diff --git a/data_all_eng_slimpj/shuffled/split2/finalzdpp b/data_all_eng_slimpj/shuffled/split2/finalzdpp new file mode 100644 index 0000000000000000000000000000000000000000..2beace4fc2e8e7a64fe587def8f2ebcb28710c64 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzdpp @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nOne of the elemental forms of document processing includes classification. \nSince the last couple of years, it is in demand because of the increasing \navailability of data in digital format which has resulted into the requirement of systematization of \nthat data. Manual organization of huge data can be tedious if strict time \nconstraints are set, increasing the necessity of automated document classification. The contexts of words in the documents play a very important role in deciding the category of the document. The human brain is very effective in consideration of contexts in the incoming information for taking the appropriate action. \n\n\\smallskip\n\nThe principles of HTM theory can be used to meet the requirements of organizing of data. \nHTM \ntakes inspiration from the mammalian brain which has been evolving over \nmillions of years and is able to process data efficiently. As HTM is \nbiologically plausible, it is based on simple rules and not complex mathematics. HTM theory is being developed by a US based company called Numenta, Inc.\n\\smallskip\n\n\\section{Related Work}\nSome of the conventional methods for text\/document classification are mentioned below:\n\\subsection{Naive Bayes}\nThe Naive Bayes classifier is a probabilistic classifier and is based on the Bayes theorem. It works well with small samples of data. The posterior probability of a particular document belonging to various classes is calculated. The document is assigned to the class with the highest posterior probability. The Naive Bayes classifier assumes strong independence between the features. This is a major limitation of this classifier and hence has low performance in cases where the features are correlated \\cite{comparison}.\n\n\\subsection{Support Vector Machines}\nSupport Vector Machines (SVMs) are supervised machine\nlearning algorithms. In case of a multi class problem, first\nthe problem has to be decomposed into two separate class\nproblems as SVM can work only with binary classification\nproblem. They will probably give poor results when total\nnumber of samples are very less than the total number of\nfeatures. In comparison with decision making classifier and\nlogistic regression, SVM takes more time for computation \\cite{comparison}.\n\n\\subsection{K-Nearest Neighbour}\nK-Nearest Neighbour (KNN) is used for classification of objects by calculating the distance of training samples from each object. KNN classification is a simple and widely used approach for text classification. However, it is computationally intensive and\nclassification time is high \\cite{comparison}. Also, it is difficult to find the ideal value of k \\cite{knn}.\n\n\n\\subsection{Convolutional Neural Network}\nConvolutional Neural Network (CNN) works well with static text classifications. CNN is a type of feed forward neural network, comprising of neurons with trainable weights and biases. CNN comprises of a number of convolutional layers with nonlinear activation functions like ReLU or tanh applied to the results. CNN suffers from the limitations of the requirement of large data and big processing power to be able to predict accurately \\cite{cnn}.\n\nHTM theory is primarily used for Classification, Prediction and Anomaly Detection purposes. One of its application for Classification is mentioned below:\n\n\\subsection{Land - forms classification}\nAs HTM based models have a common learning algorithm, it can be used for classifying images. HTM theory has been used for classifying different land-forms like trees, roads, buildings and farms using the images obtained from satellites. The framework used achieved an accuracy of 90.4\\%,\\cite{perea2009application} which is at par with the conventional machine learning techniques for image classification.\n\nSince HTM theory can be used for image classification purposes, it can hold a promise to classify text\/documents.\n\n\\section{Overview of Hierarchical Temporal Memory}\nHTM is a theory which seeks to apply the \nstructural as well as algorithmic properties of the neocortex to machine \nlearning problems \\cite{hawkins2010hierarchical}. The neocortex proves to be the center of intelligence in the \nmammalian brain. It is responsible for processing complex activities such as \ncommunication, planning and prediction. Structurally, neocortex is a 2 mm thick \ntissue divided into a number of different regions. A region is a network of \ninterconnected neurons \\cite{hawkins2010hierarchical}. This attributes to the presence of input connections \nfrom different sensory organs \\cite{hawkins2006hierarchical,onintelligence} like eyes, ears etc. The term \"Hierarchical\" in the theory is owing to \nthe fact that, HTM network contains a hierarchy of levels arranged in the \npyramid-like structure. These levels are present in the form of regions that \nare again composed of columns which finally consist of neurons. These neurons need \nnot be physically arranged in a hierarchy, but are logically arranged in the \nhierarchical format. The lower levels in hierarchy represent data having lower \nabstraction\/complexity. As we go higher in the hierarchy, the data abstraction stored in the memory increases. Time plays a crucial role in the \nway data is stored in mammalian brain. \"Temporal\" implies that the HTM network takes \ninto consideration the sequence of the incoming data. A continuous stream of \ninput data is aptly learned as spatial and temporal sequences.\n\\smallskip\n\nA remarkable property of the neocortex is that the input from all the sensory \norgans is processed in the same manner. Hence, it has a common learning algorithm \nfor inputs from all types of sensory organs \\cite{hawkins2010hierarchical}. \n\n\\begin{figure*} [htb!] \n\\includegraphics[width=1\\textwidth]{process_htm_final.png}\n\\caption{\\sl System Architecture Diagram\n\\label{fig:implementation}}\n\\end{figure*} \n\n\n\n\\subsection{Structure of a Neuron}\n\n\nInside the mammalian brain, neurons play a central role in information handling. Some \nrelevant parts of the neuron for our study are mentioned below. \n\n\\subsubsection{Proximal Dendrites}\nProximal dendrites are in close proximity to the cell body. The proximal \ndendrites are connected directly to the inputs from the sensory \norgans. \n\n\n\\subsubsection{Distal Dendrites}\nDistal dendrites are the ones that are afar from the cell body. The distal \ndendrites have connections with various other neurons in the neocortex. Majority of the \nconnections to the axon are from distal dendrites as compared to the \nconnections made by proximal dendrites \\cite{synapse}. \n\n\n\\subsubsection{Synapse}\nA synapse is a connection between an axon of one neuron and dendrite of the \nother. The ongoing process of breaking and reforming these synapses between \ncells results in learning of new data and thus gradually forgetting the old \none.\\smallskip\n\nThere is a permanence value associated with every synapse and a threshold \nlinked with every neuron. Thus, for a neuron to get activated, the total number \nof synapses with permanence values higher than the threshold value must be more \nthan the stimulus threshold.\n\n\\subsection{Sparse Distributed Representation}\nThough the neocortex contains billions of neurons in highly interconnected \nmanner, only a tiny fraction of them are active for a particular input \\cite{sdr1}. Hence, only small percentage of active neurons are responsible for \nrepresenting the input information. This is called as Sparse Distributed Representation (SDR). \nEven though single activated neuron has the potential to convey some meaning, \nthe full information can only be conveyed when it is interpreted within the \ncontext of other neurons.\nAs the information is spread across a tiny percentage of the active bits, SDRs are more noise tolerant than dense representations, making them ideal for text processing.\n\n\\subsection{Spatial Pooler}\nHTM includes two important parts - Spatial Pooler (SP) and Temporal Pooler (TP). Spatial Pooler, also known as Pattern Memory, has been emphasized in this study.\n\\smallskip\n\nThe neurons in the neocortex are arranged in columns, which represent features of the input. Every neuron in a particular column, which represent different context for an input, is \nconnected to specified number of bits in the input bit array. The selection of \nbits to be connected to the neurons in a particular column is random. The bits \nwhich are connected to a particular column are known as a potential pool of \nthat particular column. Connections between input bit and the column neuron is \ncalled as a synapse. Every synapse has a value associated with it known as \npermanence value similar to that of a mammalian brain. Permanence value is always in the range of 0 and 1. There is a threshold value associated with synapse's permanence.\n\nIf the permanence value of a synapse associated to an input bit is greater than the threshold, the activation of the column of neurons \nis influenced by the input bit. The permanence value of a synapse is adjusted in the learning phase.\n\n\nThe main role of SP in HTM is finding spatial patterns in the input data. It is decomposed into three stages:\n\\medskip\n\\subsubsection{Overlap}\nIn this stage, overlap score of each column is calculated. Overlap score is \n the count of active bits in the potential pool of a particular \ncolumn having permanence value greater than the threshold. \n\\smallskip\n\\subsubsection{Inhibition}\nThe columns are sorted according to their overlap scores from highest to lowest. A particular fraction (in our study, 0.5\\%, Table \\ref{spatial_pooler_paramters}, $NumActiveColumnsPerInhArea$) of the top columns is selected (also called as active columns or the winning columns) for the learning phase. Rest other columns are inhibited from learning.\n\\smallskip\n\\subsubsection{Learning}\nDuring Learning, the permanence value of the synapses in the potential pool of the winning \ncolumns is incremented (by synPermActiveInc, Table \\ref{spatial_pooler_paramters}) or decremented (by synPermInactiveDec, Table \\ref{spatial_pooler_paramters}). When the active column is connected to an \nactive bit then the permanence value of the synapse corresponding to that \nactive bit is incremented. However, when the active column is connected to an\ninactive bit then the permanence value of the synapse corresponding to that \ninactive bit is decremented. This is the result of column expecting that bit to be active. The synapse permanence is decremented as a \npunishment. \n\n\\section{Implementation}\n\nThe flowchart in figure \\ref{fig:implementation} is our high-level \narchitecture diagram for document categorization. As the mammalian brain requires electrical \nsignals for learning, the learning algorithm i.e., Spatial Pooler also requires \nbit patterns for processing. So, to convert text into bit arrays, Latent Semantic \nIndexing (LSI) technique is used, which converts semantically similar sentences \ninto similar bit arrays. These bit arrays (which need not be sparse)\nare fed to the Spatial Pooler where it simulates the working of neurons in \nthe brain and gives SDR as the output. The \nactive bits in the SDR represent the neurons \nwhich get activated in the Spatial Pooler. Since semantically similar \ntext belong to the same category, it is easy to classify the \ntext into different categories.\n\\medskip\n\\subsection{Latent Semantic Indexing}\nAs HTM theory is modelled after the mammalian brain, its input also should be in \naccordance with the input format received by the brain. The brain receives \ninput in the form of electrical signals which correspond to bit arrays. Latent Semantic Indexing(LSI) helps in determining hidden \nfeatures in documents \\cite{papadimitriou1998latent}. Thus the technique is used to extract the \ncontextual-usage meaning of words from the documents\\cite{lsa3}. \nThe LSI framework consists of 3 steps which are mentioned below.\n\\smallskip\n\n\n\\subsubsection{Preprocessing of input data}\nIn the initial step, the input text is tokenized and stopwords are removed from \nevery document of the corpus\\footnote{We have used the wikipedia language corpus as it \nincludes a large vocabulary which is useful for generic datasets. The corpus \nwill change if the dataset is in a language other than English or contains a \nlarge number of words which are not present in the vocabulary.}. Each term in \nthe text is then represented as a tuple containing term-id and term frequency. \nA matrix is created in which the rows denote the unique terms and the \ncolumns denote the documents. Every cell denotes the term count in the corresponding document.\nThe matrix of term-frequency counts obtained from the term document matrix is \nthen modified using the TF-IDF technique so as to give more weight to rare \nterms compared to common terms across documents and also to frequently occurring \nterms in a particular document. \nThe formula for weighing each term can be represented as,\n\n\\begin{figure}\n\\begin{centering}\n\\includegraphics[width=1\\linewidth]{LSA_with_bold_new.png}\n\\caption{\\sl Latent Semantic Indexing framework\n\\label{fig:lsa_framework}}\n\\end{centering}\n\\end{figure} \n\n\n\\begin{gather}\n Document Term Weight = f_{t,d} \\times \\ln(N \/ n_t) \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l}\n f_{t,d} & count of term~$t$ in document~$d$ \\\\ \n N & the total count of documents \\\\\n n_t & the count of documents having term~$t$ \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\n\\smallskip\n\n\nThe term-document matrix gets modified to contain weights of each term \nin a given document. The dimensionality reduction of this matrix is done using \nSingular Value Decomposition (SVD).\n\\smallskip\n\\subsubsection{Singular Value Decomposition}\nLSA uses SVD for generating the vectors of a particular text \\cite{lsa1, lsa2}. The matrix $X$(term-document) is used to calculate two matrices. These are,\n\n\\begin{gather}\n Y = X^TX \\\\\n Z = XX^T \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l}\n $X$ & term - document matrix \\\\\n\t$Y$ & document - document matrix \\\\ \n $Z$ & term - term matrix \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\nAfter finding eigenvectors of $Y$ and $Z$ matrices, we get left singular matrix, $L$ \nand right singular matrix, $R$ respectively. Thus, term - document matrix, $X$, \n is divided into unique combination of three matrices as follows -\n\n\\eat{\n\\begin{gather}\n X = L \\Sigma R^T \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n \\multirow{2}{*}{$L$} & is the left singular vector matrix representing \\\\\n & weights of a term for corresponding concepts. \\\\\n \\multirow{2}{*}{$R^T$} & is the transpose of the right singular vector matrix \n\\\\\n & representing weights of documents belonging to particular concepts. \\\\\n\\smallskip\n\\multirow{2}{*}{$\\Sigma$} & is the diagonal matrix representing \\\\\n & weights of the concepts found in the text\\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n}\n\n\\begin{gather}\n\\label{4}\n X = L \\Sigma R^T \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n $L$ & Term - Concept weight matrix \\\\\n R^T & Concept - Document weight matrix \\\\\n \\Sigma & Diagonal matrix representing concept weights \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\n$\\Sigma$ is calculated by taking the square root of the eigenvalues of matrix $Y$. \n\\smallskip\n\nTo reduce the dimensionality of the matrices in equation \\ref{4}, top $k$ concepts are selected and thus \nmatrix $X$ is approximated as,\n\n\\begin{gather}\n X_k = L_k \\Sigma_k R_k^T\n\\end{gather}\n\n\nIn our study, $k$ is taken to be 400 in order to consider top 400 concepts. This \nmarks the end of the training phase. \n\nIn the testing phase, after generating \nweight matrix using the Term Frequency - Inverse Document Frequency (TF - IDF) \nmodel, input text gets converted into a query matrix, $Q$. This matrix $Q$ is then \nmultiplied with matrices $L_K$ and $\\Sigma_k$ to generate new query vectors calculated as follows:\n\n\\begin{gather}\n New Query Vectors = Q L_k \\Sigma_k\n\\end{gather}\n\n\\subsubsection{Extraction of top features}\n\nThe query vectors are converted into bit arrays of size 400. \nThe indices of the top 40 features from the query vectors represent the '1's in the bit arrays and the indices of the remaining features represent '0's.\n\n\\subsection{Spatial Pooler}\nThe bit arrays from the LSI encoder are then passed to the Spatial Pooler for \nlearning. The Spatial Pooler gives similar Sparse Distributed Representations \n(SDRs) for similar input text. The major parameters of the Spatial Pooler which \nsignificantly affect the accuracy of our model are mentioned in Table \\ref{spatial_pooler_paramters}.\n\n\\medskip\n\n\\begin{table}[htbp]\n\\caption{Spatial Pooler Parameters}\n\\label{spatial_pooler_paramters}\n\\begin{center}\n\n\\begin{tabular}{|l|c|}\n\t\\hline\n\t\\textbf{Parameters} & \\textbf{Values}\\\\\n\t\\hline\n inputDimensions & 400\\\\\n\t\\hline\n columnDimensions & 20000\\\\\n\t\\hline\n potentialRadius & 200\\\\\n\t\\hline\n numActiveColumnsPerInhArea & 100\\\\\n\t\\hline\n synPermActiveInc & 0.01\\\\\n\t\\hline\n synPermInactiveDec & 0.008\\\\\n\t\\hline\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\\smallskip\nThe active indices of the SDR are then fed to the Classifier.\n\n\\subsection{Classifier}\nIn order to predict target class labels, a sequence of N-dimensional SDRs is \nassigned to a set of k class labels. The Classifier makes \nuse of a single layer feed forward neural network. In the figure \n\\ref{fig:classifier}, the number of output neurons is equal to the number of \npredefined categories. The number of input neurons is equal to the number of \nbits in any SDR.\n\n\\begin{figure} \n\\includegraphics[width=0.9\\linewidth]{sdr_classifier.png}\n\\caption{\\sl Classifier\n\\label{fig:classifier}}\n\\end{figure} \n\nThe algorithmic description of the classifier is as follows,\n\\smallskip\n\\subsubsection{Matrix Initialisation}\nSince all classes have an equal chance of occurrence before learning, all \nvalues in the weight matrix are initialised to zero. \n\\smallskip\n\\subsubsection{Inference}\nIn this phase, the predicted class probabilities for each input pattern are \ncalculated. The calculations include two steps as mentioned below.\n\\smallskip\n\\setlength{\\parindent}{4ex}\n\n\\textit{i) Weighted sum :} Weighted sum of the input is calculated for each output neuron to determine the activation levels of the neuron. \nActivation level of an output neuron can be determined by the summation of the product of all the input bits to the input layer neurons with the weights of its corresponding connections to the output layer neuron. \nThe formula for the activation level being,\n\n\n\\begin{gather}\n a_j = \\sum_{i=1}^{N} w_{ij} \\times x_i \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n a_j & activation level of the $j^{th}$ output layer neuron \\\\\n n & number of input layer neurons\\\\\n \\eat{w_{ij} & weight of the connection from the $i^{th}$ input layer \n\\\\ & neuron to the $j^{th}$ output layer neuron \\\\} \n \\multirow{2}{*}{$w_{ij}$} & weight of the connection from the $i^{th}$ input \nneuron \\\\\n & to the $j^{th}$ output neuron. \\\\\n x_i & Input bit value. It is either 0 or 1. \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\n\\par\n\n\\setlength{\\parindent}{4ex}\n\n\\textit{ii) Softmaxing the activation levels :} The probability distribution of \nthe categories is calculated by exponentiating and normalizing the activation \nlevels of the neurons in the output array using the softmax function. The \nformula for the probability distribution being,\n\n\\begin{gather}\n P\\left[ C_k | x, w\\right] = y_k = \\frac{e^{a_k}}{\\sum_{i=1}^{k} e^{a_i}} \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n y_k & Probability of predicting the category index $k$. \\\\\n k & Number of predefined categories. \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\n\\par\n\n\\subsubsection{Learning}\nDuring each iteration, the classifier makes a prediction of the category index \nof a given SDR. This prediction is of the form of the probability distribution \nover different category indexes.\nThe connection weights are updated to learn and improve the prediction results. Connection weights are adjusted only for the active bits. \nThe Connection Weights are determined using maximum likelihood estimation (MLE) on independent \ninput SDRs. Since, the SDRs are independent of each other, they would satisfy \nthe following equation.\n\n\\begin{gather}\n P\\left[ z^1, z^2, ..., z^t\\right] = \\prod_{t} P\\left( z^t | x^t, w\\right) \\\\\n x^t = (x^{t}_1, x^{t}_2, x^{t}_3, ..., x^{t}_N) \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n z^t & actual category index of $t^{th}$ SDR. \\\\\n x^t & Sparse Distributed Representation. \\\\\n x^{t}_1 & first bit of the $t^{th}$ SDR. \\\\\n w & Connection weights. \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\nA value of $w$ is selected so that likelihood gets maximized. The loss function to select $w$ \\cite{neuneier2012train}, is as follows : \n\n\\begin{gather}\n L = -\\ln \\left( \\prod_{t} p\\left(y_t | x_t, w\\right)\\right) \\\\\n= -\\Sigma_{t}\\ln P\\left(y_t | x_t, w\\right)\n\\end{gather}\n\nGradient descent is used is to minimize the loss function.\n\n\\begin{gather}\n\\frac{\\partial L}{\\partial w_{ij}} = \\frac{\\partial L}{\\partial a_j} \\times \n\\frac{\\partial a_j}{\\partial w_{ij}} \\\\\n=\\left(y_j - z_j\\right)x_i \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n y_j & Predicted probability of $j^{th}$ category index. \\\\\n z_j & Actual probability of $j^{th}$ category index. \\\\\n x_i & Input bit value to the $i^{th}$ input neuron. \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\nError in connection weight between $i^{th}$ input neuron to the $j^{th}$ output neuron is,\n\n\\begin{gather}\nError_{ij}\\ for\\ active\\ input\\ bits = y_j - z_j\n\\end{gather}\n\n\\begin{gather}\nupdate_{ij} = \\alpha \\times error_{ij} \\\\\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n \\alpha & Learning rate. \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\nThe value $update_{ij}$ is used to update the connection weight between the \n$i^{th}$ input neuron to the $j^{th}$ output neuron using the formula,\n\n\\begin{gather}\nw_{new_{ij}} = w_{old_{ij}} + update_{ij}\n\\eat{\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n \\alpha & Learning rate. \\\\\n \\end{tabular}\\nonumber}\n\\end{gather}\n\nBut, if we just want to update connection weights for the bits which are active \nwe multiply $update_{ij}$ with $x_i$.\n\n\\begin{gather}\nw_{new_{ij}} = w_{old_{ij}} + update_{ij} \\times x_i\n\\intertext{Where:}\n \\begin{tabular}{>{$}r<{$}@{\\ :\\ }l} \n w_{new_{ij}} & updated weight of the connection from $i^{th}$ input \\\\\n & neuron to the $j^{th}$ output neuron \\\\\n \\end{tabular}\\nonumber\n\\end{gather}\n\nThe output layer neuron with the highest probability represents the category index of \nthe input text.\n\n\\section{Results}\nMany experiments were performed to test the accuracy and performance of \nour model. We selected two standard datasets for document classification,\nnamely, 20 Newsgroup dataset from the sklearn dataset repository and Movie \nReviews dataset from the NLTK corpus repository. The datasets were \nsplit into train set and test set in the ratio 9:1. The classification framework used in this study gives comparable accuracies with the models mentioned in the table \\ref{tp_rate} on the same datasets. \n\\smallskip\n\n\\begin{table}[htbp]\n\\caption{TRUE POSITIVE RATE}\n\\label{tp_rate}\n\\begin{center}\n\n\\begin{tabular}{ | m{6.5em} | m{2cm}| m{2cm} | } \n\n\\hline\n\\bfseries Classification Techniques & \\bfseries 20 newsgroup & \\bfseries Movie \nReviews \\\\ \n\\hline\nSVM\\cite{svmmr} & ---- & 84.40\\% \\\\ \n\\hline\nDecision Trees\\cite{decisiontreesmr} & ---- & 61.10\\% \\\\ \n\\hline\nNaive Bayes\\cite{naivebayes20,naivebayesmr} & 86.00\\% & 62.35\\% \\\\ \n\\hline\nB-Tree\\cite{btree20} & 82.64\\% & ---- \\\\ \n\\hline\nBayesian Networks \\cite{bayes_net}& 78.58\\% & ---- \\\\\n\\hline\nHTM & 83.19\\% & 73.60\\% \\\\\n\\hline\n\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\\smallskip\n\n\\section{Conclusion and Future Scope}\n\nThis paper puts forward the results of using the Hierarchical Temporary Memory \nmodel for document categorization. The results prove that the HTM model gives \nan accuracy comparable to the conventional techniques used for text \nclassification. \nThe number of columns and the SDR sparsity has a significant effect on the \nperformance of the spatial pooler. As per our model, The optimal values of the \nnumber of columns was 20,000 and the sparsity was 0.5\\%.\n\nThe main advantages of this model are: a limited number of parameters, can be \ntrained on small\\cite{synapse} corpus and faster training.\n\nIn future, we plan to modify the encoding process of our model and also \nincorporate the Temporal Pooler which can help to increase the accuracy of the \nmodel.\n\n\\section{Acknowledgement}\nWe are grateful to Mr. Nikhil Malhotra of Maker's Lab, Tech Mahindra Ltd. and Mr. Satish Kumbhar of College of Engineering, Pune, for guiding us through the research.\n\n\\nocite{*}\n\n\\bibliographystyle{IEEEtran}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction\\label{s.1}}\n\n\nThe construction of diffusions on infinite-dimensional manifolds and the study of the regularity properties of their induced measures have been a topic of great interest for at least the past 50 years; see for example \\cite{DalettskiSnaiderman1969,Kuo1971,Kuo1972,Elworthy1975,FdlP1995,ADK2003,AM2006,Malliavin08}, although many other references exist. The purpose of the present paper is to construct diffusions on a general class of infinite-dimensional nilpotent Lie groups, and to show that the associated heat kernel measures are quasi-invariant under appropriate translations. We demonstrate that this class of groups is quite rich. We focus here on the elliptic setting, but comment that, as nilpotent groups are standard first models for studying hypoellipticity, examples of infinite-dimensional versions of such spaces are important for the more general study of hypoellipticity in infinite dimensions. This is an area of great interest, and is of particular relevance in the study of stochastic PDEs and their applications \\cite{AiraultMalliavin2004,BakhtinMattingly2007,BaudoinTeichmann2005,HairerMattingly2011,Malliavin1990,MattinglyPardoux2006}. The present paper studies heat kernel measures for elliptic diffusions on these spaces, which is a necessary precursor to understanding the degenerate case.\n\n\n\\subsection{Main results}\nLet $(\\mathfrak{g},\\mathfrak{g}_{CM},\\mu)$ denote an abstract Wiener\nspace, where $\\mathfrak{g}$ is a Banach space equipped with a\ncentered non-degenerate Gaussian measure $\\mu$ and associated\nCameron--Martin Hilbert space $\\mathfrak{g}_{CM}$. We will assume\nthat $\\mathfrak{g}_{CM}$ additionally carries a nilpotent Lie algebra\nstructure $[\\cdot,\\cdot]: \\mathfrak{g}_{CM}\\times \\mathfrak{g}_{CM}\n\\rightarrow \\mathfrak{g}_{CM}$, and we will further assume that\nthis Lie bracket is Hilbert-Schmidt. We will call such a space an {\\it\nabstract nilpotent Lie algebra}. Via the standard\nBaker-Campbell-Hausdorff-Dynkin formula, we may then equip\n$\\mathfrak{g}_{CM}$ with an explicit group operation under which\n$\\mathfrak{g}_{CM}$ becomes an infinite-dimensional group. When\nthought of as a group, we will denote this space by $G_{CM}$. \nWe equip $G_{CM}$ with the left-invariant Riemannian metric which\nagrees with the inner product on $\\mathfrak{g}_{CM}\\cong T_eG_{CM}$, and we denote the\nRiemannian distance by $d$. \nNote that, despite the\nuse of the notation $\\mathfrak{g}$, we do not assume that the Lie bracket\nstructure extends to $\\mathfrak{g}$, and so this space is not necessarily a\nLie algebra or a Lie group. Still, when $\\mathfrak{g}$ is playing a role\ntypically played by the group, we will denote $\\mathfrak{g}$ by $G$.\n\nIf $\\mathfrak{g}$ were a Lie algebra (and thus carried an associated group\nstructure), we could construct a Brownian motion on\n$G$ as the solution to the Stratonovich stochastic differential equation\n\\[ \\delta g_t = g_t\\delta B_t := L_{g_t*}\\delta B_t, \\text{ with }\n\tg_0=\\mathbf{e}=(0,0), \\]\nwhere $L_x$ is left translation by $x\\in G$ and $\\{B_t\\}_{t\\ge0}$ is a\nstandard Brownian motion on $\\mathfrak{g}$ (as a Banach space) with $\\mathrm{Law}(B_1)=\\mu$.\nIn finite dimensions, the solution to this stochastic differential equation may be obtained\nexplicitly as a formula involving the Lie bracket. In particular, for $t>0$\nand $n\\in\\mathbb{N}$, let $\\Delta_n(t)$ denote the simplex in\n$\\mathbb{R}^n$ given by\n\\[ \\{s=(s_1,\\cdots,s_n)\\in\\mathbb{R}^n: 0\\sigma(j+1)\\}$.\nThen the Brownian motion on $G$ could be written as \n\\begin{equation}\n\\label{e.ibm}\ng_t = \\sum_{n=1}^{r-1} \\sum_{\\sigma\\in\\mathcal{S}_n} \n\t\\left( (-1)^{e(\\sigma)}\\bigg\/ n^2 \n\t\\begin{bmatrix} n-1 \\\\ e(\\sigma) \\end{bmatrix}\\right) \n\t\\int_{\\Delta_n(t)} \n\t[ [\\cdots[\\delta B_{s_{\\sigma(1)}},\\delta B_{s_{\\sigma(2)}}],\\cdots], \n\t\\delta B_{s_{\\sigma(n)}}],\n\\end{equation}\nwhere this sum is finite under the assumed nilpotence. \nAn obstacle to the development of a general theory of stochastic differential equations on infinite-dimensional Banach spaces is the lack of smoothness of the norm in a general Banach space which is necessary to define a stochastic integral on it.\nStill, in Section \\ref{s.mult}, we prove a general result to define a class of iterated stochastic integrals with respect to Brownian motion on the Banach space $\\frak{g}$ that includes the expression above. Additionally, we show that one may make\nsense of the above expression when the Lie bracket on $\\mathfrak{g}_{CM}$ does not necessarily extend to $\\mathfrak{g}$. Thus we are able to define a ``group Brownian motion'' $\\{g_t\\}_{t\\ge0}$ on $G$ via (\\ref{e.ibm}). We let $\\nu_t:=\\mathrm{Law}(g_t)$ be the heat kernel measure on $G$.\n\nIn particular, the integrals above are defined as a limit of stochastic integrals on finite-dimensional subgroups $G_\\pi$ of $G_{CM}$. We show that these $G_\\pi$ are nice in the sense that they approximate $G_{CM}$ and that there exists a uniform lower bound on their Ricci curvatures. \n\nUsing these results, we are able to prove the following main theorem. \n\n\n\\begin{thm}\n\\label{t.quasi}\nFor $h\\in G_{CM}$, let $L_h,R_h:G_{CM}\\rightarrow G_{CM}$ denote left and\nright translation by $h$, respectively. Then $L_h$ and $R_h$ define measurable\ntransformations on $G$, and for all $T>0$, $\\nu_t\\circ L_h^{-1}$ and $\\nu_t\\circ\nR_h^{-1}$ are absolutely continuous with respect to $\\nu_t$. Let \n\\[ J_t^l(h,\\cdot) := \\frac{d(\\nu_t\\circ L_h^{-1})}{d\\nu_t} \\qquad \\text{ and }\n\t\\qquad J_t^r(h,\\cdot) := \\frac{d(\\nu_t\\circ R_h^{-1})}{d\\nu_t} \\]\nbe the Radon-Nikodym derivatives, $k$ be the uniform lower bound on the Ricci\ncurvatures of the finite-dimensional approximation groups $G_\\pi$ \nand \n\\[ c(t) := \\frac{t}{e^t-1}, \\qquad \\text{ for all } t\\in\\mathbb{R}, \\]\nwith the convention that $c(0)=1$. Then, for all $p\\in[1,\\infty)$,\n$J_t^l(h,\\cdot),J_t^r(h,\\cdot)\\in L^p(\\nu_t)$ and both satisfy the estimate\n\\[ \\|J_t^*(h,\\cdot)\\|_{L^p(\\nu_t)} \\le \\exp \\left( \\frac{c(kt)(p-1)}{2t}\n\td(\\mathbf{e},h)^2\\right), \\]\nwhere $*=l$ or $*=r$.\n\\end{thm}\n\nThe fact that one may define a measurable left or right action on $G$ by an\nelement of $G_{CM}$ is discussed in Section \\ref{s.mga}. The lower bound on\nthe Ricci curvature is proved in Proposition \\ref{p.Ric}.\n\n\n\\subsection{Discussion}\n\nThe present paper builds on the previous work in \\cite{DriverGordina2008} and\n\\cite{Melcher2009}, significantly generalizing\nthese previous works in several ways.\nIn particular, the paper \\cite{Melcher2009} considered\nanalogous results for ``semi-infinite Lie groups'', which are \ninfinite-dimensional nilpotent Lie groups\nconstructed as extensions of finite-dimensional nilpotent Lie groups\n$\\mathfrak{v}$ by an\ninfinite-dimensional abstract Wiener space (see Example \\ref{ex.ext}). \nAt several points in the analysis\nthere, the fact that $\\mathrm{dim}(\\mathfrak{v})<\\infty$ was used in a\ncritical way. In particular, it was used to show that the stochastic\nintegrals defining the Brownian motion on $G$ as in equation\n(\\ref{e.ibm}) were well-defined. \nIn the present paper, we have removed this restriction, as well as removing the\n``stratified'' structure implicit in the construction as a Lie group extension.\n\nAgain, we note that, despite the\nuse of the notation $\\mathfrak{g}$, it is not assumed that the Lie bracket\nstructure on $\\mathfrak{g}_{CM}$ extends to $\\mathfrak{g}$, and so $\\mathfrak{g}$ itself is not necessarily a\nLie algebra or Lie group. In \\cite{DriverGordina2008} and \\cite{Melcher2009}, it\nwas assumed that the Lie bracket was a continuous map defined on\n$\\mathfrak{g}$. However, it turns out that\nthe group construction on $\\mathfrak{g}_{CM}$ is the only\nnecessary structure for the subsequent analysis.\nAs is usual for the infinite-dimensional setting,\nwhile the heat kernel measure is itself supported on the larger space\n$\\mathfrak{g}$, its critical analysis depends more on the structure of $\\mathfrak{g}_{CM}$.\nStill, as was originally done in \\cite{DriverGordina2008} and then in \\cite{Melcher2009},\none may instead define an abstract nilpotent Lie algebra starting \nwith a continuous nilpotent bracket\n$[\\cdot,\\cdot]:\\mathfrak{g}\\times\\mathfrak{g}\\rightarrow\\mathfrak{g}$. For\nexample, in the event that $\\mathfrak{g}=W\\times\\mathfrak{v}$ where\n$\\mathfrak{v}$ is a finite-dimensional Lie algebra and\n$[\\cdot,\\cdot]:\\mathfrak{g}\\times\\mathfrak{g}\\rightarrow\\mathfrak{v}$, it is\nwell-known that this implies that the restriction of the bracket to\n$\\mathfrak{g}_{CM}=H\\times\\mathfrak{v}$ is Hilbert-Schmidt. (For\nany continuous bilinear $\\omega:W\\times W\\rightarrow K$ where $K$ is a Hilbert\nspace, one has that $\\|\\omega\\|_{(H^{\\otimes 2})^*\\otimes K}<\\infty$; this\nfollows for example from Corollary 4.4 of \\cite{Kuo75}.)\nMore generally, in order for the subsequent theory to make sense, one would\nnaturally need\nto require that $\\mathfrak{g}_{CM}$ be a Lie subalgebra of $\\mathfrak{g}$, that is, for\nthe restriction of the Lie bracket to $\\mathfrak{g}_{CM}$ to preserve\n$\\mathfrak{g}_{CM}$. As the proofs in the sequel rely strongly on the bracket\nbeing Hilbert-Schmidt, it would be then necessary to add the Hilbert-Schmidt hypothesis as it \ndoes not follow immediately if one only assumes a continuous bracket on\n$\\mathfrak{g}$ which preserves $\\mathfrak{g}_{CM}$. \n\n\n\n\nAdditionally, the spaces studied in the present paper are well-designed for the study of\ninfinite-dimensional hypoelliptic heat kernel measures, and there has already\nbeen progress on proving quasi-invariance and stronger smoothness properties\nfor these measures in the simplest case of a step two Lie algebra with\nfinite-dimensional center; see \\cite{BGM2013} and\n\\cite{DEM2013}. More generally, the paper \\cite{Pickrell2011} explores related interesting lines of inquiry for heat kernel measures on infinite-dimensional groups, largely in the context of groups of maps from manifolds to Lie groups.\n\n\n\n{\\it Acknowledgements. } The author thanks Bruce Driver and Nathaniel Eldredge for\nhelpful conversations during the writing of this paper.\n\n\n\n\\section{Iterated It\\^o integrals}\n\\label{s.mult}\n\nRecall the standard construction of abstract Wiener spaces. \nSuppose that $W$ is a real separable Banach space and $\\mathcal{B}_{W}$ is\nthe Borel $\\sigma$-algebra on $W$.\n\n\\begin{defn}\n\\label{d.2.1} \nA measure $\\mu$ on $(W,\\mathcal{B}_{W})$ is called a (mean zero,\nnon-degenerate) {\\em Gaussian measure} provided that its characteristic\nfunctional is given by\n\\[\n\\hat{\\mu}(u) := \\int_W e^{iu(x)} d\\mu(x)\n\t= e^{-\\frac{1}{2}q(u,u)}, \\qquad \\text{ for all } u\\in W^*,\n\\]\nfor $q=q_\\mu:W^*\\times W^*\\rightarrow\\mathbb{R}$ a symmetric, positive\ndefinite quadratic form.\nThat is, $q$ is a real inner product on $W^*$.\n\\end{defn}\n\n\n\n\\begin{thm}\n\\label{t.2.3}\nLet $\\mu$ be a Gaussian measure on $W$. \nFor $w\\in W$, let\n\\[ \n\\|w\\|_H := \\sup\\limits_{u\\in W^*\\setminus\\{0\\}}\\frac{|u(w)|}{\\sqrt{q(u,u)}},\n\\]\nand define the {\\em Cameron--Martin subspace} $H\\subset W$ by\n\\[ H := \\{h\\in W : \\|h\\|_H < \\infty\\}. \\]\nThen $H$ is a dense subspace of $W$, and there exists a unique inner\nproduct $\\langle\\cdot,\\cdot\\rangle_H$ on $H$ such that $\\|h\\|_H^2\n= \\langle h,h\\rangle_H$ for all $h\\in H$, and $H$ is a separable\nHilbert space with respect to this inner product. For any $h\\in\nH$, $\\|h\\|_W \\le C \\|h\\|_H$ for some $C<\\infty$. \n\\end{thm}\n\nAlternatively, given $W$ a\nreal separable Banach space and $H$ a real separable Hilbert space \ncontinuously embedded in $W$ as a dense subspace, then for each $w^*\\in W^*$ there\nexists a unique $h_{w^*}\\in H$ such that $\\langle h,w^*\\rangle = \\langle h,\nh_{w^*}\\rangle_H$ for all $h\\in H$. Then $W^*\\ni w^*\\mapsto h_{w^*}\\in\nH$ is continuous, linear, and one-to-one with a dense range\n\\begin{equation}\n\\label{e.H*} \nH_* := \\{h_{w^*}:w^*\\in W^*\\},\n\\end{equation}\nand $W^*\\ni w^*\\mapsto h_{w^*}\\in\nW$ is continuous. A Gaussian measure on $W$ is a Borel\nprobability measure $\\mu$ such that, for each $w^*\\in W^*$, the random\nvariable $w\\mapsto\\langle w,w^*\\rangle$ under $\\mu$ is a centered Gaussian\nwith variance $\\|h_{w^*}\\|_H^2$.\n\n\n\n\nSuppose that $P:H\\rightarrow H$ is a finite rank orthogonal projection\nsuch that $PH\\subset H_*$. Let $\\{h_j\\}_{j=1}^m$ be an orthonormal basis for\n$PH$. Then we may extend $P$ to a (unique) continuous operator\nfrom $W\\rightarrow H$ (still denoted by $P$) by letting\n\\begin{equation}\n\\label{e.proj}\nPw := \\sum_{j=1}^m \\langle w, h_j\\rangle_H h_j\n\\end{equation}\nfor all $w\\in W$. \n\\begin{nota}\n\\label{n.proj}\nLet $\\mathrm{Proj}(W)$ denote the collection of finite rank projections\non $W$ such that $PW\\subset H_*$ and $P|_H:H\\rightarrow H$ is an orthogonal\nprojection, that is, $P$ has the form given in equation\n\\eqref{e.proj}.\n\\end{nota}\n\nLet $\\{B_t\\}_{t\\ge0}$ be a Brownian motion on $W$ with variance determined by\n\\[\n\\mathbb{E}\\left[\\langle B_s,h\\rangle\n\t\t_{H} \n\t\t\\langle B_t,k\\rangle\n\t\t_H\\right] \n\t= \\langle h,k \\rangle_{H} \\min(s,t),\n\\]\nfor all $s,t\\ge0$ and $h,k\\in H_*$, where $H_*$ is as in (\\ref{e.H*}). Note that for any $P\\in\\mathrm{Proj}(W)$, $PB$ is a Brownian motion on $PH$. In the rest of this section, we will verify the existence of martingales defined as certain iterated stochastic integrals with respect to $B_t$.\n\n\nThe following is Proposition 4.1 of \\cite{Melcher2009}. Note that again this\nwas stated in the context where $H=\\mathfrak{g}_{CM}$ was a ``semi-infinite Lie algebra'', but \na brief inspection of the proof shows that this is a general statement about stochastic\nintegrals on Hilbert spaces.\n\n\\begin{prop}\n\\label{p.int1}\nLet $\\{P_m\\}_{m=1}^\\infty\\subset\\mathrm{Proj}(W)$ such that $P_m|_H\\uparrow\nI_H$. Then, for $\\xi\\in L^2(\\Delta_n(t),H^{\\otimes n})$ a continuous \nmapping, let\n\\begin{align*}\nJ_n^m(\\xi)_t &:= \\int_{\\Delta_n(t)} \\langle P_m^{\\otimes n} \\xi(s), dB_{s_1}\n\t\t\\otimes\\cdots\\otimes dB_{s_n}\n\t\t\\rangle_{H^{\\otimes n}} \\\\\n\t&= \\int_{\\Delta_n(t)} \\langle \\xi(s), dP_m B_{s_1}\n\t\\otimes\\cdots\\otimes dP_m B_{s_n}\n\t\\rangle_{H^{\\otimes n}}. \n\\end{align*}\nThen $\\{J_n^m(\\xi)_t\\}_{t\\ge0}$ is a continuous $L^2$-martingale, \nand there exists a\ncontinuous $L^2$-martingale $\\{J_n(\\xi)_t\\}_{t\\ge0}$ such that\n\\begin{equation} \n\\label{e.Jnm}\n\\lim_{m\\rightarrow\\infty} \\mathbb{E}\\left[ \\sup_{\\tau\\le t} \n\t|J_n^m(\\xi)_\\tau-J_n(\\xi)_\\tau|^2 \\right] = 0\n\\end{equation}\nand \n\\begin{equation} \n\\label{e.xi}\n\\mathbb{E}|J_n(\\xi)_t|^2\\le\n\t\\|\\xi\\|^2_{L^2(\\Delta_n(t),H^{\\otimes n})}\n\\end{equation}\nfor all $t<\\infty$. The process\n$J_n(\\xi)$ is well-defined independent of the choice of increasing\northogonal projections $\\{P_m\\}_{m=1}^\\infty$ into $H_*$, and so will be denoted by\n\\[ J_n(\\xi)_t \n\t= \\int_{\\Delta_n(t)} \\langle \\xi(s), dB_{s_1}\\otimes\\cdots\\otimes dB_{s_n}\n\t\\rangle_{H^{\\otimes n}}.\n\\]\n\\end{prop}\n\n\\iffalse\n\\begin{proof} \nNote first that, \n\\[ J_n^m(\\xi)_t = \\sum_{i_1,\\ldots,i_n=1}^m \n\t\\int_{\\Delta_n(t)} \\langle\\xi(s),h_{i_1}\\otimes\\cdots\\otimes\n\th_{i_n}\\rangle_{H^{\\otimes n}}\n\tdB_{s_1}^{i_1}\\cdots dB_{s_n}^{i_n} \n\\]\nwhere $\\{B^i\\}_{i=1}^m$ are independent real\nvalued Brownian motions. Let $\\xi_{i_1,\\ldots,i_n} :=\n\\langle\\xi,h_{i_1}\\otimes\\cdots\\otimes h_{i_n}\\rangle$. Then\n\\[ |\\xi_{i_1,\\ldots,i_n}(s) |^2\\le \\|\\xi(s)\\|^2\n\t_{H^{\\otimes n}} \\]\nand $\\xi_{i_1,\\ldots,i_n} \\in L^2(\\Delta_n(t))$. Thus, $J_n^m(\\xi)_t$ is \ndefined as a\n(finite dimensional) vector-valued multiple Wiener-It\\^o integral, see for\nexample \\cite{Ito51,Shigekawa04}.\n\nNow note that\n\\begin{align*} \ndJ&_n^m(\\xi)_t \n\t= \\int_{\\Delta_{n-1}(t)} \\langle \\xi(s_1,\\ldots,s_{n-1},t),\n\t\tdP_mB_{s_1}\\otimes\\cdots\\otimes dP_mB_{s_{n-1}} \\otimes dP_mB_t\n\t\t\\rangle_{H^{\\otimes n}} \\\\\n\t&= \\sum_{i=1}^m \\int_{\\Delta_{n-1}(t)} \\langle \\xi(s_1,\\ldots,s_{n-1},t),\n\t\tdP_mB_{s_1}\\otimes\\cdots\\otimes dP_mB_{s_{n-1}} \\otimes h_i\n\t\t\\rangle_{H^{\\otimes n}} dB_t^i.\n\\end{align*}\nThus, the quadratic variation $\\langle J_n^m(\\xi) \\rangle_t$ is given by\n\\[ \\sum_{i=1}^m \\int_0^t \n\t\\bigg|\\int_{\\Delta_{n-1}(\\tau)}\n\t\\langle \\xi(s_1,\\ldots,s_{n-1},\\tau),\n\tdP_mB_{s_1}\\otimes\\cdots\\otimes\n\tdP_mB_{s_{n-1}}\\otimes h_i \\rangle\n\t_{H^{\\otimes n}}\\bigg|^2 d\\tau,\n\\]\nand\n\\begin{align*}\n\\mathbb{E}&|J_n^m(\\xi)_t|^2 \n\t= \\mathbb{E} \\langle J_n^m(\\xi) \\rangle_t \\\\\n\t&= \\sum_{i_1=1}^m \\int_0^t \n\t\t\\mathbb{E}\\bigg[\\sum_{i_2=1}^m \\int_0^{\\tau_1}\n\t\t\\bigg|\\int_{\\Delta_{n-2}(\\tau_2)}\n\t\t\\langle \\xi(s_1,\\ldots,s_{n-2},\\tau_2,\\tau_1),\n\t\tdP_mB_{s_1}\\otimes\\cdots \\\\\n\t&\\qquad\\qquad\\qquad\\qquad\\qquad\\qquad\\qquad\\qquad \n\t\t\\cdots \\otimes dP_mB_{s_{n-2}}\n\t\t\\otimes h_{i_2}\\otimes h_{i_1}\\rangle\n\t\t_{H^{\\otimes n}}\\bigg|^2d\\tau_2 \\bigg]d\\tau_1.\n\\end{align*}\nIterating this procedure $n$ times gives\n\\begin{align*}\n\\mathbb{E}|J_n^m(\\xi)_t|^2 \n\t&= \\sum_{i_1,\\ldots,i_n=1}^m \\int_{\\Delta_n(t)} \n\t\t\\left|\\langle \\xi(\\tau_1,\\cdots,\\tau_n), \n\t\th_{i_1}\\otimes\\cdots\\otimes h_{i_n} \\rangle\n\t\t_{H^{\\otimes n}}\\right|^2 d\\tau_1\\cdots d\\tau_n \\\\\n\t&\\notag\n\t= \\int_{\\Delta_n(t)} \\|P_m^{\\otimes n} \\xi(s) \\|\n\t\t_{H^{\\otimes n}}^2\n\t\\le \\|\\xi \\|_{L^2(\\Delta_n(t),H^{\\otimes n})}^2,\n\\end{align*}\nand thus, for each $n$, $J_n^m(\\xi)_t$ is bounded uniformly in $L^2$\nindependent of $m$. \n\n\nNow, for $P\\in\\mathrm{Proj}(W)$, let \n$\nJ_n^P(\\xi)_t\n\t:= \\int_{\\Delta_n(t)} \\langle P^{\\otimes n} \\xi(s), d B_{s_1}\n\t\\otimes\\cdots\\otimes d B_{s_n}\n\t\\rangle_{H^{\\otimes n}}. \n$\nFor $P,Q\\in\\mathrm{Proj}(W)$, a similar argument to the above implies that \n\\begin{equation}\n\\label{e.gub}\n\\mathbb{E}|J_n^P(\\xi)_t - J_n^Q(\\xi)_t|^2 \t\n\t= \\int_{\\Delta_n(t)} \\|P^{\\otimes n} \\xi(s) - Q^{\\otimes n}\n\t\t\\xi(s)\\|^2_{H^{\\otimes n}}\\,ds.\n\\end{equation}\nIn particular, take $P=P_m$ and $Q=P_\\ell$ with $\\ell\\le m$, and note that\n\\begin{align}\n\\notag\n&\\|P_m^{\\otimes n} \\xi(s) - P_\\ell^{\\otimes n}\n\t\t\\xi(s)\\|^2_{H^{\\otimes n}} \\\\\n\t&\\notag\\quad= \\sum_{i_1,\\ldots,i_n=1}^\\infty\n\t\t\\bigg| \\sum_{j=1}^n \\langle P_m^{\\otimes j-1} \\otimes \n\t\t(P_m-P_\\ell)\\otimes P_\\ell^{n-j-1}\\xi(s), h_{i_1}\\otimes\n\t\t\\cdots\\otimes\n\t\th_{i_n} \\rangle_{H^{\\otimes n}}\\bigg|^2 \\\\\n\t&\\label{e.gab}\n\t\\quad\\le n \\sum_{j=1}^n \\sum_{i_1,\\ldots,i_n=1}^\\infty\n\t\t\\bigg| \\langle P_m^{\\otimes j-1} \\otimes \n\t\t(P_m-P_\\ell)\\otimes P_\\ell^{n-j-1}\\xi(s), h_{i_1}\\otimes\n\t\t\\cdots\\otimes\n\t\th_{i_n} \\rangle_{H^{\\otimes n}}\\bigg|^2 \\\\\n\t&\\notag\n\t\\quad= n \\sum_{j=1}^n \\sum_{i_1,\\ldots,i_{j-1}=1}^m \\sum_{i_j=\\ell+1}^m\n\t\t\\sum_{i_{j+1},\\ldots,i_n=1}^\\ell \n\t\t\\left|\\langle \\xi(s), h_{i_1}\\otimes\\cdots\\otimes h_{i_n} \\rangle\n\t\t_{H^{\\otimes n}}\\right|^2 \n\t\\rightarrow 0,\n\\end{align}\nas $\\ell,m\\rightarrow\\infty$, for all $s\\in\\Delta_n(t)$, since \n$\\|\\xi\\|_{L^2(\\Delta_n(t),H^{\\otimes n})}^2<\\infty$.\nThus, equation (\\ref{e.gub}) implies that\n\\[ \\lim_{\\ell,m\\rightarrow \\infty}\n\t\\mathbb{E}\\left|J_n^m(\\xi)_t - J_n^\\ell(\\xi)_t \\right|^2\n\t= 0, \\]\nby dominated convergence, and $\\left\\{J_n^m(\\xi)_t\\right\\}_{m=1}^\\infty$ \nis Cauchy in $L^2$. \nSince the space of continuous $L^2$-martingales is complete in the\nnorm $M\\mapsto \\mathbb{E}|M_t|^2$, there exists a continuous martingale\n$\\{J_n(\\xi)_t\\}_{t\\ge0}$ such that\n\\[\n\\lim_{m\\rightarrow\\infty} \\mathbb{E}|J_n^m(\\xi)_t-J_n(\\xi)_t|^2 = 0. \n\\]\nCombining this with Doob's\nmaximal inequality proves equation (\\ref{e.Jnm}).\n\n\nTo see that $J_n(\\xi)_t$ is independent of the choice of basis, suppose now that\n$\\{h'_j\\}_{j=1}^\\infty\\subset H_*$ is another orthonormal\nbasis for $H$, and let $P_m':W\\rightarrow H_*$ \nbe the corresponding orthogonal projections.\nConsider the inequality (\\ref{e.gab}) with $P_\\ell$ replaced by $P_m'$.\nWriting $P_m-P_m'=(P_m-I) + (I-P_m')$, and \nconsidering terms for each fixed $j$, we have \n\\begin{align*}\n& \\sum_{i_1,\\ldots,i_n=1}^\\infty\n\t\t\\bigg| \\langle P_m^{\\otimes j-1} \\otimes \n\t\t(P_m-I)\\otimes(P_m')^{n-j-1}\\xi(s), h_{i_1}\\otimes\n\t\t\\cdots\\otimes\n\t\th_{i_n} \\rangle_{H^{\\otimes n}}\\bigg|^2 \\\\\n\t&= \\sum_{i_1,\\ldots,i_{j-1}=1}^m \\sum_{i_j=m+1}^\\infty\n\t\t\\sum_{i_{j+1},\\ldots,i_n=1}^\\infty\n\t\t\\bigg|\\langle \\xi(s), h_{i_1}\\otimes\n\t\t\\cdots \\otimes h_{i_j} \n\t\t\\otimes P_m' h_{i_{j+1}}\n\t\t\\otimes\\cdots\\otimes P_m' h_{i_n} \\rangle\n\t\t_{H^{\\otimes n}}\\bigg|^2 \\\\\n\t&\\le \\sum_{i_1,\\ldots,i_{j-1}=1}^m \\sum_{i_j=m+1}^\\infty\n\t\t\\sum_{i_{j+1},\\ldots,i_n=1}^\\infty\n\t\t\\left|\\langle \\xi(s), h_{i_1}\\otimes\n\t\t\\cdots\\otimes h_{i_n} \\rangle\n\t\t_{H^{\\otimes n}}\\right|^2\n\t\\rightarrow 0,\n\\end{align*}\nas $m\\rightarrow\\infty$. Similarly,\n\\begin{align*}\n&\\sum_{i_1,\\ldots,i_n=1}^\\infty\n\t\t\\bigg| \\langle P_m^{\\otimes j-1} \\otimes \n\t\t(I-P_m')\\otimes(P_m')^{n-j-1}\\xi(s), h_{i_1}\\otimes\n\t\t\\cdots\\otimes\n\t\th_{i_n} \\rangle_{H^{\\otimes n}}\\bigg|^2 \\\\\n\t&\\quad= \\sum_{i_1,\\ldots,i_n=1}^\\infty\t\n\t\t\\bigg|\\langle P_m^{\\otimes j-1} \\otimes \n\t\t(I-P_m')\\otimes(P_m')^{n-j-1}\\xi(s), h_{i_1}'\\otimes\n\t\t\\cdots\\otimes h_{i_n}' \\rangle\n\t\t_{H^{\\otimes n}} \\bigg|^2 \n\t\\rightarrow 0,\n\\end{align*}\nas $m\\rightarrow\\infty$. Thus,\n\\[ \\lim_{m\\rightarrow\\infty} \\|P_m^{\\otimes n} \\xi(s) - (P_m')^{\\otimes n}\n\t\t\\xi(s)\\|^2_{H^{\\otimes n}} = 0, \\]\nfor each $s\\in\\Delta_n(t)$. \nThus, for $J_n^{m'}(\\xi)_t := J_n^{P_m'}(\\xi)_t$,\nusing equation (\\ref{e.gub}) with $P=P_m$ and $Q=P_m'$ shows that\n\\[ \\lim_{m\\rightarrow\\infty} \\mathbb{E}|J_n^m(\\xi)_t \n\t- J_n^{m'}(\\xi)_t |^2 = 0, \\]\nagain by dominated convergence. \n\\end{proof}\n\\fi\n\n\n\n\nNow we may use this result to define stochastic integrals taking values in\nanother Hilbert space $K$.\n\n\\begin{prop}\n\\label{p.int2}\nLet $K$ be a Hilbert space and $F\\in L^2(\\Delta_n(t),(H^{\\otimes\nn})^*\\otimes K)$ be a continuous map. That is, $F:\\Delta_n(t)\\times H^{\\otimes\nn}\\rightarrow K$ is a map continuous in $s$ and linear on $H^{\\otimes n}$ such that\n\\[ \\int_{\\Delta_n(t)} \\|F(s)\\|_2^2\\,ds \n\t= \\int_{\\Delta_n(t)} \\sum_{j_1,\\ldots,j_n=1}^\\infty\n\\|F(s)(h_{j_1}\\otimes\\cdots\\otimes h_{j_n})\\|_K^2\\,ds <\\infty. \\]\nThen\n\\[ J_n^m(F)_t := \\int_{\\Delta_n(t)} F(dP_m B_{s_1}\n\t\\otimes\\cdots\\otimes dP_m B_{s_n}) \n\\]\nis a continuous $K$-valued $L^2$-martingale, and there exists a continuous $K$-valued $L^2$-martingale\n$\\{J_n(F)_t\\}_{t\\ge0}$ such that\n\\begin{equation}\n\\label{e.intF}\n\\lim_{m\\rightarrow\\infty} \\mathbb{E}\\left[ \\sup_{\\tau\\le t} \n\t\\|J_n^m(F)_\\tau-J_n(F)_\\tau\\|_{K}^2 \\right] = 0,\n\\end{equation}\nfor all $t<\\infty$. The martingale $J_n(F)$ is well-defined independent of \nthe choice of orthogonal projections, \nand thus will be denoted by\n\\[\nJ_n(F)_t = \\int_{\\Delta_n(t)} F(dB_{s_1}\\otimes\\cdots\\otimes dB_{s_n}).\n\\]\n\\end{prop}\n\n\\begin{proof}\nLet $\\{e_j\\}_{j=1}^{\\infty}$ be an orthonormal basis of $K$. \nSince $\\langle F(s)(\\cdot), e_j \\rangle$ is linear on\n$H^{\\otimes n}$, for each $s$ there exists\n$\\xi_j(s)\\in H^{\\otimes n}$ such that\n\\begin{equation}\n\\label{e.xij} \n\\langle \\xi_j(s), k_1\\otimes\\cdots\\otimes k_n \\rangle \n\t= \\langle F(s)(k_1\\otimes\\cdots\\otimes k_n), e_j \\rangle. \n\\end{equation}\nIf $\\xi_j:\\Delta_n(t)\\rightarrow H^{\\otimes n}$ \nis defined by equation (\\ref{e.xij}), \nthen clearly $\\xi_j\\in L^2(\\Delta_n(t),H^{\\otimes n})$ and in particular\n\\begin{align*}\n\\|F\\|_{L^2(\\Delta_n(t)\\times H^{\\otimes n},K)}^2\n\t&= \\sum_{j=1}^\\infty \\|\\xi_j\\|_{L^2(\\Delta_n(t),H^{\\otimes n})} <\\infty.\n\\end{align*}\nThus, for $J_n(\\xi_j)$ as defined in Proposition \\ref{p.int1},\n\\begin{align*}\n\\mathbb{E}\\left[ \\sum_{j=1}^\\infty |J_n(\\xi_j)_t|^2\\right] \n\t&\\le \\frac{t^n}{n!}\\mathbb{E}\\left[ \\int_{\\Delta_n(t)} \\sum_{j=1}^\\infty |\\langle \\xi_j(s),\n\t\tdB_{s_1}\\otimes\\cdots\\otimes dB_{s_n}\\rangle_{H^{\\otimes n}}|^2\n\t\t\\right] \\\\\n\t&= \\frac{t^n}{n!}\\sum_{j=1}^\\infty \\|\\xi_j\\|^2_{L^2(\\Delta_n(t),H^{\\otimes n})}\n\t<\\infty,\n\\end{align*}\nand so we may write\n\\begin{align*} \n\\sum_{j=1}^\\infty J_n(\\xi_j)_t e_j \n\t&= \\sum_{j=1}^\\infty \\int_{\\Delta_n(t)} \n\t\t\\langle \\xi_j(s),\n\t\tdB_{s_1}\\otimes\\cdots\\otimes dB_{s_n}\\rangle_{H^{\\otimes n}} e_j \\\\\n\t&= \\int_{\\Delta_n(t)} \\sum_{j=1}^\\infty\n\t\t\\langle F(s)(\n\t\tdB_{s_1}\\otimes\\cdots\\otimes dB_{s_n}),e_j\\rangle_K e_j \\\\\n\t&= \\int_{\\Delta_n(t)} \n\t\tF(s)(dB_{s_1}\\otimes\\cdots\\otimes dB_{s_n}).\n\\end{align*}\nThus, taking $J_n(F)_t := \\sum_{j=1}^\\infty J_n(\\xi_j)_t e_j$,\nwe also have that \n\\begin{align*}\n\\mathbb{E}\\|J_n(F)_t - J_n^m(F)_t\\|_K^2\n\t&= \\mathbb{E}\\left[\\sum_{j=1}^\\infty\n\t\t|J_n(\\xi_j)_t-J_n^m(\\xi_j)_t|^2\\right]\n\t\\rightarrow 0\n\\end{align*}\nas $m\\rightarrow\\infty$ by (\\ref{e.Jnm}) and dominated convergence since\n\\[ \\mathbb{E}|J_n(\\xi_j)_t-J_n^m(\\xi_j)_t|^2 \n\t\\le 4\\|\\xi_j\\|_{L^2(\\Delta_n(t),H^{\\otimes n})}^2 \\]\nby (\\ref{e.xi}). Then equation (\\ref{e.intF}) holds by Doob's maximal\ninequality.\n\\end{proof}\n\nNote that the preceding results then imply that one may define the above\nstochastic integrals with respect to {\\it any} increasing sequence of\northogonal projections -- that is, we need not require that the projections\nextend continuously to $W$.\n\n\n\\begin{prop}\n\\label{p.bad}\nLet $V$ be an arbitrary finite-dimensional subspace of $H$, and let\n$\\pi:H\\rightarrow V$ denote orthogonal projection onto $V$. Then for any\nHilbert space $K$ and $F\\in L^2(\\Delta_n(t),(H^{\\otimes\nn})^*\\otimes K)$ a continuous map, the\nstochastic integral \n\\[ J_n^\\pi(F)_t := \\int_{\\Delta_n(t)} F(d\\pi B_{s_1}\n\t\\otimes\\cdots\\otimes d\\pi B_{s_n}) \n\\]\nis well-defined, and $\\{J_n^\\pi(F)_t\\}_{t\\ge0}$\nis a continuous $K$-valued $L^2$-martingale. Moreover, if $V_m$ is an increasing sequence of\nfinite-dimensional subspaces of $H$\nsuch that the corresponding orthogonal projections $\\pi_m\\uparrow I_H$, then \n\\begin{equation*} \n\\lim_{m\\rightarrow\\infty} \\mathbb{E}\\left[ \\sup_{\\tau\\le t} \n\t\\|J_n^{\\pi_m}(F)_\\tau-J_n(F)_\\tau\\|^2 \\right] = 0,\n\\end{equation*}\nwhere $J_n(F)$ is as defined in Proposition \\ref{p.int2}.\n\\end{prop}\n\n\\begin{proof}\nFirst consider the case that $K=\\mathbb{R}$, and thus $F(s)=\\langle\n\\xi(s),\\cdot\\rangle$ for a continuous $\\xi\\in L^2(\\Delta_n(t),H^{\\otimes n})$.\nSince $\\pi^{\\otimes n}\\xi\\in L^2(\\Delta_n(t),H^{\\otimes n})$, the definition\nof $J_n^\\pi(\\xi)=J_n(\\pi^{\\otimes n}\\xi)$ follows from Propostion \\ref{p.int1}. Moreover, by equation\n(\\ref{e.xi}), \n\\begin{align*}\n\\mathbb{E}|J_n^{\\pi_m}(\\xi)_t - J_n(\\xi)_t|^2\n\t&= \\mathbb{E}|J_n(\\pi_m^{\\otimes n}\\xi)_t - J_n(\\xi)_t|^2 \n\t= \\mathbb{E}|J_n(\\pi_m^{\\otimes n}\\xi-\\xi)_t|^2 \\\\\n\t&\\le \\|\\pi_m^{\\otimes n}\\xi-\\xi\\|_{L^2(\\Delta_n(t),H^{\\otimes n})}\n\t\\rightarrow 0\n\\end{align*}\nas $m\\rightarrow\\infty$. Now the proof for general $F$ follows just as in\nProposition \\ref{p.int2}.\n\\end{proof}\n\n\n\n\n\\section{Abstract nilpotent Lie algebras and groups}\n\\label{s.prelim}\n\n\n\n\\begin{defn}\n\\label{d.semi}\nLet $(\\mathfrak{g},\\mathfrak{g}_{CM},\\mu)$ be an abstract\nWiener space such that $\\mathfrak{g}_{CM}$ is equipped with a nilpotent\nHilbert-Schmidt Lie bracket. Then we will call\n$(\\mathfrak{g},\\mathfrak{g}_{CM},\\mu)$ an {\\it abstract nilpotent Lie\nalgebra}.\n\\end{defn}\n\n\nThe Baker-Campbell-Hausdorff-Dynkin formula implies that\n\\[ \\log(e^A e^B) = A+B+\\sum_{k=1}^{r-1} \n\t\t\\sum_{(n,m)\\in\\mathcal{I}_k} \n\t\ta_{n,m}^k\\mathrm{ad}_A^{n_1} \\mathrm{ad}_B^{m_1} \\cdots\n\t\t\\mathrm{ad}_A^{n_k} \\mathrm{ad}_B^{m_k} A,\n\\]\nfor all $A,B\\in\\mathfrak{g}_{CM}$, where \n\\begin{equation*} \na_{n,m}^k := \\frac{(-1)^k}{(k+1)m!n!(|n|+1)}, \n\\end{equation*}\n$\\mathcal{I}_k := \\{(n,m)\\in\\mathbb{Z}_+^k\\times\\mathbb{Z}_+^k : \nn_i+m_i>0 \\text{ for all } 1\\le i\\le k \\}$, and for each multi-index\n$n\\in\\mathbb{Z}_+^k$,\n\\[ n!= n_1!\\cdots n_k! \\quad \\text{ and } \\quad |n|=n_1+\\cdots+n_k; \\]\nsee, for example, \\cite{duiskolk}. If $\\mathfrak{g}_{CM}$ is nilpotent of step\n$r$, then\n\\[ \\mathrm{ad}_A^{n_1} \\mathrm{ad}_B^{m_1} \\cdots\n\t\t\\mathrm{ad}_A^{n_k} \\mathrm{ad}_B^{m_k} A = 0 \\quad\n\\text{if } |n|+|m|\\ge r. \\]\nfor $A,B\\in\\mathfrak{g}_{CM}$. \nSince $\\mathfrak{g}_{CM}$ is simply connected and nilpotent, \nthe exponential map is a global diffeomorphism (see, for\nexample, Theorems 3.6.2 of \\cite{Varadarajan} or 1.2.1 of \\cite{CorGrn90}). \nIn particular, we may view $\\mathfrak{g}_{CM}$ as both a Lie algebra and Lie group, and \none may verify that\n\\begin{align}\n\\label{e.mult}\ng\\cdot h &= g+h+\\sum_{k=1}^{r-1} \n\t\t\\sum_{(n,m)\\in\\mathcal{I}_k} \n\t\ta_{n,m}^k\\mathrm{ad}_g^{n_1} \\mathrm{ad}_h^{m_1} \\cdots\n\t\t\\mathrm{ad}_g^{n_k} \\mathrm{ad}_h^{m_k} g\n\\end{align}\ndefines a group structure on $\\mathfrak{g}_{CM}$. Note that $g^{-1}=-g$ and \nthe identity $\\mathbf{e}=(0,0)$.\n\n\\begin{defn}\nWhen we wish to emphasize the group structure on $\\mathfrak{g}_{CM}$, we will\ndenote $\\mathfrak{g}_{CM}$ by $G_{CM}$.\n\\end{defn}\n\n\n\n\n\\begin{lem}\n\\label{l.cts}\nThe Banach space topology on $\\mathfrak{g}_{CM}$ makes $G_{CM}$ into a topological group.\n\\end{lem}\n\\begin{proof}\nSince $\\mathfrak{g}_{CM}$ is a topological vector space, \n$g\\mapsto g^{-1}=-g$ and $(g_1,g_2)\\mapsto g_1+g_2$ are continuous by\ndefinition. The map $(g_1,g_2)\\mapsto [g_1,g_2]$ is continuous in \nthe $\\mathfrak{g}_{CM}$ topology by the boundedness of the Lie bracket.\nIt then follows from (\\ref{e.mult}) that $(g_1,g_2)\\mapsto g_1\\cdot g_2$\nis continuous as well.\n\\end{proof}\n\n\n\n\\label{s.mga}\\subsection{Measurable group actions on $G$}\n\nAs discussed in the introduction, given a Hilbert-Schmidt Lie bracket on\n$\\mathfrak{g}_{CM}$ and a subsequently defined group operation on $G_{CM}$,\none may define a measurable action on $G$ by left or right multiplication by\nan element of $G_{CM}$. \n\nIn particular, let $\\{e_n\\}_{n=1}^\\infty$ be an orthonormal basis of $\\mathfrak{g}_{CM}$. \nFor now, fix $n$ and consider the mapping $\\mathfrak{g}_{CM}\\rightarrow \\mathfrak{g}_{CM}^*$ given by $h\\mapsto \\langle\n\\mathrm{ad}_h\\cdot,e_n\\rangle$. Then this is a continuous linear map on $\\mathfrak{g}_{CM}$\nand in the usual way we may make the identification of\n$\\mathfrak{g}_{CM}^*\\cong \\mathfrak{g}_{CM}$ so that we\ndefine the operator $A_n:\\mathfrak{g}_{CM}\\rightarrow \\mathfrak{g}_{CM}$ given by\n\\[ \\langle A_nh, k\\rangle = \\langle \\mathrm{ad}_h k,e_n\\rangle; \\]\nin particular, $A_nh=\\mathrm{ad}_h^*e_n$. Note that, for any $h,k\\in \\mathfrak{g}_{CM}$\n\\[ \\langle A_n^*h,k\\rangle \n\t= \\langle A_nk,h\\rangle \n\t= \\langle \\mathrm{ad}_k^* e_n,h\\rangle\n\t= \\langle e_n , \\mathrm{ad}_k h \\rangle\n\t= - \\langle e_n , \\mathrm{ad}_h k \\rangle\n\t= - \\langle \\mathrm{ad}_h^* e_n,k\\rangle \\]\nand thus $A_n^*=-A_n$.\nNow fix $h\\in G_{CM}=\\mathfrak{g}_{CM}$. Then for $\\mathrm{ad}_h:\\mathfrak{g}_{CM}\\rightarrow \\mathfrak{g}_{CM}$ we may write\n\\[\n\\mathrm{ad}_h k = \\sum_n \\langle \\mathrm{ad}_hk, e_n\\rangle e_n\n\t= \\sum_n \\langle A_nh,k\\rangle e_n.\n\\]\nSince each $\\langle A_nh,\\cdot\\rangle\\in \\mathfrak{g}_{CM}^*$ has a measurable linear\nextension to $G$ such that $\\|\\langle\nA_nh,\\cdot\\rangle\\|_{L^2(\\mu)} = \\|A_nh\\|_{\\mathfrak{g}_{CM}}$ \n(see, for example, Theorem 2.10.11 of \\cite{Bogachev1998}), we may extend\n$\\mathrm{ad}_h$ to a measurable linear transformation from $G=\\mathfrak{g}$\nto $G_{CM}=\\mathfrak{g}_{CM}$ (still denoted by $\\mathrm{ad}_h$) given by\n\\[ \n\\mathrm{ad}_h g := \\sum_n \\langle A_nh,g\\rangle e_n. \\]\nNote that here we are using the fact that\n\\begin{align*} \\sum_n \\|\\langle A_nh,\\cdot\\rangle\\|_{L^2(\\mu)}^2\n\t= \\sum_n \\| A_nh\\|_{H}^2\n\t&= \\sum_{n,m} \\langle A_nh,e_m\\rangle^2 \\\\\n\t&= \\sum_{n,m} \\langle \\mathrm{ad}_{h}e_m,e_n\\rangle^2 \n\t\\le \\|h\\|^2 \\|[\\cdot,\\cdot]\\|_{HS}^2\n\\end{align*}\nwhich implies that\n\\[ \\sum_n \\langle A_nh,g\\rangle ^2 < \\infty \\quad g\\text{-a.s.} \\]\nSimilarly, we may define\n\\[ \\mathrm{ad}_g h := -\\mathrm{ad}_h g\n\t= - \\sum_n \\langle A_nh,g\\rangle e_n. \\]\n\nIn a similar way, note that we may write, for $h,k\\in G_{CM}$ and $m< r$,\n\\begin{align*} \n\\mathrm{ad}_h^m k \n\t&= \\sum_{\\ell_1}\\cdots \\sum_{\\ell_m} \\left(\\prod_{b=1}^{m-1} \n\t\t\\langle A_{\\ell_{b}}h, e_{\\ell_{b+1}}\\rangle \\right)\\langle\n\t\tA_{\\ell_m}h,k\\rangle e_{\\ell_1} \\\\\n\t&= (-1)^m \\sum_{\\ell_1}\\cdots \\sum_{\\ell_m} \\left(\\prod_{b=1}^{m-1} \n\t\t\\langle A_{\\ell_{b}}e_{\\ell_{b+1}},h \\rangle \\right)\\langle\n\t\tA_{\\ell_m}k,h\\rangle e_{\\ell_1}.\n\\end{align*}\nand thus for $h,k,x\\in G_{CM}$ and $n+m< r$\n\\begin{multline*} \n\\mathrm{ad}_k^n \\mathrm{ad}_h^m x \n\t= (-1)^n \\sum_{j_1}\\cdots \\sum_{j_n}\n\t\t\\sum_{\\ell_1}\\cdots\\sum_{\\ell_m} \n\t\t\\left(\\prod_{a=1}^{n-1} \n\t\t\t\\langle A_{j_{a}}e_{j_{a+1}},k \\rangle \\right)\n\t\t\\langle A_{j_n}e_{\\ell_1},k\\rangle \\\\\n\t\t\\left(\\prod_{b=1}^{m-1} \n\t\t\t\\langle A_{\\ell_{b}}h, e_{\\ell_{b+1}} \\rangle \\right)\n\t\t\\langle A_{\\ell_m}h,x\\rangle e_{j_1}.\n\\end{multline*}\n\n\nMore generally for $|n|+|m|-\\infty$ and $k$ is the largest constant such that\n\\[ \\langle \\mathrm{Ric}^\\pi X,X\\rangle_{\\mathfrak{g}_\\pi} \\ge \n\tk \\|X\\|^2_{\\mathfrak{g}_\\pi}, \n\t\\quad \\text{ for all } X \\in\\mathfrak{g}_\\pi, \\]\nholds uniformly for all $\\mathfrak{g}_\\pi$ finite-dimensional Lie subalgebras\nof $\\mathfrak{g}_{CM}$.\n\\end{prop}\n\n\\begin{proof}\nFor $\\mathfrak{g}$ any nilpotent Lie algebra with orthonormal basis\n$\\Gamma$, \n\\begin{align*}\n\\langle \\mathrm{Ric}\\, X,X\\rangle \n\t&= \\frac{1}{4}\\sum_{Y\\in\\Gamma} \\|\\mathrm{ad}^*_Y X\\|^2 \n\t\t- \\frac{1}{2}\\sum_{Y\\in\\Gamma} \\|\\mathrm{ad}_Y X\\|^2 \n\t\\ge - \\frac{1}{2}\\sum_{Y\\in\\Gamma} \\|[Y,X]\\|^2\n\\end{align*}\nfor all $X\\in\\mathfrak{g}$. Thus, for $\\mathfrak{g}_\\pi$ any\nfinite-dimensional Lie algebra\n\\[ \\langle \\mathrm{Ric}^\\pi X,X\\rangle_{\\mathfrak{g}_\\pi} \\ge \n\tk_\\pi \\|X\\|^2_{\\mathfrak{g}_\\pi}, \n\t\\quad \\text{ for all } X \\in\\mathfrak{g}_\\pi, \\]\nwhere\n\\begin{equation}\n\\label{e.pah}\n k_\\pi := - \\frac{1}{2}\\sup \\left\\{ \n\t\t\\|[\\cdot,X]\\|^2_{\\mathfrak{g}_\\pi^*\\otimes\\mathfrak{g}_\\pi}\n \t\t:\\, \\|X\\|_{\\mathfrak{g}_\\pi}=1 \\right\\} \n\t\\ge - \\frac{1}{2}\\|[\\cdot,\\cdot]\\|_2^2 > -\\infty.\n\\end{equation}\nTaking the infimum of $k_\\pi$ over all $\\mathfrak{g}_\\pi$ completes the proof.\n\\end{proof}\n\n\n\\section{Brownian motion on $G$}\n\\label{s.BM}\nSuppose that $B_t$ is a smooth curve in $\\mathfrak{g}_{CM}$ with\n$B_0=0$, and consider the differential equation\n\\[ \\dot{g}_t = g_t \\dot{B}_t \n\t:= L_{g_t*}\\dot{B}_t, \\quad \\text{ with } g_0=\\mathbf{e}. \\] \nThe solution $g_t$ may be written as follows (see\n\\cite{Strichartz87}): For $t>0$, let $\\Delta_n(t)$ denote the simplex in\n$\\mathbb{R}^n$ given by\n\\[ \\{s=(s_1,\\cdots,s_n)\\in\\mathbb{R}^n: 0\\sigma(j+1)\\}$. Then\n\\begin{multline}\n\\label{e.ode}\ng_t = \\sum_{n=1}^r \\sum_{\\sigma\\in \\mathcal{S}_n} \n\t\\left( (-1)^{e(\\sigma)}\\bigg\/ n^2 \n\t\\begin{bmatrix} n-1 \\\\ e(\\sigma) \\end{bmatrix}\\right) \\times \\\\\n\t\\int_{\\Delta_n(t)} [ \\cdots[\\dot{B}_{s_{\\sigma(1)}},\n\t\\dot{B}_{s_{\\sigma(2)}}],\\ldots,] \\dot{B}_{s_{\\sigma(n)}}]\\, ds,\n\\end{multline}\nwhere the $n=1$ term is understood to be $\\int_0^t dB_s = B_t$.\nUsing this as our motivation, we first explore stochastic integral\nanalogues of equation (\\ref{e.ode}) where the smooth curve $B$ is replaced by\nBrownian motion on $\\mathfrak{g}$.\n\n\n\n\n\n\n\n\\subsection{Brownian motion and finite-dimensional approximations}\n\n\nWe now return to the setting of an abstract Wiener space\n$(\\mathfrak{g},\\mathfrak{g}_{CM},\\mu)$ endowed with a nilpotent\nHilbert-Schmidt Lie bracket on $\\mathfrak{g}_{CM}$.\nAgain, let $B_t$ denote Brownian motion on $\\mathfrak{g}$.\nBy equation (\\ref{e.ode}), the solution to the Stratonovich\nstochastic differential equation\n\\[ \\delta g_t = L_{g_t*} \\delta B_t, \\quad \\text{ with } g_0=\\mathbf{e}, \\]\nshould be given by\n\\begin{equation}\n\\label{e.gt}\ng_t = \\sum_{n=1}^{r} \\sum_{\\sigma\\in\\mathcal{S}_n} c^\\sigma_n \\int_{\\Delta_n(t)} \n\t[ [\\cdots[\\delta B_{s_{\\sigma(1)}},\\delta B_{s_{\\sigma(2)}}],\\cdots], \n\t\\delta B_{s_{\\sigma(n)}}],\n\\end{equation}\nfor coefficients $c_n^\\sigma$ determined by equation (\\ref{e.ode}).\n\nTo understand the integrals in (\\ref{e.gt}), consider the following heuristic\ncomputation.\nLet $\\{M_n(t)\\}_{t\\ge0}$ denote the process in $\\mathfrak{g}^{\\otimes n}$ \ndefined by\n\\[ \nM_n(t) := \\int_{\\Delta_n(t)} \\delta B_{s_1}\\otimes\\cdots\\otimes \\delta\n\tB_{s_n}.\n\\]\nBy repeatedly applying the definition of the Stratonovich integral, \nthe iterated Stratonovich integral $M_n(t)$ \nmay be realized as a linear combination of iterated It\\^o integrals:\n\\[ M_n(t) = \\sum_{m=\\lceil n\/2\\rceil}^n \\frac{1}{2^{n-m}}\n\t\t\\sum_{\\alpha\\in\\mathcal{J}_n^m} I^n_t(\\alpha), \\]\nwhere\n\\[ \\mathcal{J}_n^m := \\left\\{(\\alpha_1,\\ldots,\\alpha_m)\\in\\{1,2\\}^m :\n\t\\sum_{i=1}^m \\alpha_i = n \\right\\}, \\]\nand, for $\\alpha\\in\\mathcal{J}_n^m$, $I_t^n(\\alpha)$ is the iterated \nIt\\^o integral\n\\[ I_t^n(\\alpha) = \\int_{\\Delta_m(t)} dX^1_{s_1}\\otimes\\cdots\\otimes \n\tdX^m_{s_m} \\]\nwith\n\\[ dX_s^i = \\left\\{ \\begin{array}{cl} dB_s & \\text{if } \\alpha_i=1 \\\\\n\t\\sum_{j=1}^\\infty h_j \\otimes h_j \\, ds & \\text{if } \\alpha_i=2\n\t\\end{array} \\right. ; \\]\ncompare with Proposition 1 of \\cite{BenArous89}. This change from multiple\nStratonovich integrals to multiple It\\^o integrals may also be recognized as a\nspecific case of the Hu-Meyer formulas \\cite{HuMeyer88-2,HuMeyer88-1},\nbut we will compute more explicitly to verify that our integrals are well-defined.\n\n\nDefine $F_1:\\mathfrak{g}_{CM}\\to\\mathfrak{g}_{CM}$ by $F_1(k)=k$, and for $n\\in\\{2,\\cdots,r\\}$ define $F_n:\\mathfrak{g}_{CM}^{\\otimes n}\n\\rightarrow\\mathfrak{g}_{CM}$ by\n\\begin{equation}\n\\label{e.1Fn}\n F_n(k_1\\otimes\\cdots\\otimes k_n) \n\t:= [ [ [\\cdots[k_1,k_2],k_3],\\cdots],k_n]. \n\\end{equation}\nFor each fixed $n$ and $\\sigma\\in\\mathcal{S}_n$, define $F_n^\\sigma:\\mathfrak{g}_{CM}^{\\otimes n}\n\\rightarrow\\mathfrak{g}_{CM}$ by\n\\begin{equation}\n\\label{e.Fn}\n\\begin{split}\nF_n^\\sigma(k_1\\otimes\\cdots\\otimes k_n) \n\t&:= F_n(k_{\\sigma(1)}\\otimes\\cdots\\otimes k_{\\sigma(n)}) \\\\\n\t&= [ [\\cdots[k_{\\sigma(1)},k_{\\sigma(2)}],\\cdots], \n\t\tk_{\\sigma(n)}].\n\\end{split}\n\\end{equation}\nThen we may write\n\\[\ng_t = \\sum_{n=1}^{r} \\sum_{\\sigma\\in\\mathcal{S}_n} \n\tc^\\sigma_n F^\\sigma_n (M_n(t))\n\t= \\sum_{n=1}^{r} \\sum_{\\sigma\\in\\mathcal{S}_n}\n\t\\sum_{m=\\lceil n\/2\\rceil}^n \\frac{c^\\sigma_n }{2^{n-m}}\n\t\t\\sum_{\\alpha\\in\\mathcal{J}_n^m} F^\\sigma_n (I^n_t(\\alpha)),\n\\]\npresuming we can make sense of the integrals $F_n^\\sigma(I_t^n(\\alpha))$.\n\nFor each $\\alpha$, let $p_\\alpha=\\#\\{i:\\alpha_i=1\\}$ and $q_\\alpha=\\#\\{i:\n\\alpha_i=2\\}$ (so that $p_\\alpha+q_\\alpha=m$ when $\\alpha\\in\\mathcal{J}_n^m$),\nand let \n\\[ \\mathcal{J}_n := \\bigcup_{m=\\lceil n\/2\\rceil}^n \\mathcal{J}_n^m. \\]\nThen, for each $\\sigma\\in\\mathcal{S}_n$ and $\\alpha\\in\\mathcal{J}_n$, \n\\[ F_n^\\sigma(I_t^n(\\alpha))\n\t= \\int_{\\Delta_{p_\\alpha}(t)} f_\\alpha(s,t) \\hat{F}_n^{\\sigma,\\alpha}\n\t\t(dB_{s_1}\\otimes\\cdots\\otimes dB_{s_{p_\\alpha}}),\n\\]\nwhere $\\hat{F}_n^{\\sigma,\\alpha}$ and $f_\\alpha$ are as follows.\n\nThe map $\\hat{F}_n^{\\sigma,\\alpha}:\\mathfrak{g}^{\\otimes p_\\alpha}\n\\rightarrow\\mathfrak{g}$ is defined by\n\\begin{multline}\n\\label{e.Fhat}\n\\hat{F}_n^{\\sigma,\\alpha}(k_1\\otimes\\cdots\\otimes k_{p_\\alpha}) \\\\\n\t:= \\sum_{j_1,\\ldots,j_{q_\\alpha}=1}^\\infty \n\t\tF_n^{\\sigma'}(k_1\\otimes\\cdots\\otimes k_{p_\\alpha}\n\t\t\\otimes h_{j_1}\\otimes h_{j_1}\n\t\t\\otimes\\cdots\\otimes h_{j_{q_\\alpha}}\\otimes h_{j_{q_\\alpha}}), \n\\end{multline}\nfor $\\{h_j\\}_{j=1}^\\infty$ an orthonormal basis of $\\mathfrak{g}_{CM}$ and\n$\\sigma'=\\sigma'(\\alpha)\\in\\mathcal{S}_n$ given by \n$\\sigma'=\\sigma\\circ\\tau^{-1}$, for any $\\tau\\in\\mathcal{S}_n$ such that\n\\begin{multline*} \n\\tau(dX^1_{s_1}\\otimes\\cdots\\otimes dX^m_{s_m}) \\\\\n\t= \\sum_{j_1,\\cdots,j_{q_\\alpha}=1}^\\infty dB_{s_1}\\otimes\\cdots\n\t\\otimes dB_{s_{p_\\alpha}}\\otimes h_{j_1}\\otimes h_{j_1}\\otimes\\cdots\n\t\\otimes h_{j_{q_\\alpha}}\\otimes h_{j_{q_\\alpha}} ds_1\\cdots\n\tds_{q_\\alpha}.\n\\end{multline*}\n\nTo define $f_\\alpha$, first consider the polynomial of order $q_\\alpha$,\nin the variables $s_i$ with $i$ such that $\\alpha_i=1$ and in the variable $t$, given by evaluating the integral\n\\begin{equation}\n\\label{e.fprime} \nf_\\alpha'( (s_i:\\alpha_i=1),t)\n\t= \\int_{\\Delta'_{q_\\alpha}(t)} \\prod_{i: \\alpha_i=2} ds_i,\n\\end{equation}\nwhere $\\Delta'_{q_\\alpha}(t)=\\{s_{i-1}N$, $A_{\\sigma(j)}=k_i$ for some \n\\[ i\\in I := I(\\sigma):= \\{ \\sigma(j):j=N+1,\\ldots,n\\} \\subseteq\n \\{1,\\ldots,p_\\alpha\\}= \\{1,\\ldots,n-2\\}. \\]\nThus, writing\n\\[ \\mathcal{A}(h_1, k_i : i\\in I^c) \n\t:= [ [ \\ldots[A_{\\sigma(1)},A_{\\sigma(2)}],\\ldots],A_{\\sigma(N-1)}], \\]\nwe have that\n\\begin{multline*}\nF_n^\\sigma(k_1\\otimes\\cdots\\otimes k_{n-1}\\otimes h_1\\otimes h_1) \\\\\n\t= \\sum_{e_1}\n\t\t\\,\\langle \\mathcal{A}(h_1,k_i:i\\in I^c),e_1\\rangle_{\\mathfrak{g}_{CM}}\n\t\t[ [ \\ldots[e_1,h_1],A_{\\sigma(N+1)}],\\ldots,A_{\\sigma(n)}],\n\\end{multline*}\nand so\n\\begin{align*}\n\\|\\hat{F}_n^{\\sigma,\\alpha}\\|_2^2\n\t&= \\sum_{k_1,\\ldots,k_{n-1}}\\left\\|\n\t\t\\sum_{h_1} \\,F_n^\\sigma(k_1\\otimes\\cdots\\otimes k_{n-1}\\otimes h_1\\otimes h_1)\n\t\t\\right\\|_{\\mathfrak{g}_{CM}}^2 \\\\\n\t&\\le \\sum_{k_1,\\ldots,k_{n-1}} \\left( \\sum_{h_1,e_1} \n\t\t|\\langle \\mathcal{A}(h_1,k_i :i\\in I^c),e_1\\rangle\n\t\t_{\\mathfrak{g}_{CM}}|^2 \\right) \\\\\n\t&\\qquad\\qquad\\qquad\\quad\\times\n\t\t\\left( \\sum_{h_1,e_1} \n\t\t\\|[ [[e_1,h_1],A_{\\sigma(N+1)}],\\ldots,A_{\\sigma(n)}]\\|\n\t\t_{\\mathfrak{g}_{CM}}^2 \\right) \\\\\n\t&= \\left(\\sum_{k_i:i\\in I^c,h_1,e_1} |\\langle \\mathcal{A}(h_1,k_i :i\\in I^c),e_1\\rangle\n\t\t_{\\mathfrak{g}_{CM}}|^2 \\right) \\\\\n\t&\\qquad\\qquad\\qquad\\quad \\times \n\t\t\\left( \\sum_{k_i:i\\in I,h_1,e_1} \n\t\t\\|[ [\\ldots[[e_1,h_1],A_{\\sigma(N+1)}],\\ldots],A_{\\sigma(n)}]\\|\n\t\t_{\\mathfrak{g}_{CM}}^2 \\right) \\\\\n\t&\\le \\|[ [ \\ldots[\\cdot,\\cdot],\\ldots],\\cdot]\\|\n\t\t_{(\\mathfrak{g}_{CM}^*)^{\\otimes N-1}\\otimes\\mathfrak{g}_{CM}}^2\n\t\t\\|[ [ \\ldots[\\cdot,\\cdot],\\ldots],\\cdot]\\|\n\t\t_{(\\mathfrak{g}_{CM}^*)^{\\otimes n-N+1}\\otimes\\mathfrak{g}_{CM}}^2.\n\\end{align*}\n \nNow more generally when $q_\\alpha\\ge2$, we may similarly ``separate'' the\npairs of $h_j$'s as above. More precisely, define\n\\[ \\Phi(b) := \\Phi_\\alpha(b) := \\left\\{ \\begin{array}{ll} \n\tb & \\text{if } b=1,\\ldots, p_\\alpha \\\\\n\t\\lceil\\frac{b-p_\\alpha}{2}\\rceil+p_\\alpha & \\text{if } b=p_\\alpha+1,\\ldots,n\n\\end{array}\\right. .\\]\nLet $N_0=1$, and set\n\\[ \\Omega_1^j := \\{ \\Phi(\\sigma(\\ell)) : \\ell=N_0,\\ldots,j-1\\} \\quad\n\\text{ and } \\quad N_1 := \\min\\{ j>N_0:\n\t\\Phi(\\sigma(j))\\in\\Omega_1^j\\}, \\]\n\\[ \\Omega_2^j := \\{\\Phi(\\sigma(\\ell)):\\ell=N_1,\\ldots,j-1\\} \\quad\n\\text{ and } \\quad N_2 := \\min\\{ j>N_1:\n\t\\Phi(\\sigma(j))\\in\\Omega_2^j\\}. \\]\nSimilarly, we define \n\\[ \\Omega_{2m+1}^j := \\{\\Phi(\\sigma(\\ell)):\\ell=N_{2m},\\ldots,j-1 \\}, \n\t \\]\n\\[ N_{2m+1} := \\min\\left\\{ j>N_{2m}: \\Phi(\\sigma(j))\\in \\bigcup_{i=0}^{m-1}\n\t\\Omega_{2i+1}^{N_{2i+1}} \\cup \\Omega_{2m+1}^j \\right\\}, \\]\n\\[ \\Omega_{2m}^j := \\{\\Phi(\\sigma(\\ell)):\\ell=N_{2m-1},\\ldots,j-1 \\}, \\text{\n\tand} \n\\]\n\\[ N_{2m} := \\min\\left\\{ j>N_{2m-1}: \\Phi(\\sigma(j))\\in\n\t\\bigcup_{i=1}^{m-1} \\Omega_{2m}^{N_{2m}}\\cup \\Omega_{2m}^j\\right\\}.\n\\]\n\n\nThen there is an $M0$, let $\\nu_t=\\mathrm{Law}(g_t)$ be the {\\em heat kernel measure at\ntime $t$}, a probability measure on $G$.\n\\end{defn}\n\n\n\n\\begin{prop}[Finite-dimensional approximations]\n\\label{p.approx}\nFor $G_\\pi$ a finite-dimensional Lie subgroup of\n$G_{CM}$, let $\\pi$ denote orthogonal projection of\n$G_{CM}$ onto\n$G_\\pi$ and let $g^\\pi_t$ be the continuous process on $G_\\pi$\ndefined by\n\\[ g_t^\\pi = \\sum_{n=1}^r \\sum_{\\sigma\\in\\mathcal{S}_n}\n\t\\sum_{m=\\lceil n\/2\\rceil}^n \\frac{c^\\sigma_n }{2^{n-m}}\n\t\t\\sum_{\\alpha\\in\\mathcal{J}_n^m}\n\t\\int_{\\Delta_{p_\\alpha}(t)} f_\\alpha(s,t) \\hat{F}_n^{\\sigma,\\alpha}\n\t(d\\pi B_{s_1}\\otimes\\cdots\\otimes d\\pi B_{s_{p_\\alpha}}), \\]\nwhere the stochastic integrals are defined as in Proposition \\ref{p.bad}.\nThen $g_t^\\pi$ is Brownian motion on $G_\\pi$. In particular, for\n$G_\\ell=G_{\\pi_\\ell}$ an increasing sequence of finite-dimensional Lie\nsubgroups such that the associated orthogonal projections $\\pi_\\ell$ are\nincreasing to $I_{\\mathfrak{g}_{CM}}$, let\n$g^\\ell_t=g_t^{\\pi_\\ell}$. Then, for all $t<\\infty$,\n\\begin{equation} \n\\label{e.b}\n\\lim_{\\ell\\rightarrow\\infty}\\mathbb{E}\n\t\\left\\|g^\\ell_t-g_t\\right\\|_\\mathfrak{g}^2 = 0. \n\\end{equation}\n\\end{prop}\n\\begin{proof}\nFirst note that $g_t^\\pi$ solves the Stratonovich equation \n$\\delta g_t^\\pi = L_{g_t^\\pi*}\\delta \\pi B_t$ with $g_0^\\pi=\\mathbf{e}$, see\n\\cite{BenArous89,Castell93,Baudoin04} where $\\langle \\pi B\\rangle_t$ is a\nstandard $\\mathfrak{g}_\\pi$-valued Brownian motion. \nThus, $g_t^\\pi$ is a $G_\\pi$-valued\nBrownian motion. \n\n\n\nBy equation (\\ref{e.a}) and its preceding discussion,\n\\[ g_t^\\ell = \\sum_{n=1}^r \\sum_{\\sigma\\in\\mathcal{S}_n}\n\t\\sum_{m=\\lceil n\/2\\rceil}^n \\frac{c^\\sigma_n }{2^{n-m}}\n\t\t\\sum_{\\alpha\\in\\mathcal{J}_n^m}\n\t\\sum_{a=0}^{q_\\alpha} b_\\alpha^a t^a J_n^\\ell(\\tilde{f}_\\alpha\n\t\t\\hat{F}_n^{\\sigma,\\alpha})_t, \\]\nand thus, to verify (\\ref{e.b}), it suffices to show that\n\\[ \\lim_{\\ell\\rightarrow\\infty} \\mathbb{E}\\|\\pi_{\\ell}B_t- B_t\\|_\\mathfrak{g}^2 =\n0 \\]\nand\n\\[ \\lim_{\\ell\\rightarrow\\infty} \\mathbb{E} \n\t\\left\\|J_n^\\ell(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_t -\n\tJ_n(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_t\\right\\|^2\n\t = 0, \\]\nfor all $n\\in\\{2,\\ldots,r\\}$, $\\sigma\\in\\mathcal{S}_n$ and\n$\\alpha\\in\\mathcal{J}_n$.\n\nSo let $\\mu_t=\\mathrm{Law}(B_t)$. Then it is known that, if $V$ is a finite-dimensional subspace\nof $\\mathfrak{g}_{CM}$ and $\\pi_V$ is the orthogonal projection from\n$\\mathfrak{g}_{CM}$ to $V$, then $\\pi_V$ admits a $\\mu_t$-a.s.~unique\nextension to $\\mathfrak{g}$. Moreover, if $V_n$ is an increasing sequence of\nfinite-dimensional subspaces, then \n\\[ \\lim_{n\\rightarrow\\infty} \\mathbb{E}\\|\\pi_{V_n}B_t- B_t\\|_\\mathfrak{g}^2 = 0; \\]\nsee for example Section 8.3.3 of \\cite{Stroock2011}.\n\nBy Proposition \\ref{p.HS},\n$\\hat{F}_n^{\\sigma,\\alpha}$ is Hilbert-Schmidt, and recall that\n$\\tilde{f}_\\alpha$ is a deterministic polynomial function in $s$. Thus\n$J_n^\\ell(\\tilde{f}_\\alpha \\hat{F}_n^{\\sigma,\\alpha})$ and\n$J_n(\\tilde{f}_\\alpha \\hat{F}_n^{\\sigma,\\alpha})$ are \n$\\mathfrak{g}_{CM}$-valued martingales as defined in Proposition \\ref{p.bad},\nand Proposition \\ref{p.bad} gives the desired convergence as well (in\n$\\mathfrak{g}_{CM}$ and thus in $\\mathfrak{g}$).\n\\end{proof}\n\n\\begin{remark}\nIn fact, for each of the stochastic integrals $J_n(\\tilde{f}_\\alpha\n\\hat{F}_n^{\\sigma,\\alpha})$, it is possible to prove the stronger convergence\nthat, for all $p\\in[1,\\infty)$,\n\\[ \\lim_{\\ell\\rightarrow\\infty} \\mathbb{E}\\left[ \\sup_{\\tau\\le t} \n\t\\left\\|J_n^\\ell(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_\\tau -\n\tJ_n(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_\\tau\\right\\|^p\n\t\\right] = 0, \\]\nfor all $n\\in\\{2,\\ldots,r\\}$, $\\sigma\\in\\mathcal{S}_n$ and\n$\\alpha\\in\\mathcal{J}_n$. Again, Proposition \\ref{p.bad} gives the limit for $p=2$ and thus for\n$p\\in[1,2]$. \nFor $p>2$, Doob's maximal inequality implies it suffices to show that \n\\[ \\lim_{\\ell\\rightarrow\\infty} \\mathbb{E}\n\t\\left\\|J_n^\\ell(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_t -\n\tJ_n(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_t \n\t\\right\\|^p = 0. \\] \nSince each $J_n^\\ell(\\tilde{f}_\\alpha \\hat{F}_n^{\\sigma,\\alpha})$ and\n$J_n(\\tilde{f}_\\alpha \\hat{F}_n^{\\sigma,\\alpha})$ has chaos expansion\nterminating at degree $n$, a theorem of Nelson (see Lemma 2 of\n\\cite{Nelson73b} and pp. 216-217 of \\cite{Nelson73c}) implies that,\nfor each $j\\in\\mathbb{N}$, there exists $c_j<\\infty$ such that \n\\[ \\mathbb{E}\\left\\|J_n^\\ell(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_t -\n\t\tJ_n(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_t \n\t\t\\right\\|^{2j}\n\t\\le c_j \\left(\\mathbb{E}\\left\\|\n\t\tJ_n^\\ell(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_t -\n\t\tJ_n(\\tilde{f}_\\alpha\\hat{F}_n^{\\sigma,\\alpha})_t \n\t\t\\right\\|^2\\right)^j. \\]\n\\end{remark}\n\nIn a similar way, one may prove the following convergence for the Brownian\nmotions under right translations by elements of $G_{CM}$.\n\n\\begin{prop}\n\\label{p.rconv}\nFor any $y\\in G_{CM}$, \n\\[ \\lim_{\\ell\\rightarrow\\infty}\n\t\\mathbb{E}\\|g_t^\\ell y -g_t y\\|^2_{\\mathfrak{g}} = 0. \\]\nwhere $g_ty$ is the measurable right group action of $y\\in G_{CM}$ on $g_t\\in\nG$, as in Proposition \\ref{p.gp}.\n\\end{prop}\n\n\\iffalse\n\\begin{proof}\nNote that $g_t=B_t+I_t$ where $B_t$ is $\\mathfrak{g}$-valued Brownian motion\nand $I_t$ is a finite sum of stochastic integrals taking values in\n$\\mathfrak{g}_{CM}$. Thus,\n\\begin{align*}\ng_t\\cdot h &= g_t+h+\\sum_{k=1}^{r-1} \n\t\t\\sum_{(n,m)\\in\\mathcal{I}_k} \n\t\ta_{n,m}^k\\mathrm{ad}_{g_t}^{n_1} \\mathrm{ad}_h^{m_1} \\cdots\n\t\t\\mathrm{ad}_{g_t}^{n_k} \\mathrm{ad}_h^{m_k} g_t\n\\end{align*}\nFor any $x,y\\in W$, \n\\begin{align*} \n\\mathrm{ad}_{x+y}&^{n_1} \\mathrm{ad}_h^{m_1} \\cdots\n\t\t\\mathrm{ad}_{x+y}^{n_k} \\mathrm{ad}_h^{m_k} (x+y) \\\\\n\t&= (\\mathrm{ad}_x + \\mathrm{ad}_y)^{n_1} \\mathrm{ad}_h^{m_1} \\cdots\n\t\t(\\mathrm{ad}_{x} +\\mathrm{ad}_{y})^{n_k} \\mathrm{ad}_h^{m_k} (x+y)\n\t\t\\\\\n\t&= \\sum_{\\ell}\n\t\t\\mathrm{ad}_x^{\\ell_1^1}\\mathrm{ad}_{y}^{\\ell_2^1} \\cdots\n\t\t\\mathrm{ad}_x^{\\ell_{n_1-1}^1}\\mathrm{ad}_{y}^{\\ell_{n_1}^1} \\mathrm{ad}_h^{m_1}\\cdots\n\t\t\\mathrm{ad}_x^{\\ell_1^k}\\mathrm{ad}_{y}^{\\ell_2^k} \\cdots\n\t\t\\mathrm{ad}_x^{\\ell_1^k}\\mathrm{ad}_{y}^{\\ell_2^k} \\mathrm{ad}_h^{m_k} (x+y)\n\\end{align*}\n\n\\end{proof}\n\\fi\n\n\n\\begin{remark}\n\tNote that, while the present paper focuses on the case where $\\mu$ is non-degenerate and $B$ is Brownian motion on $G$, the above construction and finite-dimensional approximations would all follow with essentially no modification if one considered instead a Gaussian measure $\\mu$ whose support was, for example, a subspace $\\frak{h}$ of $\\frak{g}$ such that $\\frak{h}$ generates the span of $\\frak{g}$ via the Lie bracket.\n\\end{remark}\n\n\n\\subsection{Quasi-invariance and log Sobolev}\n\\label{s.hki}\n\n\n\nWe are now able to prove Theorem \\ref{t.quasi}, which states that the heat kernel measure $\\nu_t =\n\\mathrm{Law}(g_t)$ is\nquasi-invariant under left and right translation by elements of $G_{CM}$ and\ngives estimates for the Radon-Nikodym derivatives of the ``translated'' measures. Given the results so far, the proof could be given as an application of Theorem 7.3 and Corollary 7.4 of \\cite{DriverGordina2009}. However, we provide here a full proof for the reader's convenience.\n\n{\\it Proof of Theorem \\ref{t.quasi}.}\nFix $t>0$ and $\\pi_0$ an orthogonal projection onto a finite-dimensional\nsubspace $G_0$ of $\\mathfrak{g}_{CM}$. Let $h\\in G_0$, and\n$\\{\\pi_n\\}_{n=1}^\\infty$ be an increasing sequence of projections such that\n$G_0\\subset \\pi_nG_{CM}$ for all $n$ and $\\pi_n|_{G_{CM}}\\uparrow\nI_{G_{CM}}$. Let $J^{n,r}_t(h,\\cdot)$ denote the Radon-Nikodym derivative of\n$\\nu_t^n\\circ R_h^{-1}$ with respect to $\\nu_t^n$. Then for each $n$ and for\nany $q\\in[1,\\infty)$, we have the following integrated Harnack inequality\n\\[ \\left(\\int_{G_n} \\left(J^{n,r}_t(h,g)\\right)^q\n\td\\nu_t^n(g)\\right)^{1\/q} \\le\n\\exp\\left(\\frac{(q-1)k}{2(e^{kt}-1)}d_n(e,h)^2\\right)\n\\]\nwhere $k$ is the uniform lower bound on the Ricci curvature as in Proposition\n\\ref{p.Ric} and $d_n$ is Riemannian distance on $G_n$;\nsee for example Theorem 1.6 of \\cite{DriverGordina2009}.\n\n\nBy Proposition \\ref{p.rconv}, we have that for any $f\\in C_b(G)$, the class of bounded continuous\nfunctions on $G$\n\\begin{equation}\n\\label{e.5.7}\n\\begin{split}\n\\int_{G} f(gh) d \\nu_{t}(g)\n\t&= \\mathbb{E}[f(g_th)] \\\\\n\t&= \\lim_{n\\rightarrow\\infty} \\mathbb{E}[f(g_t^nh)]\n\t= \\lim_{n\\rightarrow\\infty}\\int_{G_{n}} (f\\circ i_n)(gh) \\,\nd\\nu_t^{n}(g),\n\\end{split}\n\\end{equation}\nwhere $ i_n:G_n\\rightarrow G$ denotes the inclusion map. Note that for any $n$\n\\begin{align*}\n\\int_{G_n} |(f\\circ i_n)(gh)|\\,d\\nu_t^n(g)\n\t&= \\int_{G_n} J^{n,r}_t(h,g)|(f\\circ i_n)(g)|d\\nu_t^n(g) \\\\\n\t&\\le \\|f\\circ i_n\\|_{L^{q'}(G_n,\\nu_t^n)} \\exp\\left(\n\t\t\\frac{k(q-1)}{2(e^{kt}-1)}d_n(e,h)^2\n\t\t\\right),\n\\end{align*}\nwhere $q'$ is the conjugate exponent to $q$.\nAllowing $n\\rightarrow\\infty$ in this last inequality yields\n\\begin{equation}\n\\label{e.c}\n\\int_G |f(gh)|\\,d\\nu_t(g)\n\t\\le \\|f\\|_{L^{q'}(G,\\nu_t)} \\exp\\left(\n\t\t\\frac{k(q-1)}{2(e^{kt}-1)}d(e,h)^2\n\t\t\\right),\n\\end{equation}\nby equation \\eqref{e.5.7} and the fact that\nthe length of a path in $G_{CM}$\ncan be approximated by the lengths of paths in the finite-dimensional\nprojections. That is, for any\n$\\pi_0$ and $\\varphi\\in C^1([0,1],G_{CM})$ with\n$\\varphi(0)=\\mathbf{e}$, there exists an increasing sequence\n$\\{\\pi_n\\}_{n=1}^\\infty$ of orthogonal projections such that $\\pi_0\\subset \\pi_n$,\n$\\pi_n|_{\\mathfrak{g}_{CM}}\\uparrow I_{\\mathfrak{g}_{CM}}$, and \n\\[ \\ell_{CM}(\\varphi) = \\lim_{n\\rightarrow\\infty}\n\t\\ell_{G_{\\pi_n}}(\\pi_n\\circ\\varphi). \\]\nTo see this, let $\\varphi$ be a path in $G_{CM}$. Then one may show that\n\\begin{align*}\n\\ell_{G_{\\pi_n}}(\\pi_n\\circ\\varphi)\n\t&=\\int_0^1 \\left\\|\\pi_n\\varphi'(s) + \\sum_{\\ell=1}^{r-1}\n\t\tc_\\ell \\mathrm{ad}_{\\pi_n\\varphi(s)}^\\ell \\pi_n\\varphi'(s)\n\t\t\\right\\|_{\\mathfrak{g}_{CM}}\\,ds \n\\end{align*}\nfor appropriate coefficients $c_\\ell$; see for example Section 3 of\n\\cite{Melcher2009}.\nThus, we have proved that \\eqref{e.c} holds for $f\\in C_b(G)$ and $h\\in\n\\cup_{\\pi} G_\\pi$. As this union is dense in $G$ by\nProposition \\ref{p.length},\ndominated convergence along with the continuity of $d(e,h)$ in $h$ implies\nthat \\eqref{e.c} holds for all $h\\in G_{CM}$.\n\nSince the bounded continuous functions are dense in $L^{q'}(G,\\nu_t)$ (see for\nexample Theorem A.1 of \\cite{Janson1997}), the inequality in (\\ref{e.c}) implies that the\nlinear functional $\\varphi_h:C_b(G)\\rightarrow\\mathbb{R}$ defined by\n\\[ \\varphi_h(f) = \\int_G f(gh)\\,d\\nu_t(g) \\]\nhas a unique extension to an element, still denoted by\n$\\varphi_h$, of $L^{q'}(G,\\nu_t)^*$ which satisfies the bound\n\\[ |\\varphi_h(f)| \\le \\|f\\|_{L^{q'}(G,\\nu_t)}\n\t\\exp\\left(\n\t\t\\frac{k(q-1)}{2(e^{kt}-1)}d(e,h)^2\n\t\t\\right) \\]\nfor all $f\\in L^{q'}(G,\\nu_t)$. Since $L^{q'}(G,\\nu_t)^*\\cong L^q(G,\\nu_t)$, there\nthen exists a function $J_t^r(h,\\cdot)\\in\nL^q(G,\\nu_t)$ such that\n\\begin{equation}\n\\label{e.d}\n\\varphi_h(f) = \\int_G f(g)J_t^r(h,g)\\,d\\nu_t(g),\n\\end{equation}\nfor all $f\\in L^{q'}(G,\\nu_t)$, and\n\\[ \\|J_t^r(h,\\cdot)\\|_{L^q(G,\\nu_t)}\n\t\\le \\exp\\left(\n\t\t\\frac{k(q-1)}{2(e^{kt}-1)}d(e,h)^2\n\t\t\\right). \\]\n\nNow restricting (\\ref{e.d}) to $f\\in C_b(G)$, we may rewrite this equation as\n\\begin{equation}\n\\label{e.last}\n\\int_G f(g)\\,d\\nu_t(gh^{-1})\n\t= \\int_G f(g) J_t^r(h,g)\\,d\\nu_t(g).\n\\end{equation}\nThen a monotone class argument (again use Theorem A.1 of\n\\cite{Janson1997}) shows that (\\ref{e.last}) is valid for all\nbounded measurable functions $f$ on $G$. Thus,\n$d(\\nu_t\\circ R_h^{-1})\/d\\nu_t$ exists and is given by $J_t^r(h,\\cdot)$, which is in\n$L^q$ for all $q\\in(1,\\infty)$ and satisfies the desired bound.\n\nA parallel argument gives the\nanalogous result for $d(\\nu_t\\circ L_h^{-1})\/d\\nu_t$. Alternatively, one\ncould use the right translation invariance just proved along with the facts that $\\nu_t$\ninherits invariance under the inversion map $g\\mapsto g^{-1}$ from its\nfinite-dimensional projections and that $d(e,h^{-1})=d(e,h)$.\n\\hfill$\\square$\n\n\nThe following also records the straightforward fact that the heat kernel measure does not charge $G_{CM}$.\n\n\\begin{prop}\nFor all $t>0$, $\\nu_t(G_{CM})=0$.\n\\end{prop}\n\n\\begin{proof}\nThis follows trivially from the fact that $g_t$ is the sum of a Brownian motion $B_t$ on\n$\\mathfrak{g}$ with a finite sequence of stochastic integrals taking values in\n$\\mathfrak{g}_{CM}$.\n\\end{proof}\n\nThus, $G_{CM}$ maintains its role as a dense subspace of $G$ of\nmeasure 0 with respect to the distribution of the ``group Brownian\nmotion''. \n\n\\begin{defn}\n\\label{d.cyl} \nA function $f:G\\rightarrow\\mathbb{R}$ is said to be a\n{\\it (smooth) cylinder function} if $f=F\\circ\\pi$ for some\nfinite-dimensional projection $\\pi$ and\nsome (smooth) function $F:G_\\pi\\rightarrow\\mathbb{R}$. Also, $f$ is a \n{\\it cylinder polynomial} if $f=F\\circ\\pi$ \nfor $F$ a polynomial function on $G_\\pi$.\n\\end{defn}\n\n\n\\begin{thm}\n\\label{t.logsob}\nGiven a cylinder polynomial $f$ on $G$, let\n$\\nabla f:G\\rightarrow\\mathfrak{g}_{CM}$ be the gradient of $f$,\nthe unique element of $\\mathfrak{g}_{CM}$ such that\n\\[ \\langle\\nabla f(g), h \\rangle_{\\mathfrak{g}_{CM}} = \\tilde{h}f(g)\n\t:= f'(g)(L_{g*}h_\\mathbf{e}), \\]\nfor all $h\\in\\mathfrak{g}_{CM}$. \nThen for $k$ as in Proposition \\ref{p.Ric},\n\\[ \\int_G (f^2\\ln f^2)\\,d\\nu_t -\n\t\t\\left(\\int_G f^2\\,d\\nu_t \\right)\\cdot\\ln\\left(\\int_G\n\t\tf^2\\,d\\nu_t\\right)\n\t\\le 2\\frac{1-e^{-kt}}{k} \\int_G \\|\\nabla f\\|_{\\mathfrak{g}_{CM}}^2\n\t\t\\,d\\nu_t. \\]\n\\end{thm}\n\n\\begin{proof}\nFollowing the method of Bakry and Ledoux applied to $G_P$ (see Theorem 2.9 of \n\\cite{Driver96} for the case needed here) shows that\n\\[ \\mathbb{E}\\left[\\left(f^2\\ln f^2\\right)\\left(g^\\pi_t\\right)\\right] \n\t\t- \\mathbb{E}\\left[f^2\\left(g^\\pi_t\\right)\\right] \n\t\t\\ln\\mathbb{E}\\left[f^2\\left(g_t^\\pi\\right)\\right]\n\t\\le 2 \\frac{1 - e^{-k_\\pi t}}{k_\\pi} \\mathbb{E}\\left\\|(\\nabla^\\pi\n\t\tf)\\left(g^\\pi_t\\right)\\right\\|^2_{\\mathfrak{g}_\\pi},\n\\]\nfor $k_\\pi$ as in equation (\\ref{e.pah}). Since the function $x\\mapsto\n(1-e^{-x})\/x$ is decreasing and $k\\le k_\\pi$ for all finite-dimensional\nprojections $\\pi$, \nthis estimate also holds with $k_\\pi$ replaced with $k$. Now applying\nProposition \\ref{p.approx} to pass to the limit as $\\pi\\uparrow I$ gives \nthe desired result.\n\\end{proof}\n\n\n\\bibliographystyle{amsplain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\begin{comment}\nThroughout this paper, the term ``knot\" is used both for a knot and a link. That is, a knot in this paper may have more than one component. In the classical knot theory, knots are treated as combinatorial\nobjects, for example as equivalence classes of polygons in the\nthree-dimensional space or as equivalence classes of diagrams that are related by Reidemeister moves.\nAiming at classifying knots, many knot invariants have been\nintroduced, for instance various polynomials, or integer valued invariants such as the bridge index, the braid index of the genus \\cite{BZ, cromwell2004} .\nWhile the classification of all knots is wide open, many knot families have been classified.\nFor example the 2-bridge knots have been completely classified in the 1950s\nby the work of Schubert~\\cite{schubert}.\nThe latter has stimulated the development of the theory of $3$-manifolds.\n\\end{comment}\n\nIn his famous paper \\cite{milnor}, Milnor showed how a knot invariant may be related to geometric properties of the space curve representing a knot by providing a lower bound of the\ntotal curvature of an embedded closed curve explicitly in terms of its bridge index of the knot the curve represents. In a similar spirit, Langer and Singer~\\cite{LS} raised a question concerning the equilibrium shape (a physical property) of a closed springy wire and the knot invariants of the knot the closed wire represents. Gallotti and Pierre-Louis~\\cite{gallotti} conjectured that the equilibrium shapes within knot types $K$ where braid index $\\qopname\\relax o{braid}(K)$ and\nbridge index $\\qopname\\relax o{bridge}(K)$ coincide, will be the $\\qopname\\relax o{bridge}(K)$-times covered circle. This conjecture has recently been proven in the case of 2-bridge knots~\\cite{GRvdM}. For the sake of convenience, we shall call a knot type whose bridge index and braid index coincide a {\\em BB knot}.\n\nHeuristically, it is plausible that the equilibrium shape\nof a very thin springy wire should be, if topologically possible,\nvery close to a $k$-times covered circle.\nRecalling the F\\'ary--Milnor inequality~\\cite{milnor},\nthe minimum value would be\n$k=\\qopname\\relax o{bridge}(K)$.\nOn the other hand, a configuration of a wire that passes\n$\\qopname\\relax o{bridge}(K)$-times around a circle constitutes in\nfact a braid presentation which requires that $k\\ge \\qopname\\relax o{braid}(K)$\nwhich by $\\qopname\\relax o{braid}(K)\\ge\\qopname\\relax o{bridge}(K)$ implies $\\qopname\\relax o{bridge}(K)=\\qopname\\relax o{braid}(K)$. The left of Figure \\ref{experiment} shows an experiment of the equilibrium state of the figure eight knot realized by a closed springy wire, which is clearly not close to a multiply covered circle. Notice that the figure eight knot is not a BB knot since its bridge index is 2 and braid index is 3. On the other hand, our numerical simulations\nindicate that an equilibrium state of a BB knot $K$ is\nclose to a $\\qopname\\relax o{bridge}(K)$-times covered circle as shown in Figure \\ref{fig:sims}.\n\nThe class of BB knots might also be of potential interest to scientists looking for potential candidates of knot types that can be constructed via molecular knots.\nIn~\\cite{micheletti} the authors give a list of knots that either have been constructed or could potentially be constructed as different molecular knots. At least in the symmetric cases, we observe that there seems to be a prevalence for BB knots, in particular among knot classes with larger crossing number.\n\nIn this light, it is a natural question to investigate the\nfamily of all knot or link classes $K$ with $\\qopname\\relax o{bridge}(K)=\\qopname\\relax o{braid}(K)$. This is the main focus of this paper.\nAs a first step towards a complete classification (which might or might\nnot be feasible) we searched the database KnotInfo \\cite{knotinfo} and found that there are 182 one component BB knots with crossing number up to $12$ (they are listed in Table~\\ref{table}).\nNext, we consider certain families of classes\n(namely torus knots\/links, 2-bridge knots\/links, Montesinos links,\nConway algebraic knots)\nand identify (infinite) subfamilies of BB knots within. \n\nMoreover, there are a number of interesting questions\nconcerning the number $\\mathcal B_{n}$ of BB knots with crossing number $n$.\nDoes $\\mathcal B_{n}$ grow exponentially?\nWhat about the ratio of $\\mathcal B_{n}$ over the number of \\emph{all} knots\nwith with crossing number $n$~?\n\n\\begin{figure}[!h]\n\\label{experiment}\n\\includegraphics[scale=.18,trim=20 0 180 0,clip]{figure8_1}\n\\quad%\n\\rotatebox[origin=c]{-20}{\\reflectbox{\\includegraphics[scale=.36]{bribra\/4_1.png}}}\n\\caption{Left: An experiment with a springy wire belonging to the figure-eight class (Wire model manufactured by \\textsc{why knots}, Aptos, in 1980, photographed by Bernd Bollwerk, Aachen);\nRight: A local minimizer produced by numerical simulation featuring nearly the same shape (see~\\cite{BR} for details).\n}\n\\end{figure}\n\n\nBefore we proceed to the next section, we need to clarify a couple of key terms and concepts used in this paper. First, in what follows we will use the word ``knot'' to indicate both a knot type or a particular knot embedding. What we mean will depend on the context and should be clear to the reader. We will use the term ``knot class'' when we talk about the set of embeddings of a knot type with certain geometric restrictions.\nMoreover, the term ``knot\" in this paper is also used for the commonly used term ``link\" in other literature. That is, a knot in this paper may have more than one component. When there is a need, we shall make this clear by specifying the number of components in the knot being discussed. For example, the BB knots listed in Table \\ref{table} are all knots with one component. Second, in the definition of BB knots, the knots are un-oriented (since orientation plays no role to the physical property of interest that leads to the definition of BB knots). However our approach to this problem requires the use of the braid index defined for oriented knots. We would like to point out the connection between these two definitions to avoid confusion. Let $K$ be an un-oriented knot, $\\vec{K}$ be an oriented knot corresponding to $K$ (that is, each component in $K$ has been assigned an orientation) and $\\qopname\\relax o{braid}(\\vec{K})$ be the braid index of $\\vec{K}$ (as an oriented knot). Then un-oriented braid index $\\qopname\\relax o{braid}(K)$ of $K$, which is what we have used in the above in the definition of BB knots, is defined as the minimum of $\\vec{K}$ where $\\vec{K}$ runs over all possible orientation assignments to the components of $K$. It is known (a rather obvious fact) that $\\qopname\\relax o{braid}(\\vec{K})\\ge \\qopname\\relax o{bridge}(K)$. Thus if $\\qopname\\relax o{braid}(\\vec{K})= \\qopname\\relax o{bridge}(K)$ for some $\\vec{K}$, then we must have $\\qopname\\relax o{braid}(\\vec{K})= \\qopname\\relax o{braid}(K)$. Of course, if $k$ has only one component then for any choice of orientation $\\qopname\\relax o{braid}(\\vec K)=\\qopname\\relax o{braid} (K)$.\n\n\n\nWe organize the rest of the paper as follows: In Sections \\ref{sec:bend} and \\ref{numerics} we discuss BB knots made of thin springy wires (so called \\emph{elastic knots}) and consider the bending energy of elastic knots in a numerical experiment. \nIn Section \\ref{knotfamilies} we identify BB knots among several knot families. In Section \\ref{numberofknots} we show that the number of BB knots with a given crossing number grows exponentially as a function of the crossing number\nand state a few questions.\n\n\\section{Bending energy minimizers within knot classes}\\label{sec:bend}\n\nThe aim of this section is to explain how the BB knots appear in an elementary model\nfor the experiment described in the introduction.\nFor convenience, we will present here a simplified version\nof the model of \\emph{elastic knots} which is described in Section~\\ref{numerics}\nbelow.\n\n\\subsection{A variational problem}\n\nNeglecting all other physical forces such as friction, shear, and twist,\nwe will assume that the behavior of that springy wire is only affected\nby the bending energy of its centerline $\\gamma:\\mathbb{R}\/\\mathbb{Z}\\to\\mathbb{R}^{3}$\n\\[ E_{\\mathrm{bend}}(\\gamma)=\\int_{\\gamma}(\\kappa(s))^{2} ds \\]\nwhere $\\kappa(s)$ denotes the curvature of $\\gamma$ at $s$\nwhile $s$ is an arc-length parameter. Without loss of generality\nwe may assume that $\\gamma$ is parametrized by arc-length which gives\n$E_{\\mathrm{bend}}(\\gamma)=\\|\\gamma''\\|_{L^{2}}^{2}$.\n\nOur aim would be to find global minimizers of $E_{\\mathrm{bend}}$ within a given knot class $K$.\nAs $E_{\\mathrm{bend}}$ is not invariant under scaling, we will consider only unit length embeddings\nwhich leads to considering the set\n\\[ \\mathscr C(K) = \\left\\{\\gamma\\in W^{2,2}(\\mathbb{R}\/\\mathbb{Z},\\mathbb{R}^{3}) \\middle|\n|\\gamma'(s)|\\equiv1, \\gamma(0)=0_{\\mathbb{R}^{3}},\\gamma\\in K\\right\\}. \\]\nHere we fix $\\gamma(0)$ just for technical reasons.\n\nWe will always assume that $K$ is a non-trivial tame knot class,\nfor the other cases are uninteresting.\nThe global minimizer of $E_{\\mathrm{bend}}$ within the unknot class is\njust the circle.\nAs wild knots are not $C^{1}$ and $W^{2,2}\\hookrightarrow C^{1,1\/2}$\nby the Sobolev embedding theorem, $\\mathscr C(K)=\\emptyset$ for wild $K$.\n\n\\subsection{Minimizers}\n\nIt turns out that, except for the trivial knot class, there are no such minimizers:\nAny potential minimizer within $K$ would be $C^{1}$ (due to the Sobolev embedding\n$W^{2,2}\\hookrightarrow C^{1,1\/2}$) and embedded (since it is a knot).\nAccording to~\\cite{DEJvR} %\nthere is a $C^{1}$-neighborhood consisting of knotted curves belonging to $K$ as well.\nIn particular, this neighborhood contains a $W^{2,2}$-neighborhood\nwith the same property which demonstrates that $\\gamma$ is not only\na global minimizer of $E_{\\mathrm{bend}}$ within $K$ but also a local minimizer\nof $E_{\\mathrm{bend}}$ with respect to \\emph{all} curves.\nNow, by Langer and Singer's seminal result~\\cite{LS},\nthe circle is the only curve with the latter property.\nThis fact reflects the observation that there are indeed points of\nself-contact present in physical models.\n\nNevertheless we can consider a minimal sequence $(\\gamma_{k})_{k\\in\\mathbf N}$ with respect to $E_{\\mathrm{bend}}$\nin $\\mathscr C(K)$,\n{\\em i.e.}, $\\gamma_{k}\\in\\mathscr C(K)$ for all $k\\in\\mathbf N$ with $E_{\\mathrm{bend}}(\\gamma_{k})\\to\\inf_{\\mathscr C(K)}E_{\\mathrm{bend}}$ as $k\\to\\infty$.\nBy the fact that $E_{\\mathrm{bend}}(\\gamma)=\\|\\gamma''\\|_{L^{2}}^{2}$\nand $\\gamma(0)=0_{\\mathbb{R}^{3}}$ for all $\\gamma\\in\\mathscr C(K)$\nwe deduce that this minimal sequence is uniformly bounded in $W^{2,2}$.\nBeing a reflexive space, bounded sets in $W^{2,2}$\nare weakly compact.\nTherefore we may extract a subsequence converging to some limit curve $\\gamma_{0}$\nwith respect to the weak $W^{2,2}$-topology.\nAny such limit curve belongs to the weak $W^{2,2}$-closure\nof $\\mathscr{C}(K)$ which will be denoted by $\\overline{\\mathscr{C}(K)}$.\n\nPotentially there can be many of those limit curves, depending on the\nminimal sequence.\nAny of these limit curves has the following properties:\nIt is not embedded (due to~\\cite{LS}, unless $K$ is trivial), so it\nbelongs to the weak $W^{2,2}$-\\emph{boundary} of $\\mathscr C(K)$.\nIts bending energy provides\na lower bound on the bending energy of any curve in $\\mathscr C(K)$.\nThis is due to the fact that $E_{\\mathrm{bend}}$ is lower \\emph{semi}continuous with respect to weak $W^{2,2}$-convergence.\n\n\\subsection{The case of BB knots}\n\nRecall that the F\\'ary--Milner inequality~\\cite{milnor} bounds the\ntotal curvature $\\int\\kappa(s)ds$ of a curve belonging to $K$ by $2\\pi \\qopname\\relax o{bridge}(K)$ such that\nwe have for $\\gamma\\in\\mathscr C(K)$\n\\[ 2\\pi \\qopname\\relax o{bridge}(K)<\\int_{\\gamma}\\kappa(s)ds=\\|\\gamma''\\|_{L^{1}}. \\]\nAs $\\gamma\\mapsto{}\\|\\gamma''\\|_{L^{1}}$ is not continuous with respect to weak\n$W^{2,2}$-convergence it is not clear whether this lower bound\nalso holds for $\\overline{\\mathscr C(K)}$.\nIn fact, using the existence of \\emph{alternating} quadrisecants\nestabished by Denne~\\cite{denne}, this has been\nproven~\\cite[Appendix]{GRvdM} for $\\qopname\\relax o{bridge}(K)=2$. In the following\nwe \\emph{assume} that it also holds for higher-order bridge indices.\nInvoking the Cauchy--Schwarz inequality, the limit curve $\\gamma_{0}\\in\\overline{\\mathscr C(K)}$ satisfies\n\\[ 2\\pi \\qopname\\relax o{bridge}(K)\\le\\int_{\\gamma_{0}}\\kappa(s)ds=\\|\\gamma_{0}''\\|_{L^{1}}\n\\le\\|\\gamma_{0}''\\|_{L^{2}} = \\left(E_{\\mathrm{bend}}(\\gamma_{0})%\n\\right)^{1\/2}. \\]\nUsing a braid representation we can construct\n$C^{2}$-smooth (even $C^{\\infty}$-smooth) curves belonging to $K$ inside the standard\n$\\varrho$-torus in $\\mathbb{R}^{3}$, {\\em i.e.}, the uniform neighborhood of\nwidth $\\varrho\\in(0,1)$ of the unit circle,\nsuch that each disk of the $\\varrho$-torus is intersected $\\qopname\\relax o{braid}(K)$-times.\nTranslating and rescaling,\nwe obtain a family $(\\beta_{\\varrho})_{\\varrho\\in(0,1)}\\subset\\mathscr C(K)$ such that \n$\\beta_{\\varrho}$ converges (with respect to the $W^{2,2}$-norm) to a $\\qopname\\relax o{braid}(K)$-times\ncovered circle of length one as $\\varrho\\searrow0$.\nOf course, the latter has bending energy $(2\\pi \\qopname\\relax o{bridge}(K))^{2}$.\nAs $\\gamma_{0}$ is an $E_{\\mathrm{bend}}$-minimizer within $\\overline{\\mathscr C(K)}$,\nwe arrive, for any $\\varrho\\in(0,1)$, at\n\\[ (2\\pi \\qopname\\relax o{bridge}(K))^{2}\\le\\left(\\int_{\\gamma_{0}}\\kappa(s)ds\\right)^{2}=\\|\\gamma_{0}''\\|_{L^{1}}^{2}\n\\le\\|\\gamma_{0}''\\|^2_{L^{2}} = E_{\\mathrm{bend}}(\\gamma_{0}) \\le E_{\\mathrm{bend}}(\\beta_{\\varrho})\n\\xrightarrow{\\varrho\\searrow0} (2\\pi \\qopname\\relax o{braid}(K))^{2}. \\]\nHere the condition $\\qopname\\relax o{braid}(K)=\\qopname\\relax o{bridge}(K)$ comes into play for it implies that\nall terms in the previous line are equal. Equality in the Cauchy--Schwarz inequality implies\nthat the integrand is constant a.e. Therefore the curvature of the minimizer\n$\\gamma_{0}$ must be equal to $2\\pi \\qopname\\relax o{bridge}(K)$ a.e.\n\n\\subsection{Caveat}\n\nIt is important to note that the latter condition does \\emph{not}\nimply that $\\gamma_{0}$ is a $\\qopname\\relax o{bridge}(K)$-times covered circle.\nIndeed, that would be wrong.\nIn case $\\qopname\\relax o{bridge}(K)=2$ one can rigorously prove~\\cite{GRvdM} that\nany $E_{\\mathrm{bend}}$ minimizer $\\gamma_{0}$ within $\\mathscr C(K)$\nconsists of two circles that either coincide or tangentially meet in\nprecisely one point.\nUp to isometries and reparametrization, the set of those minimizers can be\nparametrized by the angle between the two circles.\nIn the general situation we would expect a number of $\\qopname\\relax o{bridge}(K)$ circles\ntangentially meeting in (at least) one point.\n\nHowever, adding a positive thickness restriction to $\\mathscr C(K)$ or a penalty term to $E_{\\mathrm{bend}}$\nacts like a choice criterion that selects the $\\qopname\\relax o{bridge}(K)$-times covered circle from that family of $E_{\\mathrm{bend}}$-minimizers.\nThis will be outlined below in Section~\\ref{numerics}.\n\n\\section{BB knots realized by elastic wires}\\label{numerics}\n\n\n\\subsection{Elastic knots}\n\nIn Section~\\ref{sec:bend} above we considered the problem\nto minimize $E_{\\mathrm{bend}}$ in the class $\\mathscr C(K)$ of curves\nin the knot class~$K$. In order to do so, we looked at a\nminimal sequence of curves and extracted a subsequence\nthat converges to some limit curve $\\gamma_{0}$.\nThe problem is that these limit curves are not unique.\nFor instance, in the case of 2-bridge torus knots ({\\em i.e.}, the knots $T(n,2)$) we\nobtain an entire one-parameter family of minimizers.\nWhich of them is the most ``realistic'' one,\n{\\em i.e.}, one that would be observed in physical experiments?\n\nTo answer this question,\nwe need to incorporate the fact that we are looking at\nphysical ropes that have some very small thickness in our simulation model.\nThis can be done by either imposing a constraint to the space of curves~\\cite{gallotti,vdM:eke3}, \nor by adding a penalty term to the bending energy~\\cite{sossinsky,GRvdM,vdM:meek}.\nWe will discuss the latter approach, {\\em i.e.}, we shall adopt the following model\n\\[ E_{\\vartheta} = E_{\\mathrm{bend}}+\\vartheta\\mathcal R, \\qquad\\vartheta>0, \\]\nwhere $\\mathcal R$ denotes a self-avoiding functional\nsuch as the ropelength, {\\em i.e.}, the quotient of length over thickness~\\cite{Buck,DEJvR,GM99}.\nNow the minimization problem is well defined~\\cite{GRvdM,vdM:meek},\n{\\em i.e.}, for any $\\vartheta>0$ there is a curve $\\gamma_{\\vartheta}\\in\\mathscr C(K)$\nsuch that $E_{vartheta}(\\gamma_{\\vartheta})=\\inf_{\\mathscr C(K)} E_{\\vartheta}$.\nIn particular, for $\\vartheta\\in(0,1)$ we have that\n\\[ \\|\\gamma_{\\vartheta}''\\|_{L^{2}}^{2}\n\\le E_{\\vartheta}(\\gamma_{\\vartheta})\n= \\inf_{\\mathscr C(K)} E_{\\vartheta}\n\\le E_{\\vartheta}(\\gamma_{1})\n\\le E_{\\mathrm{bend}}(\\gamma_{1}) + \\mathcal R(\\gamma_{1}) \\]\nis uniformly bounded, %\nso we can extract a subsequence as detailed in Section~\\ref{sec:bend}.\nThe limit curve $\\gamma_{0}$ is referred to as an \\emph{elastic knot} for the knot class $K$~\\cite{GRvdM,vdM:meek}.\nNote that, unless $K$ is trivial, $\\gamma_{0}$ is not embedded.\n\nThere are other names for related concepts in the literature, namely\n``stiff knot'' (Gallotti and Pierre-Louis~\\cite{gallotti}),\nand ``normal form'' (Sossinsky who even thought of a complete classification of\nknot classes by this concept, see~\\cite{sossinsky} and references therein).\n\nNote that, a priori, we cannot expect either the minimizers\n$(\\gamma_{\\vartheta})_{\\vartheta>0}$ or the elastic knot $\\gamma_{0}$\nto be unique. A posteriori, one can show that the elastic knot\nfor the 2-bridge torus knot classes is (up to isometries) the doubly covered circle~\\cite{GRvdM}.\nThe proof relies on a generalization of the crookedness estimate in Milnor's proof~\\cite{milnor}.\nCurrently there are no rigorous results concerning elastic knots\nfor other knot classes. However, the $\\qopname\\relax o{braid}(K)$-times covered circle\nis a candidate for an elastic knot for any BB class~$K$.\nVice versa, one might speculate that the BB classes\nare the only ones whose elastic knots are several times\ncovered circles.\n\n\n\\subsection{Simulation of elastic knots}\n\nPhysical and numerical simulations related to elastic knots\nhave been carried out so far\nby Avvakumov and Sossinsky~\\cite{sossinsky}, Gallotti and Pierre-Louis~\\cite{gallotti},\n Gerlach et al.~\\cite{GRvdM}, as well as by one of the authors and colleagues recently in~\\cite{BR,BRR}.\nThe recently launched web application \\textsc{knotevolve}~\\cite{knotevolve} allows for carrying out a large variety of new experiments.\n\nNumerically, it is a challenging problem to approximate elastic knots as we face two forces which push the elastic knots in different directions. The experiments carried out in~\\cite{BR} shed some light on the energy landscape.\nFor instance, it is unlikely that the configuration shown in\nFigure~\\ref{experiment} is an elastic knot for the figure-eight class; see also the discussion in~\\cite[Section~6.3]{GiRvdM}.\n\n\nIn Figure~\\ref{fig:sims} we show some simulation results for BB knots with braid index up to 3. To this end, we applied the algorithm introduced in~\\cite{bartels13,BRR,BR,knotevolve}\nto the regularization parameter $\\vartheta{}=10^{-4}$\nand initial configurations from the Knot Server~\\cite{knotserver}.\nFor the BB knots with braid index 3, these simulation results still show visible deviation from the\nthree-times covered circle, in spite of \nthe relatively small regularization parameter.\n\n\n\n\\newcommand{\\knot}[3][.2]{%\n\\fbox{\\begin{minipage}[b][38mm]{.22\\textwidth}\n\\includegraphics[scale=#1]{bribra\/#2_#3.png}\\par\\vfill\n\\includegraphics[scale=#1]{bribra\/#2_#3f.png}\n\\end{minipage}}\\makebox[0cm][r]{\\raisebox{1ex}{$#2_{#3}$\\ }}}\n\\newcommand{\\sknot}[3][.15]{%\n\\fbox{\\begin{minipage}[b][25mm]{.17\\textwidth}\n\\includegraphics[scale=#1]{bribra\/#2_#3.png}\\par\\vfill\n\\includegraphics[scale=#1]{bribra\/#2_#3f.png}\n\\end{minipage}}\\makebox[0cm][r]{\\raisebox{1ex}{$#2_{#3}$\\ }}}\n\n\n\\begin{figure}%\n\\knot3{1}\\hfill\n\\knot5{1}\\hfill\n\\knot7{1}\\hfill\n\\knot9{1}\\medskip\n\n\\sknot[.19]8{5}\\hfill\n\\sknot8{10}\\hfill\n\\sknot[.19]8{16}\\hfill\n\\sknot[.17]8{17}\\hfill\n\\sknot8{18}\\medskip\n\n\\knot8{19}\\hfill\n\\knot8{20}\\hfill\n\\knot8{21}\\hfill\n\\knot9{16}\n\n\\caption{Approximations of elastic knots for all thirteen BB classes\nwith crossing number at most nine.\nEach curve is shown from top and front view;\ncolors correspond to local curvature.\nThe knots in the first row as well as $8_{19}$ are torus knots.}\\label{fig:sims}\n\n\\end{figure}\n\n\n\n\n\\section{BB knot identification in knot families}\n\\label{knotfamilies}\n\nIn this section we will consider knot families including the torus knots, the 2-bridge knots, the alternating Montesinos knots, the non-alternating Montesinos knots, and the Conway algebraic knots. For the torus knots, the 2-bridge knots and the alternating Montesinos knots we are able to identify all knots within these families that have equal bridge index and braid index. For the non-alternating Montesinos knots and the Conway algebraic knots, we have some partial results. The results for the torus knots and the 2-bridge knots are known, we decide to state them here for the sake of completeness. We would like to point out that the proof for the case of 2-bridge knots is new.\n\n\\subsection{Torus knots} \\label{A}\nEvery chiral pair of torus knots has a representative that can be presented by a pair of positive integers $p$, $q$ such that $p\\ge q\\ge2$ and is denoted by $T(p,q)$, with the number of components in $T(p,q)$ given by $\\gcd(p,q)$. It is well known that $\\qopname\\relax o{braid}(T(p,q))=\\qopname\\relax o{bridge}(T(p,q))=q$ \\cite{Mu2,S2} or \\cite{Sch2007} for a more recent proof. That is, every torus knot has equal bridge index and braid index. It is known that the crossing number of $T(p,q)$ is $(q-1)p$ \\cite{Mu2} hence the number of torus knots with a given crossing number $n$ is at most of the order of $n$. In other words, the number of torus knots will not contribute to the exponential growth of knots with equal bridge index and braid index. It is worthwhile for us to point out that when a torus knot has more than one component, it is assumed that all components are assigned parallel orientations. One component BB torus knots with crossing number up to 12 are listed in Table \\ref{table} marked with a superscript $^t$. \n\n\\subsection{ 2-bridge knots} \n\nThis case is easy to deal with, since the only knots that can arise a 2-braids are the $T(n,2)$ torus knots. Thus we have by default the following theorem:\n\n\\begin{theorem}\\label{torustwobridge} \nLet $K=B(\\alpha,\\beta)$ be a 2-bridge knot. Then $K$ is a BB knot if and only if $K$ is a $T(n,2)$ torus knot.\n\\end{theorem}\n\n\nIn the following we give a second proof of the above theorem, introducing a method that will help us to deal with Montesinos knots in the next subsection and will be used in the proof of Theorem \\ref{exp_thm}. To explain this method we need to describe the family of 2-bridge knots in more detail.\nIt is known that every 2-bridge knot can be represented by an alternating diagram associated with two co-prime positive integers $0<\\beta<\\alpha$ in the following way. A vector $(a_1,a_2,...,a_n)$\nis called a {\\it standard continued fraction decomposition} of $\\frac{\\beta}{\\alpha}$ if $n$ is odd and all $a_i>0$ and\n$$\n\\frac{\\beta}{\\alpha}=\\frac{1}{a_1+\\frac{1}{a_{2}+\\frac{1}{.....\\frac{1}{a_n}}}}. \n$$\nIt may be necessary to allow $a_n=1$ in order to guarantee that the length of the vector $(a_1,a_2,...,a_n)$ is odd. Under these conditions the standard continued fraction expansion of $\\frac{\\beta}{\\alpha}$ is unique. An oriented 2-bridge knot (also called a {\\em 4-plat} or a {\\em rational knot}), denoted by $\\vec{K}=\\vec{B}(\\alpha,\\beta)$, is then presented by the {\\em standard diagram} as shown in Figure \\ref{2bridgeone} using the vector $(a_1,a_2,...,a_n)$. Furthermore, without loss of generality for a standard diagram we can assign the component corresponding to the long arc at the bottom of Figure \\ref{2bridgeone} the orientation as shown, since 2-bridge knots are known to be invertible. When a 2-bridge knot has two components, there are two choices for the orientation of the other component. Usually the two different orientations of the other component lead to two different oriented 2-bridge knots \\cite{BZ, cromwell2004} which may have different braid indices. For example, the braid index of the two-bridge link in Figure \\ref{2bridgeone} is ten, however if we re-orient one of the two components the braid index changes to nine, see Theorem \\ref{2bridge_theorem} below.\n\n\\begin{figure}[htb!]\n\\includegraphics[scale=1]{Figures2018\/fig11}\n\\caption{The 2-bridge knot $\\vec{B}(17426,5075)$ given by $(3,2,3,3,1,2,3,4,4)$.}\n\\label{2bridgeone}\n\\end{figure}\n\nSince all crossings corresponding to a given $a_i$ have the same crossing sign under the given orientation, we will define a signed vector $(b_1,b_2,...,b_n)$ where $b_i =\\pm a_i$ with its sign given by the crossing sign of the crossings corresponding to $a_i$. For example, for $\\vec{K}=\\vec{B}(17426,5075)$ with the orientation shown in Figure \\ref{2bridgeone} we obtain the signed vector $(3,2,3,3,-1,-2,-3,4,-4)$. In \\cite{DEHL2018} the following theorem was established:\n\n\\begin{theorem}\\label{2bridge_theorem} \\cite{DEHL2018} \nLet $\\vec{K}=\\vec{B}(\\alpha,\\beta)$ be an oriented 2-bridge link diagram with signed vector $(b_1,b_2,...,b_{2k+1})$ in the standard form, then its braid index is given by\n\\begin{equation}\\label{2bridgeformula}\n\\textbf{b}(K)=1+\\frac{2+\\mbox{\\rm sign}(b_1)+\\mbox{\\rm sign}(b_{2k+1})}{4}+\\sum_{b_{2j}>0,1\\le j\\le k}\\frac{b_{2j}}{2}+\\sum_{b_{2j+1}<0,0\\le j\\le k}\\frac{|b_{2j+1}|}{2}.\n\\end{equation}\n\\end{theorem}\n\nThe above theorem allows the computation of the braid index of any 2-bridge link using a minimal diagram. The computation of the braid index of an oriented 2-bridge link was first given by Murasugi \\cite{Mu} using a different method depending on a continued fraction expansion of $\\beta\/\\alpha$ using only even integers. That the two methods are equivalent was shown in \\cite{DEHL2018}. The formulation of Theorem \\ref{2bridge_theorem} allows us to prove Theorem \\ref{torustwobridge} in a way that can be generalized to Montesinos knots, see the next subsection.\n\n\n\\begin{proof}\nThe only if part is trivially true since every torus knot has equal bridge index and braid index as we discussed in Subsection \\ref{A}. Now, if $K=B(\\alpha,\\beta)$ is a BB knot, then we must have $\\qopname\\relax o{braid}(K)=2$ hence there exists an oriented version $\\vec{K}=\\vec{B}(\\alpha,\\beta)$ such that $\\qopname\\relax o{braid}(\\vec{K})=2$. By (\\ref{2bridgeformula}) we have \n$$\n2=1+\\frac{2+\\mbox{\\rm sign}(b_1)+\\mbox{\\rm sign}(b_{2k+1})}{4}+\\sum_{b_{2j}>0,1\\le j\\le k}\\frac{b_{2j}}{2}+\\sum_{b_{2j+1}<0,0\\le j\\le k}\\frac{|b_{2j+1}|}{2},\n$$\nwhere $(b_1,b_2,...,b_{2k+1})$ is the signed vector of $\\vec{K}=\\vec{B}(\\alpha,\\beta)$. Notice that $\\mbox{\\rm sign}(b_1)=\\mbox{\\rm sign}(b_2)$ if $b_2\\not=0$. Furthermore, if $b_3>0$ then $b_2$ is even hence $b_2>1$. A proof of this can be found in \\cite{DEHL2018}, a reader can also prove this directly by considering the Seifert circle decomposition of $\\vec{K}$. We will prove the theorem in two separate cases: $\\mbox{\\rm sign}(b_1)=1$ and $\\mbox{\\rm sign}(b_1)=-1$. \n\nCase 1. $\\mbox{\\rm sign}(b_1)=1$. If $\\mbox{\\rm sign}(b_{2k+1})=1$, then\n$$\n0=\\sum_{b_{2j}>0,1\\le j\\le k}\\frac{b_{2j}}{2}+\\sum_{b_{2j+1}<0,0\\le j\\le k}\\frac{|b_{2j+1}|}{2}\\ge \\frac{b_{2}}{2}\\ge 0,\n$$\nhence $b_2=0$. So $k=0$ and $K=T(n,2)$ for some integer $n\\ge 2$. On the other hand, if $\\mbox{\\rm sign}(b_{2k+1})=-1$, then \n\\begin{eqnarray*}\n&&1+\\frac{2+\\mbox{\\rm sign}(b_1)+\\mbox{\\rm sign}(b_{2k+1})}{4}+\\sum_{b_{2j}>0,1\\le j\\le k}\\frac{b_{2j}}{2}+\\sum_{b_{2j+1}<0,0\\le j\\le k}\\frac{|b_{2j+1}|}{2}\\\\\n&\\ge &\n2+\\sum_{b_{2j}>0,1\\le j\\le k}\\frac{b_{2j}}{2}+\\sum_{b_{2j+1}<0,0\\le j\\le k-1}\\frac{|b_{2j+1}|}{2}> 2,\n\\end{eqnarray*}\nwhich is a contradiction hence this case is not possible.\n\nCase 2. $\\mbox{\\rm sign}(b_1)=-1$. If $b_2=0$, then \n\\begin{eqnarray*}\n2&=&1+\\frac{2+\\mbox{\\rm sign}(b_1)+\\mbox{\\rm sign}(b_{2k+1})}{4}+\\sum_{b_{2j}>0,1\\le j\\le k}\\frac{b_{2j}}{2}+\\sum_{b_{2j+1}<0,0\\le j\\le k}\\frac{|b_{2j+1}|}{2}\\\\\n&= &\n1+\\frac{|b_{1}|}{2},\n\\end{eqnarray*}\nhence $b_1=-2$ and $K$ is the Hopf link that is the mirror image of $T(2,2)$ discussed in Case 1 if we ignore the orientation. If $b_2\\not=0$, then $b_2<0$ and it is necessary that $b_1=-1$ and $b_3<0$ in this case (again this can be observed by considering the Seifert circle decomposition of $K$). It follows that \n\\begin{eqnarray*}\n2&=&1+\\frac{2+\\mbox{\\rm sign}(b_1)+\\mbox{\\rm sign}(b_{2k+1})}{4}+\\sum_{b_{2j}>0,1\\le j\\le k}\\frac{b_{2j}}{2}+\\sum_{b_{2j+1}<0,0\\le j\\le k}\\frac{|b_{2j+1}|}{2}\\\\\n&\\ge &\n1+\\frac{1+\\mbox{\\rm sign}(b_{2k+1})}{4}+\\frac{1+|b_{3}|}{2}.\n\\end{eqnarray*}\nThus we must have $b_3=-1$ and $\\mbox{\\rm sign}(b_{2k+1})=-1$. However $b_3=-1$ implies either $b_4=0$ or $b_4>0$. Since $b_4>0$ leads to $\\qopname\\relax o{braid}(\\vec{K})>2$, we must have $b_4=0$, hence $k=1$ and the signed vector of $K$ is of the form $(-1,-(n-2),-1)$ for some $n>2$. This is the mirror image of the torus knot $T(n,2)$ discussed in Case 1 above if we ignore the orientation.\n\\end{proof}\n\n\\subsection {Alternating Montesinos knots} We now consider the set of all alternating Montesinos knots, a family that is much larger than the family of 2-bridge knots. In general, a Montesinos knot $K=M(\\beta_1\/\\alpha_1,\\ldots, \\beta_k\/\\alpha_s,\\delta)$ is a knot with a diagram as shown in Figure \\ref{Montesinos}, where each diagram within a topological circle (which is only for the illustration and not part of the diagram) is a rational tangle $A_j$ that corresponds to some rational number $\\beta_j\/\\alpha_j$ with $|\\beta_j\/\\alpha_j|<1$ and $1\\le j\\le s$ for some positive integer $s\\ge 2$, and $\\delta$ is an integer that stands for an arbitrary number of half-twists, see Figure \\ref{Montesinos}.\n\n\\begin{figure}[htb!]\n\\includegraphics[scale=.4]{Figures2018\/fig14}\n\\caption{A diagram depicting a general Montesinos knot with $s$ rational tangles and $\\delta$ horizontal half-twists. The arrows indicate the potential orientation assignments if the knot is to be oriented.}\n\\label{Montesinos}\n\\end{figure}\n\nThe bridge index of a general Montesinos knot (alternating or nonalternating) is known and given by the following theorem.\n\n\\begin{theorem} \\cite{BoZi} \n\\label{bridgeMknot}\nLet $K=M(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,\\delta)$ be a Montesinos knot with $|\\beta_j\/\\alpha_j|<1$, $1\\le j\\le s$, then \n$\\qopname\\relax o{bridge}(K)=s$.\n\\end{theorem} \n\nAn explicit formula for the braid index of any alternating Montesinos knot is a rather new result \\cite{DEHL2018}. The discussion from here to (and including) Theorem \\ref{Montesinos_formula} is modified from \\cite{DEHL2018}, as it is needed in order to understand the concepts and the formulas used in Theorem \\ref{Montesinos_formula}. \n\nLet $\\vec{K}=\\vec{M}(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,\\delta)$ be an oriented Montesinos knot. Following \\cite{DEHL2018}, we will use the following conventions on $\\vec{K}$.\nIf $\\vec{K}$ is alternating then all fractions $\\beta_j\/\\alpha_j$ have the same sign and this is matched by the sign of $\\delta$ representing the $|\\delta|$ half-twists. As in the case of 2-bridge knots the sign of $\\delta$ and the $\\beta_j\/\\alpha_j$ should not be confused with the sign of individual crossings. For example, the signs of the crossings represented by $\\delta$ may not coincide with the sign of $\\delta$. The sign of the crossings represented by $\\delta$ depends on the orientations of the two strings in the $\\delta$-half twists. Since a knot and its mirror image have the same braid index, for alternating Montesinos knots we only need to consider the case $\\beta_j\/\\alpha_j>0$ for each $j$. \n\n\n\nand that the crossings in the tangle diagrams are as chosen in the standard drawings of 2-bridge knots as shown in Figure \\ref{tangle}. The conclusion we reach will then be applicable to the case $\\beta_j\/\\alpha_j<0$ for each $j$ that mirrors the Montesinos knots discussed below. Furthermore, if $s=2$, the a Montesinos knot is actually a 2-bridge knot and thus we only need to consider the case $s\\ge 3$. \nFor our purpose, we can always orient the top long strand in a Montesinos knot diagram from right to left as shown in Figure \\ref{Montesinos} since reversing the orientations of all components in a knot does not change its braid index. \n\nWe will use a standard drawing for each rational tangle $A_j$ which is given by the continued fraction of the rational number $\\beta_j\/\\alpha_j$ and contains an odd number of positive entries, exactly like what we did in the case of 2-bridge knots. That is, we assume that $0<\\beta_j<\\alpha_j$ and $\\beta_j\/\\alpha_j$ has a continued fraction decomposition of the form $(a_{1}^j,a^j_2,...,a_{2q_j+1}^j)$. The four strands that entering\/exiting each tangle are marked as NW, NE, SW and SE. One example is shown at the left of Figure \\ref{tangle}. Here we note that $a_{2q_j+1}^j$ is allowed to equal one if needed to ensure that the vector $(a_{1}^j,a^j_2,...,a_{2q_j+1}^j)$ has odd length. We have\n$$\n\\frac{\\beta_j}{\\alpha_j}=\\frac{1}{a_1^j+\\frac{1}{a_2^j+\\frac{1}{.....\\frac{1}{a_{2q_j+1}^j}}}}. \n$$\nThe closure of a rational tangle is obtained by connecting its NW and SW end points by a strand and connecting its NE and SE end points with another strand (as shown at the left side of Figure \\ref{tangle}). This closure is called the denominator $D(A_j)$ of the rational tangle $A_j$. Notice that $D(A_j)$ results in a normal standard diagram of the oriented 2-bridge knot $\\vec{B}(\\alpha_j, \\beta_j)$ given by the vector $(a_{1}^j,a^j_2,...,a_{2q_j+1}^j)$ (as shown at the right side of Figure \\ref{tangle}). Finally, we define $(b_{1}^j,b^j_2,...,b_{2q_j+1}^j)$ by $|b^j_m|=a^j_m$ with its sign matching the signs of the corresponding crossings in $\\vec{K}$ under the given orientation. Notice that $(b_{1}^j,b^j_2,...,b_{2q_j+1}^j)$ may be different from that defined for 2-bridge knots since the orientations of the strands here are inherited from $\\vec{K}$. We will use the notation $A_j(b_{1}^j,b^j_2,...,b_{2q_j+1}^j)$ to denote the tangle $A_j$ and the signed vector associated with it that is inherited from the orientation of $\\vec{K}$. Notice further that in a standard Montesinos knot diagram, each rational tangle has at the bottom a vertical row of $a_1$ twists corresponding to the condition $|\\beta_j\/\\alpha_j|<1$. (Tangles with $|\\beta_j\/\\alpha_j|\\ge1$ end with a row of horizontal twists on the right, and these twists can be combined via flypes with the $\\delta$ half twists to reduce the tangle to $|\\beta_j\/\\alpha_j| \\mod(1)$.)\n\n\\begin{figure}[htb!]\n\\includegraphics[]{fig15x.pdf}\n\\caption{Left: A standard drawing of the rational tangle $56\/191 = A(3,2,2,3,3)$; Right: The denominator $D(A(3,2,2,3,3))$ is a standard 2-bridge knot diagram of the 2-bridge knot $K(191,56)$.}\n\\label{tangle}\n\\end{figure}\n\nLet us now consider the Seifert circle decomposition of $\\vec{K}$ by first examining how the arcs of Seifert circles entering and exiting each $A_j(b_{1}^j,b^j_2,...,b_{2q_j+1}^j)$ might look. Figure \\ref{decomp} lists all eight possibilities for these arcs. Small Seifert circles within each tangle are not shown in Figure \\ref{decomp}. Observing (from Figure \\ref{tangle}) that the SW and SE strands meet at the last crossing in $b_{1}^j$, therefore if these two strands belong to two different Seifert circles, then they must have parallel orientation. Thus (vi) and (viii) are not possible. Furthermore, since we have assigned the top long arc in the Montesinos knot diagram the orientation from right to left, (iii) is not possible either. We say that $A_j$ is of {\\em Seifert Parity 1} if it decomposes as (i) in Figure \\ref{decomp}, of {\\em Seifert Parity 2} if it decomposes as (ii) or (iv) in Figure \\ref{decomp} and of {\\em Seifert Parity 3} if it decomposes as (v) or (vii) in Figure \\ref{decomp}. Note that the Seifert Parity of a tangle $A_j$ is not a property of the tangle alone, but depends on the structure of the diagram that contains $A_j$. (It should not be confused with the the term {\\em parity of a tangle}, which denotes how the arcs in a tangle are connected.)\n\n\\begin{figure}[htb!]\n\\includegraphics{fig16x}\n\\caption{Of the eight cases listed, (iii), (vi) and (viii) are not possible.}\n\\label{decomp}\n\\end{figure}\n\n\\medskip\n\\begin{remark}\\label{bj_sign}{\\em \nBy assigning the appropriate orientations to the two strands at SW and SE in the bottom portion of Figure \\ref{tangle}, one can relate the sign of $b_1^j$ to the Seifert Parity of $A_j$ as follows: $b_1^j<0$ if $A_j$ is of Seifert Parity 1 or 2 and \n$b_1^j>0$ if $A_j$ is of Seifert Parity 3.}\n\\end{remark}\n\n\\begin{comment}\nThus, it becomes clear that the Seifert circle decomposition of $\\vec{K}$ contains the following: the Seifert circle(s) that contain the top and bottom long strands in $\\vec{K}$ (called {\\em huge} Seifert circle(s)), the Seifert circles that do not contain these long strands, but contain strands that enter\/exit one or more tangles (these will be called {\\em large} Seifert circles), and Seifert circles that are completely within a tangle (and these will be called {\\em medium} Seifert circles if they share crossings with a large Seifert circle, and {\\em small} Seifert circles otherwise). Notice that small Seifert circles are generated by anti-parallel half twists given by a single entry $b_{i}^j$ of a tangle $A_j$. $\\vec{K}$ can be classified into one of the following three classes. \n\\end{comment}\n\nLet us call a Seifert circle in the Seifert circle decomposition of $\\vec{K}$ a {\\em long} Seifert circle if it contains either the top long strand or\/and the bottom long strand. $\\vec{K}$ can be partitioned into the following three classes. \n\nClass M1. The top long strand and the bottom long strand belong to two different long Seifert circles, and the bottom long strand also has the orientation from right to left. Notice that $\\vec{K}$ is of Class M1 if and only if every $A_j$ is of Seifert Parity 1 and all crossings in $\\delta$ (if there are any) have negative signs.\n\nClass M2. The top long strand and the bottom long strand belong to two different long Seifert circles and the bottom long strand has the orientation from left to right. Notice that $\\vec{K}$ is of Class M2 if and only if every $A_j$ is of Seifert Parity 2 (more precisely case (ii) in Figure \\ref{decomp}) and in this case $\\delta=0$.\n\nClass M3. The top long strand and the bottom long strand belong to the same long Seifert circle. Notice that $\\vec{K}$ is of Class M3 if and only if all crossings in $\\delta$ (if there are any) have positive signs and at least one tangle is of Seifert Parity 3 where we consider the horizontal half twists given by $\\delta$ as an additional tangle besides the $A_j$ tangles.\n\n\\noindent\nLet $i=1$, 2 or 3 be the Seifert Parity type of $A_j$ and define \n\n\\begin{eqnarray}\n\\Delta_1(A_j)&=&\\Delta_3(A_j)=\\frac{(-1+\\mbox{\\rm sign}(b^j_{2q_j+1}))}{4}+\\Delta(A_j), \\label{Deltaoneorthree}\\\\\n\\Delta_2(A_j)&=&\\frac{(2+\\mbox{\\rm sign}(b^j_1)+\\mbox{\\rm sign}(b^j_{2q_j+1}))}{4}+\\Delta(A_j),\\label{Deltatwo}\n\\end{eqnarray}\nwhere \n$$\n\\Delta(A_j)=\\sum_{b^j_{2m}>0,1\\le m\\le q_j}b^j_{2m}\/2+\\sum_{b^j_{2m+1}<0,0\\le m\\le q_j}|b^j_{2m+1}|\/2.\n$$\n\nThe braid index for an oriented and alternating Montesinos knot $\\vec{K}$ is given by the following theorem.\n\n\\begin{theorem}\\label{Montesinos_formula} \\cite{DEHL2018} \nLet $\\vec{K}=\\vec{M}(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,\\delta)=\\vec{M}(A_1,A_2,\\ldots, A_s,\\delta)$ be an oriented and alternating Montesinos knot with a normal standard diagram and the signed vector $(b_{1}^j,b^j_2,...,b_{2q_j+1}^j)$ for $A_j$, then we have\n\\begin{eqnarray*}\n\\qopname\\relax o{braid}(\\vec{K})&=&2+\\sum_{1\\le j\\le s}\\Delta_1(A_j)\\ {\\rm{if}}\\ \\vec{K}\\ {\\rm{belongs\\ to\\ Class\\ M1}};\\\\%\n\\qopname\\relax o{braid}(\\vec{K})&=&1+\\sum_{1\\le j\\le s}\\Delta_2(A_j) \\ {\\rm{if}}\\ \\vec{K}\\ {\\rm{belongs\\ to\\ Class\\ M2}};\\\\%\n\\qopname\\relax o{braid}(\\vec{K})&=&\\Delta_0(\\vec{K})+\\sum_{A_j \\in \\Omega_2}\\Delta_2(A_j)+\\sum_{A_j \\in \\Omega_3}\\Delta_3(A_j) \\ {\\rm{if}}\\ \\vec{K}\\ {\\rm{belongs\\ to\\ Class\\ M3}},%\n\\end{eqnarray*}\nwhere $\\Omega_2$, $\\Omega_3$ are the sets of $A_j$'s that have Seifert Parity 2 and 3 respectively, $\\Delta_0(\\vec{K})=\\eta+\\delta-\\min\\{(\\eta+\\delta)\/2-1,\\delta\\}$ and $\\eta=\\vert \\Omega_3\\vert$. \n\\end{theorem}\n\nUsing Theorem \\ref{Montesinos_formula} we can now identify the alternating Montesinos knots where the bridge index equals the braid index.\n\n\\begin{theorem}\n\\label{braidequalsbridgeMknot}\nLet $ \\vec{K}=\\vec{M}(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,\\delta)$ be an oriented and alternating Montesinos knot. Then $\\qopname\\relax o{braid}( \\vec{K})=\\qopname\\relax o{bridge}( K)=s$ if and only if the following conditions hold:\n\n(i) $\\vec{K}$ is of Class M3.\n\n(ii) $\\eta\\ge \\delta+2$ where $\\eta=\\vert \\Omega_3\\vert$ is the number of tangles $A_j$ with Seifert Parity 3.\n\n(iii) If $A_j$ has Seifert Parity 2 then $A_j=(-1,-s_j,-1)$ for some $s_j\\ge 0$. If $A_j$ has Seifert Parity 3 then $A_j=(s_j)$ for some $s_j> 0$.\n\\end{theorem}\n\n\\begin{proof}\n``$\\Longrightarrow$\": Assume that $\\vec{K}=\\vec{M}(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,\\delta)$ is an oriented alternating Montesinos knot with $\\qopname\\relax o{bridge}(K)=\\qopname\\relax o{braid}(\\vec{K})=s$.\n\nIf $\\vec{K}$ is of Class M1 then every $A_j$ has Seifert Parity 1 and there are at least two $A_j$'s with $\\Delta_1(A_j)=0$ since $\\Delta_1(A_j)\\ge 0$ for each $j$.\nBy Remark \\ref{bj_sign}, $\\mbox{\\rm sign}(b^j_1)=-1$. Thus \n$\\Delta_1(A_j)=0$ is possible only if $\\mbox{\\rm sign}(b^j_{2q_j+1})=-1$ in (\\ref{Deltaoneorthree}).\nHowever then $\\Delta_1(A_j)\\ge \\sum_{b^j_{2m+1}<0,0\\le m\\le q_j}|b^j_{2m+1}|\/2>0$ and therefore $\\Delta_1(A_j)=0$ is not possible.\n\nSimilarly if $\\vec{K}$ is of Class M2 then there exists at least one tangle $A_j$ with $\\Delta_2(A_j)=0$. This is only possible if $\\mbox{\\rm sign}(b^j_1)=\\mbox{\\rm sign}(b^j_{2q_j+1})=-1$ in (\\ref{Deltatwo}).\nHowever in such a case \\newline\n$\\Delta_2(A_j)\\ge \\sum_{b^j_{2m+1}<0,0\\le m\\le q_j}|b^j_{2m+1}|\/2>0$ therefore $\\Delta_2(A_j)=0$ is not possible either. This proves that $\\vec{K}$ must of Class M3 and therefore all tangles must have Seifert Parity 2 or 3.\n\nLet $\\eta$ be the number of tangles $A_j$ with Seifert Parity 3 and $s-\\eta$ be the number of tangles $A_j$ with Seifert Parity 2. We note that the contribution $\\Delta_2(A_j)$ of a tangle with Seifert Parity 2 to the braid index is always an integer (it is just one less than the braid index of the corresponding two bridge link, see Theorem \\ref{2bridgeformula}) and therefore we know that a tangle of Seifert parity 2 must have $\\Delta_2(A_j)\\ge1$. We have\n$$\ns=\\qopname\\relax o{braid}(\\vec{K})\\ge \\Delta_0(\\vec{K})+s-\\eta +\\sum_{A_j \\in \\Omega_3}\\Delta_3(A_j) \\ge s+\\delta-\\min\\{(\\eta+\\delta)\/2-1,\\delta\\}.\n$$\nThis implies that $0=\\delta-\\min\\{(\\eta+\\delta)\/2-1,\\delta\\}$, hence $\\eta\\ge \\delta+2$.\nMoreover we must have $\\Delta_2(A_j)=1$ for each $A_j$ with Seifert Parity 2 and $\\Delta_3(A_j)=0$ for each $A_j$ with Seifert Parity 3.\n\nIf $A_j$ has Seifert Parity 2 and $\\Delta_2(A_j)=1$, then $\\mbox{\\rm sign}(b^j_1)=-1$ by Remark \\ref{bj_sign} and equation (\\ref{Deltatwo}) becomes\n$$\n1=\\Delta_2(A_j)=\\frac{|b^j_1|}{2}+\\frac{(1+\\mbox{\\rm sign}(b^j_{2q_j+1}))}{4}+\\sum_{b^j_{2m}>0,1\\le m\\le q_j}b^j_{2m}\/2+\\sum_{b^j_{2m+1}<0,0< m\\le q_j}|b^j_{2m+1}|\/2\\ge \\frac{|b^j_1|}{2}.\n$$\nThus $|b^j_1|\\le 2$. If $b^j_1=-2$ then we must have $\\mbox{\\rm sign}(b^j_{2q_j+1})=-1$, however $1=\\Delta_2(A_j)$ is only possible if $q_j=0$ and $A_j=(-2)$, which can be written as $(-1,0,-1)$ since $(2)$ and $(1,0,1)$ are both continued fraction decompositions of $1\/2$. If $b^j_1=-1$ then $2q_j+1\\ge 3$ and we must have $\\mbox{\\rm sign}(b^j_{2q_j+1})<0$ hence $b^j_{2q_j+1}=-1$ as well. In addition we can see that both $\\mbox{\\rm sign}(b^j_2)$ and $\\mbox{\\rm sign}(b^j_3)$ are negative. Thus we must have $2q_j+1= 3$, that is, $A_j=(-1,-s_j,-1)$ for some integer $s_j>0$. \n\nIf $A_j$ has Seifert Parity 3, then $\\Delta_3(A_j)=0$. $b^j_1>0$ by Remark \\ref{bj_sign}. If $2q_j+1\\ge 3$ then one can easily check that $\\mbox{\\rm sign}(b^j_2)>0$, which would lead to $\\Delta_3(A_j)>0$. Thus $2q_j+1=1$ and $A_j=(s_j)$ for some integer $s_j>0$. \nThus we have shown that conditions (i), (ii) and (iii) are satisfied.\n\n``$\\Longleftarrow$\": This is straight forward by Theorem \\ref{Montesinos_formula}.\n \\end{proof}\n \n\n\\begin{definition}\\label{v_h}{\\em \nLet us call a tangle $A_j$ corresponding to a rational number of the form $\\beta\/\\alpha=1\/s_j$ for some $s_j\\ge 2$ a {\\em vertical} tangle, and a tangle $A_j$ corresponding to a rational number of the form $\\beta\/\\alpha=(s_j+1)\/(s_j+2)$ for some $s_j\\ge 1$ a {\\em horizontal} tangle. \n}\n\\end{definition}\n\nNote that the standard continued fraction decompositions of a vertical and a horizontal tangle are of the form $(s_j)$ and $(1,s_j,1)$ respectively. Using Theorem \\ref{braidequalsbridgeMknot}, we can then identify all BB knots in the set of all un-oriented alternating Montesinos knots as stated in the following theorem.\n \n\\begin{theorem}\n\\label{unorientedM_BBknots}\nLet ${K}={M}(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,\\delta)$ be an un-oriented alternating Montesinos knot. Then $\\qopname\\relax o{braid}({K})=\\qopname\\relax o{bridge}( K)=s$ if and only if $K$ or its mirror image satisfies the following conditions:\n\n(i) every $A_j$ is either a vertical tangle or a horizontal tangle;\n\n(ii) $\\eta\\ge \\delta+2$ where $\\eta$ is the number of $A_j$'s that are vertical.\n\\end{theorem}\n\nThe key observation one needs to make in the proof of Theorem \\ref{unorientedM_BBknots} is that under condition (ii), we can oriented $K$ so that the resulting $\\vec{K}$ belongs to Class M3. We leave the details to the reader.\n \nThe BB alternating Montesinos knots with one component and crossing number up to 12 are listed in Table \\ref{table} indicated by a superscript $^\\dagger$.\n \n\n\\subsection{Non alternating Montesinos knots}\n\nA classification of Montesinos knots (including both the alternating and non-alternating cases) can be found in \\cite{BZ}. A non alternating Montesinos knot will have a minimum diagram when there are no integral twists on the right, that is, $\\delta=0$. We have the following:\n\n\\begin{theorem}\n\\label{braidequalsbridgeMknotnonalt}\nLet $\\vec{K}=\\vec{M}(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,0)$ be an oriented Montesinos knot. If $\\vec{K}$ belongs to Class M3, $A_j=(-1,-s_j,-1)$ for some integer $s_j\\ge 0$ if it has Seifert Parity 2 and $A_j=(s_j)$ for some integer $s_j\\ge 2$ if it has Seifert Parity 3, then $\\qopname\\relax o{bridge}(K)=\\qopname\\relax o{braid}(\\vec{K})$.\n\\end{theorem}\n\n\\begin{proof} Let $\\vec{K}$ be an oriented Montesinos knot satisfying conditions (i) and (ii). \nIt is known that $ \\qopname\\relax o{braid}(\\vec{K})\\le \\gamma(\\vec{K})$ \\cite{Ya}, where $\\gamma(\\vec{K})$ denotes the number of Seifert circles in the Seifert circle decomposition of $\\vec{K}$. Thus we have $\\qopname\\relax o{bridge}(K)\\le \\qopname\\relax o{braid}(\\vec{K})\\le \\gamma(\\vec{K})$, and the result of the theorem follows if we can show that $\\gamma(\\vec{K})=s$ since $s=\\qopname\\relax o{bridge}(K)$ by Theorem \\ref{bridgeMknot}.\nEach tangle $A_j$ with Seifert Parity 2 contributes a Seifert circle (which is contained within the tangle $A_j$).\n Since each Seifert circle that is not completely contained within a tangle (this includes the large Seifert circle) must contain two vertical arcs: one each from a tangle of case (v) and from a tangle of case (vii) as defined in Figure \\ref{decomp} hence each tangle $A_j$ of Seifert Parity 3 also contributes one Seifert circle. It follows that $\\vec{K}$ has exactly $s$ Seifert circles.\n\\end{proof}\n\n\\begin{corollary}\\label{non_un_cor}\nLet ${K}={M}(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,0)$ be a Montesinos knot with $s\\ge 3$ (that is not necessarily alternating). If each $A_j$ or its mirror image is either a vertical or a horizontal tangle (as defined in Definition \\ref{v_h}) and at least two of them are vertical, then $\\qopname\\relax o{bridge}(K)=\\qopname\\relax o{braid}({K})$.\n\\end{corollary}\n\nIf $K$ satisfies the conditions in the corollary, then we can oriented it in a way that it satisfies the conditions of Theorem \\ref{braidequalsbridgeMknotnonalt}. The details are left to the reader.\n\n\\begin{remark}{\\em\nWe note that the proof of Theorem \\ref{braidequalsbridgeMknotnonalt} does not depend on whether $\\vec{K}$ is alternating, thus it also works for an oriented alternating Montesinos knot $\\vec{K}=\\vec{M}(\\beta_1\/\\alpha_1,\\ldots, \\beta_s\/\\alpha_s,\\delta)$ where $\\delta=0$. However this method is not powerful enough to prove Theorem \\ref{braidequalsbridgeMknot} since the Seifert circle decomposition of $\\vec{K}$ contains $s+\\delta$ Seifert circles when $\\vec{K}$ satisfies the conditions of Theorem \\ref{braidequalsbridgeMknot}. That is, $\\vec{K}$ (in its standard diagram form) does not minimize the number of Seifert circles. In \\cite{DEHL2018} we explain an algorithm that converts $\\vec{K}$ to a non alternating and non minimal diagram $D$ that minimizes the number of Seifert circles via so called reduction moves.}\n\\end{remark}\n\nAll non alternating Montesinos knots with one component and up to 12 crossings that satisfy the condition of \nCorollary \\ref{non_un_cor} are listed in Table \\ref{table} and are marked with a superscript $^\\ddagger$. It turns out that this is a complete list of one component BB non alternating Montesinos knots with crossing number up to 12. In fact, we conjecture that this is generally true. See Conjecture \\ref{conjecture1} at the end of the paper.\n\n\\subsection{Knots using Conway basic polyhedra}\n\nIn \\cite{Con67} the concept of Conway basic polyhedra was introduced. Here we will only concentrate on one class of such polyhedra.\nThe Conway basic polyhedra $n^*$ for $n=2k$ and $k\\ge 3$ can be thought of as taking a regular $k$-gon into which we inscribe a second regular $k$-gon such that its vertices touch the midpoints of the sides of the original $k$-gon. Into the smaller $k$-gon we inscribe a third $k$-gon in the same way. The left of Figure \\ref{eightstar} illustrate the case of $k=4$. This will form a knot diagram $D$ with $n$ crossings if we make the diagram alternating. Moreover the writhe $w(D)=0$ (with proper orientation in case of a link), three Seifert circles and braid index 3. For example $8^*$ is the knot $8_{18}$ (the right of Figure \\ref{eightstar}) and $10^*$ is the knot $10_{123}$. \nWe note that for $n\\not\\equiv 0\\mod(3)$ we obtain a knot diagram and for $n\\equiv 0\\mod(3)$ we obtain a 3-component knot diagram, for example for $k=3$ we obtain the Borromean rings ($6_2^3$ in Rolfsen notation). Note that when he first introduced these concepts, Conway used two symbols for the six crossing diagram ($k=3$) denoted by $6^{*}$ and $6^{**}$. The two symbols denote isomorphic graphs that were introduced to make the obtained notation for knots to be nicer. However they are essentially the same basic polyhedron with alternative way to insert tangles. Conway developed a shorthand notation where the two symbols $6^{**}$ and $6^{*}$ are omitted. If there is an initial dot in the beginning of the symbol, then it is $6^{**}$. If there are more than one tangle symbols separated by dots without the basic polyhedron symbol, then it is meant to be $6^{**}$. If there is also a dot in the beginning of the symbol, then it is $6^{*}$.\nIf the tangle substitutions are simple then there is a natural way to view these diagrams as a 3 string braids without increasing the crossing number and we obtain a braid of three strings and bridge number three.\n\n\\begin{figure}[htb!]\n\\includegraphics[scale=.15]{Figures2018\/eightstar.pdf}\\qquad\n\\includegraphics[scale=.15]{Figures2018\/eighteighteen.pdf}\n\\caption{Left: The Conway basic polyhedra $8^*$. Right: The knot $8_{18}$. }\n\\label{eightstar}\n\\end{figure}\n\nUsing the $6^{*}$, $6^{**}$, $8^{*}$ and $10^{*}$ diagrams, we are able to identify all one component BB knots with crossing number up to 12 that can be constructed using Conway basic polyhedra. They are listed in \nTable \\ref{table} and are marked with a superscript $^*$. We would like to point out that \nwhile the above knot construction using Conway basic polyhedra can be generalized, the generalized construction may not yield a BB knot.\nOne such example is $n^*$ with $n = 3k$ that can be thought of as inscribing four $k$-gons into each other. The simplest example is $9^*$ which forms the knot $9_{40}$. It has a diagram with 4 Seifert circles and its braid index is four, but its bridge number is only three.\n\n\\subsection{ Arborescent knots or Conway algebraic knots.}\n\nConway algebraic knots are a super family of the Montesinos knots \\cite{BoSi, Con67}.\nThe following describes Conway algebraic knots with bridge and braid index three.\nFirst, they are represented in the Conway notation as $[(a_1;b_1)(a_2;b_2)]$ with $|a_i|\\ge 2$ and $|b_i|\\ge 2$ \\cite{Con67}. These Conway algebraic knots may have one or more components, depending on the parity of $a_i$ and $b_i$. For example, if $a_1$, $a_2$ are both even and $b_1$, $b_2$ are both odd then the knot has only one component. In the case of multiple components we need to assume that the components are oriented such that $a_i$ and $b_i$ represent parallel oriented half twists. These knots can be either alternating or non-alternating. In the non alternating case they can also be denoted by $[(a_1;b_1)-(a_2;b_2)]$, $[(a_1;b_1)(a_2-1,1;-b_2)]$, $[(a_1-1,1;-b_1+1,-1)(a_2;b_2)]$ or $[(-a_1+1,-1;b_1-1,1)(a_2;b_2)]$. The braid index of such a Conway algebraic knot is at most $3$ since the standard diagram given by the Conway notation has 3 Seifert circles in its Seifert circle decomposition. Since these are not 2-bridge knots, their bridge index is at least 3. Thus if $K$ is a Conway algebraic knot described here, then we have $\\qopname\\relax o{braid}(K)=\\qopname\\relax o{bridge}(K)=3$. \n\n\\subsection{Summary of BB knots with crossing number up to 12.}\n\nTable \\ref{table} lists all one component BB knots with crossing number up to 12. The superscriptions are used in the table as follows. $^t$: torus knots; $^\\dagger$: alternating Montesinos knots; $^\\ddagger$: non alternating Montesinos knots; $^*$: knots constructed from Conway basic polyhedra; $^\\flat$: Conway algebraic knots. The symbols used for the knots follow the historical notation for knots with at most 10 crossings \\cite{rolfsen2003knots} and the notation created for the program Knotscape, developed by Morwen and Jim Hoste \\cite{hoste1999knotscape} for knots with more than 10 crossings, where the letters a and n denote alternating and non-alternating knots, respectively. We would like to point out that a few BB knots in Table \\ref{table} belonging to several families discussed above. For example the knot $8_{19}$ is the torus knot $T(4,3)$, the non alternating Montesinos knot corresponding to $[3;3;2;-]$, as well as the knot constructed using the $8^*$ Conway basic polyhedron. Another example is the knot $10_{124}$: it is the torus knot $T(5,3)$ and also the non alternating Montesinos knot $[5;3;2;-]$.\n\n\\begin{center}\n\\begin{longtable}{|llllllll|}\n\\hline\\hline\n $(3_1)^t$&&&&&&&\\\\ \\hline\n $(5_1)^t$&&&&&&&\\\\ \\hline\n $(7_1)^t$&&&&&&&\\\\ \\hline\n $(8_{5})^\\dagger$&$(8_{10})^\\dagger$&$(8_{16})^\\dagger$&$(8_{17})^*$&$(8_{18})^*$&\n $(8_{19})^{t*\\ddagger}$&$(8_{20})^\\ddagger$&$(8_{21})^\\ddagger$\\\\ \\hline\n $(9_1)^t$&$(9_{16})^\\dagger$&&&&&&\\\\ \\hline\n $(10_{46})^\\dagger$&$(10_{47})^\\dagger$&\n $(10_{48})^\\dagger$&$(10_{62})^\\dagger$&$(10_{64})^\\dagger$&$(10_{79})^\\flat$&\n$(10_{82})^*$&$(10_{85})^*$\\\\ \\hline\n$(10_{91})^*$&$(10_{94})^*$&\n$(10_{99})^*$&$(10_{100})^*$&$(10_{104})^*$&$(10_{106})^*$&\n$(10_{109})^*$&$(10_{112})^*$\\\\ \\hline\n$(10_{116})^*$&$(10_{118})^{t*}$&$(10_{123})^*$&$(10_{124})^{t\\ddagger}$&\n$(10_{125})^\\ddagger$&$(10_{126})^\\ddagger$&$(10_{127})^\\ddagger$&$(10_{139})^\\ddagger$\\\\ \\hline\n$(10_{141})^\\ddagger$&$(10_{143})^\\ddagger$&$(10_{148})^\\flat$&$(10_{149})^\\flat$&\n$(10_{152})^\\flat$&$(10_{155})^*$&$(10_{157})^*$&$(10_{159})^*$\\\\ \\hline\n$(10_{161})^*$&&&&&&&\\\\ \\hline\n$(11a_{44})^\\dagger$&$(11a_{47})^\\dagger$&$(11a_{57})^\\dagger$&$(11a_{231})^\\dagger$&\n$(11a_{240})^\\dagger$&$(11a_{263})^\\dagger$&$(11a_{338})^\\dagger$&$(11a_{367})^t$\\\\ \\hline\n$(11n_{71})^\\ddagger$&$(11n_{72})^\\ddagger$&$(11n_{73})^\\ddagger$&$(11n_{74})^\\ddagger$&\n$(11n_{75})^\\ddagger$&$(11n_{76})^\\ddagger$&$(11n_{77})^\\ddagger$&$(11n_{78})^\\ddagger$\\\\ \\hline\n$(11n_{81})^\\ddagger$&&&&&&&\\\\ \\hline\n$(12a_{146})^\\dagger$&$(12a_{167})^\\dagger$&$(12a_{369})^\\dagger$&$(12a_{576})^\\dagger$&\n$(12a_{692})^\\dagger$&$(12a_{801})^\\dagger$&$(12a_{805})^*$&\n$(12a_{815})^\\flat$\\\\ \\hline\n$(12a_{819})^*$&$(12a_{824})^\\flat$&$(12a_{835})^\\dagger$&\n$(12a_{838})^\\dagger$&$(12a_{850})^*$&$(12a_{859})^*$&$(12a_{864})^*$&\n$(12a_{869})^*$\\\\ \\hline\n$(12a_{878})^\\dagger$&$(12a_{898})^*$&$(12a_{909})^*$&\n$(12a_{916})^*$&$(12a_{920})^*$&$(12a_{981})^*$&$(12a_{984})^*$&\n$(12a_{999})^*$\\\\ \\hline\n$(12a_{1002})^*$&$(12a_{1011})^*$&$(12a_{1013})^*$&\n$(12a_{1027})^\\dagger$&$(12a_{1047})^*$&$(12a_{1051})^*$&$(12a_{1114})^*$&\n$(12a_{1120})^*$\\\\ \\hline\n$(12a_{1168})^*$&$(12a_{1176})^*$&$(12a_{1191})^*$&\n$(12a_{1199})^*$&$(12a_{1203})^*$&$(12a_{1209})^*$&$(12a_{1210})^*$&\n$(12a_{1211})^*$\\\\ \\hline\n$(12a_{1212})^*$&$(12a_{1214})^\\dagger$&$(12a_{1215})^*$&\n$(12a_{1218})^*$&$(12a_{1219})^*$&$(12a_{1220})^*$&$(12a_{1221})^*$&\n$(12a_{1222})^*$\\\\ \\hline\n$(12a_{1223})^*$&$(12a_{1225})^*$&$(12a_{1226})^*$&\n$(12a_{1227})^*$&$(12a_{1229})^*$&$(12a_{1230})^*$&$(12a_{1231})^*$&\n$(12a_{1233})^\\dagger$\\\\ \\hline\n$(12a_{1235})^*$&$(12a_{1238})^*$&$(12a_{1246})^*$&\n$(12a_{1248})^*$&$(12a_{1249})^*$&$(12a_{1250})^*$&$(12a_{1253})^*$&\n$(12a_{1254})^*$\\\\ \\hline\n$(12a_{1255})^*$&$(12a_{1258})^*$&$(12a_{1260})^*$&\n$(12a_{1283})^\\dagger$&$(12a_{1288})^\\flat$&$(12n_{113})^\\flat$&$(12n_{114})^\\flat$&\n$(12n_{190})^\\flat$\\\\ \\hline\n$(12n_{191})^\\flat$&$(12n_{233})^\\ddagger$&$(12n_{234})^\\ddagger$&\n$(12n_{235})^\\ddagger$&$(12n_{242})^\\ddagger$&$(12n_{344})^\\flat$&$(12n_{345})^\\flat$&\n$(12n_{417})^*$\\\\ \\hline\n$(12n_{466})^\\ddagger$&$(12n_{467})^\\ddagger$&$(12n_{468})^\\ddagger$&\n$(12n_{472})^\\ddagger$&$(12n_{570})^\\ddagger$&$(12n_{571})^\\ddagger$&$(12n_{574})^\\ddagger$&\n$(12n_{604})^\\flat$\\\\ \\hline\n$(12n_{640})^*$&$(12n_{647})^*$&$(12n_{666})^*$&\n$(12n_{674})^\\flat$&$(12n_{675})^\\flat$&$(12n_{679})^\\flat$&$(12n_{683})^*$&\n$(12n_{684})^*$\\\\ \\hline\n$(12n_{688})^\\flat$&$(12n_{707})^*$&$(12n_{708})^*$&\n$(12n_{709})^*$&$(12n_{721})^\\ddagger$&$(12n_{722})^\\ddagger$&$(12n_{725})^\\ddagger$&\n$(12n_{747})^*$\\\\ \\hline\n$(12n_{748})^*$&$(12n_{749})^*$&$(12n_{750})^*$&\n$(12n_{751})^*$&$(12n_{767})^*$&$(12n_{820})^*$&$(12n_{821})^*$&\n$(12n_{822})^*$\\\\ \\hline\n$(12n_{829})^*$&$(12n_{830})^*$&$(12n_{831})^*$&\n$(12n_{850})^*$&$(12n_{882})^*$&$(12n_{887})^*$&$(12n_{888})^\\flat$& \n \\\\\\hline\\hline\n \\caption{List of one component BB knots with crossing number up to 12. \\label{table}}\n\\end{longtable}\n\\end{center}\n\n\\vspace{-0.7in}\n\\section{The number of BB knots with a given crossing number}\n\\label{numberofknots}\n\nTable \\ref{table2} summarizes the number of one component BB knots with crossing numbers up to 12. By separating the knots with odd crossing numbers and the knots with with even crossing number, we observe that the number of one component BB knots with a given crossing number $n$ increases as the crossing number increases and it seems that there are more BB knots in the case of an even crossing number greater than six; and (ii) the percentage of BB knots among all knots with the same crossing number decreases as the crossing number increases. What are the general behaviors of these numbers and percentages? More specifically, if we let $\\mathcal{K}_n$ and $\\mathcal{B}_n$ be the numbers of knots and BB knots with crossing number $n$ respectively, then how do $\\mathcal{B}_n$ and $\\mathcal{B}_n\/\\mathcal{K}_n$ behave? While it is \nplausible that $\\lim_{n\\to\\infty}\\mathcal{B}_n\/\\mathcal{K}_n=0$, we do not have a proof of it. However, we can prove that $\\lim_{n\\to\\infty}\\mathcal{B}_n=\\infty$ and does so exponentially. This is stated in Theorem \\ref{exp_thm}.\n\n\n\\begin{table}[!hb]\n\\begin{center}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|} \n\\hline crossing \\# &3&4&5&6&7&8&9&10&11&12\\\\\n\\hline \\# of knots &1&1&2&3& 7&21&49&165&552&2176\\\\\n\\hline \\# of BB knots &1&0&1&0&1&8&2&33&17&119\\\\\n\\hline $\\approx$ percentage &100\\%&0\\%&50\\%&0\\%& 14.3\\%&38.1\\%&4.1\\%&20.0\\%&3.1\\%&5.5\\%\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n \\caption{Number of one component BB knots with respect to the crossing number. \\label{table2}}\n\\end{table}\n\n\\begin{theorem}\\label{exp_thm}\nThe number $\\mathcal{B}_n$ grows exponentially with $n$. Moreover the number of one component BB knots with crossing number $n$ also grows exponentially with $n$.\n\\end{theorem}\n\\begin{proof}\nIn fact, we will prove that the number of BB knots with braid index 3 grows exponentially with the crossing number $n$. Assume that we only generate alternating 3-braids. Then a word representing such a braid can be generated by two symbols $\\sigma_1$ and $\\sigma_2^{-1}$. Using these two symbols there are $2^n$ words of length $n$ each of which representing an alternating braid with $n$ crossings.\nIf we close such a braid we obtain an alternating knot or link diagram with $n$ crossings and at most 3 components.\nNow we need to estimate how many of these diagrams represent distinct knots of $n$ crossings. There are several points we need to consider:\n\n(i) If $w$ is a braid word then any cyclic permutation of this word will yield the same link. Thus up to cyclic permutation there are at least $\\frac{2^n}{n}$ different words.\n\n(ii) Switching between $\\sigma_1$ and $\\sigma_2^{-1}$ in a braid word will result in the same knot. Thus up to cyclic permutation and exchange of $\\sigma_1$ and $\\sigma_2^{-1}$ there are $\\frac{2^{n-1}}{n}$ different words (at least).\n\n(iii) The diagram generated by the braid closure is a reduced alternating knot diagram if $\\sigma_1$ and $\\sigma_2^{-1}$ both occur in the word more than once. \n\n\n(iv) Using the classification theorem in \\cite{Bir93}, we see that a knot diagram obtained by closing a braid satisfying condition (iii) above admits flypes only if the braid is of the form $\\sigma_1^u \\sigma_2^{-1}\\sigma_1^z\\sigma_2^{-v}$, $\\sigma_2^{-u}\\sigma_1\\sigma_2^{-z}\\sigma_1^{v}$, $\\sigma_1^u\\sigma_2^{-v}\\sigma_1^z\\sigma_2^{-1}$ or $\\sigma_2^{-u}\\sigma_1^v\\sigma_2^{-z}\\sigma_1^{1}$ for some positive integers $u$, $z$, and $v$. We note that $\\sigma_1^u \\sigma_2^{-1}\\sigma_1^z\\sigma_2^{-v}$ is flype equivalent to $\\sigma_1^u\\sigma_2^{-v}\\sigma_1^z \\sigma_2^{-1}$ and $\\sigma_2^{-u}\\sigma_1\\sigma_2^{-z}\\sigma_1^{v}$ is flype equivalent to $\\sigma_2^{-u}\\sigma_1^v\\sigma_2^{-z}\\sigma_1^{1}$.\n\nIt is easy to see that the number of braids satisfying condition (iii) and also admitting flypes is bounded above by $4\\cdot {{n-2}\\choose{2}}0$ such that when $n$ is large enough the number of such knots is bounded below by $2^{n-5}\/n-n^3> 2^{cn}$. \nNext, we need to estimate how many of these 3 braids are 2-bridge knots using Theorem \\ref{2bridge_theorem}. Using a case by case analysis similar to the method used in the proof of Theorem \\ref{torustwobridge} we can list all vectors of 2-bridge knots with braid index three. If we represent the these 2-bridge knots with an odd length vector using positive integers than we can show that these vector are of the following form:\n$211$, $a12$, $a2b$ and $3a1$ for length three vectors and $a11b1$ and $1a3b1$ for length five vectors,\nwhere $a,b$ are positive integers. No vectors of a length greater than five can have a braid index of three. Thus for a fixed crossing number $n$ the number of different 2-bridge knots with braid index three is linearly bounded above by $c_1 n$ some fixed constant $c_1>0$. So, the number of 3-braids with $\\qopname\\relax o{bridge}(K)=\\qopname\\relax o{braid}(K)=3$ will still growth exponentially. Roughly a third of these will be one component knots hence the number of one component BB knots also grows exponentially with $n$.\n\\end{proof}\n\nWe end our paper with the following two questions and a conjecture.\n\n\\begin{question} {\\em %\nIs $\\lim_{n\\rightarrow\\infty}\\mathcal{B}_n\/{{\\mathcal{K}_n}}=0$ and does this limit approach zero exponentially fast?\n}\n\\end{question}\n\n\n\n\\begin{question} {\\em Is it true that $\\mathcal{B}_{2n}>\\mathcal{B}_{2n+1}$ for all $n\\ge 4$? And why?}\n\\end{question}\n\n\\begin{conjecture}\\label{conjecture1}{\\em \nThe non alternating Montesinos knots described in Theorem \\ref{braidequalsbridgeMknotnonalt} (or Corollary \\ref{non_un_cor}) are the only non alternating Montesinos knots that are BB knots. \n}\n\\end{conjecture}\n\n\\subsection*{Acknowledgement}\nPh.~R.\\@ was supported by German Research Foundation\ngrant RE~3930\/1--1. We would like to thank S\\\"oren Bartels for fruitful\ndiscussions on the numerical simulation of elastic knots.\n\n\n\\newcommand{\\href}[2]{#2}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe problem of noise and decoherence plays the important role both for applications and for fundamental\nphysics. Noise parameters for electron transport provide a valuable information, which is not available from average\ncurrent-voltage dependences. This stimulates development of effective methods of noise investigation. In particular, a lot of\nattention is devoted to shot noise\n\\cite{Blanter}, which is related to discreteness of charge transport and yields direct information on carrier charges. \nEfforts of theorists were invested to studying full counting statistics of shot noise, its non-Gaussian\ncharacter \\cite{Lez} and asymmetry (odd moments) \\cite{Odd,BKN}. They are also objects of intensive\nexperimental investigations\n\\cite{Reul,Rez}. \n\nRecently it was shown that the zero-bias\nanomaly of the incoherent Cooper-pairs current in a Coulomb blockaded Josephson junction is very\nsensitive to shot noise from an independent source and therefore can be used for noise investigation\n\\cite{SN}. Further experimental and theoretical investigations \\cite{exp,SN-T,Heik} have demonstrated\nthat this method especially useful for studying asymmetry of shot noise, which is connected with \nits non-Gaussian character and is very difficult for detection by other methods of noise spectroscopy\n\\cite{Reul,Rez}. In addition, other methods to use the Josephson junction as a noise probe were discussed.\n Deblock {\\em et al.} \\cite{Delft} suggested to use the quasiparticle current for noise detection. The\nscheme to use the Josephson junction as a threshold detector of electron counting statistics has also been\ninvestigated theoretically and experimentally \\cite{TN,Pek,Grab}. The scheme exploited the effect of\nnoise on macroscopical quantum tunneling at currents close to the critical current.\n\nThe goal of this paper is to extend the theoretical analysis of the effect of noise on the low-bias part of\nthe $IV$ curve of a mesoscopic tunnel junction. The previous analysis \\cite{SN,exp,SN-T} addressed the\neffect of shot noise on a Coulomb blockaded Josephson junction in the weak-coupling limit, when \n the Josephson coupling energy $E_J$ was small compared to the Coulomb energy $E_c=e^2\/C$. Here $C$ is the\nrelevant capacitance. Shot noise originated from a low current through a parallel junction, and its effect\non the $IV$ curve of the Josephson junction had asymmetry connected with the non-Gaussian character of shot\nnoise. The effect was insensitive to counting statistics of the noise current since the latter was so low\nthat the single-electron tunneling events were well separated in time and their correlation was not\nessential. On the basis of the fulfilled analysis one could expect that a Coulomb blockade normal junction\nis also able to effectively probe noise, and other types of noise different from shot noise can be\ninvestigated, but in order to check these expectations an additional analysis was needed. The present paper\nis to give answers to the following questions:\n\\begin{itemize}\n\\item\nCan the zero-bias anomaly of the probing junction be sensitive to the counting statistics of electrons tunneling through\nthe noise source (noise junction) when the current through the noise junction grows?\n\n\\item Is the zero-bias anomaly of the Coulomb blockaded {\\em normal} tunnel junction also sensitive to noise similarly to\nthe Coulomb blockaded Josephson junction?\n\n\\item Is the zero-bias anomaly of the Coulomb blockaded tunnel junction sensitive to other types of noise different from\nshot noise?\n\n\\item Can the Josephson junction in the strong coupling limit $E_J\\gg E_C$ also probe noise?\n\\end{itemize}\n\nThe paper gives positive answers to all these questions. The content of the paper is the following. Section \\ref{sec2}\nreminds the previous analysis \\cite{SN,exp,SN-T} for the Coulomb blockade Josephson junction in the weak coupling limit $E_J\n\\ll E_c$ and extends it on the case of higher noise currents, when the zero-bias anomaly is expected to be sensitive to the\nelectron counting statistics. The analysis confirms this expectation. At high currents the\neffect is essentially different for the cases of strictly periodical sequence of pulses and of random pulses governed\nby the Poissonian statistics. Section \\ref{sec3} considers\nanother type of noise: phase fluctuations in a monochromatic microwave signal, which result in decoherence of the\nsignal. A similar phase noise is well known in quantum optics. It is shown that the effect of the microwave on the\n$IV$ curve of the Josephson junction gives direct information on the decoherence time. Section \\ref{sec4} addresses the\neffect of shot noise on the zero-bias anomaly of a normal tunnel junction. The latter is also very sensitive to shot\nnoise and phase fluctuations in an AC input, and therefore can be exploited as a noise detector. Section \\ref{sec5} considers the\nJosephson junction in the strong coupling limit in low-impedance environment. This case was chosen because it allows to exploit its\nduality to the case of the Josephson junction in the weak coupling limit in high-impedance environment, which was considered in the\nprevious sections. But duality is valid mostly for the equilibrium {Nyquist-Johnson noise. For shot noise\nduality does not work, and the effect of noise on the Josephson junction in low impedance environment \n(superconductive regime) is different from that in high impedance environment (Coulomb blockade regime). The\nimportant feature of the low-impedance case is the perspective to measure the intrinsic tunneling time,\nwhich is discussed in the end of Sec.\n\\ref{sec5}. \n\n\n\\section{Josephson junction, weak coupling limit, shot noise} \\label{sec2}\n\n\\subsection{Without shot noise}\n\n Let us review well known results concerning the\n$IV$ curve without shot noise. We assume that the Josephson coupling\nenergy $E_J\\cos \\varphi$ ($\\varphi$ is the Cooper-pair phase difference between two banks of the Josephson\njunction) is weak in comparison with the Coulomb energy $E_c =e^2\/2C$, where\n$C$ is the relevant capacitance (see below), and the Josephson coupling can be considered as a time-dependent\nperturbation. Then the\nGolden Rule gives for the current of Cooper pairs \\cite{Aver,SZ,IN}:\n\\begin{equation}\nI={\\pi eE_J^2 \\over \\hbar}[P(2eV)-P(-2eV)]~, \n \\label{IP}\\end{equation}\nwhere the function\n\\begin{equation}\nP(E)={1\\over 2\\pi \\hbar}\\int_0^\\infty dt\\, \\left[e^{iEt\/\\hbar}\\left\\langle\ne^{i\\varphi(t_0)}e^{-i\\varphi (t_0-t)}\\right\\rangle+ e^{-iEt\/\\hbar}\\left\\langle\ne^{i\\varphi (t_0-t)} e^{-i\\varphi(t_0)}\\right\\rangle\\right]\n \\label{PE} \\end{equation}\ncharacterizes the probability to transfer the energy $E>0$ to environment (or to\nabsorb the energy $|E|$ from environment if $E<0$) at the time $t_0$. In many cases (equilibrium and shot noise included) the\naverage phase correlators do not depend on the time $t_0$ after averaging, and Eq. (\\ref{PE}) is reduced to the Fourier component\nof the average phase correlator \\cite{IN}:\n\\begin{equation}\nP(E)={1\\over 2\\pi \\hbar}\\int_{-\\infty}^\\infty dt\\, e^{iEt\/\\hbar}\\left\\langle\ne^{i\\varphi(t)}e^{-i\\varphi (0)}\\right\\rangle~.\n \\end{equation}\nHowever studying the linear response to the AC input in Sec. \\ref{AClin} we shall need the more\ngeneral expression Eq.~(\\ref{PE}).\n\n\nSince we use the\nperturbation theory with respect to $E_J$ we can calculate phase fluctuations neglecting $E_J$, i.e.,\ntreating the Josephson junction as a capacitor. The crucial assumption in the phase-fluctuation theory (or\n$P(E)$ theory) is that the phase fluctuations are Gaussian \\cite{IN} and \n\\begin{equation}\n\\langle e^{i\\varphi_0(t_0)}e^{-i\\varphi_0 (t_0-t)}\\rangle \\approx e^{J_0(t)} ~,\n \\label{Gauss} \\end{equation}\nwhere the phase--phase correlator\n\\begin{eqnarray}\nJ_0(t) =\\langle[ \\varphi_0(t)\n-\\varphi_0(0)]\\varphi_0(0)\\rangle=J_R(t)+ iJ_I(t) \n= 2\\int_{-\\infty}^\\infty {d\\omega \\over \\omega}\n\\frac{\\mbox{Re} Z(\\omega)}{R_Q}\n \\frac{e^{-i\\omega t} -1}{1-e^{-\\beta \\hbar \\omega}}\n \\label{phaseCor} \\end{eqnarray}\nis a complex function of time. The subscript 0 points out that the phase fluctuation $\\varphi_0$ is determined by the\nequilibrium Johnson-Nyquist noise in the environment, i.e., in the electric circuit with the impedance\n$Z(\\omega)$. Here $R_Q=h\/4e^2=\\pi \\hbar\/2e^2$ is the quantum resistance for Cooper pairs and $\\beta =1\/k_BT$ is the inverse\ntemperature. Then\n\\cite{IN}\n\\begin{equation}\nP(E)={1\\over \\pi \\hbar}\\mbox{Re} \\left\\{ \\int_0^\\infty dt \\exp\\left[ J_0(t) +\n\\frac{iEt}{\\hbar}\\right]\\right\\}\\, , \n \\label{P-E} \\end{equation}\nand the current is\n\\begin{eqnarray}\nI=-{2 eE_J^2 \\over \\hbar^2}\\mbox{Im}\\left\\{\\int_0^\\infty dt e^{J_0(t)}\n \\sin \\left(2eVt\\over \\hbar\\right)\\right\\}~.\n \\label{WC} \\end{eqnarray}\nThe zero-bias conductance is given by\n\\begin{equation}\nG_0=\\left.{dI\\over dV}\\right|_{V\\rightarrow 0}=-{4 e^2E_J^2 \\over\n\\hbar^3}\\mbox{Im}\\left\\{\\int_0^\\infty t\\,dt e^{J_0(t)}\\right\\} \\,.\n \\label{G-0}\\end{equation}\n\n\n\\begin{figure\n \\begin{center}\n \\leavevmode\n \\includegraphics[width=0.5\\linewidth]{ShotL-1.eps}\n \n \\caption{Electric circuit. a) The Josephson junction voltage-biased through the shunt resistance $R$. b) Parallel to\nthe Josephson junction the normal junction with the capacitance $C_s$ is switched on, which produces shot noise affecting the\ncurrent through the Josephson junction. The large shunt capacitance $C_{sh}$ transforms at finite frequencies the current bias\ninto the voltage bias.}\n \\label{fig1}\n \\end{center}\n \\end{figure}\n\n\nThe electric circuit for our analysis is shown in Fig. \\ref{fig1}a. The\nJosephson tunnel junction with capacitance $C_J$ is voltage-biased via the shunt\nresistor $R$. Thus in our case $Z(\\omega) =(1\/R +i\\omega C)^{-1}$ with $C=C_J$, and \n\\begin{eqnarray}\nJ_0(t) = 2\\rho\\int_{-\\infty}^\\infty {d\\omega \\over \\omega}\n\\frac{1}{1 +\\omega^2\\tau^2}\n \\frac{e^{-i\\omega t} -1}{1-e^{-\\beta \\hbar \\omega}}~,\n \\label{phaseCorT} \\end{eqnarray}\nwhere $\\tau=RC$ and $\\rho=R\/R_Q$. At $T \\to 0$\n($t>0$):\n\\begin{eqnarray}\nJ_0(t) = \\rho \\left[-e^{t\/\\tau}\\mbox{E}_1\\left({t\\over \\tau}\\right)-\ne^{-t\/\\tau}\\mbox{E}_1\\left(-{t\\over \\tau}+i0\\right) \n-2 \\ln{ t \\over \\tau}\n -2\\gamma -i\\pi\\right] \\,,\n \\label{J-0} \\end{eqnarray}\n where $\\gamma=0.577$ is the Euler constant, and\n$\\mbox{E}_1(z)=\\int_1^\\infty (e^{-zt}\/t)\\,dt$ is the exponential integral\n\\cite{AS}. The small imaginary correction $+i0$ to the argument of one of the\nexponential integrals is important for analytic continuation of $\\mbox{E}_1(z)$ from real\n$t$ to the complex plane \\cite{AS} (see below). At $T=0$ $P(E)$ vanishes for $E<0$ since it is the probability of the transfer of\nthe energy $|E|$ from the environment to the junction, which is impossible if $T=0$.\n\nFor further analysis we need the expressions for $J_0$ in the limits of short time\n$t \\ll \\tau$:\n\\begin{eqnarray}\nJ_0(t) \\approx \\rho \\left[{t^2 \\over \\tau^2}\\left(\\ln{t\\over\n\\tau}+\\gamma -{3\\over 2}\\right) -{i\\pi t\\over \\tau}\\right]\\,,\n \\label{J-0s} \\end{eqnarray}\nand long time $t \\gg \\tau$:\n\\begin{eqnarray}\nJ_0(t) = - \\rho \\left(2 \\ln{ t \\over \\tau}\n +2\\gamma +i\\pi\\right) \\, .\n \\label{J-0l} \\end{eqnarray}\n\nThe function $P(E)$, as well as the current $I$, which it determines, have been carefully studied and calculated for arbitrary\n$\\rho$ \\cite{Ing}. But for the goals of our analysis it is useful to present a simplified calculation valid only in the high\nimpedance limit $\\rho \\gg 1$. In this limit integration in the expression for the conductance $G_0$, Eq. (\\ref{G-0}),\nshould be done over rapidly oscillating functions and it is difficult to see that the integral exactly vanishes at $T=0$.\nBut it becomes evident after rotation of the integration path in the plane of complex time, which corresponds to replacing\n$t$ by $-iy$. This rotation transforms the integration over the positive real semiaxis into the integration over the\nnegative imaginary semiaxis. After this transformation, the complex function $J_0(t) \\to J_0(-iy)$ becomes a\npurely real function, and the conductance $G_0$, which is given by the imaginary part of this function, vanishes. This\nis true for any term\n$V^k$ in the expansion of the current $I$ in voltage $V$ as far as the integral over $t$, which determines this\nterm, converges at long time. Using the asymptotic expression Eq. (\\ref{J-0l}) one can see that the integral is\ndivergent if\n$k>2\\rho -2$. So at zero temperature the current $I$ as a function of $V$ does not vanish completely but is given by a\nsmall nonanalytic power-law term, which can be found in the limit of high $\\rho$ from the following simple steepest\ndescent estimation of the integral.\n\nExpecting that the main contribution comes from long times one can rewrite the expression (\\ref{P-E}) for the $P(E)$ function\nusing the asymptotic expression (\\ref{J-0l}) for $J_0$:\n\\begin{eqnarray}\nP(E)= {1\\over \\pi \\hbar}\\mbox{Re}\\left\\{\\int _0^\\infty dt\\,\\exp \\left[ - \\rho \\left(2 \\ln{ t \\over \\tau}\n +2\\gamma +i\\pi\\right) +{iE t\\over \\hbar}\\right]\\right\\}~.\n \\end{eqnarray}\nThe saddle point is determined by\nthe condition of vanishing first derivative of the argument of the exponential function: $\n-{2\\rho \/ t_0} + {iE \/\\hbar}=0$. This yields $t_0=-2i\\hbar\/E$. Expanding the argument of the exponential\nfunction around the saddle point ($t'=t-t_0$) we receive \n\\begin{eqnarray}\n P(E)= {1\\over \\pi \\hbar}\\mbox{Re}\\left\\{\\exp\\left[\\rho\\left(-2\\gamma -\n2\\ln {2\\rho\\hbar\\over iE\\tau}\n-i\\pi\\right)+2\\rho\\right]\\right\\} \\int_{-\\infty}^\\infty\ndt'\\,\\exp\\left(-{E^2\\over 4\\rho\\hbar^2} t'^2\\right)\\nonumber \\\\\n=\\exp[2\\rho(1-\\gamma)]{\\tau \\over \\sqrt{\\pi\\rho} \\hbar} \\left(E\\tau\n\\over 2\\rho\\hbar\\right)^{2\\rho-1}~.\n \\end{eqnarray}\nFinally \n\\begin{equation}\nI={\\pi eE_J^2 \\over \\hbar}P(2eV)=\\sqrt{\\pi \\over {\\rho} } \\exp[2\\rho(1-\\gamma)] {eE_J^2 \\tau\\over \\hbar^2}\\left(eV\\tau \\over\n\\rho\\hbar\\right)^{2\\rho-1}~. \n \\label{curr0}\\end{equation}\n\n\nIn summary, if environment provides only the equilibrium noise, the low-bias part of the $IV$ curve is governed by phase\ncorrelations at very long time, which yield in the limit $\\rho \\gg 1$ an extremely small nonanalytic current.\n\n\n\\subsection{With shot noise}\n\nNow we consider the effect of shot noise from an independent source. Parallel to the Josephson junction there is another junction\n(noise junction) with the capacitance $C_s$ connected with an independent\nDC current source (see Fig. \\ref{fig1}b). The resistance of the noise junction is very large compared to the\nshunt resistance $R$. The role of very large capacitance\n$C_{sh}$ is to shortcircuit the large ohmic resistance of the current source for finite frequencies. The\ncurrent\n$I_s$ through the noise junction produces shot noise, which affects the $IV$\ncurve of the Josephson junction. \n\nIn the presence of shot noise the fluctuating phase $\\varphi=\\varphi_0+\\varphi_s$ consists of\ntwo terms: $\\varphi_0$ from Johnson-Nyquist noise, and $\\varphi_s$ from shot noise. \nFor calculation of the shot-noise fluctuations $\\varphi_s$ we assume that\nthe charge transport through the noise junction is a sequence of current\npeaks $\\delta I=\\mbox{sign}(I_s)e\\sum_i\\delta (t-t_i)$, where $t_i$ are random\nmoments of time when an electron crosses the junction \\cite{Blanter}. We neglect \nduration of the tunneling event itself. The positive sign of\n$I_s$ corresponds to the current shown in Fig. \\ref{fig1}b. Any peak\ngenerates a voltage pulse at the Josephson junction:\n$V_s(t)=\\mbox{sign}(I_s)(e\/C)\\sum_i \\Theta (t-t_i) e^{-(t-t_i)\/\\tau}$, where\n$\\Theta(t)$ is the step function and $C=C_J+C_s$. The voltage pulses result in phase jumps determined by\nthe Josephson relation $\\hbar \\partial \\varphi_s\/\\partial t=2eV_s$. The sequences of current and voltage\npeaks and phase jumps are shown in Fig. \\ref{fig2}.\n\nCalculating the contribution from shot\nnoise fluctuations $\\varphi_s$ to the phase correlators one should abandon the assumption that noise is Gaussian\n[Eq. (\\ref{Gauss})]. On the\nother hand, we assume that the phase fluctuation\n$\\varphi_s$ is classical and the values of $\\varphi_s$ at different moments of\ntime commute. Since equilibrium noise and shot noise are uncorrelated, the\ngeneralization of Eq. (\\ref{WC}) is \n\\begin{eqnarray}\nI=-{2 eE_J^2 \\over \\hbar^2}\\mbox{Im}\\left\\{\\int_0^\\infty dt e^{J_0(t)}\n\\left\\langle \\sin \\left({2eVt\\over \\hbar}+\\Delta\n\\varphi_s\\right)\\right\\rangle\\right\\} .\n \\label{curG} \\end{eqnarray}\n\nThe phase difference between two moments $t_0$ and $t_0-t$, $\\Delta \\varphi_s=\n\\varphi_s(t_0)-\\varphi_s(t_0-t)=\\sum _i \n\\delta \\varphi_s(t,t_0-t_i)$, is a sum of contributions from random current peaks, each of them determined\nby\n\\begin{eqnarray}\n\\delta \\varphi_s(t,\\tilde t) \n=\\mbox{sign}(I_s)\\pi \\rho\\left\\{ \\Theta (\\tilde t)\\left [ 1-\ne^{-(\\tilde t)\/\\tau}\\right] \n- \\Theta (\\tilde t-t)\\left [ 1-\ne^{-(\\tilde t-t)\/\\tau}\\right]\\right\\}~, \n \\end{eqnarray}\nwhere $\\tilde t=t_0-t_i$. Since phase jumps are not small\nfor $\\rho \\gg 1$, one cannot use the perturbation theory with respect to them. \nThe interpretation of the expression Eq. (\\ref{curG}) for the current is straightforward: As far as the\nrandom phase difference $\\Delta \\varphi_s=2e\\int^{t_0}_{t_0-t}\nV_s(t')dt'\/\\hbar$ is classical, it should be simply added to the phase difference $\\varphi_V=2eVt\/\\hbar$\ngenerated by the constant voltage bias $V$.\n\n\\begin{figure\n \\begin{center}\n \\leavevmode\n \\includegraphics[width=0.9\\linewidth]{ShotL-2.eps}\n \n \\caption{Current and voltage pulses, phase jumps.}\n \\label{fig2}\n \\end{center}\n \\end{figure}\n\nLet us consider the case of the Poissonian statistics. If one performs observation during a very long\nperiod of time\n$\\tau_\\infty$, the number of pulses during this period is large and close to $N\\sim |I_s| \\tau_\\infty\/e$. \nKeeping in mind the absence of correlation between pulses, the phase correlator after averaging over\nthe long time $\\tau_\\infty$ is\n\\begin{eqnarray}\n \\left\\langle e^{i\\Delta \\varphi_s}\\right\\rangle =\n \\left\\langle e^{i\\sum _j\\delta \\varphi_s(t, t_0-t_j) }\\right\\rangle \n= \\left\\langle e^{i\\delta \\varphi_s }\\right\\rangle^N \n\\approx \\left(1+ {\\Phi(t)\\over \\tau_\\infty}\\right)^N ~,\n \\end{eqnarray}\nwhere $\\Phi(t)=\\Phi_c(t)+i\\Phi_s(t)$\nis determined by the integrals over a single phase jump (corresponding to a single current pulse):\n\\begin{eqnarray}\n\\Phi_s(t) = \\int_{-\\infty}^\\infty d\\tilde\nt \\sin \\delta \\varphi_s(t,\\tilde t)\n= \\tau {I_s\\over |I_s|} \\left\\{{\\pi \\over 2}+ \\mbox{si} \\left[r\\left(1-\ne^{-t\/\\tau} \\right) \\right] +\\sin r \\left[\\mbox{ci} r -\n\\mbox{ci} \\left(r e^{-t\/\\tau} \\right)\\right] -\\cos r\n\\left[\\mbox{si} r - \\mbox{si} \\left(r \ne^{-t\/\\tau} \\right)\\right] \\right\\}\n \\label{sin} \\end{eqnarray}\nand\n\\begin{eqnarray}\n\\Phi_c(t)=\\int_{-\\infty}^\\infty d\\tilde\nt\n\\left[\\cos \\delta \\varphi_s(t, \\tilde t) -1\\right] \n= \\tau \\left\\{ \\mbox{ci} \\left[r\\left(1-e^{-t\/\\tau} \\right) \\right]+\n\\cos r\n\\left[\\mbox{ci} r -\n\\mbox{ci} \\left(r e^{-t\/\\tau} \\right)\\right] \\right.\\nonumber \\\\ \\left.\n+ \\sin r\n\\left[\\mbox{si} r - \\mbox{si} \\left(r \ne^{-t\/\\tau} \\right)\\right] \n-\\gamma-\\ln \\left[r\\left(1-\ne^{-t\/\\tau} \n\\right) \\right] \\right\\}-t~. \n \\label{cos} \\end{eqnarray}\nHere $\\mbox{si}(x)=-\\int_x^\\infty\n\\sin t\\, dt\/t$ and $\\mbox{ci}(x)=-\\int_x^\\infty \\cos t\\, dt\/t$ are sine and cosine\nintegral functions \\cite{AS}, and $r=\\pi \\rho$. \nIn the limit $\\tau_\\infty\\to \\infty$ and $N\\to \\infty$ at $N\/\\tau_\\infty=|I_s|\/e$ the phase correlator is \n\\begin{eqnarray}\n \\left\\langle e^{i\\Delta \\varphi_s}\\right\\rangle= \n\\exp\\left[{N\\Phi(t)\\over \\tau_\\infty}\\right] =\\exp\\left[{|I_s|\\over e}\\Phi(t)\\right]~.\n \\label{Pois} \\end{eqnarray}\nFinally the expression for the current, Eq. (\\ref{curG}), can be written as\n\\begin{eqnarray}\nI=-{2 eE_J^2 \\over \\hbar^2}\\mbox{Im}\\left\\{\\int_0^\\infty dt e^{J_0(t)}\n\\exp \\left({|I_s| \\over e}\\Phi_c \\right) \\sin \\left({2eVt\\over\n\\hbar}+{|I_s| \\over e} \\Phi_s\\right) \\right\\}.\n \\label{curGg} \\end{eqnarray}\n\nIn contrast to the case without shot noise, when the expansion of the current $I$ in a small voltage bias\nstarts from the nonanalytic term $\\propto V^{2\\rho-1}$, in the presence of shot noise\nthe expansion in $V$ starts with analytic terms:\n\\begin{equation}\nI=I_0 +G_s V+ a V^2+bV^3~,\n \\label{Vexp} \\end{equation}\nwhere \n\\begin{eqnarray}\nG_s=-{ 4e^2 E_J^2 \\over \\hbar^3 }\\int_0^\\infty t\\, dt \\mbox{Im} \\left\\{e^{J_0(t)}\\right\\}[\\left\\langle\\cos\n\\Delta\\varphi_s\\right\\rangle-1]\n \\end{eqnarray}\nis the shot-noise conductance, \n\\begin{eqnarray}\nI_0=-{ 2e E_J^2 \\over \\hbar^2 } \\int_0^\\infty dt \\mbox{Im} \\left\\{e^{J_0(t)}\\right\\}\n\\left\\langle\\sin \\Delta\\varphi_s \\right\\rangle\n \\label{ratchet} \\end{eqnarray}\nis the ratchet current, and the parameters \n\\begin{eqnarray}\na={ 4e^3 E_J^2 \\over \\hbar^4}\\int_0^\\infty t^2\\, dt \\mbox{Im} \\left\\{e^{J_0(t)}\\right\\}\n\\left\\langle\\sin \\Delta\\varphi_s \\right\\rangle\n \\end{eqnarray}\nand\n\\begin{eqnarray}\nb={ 8 e^4 E_J^2 \\over 3 \\hbar^5} \\int_0^\\infty t^3\\, dt \\mbox{Im} \\left\\{e^{J_0(t)}\\right\\}[\\left\\langle\\cos\n\\Delta\\varphi_s\\right\\rangle-1]\n \\end{eqnarray}\ndetermine the curvature of the conductance-voltage plot and the shift of the conductance minimum. Without\nshot noise all these integrals vanish at $\\rho \\gg 1$ [see the paragraph after Eq. (\\ref{J-0l})].\n\nIn Refs. \\onlinecite{exp,SN-T} the parameters of the analytic expansion of $I(V)$ were calculated for low\ncurrents $|I_s|\\ll e\/\\tau$ through the noise junction, when voltage pulses and phase jumps are well\nseparated in time (see Fig.\n\\ref{fig2}). Then the relevant phase correlators are proportional to the density\n$|I_s|\/e$ of pulses in time \\cite{SN-T}:\n\\begin{equation}\n\\langle e^{i \\Delta \\varphi_s} \\rangle -1= \\langle \\cos \\Delta \\varphi_s \\rangle-1\n+i\\langle \\sin \\Delta \\varphi_s \\rangle = {|I_s| \\over e}\\Phi(t)={|I_s| \\over e}[\\Phi_c(t)+i\\Phi_s(t)]~.\n\\end{equation}\n The parameters \ndetermined by $\\left\\langle\\cos \\Delta\\varphi_s\\right\\rangle$ correspond to ``even'' effects (the conductance\nis symmetric with respect to voltage inversion $V\\to -V$), which are present also without shot noise. Therefore the\ncontribution from shot noise to these parameters should be\nadded to the values derived from Gaussian equilibrium noise. In contrast, the parameters determined by\n$\\left\\langle\\sin \\Delta\\varphi_s \\right\\rangle$ correspond to ``odd'' (asymmetric) effects, which are absent for \nequilibrium noise and are related to the non-Gaussian character of shot noise.\n\nIn the high-impedance limit $\\rho \\gg 1$ it is possible to calculate the parameters\nof the $IV$ curve analytically. As one can see below, the most important\ncontributions to the integrals, which determine $G_s$, $a$, and $b$, come from times $t \\sim \\tau\n\/\\sqrt{\\rho \\ln \\rho}$ short compared to $\\tau $, and one may use the small-argument expansion for the\nJohnson-Nyquist correlator given in Eq. (\\ref{J-0s}). On the other hand, these times are long enough for using\nasymptotic expansions for the sine and the cosine integrals:\n$\\mbox{si}(x) \\sim -\\cos x\/x -\\sin x\/x^2 $, $\\mbox{ci}(x) \\sim \\sin x\/x -\\cos x\/x^2$. Then one can rewrite Eq. (\\ref{sin}) as \n\\begin{eqnarray}\n\\Phi_s(t) \\approx \\tau \\left\\{{\\pi \\over 2}+ {1\\over r} -{\\cos \\left[r\\left(1-\ne^{-t\/\\tau} \\right) \\right] \\over re^{-t\/\\tau}}-{\\cos \\left[r\\left(1-\ne^{-t\/\\tau} \\right) \\right] \\over r(1-e^{-t\/\\tau})} \\right. \\nonumber \\\\ \\left.\n+{\\sin \\left[r\\left(1-\ne^{-t\/\\tau} \\right) \\right] \\over r^2e^{-2t\/\\tau}}-{\\sin \\left[r\\left(1-\ne^{-t\/\\tau} \\right) \\right] \\over r^2(1-e^{-t\/\\tau})^2} \\right\\}\n\\approx \\tau \\left[{\\pi \\over 2}-{\\tau\\over rt} \\cos {rt\\over \\tau} -{\\tau^2\\over r^2t^2} \\sin {rt\\over\n\\tau}\n\\right] ~.\n \\label{sinAs} \\end{eqnarray}\nIn Eq. (\\ref{cos}) it is enough to keep only the main asymptotic terms $\\propto 1\/r$:\n\\begin{eqnarray}\n\\Phi_c(t) \\approx \\tau \\left\\{-{t \\over\\tau} \n+{\\sin \\left[r\\left(1-e^{-t\/\\tau} \\right) \\right] \\over re^{-t\/\\tau}}+{\\sin \\left[r\\left(1-e^{-t\/\\tau} \\right) \\right] \\over\nr(1-e^{-t\/\\tau})}\n -\\gamma-\\ln \\left[r\\left(1-e^{-t\/\\tau} \\right) \\right] \\right\\}\\nonumber \\\\\n\\approx \\tau \\left[-{t \\over\\tau} -\\ln {rt\\over \\tau} -\\gamma\n+{\\tau \\over rt}\\sin {rt\\over \\tau} \\right]~. \n \\label{cosAs} \\end{eqnarray}\n\nLet us consider first the parameters connected with ``even'' effects. For low currents $|I_s| \\ll\ne\/\\tau $:\n\\begin{eqnarray}\nG_s\\approx -{ 4e E_J^2 \\tau \\over \\hbar^3 }|I_s|\\int_0^\\infty t\\, dt \\mbox{Im}\n\\left\\{e^{J_0(t)}\\right\\}{\\tau \\over rt}\\sin {rt\\over \\tau} \\nonumber \\\\ \n\\approx { 4e E_J^2 \\tau^2\\over \\hbar^3 r}|I_s| \\int_0^\\infty dt\n\\exp \\left(-\\rho{t^2 \\over \\tau^2}\\ln{\\tau \\over\nt}\\right) \\sin ^2 {rt \\over\\tau} \n\\approx {\\sqrt{2} \\pi e E_J^2 \\tau^3\\over \\hbar^3 r^{3\/2}\\sqrt{\\ln r}}|I_s|\n\\approx {\\pi^{5\/2}E_J^2 C^3 \\over 4\ne^5 \\sqrt{2\\ln \\rho}}\\rho^{3\/2}|I_s|~,\n \\label{integr} \\end{eqnarray}\n\\begin{eqnarray}\nb={ 8 e^3 E_J^2\\tau \\over 3 \\hbar^5}|I_s| \\int_0^\\infty t^3\\, dt \\mbox{Im} \\left\\{e^{J_0(t)}\\right\\}{\\tau \\over rt}\\sin {rt\\over\n\\tau} \n\\nonumber \\\\\n\\approx -{ 8 e^3 E_J^2\\tau^2 \\over 3 \\hbar^5 r}|I_s| \\int_0^\\infty t^2\\, dt \\exp \\left(-\\rho{t^2 \\over \\tau^2}\\ln{\\tau \\over\nt}\\right) \\sin ^2 {rt \\over\\tau} \\approx -{ 2\\pi^2\\sqrt{2} e^3 E_J^2\\tau^5 \\over 3 \\hbar^5 r^{5\/2} (\\ln r)^{3\/2}}|I_s|\n\\approx -{\\pi^2 C^2\\rho\\over 6 e^2\\ln \\rho}G_s ~.\n \\label{integr-b} \\end{eqnarray}\nThe main contributions to these integrals come from the last term in the asymptotic expansion Eq.\n(\\ref{cosAs}). The other terms either vanish (term $\\propto \\gamma$) or are of higher orders (terms $\\propto\nt$ and $\\propto \\ln t$) with respect to $1\/r$. A negative sign of $b$ means that in the center of the\nzero-bias anomaly the curve ``conductance vs. voltage'' has a maximum but not a minimum. However this is\npossible to see only at very low temperatures since finite temperatures give a positive contribution to the\ncurvature parameter $b$.\n\n\nIn a similar way one can calculate the asymmetry integral \n\\begin{eqnarray}\na=-{ 4e^2 E_J^2 \\tau \\over \\hbar^4}I_s\\int_0^\\infty t^2\\, dt \\mbox{Im} \\left\\{e^{J_0(t)}\\right\\}\n{\\tau^2\\over r^2t^2} \\sin {rt\\over \\tau}={I_s \\over |I_s|}{ C\\over 2e }G_s ~.\n \\label{integr-a} \\end{eqnarray}\nHere the main contribution comes from the last term of the asymptotic expansion Eq. (\\ref{sinAs}). \n\n For the integral in Eq.\n(\\ref{ratchet}), which determines the ratchet current $I_0$, the relevant times are shorter than for the\nother integrals:\n$t\n\\sim \\tau\/\\rho \\sim R_QC$. Therefore one cannot use the asymptotic expansion Eq. (\\ref{sinAs}). Instead one can\nintegrate by parts, and for low noise currents: \n\\begin{eqnarray}\nI_0=-{ 2e E_J^2 \\over \\hbar^2 } {|I_s|\\over e} \\int_0^\\infty dt \\mbox{Im} \\left\\{e^{J_0(t)}\\right\\}\n\\Phi_s(t) \\approx { 2 E_J^2 \\over \\hbar^2 }|I_s| \\int_0^\\infty dt \\Phi_s(t) \\sin {r t\\over \\tau}\n = { 2 E_J^2\\tau \\over \\hbar^2 r } |I_s| \\int_0^\\infty dt \\frac {d\\Phi_s(t)}{dt}\\cos {r t\\over \\tau}\n\\nonumber \\\\\n\\approx { 2E_J^2\\tau^2 \\over \\hbar^2 r }I_s \\int_0^\\infty {dt\\over t} \\cos {r t\\over \\tau}\n\\sin {r t\\over \\tau}={ \\pi E_J^2\\tau^2 \\over 2 \\hbar^2 r }I_s= {\\pi^2 E_J^2 C^2\\over 8e^4 }\\rho I_s\\, .\n \\label{integr-r} \\end{eqnarray}\n\n\\subsection{Shot noise from high currents and electron counting statistics} \\label{sec2c}\n\n\nShot noise is non-Gaussian and asymmetric, but in the case of low noise currents its effect does not\ndepend on statistics of electron transport and is determined only by density of current pulses in time.\nEven if pulses were not random and formed a strictly periodical sequence the effect would be the same. In\norder to obtain information on counting statistics from measurements of the $IV$ curve the noise current\nshould be not small compared with the width of the voltage pulse $\n\\sim \\tau$. One could expect \\cite{SN-T} that in the limit of high noise current $I_s$ discreteness of the\nelectron transport through the junction would be less and less essential and eventually the effect of the\ncurrent\n$I_s$ would be reduced to the constant voltage drop $V_s =I_s R$ on the shunt resistor $R$ resulting\nin a trivial shift of the voltage applied to the Josephson junction. If it were the case, the long-time asymptote for\nthe phase correlator would be\n\\begin{eqnarray}\n \\langle e^{i\\Delta \\varphi_s }\\rangle \\to \\exp \\left(2ie V_s t\\over \\hbar \\right)\n=\\exp \\left(2ie I_s Rt\\over \\hbar \\right)=\\exp \\left(ir {I_s t\\over e} \\right)~.\n \\label{asyTr} \\end{eqnarray} \nThis asymptotic behavior really takes place for a strictly periodic sequence of current pulses with period\n$T_0=e\/|I_s|$. In this case the phase variation for the time interval $nT_0 10^{-13}$ s. On the other hand,\nwhatever disagreement on definition of the tunneling time, usually it is estimated as\n$10^{-15}~-~10^{-14}$ s. This yields the hope that further progress in miniaturization of the\nexperimental set up or searching for cases with longer tunneling time could open a new possibility to investigate\nthe tunneling time experimentally.\n\n\\section{Conclusions}\n\nWe have presented the theory of the effect of shot noise from an independent source on the Coulomb blockaded\nJosephson junction in high-impedance environment. The analysis takes into account asymmetry of \nshot noise characterized by its odd moments. For high impedance environment the effect is so strong that the expansion in\nmoments is not valid and was not used in the analysis. Asymmetry of shot noise results in asymmetry of the $IV$\ncurve: the shift of the conductance minimum from the zero bias and the ratchet effect, which\nhave been observed experimentally \\cite{SN,exp}. At low noise currents (currents responsible for shot noise) the effect is\nproportional to the noise current independently of counting statistics of electron transport. However, at high noise\ncurrents the effect of noise on the $IV$ curve is very sensitive to electron counting statistics. This high\nsensitivity is explained by the fact that in contrast to usual methods of noise detection the Coulomb\nblockaded junction probes phase but not voltage.\n\nThe theory was generalized on another type of noise (phase noise of a monochromatic AC input) and on a normal\nCoulomb blockaded tunnel junction, In the both cases the effect of noise strongly affects the Coulomb\nzero-bias anomaly of the $IV$ curve. The effect of shot noise on the superconducting Josephson junction in\nlow-impedance environment was also considered. If environment generates only the equilibrium Johnson-Nyquist\nnoise this case can be analyzed using the duality relations with the case of the Coulomb blockaded Josephson\njunction in high-impedance environment. However, shot noise breaks duality relations between the two cases. An\ninteresting feature of the superconducting Josephson junction in low-impedance environment has been revealed:\nthe effect of shot noise on its $IV$ curve can give information on the time of electron tunneling through the\njunction responsible for shot noise.\n\nAltogether, the analysis demonstrates, that the low-bias part of the $IV$ curves of tunnel junctions, both Coulomb\nblockaded and superconducting, can be exploited as a\nsensitive detector of various types of noise. \n\n\n\\section*{Acknowledgements}\n\nThe author appreciates collaboration and discussions with Julien Delahaye, Pertti Hakonen,\nTero Heikkil\\\"a, Rene Lindell, Mikko Paalanen, Jukka Pekola, and Mikko Sillanp\\\"a\\\"a. The author also thanks Markus\nB\\\"uttiker, Rosario Fazio, and Bertrand Reulet for interesting discussions of the results of the paper. The work was supported by \nthe Large Scale Installation Program ULTI-3 of the European Union and by the grant of the Israel Academy of Sciences and\nHumanities.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe low-energy properties of frustrated quantum spin systems -- loosely speaking,\nsystems of interacting spins in which the local energetic constraints cannot\nall be simultaneously satisfied -- have fascinated researchers for\nmany decades. These systems arise in the description of the spin degrees\nof freedom of Mott insulators, i.e. insulating states where the fluctuations of\nthe charge degrees of freedom have been suppressed by interactions\nwhile the spin degrees of freedom remain free to form non-trivial quantum\nphases. Such states are found in many materials, but can also be\nartificially made in the lab using cold atomic gases~\\cite{greiner2002,jordens2008}.\nIn most situations, the spins collectively order into some pattern\nthat can be described through a local order parameter. A more exciting possibility,\ncoined {\\it spin liquid phase}~\\cite{balents2010}, is that the spins do not order into\nsuch a local pattern; instead, a more exotic state governed by strong quantum\nfluctuations emerges.\n\nIn a famous paper in 1987, Kalmeyer and Laughlin~\\cite{kalmeyer1987}\nhypothesized a scenario where a chiral topological\nspin liquid (CSL) is formed. In this elusive phase of matter, the spins form\na collective state that can only be described in terms of emergent, non-local\ntopological properties. So far, this behavior has experimentally only been observed in fractional\nQuantum Hall systems~\\cite{tsui1982,laughlin1983}.\nSuch topologically ordered liquids~\\cite{wen1990-1} are characterized through a\nnumber of universal properties ranging from topologically protected gapless\nedge states~\\cite{halperin1982,wen1990} and a ground state degeneracies\nthat depend on the topology of the sample~\\cite{wen1990-1} to exotic\nexcitations that carry fractional charge and satisfy neither fermionic nor\nbosonic exchange statistics~\\cite{moore1991}. These \\emph{anyonic particles}~\\cite{wilczek1982}\ncan serve as key ingredients in topological quantum computers~\\cite{nayak2008},\nmaking them relevant also for technological applications.\n\nIn the specific case of the chiral spin liquid as proposed by Kalmeyer and\nLaughlin, the universal properties of the\nground state are captured by the bosonic $\\nu=1\/2$ Laughlin\nstate~\\cite{laughlin1981}. Probably the most striking property of this state\nare its semionic bulk excitations: When exchanging two such semions,\nthe wave function describing the collective state of the system acquires\na complex phase $i$, in stark contrast\nto conventional bosons or fermions, where the factor is 1 and $-1$,\nrespectively. Equally striking is the emergence of a topologically\nprotected chiral edge state with a universal spectrum at the boundary\nof the sample. This leads to unidirectional transport along the\nboundary of the sample, while the bulk remains insulating. The \ncorrespondence between edge and bulk physics has\nbeen used as a powerful experimental probe into the physics of\nfractional quantum Hall systems. Finally, the\nground state degeneracy of this state depends on the topology of the\nunderlying manifold; for example, when placed on a torus, two ground\nstates are found.\n\n\\begin{figure}\n \\includegraphics[width=1.5in]{kagome.pdf}\n \\includegraphics[width=1.5in]{hom.pdf}\n \\caption{\n {\\it Left panel:} Kagome lattice considered in this manuscript, where grey shading\n indicates the elementary triangles. Arrows on the bonds indicate the direction\n induced by the magnetic flux $\\Phi$ enclosed in each triangle. \n {\\it Right panel:} Visualization of the network-model perspective on the chiral spin liquid phase arising from Hamiltonian~\\eqnref{eqn:ham}. Consistent with a chiral topological phase, a collective edge state encircles the whole systems. In this particular model, additional closed edges encircle each hexagon.\n \\label{fig:kagome}\n }\n\\end{figure}\n\nOver the last decades, much research has been devoted to finding\nrealistic spin Hamiltonians that have such a chiral topological phase\nas their ground state, but to this date, the only known examples are\nHamiltonians that are unlikely to be relevant for any material~\\cite{yao2007,schroter2007,tang2011,sun2011,neupert2011,nielsen2013}.\nHere, we study a simple spin model on the Kagome lattice (left panel of Fig.~\\ref{fig:kagome}) that is derived from\nthe Hubbard model, which is the minimal relevant\nmodel for itinerant interacting electrons,\nin the presence of time-reversal symmetry breaking.\nThe Hubbard Hamiltonian reads\n\\begin{equation} \\label{eqn:hubb} \\begin{split}\nH =& - \\sum_{\\langle i,j \\rangle, \\sigma} (t_{ij} c_{i \\sigma}^\\dagger c_{j \\sigma} + t_{ij}^* c_{j \\sigma}^\\dagger c_{i \\sigma}) \\\\\n& + \\frac{h_z}{2} \\sum_i (n_{i \\uparrow} - n_{i \\downarrow}) + U \\sum_{i} n_{i \\uparrow} n_{i \\downarrow}.\n\\end{split} \\end{equation}\nHere, a magnetic field induces both a Zeeman term $h_z$ as well as a flux $\\Phi$ through\neach elementary triangle of the lattice, such that for $i,j,k$ clockwise around a triangle\nwe have $t_{ij} t_{jk} t_{ki} = t^3 \\exp(i \\Phi)$, as indicated by the arrows in the left panel of\nFig.~\\ref{fig:kagome}.\nWhen $\\Phi \\neq 0$ or $h_z \\neq 0$, time-reversal symmetry is broken.\nWhen considered at half-filling, $\\langle n \\rangle = 1$, and in the limit\nof large repulsive interaction strength $U$, a Mott insulating state is formed and an effective\nspin model can be derived from perturbation theory in $t\/U$~\\cite{motrunich2006}.\n\nHere, we will demonstrate conclusively that in a very wide parameter regime where\na large enough magnetic flux $\\Phi$ breaks time-reversal symmetry,\nthe ground state of the effective spin model -- and hence also the Hubbard model in an\nappropriate parameter regime -- is a chiral topological spin liquid with\nemergent anyonic excitations.\n\n\\section{Model} \\label{sec:model}\n\nStarting from from the Hubbard model of Eqn.~\\eqnref{eqn:hubb}, a $t\/U$ expansion\nat half filling gives the following spin Hamiltonian~\\cite{motrunich2006}:\n\\begin{equation} \\label{eqn:ham} \\begin{split}\nH =&~ J_\\text{HB} \\sum_{\\langle i,j \\rangle} \\vec{S}_i \\cdot \\vec{S}_j + h_z \\sum_i S^z_i \\\\\n& +J_\\chi \\sum_{i,j,k \\in \\bigtriangleup} \\vec{S}_i \\cdot (\\vec{S}_j \\times \\vec{S}_k) + \\ldots,\n\\end{split} \\end{equation}\nwhere for the three-spin term, the $i,j,k$ are ordered clockwise around the elementary triangles of the\nKagome lattice. The term $\\chi_{ijk} = \\vec{S}_i \\cdot (\\vec{S}_j \\times \\vec{S}_k)$, referred to\nas the scalar spin chirality~\\cite{wen1989,baskaran1989},\nbreaks time-reversal symmetry and parity, but preserves SU(2) symmetry.\nTo lowest order, the coupling parameters depend on the parameters of\nthe Hubbard model as $J_\\text{HB} \\sim t^2\/U$ and $J_\\chi \\sim \\Phi t^3\/U^2$,\nignoring further subleading terms. We choose\nto parametrize the model using $J_\\text{HB} = J \\cos \\theta$ and $J_\\chi = J \\sin \\theta$\nand set $J=1$.\n\nIn the absence of time-reversal symmetry breaking ($\\theta=0$ and $h_z=0$),\nthis is the Kagome lattice nearest-neighbor Heisenberg antiferromagnet, which has become\na paradigmatic model for frustrated magnetism~\\cite{elser1989,marston1991,sachdev1992} with possible\nspin liquid ground state and relevance to the description of materials such as Herbertsmithite and\nVolborthite~\\cite{lee2007,han2012}.\nRecent numerical work~\\cite{yan2011,jiang2012-1,depenbrock2012} has indicated \nthat this model may realize a time-reversal symmetric $\\mathbb{Z}_2$ topological spin liquid,\nwhile other numerical results give evidence for a gapless spin liquid phase~\\cite{iqbal2011,clark2012}.\n\nHere, we explore the ground state phase diagram of~\\eqnref{eqn:ham} away from\nthe time-reversal invariant Heisenberg point $\\theta=0$. In particular, we find an\nextended chiral spin liquid phase around the point $\\theta=\\pi\/2$ and $h_z=0$,\nwhere the Hamiltonian reduces to the three-spin term,\n\\begin{align} \\label{eqn:csl-ham}\nH_{\\text{CSL}} &= \\sum_{i,j,k \\in \\bigtriangleup} \\chi_{ijk}.\n\\end{align}\nOur numerical results indicate that the CSL is in fact stable almost up to the\nHeisenberg point, namely for all $\\theta \\geq 0.05 \\pi$.\nWe also establish an extended range of stability against other perturbations, including\n(i) the Zeeman field,\n(ii) an easy-axis spin anisotropy in the Heisenberg term,\n(iii) a next-nearest neighbor Heisenberg term, and\n(iv) the Dzyaloshinsky-Moriya (DM) interaction induced by Rashba-type spin-orbit coupling for the fermions.\nWhile the aim of this paper is not to examine the nature of the\ntransitions out of the chiral spin liquid phase, e.g. the expected phase transition\nto the time-reversal symmetric spin liquid of the Heisenberg antiferromagnet,\nthese questions should be addressed in future work.\n\nIn the following, we will use two complementary routes to show that the ground\nstate of Eq.~\\eqnref{eqn:csl-ham} is indeed a chiral spin liquid.\nFirst, we argue for this from a powerful perspective rooted in the physics of network models of\nedge states akin to the Chalker-Coddington network model for the integer\nquantum Hall transition~\\cite{chalker1988}. We will then turn to powerful numerical\ntools to unambiguously identify the universal properties of the chiral spin liquid.\n\n\\begin{figure}\n \\begin{tabular}{c}\n \\includegraphics[width=3in]{puddle1.pdf} \\\\ \\hline\n \\vspace{0.1in} \\includegraphics[width=2.5in]{puddles_2.pdf} \n \\end{tabular}\n \\caption{(color online)\n {\\it Top:} Sketch of a puddle of topological phase replacing each triangle of three spins.\n {\\it Bottom:} Behavior of two corner-sharing triangular puddles of the topological phase. \\label{fig:puddle} }\n\\end{figure}\n\nThe key step of our first argument is to view each triangle of spins\nas the seed of a chiral topological phase, a puddle encircled by\nan edge state, as illustrated in the top panel of Fig.~\\ref{fig:puddle}. The natural\ncandidate for the phase filling the puddle is the bosonic $\\nu=1\/2$\nLaughlin state~\\cite{halperin1983}\\nocite{moore1991}, which is the simplest\nbosonic quantum Hall state known to possess the SU(2) symmetry required by our construction.\nIt is also the state envisioned by Kalmeyer and Laughlin~\\cite{kalmeyer1987}.\nForming a lattice out of the elementary triangles, we should then consider\na situation with many individual puddles of this topological phase. To\nsee what collective state is formed, we have to understand how two\ncorner-sharing triangles of the Kagome lattice are joined.\nThis situation of edges meeting at the corner spin shared by two triangles\nis an incarnation of two-channel Kondo physics~\\cite{AffleckLudwig1991,MaldacenaLudwig1997},\nfor which it is well-known that the edges will {\\it heal}~\\cite{eggert1992,kane1992}\nif the coupling\nto the center spin is symmetric, as illustrated in the lower panel\nof Fig.~\\ref{fig:puddle} and discussed in more detail in the appendix. Thus, the corner spin has\nmerged the two triangles to form a larger puddle encircled by\na single edge state, i.e. to form a larger region of the topological phase.\nWe can repeat the above step (Fig.~\\ref{fig:puddle}) for all pairs of\ncorner-sharing triangles of the Kagome lattice. The system then forms\none macroscopic, extended region of a \\emph{single} topological\nphase with one edge state encircling its outer boundary, as illustrated\nin the right panel of Fig.~\\ref{fig:kagome}, and with closed loops encircling the interior\nhexagons of the Kagome lattice. We thus obtain a direct realization of the\nKalmeyer-Laughlin state for a chiral topological spin liquid phase.\n\n\\section{Numerical identification of the CSL} \\label{sec:numerics}\n\nWe now turn to a numerical identification of the CSL \nat the chiral point $\\theta=\\pi\/2$ of Hamiltonian~\\eqnref{eqn:ham} and\nin its vicinity by studying the three\nhallmark properties of a chiral topological phase: the presence of\n(i) a gapped spectrum with a topological degeneracy on the torus,\n(ii) a gapless edge state with a universal spectrum of low-energy excitations,\nand (iii) anyonic bulk excitations.\nOn a technical level, we resort to exact diagonalization and DMRG calculations\nto extract energy spectra, entanglement spectra, and modular matrices for\nvarious system configurations.\nTo label their diameter and boundary condition, we will\nuse the notation introduced in Ref.~\\onlinecite{yan2011}; see also\nthe Methods section.\n\n\\begin{figure}\n \\includegraphics[width=\\columnwidth]{spectrum.pdf}\n \\caption{\n {\\it (a)} Exact diagonalization excitation energies on small tori of type XT4-0 up to length 5. \n {\\it (b)} Energy gap on a thin, long strip of width 4 sites; dashed lines indicate the extrapolation to $N \\rightarrow \\infty$. The two different branches denote systems with an even (black) and odd (blue) number of unit cells.\n {\\it (c)} Entanglement entropy at the center of the same system as (b) on a semi-logarithmic scale for even (black) and odd (blue) number of unit cells; dashed lines indicate a fit. Both fits are consistent with $c=1$.\n In all panels, $N$ is the total number of lattice sites.\n \\label{fig:spectrum} }\n\\end{figure}\n\nWe first demonstrate that the system has a finite gap in the thermodynamic limit.\nTo this end, we consider a sequence of XT4-0 tori of length up to 5 unit cells, shown in the left panel\nof Fig.~\\ref{fig:spectrum}.\nFor systems with $N \\geq 18$ sites, there clearly is a low-lying excitation, which can be attributed\nto a two-fold near-degeneracy of the ground state. All excitations above these near-degenerate ground\nstates are separated by a spectral gap of roughly $\\Delta \\approx 0.05$.\nFurther consistent evidence for the gap can be obtained from exact diagonalization of a 36-site\nXT6-3 cluster and on XT4-2 clusters of size up to 30 sites (not shown).\nAs a further consistency check, we can extract the gap for long, thin cylinders using DMRG.\nPerforming this for cylinders of type XC4-0 with up to 100 sites,\nwe confirm that the triplet gap does not depend significantly on the length\nof the system, ruling out the presence of gapless modes propagating along the cylinder.\n\nWe conclude from this that the gap remains finite in the thermodynamic limit. The qualitative\nagreement between the spectral gap extracted for different system sizes and boundary conditions\nis also a strong indication\nthat the correlation length of the system is short compared to the system sizes we are able to\nstudy numerically. To further support this, we have calculated the spin-spin and dimer-dimer\ncorrelation functions as well as an upper bound on the asymptotic correlation\nlength on infinite cylinders. All of these indicate a correlation length on the order\nof {\\it one} unit cell. Taken together, this gives strong evidence that the properties observed on\nsmall tori and quasi-one-dimensional systems are representative of the\ntwo-dimensional phase in the thermodynamic limit.\n\nThe observed two-fold near-degeneracy is consistent with what is\nexpected for the CSL, namely a\n$2^g$-fold ground state degeneracy on a manifold of genus $g$, which\nwill be split by an amount that is exponentially small in the system size.\nWe also find two states $\\ket{\\Psi_a}$ with very similar energy densities for infinite\ncylinders of type XC8-4 and XC12-6. As explained in the\nMethods section, the two states can be identified by\ntheir well-defined topological flux $a$ through the cylinder,\nwhich for the $\\nu=1\/2$ Laughlin state can be the identity\n($a = \\mathds{1}$) or a semion ($a=\\mathrm{s}$).\n\n\\subhead{Edge physics}\nPlacing a chiral topological phase on a cylinder or disk, a gapless chiral edge state emerges with\na universal spectrum governed by a conformal field theory~\\cite{halperin1982,wen1990}.\nTo observe this, we consider the spectral gap of the system\nwhen placed on a thin, long strip with a fixed width of 4 sites.\nIn stark contrast with the case of a thin long cylinder, the spin gap\nvanishes as $a\/L+b\/L^2$, where $a$ and $b$ are\nparameters of the fit (Fig.~\\ref{fig:spectrum}(b)).\nThis is a hallmark signature of a gapless edge mode. We can further\npinpoint the universality class of the edge theory by extracting its central charge $c$\nfrom the entanglement entropy. As shown in the Fig.~\\ref{fig:spectrum}(c), we find good agreement\nwith a value of $c=1$, which is precisely that expected for the chiral SU(2)$_1$ Wess-Zumino-Witten\nconformal field theory describing the edge of a $\\nu=1\/2$ Laughlin state.\n\n\\begin{figure}\n \\includegraphics[width=\\columnwidth]{cft.pdf}\n \\caption{Entanglement spectrum of the reduced density matrix $\\rho_a$ for one half of an infinite cylinder obtained for both ground states $\\ket{\\Psi_\\mathds{1}}$ (left) and $\\ket{\\Psi_\\mathrm{s}}$ (right panel). The entanglement energies shown on the vertical axis, up to the global shift and rescaling, are given by $E_{a,\\sigma} = -\\log (p_{a,\\sigma})$, where $p_{a,\\sigma}$ are the eigenvalues of $\\rho_a$. The horizontal axis shows the momentum in the transverse direction of the corresponding eigenvectors of $\\rho_a$. Each tower is identified by its $S^z$ quantum number as indicated by the blue label; we have offset the momentum of different towers by $2\\pi$ to improve clarity. The cylinder used here is XC12-6.\n }\n \\label{fig:cft}\n\\end{figure}\n\nAs an even more refined probe, we use the entanglement spectrum, which\nreflects the same universal properties as the physical edge spectrum~\\cite{li2008,qi2012,chandran2011,dubail2012,swingle2012}.\nFor each of the two ground states $\\ket{\\Psi_a}$ obtained for an infinite cylinder,\nthe entanglement spectrum, see Fig.~\\ref{fig:cft}, is consistent with\nthe corresponding sector of the\nchiral SU$(2)_1$ Wess-Zumino-Witten conformal field theory:\nThe entanglement spectrum of $\\ket{\\Psi_\\mathds{1}}$ displays precisely the sequence\nof degeneracies of the tower of Kac-Moody descendants\nof the identity primary field (1-1-2-3-5-...). These are reproduced by counting\nthe number of low-lying close-by states in each tower grouped by momentum and spin quantum numbers.\nSimilarly, the entanglement spectrum of $\\ket{\\Psi_\\mathrm{s}}$ displays the degeneracies of\nthe spin-1\/2 primary field and its descendants (also 1-1-2-3-5-...).\nWe note that in the identity sector, all towers carry integer representations of the spin quantum number, whereas in the semion\nsector they carry half-integer representations. In both ground states, the levels can be grouped into\nSU(2) multipletts.\n\n\\subhead{Emergent anyons}\nThe bulk of the chiral spin liquid phase has anyonic excitations, referred to as semions.\nThe topological properties of these quasiparticles can be characterized through their modular\n$T$ and $S$ matrices~\\cite{nayak2008}. The $T$ matrix contains the\ncentral charge $c$ and the self-statistics of the anyonic particles, i.e. the phase\nthat is obtained when two particles of the same kind are exchanged.\nThe $S$ matrix contains the mutual statistics of the anyonic quasiparticles, their\nquantum dimensions (counting the internal degrees of freedom\nof each particle), and the total quantum dimension of the phase. More detailed\ndefinitions of these quantities are given in the Methods summary.\n\nFor a fixed number of quasiparticles, only a finite number of\npossible $S$ and $T$ matrices exist~\\cite{rowell2009,bruillard2013}. For two types of\nquasiparticles (as in the case of the $\\nu=1\/2$ bosonic Laughlin state) only two choices are possible~\\cite{rowell2009}.\nTherefore, by numerically calculating the $S$ and $T$ matrices and\ncomparing them against the two possibilities, we have fully identified\nthe universal properties of the topological phase.\nFor the $\\nu=1\/2$ Laughlin state, the modular matrices are\n\\begin{align} \\label{TS}\nT&=e^{-i \\frac{2\\pi}{24}} \\left[\\begin{matrix} 1 & 0 \\\\ 0 & i \\end{matrix} \\right]\n&S&=\\frac{1}{\\sqrt{2}}\\left[\\begin{matrix} 1 & 1 \\\\ 1 & -1 \\end{matrix}\\right].\n\\end{align}\n\nFor an XT8-4 torus of $48$ sites at $\\theta=0.05 \\pi$, where the\nfinite-size corrections to this quantity are minimal, we obtain\n\\begin{eqnarray}\nT &=& e^{-i \\frac{2\\pi}{24} 0.988} \\left[\n\\begin{matrix}\n1 & 0 \\\\\n0 & i \\cdot e^{-i0.0021 \\pi}\n\\end{matrix}\n\\right] \\ , \\label{numT} \\label{numS} \\\\\nS &=& \\frac{1}{\\sqrt{2}} \\left[\n\\begin{matrix}\n0.996 & 0.995 \\\\\n0.996 & -0.994 e^{-i 0.0019 \\pi}\n\\end{matrix} \\right] \\ . \\nonumber\n\\end{eqnarray}\nThis is in very good agreement with the $T$ and $S$ matrices for the $\\nu=1\/2$ Laughlin state\ngiven in Eqn.~\\eqnref{TS} and provides the strongest confirmation of the nature of the\nbulk topological phase.\nThe correct normalization of the first row or column of the $S$ matrix\nindicates that we have indeed obtained a full set of ground states.\nWe can also read off the total quantum dimension $\\mathcal{D}=1\/S_{\\mathds{1} \\mathds{1}}=\\sqrt{2}\/0.996$ of the phase,\nwhich determines the topological entanglement entropy~\\cite{kitaev2006-tee,levin2006}\nthat has been widely used to identify topological phases.\nFurthermore, the central charge $c=0.988$ is in excellent agreement with the prediction\nand the value extracted from the edge above.\n\n\\begin{figure}\n \\includegraphics[width=\\columnwidth]{pd.pdf}\n \\caption{\n Singlet and triplet gaps as a function of $\\theta$ for an infinite XC8-4 cylinder.\n The triplet gap is a lower bound on the critical magnetic field $h_c$; hence, the shaded region in\n the middle panel indicates the minimal stability of the phase in the $\\theta$-$h_z$ phase diagram.\n \\label{fig:pd} }\n\\end{figure}\n\n\\subhead{Stability of the chiral spin liquid}\nTo establish the region in which the phase persists as $\\theta$ is tuned in the range $\\theta \\in [0,\\pi\/2]$,\nwe first consider the fidelity~\\cite{zanardi2006} $F(\\theta) = \\langle \\Psi_a(\\theta-\\epsilon) | \\Psi_a(\\theta+\\epsilon) \\rangle$\nshown in the bottom panel of Fig.~\\ref{fig:pd} (for the precise definition of this quantity for infinite\nsystems, see the appendix). The fidelity remains near unity as $\\theta$ is tuned away from\nthe chiral point $\\theta=\\pi\/2$, until it suddenly drops for $\\theta < 0.05 \\pi$, indicating a transition.\nFurther support for the extended stability of the CSL is found in various other characteristics,\nincluding the spectral gap, the modular matrices and the entanglement spectrum.\nThis is remarkable as it indicates that tuning away from the Heisenberg model ($\\theta=0$)\nwith a small critical chiral coupling of $(J_\\chi\/J_\\text{HB})_\\text{crit} \\leq \\tan(0.05 \\pi) \\approx 0.16$\nis sufficient to drive the system into the chiral phase. \n\nIn experimental scenarios, a Zeeman magnetic field $h_z$ is likely to be generated along with\nthe orbital magnetic field that induces the three-spin chiral term. The relative strength of the orbital\nmagnetic field and the Zeeman field is determined by the $g$-factor and the ratio $t\/U$.\nThe energy gap in the triplet sector gives a lower bound on the critical field strength $h_c$ up \nto which the CSL phase is stable. The values for the triplet gap shown in the middle panel of Fig.~\\ref{fig:pd}\nremain large all the way from the fully chiral point ($\\theta=\\pi\/2$) to the transition out of the\nCSL towards the Heisenberg point ($\\theta=0$).\n\nIn the top panel of Fig.~\\ref{fig:pd}, we also show the singlet gap, i.e. the gap to the lowest excitation\nin the $S_z=0$ sector. As opposed to the triplet gap, which appears to remain large across the\ntransition, the singlet gap decreases as the transition out of the CSL is approached. Note that the gaps may\nbe rounded off at the transition by effects due to either the finite diameter or the finite bond dimension\nof the matrix-product state ansatz.\nWe point out, however, that a closing only of the singlet and not the triplet\ngap is consistent with the scenario for the transition from the chiral spin liquid into a\ndoubled semion phase (twisted $\\mathbb{Z}_2$ topological phase) studied in Ref.~\\onlinecite{barkeshli2013}.\n\n\\section{Outlook}\n\nWe have taken an important step towards finding realistic models for a chiral spin\nliquid in a frustrated spin system. \nWe believe that this will nucleate new research efforts both by theorists and\nexperimentalists. From a theoretical point of view, studying the transition from\nthe chiral spin liquid to the putative time-reversal symmetric spin liquid in the\nHeisenberg model will provide the unique opportunity to study a topological\nphase transition in a realistic model, and may provide invaluable insights into\nthe physics of frustrated spins on the Kagome lattice. For experimentalists, our\nwork will provide a guide in searching for realizations of bosonic fractional\nQuantum Hall physics in the lab, be it in materials that have Kagome lattice\nstructure and form Mott insulators, or by engineering such systems in cold atomic gases.\n\n \\vspace{0.15in}\n{\\small During completion of this work, we became aware of related work in Refs.~\\onlinecite{he2013,gong2013}.}\n\n\\acknowledgements\nThe DMRG code was developed with support from the Swiss Platform for High-Performance\nand High-Productivity Computing (HP2C) and based on the ALPS libraries~\\cite{bauer2011-alps}.\nS.T. was supported, in part, by SFB TR 12 of the DFG. A.W.W.L was supported, in part, by\nNSF DMR-1309667. We acknowledge illuminating discussions with participants of the KITP\nworkshop \\emph{Frustrated Magnetism and Quantum Spin Liquids} (Fall 2012) as well as\nM. Barkeshli and P. Bonderson. This research was supported in part by Perimeter Institute for\nTheoretical Physics. Research at Perimeter Institute is supported by the Government of Canada\nthrough Industry Canada and by the Province of Ontario through the Ministry of Research and Innovation.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbkgg b/data_all_eng_slimpj/shuffled/split2/finalzzbkgg new file mode 100644 index 0000000000000000000000000000000000000000..ce77b7510930622882b17d9aac0aa660e4aec6ec --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbkgg @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nMrk~766 is a bright ($F_{(2-10)}\\sim 2 \\times 10^{-11} \\rm erg\\,\ncm^{-2} s^{-1}$), soft ($\\Gamma_{0.1-2.4} \\sim 2.7$) X-ray source at\nredshift $z=0.012$. The spectrum measured with the {\\it Einstein} IPC\nand MPC was complex and ultra-soft ($\\Gamma=1.77$; $kT=18.6 \\rm eV$;\nUrry {\\it et al.}\n\\markcite{34} 1990). A shortest time scale of variability of 1000\nseconds and a steep and variable power law index was found in a long\nobservation using {\\it EXOSAT} (Molendi, Maccacaro \\& Schaeidt\n\\markcite{20} 1993). During the {\\it ROSAT} All Sky Survey, \nMrk~766 was bright ($F_{0.1-2.4} \\sim 1.5\n\\times 10^{-10} \\rm erg\\, cm^{-2} s^{-1}$ (unabsorbed)) and\nvariability by a factor of three with no accompanying spectral\nvariability was observed in 10--12 hours (Molendi, Maccacaro \\&\nSchaeidt \\markcite{20} 1993). Pointed {\\it ROSAT} observations\nrevealed spectral variability that Netzer, Turner\n\\& George (1994) \\markcite{24} showed could not be explained by a\nchange in ionization of a warm absorber, and Molendi \\& Maccacaro\n(1994) \\markcite{19} attributed to a change in the accretion rate.\n\nMrk~766 is a member of the X-ray narrow line Seyfert 1 (NLS1) galaxy\nclass (Osterbrock \\& Pogge \\markcite{25} 1985; Goodrich \\markcite{13}\n1989). {\\it ROSAT} observations of NLS1s find soft 0.1--2.4 keV X-ray\nspectra and rapid, large amplitude soft X-ray variability. The soft\nX-ray spectra of NLS1s are systematically steeper than the spectra of\nbroad-line Seyfert 1 galaxies (Boller, Brandt \\& Fink \\markcite{3}\n1996 and references therein). A harder high energy power law component\ngenerally was not observed in the relatively soft {\\it ROSAT} band.\nOnly a few observations at higher energies have been reported. The\n{\\it ASCA} spectrum of the NLS1 object IRAS~13224-3809 is dominated\nbelow $\\sim 2$ keV by a soft excess and from 2 to 10 keV by a hard\n($\\Gamma \\sim 1.3$) power law (Otani \\markcite{26} 1995). In\ncontrast, a very steep spectrum with $\\Gamma_{(2-10keV)} \\sim 2.6$ was\nfound from NLS1 object RE~1034+39 (Pounds, Done \\& Osborne\n\\markcite{39} 1995).\n\nWe report the results from December 1993 {\\it ROSAT} and {\\it ASCA}\nobservations of Mrk~766. Timing analyses of two {\\it ROSAT} archival\nobservations are also presented. In section 2 the data reduction is\ndiscussed briefly. In section 3 timing analyses using normalized\nvariability amplitudes and hardness ratios are presented. In section\n4 the spectral analysis of the {\\it ASCA} data is described. The\nresults are discussed in terms of standard models in Section 5 and\ncompared with reported results from other NLS1s. A summary and\nconclusions are given in Section 6.\n\n\\section{{\\it ASCA} and {\\it ROSAT} Observations of Mrk~766}\n\nWe observed Mrk~766 with {\\it ASCA} and the {\\it ROSAT} PSPC during\nDecember 1993. It had been previously observed with the {\\it ROSAT}\nPSPC several times. Two longer observations were made 1991 June 15 and\n1992 December 21 and these data were extracted from the {\\it ROSAT}\narchive. The observation log is given in Table 1.\n\nThe data were reduced using Xselect. To ensure all soft photons were\ncollected, {\\it ROSAT} extraction regions of $3 ^\\prime$ for the\non-axis 1993 observation and $4^\\prime$ for the off-axis 1991 and 1992\nobservations were used. Extraction of background subtracted light\ncurves from the events files was done using IDL software. The light\ncurves from the off-axis 1991 and 1992 observations were corrected for\nvignetting. In the 1991 observation, Mrk~766 was periodically occulted\nby the detector rib so time periods in which the flux dropped to zero\nbecause of occultation were excluded (e.g. Brandt et al.\n\\markcite{2} 1993). A region of the same radius located diametrically\nacross the detector and subjected to the same good time interval\nselection provided an approximately correctly normalized background.\n\n{\\it ASCA} data were reduced using standard selection and cleaning\ncriteria. Background for the GIS was obtained from a source free\nregion in the GIS field of view approximately the same distance from\nthe optical axis as the source. Background SIS spectra were obtained\nfrom blank sky fields, while background count rates for the light\ncurves were determined from the edges of the SIS chips. The time\ndependent gain shift in the SIS was accounted for by filling and using\nPI columns. Spectra were extracted using Xselect, and background\nsubtracted light curves were obtained from the cleaned events files\nusing IDL.\n\nFigure~\\ref{fig1}a shows the {\\it ROSAT} PSPC and {\\it ASCA} SIS0\nlight curves from the most recent, quasi-simultaneous 1993\nobservations. Error bars represent $1\\sigma$ statistical error. The\n{\\it ROSAT} observation started $\\sim 20,000$ seconds before the {\\it\nASCA} observation but was unfortunately cut short as the satellite\nwent into safe hold mode. The {\\it ROSAT} PSPC light curves from the\n1991 and 1992 observations are shown in Figure~\\ref{fig1}b. Mrk~766\nwas brightest in the {\\it ROSAT} band in 1992 at $<5 \\rm\\,\ncnts\\,s^{-1}$. This is comparable to the flux level observed during\nthe {\\it ROSAT} All Sky Survey observation (Molendi, Maccacaro \\&\nSchaeidt \\markcite{20} 1993). In order to make a direct comparison\nwith the other data, only the first $\\sim 80$ks of the 1992\nobservation was considered. The remainder consists of data from only\ntwo orbits, is separated from the main part of the observation by\nnearly one day and the full light curve is shown in Netzer, George \\&\nTurner \\markcite{24} 1994.\n\n\\section{Timing Analysis}\n\n\\subsection{Fastest Observed Variability}\n\nThe {\\it ASCA} light curves were examined in order to find instances\nof rapid variability that could be clearly identified in all four\ndetectors. A dip in flux occurred at $\\sim 50,000\\rm \\, s$ from the\nstart of the observation; the light curve from the SIS0 detector is\nshown embedded in Figure~\\ref{fig1}a. At the end of the dip, the flux\nincreased by nearly a factor of two in $\\sim 1000$ s. This timescale\nis the same order as those observed in {\\it ASCA} data from other\nSeyfert 1 nuclei, including NGC 4051 ($\\Delta t \\sim 200 \\rm \\,s$;\nMihara et al. \\markcite{18} 1994).\n\nAssuming that the X-ray emission originates from a single region, the\nminimum flux doubling time scale $\\Delta t$ gives an upper limit on\nthe source size as $R \\sim c \\Delta t$ of $3 \\times 10^{13}\\, \\rm cm$,\nwhere $c$ is the speed of light. This estimate breaks down if the\nX-rays are emitted from many small regions.\n\nBecause of the telescope wobble, variability on timescales less than\n$400\\rm \\, s$ generally cannot be detected in {\\it ROSAT} data.\nSignificant variability was observed consistently between adjacent\norbits ($\\Delta t \\sim 6000$ s). The fastest variability of the three\nobservations was found when it was brightest during the 1992\nobservation. A 30\\% increase in 2400 s occurred 3400 s from the\nbeginning of the observation (Figure~\\ref{fig1}b).\n\n\\subsection{Variance analysis}\n\nThe normalized variability amplitude (NVA), defined to be the standard\ndeviation divided by the mean intensity, provides a simple way to\nquantify the variability in different energy bands (e.g. Edelson\n\\markcite{5} 1992). If the width of the energy band is chosen so that the\nnumber of photons in each light curve is the same, the NVA collapses\nto the square root of the variance.\n\nThe {\\it ASCA} light curves from each detector were accumulated with\n100 s binning in the 4--10 keV band where the power law component\nshould dominate the spectrum. The light curves were not background\nsubtracted. However, the dilution of the variability by the constant\nbackground was small since the background rate in the highest energy\nband where contribution is largest was only e.g. $\\sim 8$\\% of the\nSIS0 count rate. The data below 4 keV were divided into energy bands\nwith bounds chosen so that the light curves had the same mean square\nmeasurement error $\\sigma^2_{err}$ as in the 4--10 keV band. Note\nthat each resulting band was wider than the energy resolution at that\nband. The true variance of the data given by\n$\\sigma^2_{int}=\\sigma^2_{obs}-\\sigma^2_{err}$ is plotted as a\nfunction of energy in Figure~\\ref{fig2}. The $1\n\\sigma$ uncertainties in the variance are less than 10\\% of the values\n(Equation 2 of Done et al. \\markcite{4} 1992). These variance plots\ndemonstrate that the variability amplitude over the whole observation\nis smallest at hard energies, peaks near 1 keV, and decreases towards\nlower energies in the SISs.\n\nFor the {\\it ROSAT} light curves, three standard energy bands were\nconsidered: the `{\\it a} band' (channels 11--41, energy 0.1--0.4 keV),\nthe `{\\it c} band' (channels 52--90, energy 0.5--0.9 keV) and the\n`{\\it d} band' (channels 91--201, energy 0.9--2 keV). These bands are\nindependent and roughly correspond to regions where different spectral\nfeatures will dominate: the soft excess in the {\\it a} band, the warm\nabsorber in the {\\it c} band, and the power law in the {\\it d} band.\nThe binning of the background subtracted light curves was chosen to be\n400~s to account for the telescope wobble. \n\nBecause of the rib occultations in the 1991 data, it was necessary to\ninclude data with net exposure per bin of 200--400~s, even though the\nshorter exposure bins add noise to the light curve. The light curves\nin these three bands are shown on the left in Figures~\\ref{fig3}a, b\nand c. Mrk~766 is bright enough and the bin size is long enough that\nthe signal to noise is better than 6 in all bins. Variability was\ndetected in each energy band and the $\\chi^2$ values for a constant\nhypothesis model fit and the computed NVAs are listed in Table 2. The\nNVAs show that in all observations the source is significantly less\nvariable in the lowest energy band compared with the higher energy\nbands.\n\n\\subsection{Flux Ratios}\n\nThe hardness ratio (4.0--10.5 keV\/1.0--1.35 keV) and softness ratio\n(0.4--0.7 keV\/1.0--1.35 keV) light curves from the {\\it ASCA} SIS0\ndetector are shown in Figures~\\ref{fig4}a and 4b, respectively.\nFollowing Ptak et al. \\markcite{27} (1994) the values were computed\nusing variable bin sizes corresponding to good time intervals longer\nthan 300 seconds. A large decrease in hardness corresponding to a\nsoftening of the spectrum was observed in the hardness ratio light\ncurve at about 20,000~s from the beginning of the observation\n(Figure~\\ref{fig4}a). The light curves show that the spectral change\nis due to a large increase in 1.0--1.35 keV flux while the 4--10 keV\nflux remained nearly constant. Significant variability in the\nsoftness ratio was also observed at the same time, such that the\nspectrum below 1 keV hardens when the flux increases\n(Figure~\\ref{fig4}b). The hardening of the spectrum may be more\ngradual and it is not clearly completed until after $\\sim 25,000$~s.\nVariability was observed in both energy bands but the amplitude is\nlarger in the 1.0--1.35 keV band. The spectral variability is\nconfined to the region around 20,000~s elapsed time. Other instances\nof large amplitude flux variability occurred (e.g. at $\\sim 35,000$ s)\nwith no corresponding spectral change. Thus, the spectral variability\nis not strictly correlated with the flux.\n\n{\\it ROSAT} softness ratio light curves (0.1--0.4 keV\/0.9--2.0 keV and\n0.5--0.9 keV\/0.9--2.0 keV) are shown on the right sides of\nFigures~\\ref{fig3}a, b and c. Spectral variability is most pronounced\nbetween the hardest and softest bands, with the spectrum generally\nhardening as the flux increased. This result is also true from\nobservation to observation; the spectrum was hardest when the source\nwas brightest in 1992, and softest when it was dimmest in 1991. On\nlong time scales the softest band varies proportionately less than the\nharder bands. On short time scales the spectral variability is not\ncorrelated with the flux. At high flux, when the source was most\nrapidly variable, correlated variability occurred with no change in the\nhardness ratio (1992: at the beginning of the observation). Similar\ncorrelated variability was observed during the {\\it ROSAT} All Sky\nSurvey when the source was also quite bright (Molendi, Maccacaro and\nSchaeidt \\markcite{20} 1993). In contrast, at low flux in the 1991\nand 1992 observations, orbit to orbit deviations in the hardest band\noccurred which were not followed in the softest band (1991: at $\\sim\n23000$ and $\\sim 58000$~s; 1992: at $\\sim 66000$~s). These short time\nscale hard band excursions result in dips in the softness ratio.\n\n\\section{Spectral Analysis}\n\nBased on the softness and hardness ratios, the {\\it ASCA} data were\ndivided in two as shown in Figures~\\ref{fig4}a and 4b, and spectra were\naccumulated avoiding the transition region between $\\sim 15,000$ and\n$\\sim 25,0000$~s from the beginning of the observation. These spectra\nare referred to as the low state and high state spectra and represent\nexposures of $\\sim 8,000$ and $20,000$~s respectively.\nFigure~\\ref{fig5} shows the pha ratios from the low state divided by\nthe high state of the summed SIS0 and SIS1 spectra in the top panel\nand of the summed GIS2 and GIS3 spectra in the bottom panel. These\nconfirm the results of the variance analysis which were that the\nspectrum is most variable at around 1 keV, the variability amplitude\ndecreases to lower and higher energies, and the flux is essentially\nconstant at about 10 keV.\n\n\\subsection{The Hard X-ray Spectrum}\n\nAn estimation of the power law index, assuming that any warm absorber\ndoes not have a large column density, was obtained by fitting the four\nspectra above 2 keV with a power law plus iron $K\\alpha$ line model.\nThe low and high state spectral indices were $1.57\\pm 0.07$ and\n$2.00^{+0.03}_{-0.04}$ respectively, and the results from fitting each\ndetector separately were consistent. Throughout this paper, quoted\nuncertainties are determined assuming 90\\% confidence and 1 parameter\nof interest ($\\Delta \\chi^2=2.71$).\n\n\\subsection{The Soft X-ray Spectrum}\n\nThe shape of the soft X-ray spectrum can be seen by fixing the power\nlaw indices to the values found above 2 keV, including the Galactic\nabsorption ($1.77 \\times 10^{20} \\rm \\, cm^{-2}$; Elvis, Wilkes \\&\nLockman\n\\markcite{6} 1989), and plotting the ratio of the data to the model.\nThese plots are shown in Figure~\\ref{fig6}a and 6b for the SIS0 and\nGIS2 low state and high state spectra respectively. In the low state\nthere is excess emission below 0.8 keV, suggesting the presence of a\nsoft excess component (e.g. Mihara et al. \\markcite{18} 1994). In\nthe high state there is a deficit between 0.8 and 1.3 keV, suggesting\nthe presence of a partially ionized absorber (e.g. Fabian et al.\n\\markcite{8} 1994). It is clear that an absorbed power law plus\nnarrow iron line cannot model the spectra. Spectral fitting results\nof the SIS0:GIS2 and SIS1:GIS3 pairs are listed in Table 3 for low and\nhigh states separately. The iron line is discussed separately in\nSection 4.5 and fit results are given in Table 4.\n\nThe soft excess component and warm absorber can be simply\nparameterized using a black body model and an absorption edge,\nrespectively. The results from adding each of these components\nseparately are given in the second and third panels of Table 3.\nAddition of either model component significantly improved the fits of\nboth the low and high flux spectra. However, the addition of the\nblack body improved the fit more than the edge in the low flux case,\nwhile the reverse was true for the high flux case, as expected from\nthe residuals shown in Figure~\\ref{fig6}.\n\nThe fourth panel of Table 3 lists results from fitting with a model\nincluding both a blackbody and an edge. These fit results confirm the\nimportance of the black body component in the low state spectra and\nthe edge in the high state spectra. The black body temperatures and\nedge energies of the low and high state spectra are consistent. The\nedge energy near $0.74\\rm \\, keV$ is consistent with an origin of\ntransmission through a partially ionized absorber dominated by\n\\ion{O}{7}. The power law slope of the low flux spectra remains\nsignificantly flatter than that of the high flux spectra ($\\Delta\n\\Gamma \\sim 0.35$).\n\nNext, to better simulate the warm absorber, a two edge plus power law\nmodel was tried and the results are listed in the fifth panel of Table\n3. The power law indices are steep in both the low and high state,\nand the implied change in index is much smaller ($\\Delta\\chi^2\n\\sim 0.1$). This model has the same number of degrees of freedom as\nthe power law plus black body and edge model, but the fit is much\npoorer for the low flux spectra ($\\Delta \\chi^2$ of 24 and 50 for the\nSIS0:GIS2 and SIS1:GIS3 pairs, respectively). In contrast, the fits\nof the high flux spectra are improved somewhat by the additional edge\n($\\Delta \\chi^2$ of 9 and 11). Addition of a black body component,\nwhile not necessary for the high flux spectra, greatly improves the\nfit of the low flux spectra ($\\Delta \\chi^2$ of 28 and 52), and\nresults in a decrease in the low flux photon index and an increase in\nthe implied index change ($\\Delta\\Gamma \\sim 0.35$). The temperature\nof the black body component in the low and high states is consistent\nat $kT \\sim 120\\rm eV$, although the limits on the temperature in the\nhigh state cannot be determined well for the SIS1:GIS3 pair. The edge\nenergies near 0.74 and $0.87\\rm \\, keV$ found in the high state are\napproximately consistent with absorption by \\ion{O}{7} and \\ion{O}{8}.\nIn the low state, the \\ion{O}{7} edge is clearly detected but the\nsecond edge is not necessary. This suggests that the ionization of\nthe gas in the high state is higher than in the low state.\n\nWe also modeled the warm absorber using a table model (e.g. Yaqoob,\nWarwick \\& Pounds \\markcite{37} 1989). The model used here assumes\nthat the power law is the sole source of ionizing photons. The density\nof the gas was assumed to be $10^{9.5} \\rm cm^{-3}$. An analytic\napproximation based on a large number of CLOUDY runs was used to model\nthe temperature as a function of ionization parameter. It was found\nto range between $\\sim 5 \\times 10^4$K and $\\sim 10 \\times 10^4$K. The\nadvantages of using the warm absorber table model compared with the\ntwo edge model are that there are two fewer parameters, and the\nparameters ($log(U)$ and $log(N_w)$) can be directly interpreted. The\ndisadvantage is that we must assume a particular model for the\nionizing spectrum and gas. Further, in this model, only absorption is\nconsidered, and the emission lines expected if the warm absorber has a\nlarge covering factor are ignored (e.g. Netzer \\markcite{23} 1993).\nHowever, this model is sufficient for a general discussion given the\nstatistical quality of these data (but see Section 4.3.1).\n\nThe four groups of spectra were first fit with a power law plus warm\nabsorber model (Table 3). As found using the two edge model, the fits\nof the high flux spectra are acceptable, but the fits of the low flux\nspectra are unacceptable. Addition of a black body component\nsubstantially improves the fit of the low flux spectra, as shown in\nthe eighth panel of Table 3, but it is not necessary to model the high\nflux spectra. The ionized column density is consistent between the\nlow and high flux states at $log(N_w) \\sim 21.8$, while the ionization\nstate $\\log(U)$ is lower for the low flux spectra ($-0.78$) compared\nto the high flux spectra ($-0.4$). In this model, ionization states\n$log(U)$ below and above $\\sim -0.4$ are dominated by \\ion{O}{7} and\n\\ion{O}{8} absorption, respectively. In contrast with the two edge\ndescription of the warm absorber, the use of the warm absorber table\nmodel resulted in a higher black body temperature in the high flux\nstate; however, the black body was barely detected in the high flux\nspectra ($\\Delta \\chi^2$ of 4 and 5). Thus the temperature of the\nsoft component is model dependent, but the necessity of this component\nto model the low flux spectra is not. The table model description of\nthe warm absorber results in a slightly steeper index compared with\nthe two edge description. This is because the table model properly\ntreats the absorption by gas with cosmic abundances (i.e. not only\noxygen) producing curvature in the model between 1.5 and 2.5 keV.\nThus the value of the indices is slightly model dependent, but the\nchange in index between the low and high states is not.\n\nIn summary, these results show that to model the high and low flux\nspectra consistently, both a soft excess component and a warm absorber\nare required. When both of these components are included in the\nspectrum, the low flux spectral index is significantly flatter than\nthe high flux index ($\\Delta\\Gamma \\sim 0.35$).\n\n\\subsection{Other Models}\n\n\\subsubsection{Other Soft Excess Models}\n\nThe soft excess component in the low flux spectra was modeled\nadequately with a black body (with a single edge, $\\chi^2 = 230$\/266\nd.o.f. for the SIS0:GIS2 pair). For comparison, other soft excess\nmodels including Raymond--Smith (cosmic abundances), bremsstrahlung,\ndisk black body, and power law were tried. The power law plus soft\ncomponent model alone did not fit the low flux spectra well. The disk\nblack body model fit the best, with $\\chi^2$ of 247\/268 d.o.f. Including\nan edge generally improved the fits, but the Raymond--Smith and the\npower law models could not describe the low flux spectra well ($\\chi^2$ of\n277 and 259\/266 d.o.f. respectively), while the bremsstrahlung and the\ndisk black body models could ($\\chi^2$ of 234 and 231\/266 d.o.f.\nrespectively). A slightly higher temperature was found using these\nmodels ($kT=200$ eV and $kT=145$ eV) compared with the black body\nmodel ($kT=117$ eV) but nearly consistent edge energy, edge depth and\nphoton index were found. The indices obtained were flat ($\\Gamma =\n1.57$ in both cases).\n\nThe warm absorber table model used thus far models only the absorption\nedges of various ionized species. Since the line emission expected\nfrom a physical warm absorber might appear as an excess emission\ncomponent, we also tested models calculated using XSTAR which include\nemission lines from the warm absorber (Kallman \\& Krolik\n\\markcite{16} 1993). Fitting the SIS0:GIS2 pair with a power law\nplus emission only from a physical warm absorber in the line of sight\nresulted in a better fit with $\\chi^2$ of 292\/277 d.o.f. Including an\nedge, to simulate the absorption by a warm absorber, resulted in a $\\chi^2$ \nof 261\/268 d.o.f. However, a significantly better fit was found when\na blackbody was also included ($\\chi^2$ = 228\/264 d.o.f.) and the resulting\npower law index was again flat ($\\Gamma = 1.55$). Similarly, emission\nfrom reflection by a warm absorber could not alone describe the\nspectrum (alone: $\\chi^2=299\/268$ d.o.f.; with an edge:\n$\\chi^2=268\/266$ d.o.f.; with an edge and a black body:\n$\\chi^2=227\/264$ d.o.f.). In the final case, the index was flat\n($\\Gamma = 1.55$).\n\nSoft excess emission can also be produced by reflection from an\nionized disk (Ross \\& Fabian \\markcite{28} 1993; Zycki et al.\n\\markcite{38} 1994). We fit the low flux spectrum with an ionized\ndisk table model computed according to Zycki et al. \\markcite{38}\n1994. For power law plus disk emission only the $\\chi^2$ was 262\/269\nd.o.f., but the ratio of the reflected flux to direct emission was\n5.8. Since the ratio should be near 1 for the isotropic static case.\nwe considered this result to be unphysical. Addition of an edge gave\n$\\chi^2$ of 248\/267 d.o.f., but again the ratio was too large at 5.5.\nAddition of a black body gave $\\chi^2=228\/265$ d.o.f., with the ratio\nreduced to a physical value of $R=1.23$ and a flat index $\\Gamma =\n1.61$.\n\nThese results show that soft excess models without line emission fit\nthe spectra well, but we cannot distinguish among them, possibly\nbecause of the poor statistics due to the relatively short exposure in\nthe low state and the decrease in sensitivity toward low energies of\nthe {\\it ASCA} SIS. Further, the flat spectral index obtained in the\nlow state is robust against changes in the soft excess model.\n\n\\subsubsection{Partial Covering Models}\n\nAnother possible origin of soft emission is leakage through a\npartially covering absorber (e.g. NGC 4151: Weaver et al.\n\\markcite{35} 1994). The spectral variability would then result from\na change in the covering fraction. However, the partial covering\nmodel does not fit the low flux SIS0 and GIS2 spectra well\n($\\chi^2=298$ for 268 d.o.f.), the resulting power law is forced to be\nsteep ($\\Gamma=2.42$), and both low and high energy residuals are\nseen. These can be modeled by reflection, in which case the fit is\ngood ($\\chi^2=225$\/263 d.o.f.), but the power law is very steep\n($\\Gamma=3.0$) and the ratio of the reflected emission to primary\nemission is required to be 20. This model is unphysical so we\nconclude that partial covering cannot adequately model the soft excess\ncomponent. Finally, a decrease in the fraction of the source covered can\nonly produce a steepening of the spectrum with an increase in flux and\ncannot explain the hardening of the spectrum below 1 keV indicated by\nthe softness ratio (Figure~\\ref{fig4}b).\n\nA scattering and dual absorber model was used to describe the complex\nX-ray spectrum of NGC 4151 (Weaver et al. \\markcite{35} 1994). For\nMrk~766 this model does not give a good fit ($\\chi^2=244$\/266 d.o.f.)\nand the photon index is steep ($\\Gamma=2.84$). Low energy residuals\nsuggest an unmodeled absorption edge. When an edge is included the\nfit is good ($\\chi^2$ =222\/264 d.o.f.), but the power law index is very\nsteep ($\\Gamma=2.94$). Again, this steep index seems unphysical, so\nthe dual absorber model is rejected.\n\n\\section{Reflection}\n\nThe spectral index change is robust against changes in models of the\nsoft excess component and warm absorber because the low flux spectrum\nis flat at high energies. The reflection spectrum is also flat (e.g.\nGeorge \\& Fabian \\markcite{9} 1991), and could produce a hard tail if\nthe reflection component normalization is high compared with the power\nlaw normalization. This could be observed if the response of the\nreflection component lags variability of the incident X-rays and would\nbe expected if the light crossing timescale of the reflection region\nis long compared with the source variability timescale, or if the\nreflection region is located far from the X-ray source (e.g. in the\nmolecular torus; Ghisellini, Haardt\n\\& Matt \\markcite{12} 1994; Krolik, Madau \\& Zycki\n\\markcite{17} 1994; Leighly et al. \\markcite{42} 1996).\n\nWe define the reflection ratio to be 1 under the conditions that\nnonvarying primary power law emission from an isotropic point source\nilluminates an infinite optically thick disk. In this case, the\nreflection spectral component is not important in the spectrum below 5\nkeV. Thus, we can estimate the contribution of the reflection by\nfitting the spectra below 5 keV and comparing with the fit results\nover the full range. A power law plus two edge fit of the SIS0:GIS2\nspectra results in a steep photon index (low state: $\\Gamma=1.95$ and\n$\\chi^2$ = 214.4\/230 d.o.f., high state: $\\Gamma=2.02$ and $\\chi^2$ =524\/519\nd.o.f.). Addition of a black body component to the model improved the\nfit of the low flux spectra and flattened the photon index\n($\\Gamma=1.67$ and $\\chi^2=197\/228$ d.o.f.), but had no effect on the\nhigh flux spectral fit. If the table model is used to model the warm\nabsorber, similar results are obtained although both indices are found\nto be slightly steeper. Thus, fitting below 5 keV shows that the\nindex variability is still required and also that the measured low\nenergy index is consistent with the data above $\\sim 5 \\rm keV$.\n\nA reflection ratio much larger than 1 results in a flat spectrum\ntowards high energies. Fitting the spectra with a power law plus 2\nedges and reflection allowing the ratio to be free produces a good fit\nwith a steep index which is consistent between the low and high flux\nspectra (Table 3). However, to explain the low flux spectrum, the\nreflection ratio must be 5--8, while the high flux spectra require a\nreflection ratio of only 0.5. Most importantly, the reflection\ncomponent normalization is required to be significantly {\\it higher}\nin the low flux state compared with the high flux state, so a\nreflection lag cannot explain the spectral variability. Further, such\na large reflection ratio should be accompanied by a large equivalent\nwidth narrow iron line in the low state spectrum and such a line is\nnot observed (Section 4.5).\n\nThe timing analysis results also rule out a lag in neutral reflection as\nthe origin of the spectral variability. Since the neutral reflection\nspectrum flux decreases towards low energies, only spectral softening\nwith an increase in power law flux is predicted, whereas a hardening\nof the spectrum below 1 keV was observed. If the surface of the\nreflector is ionized, the opacity is reduced at low energies and a\nsoft X-ray reflection component plus emission lines should be observed\n(Ross \\& Fabian \\markcite{28} 1993; Zycki et al. \\markcite{38} 1994),\nwhich results in hardening of the low flux spectrum with an increase\nin power law flux. It was shown in Section 4.3.1 that the ionized\ndisk model does not produce a good fit alone, mostly because the soft\nexcess does not show evidence for line emission.\n\n\\subsection{The Iron Line}\n\nThe presence of the iron $K\\alpha$ line in Seyfert 1 nuclei spectra\nis well established (e.g. Nandra \\& Pounds \\markcite{22} 1994). Mrk\n766 was not observed using Ginga, and a iron $K\\alpha$ line had not\nyet been observed in its spectrum. To look for the iron line, we fit\nthe spectra from all four detectors simultaneously above 2 keV.\nBecause of the continuum spectral variability the low and high state\nspectra were fit separately. A power law model resulted in\n$\\chi^2$\/d.o.f. of 206\/253 and 806\/759 for the low and high states\nrespectively. Addition of a narrow ($\\sigma=0$ keV) redshifted line\nwith the energy fixed at 6.4 keV gave the results presented in Table\n4. There is no strong evidence for a narrow line with rest energy 6.4\nkeV in the low flux spectra ($\\Delta\\chi^2 \\sim 2$). In the high flux\nspectra $\\Delta\\chi^2$ is 13 corresponding to an F statistic value of\n12.4, indicating the presence of a line with confidence greater than\n99.9\\%. Thus the presence of an iron emission line is confirmed in\nthis source. The ratio of data to power law model for the summed SIS0\nand SIS1 spectra shows the narrow line and ionized iron edge\n(Figure~\\ref{fig7}). The non-detection in the low flux spectra can be\nexplained by the poor statistics resulting from lower flux and shorter\nexposure. If the high flux data are divided into several spectra\ncharacterized by shorter exposures, the presence of a line in the\nspectra from all detectors separately cannot be confirmed. Next, the\nline energy was allowed to be free. The high state line energy is\nconsistent with 6.4 keV and a lower energy line is marginally detected\nin the low state spectra. The line energy in both states is\nconsistent with an origin in primarily neutral material, and emission\nfrom highly ionized material is excluded. The line equivalent width is\n$\\sim 100 eV$ consistent with that expected from emission from an\naccretion disk (e.g. George\n\\& Fabian \\markcite{9} 1991). Broad iron lines have been discovered in the\n{\\it ASCA} spectra of several AGN (e.g. Mushotzky et al.\n\\markcite{21} 1995). A broad line is preferred in the low flux\nspectra, although the addition of another parameter reduces the $\\chi^2$ by\nonly 5, so again the detection is marginal. The line is narrow in the\nhigh flux spectra, constrained with $\\sigma < 0.2\\,\\rm keV$. The\nshape of the high energy continuum changes the measured line\nparameters. When reflection with ratio fixed to 1 is included,\nsimilar results were obtained as before, but the measured equivalent\nwidths were smaller (Table~4).\n\nThese results also support the hypothesis that a lag in the reflection\ncomponent cannot be the origin of the spectral variability. A lag\nimplies that the bulk of the reflected emission should come from a\nsubstantial distance from the source, and so the line would be\nexpected to be narrow. However, the large reflection ratio required\nto fit the low flux spectra predicts a very large equivalent width ($>\n\\sim 500\\rm eV$) narrow line, which would be easily detected if\npresent.\n\n\\subsection{Quantifying the Spectral Variability}\n\nThe results of the previous sections indicate that the power law with\nblack body and warm absorber model provides the best fit to both the\nlow and high flux spectra. A change in spectral index seems to be\nindicated. However, since the model is complex, spectra from the two\nstates must be fit simultaneously to determine which parameters\nnecessarily change. Neutral absorption, originating in our Galaxy and\nthe host galaxy, was assumed not to change, and the iron line was\nmodeled as narrow with fixed energy. Combined fits were done using both\ndescriptions of the warm absorber.\n\nWhen two edges were used to model the warm absorber, the edge energies\nwere equated in the high and low state models. This was done because\nthe low state spectra could not constrain the higher edge energy and\nsince the edges are identified as \\ion{O}{7} and\n\\ion{O}{8} edges, no change is expected in the energies. This model fit the\nlow and high spectra well ($\\chi^2$ of 860.1\/859 d.o.f. and 789.8\/844\nd.o.f. for the SIS0:GIS2 and SIS1:GIS3 spectra respectively). The\ntemperatures of the black body were consistent between the low and\nhigh states so these were equated resulting in a negligible increase\nin $\\chi^2$. No other parameters could be equated without resulting\nin a large change in $\\chi^2$. The results are listed in the top\npanel of Table~5, and they indicate that a significant change in index\nand black body normalization occurred. The edge energies are roughly\nconsistent with absorption by \\ion{O}{7} and \\ion{O}{8}. The optical\ndepths of these edges are consistent between the low and high state,\nso no change in the ionization of the warm absorber can be determined\nfrom these fits. However, the best fit value of $\\tau_{OVII}$ is\nlarger in the low state than in the high state, and the reverse is\napproximately true for $\\tau_{OVIII}$. This suggests that an increase\nin the ionization occurred, but the statistics are too poor to require\nthis conclusion.\n\nThe results of the combined fits using the warm absorber table model\nare listed in the second panel of Table 5. The ionized column\ndensities were consistent so they were equated in the spectral\nfitting. As noted previously, when the warm absorber was described\nusing the table model, the black body temperature was found to be\nhigher in the high state than in the low state (Table 3). When the\ntemperatures are equated in the combined fits, the increase in\n$\\chi^2$ is 2.7 and 5.7 for the SIS0:GIS2 and SIS1:GIS3 respectively,\nsignificant with 90\\% and 97.5\\% confidence. However, this effect is\nclearly model-dependent and may be due to the shape of the warm\nabsorber model and possibly the absence of emission lines in the\nmodel, and thus the implied black body temperature change is unlikely\nto be physical. Three parameters changed between the low and high\nstates: the power law index, the black body normalization, and the\nionization parameter. As these parameters are coupled in the spectral\nfitting, to evaluate the significance of the change, the $\\chi^2$ contours\nwere plotted for each pair of parameters (Figure~\\ref{fig8}a, b, and\nc). These show that the results are consistent between the SIS0:GIS2\nand SIS1:GIS3 spectral pairs, and that the index change, the\nionization state change and the black body normalization change are\nsignificant with $>99$\\%, 90\\% and 68\\% confidence respectively. We\nnote that the change in the ionization state is a model dependent\nresult, as we cannot demonstrate a change in the optical depths of the\ntwo oxygen edges. Figure~\\ref{fig9}a and b show the best fitting\nmodels, spectra, and ratios between spectra and model for the low and\nhigh state SIS0:GIS2 spectra. Figure~\\ref{fig10} shows the best fit\nmodels for the low and high state SIS0 spectra. Note that the pivot\npoint for the power law change is $\\sim 9$ keV.\n\n\\subsection{{\\it ROSAT} Spectral Fitting}\n\nThe {\\it ASCA} data show that the soft X-ray spectrum is complex,\ncomprised of a power law with variable index, a warm absorber with\nvariable ionization state and a black body with marginally variable\nnormalization and model dependent temperature. Seven parameters are\nrequired to describe the spectrum in the {\\it ROSAT} band. Because of\nthe poor spectral resolution, the PSPC spectra have 5 independent\nchannels. Thus, detailed fitting of the PSPC spectra is of limited\nvalue, as multiple models are acceptable. Qualitatively, the spectra\nfrom the 1992 and 1993 observations are very soft and cannot be\nadequately modeled using a single power law plus absorption. A soft\ncomponent like a black body gives a good fit, with $kT\n\\sim$70--90 eV, and a power law with index $\\sim $1.9--2.0. An edge\ncan also model the spectrum, but the spectral index is steeper at\n$\\sim 2.5$.\n\n\\section{Discussion}\n\n\\subsection{The Hard Spectral Variability}\n\nThe most significant result of this study was the observation of\ndramatic photon index variability from $\\sim 1.6$ to $\\sim 2.0$, over\nseveral thousand seconds and confined to a single event. We discuss this\nresult in light of current models of the X-ray power law emission in AGN.\n\n\\subsubsection{General Considerations}\n\nThe high energy power law observed from AGN can be successfully and\nplausibly explained by inverse Comptonization by high energy electrons\nof soft UV photons likely originating in the accretion disk. The rapid\nX-ray variability of some AGN implies a high radiation density in the\nnucleus which results in production of electron-positron pairs. The\nimportance of pair production is determined by the compactness\nparameter, $$l= L\\sigma_T\/R m_e c^3,\\eqno(1)$$ where $L$ is the\nluminosity, $R$ is the source size, $\\sigma_T$ is the Thompson\nscattering cross section, $m_e$ is the mass of the electron, and $c$\nis the speed of light. If the compactness is high, the optical depth\nto pair production will exceed unity and pairs will be produced which\ncan substantially modify the emerging spectrum. Generally speaking,\nthese models can be differentiated by whether the high energy\nelectrons are thermal or accelerated by non-thermal processes, since\npair production limits the highest energy attainable in the thermal\nplasma. In rapidly variable AGN, however, both thermal and\nnon-thermal processes may be present (Ghisellini, Haardt \\& Fabian\n\\markcite{10} 1993).\n\nThe results presented here suggest that Mrk~766 is compact enough that\npairs should be produced in the nucleus. The X-ray flux was observed\nto change by a factor of two in $\\sim 1000$ seconds. This corresponds\nto a source size upper limit of $R < c\\Delta t \\sim 3 \\times 10^{13}\n\\rm \\, cm$. The 2--10 keV luminosity is $1.3 \\times 10^{43}\\, \\rm ergs \\,\ns^{-1}$ in the high state, implying an X-ray compactness parameter of\n$l_x \\sim 12$. The hard compactness parameter, proportional to the\ntotal luminosity in the hard component, could be substantially larger.\nIf the compactness parameters are larger than 10, pair production\nshould be important if there are an adequate number of $\\gamma$-ray\nphotons present. In non-thermal models, the $\\gamma$-rays are\nproduced through upscattering of soft photons by extremely\nrelativistic electrons. In thermal models, the origin is primarily the\nhigh energy tail of the thermal spectrum and thus the number of\n$\\gamma$-rays depends on the temperature of the plasma. OSSE\nobservations of a few AGN find that the temperature is large enough\nthat electron positron pairs should be produced (e.g. see Figure 1 of\nFabian \\markcite{7} 1994); however, there have been no high energy\nspectra obtained from Mrk~766.\n\n\\subsubsection{Simple Thermal Comptonization Models}\n\nIf the source does not contain many pairs, and if the power law\nresults from unsaturated Comptonization of soft photons, the spectral\nparameters are very simply related to one another (e.g. Rybicki \\&\nLightman \\markcite{29} 1979). The slope of the power law depends\ninversely on the Compton $y$ parameter which is proportional to a\npower of the temperature, so an increase in temperature implys a\ndecrease in index. However, if the soft photon input is constant, the\npivot point energy of the photon index change should be the energy of\nthe soft input photons. In contrast, we observe the pivot point to be\nmuch higher, at $\\sim 9$ keV.\n\nIf the thermal plasma is pair dominated, Ghisellini \\& Haardt (1994)\n\\markcite{11} show that there is a one--to--one mapping of the\nobservables ($kT$ and $\\alpha$, where $\\alpha=\\Gamma-1$ is the energy\nindex) to the plasma parameters ($l_H$ and $l_H\/l_S$, where $l_H$ and\n$l_S$ are the hard and soft compactnesses, characteristic of the\nrelativistic electrons and the soft (UV) seed photons, respectively).\nOur observed increase in photon index by $\\Delta \\alpha \\sim 0.4$\nimplies a decrease in $l_H\/l_S$ by a factor of 10 (Figure 2 of\nGhisellini \\& Haardt \\markcite{11} 1994). We observed a 2--10 keV\nflux increase by a factor of 1.3, but since the power law pivot point\nis $\\sim 9\\rm keV$, integration to high energies may show that the low\nflux state luminosity is actually larger than the high flux state\nluminosity. OSSE observations of several Seyfert 1 galaxies have\nfound that the power spectrum is cut off above several hundred keV\n(e.g. Fabian \\markcite{7} 1994). Integration of the power law from\n2~keV to the generous upper limit of 500~keV shows that the low state\n$l_H$ is at most a factor of 2 larger than the high state $l_H$,\npredicting an increase in index by only $\\sim 0.1$. Further, the time\nscale of the spectral variability, less than 10,000 seconds, precludes\na large increase in $l_S$, since this short time scale would be the\norder of the orbital period at the innermost stable orbit for a $< 5\n\\times 10^{7} M_\\odot$ black hole. A change in the accretion rate\nshould be characterized by the viscous or radial drift time scale,\nestimated by Molendi \\& Maccacaro (1994) \\markcite{19} to be 2.6 days.\nFurther, the {\\it ROSAT} spectral variability can be most naturally\nexplained by a constant (on time scales of one day) soft component\ndominating the softest X-ray band (see Section 5.2). Finally, as\nGhisellini \\& Haardt (1994) \\markcite{11} note, if reprocessing in the\ndisk is important, $l_H\/l_S$ would be expected to remain approximately\nconstant, and little intrinsic index variability should be observed.\nHowever, this model is very simple, and predictions may change\nsubstantially if a realistic geometry or self-consistent treatment of\nthe two phases is considered.\n\n\\subsubsection{Nonthermal Comptonization Models}\n\nStatic nonthermal models of X-ray emission have been investigated by\nseveral authors (e.g. Svensson \\markcite{31} 1994 and references\ntherein) and 2--10 keV spectral index variability has been studied by\nYaqoob \\markcite{36} (1992). Most simply and generally, soft photons\nwith dimensionless frequency $x_S$ and compactness $l_S$ are scattered\nby relativistic electrons with Lorentz factor $\\gamma_0$ and\ncompactness $l_H$. First order scattering produces a flat photon\nspectrum with $\\Gamma \\sim 1.5$ extending to\n$x_{max,1}=max[4\/3\\gamma_0^2 x_s,\\gamma_0]$ (Svensson \\markcite{30}\n1987). Pairs are produced if the photon spectrum extends to\nsufficiently high energies and if the optical depth to pair production\nis greater than unity. Soft photons reprocessed by pairs have a\nsteeper spectrum with $\\Gamma=1.75$ breaking sharply at $x_B=2\n\\gamma_0^4 (2\/3 x_s)^3$. If the energy of pair reprocessed photons is\nrelatively low, they could be observed as an X-ray soft excess. If\nthe energy of reprocessed photons is high enough, additional pair\ngenerations will be produced resulting in a pair cascade. In that\ncase, the photon spectrum is steep with $\\Gamma$ approaching 2 (e.g.\nSvensson \\markcite{30} 1987).\n\nA nonthermal model can naturally explain the observed change in index,\nthe disappearance of the soft excess component and the confinement of\nthe spectral variability to a single event. The photon index\nvariability could result from a transition from a first order pair\nspectrum to a cascade caused by a sudden increase in the Lorentz\nfactor of the relativistic electrons. Thus, the low flux spectrum is\ncomprised of the inverse Compton cooling spectrum, characterized by\nthe hard X-ray power law with photon index near $1.5$, and the first\norder optically thin pair reprocessed spectrum, observed as the soft\nexcess component. The high flux spectrum is comprised of a pair\ncascade spectrum, characterized by the hard X-ray power law with\nphoton index near 2. The soft excess component disappears in the high\nflux spectrum as the maximum energy of the pair reprocessed spectrum\nincreases far beyond the observed X-ray band, resulting in the\ncascade. The change in flux in the X-ray band depends on the change\nof hard compactness $l_H$ which can be expected to accompany the\nchange in Lorentz factor, and flux variability uncorrelated with\nspectral variability would occur through variation in $l_H$ alone.\n\nA possible difficulty with non-thermal models which produce flat\nspectra is that, in general, they overpredict the gamma ray\nbackground. However, recent work shows that if the non-thermal plasma\nis in a corona above a disk, $\\gamma$-ray photons are more efficiently\ndepleted and, depending on the compactness, a strong spectral cut off\nbelow 200 keV is predicted (Tritz \\markcite{32} 1990; Tsuruta \\&\nKellen \\markcite{40} 1995). \n\n\\subsection{The Soft Spectral Variability}\n\nThe {\\it ROSAT} spectrum was observed to become harder as the flux\nincreased, consistent with the lower amplitude variability observed at\nsoftest energies. A most natural explanation for this behavior is\nvariability between the relative normalizations of the power law and\nsoft excess component. This would be observed if the flux of the soft\ncomponent were nearly constant on the time scale of an observation.\nIn terms of current physical models it could imply that the soft\ncomponent is dominated by primary emission from an accretion disk and\nreprocessed hard emission is relatively less important. The fraction\nof black body flux in the softest band can be estimated by comparing\nthe relative variability of the hardest band where the power law\ndominates with the relative variability of the softest band where the\nsoft component dominates. This scenario predicts that if a static\nsoft excess component comprises a large fraction of the flux in the\nsoftest band, any flux change must be accompanied by a hardness ratio\nchange, while if the black body comprises only a small fraction of the\nflux, variability in hard and soft bands should be correlated.\n\nOverall, the {\\it ROSAT} PSPC data are qualitatively consistent with\nthis scenario. In the 1992 observation, the spectrum was harder at\nhigh flux and correlated variability was observed, while at low flux\nuncorrelated variability was found. In the 1991 observation, when the\nflux was lower and the spectrum generally softer, only uncorrelated\nvariability was observed. This scenario can also explain the lack of\nspectral variability seen in the {\\it ROSAT} All Sky Survey data\n(Molendi, Maccacaro and Schaeidt \\markcite{20} 1993) since at that\ntime the source was bright and power law emission may have dominated\nthe soft component emission. Qualitatively and on short time scales\nthere are difficulties with this scenario. In 1992, the hardest and\nsoftest bands decrease by 50\\% and 25\\% respectively overall, implying\nthe soft component must contribute the same percentages of the soft\nband flux in the high and low states respectively. However, at high\nflux there is a dip in flux by 30\\% just after the start of the\nobservation, and no change in the softness ratio was observed.\nSimilarly, at low flux, there is an increase by 30\\% in the hard band\napproximately $6\\times 10^4\\,\\rm s$ after the start of the observation,\nbut no change in the soft band emission was observed. In combination,\nthese two results cannot be explained if the black body flux is constant\nand no other parameters change. Reprocessing, neglected so far, may\nbe able to explain large amplitude correlated variability at high\nflux.\n\nOther models cannot explain the observed spectral variability.\nNetzer, Turner \\& George (1994) \\markcite{24} showed that variability\nof the warm absorber in response to ionizing flux changes could not\nexplain the spectral variability found during the 1992 observation.\nIn general, if the soft component were dominated by reprocessing, the\nsoft X-ray variability would be expected to track the hard component\nvariability. However, substantial spectral variability of the\nincident continuum could result in some spectral variability of the\nreprocessed component. Changes in the accretion rate (Molendi \\&\nMaccacaro \\markcite{19} 1994) cannot explain the orbit-to-orbit\nspectral variability as the predicted time scales are much longer.\n\n\\subsection{The Change in the Warm Absorber}\n\nIn the {\\it ASCA} spectra, we found that there was no evidence that\nthe ionized absorption column density changed between the high state\nand the low state; however, we found that the ionization parameter\nchanged with 90\\% confidence. This result is model dependent, as we\ncould not demonstrate that the optical depth of the oxygen edges\nchanged significantly.\n\nThe best fit ionization parameter $\\log(U)$ changed from $\\sim -0.85$\nin the low state to $\\sim -0.42$ in the high state, implying an\nincrease in flux of ionizing photons by a factor of 2.7. It is\ninteresting that this is quite close to the implied change in flux by\na factor of 3.1 of the intrinsic power law at 0.7 keV. The observed\nphoton index variability implies a larger change in the flux of\nionizing photons, assuming the power law extends to low energies. The\nspectral changes occurred over a time scale of several thousand\nseconds. The recombination time scale for \\ion{O}{8} is about $2\n\\times 10^{11}T_e^{0.5} n^{-1}$ seconds (e.g. Turner et al.\n\\markcite{33} 1993) or about 5.5 hours using the parameters assumed in\nthis model (or longer if the gas is rarer). Thus the gas may not be\nin photoionization equilibrium in the high flux state, and the larger\npopulation of \\ion{O}{8} implied by the increase in the ionization\nparameter may result from ions which are directly stripped of an\nelectron by the increased number of photons with energy near 0.7 keV.\nFurther observations are necessary to determine the response of the\nionized material to a decrease in flux since a change in ionization\nwould be observed only if recombination had occurred.\n\nA change in the ionization correlated with an increase in flux has not\nbeen previously reported from {\\it ASCA} data. An increase in column\ndensity and no change in ionization accompanied an increase in flux in\nMCG--6-30-15 (Fabian et al. \\markcite{8} 1994). Explaining the\nspectral variability during an increase in flux by a change in warm\nabsorber properties in NGC~3227 required a decrease in ionization and\nan increase in column (Ptak et al. \\markcite{27} 1994). In another\nobservation of MCG--6-30-15, an increase in the optical depth of the\n\\ion{O}{8} edge during a flux decrease was interpreted as evidence for\nrecombination of \\ion{O}{9} (Otani \\markcite{26} 1995).\n\n\\subsection{Hard X-ray Emission from Narrow Line Seyfert 1s}\n\\vskip 1pc\n\nThe soft X-ray properties of narrow line Seyfert 1s are well studied\n(Boller, Brandt \\& Fink \\markcite{1} 1996) but few hard X-ray\nobservations have been reported. The {\\it ASCA} observation of Mrk~766\nrepresents one of the first observations of the hard emission from\nthese objects.\n\nWe found a hard power law with variable photon index in Mrk~766, and\nthe spectral variability which was not strictly flux correlated.\nSimilar photon index variability which was flux correlated has been\ndiscovered from NGC~4051 (Guainazzi et al. \\markcite{15} 1996), a\nSeyfert 1 galaxy which shares many properties with NLS1s. In\ncontrast, a very steep spectrum with photon index $\\sim 2.6$, a\ndominate soft excess and no variability was observed from narrow-line\nSeyfert 1 RE~1034+39 (Pounds, Done \\& Osborne \\markcite{39} 1995).\nBecause these spectral and variability properties are similar to those\ncharacteristic of black hole candidates in the high state (e.g. Nowak\n\\markcite{41} 1991) it was postulated that RE~1034+39 represents a\nSeyfert 1 galaxy in the high state (Pounds, Done \\& Osborne\n\\markcite{39} 1995).\n\nThe marked differences between the hard X-ray properties of Mrk~766\nand RE~1034+39 are interesting. Black hole candidates in the low\nstate are characterized by a flat hard X-ray power law and more rapid,\nlarger amplitude hard X-ray variability (e.g. Nowak \\markcite {41}\n1995). These properties more closely resemble the observational\nresults from Mrk~766 and NGC~4051 than do the properties of black hole\ncandidates in the high state. Further observations of NLS1s may find\nthat the spectral and variability properties fall into two classes:\nthose with steep hard X-ray spectra, dominant soft X-ray emission and\nlower amplitude short term hard X-ray variability, and those with flat\nhard X-ray spectra, less soft X-ray emission and rapid hard X-ray\nvariability. There is perhaps already some evidence for such a\ndivision. While many NLS1s are very bright soft X-ray objects commonly\nfound in soft X-ray samples, relatively few have hard X-ray detections\nby HEAO-1 A2.\n\nFurther support for this scenario may come if repeated X-ray\nobservations discover that some objects have made the transition\nbetween two states. This could have been what had occurred in objects\nobserved to have varied by factors of 10 or more between two {\\it\nROSAT} observations (e.g. Zwicky 159.034, Brandt, Pounds \\& Fink\n\\markcite{3} 1995; WPVS007, Grupe et al. \\markcite{14} 1995).\nWell-studied broad-line Seyfert 1 galaxies are not observed to undergo\nthis kind of transition. The fact that many well-studied AGN are hard\nX-ray selected while NLS1s are clearly soft X-ray selected objects\nfurther supports this hypothesis.\n\nIn black hole candidates, it is widely believed that the high state\nis characterized by a relatively larger accretion rate compared with\nthe low state. Thus the behavior of NLS1s may result from a\nrelatively larger accretion rate. If the processes fueling AGNs are\ncommon for Seyferts, a relatively larger accretion rate would be\nobserved in systems with relatively small black hole masses.\n\n\\section{Conclusions}\n\nWe report analysis of {\\it ASCA} and {\\it ROSAT} observations of the\nnarrow-line Seyfert 1 galaxy Mrk~766. In the {\\it ASCA} observation\nrapid variability with doubling time scale of order $\\sim 1000$\nseconds was observed, and dramatic spectral variability over as time\nperiod of less than $\\sim 10,000\\,\\rm s$ was discovered. Confined to a\nsingle event, during a 2--10 keV flux increase the spectrum above and\nbelow $\\sim 1$ keV softened and hardened respectively. The low and\nhigh flux spectra could be described with a model consisting of a\npower law, iron line, warm absorber and soft excess modeled as a black\nbody. The spectral variability was a result of a highly significant\nincrease in the intrinsic power law index from $\\sim 1.6$ to $\\sim\n2.0$ with the pivot point at $\\sim 9$ keV, a model dependent increase\nin the ionization of the warm absorber, and a marginal decrease in the\nsoft excess component. A $100 \\rm \\, eV$ equivalent width narrow iron\nline was detected in the high flux spectrum but not in the low flux\nspectrum, most likely because of poor statistics. Variability on time\nscales as short as $\\sim 2400$ seconds was found in the {\\it ROSAT}\ndata. Because the variability in the softest {\\it ROSAT} band, below\n0.4 keV, had relatively lower amplitude than the harder bands,\nspectral hardening during flux increases was detected on time scales\nas short as the orbital period of $ \\sim 6000 \\,\\rm s$.\n\nThe spectral index change, the disappearance of the soft component in\nthe {\\it ASCA} band and the confinement of the spectral variability to\na single event could be naturally explained in terms of non-thermal\nComptonization models. We postulate that the index change occurred\nthrough a transition from a first order pair reprocessed spectrum to a\npair cascade spectrum brought about by a sudden increase in the\nLorentz factor of the injected relativistic electrons. The first\norder pair reprocessed spectrum observed in the low state as a soft\nexcess disappeared in the high state cascade spectrum. Variations in\nthe hard compactness resulted in pure flux variability. The measured\nincrease in the warm absorber ionization corresponds to the increase\nin flux near the oxygen edges resulting from the power law index\nchange. The spectral variability in the {\\it ROSAT} data was most\nnaturally explained by a variable hard component and a nonvariable\nsoft component which dominated the softest band and may be primary\nemission from an accretion disk perhaps implying that reprocessing is\nrelatively less important in this object.\n\nThe flat and variable hard power law index observed in Mrk~766 is\nsimilar to that observed in NGC 4051 (Guainazzi et al. \\markcite{15}\n1996), a Seyfert 1 with many properties common to NLS1s, but contrasts\nmarkedly with the very steep hard X-ray index $\\Gamma \\sim 2.6$ found\nin NLS1 object RE~1034+39 (Pounds, Done \\& Osborne \\markcite{39} 1995).\nFurther hard X-ray observations of NLS1s using {\\it ASCA} are\nnecessary to clearly understand the hard X-ray properties of these\nsources.\n\n\\acknowledgements\n\nThis research has made use of data obtained through the High Energy\nAstrophysics Science Archive Research Center Online Service, provided\nby the NASA-Goddard Space Flight Center. The authors thank T. Kallman\nand P. Zycki for the use of their spectral table models. KML\nacknowledges receipt of a National Research Council fellowship at\nNASA\/GSFC and a Japanese Science and Technology Agency fellowship at\nRIKEN and useful conversations with M. Cappi. KML acknowledges\nsupport by a {\\it ROSAT} AO4 guest observer grant and an {\\it ASCA}\nAO1 guest observer grant.\n\n\\clearpage\n\n\\begin{deluxetable}{lrrrrr}\n\\tablewidth{0pc}\n\\tablenum{1}\n\\tablecaption{Observing Log}\n\\tablehead{\n\\colhead{Instrument} & \\colhead{Observation} & \n\\colhead{Exposure} & \\colhead{Total Counts} &\n\\colhead{Background} \\\\\n\\colhead{} & \\colhead{Date} &\n\\colhead{(s)} & \\colhead{} &\n\\colhead{Count Rate} }\n\n\\startdata\n{\\it ROSAT} PSPC & 15\/06\/91 & 15179\\tablenotemark{a} & 21804 & 0.081 \\nl\n{\\it ROSAT} PSPC & 21\/12\/92 & 16303\\tablenotemark{b} & 53865 & 0.056 \\nl\n{\\it ROSAT} PSPC & 17\/12\/93 & 3146 & 7309 & 0.033 \\nl\n{\\it ASCA} S0 & 18\/12\/93 & 32947 & 32823 & 0.030 \\nl\n\\hphantom{{\\it ASCA}} S1 & & 32735 & 26853 & 0.020 \\nl\n\\hphantom{{\\it ASCA}} G2 & & 35805 & 16991 & 0.033 \\nl\n\\hphantom{{\\it ASCA}} G3 & & 35808 & 19532 & 0.032 \\nl\n\\tablecomments{Column 4 is the total (not background subtracted) counts in the\nsource extraction region. \nColumn 5 is the background rate scaled to the source\nextraction region. }\n\\tablenotetext{a} {Exposure time after data selection to remove periods\nwhere the source was occulted by a rib.}\n\\tablenotetext{b} {Exposure time in the first 85,000 seconds of\nobservation. The total observation time was 19870 seconds.}\n\\enddata\n\\end{deluxetable}\n\\clearpage\n\n\\begin{deluxetable}{lrrr}\n\\tablewidth{0pc}\n\\tablenum{2}\n\\tablecaption{PSPC Variability}\n\\tablehead{\n\\colhead{Band} & \\colhead{Mean} & \n\\colhead{$\\chi^2_\\nu$} & \\colhead{NVA}}\n\n\\startdata\n\n\\multicolumn{4}{l}{1991 Data (44 points):} \\nl\nTotal & 1.35 & 17.6 & 0.22 \\nl\n0.2--0.5 & 0.84 & 6.0 & 0.16 \\nl\n0.5--0.9 & 0.20 & 7.05 & 0.36 \\nl\n0.9--2.0 & 0.22 & 8.54 & 0.37 \\nl\n\\tableline\n\\multicolumn{4}{l}{1992 Data (41 points):} \\nl\nTotal & 3.06 & 77.5 & 0.26 \\nl\n0.2--0.5 & 1.63 & 22.7 & 0.19 \\nl\n0.5--0.9 & 0.58 & 24.9 & 0.34 \\nl\n0.9--2.0 & 0.67 & 35.1 & 0.38 \\nl\n\\tableline\n\\multicolumn{4}{l}{Quasi-simultaneous} \\nl\n\\multicolumn{4}{l}{Data (8 points):} \\nl\nTotal & 2.24 & 29.9 & 0.19 \\nl\n0.2--0.5 & 1.33 & 8.2 & 0.13 \\nl\n0.5--0.9 & 0.39 & 13.9 & 0.31 \\nl\n0.9--2.0 & 0.39 & 13.6 & 0.31 \\nl\n\\enddata\n\\end{deluxetable}\n\\clearpage\n\n\\begin{deluxetable}{lcccc}\n\\footnotesize\n\\tablewidth{0pc}\n\\tablenum{3}\n\\tablecaption{{\\it ASCA} Spectral Fitting Results}\n\\tablehead{\n\\colhead{Parameter} & \\multicolumn{2}{c}{Low State} &\n\\multicolumn{2}{c}{High State} \\\\\n\\colhead{} & \\colhead{SIS0:GIS2} & \n\\colhead{SIS1:GIS3} & \\colhead{SIS0:GIS2} &\n\\colhead{SIS1:GIS3}}\n\n\\startdata\n\\multicolumn{5}{l}{Power law model:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ &\n $\\rm Gal < 0.19$ & $\\rm Gal<0.20$ & $\\rm Gal<0.19$ & $\\rm Gal<0.192$ \\nl\nIndex & $1.65 \\pm 0.04$ & $1.64^{+0.04}_{-0.05}$ & $1.92 \\pm 0.02$ & \n$1.91^{+0.01}_{-0.02}$ \\nl\n$\\chi^2$\/d.o.f. & 422\/270 & 341\/254 &\n 819\/598 & 765\/597 \\nl\n\\tableline\n\\multicolumn{5}{l}{Power law plus black body:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & $0.84^{+0.51}_{-0.48}$ & \n$0.75^{+0.53}_{-0.49}$\n& $0.95^{+0.22}_{-0.20}$ & $1.02^{+0.22}_{-0.20}$ \\nl\nIndex & $1.54^{+0.06}_{-0.07}$ & $1.54^{+0.08}_{-0.07}$ & $1.96 \\pm 0.03$ &\n$1.98^{+0.04}_{-0.03}$ \\nl\nSIS PL norm\\tablenotemark{a} & $3.0^{+0.3}_{-0.2}$ & $3.0^{+0.3}_{-0.2}$ &\n$7.7 \\pm 0.03$ & $7.9 \\pm 0.03$ \\nl\nkT (eV) & $88^{+8}_{-6}$ & $87^{+9}_{-7}$ & $77^{+5}_{-4}$ & $72 \\pm 5$ \\cr\nSIS bb norm\\tablenotemark{b} & $2.5^{+2.1}_{-1.2}$ & $2.3^{+2.1}_{-1.2}$ &\n$4.2^{+1.5}_{-1.3}$ & $5.4^{+2.0}_{-1.7}$ \\nl\n$\\chi^2$\/d.o.f. & 245\/268 & 208\/254 & 681\/596 & 647\/595 \\nl\n\\tableline\n\\multicolumn{5}{l}{Power law plus edge:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & $\\rm Gal < 0.18$ & $\\rm Gal<0.20$ & $\\rm Gal<0.26$ &\n$\\rm Gal< 0.23$ \\nl\nIndex & $1.87 \\pm 0.04$ & $1.83^{+0.06}_{-0.05}$ & $2.01 \\pm 0.02$ &\n$2.01^{+0.03}_{-0.02}$ \\nl\nSIS PL norm\\tablenotemark{a}\n & $4.38 \\pm 0.18$ & $4.23^{+0.24}_{-0.23}$ & $8.06^{+0.17}_{-0.15}$\n& $8.06^{+0.23}_{-0.17}$ \\nl\nEdge Energy (keV) & $0.81 \\pm 0.02$ & $0.82^{+0.02}_{-0.03}$ &\n$0.78^{+0.01}_{-0.03}$ & $0.75 \\pm 0.02$ \\nl\n$\\tau$ & $1.01 \\pm 0.15$ & $0.89^{+0.21}_{-0.19}$ & $0.49^{+0.07}_{-0.06}$ &\n$0.53 \\pm 0.70$ \\nl\n$\\chi^2$\/d.o.f. & 344\/268 & 279\/254 & 651\/596 & 608\/595 \\nl\n\\tableline\n\\multicolumn{5}{l}{Power law, black body and edge:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & $\\rm Gal<0.28<0.91$ & $\\rm Gal<0.42<0.88$ &\n$0.43^{+0.18}_{-0.20}$ & $\\rm Gal<0.35<0.63$ \\nl\nIndex & $1.55^{+0.09}_{-0.05}$ & $1.57 \\pm 0.07$ & $1.99^{+0.02}_{-0.04}$ &\n$2.00^{+0.04}_{-0.03}$ \\nl\nSIS PL norm\\tablenotemark{a} & $3.1^{+0.3}_{-0.2}$ & $3.2 \\pm 0.30$ &\n $7.9^{+0.2}_{-0.3}$ & $8.0^{+0.4}_{-0.3}$ \\nl\nEdge energy (keV) & $0.76^{+0.03}_{-0.04}$ & $0.74 \\pm 0.02$ &\n$0.74^{+0.03}_{-0.02}$ & $0.75^{+0.02}_{-0.03}$ \\nl\n$\\tau$ & $0.60^{+0.28}_{-0.25}$ & $0.77^{+0.28}_{-0.30}$ & \n$0.43^{+0.09}_{-0.10}$ & $0.46^{+0.12}_{-0.11}$ \\nl \n$kT$ (eV) & $117 \\pm 18$ & $121^{+21}_{-16}$ & $101^{+19}_{-13}$\n & $91^{+48}_{-35}$ \\nl\nSIS bb norm\\tablenotemark{b}\n & $0.95^{+1.19}_{-0.26}$ & $1.12^{+0.82}_{-0.44}$ & $0.81^{+0.56}_{-0.50}$\n& $0.54^{+1.04}_{-0.52}$ \\nl\n$\\chi^2$\/d.o.f. & 230\/266 & 191\/253 & 640\/594 & 605\/593 \\nl\n\\tableline\n\\multicolumn{5}{l}{Power law and 2 edges:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & $\\rm Gal<0.22$ & $\\rm Gal<0.22$ & $1.1<2.8$ &\n$2.5<3.4$ \\nl\nIndex & $1.91^{+0.05}_{-0.04}$ & $1.90 \\pm 0.05$ & $2.02^{+0.05}_{-0.01}$ &\n$2.05 \\pm 0.04$ \\nl\nSIS PL norm\\tablenotemark{a} & $5.0 \\pm 0.20$ & $4.9 \\pm 0.30$ & \n$8.2^{+0.5}_{-0.2}$ & $8.5^{+0.5}_{-0.4}$ \\nl\nEdge energy (keV) & $0.78^{+0.01}_{-0.02}$ & $0.77 \\pm 0.03$ &\n$0.74^{+0.02}_{-0.04}$ & $0.73 \\pm 0.02$ \\nl\n$\\tau$ & $1.00^{+0.18}_{-0.15}$ & $1.01^{+0.23}_{-0.18}$ &\n $0.43^{+0.08}_{-0.19}$ & $0.51^{+0.09}_{-0.07}$ \\nl\nEdge energy (keV) & $1.19^{+0.05}_{-0.03}$ & $1.24^{+0.07}_{-0.14}$ &\n$0.94^{+0.06}_{-0.24}$ & $0.98^{+0.05}_{-0.04}$ \\nl\n$\\tau$ & $0.55 \\pm 0.11$ & $0.49^{+0.15}_{-0.11}$ & $0.16^{+0.15}_{-0.06}$ &\n$0.14^{+0.07}_{-0.06}$ \\nl\n$\\chi^2$\/d.o.f. & 254\/266 & 241\/252 & 631\/594 & 594\/593 \\nl\n\\tableline\n\\tablebreak\n\\multicolumn{5}{l}{Power law, 2 edges and black body:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & $Gal<0.32<0.81$ & $\\rm Gal<0.70$ &\n$\\rm Gal<0.29<0.44$ & $\\rm Gal<0.49<0.52$ \\nl\nIndex & $1.64^{+0.10}_{-0.09}$ & $1.62 \\pm 0.09$ & $2.01 \\pm 0.04$ &\n$2.05 \\pm 0.04$ \\nl\nSIS PL norm\\tablenotemark{a} & $3.5^{+0.5}_{-0.4}$ & $3.4^{+0.4}_{-0.3}$ &\n$8.2 \\pm 0.4$ & $8.5^{+0.5}_{-0.4}$ \\nl\nEdge energy (keV) & $0.76 \\pm 0.03$ & $0.75^{+0.02}_{-0.03}$ &\n$0.73^{+0.02}_{-0.04}$ & $0.73 \\pm 0.02$ \\nl\n$\\tau$ & $0.67^{+0.26}_{-0.27}$ & $0.84^{+0.37}_{-0.30}$ & \n$0.40^{+0.15}_{-0.16}$ & $0.51^{+0.08}_{-0.07}$ \\nl\n$kT$ (eV) & $115 \\pm 21$ & $134^{+31}_{-33}$ & $126^{+34}_{-29}$ & \n84\\tablenotemark{c} \\nl\nSIS bb norm\\tablenotemark{b} & $0.88^{+1.00}_{-0.37}$ &\n$0.69^{+1.04}_{-0.13}$ & $0.29^{+0.31}_{-0.26}$ & $0<0.08$ \\nl\nEdge energy (keV) & $1.20^{+0.07}_{-0.17}$ & 1.104\\tablenotemark{c}\n & $0.87^{+0.12}_{-0.06}$ & $0.98^{+0.05}_{-0.04}$ \\nl\n$\\tau$ & $0.18^{+0.16}_{-0.14}$ & $0<0.16<0.27$ & $0.18^{+0.14}_{-0.09}$ &\n$0.14 \\pm 0.07$ \\nl\n$\\chi^2$\/d.o.f. & 226\/264 & 189\/250 & 629\/592 & 594\/591 \\nl\n\\tableline\n\\multicolumn{5}{l}{Warm Absorber:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & $\\rm Gal<0.18$ & $\\rm Gal < 0.18$ &\n$\\rm Gal<0.23<0.30$ & $0.26^{+0.09}_{-0.08}$ \\nl\nIndex & $2.05^{+0.04}_{-0.07}$ & $1.98 \\pm 0.08$ & $2.12^{+0.05}_{-0.04}$ &\n$2.13^{+0.05}_{-0.04}$ \\nl\nSIS pl norm\\tablenotemark{a} & $6.4^{+0.5}_{-0.7}$ & $5.7^{+0.5}_{-0.3}$ &\n9$.7 \\pm 0.5$ & $9.9 \\pm 0.6$ \\nl\n$log(U)$ & $-0.17^{+0.08}_{-0.19}$ & $-0.28^{+0.06}_{-0.09}$ &\n$-0.38^{+0.05}_{-0.06}$ & $-0.43^{+0.08}_{-0.06}$ \\nl\n$log(N_w)$\\tablenotemark{c} & $22.27^{+0.09}_{-0.17}$ & $22.13 \\pm 0.09$ &\n$21.71 \\pm 0.07$ & $21.68^{+0.08}_{-0.06}$ \\nl\n$\\chi^2$\/d.o.f. & 294\/268 & 269\/254 & 634\/596 & 601\/595 \\nl\n\\tableline\n\\multicolumn{5}{l}{Warm Absorber plus black body:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & Gal<0.48<0.94 & $\\rm Gal<0.36<0.79$ &\n$\\rm Gal<0.29<0.42$ & $\\rm Gal<0.23<0.40$ \\nl\nIndex & $1.70^{+0.13}_{-0.11}$ & $1.70^{+0.14}_{-0.10}$ &\n $2.12^{+0.05}_{-0.07}$ & $2.10^{+0.07}_{-0.06}$ \\nl\nSIS PL norm\\tablenotemark{a} & $3.9^{+1.0}_{-0.6}$ & $4.0^{+1.1}_{-0.6}$ &\n$9.8^{+0.9}_{-0.8}$ & $9.5^{+1.0}_{-0.6}$ \\nl\n$log(U)$ & $-0.78^{+0.21}_{-0.32}$ & $-0.79^{+0.25}_{-0.26}$ & \n$-0.41^{+0.08}_{-0.07}$ & $-0.42^{+0.06}_{-0.05}$ \\nl\n$log(N_w)^{d}$ & $21.78^{+0.36}_{-0.39}$ & $21.84^{+0.40}_{-0.36}$ & \n$21.82^{+0.13}_{-0.20}$ & $21.83 \\pm 0.11$ \\nl\n$kT$ (eV) & $119^{+47}_{-18}$ & $131^{+55}_{-24}$ & $184^{+76}_{-103}$ &\n$230^{+105}_{-57}$ \\nl\nSIS bb norm\\tablenotemark{b}\n & $1.52^{+1.34}_{-0.66}$ & $1.37^{+1.70}_{-0.48}$ & $0.49^{+0.71}_{-0.46}$\n& $0.49^{+0.65}_{-0.44}$ \\nl\n$\\chi^2$\/d.o.f. & 234\/266 & 194\/253 & 630\/594 & 596\/593 \\nl\n\\tableline\n\\multicolumn{5}{l}{Power Law, two edges and Reflection:} \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & $\\rm Gal < 0.39$ & $\\rm Gal < 0.50$ &\n$\\rm Gal<0.23<0.37$ & $\\rm Gal<0.28<0.44$ \\nl\nIndex & $2.05^{+0.12}_{-0.06}$ & $2.04^{+0.17}_{-0.06}$ & \n$2.06^{+0.08}_{-0.05}$ & $2.07^{+0.09}_{-0.06}$ \\nl\nSIS PL norm\\tablenotemark{a} & $4.87^{+0.52}_{-0.22}$ & $4.75^{+0.83}_{-0.26}$ &\n$8.40^{+0.61}_{-0.36}$ & $8.65^{+0.58}_{-0.51}$ \\nl\nSIS Refl norm\\tablenotemark{a}\n & $2.73^{+1.83}_{-1.10}$ & $2.56^{+2.42}_{-0.94}$ & $0.21<1.24$ &\n$0.26<1.35$ \\nl\nRefl. Ratio & $5.5^{+2.5}_{-2.1}$ & $8.3^{+6.1}_{-2.6}$ & $0.4<1.5$ &\n$0.3<1.5$ \\nl\nEdge Energy (keV) & $1.18^{+0.05}_{-0.1}$ & $1.11^{+0.05}_{-0.06}$ &\n$0.94^{+0.07}_{-0.11}$ & $0.99 \\pm 0.04$ \\nl\n$\\tau$ & $0.44^{+0.08}_{-0.12}$ & $0.38^{+0.13}_{-0.12}$ & \n$0.18^{+0.06}_{-0.08}$ & $0.15 \\pm 0.07$ \\nl\nEdge Energy (keV) & $0.77 \\pm 0.02$ & $0.75 \\pm 0.02$ &\n$0.73 \\pm 0.02$ & $0.73^{+0.02}_{-0.01}$ \\nl\n$\\tau$ & $0.98 \\pm 0.17$ & $0.90^{+0.18}_{-0.17}$ & $0.44^{+0.10}_{-0.20}$ &\n$0.53\\pm 0.09$ \\nl\n$\\chi^2$\/d.o.f. & 230\/263 & 201\/250 & 631\/593 & 594\/590 \\nl\n\\tablecomments{The value ``Gal'' for the absorption refers to the spectral\nfit lower limit set to the Galactic value, $1.77 \\times 10^{20} \\rm \\,\ncm^{-2}$ (Elvis, Wilkes \\& Lockman \\markcite{6} 1989).}\n\\tablenotetext{a}{$\\times\n10^{-3} \\rm photons\\,keV^{-1}cm^{-2}s^{-1}$}\n\\tablenotetext{b}{$\\times 10^{-4} L_{39}\/{D_{10}^2}$, where \n$L_{39}$ is the source\nluminosity in $10^{39}\\rm erg\\,s^{-1}$ and $D_{10}$ is the distance to\nthe source in $10^{10} \\rm kpc$}\n\\tablenotetext{c}{Unconstrained}\n\\tablenotetext{d}{log of the ionized column in units of $\\rm cm^{-2}$.}\n\\enddata\n\n\\end{deluxetable}\n\\clearpage\n\n\n\\begin{deluxetable}{lcc}\n\\tablewidth{0pc}\n\\tablenum{4}\n\\tablecaption{Iron Line Fitting Results}\n\\tablehead{\n\\colhead{Parameter} & \\colhead{Low State} & \n\\colhead{High State}}\n\n\\startdata\n\\multicolumn{3}{l}{Power law plus narrow line:} \\nl\nIndex & $1.57 \\pm 0.07$ & $2.00^{+0.03}_{-0.04}$ \\nl\nLine Flux\\tablenotemark{a} & $1.8<3.0$ & $2.3^{+1.0}_{-1.2}$ \\nl\nLine Eq. Width (eV) & $100<170$ & $110^{+40}_{-55}$ \\nl\n$\\chi^2$\/d.o.f. & 204\/252 & 793\/758 \\nl\n\\tableline\n\\multicolumn{3}{l}{Power law, narrow line and reflection:} \\nl\nIndex & $1.66 \\pm 0.07$ & $2.08^{+0.04}_{-0.03}$ \\nl\nLine Flux\\tablenotemark{a}& $1.1<2.6$ & $1.8^{+0.9}_{-1.0}$ \\nl\nLine Eq. Width (eV) & $35<84$ & $47 \\pm 25$ \\nl\n$\\chi^2$\/d.o.f. & 204\/252 & 793\/758 \\nl\n\\tablenotetext{a}{$\\times 10^{-5} \\rm photons\\,cm^{-2}s^{-1}$ in the\nline.}\n\\enddata\n\\end{deluxetable}\n\\clearpage\n\n\n\\begin{deluxetable}{lcccc}\n\\footnotesize\n\\tablewidth{0pc}\n\\tablenum{5}\n\\tablecaption{Combined Spectral Fits}\n\\tablehead{\n\\colhead{Parameter} & \\multicolumn{2}{c}{SIS0:GIS2}\n & \\multicolumn{2}{c}{SIS1:GIS3} \\\\\n\\colhead{} & \\colhead{Low State} &\n\\colhead{High State} & \\colhead{Low State} &\n\\colhead{High State}}\n\n\\startdata\n\\multicolumn{5}{l}{Two Edge Model: } \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & \\multicolumn{2}{c}{$\\rm Gal<0.27<0.45$} &\n\\multicolumn{2}{c}{$\\rm Gal<0.25<0.37$} \\nl\nIndex & $1.58 \\pm 0.08$ & $2.02 \\pm 0.04$ & $1.60^{+0.08}_{-0.07}$ &\n$2.05 \\pm 0.04$ \\nl \nSIS PL norm\\tablenotemark{a} & $3.2 \\pm 0.30$ & $8.3^{+0.4}_{-0.3}$ \n& $3.2 \\pm 0.3$ & $8.6 \\pm 0.4$ \\nl\nkT (eV) & \\multicolumn{2}{c}{$123^{+22}_{-17}$} &\n \\multicolumn{2}{c}{$133^{+26}_{-21}$} \\nl\nSIS BB norm\\tablenotemark{b} & 0$.89^{+0.34}_{-0.22}$ & $0.19<0.61$ & \n$0.84^{+0.24}_{-0.16}$ & $0<0.23$ \\nl\nEdge Energy (keV) & \\multicolumn{2}{c}{$0.73^{+0.03}_{-0.01}$} &\n\\multicolumn{2}{c}{$0.74^{+0.01}_{-0.02}$} \\nl\n$\\tau$ & $0.66^{+0.28}_{-0.25}$ & $0.47^{+0.08}_{-0.09}$ & \n$0.83^{+0.19}_{-0.17}$ & $0.52^{+0.07}_{-0.08}$ \\nl\nEdge Energy (keV) & \\multicolumn{2}{c}{$0.97^{+0.05}_{-0.16}$} &\n\\multicolumn{2}{c}{ $0.99\\pm 0.04$ } \\nl\n$\\tau$ & $0.11<0.30$ & $0.15^{+0.06}_{-0.08}$ & $0.13<0.30$ & \n$0.14^{+0.07}_{-0.06}$\\nl\n$\\chi^2$\/d.o.f & \\multicolumn{2}{c}{860.1\/860} &\n\\multicolumn{2}{c}{790.7\/845 } \\nl \n\\tableline \n\\multicolumn{5}{l}{Warm Absorber Model: } \\nl\n$N_H (\\times 10^{21}\\rm cm^{-2})$ & \\multicolumn{2}{c}{$0.33^{+0.16}_{-0.15}$} &\n\\multicolumn{2}{c}{$0.30^{+0.17}_{-0.10}$} \\nl\nIndex & $1.66^{+0.07}_{-0.06}$ & $2.12^{+0.05}_{-0.04}$ &\n$1.67^{+0.07}_{-0.06}$ & $2.14^{+0.05}_{-0.04}$ \\nl\nSIS PL norm\\tablenotemark{a}\n & $3.7^{+0.2}_{-0.3}$ & $9.8 \\pm 0.6$ & $3.6^{+0.3}_{-0.2}$ &\n$10.0 \\pm 0.6$ \\nl \nkT (eV) & \\multicolumn{2}{c}{$117^{+13}_{-14}$} & \n\\multicolumn{2}{c}{$121^{+12}_{-14}$} \\nl\nSIS BB norm\\tablenotemark{b}\n & $1.34^{+0.43}_{-0.38}$ & $0.42<0.97$ & $1.20^{+0.49}_{-0.28}$ &\n$0.02<0.52$ \\nl\n$log(N_w)$\\tablenotemark{c} & \\multicolumn{2}{c}{$21.68^{+0.04}_{-0.08}$} \n& \\multicolumn{2}{c}{$21.69 \\pm 0.08$}\n\\nl\n$log(U)$ & $-0.85^{+0.16}_{-0.15}$ & $-0.42 \\pm 0.08$ &\n$-0.87^{+0.16}_{-0.15}$ & $-0.42^{+0.07}_{-0.09}$ \\nl\n$\\chi^2$\/d.o.f & \\multicolumn{2}{c}{867.1\/863} & \\multicolumn{2}{c}{801.8\/848} \\nl\n\\tablecomments{The value ``Gal'' for the absorption refers to the spectral\nfit lower limit set to the Galactic value, $1.77 \\times 10^{20} \\rm \\,\ncm^{-2}$ (Elvis, Wilkes \\& Lockman \\markcite{6} 1989).}\n\\tablenotetext{a} {$\\times 10^{-3} \\rm\nphotons\\,keV^{-1}cm^{-2}s^{-1}$}\n\\tablenotetext{b} {$\\times 10^{-4} L_{39}\/{D_{10}^2}$, where $L_{39}$\n is the source\nluminosity in $10^{39}\\rm erg\\,s^{-1}$ and $D_{10}$ is the distance to\nthe source in $10^{10} \\rm kpc$.}\n\\tablenotetext{c}{log of the ionized column in units of $ \\rm\ncm^{-2}$}\n\\enddata\n\\end{deluxetable}\n\\clearpage\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction\nThe coupling between density fluctuations of different wavelengths is one of the\nmost important topics in the study of large-scale structure \\cite{Bernardeau:2001qr}.\nThese couplings can be imprinted in the inflationary initial conditions or develop \nthrough gravitational evolution. In the latter class, a long-wavelength density mode\naffects the evolution of all short-wavelength modes that are embedded in it leading\nto changes in the power spectrum \\cite{Takada:2013bfn,Li:2014jra,Chiang:2014oga,Chiang:2015eza}\nand the dark matter halo abundance which gives rise to halo bias \\cite{Mo:1995cs,Seljak:2012tp}.\nIn the limit of a large separation in these scales, one can use the ``separate universe''\n(SU) approach to describe these and other effects through a change in the background\ndensity within which small scale structure evolves \n\\cite{1993MNRAS.262..717B,Baldauf:2011bh,Sherwin:2012nh}.\n\n\nThe SU approach is not only conceptually straightforward to understand but can also\nbe readily implemented in cosmological simulations, arbitrarily deep into the nonlinear\nregime where perturbation theory breaks down \\cite{Sirko:2005uz,Gnedin:2011kj,Li:2014sga,Wagner:2014aka}.\nIn particular, SU simulations have enabled studies of the squeezed-limit $n$-point\ncorrelation functions \\cite{Wagner:2015gva} and their impact on the power spectrum\ncovariance \\cite{Li:2014sga}, the halo bias \\cite{Li:2015jsz,Lazeyras:2015lgp,Baldauf:2015vio},\nand the Lyman-$\\alpha$ forest \\cite{McDonald:2001fe,Cieplak:2015kra}. Since the whole\ntime evolution of the long-wavelength mode is properly captured, as opposed to just\na single epoch such as the time of observation, temporally nonlocal effects on small-scale\nobservables such as the nonlinear power spectrum \\cite{Ma:2006zk} and halo bias\n\\cite{Senatore:2014eva,LoVerde:2014pxa} are correctly modeled.\n\n\nPrevious studies have focused on SU simulations in the $\\Lambda$CDM cosmology,\nwhere only matter clusters at low redshift. If the system contains additional\nclustering components such as dynamic dark energy or massive neutrinos, then\none has to be careful when applying the separate universe principle. Specifically,\nthe separate universe construction is only strictly true if long-wavelength\nperturbations evolve under gravitational forces alone and not internal stress\ngradients \\cite{Dai:2015jaa,Hu:2016ssz}. This means that a SU description would\nseem to require that the long-wavelength mode be larger than the Jeans or free\nstreaming scale of the system. On the other hand, if the impact on small-scale\nstructure of these extra components is only gravitational, then it can be correctly\nmodeled by matching the local expansion rate to an SU Hubble rate in a ``fake''\nSU approach which implicitly requires fictitious energy density components \\cite{Hu:2016ssz}.\n\n\nIn this work, we implement and test this multi-component SU method in simulations\nwith quintessence dark energy. In particular, the growth of long-wavelength matter\nfluctuations above or below the Jeans scale of quintessence differs due to clustering\nof the dark energy. As a result, the SU expansion history depends on the scale of\nthe long-wavelength matter fluctuation as does the response of small-scale observables\nsuch as the power spectrum and halo mass function. The latter implies that halo bias\nitself will become scale dependent.\n\n\nThe rest of the paper is organized as follows.\nIn \\refsec{theory}, we describe the mapping of perturbations in the quintessence model\nonto the SU background above and below the Jeans scale.\nIn \\refsec{sims}, we implement the SU approach in quintessence simulations.\nWe present the results of SU simulations in \\refsec{pk} and \\refsec{bias} for \nthe power spectrum response and the halo bias respectively.\nWe discuss these results in \\refsec{discussion}.\nIn \\refapp{bias_model}, we compare our results to the predictions of scale-dependent halo bias models\nin the recent literature.\n\n\nThroughout the paper, we adopt a spatially flat cosmology with a Hubble constant\n$h=0.7$, matter density $\\Omega_m=0.3$, quintessence energy density $\\Omega_Q=0.7$,\nquintessence equation of state $w_Q=-0.5$, and an initial curvature power spectrum\nwith scale-invariant tilt $n_s=1$ and amplitude which sets $\\sigma_8=1$ today.\nThese parameters are chosen to highlight the scale dependence of quintessence\nrather than for observational viability.\n\n\n\\section{Quintessential Separate Universe}\n\\label{sec:theory}\n\n\nFollowing Ref.~\\cite{Hu:2016ssz}, we review here the construction of the separate universe\nfor the case where components other than the cold dark matter possess Jeans scales. In\n\\refsec{expansion}, we show that the influence of these components is captured by a modified\nexpansion history that is defined by the growth history of the large-scale matter density\nfluctuation. We apply this construction to quintessence dark energy models in \\refsec{quintessence}.\n\n\\subsection{Expansion History}\n\\label{sec:expansion}\n\nA observer sitting within a long-wavelength matter fluctuation $\\delta_m$\nwould measure the {\\it local} mean matter density as\n\\begin{equation}\n \\bar{\\rho}_{mW}(a)=\\bar{\\rho}_m(a)[1+\\delta_m(a)] \\,,\n \\label{eq:rhoW}\n\\end{equation}\nwhere $W$ denotes a windowed average across a scale much smaller than the\nlong-wavelength mode. In the SU picture, the local mean evolves as if the\nobserver were in a SU whose scale factor \n\\begin{equation}\n a_W=\\frac{a}{(1+\\delta_m)^{1\/3}}\\approx a\\left(1-\\frac{\\delta_m}{3}\\right) \\,,\n \\label{eq:aW}\n\\end{equation}\nso that $\\bar{\\rho}_{mW} \\propto a_W^{-3}$.\nNote that at early times\n\\begin{equation}\n \\lim_{t\\to 0}\\delta_m\\to 0 \\,, ~~ \\lim_{t\\to 0}a_W\\to a \\,,\n\\end{equation}\nand the physical conditions of the local and global cosmology coincide. We\nhave implicitly assumed that there is a universal time coordinate between\nthe two and so in the relativistic limit $\\delta_m$ is specifically the\nsynchronous gauge density perturbation \\cite{Hu:2016ssz}.\n\n\nNotice that the SU construction requires only $\\delta_m(a)$ itself, not the\nevolution of any other density component in the universe. The other components \ndetermine the evolution of $\\delta_m(a)$, but they do not enter into $a_W$\nexplicitly. If these components only influence small-scale observables through\ntheir impact on $\\delta_m(a)$, their effects can be characterized by $a_W$\nand the {\\it local} Hubble expansion\n\\begin{equation}\n H_W=\\frac{\\dot{a}_W}{a_W}=H-\\frac13\\dot{\\delta}_m=H\\left(1-\\frac13\\delta'_m\\right) \\,,\n\\label{eq:H_W}\n\\end{equation}\nwhere $'\\equiv d\/d\\ln a$. This expansion history does not even need to be\ngiven by a SU Friedmann equation involving the local energy densities and\ncurvature \\cite{Gnedin:2011kj,Hu:2016ssz}. With $a_W$ and $H_W$ alone, we\ncan model the small-scale observables using $N$-body simulations with this\nSU expansion rate.\n\n\nThis construction includes cases where the other components experience\nnon-gravitational forces which define their Jeans scales. In these cases,\nthe growth history of $\\delta_m(a)$ depends on scale. Since the SU expansion\nhistory depends on the whole growth history, regions of different sizes that\nshare a common $\\delta_m$ at a fixed $a$ will produce different responses\nin the small-scale observables. In other words, these observables cannot be\ndescribed solely by the change in the local density at the time of observation\nalone. For example, as we shall see in \\refsec{bias}, the response of the\ndark matter halo abundance to $\\delta_m(a)$ leads to a halo bias that violates\nthe local bias expectation of scale independence in the linear regime.\n\n\n\\subsection{Quintessence}\n\\label{sec:quintessence}\n\n\nQuintessence or scalar field dark energy models provide a simple arena to\nexplore the response of small-scale observables to long-wavelength fluctuations,\nin particular their amplitude, scale, and growth history. The construction\nof the SU with quintessence perturbations has been extensively discussed in\nRef.~\\cite{Hu:2016ssz}. Here we only summarize the results that are related\nto simulating observable responses above and below the quintessence Jeans scale.\n\n\nThe sound speed of quintessence $c_Q$ sets the sound horizon or Jeans scale\n$r_J \\sim c_Q \/a H$ across which its influence on the evolution of $\\delta_m$\ndiffers. If $\\delta_m$ has a wavelength smaller than $r_J$, the quintessence\nperturbation is Jeans stable and becomes negligible in comparison. Thus the\nmatter fluctuations evolve under\n\\begin{equation}\n \\del{\\downarrow}''+\\left(2+\\frac{H'}{H}\\right)\\del{\\downarrow}'\n =\\frac32\\frac{H_0^2}{H^2}\\frac{\\Omega_m}{a^3}\\del{\\downarrow}\\,,\n\\label{eq:dm_subJ}\n\\end{equation}\nwhere $\\delta_m=\\del{\\downarrow}$ and the down arrow in the subscript denotes\nthe sub-Jeans case. On the other hand, if $\\delta_m$ has a wavelength larger\nthan $r_J$, then the quintessence perturbation $\\delta_Q$ has an impact on\n$\\delta_m$. Assuming that all fluctuations arise from initial curvature\nfluctuations and the sound speed of quintessence is much smaller than speed\nof light, we have for the two-component system \\cite{Hu:2016ssz}\n\\ba\n \\delta'_Q-3w_Q\\delta_Q\\:&=(1+w_Q)\\del{\\uparrow}' \\,, \\nonumber\\\\\n \\del{\\uparrow}'' +\\left(2+\\frac{H'}{H}\\right)\\del{\\uparrow}'\\:&=\n \\frac32\\frac{H_0^2}{H^2}\\left[\\frac{\\Omega_m \\del{\\uparrow}}{a^3}\n +\\frac{\\Omega_Q \\delta_Q}{a^{3(1+w_Q)}}\\right] \\,,\n\\label{eq:dm_supJ}\n\\ea\nwhere $\\delta_m =\\del{\\uparrow}$ and the up arrow in the subscript denotes\nthe super-Jeans case. For simplicity we have also taken the quintessence\nequation of state parameter $w_Q=\\bar p_Q\/\\bar \\rho_Q$ to be a constant.\nWith the assumed curvature initial conditions, the initial conditions for\nthe fields are set by taking $\\delta_m=\\del{\\uparrow}=\\del{\\downarrow}$\nand $\\delta_Q$ are all proportional to $a$ in the matter dominated limit.\n\n\nIn \\refFig{dm} we plot $\\delta_m\/\\delta_{m0}$ as a function of the global\nscale factor, where $\\delta_{m0}=\\delta_m(a=1)$ is the present-day overdensity.\nThe red solid and blue dashed lines show the sub-Jeans and super-Jeans SUs.\nNormalized to the same $\\delta_{m0}$, the super-Jeans SU is always closer\nto the global universe ($\\delta_m=0$) in the past than the sub-Jeans SU in\nits expansion history. This implies that the response of the small-scale\nobservables such as the power spectrum and halo abundance should be smaller\nin the super-Jeans than the sub-Jeans SU.\n \n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.45\\textwidth]{dm.pdf}\n\\caption{(Top) Scale-dependent growth in $\\delta_m$ as a function of the scale\nfactor for super-Jeans (red solid) and sub-Jeans (blue dashed) long-wavelength\nmodes. (Bottom) The ratio of $\\delta_m$ in super-Jeans to sub-Jeans cases. When\ncharacterized as a separate universe, the former is closer to the global universe\nthan the latter in the past for the same density fluctuation today.}\n\\label{fig:dm}\n\\end{figure}\n\n\nFinally for setting up simulations in the next section it is useful to define\nthe linear growth of short-wavelength structure in the SU. If the small-scale\nmatter fluctuations of interest are well within $r_J$, the growth function $D_W$\nis simply \\refeq{dm_subJ} with the SU expansion history\n\\begin{equation}\n \\frac{d^2D_W}{d\\ln a_W^2}+\\left(2+\\frac{d\\ln H_W}{d\\ln a_W}\\right)\\frac{dD_W}{d\\ln a_W}\n =\\frac32\\frac{H_{0W}^2}{H_W^2}\\frac{\\Omega_{mW}}{a_W^3}D_W \\,.\n\\label{eq:DW}\n\\end{equation}\nNote that \n\\begin{equation}\n \\Omega_{mW} H_{0W}^2 = \\Omega_m H_0^2 \\,,\n\\end{equation}\nand so only the SU expansion rate $H_W$ from \\refeq{H_W} is required to solve\nfor $D_W(a_W)$. As we shall see next, we can generalize this statement to nonlinear\nobservables with SU $N$-body simulations.\n\n\n\\section{Separate Universe Simulations}\n\\label{sec:sims}\n\n\nThe growth history of the long-wavelength matter fluctuation $\\delta_m(a)$ in\nthe global universe alone sets the expansion history in the separate universe.\nAll effects from long-wavelength fluctuations in other species are incorporated\ninto its growth history. In the quintessence model, within the Jeans scale dark\nenergy perturbations can be ignored and so the response of small scale observables\ncan be calibrated using $N$-body simulations with just this change in the expansion\nhistory.\n \n\nUnlike the SU technique in $\\Lambda$CDM, the change of cosmological parameters \nand their correspondence with real energy densities and curvature becomes non-trivial\nfor the quintessence model \\cite{Hu:2016ssz} whereas the direct change in the\nexpansion rate $H_W$ remains simply determined by $\\delta_m(a)$. Thus, while\nsome steps are similar to the $\\Lambda$CDM SU techniques for running and analyzing\nsimulations (see e.g. \\cite{Li:2014sga,Wagner:2014aka,Li:2015jsz,Lazeyras:2015lgp}),\nthere are some major differences in performing the SU simulations with quintessence\nwhich we now describe.\n\n\nLet us start with setting the initial conditions for the simulations. Recall that \nat high redshift the separate and global universes are identical in their physical\ndescription. To achieve this, we first compute the linear power spectrum with the\nglobal cosmology at $z=0$ using CAMB \\cite{Lewis:1999bs,Howlett:2012mh}. We then\nrescale this power spectrum to the initial redshift of the simulations $a_{Wi}=0.02$ as\n\\begin{equation}\n P_W(k,a_{Wi})=P(k,a_0)\\left[\\frac{D_W(a_{Wi})}{D(a_0)}\\right]^2 \\,,\n\\end{equation}\nwhere $D$ is the linear growth in the global universe and $D_W$ is the linear\ngrowth of the SU following \\refEq{DW}. The growth functions are normalized in\nthe matter dominated epoch as\n\\begin{equation}\n \\lim_{a \\rightarrow 0} D(a)=a, \\quad \\lim_{a_W \\rightarrow 0} D(a_W) = a_W \\,.\n\\label{eq:D_ic}\n\\end{equation}\nNote that $D_W$ in sub-Jeans and super-Jeans SUs are different, as they have different\nexpansion histories. Another subtlety is that the change in the expansion rate of the\nSU makes the traditional unit of comoving $[h\\,{\\rm Mpc}^{-1}]$ inconvenient. Throughout this paper\nwe avoid this confusion by using units of comoving $[{\\rm Mpc}]$ and convert for code\npurposes as necessary. Given the different scale factors $a$ and $a_W$, the correspondence\nbetween comoving wavenumber and physical wavenumber in the global universe differ. Since\nthis represents a simple dilation of scales, we can account for it in the interpretation\nof observable responses rather than in the simulations directly \\cite{Li:2014sga}.\n \n\nThe initial conditions are then set up using realizations of Gaussian random\nfields for the primordial fluctuations and evolved to $a_{Wi}$ using second-order\nLagrangian perturbation theory (2LPT) \\cite{Crocce:2006ve}. Usual 2LPT codes,\nsuch as the publicly available 2LPTIC \\cite{2lptic}, compute the linear growth and\ngrowth rate $f_W=d\\ln D_W\/d\\ln a_W$ at $a_{Wi}$ from the cosmological parameters.\nWe modify the pipeline such that $D_W$ and $f_W$ from the numerical solution of\n\\refEq{DW} determine the initial positions and velocities of the particles.\n\n\nWe use Gadget-2 \\cite{Springel:2005mi} to carry out the simulations. Standard\nGadget-2 computes the Hubble expansion as a function of the scale factor using\nthe input cosmological parameters. Instead of finding the corresponding cosmological\nparameters, we first compute $H_W$ as a function of $a_W$ with \\refEq{H_W} and\n\\refEqs{dm_subJ}{dm_supJ}, pass the table $(a_W,H_W)$ to the code, and then\ninterpolate the value of $H_W(a_W)$ when necessary\\footnote{Specifically, we\nonly need to modify \\texttt{driftfac.c} and \\texttt{timestep.c}. Also since\nGadget-2 checks the consistency of the input parameters, we provide the SU\n$\\Omega_{mW}$ as well as $h_W$, and $L_W$ where $L_W$ is the box size of the\nsimulations}. We have verified that the SU results are in excellent agreement\nwith those of the standard 2LPTIC Gadget-2 pipeline in $\\Lambda$CDM where the\nSU is implemented by varying cosmological parameters.\n\n\nFollowing the procedures in Ref.~\\cite{Lazeyras:2015lgp}, we identify halos with\nthe Amiga Halo Finder \\cite{Knollmann:2009pb,Gill:2004km}, which is based on the\nspherical overdensity algorithm. The key quantity of the spherical overdensity\nalgorithm is the density threshold, and we set it to be $\\Delta=200$ in the global\nuniverse. To match halos identified in the global cosmology, the threshold relative\nto the mean in the SU needs to be rescaled as \\cite{Li:2015jsz,Lazeyras:2015lgp}\n\\begin{equation}\n \\Delta_W=\\frac{\\Delta}{1+\\delta_m(t)}\\approx\\Delta[1-\\delta_m(t)] \\,.\n\\end{equation}\nIn other words, in the overdense (underdense) universe the threshold becomes smaller\n(larger) due to the background fluctuations. From each simulation we obtain one halo\ncatalog, and we consider only halos with more than 400 particles. We also neglect\nsub-halos for simplicity.\n\n\nIn this paper, we perform both the sub-Jeans and super-Jeans SU simulations with\n$\\del{\\uparrow\\downarrow 0}=\\del{\\uparrow\\downarrow}(a=1)=\\pm0.01$, totaling 4\nsimulations per set. For each of the 20 sets, we fix their initial phases so that\nwhen we take the difference of the observables between overdense and underdense\nSU simulations a large amount of noise due to sample variance is removed. We also\nrun 40 simulations of the global $\\delta_m=0$ universe in order to characterize\nthe clustering bias for comparison in \\refsec{clusteringbias}. The first 20 have\nthe same initial phases as their SU counterparts. For each of these sets we take\na comoving box size $L=1000\\,$Mpc and number of particles $N_p=1024^3$, denoted\nas small-box.\n\n\nWe also run 20 simulations with $L=2800$ Mpc and $N_p=1024^3$ particles in the global\nuniverse, denoted as big-box simulations. These big-box simulations are used to measure\nthe position-dependent power spectrum \\cite{Chiang:2014oga} for comparison with the\npower spectrum response of the sub-Jeans simulations. The details of the simulations\nare summarized in \\reftab{sims}.\n\n\n\\begin{table}[h]\n \\begin{tabular}{c c c c c c c}\n \\hline\n type & SU & $L$ [Mpc] & $N_p$ & $\\del{m0}$ & $N_{\\rm sets}$\\\\\n \\hline\n small-box & $\\uparrow\\downarrow$ &1000 & $1024^3$ & $\\pm0.01$ & 20 \\\\\n small-box & no &1000 & $1024^3$ & 0 & 40 \\\\\n big-box & no& 2800 & $1024^3$ & 0 & 20 \\\\\n \\hline\n \\end{tabular}\n \\caption{Summary of the simulations.}\n\\label{tab:sims}\n\\end{table}\n\n\n\\section{Power Spectrum Response}\n\\label{sec:pk}\n\n\nIn this section, we calibrate the responses in the locally measured power spectrum\nto a long-wavelength mode above and below the Jeans scale. In \\refsec{resp} we extract\nthese responses from the SU simulations and show that they are scale dependent and\nsmaller for modes above the Jeans scale than below. We test these responses against\npredictions from perturbation theory in \\refsec{pt} and the local, position-dependent,\npower spectrum from the big-box simulations with long-wavelength sub-Jeans scale modes\nin \\refsec{ibn}. The good agreement implies that the SU simulation technique provides\naccurate predictions for these small scale observables without the need for direct\nsimulations of quintessence clustering.\n\n\n\\subsection{Separate Universe Calibration}\n\\label{sec:resp}\n\n\nIn the presence of a long-wavelength density fluctuation $\\delta_m$, the power spectrum\nobserved locally will differ from the global average. We can characterize the fractional\nchange in the local power spectrum as a ``response'' $R_{\\rm tot}$ to $\\delta_m$\n\\begin{equation}\n \\frac{\\Delta P}{P} \\approx \\frac{d \\ln P}{d\\delta_m} \\delta_m \\equiv R_{\\rm tot} \\delta_m \\,.\n\\end{equation}\nSince to the leading order $R_{\\rm tot}$ is independent of $\\delta_m$, it can be calibrated\nusing the SU simulations once and for all rather than with simulations that follow the\ndynamics of individual long-wavelength modes. This is especially advantageous for quintessence,\nwhere super-Jeans modes require simulations with quintessence clustering.\n\n\nThis effect can be observed in a local sample of our universe by dividing it into subvolumes\nand measuring the correlation between the local power spectra and the subvolume mean overdensities,\nwhich is known as the position-dependent power spectrum \\cite{Chiang:2014oga,Chiang:2015eza}.\nEven if only the undivided volume is employed, the coherent change in the local power spectrum\n$\\Delta P(k)$ due to wavelengths larger than the sample induces a ``super-sample'' covariance\nbetween measurements of different $k$ modes \\cite{Takada:2013bfn,Li:2014sga,Li:2014jra}.\n\n\nIn practice, the calibration of the total response with SU simulations involves three pieces:\ngrowth, dilation, and reference-density \\cite{Li:2014sga}\n\\begin{equation}\n R_{\\rm tot} = R_{\\rm growth} + R_{\\rm dilation} + R_{\\bar \\rho} \\,.\n\\end{equation}\n$R_{\\rm growth}$ describes the change in the growth of a small-scale density fluctuation\nat a fixed comoving $k$ in the separate and global universe relative to their own scale\nfactors. $R_{\\rm dilation}$ changes the scale to a fixed wavenumber in the global universe\nor physical wavenumber in each. Finally $R_{\\bar \\rho}$ accounts for the different mean\ndensity of the two universes in the definition of the density fluctuation.\n\n\nTo measure the growth response from SU simulations, we first distribute the dark matter\nparticles onto a $1024^3$ grid by the cloud-in-cell (CIC) density assignment scheme to construct\nthe density fluctuation, and Fourier transform the density fluctuations with FFTW \\cite{fftw}\nto form the power spectrum. For each set of super $\\uparrow$ or sub $\\downarrow$ Jeans\nscale SU simulations with the same initial phases, we estimate the growth response,\n\\begin{equation}\n R_{\\rm growth,\\uparrow\\downarrow} \\equiv \n R_{\\uparrow\\downarrow}\n \\equiv \\frac{d\\ln P_{\\uparrow\\downarrow}}{d\\del{\\uparrow\\downarrow}}\n\\end{equation}\nas\n\\begin{equation}\n \\hat{R}_{\\uparrow\\downarrow}(k,a)=\n \\frac{\\hat{P}_{\\uparrow\\downarrow}(k,a|{\\scriptstyle +}\\del{\\uparrow\\downarrow,0})\n -\\hat{P}_{\\uparrow\\downarrow}(k,a|{\\scriptstyle -}\\del{\\uparrow\\downarrow,0})}\n {2\\hat{P}(k,a) \\del{\\uparrow\\downarrow}(a)} \\,,\n\\end{equation}\nwhere we difference the overdense and underdense pairs for each $|\\del{\\uparrow\\downarrow,0}|$.\nWe then compute the variance of $\\hat{R}_{\\uparrow\\downarrow}$ from the 20 small-box realizations.\n\n\nThe dilation response accounts for the fact that the same comoving $k$ in the SU corresponds\nto a different physical $k$ in the global universe. Given the change in the scale factor from\n\\refeq{aW}, it is analytically related to the local slope in power spectrum as \\cite{Li:2014sga}\n\\begin{equation}\n R_{\\rm dilation}(k,a)=-\\frac{1}{3}\\frac{d\\ln k^3P(k,a)}{d\\ln k} \\,.\n\\label{eq:Rdilation}\n\\end{equation}\nTo compute the dilation response from simulations, we take the log-derivative of the\nmean power spectrum measured from 40 small-box simulations with the global cosmology.\nAs a result, the dilation response is the same in both sub-Jeans and super-Jeans SUs.\nFinally the $\\bar\\rho$ response is due to the change in the definition of a density\nfluctuation\n\\begin{equation}\n \\delta_m \\equiv \\frac{\\delta\\rho_m}{\\bar\\rho_m} = \\delta_{mW} \\frac{ \\bar\\rho_{mW}}{ \\bar\\rho_m} \\,,\n\\end{equation}\nso that to the leading order \\refeq{rhoW} implies\n\\begin{equation}\n R_{\\bar \\rho}(k,a)=2 \\,.\n\\label{eq:Rreference}\n\\end{equation}\nNote that the last two responses, dilation and reference-density, do not involve\nthe SU simulations.\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.46\\textwidth]{pk_response_component_z0.pdf}\n\\caption{(Top) Different components of the separate universe power spectrum responses\nat $z=0$: growth response (sub-Jeans, red solid; super-Jeans blue dashed), absolute\nvalue or negative of the dilation response (green dot-dashed), and the reference-density\nresponse (black dotted). (Bottom) Ratios of the mean of the total response to that of\nthe sub-Jeans separate universe. Shaded bands reflect the error on the mean response\nof the simulations. The clear distinction between the sub-Jeans and super-Jeans power\nspectrum responses is the first important result of our separate universe simulations.}\n\\label{fig:resp_tot}\n\\end{figure}\n\n\nThe top panel of \\reffig{resp_tot} shows the various power spectrum responses at $z=0$,\nand the bottom panel shows the ratios of the total power spectrum response, $R_{\\rm tot}$,\nto that of the sub-Jeans SU, $R_{{\\rm tot},\\downarrow}$. We find that the response is\nroughly 2\\% smaller in super-Jeans than in sub-Jeans SUs, and the distinction is statistically\nsignificant. Note also the small errors ($\\sim0.1\\%$ at low-$k$ and $\\sim 0.3\\%$ at high-$k$)\nestimated from small-box SU simulations, demonstrating the power of the SU technique to\nprecisely characterize the response down to arbitrarily small scales.\n\n\nThe fact that the growth response is smaller in super-Jeans than in sub-Jeans SUs can be\nunderstood qualitatively from \\reffig{dm}. Normalized to a given observation redshift,\n$\\delta_m$ in the super-Jeans limit is always smaller in the past than that in the sub-Jeans\nlimit. Consequently, the super-Jeans SU is closer to the global universe along the growth\nhistory, and so the response is smaller.\n\n\nThis difference between super-Jeans and sub-Jeans scales produces an observable change\nin the local power spectrum, and so can in principle be used as a new probe of the sound\nspeed of quintessence. In the real universe, the small-scale power spectrum responds\nto long modes of all scales, the difference of the responses in sub-Jeans and super-Jeans\nlimit would thus appear as the scale-dependent squeezed-limit bispectrum for a fixed\nsmall-scale mode. However in quintessence models with initial curvature perturbations,\nthe predicted amplitude for adiabatic quintessence fluctuations is proportional to\n$(1+w_Q)$ (see Eq.~(95) of Ref. \\cite{Hu:2016ssz}) and so goes to zero as $w_Q \\rightarrow -1$.\nMore generally, this growth history dependence demonstrates that the nonlinear matter power\nspectrum cannot simply be a functional of the linear power spectrum at the same epoch as\nis commonly assumed in simple halo model and nonlinear fitting procedures (see also \\cite{Ma:2006zk}).\n\n\n\\subsection{Perturbation Theory}\n\\label{sec:pt}\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.45\\textwidth]{dlnDW.pdf}\n\\caption{The response of the separate universe growth function as a function of the global\nscale factor $a$ in the global universe for super-Jeans (red solid) and sub-Jeans (blue\ndashed) cases. Above the Jeans scale, the response to the same $\\delta_m$ at $a$ is smaller\nthan below.}\n\\label{fig:dlnDW}\n\\end{figure}\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.45\\textwidth]{dlnpk_linear.pdf}\n\\caption{Linear perturbation theory predictions for the growth responses compared with\nthe measurements from the 20 small-box separate universe simulations at $z=3$ (top)\nand 0 (bottom). The red solid and blue dashed lines with shaded areas show the sub-Jeans\nand super-Jeans separate universes measurements as in Fig.~\\ref{fig:resp_tot}, whereas\nthe red dot-dashed and blue long-dashed lines show the corresponding linear perturbation\ntheory predictions, i.e.~\\refeqs{Rgrowth}{linear}. Note that the range of $y$-axes is\nsmaller in the top than in the bottom panel. Also the cusp feature at $k\\sim0.078~{\\rm Mpc}^{-1}$\nis a visual artifact due to binning, which we choose to be $\\Delta k= 2\\pi\/L$.}\n\\label{fig:dlnpk_linear}\n\\end{figure}\n\n\nTo better understand the growth responses quantitatively, we compute them in perturbation\ntheory and check their agreement with the SU simulations at various redshifts. In perturbation\ntheory, the effect can be modeled through the SU linear growth function $D_W$ as\n\\begin{equation}\n R_{\\rm growth}(k,a)=\\frac{d\\ln P(k,a)}{d\\ln D_W(a)}\\frac{d\\ln D_W(a)}{d\\delta_m(a)} \\,.\n\\label{eq:Rgrowth}\n\\end{equation}\nIn the linear regime $P(k,a) \\approx P_{\\rm lin}(k,a) \\propto D_W^2(a)$ and so \n\\begin{equation}\n \\frac{d\\ln P(k,a)}{d\\ln D_W(a)}\\approx 2 \\,.\n \\label{eq:linear}\n\\end{equation}\nTo determine the response of $D_W$ in \\refEq{Rgrowth}, we solve \\refEq{DW} with the initial\ncondition \\refEq{D_ic}, and the result is shown in \\reffig{dlnDW}. It approaches the matter\ndominated ($\\Omega_m=1$) limit $13\/21$ at high redshift for both super-Jeans and sub-Jeans\nscale responses, and at low redshift is smaller in the super-Jeans case as expected from\n\\reffig{dm}. In \\reffig{dlnpk_linear}, we compare the measured power spectrum response in\nsub-Jeans and super-Jeans SUs to the corresponding linear perturbation theory predictions,\ni.e.~\\refeqs{Rgrowth}{linear}. We find that in both cases the linear perturbation theory\nagrees with the measured responses in the linear regime, i.e., at sufficiently low $k$ or\nhigh $z$.\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.45\\textwidth]{dlnpk_1loop.pdf}\n\\caption{Same as \\reffig{dlnpk_linear}, but for the 1-loop predictions,\ni.e. \\refeq{Rgrowth} and \\refeq{1loop}.}\n\\label{fig:dlnpk_1loop}\n\\end{figure}\n\n\nHowever, as we move to lower redshift as well as higher $k$, the measured responses\nbecome nonlinear and perturbation theory predictions deviate from SU simulation\nmeasurements. Unlike perturbation theory, the SU response calibration is not limited\nto large scales. On the other hand, we can understand the onset of nonlinearity in\nthe simulations through higher order perturbation theory. The 1-loop power spectrum\nfrom standard perturbation theory is given by (see e.g. Ref.~\\cite{Jeong:2006xd})\n\\begin{equation}\n P_{\\rm 1-loop}(k,a)=P_{\\rm lin}(k,a)+P_{22}(k,a)+2P_{13}(k,a) \\,,\n\\end{equation}\nwhere the nonlinear corrections $P_{22}$ and $P_{13}$ are proportional to $D_W^4$\nif $\\Omega_{mW}(a_W)\/f_{W}^2(a_W)\\approx1$.\nTherefore,\n\\begin{equation}\n \\frac{d\\ln P_{\\rm 1-loop}(k,a)}{d\\ln D_W(a)}\n =2\\left[1+\\frac{P_{22}(k,a)+2P_{13}(k,a)}{P_{\\rm 1-loop}(k,a)}\\right] \\,,\n \\label{eq:1loop}\n\\end{equation}\nwhich now is a function of $k$. Note that in the global cosmology $\\Omega_m\/f^2=1.034$\nat $z=3$ and 1.203 at $z=0$, and the standard perturbation theory should work better\nat $z=3$ than at $z=0$. Since the long-wavelength perturbation we consider is small\n($|\\delta_{\\uparrow\\downarrow0}|=0.01$), the standard perturbation theory should work\nas well in both the sub-Jeans and super-Jeans SUs as the global universe.\n\nIn \\reffig{dlnpk_1loop} we plot the 1-loop predictions in sub-Jeans (red dot-dashed)\nand super-Jeans (blue long-dashed). We find that the 1-loop predictions extend the\nagreement with the $N$-body measurement to smaller scales compared to the linear\npredictions. More precisely, the difference between the 1-loop model and the measurement\nat $z=3$ ($z=0$) is 1\\% (3\\%) at $k\\sim0.1~{\\rm Mpc}^{-1}$ and 4\\% (6\\%) at $k\\sim0.2~{\\rm Mpc}^{-1}$.\nAt even smaller scale or lower redshift, the nonlinearity is too large to be modeled\nby the 1-loop perturbation theory. The SU simulation calibration technique itself is\nnot limited in wavenumber and our $N$-body implementation is instead only limited by\nresolution as well as the lack of baryonic and astrophysical modeling in the deeply\nnonlinear regime.\n\n\n\\subsection{Position-Dependent Power Spectrum}\n\\label{sec:ibn}\nThe power spectrum response can also be tested in simulations and observed in surveys \nthrough the position-dependent power spectrum. As a simulation based test, it also serves\nto check the SU calibration of the power spectrum response deep into the nonlinear regime.\n\n\nSpecifically we compare the response measured from the small-box SU simulations to the\nsqueezed-limit position-dependent power spectrum measured from the big-box simulations\nwith the global cosmology (without a uniform long-wavelength density fluctuation). In\nthe latter, we assume that the dark energy does not cluster with matter and so its\nposition-dependent power spectrum should match the sub-Jeans SU prediction. \n\n\nThe procedure of measuring the position-dependent power spectrum is explained in detail\nin \\cite{Chiang:2014oga}. In short, we first distribute the dark matter particles onto\na $2048^3$ grid by the CIC density assignment scheme to construct the density fluctuation.\nWe next divide the big-box simulations in each dimension by 8, so there are $N_s=512$\nsubvolumes in total with comoving side length of $L=350$ Mpc. In each subvolume centered at\n${\\mathbf r}_L$, we measure the local power spectrum as $\\hat{P}(k,a|{\\mathbf r}_L)$\nand the mean overdensity (with respect to the entire box) as $\\hat{\\bar{\\delta}}_m({\\mathbf r}_L)$\nand construct \n\\begin{equation}\n \\frac{\\frac{1}{N_s}\\sum_{{\\mathbf r}_L}\\hat{P}(k,a|{\\mathbf r}_L)\\hat{\\bar{\\delta}}_m({\\mathbf r}_L)}\n {\\left[\\frac{1}{N_s}\\sum_{{\\mathbf r}_L}\\hat{P}(k,a|{\\mathbf r}_L)\\right]\n \\left[\\frac{1}{N_s}\\sum_{{\\mathbf r}_L}\\hat{\\bar{\\delta}}_m^2({\\mathbf r}_L)\\right]} \\,,\n\\end{equation}\nwhere the summation is over the 512 subvolumes in one big-box realization. The correlation\nbetween $\\hat{P}(k,a|{\\mathbf r}_L)$ and $\\hat{\\bar{\\delta}}_m({\\mathbf r}_L)$ quantifies\nthe integrated bispectrum, and in the squeezed limit where $k\\ll1\/L$ the integrated bispectrum\ncan be understood as the {\\it total} response of the power spectrum to the long-wavelength\noverdensity.\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.48\\textwidth]{ibn_z0.pdf}\n\\caption{Comparison of the squeezed-limit position-dependent power spectrum of big-box,\nglobal simulations (green dot-dashed), and the total power spectrum response of sub-Jeans\n(red solid) as well as super-Jeans (blue dashed) separate universe simulations at $z=0$.\nThe shaded areas show the error on the mean. This figure summarizes our main power spectrum\nresponse results: the position-dependent power spectrum and the sub-Jeans power spectrum\nresponse agree significantly better than the difference in response across the Jeans scale\nconfirming the scale dependence in this observable deep into the nonlinear regime.}\n\\label{fig:ibn}\n\\end{figure}\n\n\\refFig{ibn} shows the comparison at $z=0$ between the total power spectrum response from\nthe previous section (red solid for sub-Jeans and blue dashed for super-Jeans cases) and\nthe position-dependent power spectrum (green dot-dashed) averaged over the 20 realizations\nwith its error. To reach the squeezed limit, we require $k\\gtrsim100\/L\\sim0.3~{\\rm Mpc}^{-1}$,\nand in this regime the agreement with the SU response is better than a percent. This agreement\nis significantly better than the difference between the super-Jeans and sub-Jeans power spectrum\nresponses and thus verifies the SU calibration technique. With the SU technique tested into\nthe nonlinear regime, we can apply these results to the super-Jeans case of the position-dependent\npower spectrum without the need for costly simulations that include dark energy clustering.\n\n\n\\section{Scale-Dependent Halo Bias}\n\\label{sec:bias}\nThe SU simulations also calibrate the response of the halo mass function to a long-wavelength \nmode and hence the bias of the halo number density due to that mode. In \\refsec{responsebias},\nwe review the technique for measuring halo bias from SU simulations and show that in the\nquintessence model it acquires a scale dependence at the Jeans scale. In \\refsec{clusteringbias},\nwe test this response bias in the SU simulations against the clustering bias extracted from\n40 small-box global simulations. We discuss the implications of scale dependence for the\ntemporal nonlocality of halo bias and the observability of features in the halo power\nspectrum in \\refsec{interpretation}.\n\n\n\\subsection{Response Bias}\n\\label{sec:responsebias}\n\nThe linear density bias $b_1(M)$ of halos of mass $M$ can be defined as the response\nof the differential halo abundance $n_{\\ln M}=dn\/d\\ln M$ to the long-wavelength mode\n\\begin{equation}\n b_1(M) \\equiv \\frac{d\\delta_h}{d\\delta_m} = \\frac{d\\ln n_\\lnM}{d\\delta_m} \\,,\n \\label{eq:biasasresponse}\n\\end{equation}\nwhich we call ``response bias''. Thus by measuring the response of the halo mass\nfunction in the SU simulations we have a direct calibration of response bias\n\\cite{Li:2015jsz,Lazeyras:2015lgp,Baldauf:2015vio}. Note that the derivative in\n\\refeq{biasasresponse} is evaluated at a fixed time, but will depend on the whole\ngrowth history of $\\delta_m(a)$. This temporal nonlocality implies that response\nbias can be scale dependent if that growth history is also scale dependent. For\nquintessence, the SU simulations allow us to calibrate the bias above and below\nthe Jeans length of quintessence without direct simulations of its clustering properties. \n\n\nAs discussed in Ref.~\\cite{Li:2015jsz}, response bias largely reflects the change\nin the masses of halos due to the same local change in growth that affects the power\nspectrum. The enhanced growth in $\\delta_m>0$ regions makes halos more massive locally\nthan in their $\\delta_m<0$ counterparts. Hence halos of a fixed mass are associated\nwith the more abundant lower peaks in the initial density field in the former and\nthe less abundant higher peaks in the latter. This also means that measuring the\nchange in abundance of halos in fixed mass bins between the SU simulation pairs is\nan inefficient way to quantify response bias. Halos with small changes in mass across\nwide mass bins would register their response only when individual halos move across\nmass bins.\n\n\nWe instead adopt abundance matching as introduced in Ref.~\\cite{Li:2015jsz}, which\nwe now summarize. By finding the mass threshold above which the cumulative abundance\nis fixed, we largely eliminate the sampling noise from the discrete nature of halos.\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.45\\textwidth]{s_z0.pdf}\n\\caption{Threshold mass shift as a response of varying $\\delta_m$ at fixed cumulative abundance\nat $z=0$. The solid line and shaded region show the smoothed estimate and the bootstrap error.}\n\\label{fig:s_z0}\n\\end{figure}\n\n\nSpecifically for either the sub-Jeans or the super-Jeans case, we first combine halo catalogs\nof all realizations of the same $\\delta_m$ in the small-box suite. The masses of the $i^{\\rm th}$\nmost massive halo $M_i^\\pm$ from the $\\delta_m = \\pm 0.01$ SU simulations determine the\ndiscrete threshold mass shift\n\\begin{equation}\n s_i(\\lnM_i) = \\frac{\\lnM_i^+ - \\lnM_i^-}{2|\\delta_m|} \\,,\n\\end{equation}\nwhere $M_i$ is the geometric mean of $M_i^+$ and $M_i^-$. We then use the smoothing spline\ntechnique to estimate the ensemble average threshold mass shift $\\hat s(\\ln M)$ as well as\nthe cumulative halo abundance above threshold mass $\\hat n(\\ln M)$. \\refFig{s_z0} shows the\nmass shift measured from 20 sub-Jeans and super-Jeans SU simulations at $z=0$ as a function\nof halo mass. We find that the mass shift due to varying $\\delta_m$ is smaller above the\nJeans scale, which reflects the fact that its growth history makes it closer to the global\nuniverse than below the Jeans scale.\n\n\nThe halo mass function follows as the derivative of the cumulative mass function\n$\\hat n_\\lnM=-d\\hat n\/d\\lnM$. We can then estimate the Lagrangian halo bias above\nthreshold mass $M$ as\n\\begin{equation}\n \\hat{\\bar{b}}_1^\\Lr(M) = \\frac{\\hat n_\\lnM(\\ln M)\\,\\hat s(\\ln M)}{\\hat n(\\ln M)} \\,.\n\\label{eq:am}\n\\end{equation}\nThis quantity is the Lagrangian bias since the SU simulations are performed with the\nsame comoving rather than physical volume. The dilation of the volume from the change\nin scale factors brings the cumulative Eulerian bias to\n\\begin{equation}\n \\hat{\\bar{b}}_1(M) =1+ \\hat{\\bar{b}}_1^\\Lr(M)\\,.\n \\label{eq:eulerianb}\n\\end{equation}\nIn \\reffig{b_resp} we compare the response bias on super-Jeans (blue dashed) and sub-Jeans \n(red solid) scales as a function of halo mass at $z=0$. The bias at a fixed mass is smaller\nin the super-Jeans case. Just like for the power spectrum response, above the Jeans scale\nfor the same final $\\delta_m$, the SU is closer to global at high redshift. Thus the change\nin growth and the consequent change in halo masses and abundances is smaller. We find that\nthe mild mass dependence of the fractional difference between the super-Jeans and sub-Jeans\nresponse biases is due mainly to the dilation effect in \\refeq{eulerianb}, since that of\nthe Lagrangian bias is fairly mass independent due to the similar shapes of the mass shift\ndisplayed in \\reffig{s_z0} (see also \\reffig{bLversusmodels}).\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.46\\textwidth]{b_response_z0.pdf}\n\\caption{(Top) The $z=0$ response biases measured from 20 sub-Jeans (red solid) and\nsuper-Jeans (blue dashed) separate universe simulations. The lines and shaded areas\nshow the smoothed estimate and the bootstrap error. (Bottom) The ratios of the response\nbiases to that of the sub-Jeans response bias. The difference between the sub-Jeans\nand super-Jeans response biases, which indicates that the linear halo bias is scale\ndependent in the presence of the scale-dependent growth, is the second central result\nof our separate universe simulations.}\n\\label{fig:b_resp}\n\\end{figure}\n\n\n\\subsection{Clustering Bias}\n\\label{sec:clusteringbias}\n\nTo verify the SU calibration of halo bias through the mass function response, we can\ncompare it to how linear halo bias is commonly measured from the two-point statistics,\nwhich we call clustering bias\n\\begin{equation}\n\\bar b_1(M) = \\lim_{k\\to0}\\frac{P_{hm}(k;M)}{P_{mm}(k)} \\,,\n \\label{eq:biasascrosspower}\n\\end{equation}\nwhere $P_{hm}$ is the cumulative halo number density cross power spectrum with the\nmatter density. Where no confusion should arise, we omit the $M$ argument of the\ncumulative bias. Above the Jeans scale of quintessence, this approach would require\nsimulations of quintessence clustering even for linear halo bias. Below the Jeans\nscale, we can test the equivalence of response and clustering bias with global\nsimulations where quintessence enters only at the background level. \n\n\nIn order to extract the $k\\rightarrow 0$ limit, we first compute\n\\begin{equation}\n \\bar{q}(k)=\\frac{{P}_{hm}(k)}{P_{mm}(k)} \\,,\n\\end{equation}\nfor each of the 40 simulations of the global cosmology for a set of mass thresholds. \nMotivated by Ref.~\\cite{Assassi:2014fva}, we fit $\\bar{q}(k)$ to the model\n\\begin{equation}\n \\bar{b}(k)=\\bar{b}_1+\\sum_{i=1}^n \\bar{b}_{k^{2n}}k^{2n} \\,,\n\\end{equation}\nwhere we treat $\\bar{b}_{k^{2n}}$ as nuisance parameters that absorb the loop\ncorrections in the large-scale limit. We then get the best-fit bias parameters\nby minimizing\n\\begin{equation}\n \\chi^2=\\sum_{k}^{k_{\\rm max}}\\frac{[\\bar{q}(k)-\\bar{b}(k)]^2}{\\sigma^2[\\bar{q}(k)]} \\,,\n\\label{eq:chi2}\n\\end{equation}\nwhere $\\sigma^2[\\bar{q}(k)]$ is the variance of $\\bar{q}(k)$ measured from 40 global\nsmall-box simulations.\n\n\nTo ensure the robustness of the fitted clustering bias, especially as compared with the\nsmall predicted difference between sub-Jeans and super-Jeans response biases, we examine\nthe bias models with $n=0$, 1, and 2 for various $k_{\\rm max}$. We seek consistent result for\ndifferent bias models (different $n$) and $k_{\\rm max}$. The general principle is that the\nlarger the $k_{\\rm max}$, the larger the $n$ required to account for the nonlinearity and to\navoid underfitting. Conversely, for models with $n>0$ $k_{\\rm max}$ cannot be too small or the\nfit would suffer from overfitting.\n\n\nWith each bias model and $k_{\\rm max}$, we visually inspect its goodness of fit to $\\bar{q}(k)$\nfor various threshold halo masses. We find that across two decades in halo mass\n($2\\times10^{13}-2\\times10^{15}\\,M_\\odot$), the bias models of $n=0$, 1, and 2 with the\nbiases fitted to $k_{\\rm max}=0.014-0.028\\,{\\rm Mpc}^{-1}$, $0.042-0.049\\,{\\rm Mpc}^{-1}$,\nand $0.056-0.07\\,{\\rm Mpc}^{-1}$ are in agreement with the mean $\\bar{q}(k)$, and the\nagreement even extends to $k>k_{\\rm max}$. This shows that the fit is free from overfitting\nand underfitting problems. For a given halo mass, the best-fit clustering bias varies\nup to 0.2\\%, 0.5\\%, and 2\\% among different $n$ and $k_{\\rm max}$ at $2\\times10^{13}\\,M_\\odot$,\n$2\\times10^{14}\\,M_\\odot$, and $2\\times10^{15}\\,M_\\odot$, respectively. Given the fact\nthat the clustering bias is stable for various bias models and fitting range, we conclude\nthat systematic error due to $n$ and $k_{\\rm max}$ is at most comparable to our statistical\nerror, and is the largest at the high-mass end at which the statistical error is also large.\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.48\\textwidth]{b_clustering_ratioonly_z0.pdf}\n\\caption{The ratios of the linear biases to that of the sub-Jeans response bias at $z=0$.\nThe red solid and blue dashed lines show the sub-Jeans and super-Jeans response biases\nmeasured from 20 separate universe simulations, whereas the green dot-dashed line shows\nthe clustering bias measured from 40 global simulations. The error of the clustering bias\nis measured from the scatter of the 40 simulations. This figure summarizes the main results\non halo bias: the agreement between the clustering bias measured from global simulations\nand the sub-Jeans response bias verifies the observable difference in halo bias across the\nJeans scale inferred from the separate universe simulations.}\n\\label{fig:b_clus}\n\\end{figure}\n\n\nIn \\reffig{b_clus} we shows our fiducial results of the clustering bias measurement for\nthe quadratic model ($n=1$) with $k_{\\rm max}=0.49\\,{\\rm Mpc}^{-1}$, which gives the smallest\nstatistical errors. We find that the clustering bias is in good agreement with the sub-Jeans\nresponse bias across two decades in halo mass, confirming the validity of the SU technique.\nThis agreement is substantially better than the difference between the super-Jeans and sub-Jeans\nresponse bias at low- and mid-mass regime, even after including the systematic differences\nbetween the fitting techniques.\n\n\nTo further test robustness of the scale-dependent bias result, we also try the halo\nfinding algorithm provided in Ref.~\\cite{Li:2015jsz}, another spherical overdensity finder\nsimilar to that in Ref.~\\cite{Tinker:2008ff}. We find that the clustering bias is statistically\nin equally good agreement with the sub-Jeans response as well.\n\n\nWith this verification of the SU calibration of halo bias, our results represent the first\nsimulation confirmation of scale-dependent halo bias from scale-dependent growth. A related\neffect on the void bias has been measured in the simulations with cold dark matter and massive\nneutrinos \\cite{Banerjee:2016zaa}.\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.45\\textwidth]{phh_sup_to_sub.pdf}\n\\caption{Fractional difference or ``step'' across the Jeans scale in the matter (red solid,\n\\refeq{Smm}) versus the halo (blue dashed, \\refeq{Shh}) power spectra. The step in the halo\npower spectrum is reduced by approximately a factor of 2 compared with the matter power\nspectrum at high mass where the Lagrangian bias contribution dominates. Bias prescriptions\nwhich assume locality at either the observed or initial redshift predict the same step or\nzero step respectively at high masses.}\n\\label{fig:phh_ratio}\n\\end{figure}\n\n\\subsection{Scale-Dependent Bias and Power Spectra}\n\\label{sec:interpretation}\n\n\nSince the halo bias is smaller above versus below the Jeans scale of quintessence, its\nscale dependence counters the growth rate effects in the matter power spectrum. This\nis especially true at high masses where the Lagrangian bias dominates. The change in\nthe linear growth function above $(D^\\uparrow)$ versus below $(D^\\downarrow)$ the Jeans\nscale leads to a step in the linear matter power spectrum of approximately\n\\begin{equation}\n\\label{eq:Smm}\nS_{mm} \\equiv \n2 \\frac{D^\\uparrow - D^\\downarrow}{D^\\downarrow}\\,,\n\\end{equation}\nwhereas the step in the cumulative halo power spectrum is\n\\begin{equation}\nS_{hh}\n \\equiv\n 2 \\frac{D^\\uparrow \\bar b_1^\\uparrow - D^\\downarrow \\bar b_1^\\downarrow}{D^\\downarrow \\bar b_1^\\downarrow} \\,.\n \\label{eq:Shh}\n\\end{equation}\nIn \\reffig{phh_ratio}, we show the amplitude of these steps as a function of mass.\nAt the high mass end the halo power spectrum has half the step amplitude of the\nmatter power spectrum.\n\n\nThis result not only confirms that scale-dependent linear growth leads to scale-dependent\nbias, but it does so in a way that both reduces the observability of features in the halo\npower spectrum \\cite{LoVerde:2014pxa} and violates principles that underlie simple models\nfor bias. It is commonly assumed that the statistics of halos at any observation epoch is\ndetermined solely by the statistics of the linear density field at a single epoch, and\nhence bias is scale-free with respect to the matter power spectrum at that epoch. For\nmodels with scale-dependent growth, this epoch is commonly taken to be the initial epoch\nfor models where the growth becomes scale-free during matter domination \\cite{Parfrey:2010uy}.\n\n\nFor example in the excursion set, Lagrangian bias is given by the conditional probability\nthat the initial density field crosses some barrier at a smoothing scale $R_S$ corresponding\nto the mass $M$ at the background density given that it takes the value $\\delta_m$ at some\nlarger scale $R_L$ via a random walk between the two. For our quintessence case where these\nscales are arbitrarily well separated by our SU assumption, the lack of correlation in the\nGaussian random initial conditions between these scales means that halo bias is local in the\ninitial density field. Specifically, the conditional probability cannot depend on steps in\nthe random walk with $R>R_L$, and hence whether $\\delta_m$ was achieved from super-Jeans or\nsub-Jeans scale fluctuations. This holds regardless of the shape of the barrier, its dependence\non the redshift of observation, or some putative intermediate epoch of halo formation. As\na result, Lagrangian halo bias should be scale-free with respect to the matter power spectrum\nat the initial epoch.\n\n\nAs emphasized in Ref.~\\cite{Parfrey:2010uy}, even if the Lagrangian bias is scale-free with\nrespect to the initial power spectrum, it becomes scale dependent with respect to the matter\npower spectrum at the observation epoch. This effect is solely due to the scale-dependent\ngrowth in the latter, and hence the scale dependence takes a simple and specific form\n$b_1^{L\\uparrow} = (D^\\downarrow\/D^\\uparrow) b_1^{L\\downarrow}$. In \\refapp{bias_model},\nwe review this construction in more detail. In the quintessence model this means that the\nstep in the halo power spectrum should be absent when the Lagrangian bias dominates\n$\\lim_{M\\rightarrow \\infty}S_{hh}=0$ (see \\refeq{Shh}). Our results significantly violate\nthis prediction.\n\n\nSimilarly models of halo bias that rely on a universal mass function ansatz, characterize\nthe bias as its derivative with respect to $\\delta_m$ at the observation epoch. If this\nderivative depends {\\it only} on the local density field $\\delta_m$ at the observation epoch,\nfor example by assuming a change in the spherical collapse threshold $d\\delta_c\/d\\delta_m=-1$\n(see \\refapp{bias_model}), then the Lagrangian bias would be local and hence scale-free with\nrespect to the matter power spectrum at the observation epoch. Our results for scale-dependent\nbias directly violate this prediction and are essentially half-way between these two extreme\nmodels. We find that halo bias is nonlocal in time and cannot be characterized by the statistics\nof the density field at a single epoch, initial or observed when there is scale-dependent\nlinear growth.\n\n\nIn \\refapp{bias_model}, we show that encapsulating the dependence on the growth history of\n$\\delta_m(a)$ through its impact on the spherical collapse threshold at the observation epoch\nand assuming a universal mass function characterizes the quintessence SU simulation results\nbetter than either of these simplistic models. However given the assumptions underlying this\ntype of modeling, its validity in other contexts should be tested directly in simulations.\n\n\n\\section{Discussion}\n\\label{sec:discussion}\n\n\nQuintessence dark energy provides an arena to explore the response of small-scale\nobservables to the amplitude, scale, and growth history of long-wavelength fluctuations\nwith the separate universe technique. In the presence of quintessence fluctuations,\nthe growth of long-wavelength fluctuations differ above and below its Jeans scale. \nWe verify that even below the Jeans scale, where a naive separate universe picture\ndoes not strictly apply because the local curvature evolves due to non-gravitational\nforces which keep the quintessence smooth, the response of small-scale observables\ncan still be accurately modeled by a modified expansion history alone. One implication\nof this finding is that halo bias is not directly a response of halo number density\nto the local curvature, but rather to the local expansion history.\n\n\nUsing this technique, we show that in the presence of the scale-dependent growth,\nthe local power spectrum and halo mass function acquire a dependence on the scale\nof the long-wavelength mode. Equivalently, the squeezed bispectrum and halo bias\nbecome scale dependent. To our knowledge, our results are the first verification\nof scale-dependent bias from scale-dependent growth using simulations. Moreover\nthey violate predictions of models where bias is effectively local in the density\nfield at a single epoch, initial or observed, and show that halo bias is temporally\nnonlocal. Likewise the nonlinear matter power spectrum cannot simply be a function\nof the linear power spectrum at the same epoch.\n\n\nSpecifically, we use the separate universe (SU) technique to perform $N$-body\nsimulations in the sub-Jeans and super-Jeans SUs. By differencing pairs of\noverdense and underdense SU simulations with the same Gaussian realizations\nof initial phases, much of the sample variance is canceled, and so we can\nprecisely characterize the responses of the power spectrum (which is equivalent\nto the squeezed-limit bispectrum) and the halo mass function (which gives the\nlinear halo bias) to the long-wavelength matter fluctuation.\n\n\nWe validate the SU approach by comparing to perturbation theory predictions\nfor the power spectrum response in both the super-Jeans and sub-Jeans limits\n(see \\reffigs{dlnpk_linear}{dlnpk_1loop}). Since it is the sub-Jeans limit\nwhere the SU technique might naively fail, we further test it with direct\nsimulations that possess long-wavelength matter modes in big-box simulations\nwith smooth dark energy. We find that the squeezed-limit position-dependent\npower spectrum measured from the big-box simulations agrees with the power\nspectrum response to the resolution limit $k\\sim1\\,{\\rm Mpc}^{-1}$. Similarly,\nthe clustering bias is statistically consistent with the response bias across\ntwo decades in halo mass ($\\sim\\!10^{13}\\!-\\!10^{15}\\,M_\\odot$). Thus, with\nthe SU technique verified into the nonlinear regime, we can robustly assess\nthe scale-dependence of the power spectrum and halo density responses across\nthe Jeans scale without costly simulations that include quintessence clustering.\n\n\nWe show that for both responses there is a statistically significant distinction\nbetween sub-Jeans and super-Jeans SUs at $z=0$. More precisely, the power spectrum\nresponse in the super-Jeans SU is roughly 2\\% smaller than that in the sub-Jeans\nSU for $k\\lesssim1\\,{\\rm Mpc}^{-1}$; the halo bias in the super-Jeans SU is roughly\n1\\% and 3\\% smaller than that in the sub-Jeans SU for halo mass of $2 \\times 10^{13}$\nand $2\\times 10^{15}~M_\\odot$ respectively. The fact that the response is smaller\nin the super-Jeans SU is because quintessence enhances the growth of matter\nfluctuations there, and so the super-Jeans overdensity was smaller in the past.\nThese key SU results, along with the comparison to the global simulations,\nare summarized in \\reffig{ibn} and \\reffig{b_clus}.\n\n\nMore generally, this dependence on the growth history of the long wavelength fluctuation\nindicates that the response of small scale observables is nonlocal in time. In particular,\nthe statistically significant difference between sub-Jeans and super-Jeans response biases\nmeasured in our SU simulations falsifies the standard Lagrangian picture where the statistics\nof halos at any observation epoch is determined solely by the statistics of the linear density\nfield at a single epoch.\n\n\nThese effects are in principle important for interpreting observational tests of quintessence\nclustering from galaxy surveys and their cross correlation with the CMB (e.g. \\cite{Bean:2003fb,Hu:2004yd}).\nIn particular, the step feature in the halo power spectrum is smaller by up to a factor of 2\ncompared with the matter. However these corrections, while significant relative to the clustering\neffects on the matter power spectrum itself, are small in an absolute sense for observationally\nviable dark energy equations of state (i.e. $w_Q\\approx-1$) in the absence of quintessence\nisocurvature fluctuations \\cite{Gordon:2004ez,Hu:2016ssz}. \n\n\nOn the other hand, the same technique which has been validated here using quintessence,\ncan be applied to more observationally viable cosmological models, such as those with\nmassive neutrinos. Massive neutrinos cluster with dark matter on large scales, but their\nfree streaming sets an effective Jeans scale. This would generate not only a feature in\nthe two-point function of the total matter \\cite{Lesgourgues:2006nd}, but also influence\nthe high-order statistics \\cite{Shoji:2009gg,Blas:2014hya,Fuhrer:2014zka,Levi:2016tlf}\nas well as the halo bias \\cite{LoVerde:2014pxa}. We intend to apply the SU technique\nto study how massive neutrinos affect the small-scale structure formation in a\nfuture work.\n\n\n\\acknowledgements\nWe thank Eiichiro Komatsu and Fabian Schmidt for useful discussions.\nWe would also like to thank Alexander Knebe for guiding us to implement the dark energy model into Amiga Halo Finder. \nWH thanks the Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1066293, \nwhere part of this work was completed.\nResults in this paper were obtained using the high-performance computing system\nat the Institute for Advanced Computational Science at Stony Brook University\nand with the computation and storage resources\nprovided by the University of Chicago Research Computing Center.\nCC and ML are supported by grant NSF PHY-1316617.\nWH was supported by U.S.~Dept.\\ of Energy contract DE-FG02-13ER41958,\nNASA ATP NNX15AK22G, and the Kavli Institute for Cosmological Physics\nat the University of Chicago through grants NSF PHY-0114422\nand NSF PHY-0551142.}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\newlength{\\szerkol}\n\n\\setlength{\\szerkol}{0.5\\textwidth}\n\n\\begin{figure*}\n\\begin{center}\n\\begin{tabular}{ccc}\n\\hspace{-1.em}\\includegraphics[width=1\\szerkol,clip,viewport=2 150 575 690]{t1png_withsn.ps} &\n\\hspace{-1.5em}\\includegraphics[width=1\\szerkol,clip,viewport=2 150 575 690]{t2png_withsn.ps}\\\\\n\n\\end{tabular}\n\\end{center}\n\\caption{\nLow resolution {\\sc Hi} map\n(the beam size of $72\\arcsec\\times62\\arcsec$)\nsuperimposed on an optical image of M74.\nThe position of {SN\\,2002ap} is marked as the red dot\n(credit: Kamphuis \\& Briggs, 1992, reproduced with permission $\\copyright$ ESO).\nLeft: Zeroth moment map (integrated emission).\nRight:\nFirst moment map (velocity fields).\nThe asymmetric tail with an irregular velocity field is visible at the south-western outskirt of the atomic disc (around the position of $\\mbox{R.A.}=1^h 33^m20^s$, $\\mbox{Dec.}=15^\\circ 25^m$).\nNorth is up and east is to the left.\n}\n\\label{fig:lowres}\n\\end{figure*}\n\n\n\\setlength{\\szerkol}{0.3\\textwidth}\n\n\\begin{figure*}\n\\begin{center}\n\\begin{tabular}{ccc}\n\\includegraphics[width=\\szerkol,clip]{M74_HI_NA_mom0.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_HI_NA_mom1.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_HI_NA_mom2.eps} \\\\\n\\includegraphics[width=\\szerkol,clip]{M74_HI_RO_mom0.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_HI_RO_mom1.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_HI_RO_mom2.eps} \\\\\n\\includegraphics[width=\\szerkol,clip]{M74_CO21_mom0.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_CO21_mom1.eps} & \n\\end{tabular}\n\\end{center}\n\\caption{Gas distribution in M74. \nTop and middle: \n{\\sc Hi} data with a resolution of \n$11.9\\arcsec\\times9.3\\arcsec$ and\n$6.9\\arcsec\\times5.6\\arcsec$, respectively \\citep{walter08}. Bottom: CO(2-1) data with a resolution of $13.4\\arcsec$ \\citep{leroy09}. \nLeft: Zeroth moment maps (integrated emission).\nMiddle:\nFirst moment maps (velocity fields) relative to $z=0.00219$ (656.545\\,{\\mbox{km\\,s$^{-1}$}}).\nRight: Second moment maps (velocity dispersion). The positions of SNe are marked by grey circles. The lines outline the main spiral arm. \nEach panel is 10{\\arcmin} per side. North is up and east is to the left.\n}\n\\label{fig:image}\n\\end{figure*}\n\n\\begin{figure*}[t]\n\\begin{center}\n\\begin{tabular}{ccc}\n\\includegraphics[width=\\szerkol,clip]{M74_UVW2_cont.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_Ha_cont.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_36_cont.eps} \\\\\n\\includegraphics[width=\\szerkol,clip]{M74_80_cont.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_160_cont.eps} & \n\\includegraphics[width=\\szerkol,clip]{M74_31_cont.eps} \\\\\n\\end{tabular}\n\\end{center}\n\\caption{Ultraviolet, H$\\alpha$, mid-IR, far-IR, and radio images of M74.\nThe positions of SNe are marked by grey circles. The lines outline the main spiral arm.\nEach panel is 10{\\arcmin} per side. North is up and east is to the left.\n}\n\\label{fig:image2}\n\\end{figure*}\n\nThe nature of various types of supernovae (SNe) carry crucial information about stellar evolution.\nA subclass of SNe with no hydrogen, helium, or silicon lines in the spectrum (known as type Ic) are believed to be explosions of stars born with very high masses.\nThose exhibiting broad emission lines, indicating high velocities of the ejected material (up to a few $10^4\\,\\mbox{km\\,s$^{-1}$}$), are called `hypernovae' or SNe type Ic-BL (broad lined). Some of these SNe also show relativistic ejecta, for example SN\\,1998bw \\citep[GRB\\,980425][]{galamanature} and 2009bb \\citep{soderberg10}. These relativistic features are interpreted as a jet, and indeed some Ic-BL SNe have been associated with gamma-ray bursts (GRBs; \\citealt{hjorthsn}).\n\nObservations of atomic and molecular gas (through 21\\,cm {\\sc Hi} and carbon monoxide [CO] lines, respectively) in host galaxies of GRBs and SNe have recently been used to learn about the nature of the explosions themselves, as well as the star formation event during which their progenitors were born.\n\\citet{michalowski15hi,michalowski16,michalowski18} and \\citet{arabsalmani15b,arabsalmani19} showed that GRBs and a relativistic SN type Ic-BL exploded close to the most {\\sc Hi}-rich region of their hosts, which was interpreted as being the result of a recent gas accretion or a galaxy merger.\nWhile this conclusion was based on very small samples, if substantiated it would have important consequences for our understanding of the conditions necessary for such explosions, as well as for triggering star formation in general. This motivates us to study the gas properties of another Ic-BL SN (to date, amongst the hosts of Ic-BL SNe, atomic gas was studied in only one case;\n\\citealt{michalowski18}).\n\n{SN\\,2002ap} \\citep{nakano12circ} \nexploded $\\sim4.7\\arcmin$ ($\\sim12.7$\\,kpc)\nfrom the centre of \\object{Messier 74} (\\object{M74} or \\object{NGC\\,628}) \nand was classified as a type Ic-BL \\citep{mazzali02,kinugasa02,\ngalyam02,foley03%\n}. Its estimated progenitor mass is $20$--$25\\,\\mbox{$M_\\odot$}$, lower than other hypernovae, including SN\\,1998bw \\citep{mazzali02}. {SN\\,2002ap} was also shown to have only modest relativistic ejecta, and hence no detectable jet \\citep{berger02}. The progenitor was proposed to be a Wolf-Rayet (WR) star or a massive star in an interacting binary \\citep{smartt02,wang03%\n}.\n\nM74 has hosted three other known SNe: 2003gd (type IIP; $\\sim2.7\\arcmin$ or $\\sim7.3$\\,kpc from the galaxy centre; \\citealt{hendry05}), 2013ej (IIP; $\\sim2.2\\arcmin$ or $\\sim5.9$\\,kpc; \\citealt{valenti14}), and 2019krl (IIn; $1.9\\arcmin$ or $\\sim5.2$\\,kpc; \\citealt{ho19rep,\nandrews19atel}). The progenitor of SN\\,2003gd was confirmed to be an M-type supergiant with a mass of $\\sim8\\,\\mbox{$M_\\odot$}$,\nby examining the SN position in pre- and post-explosion images, which revealed that this star was missing in the latter\n\\citep{maund09,vandyk03,smartt04}. In a similar way, the mass of the progenitor of SN\\,2013ej was estimated to be $8.0$--$15.5\\,\\mbox{$M_\\odot$}$\\citep{fraser14,mauerhan17}.\n \nThe objectives of this paper are to {\\it i}) test whether {SN\\,2002ap} and other SNe in M74 were born in concentrations of gas indicating a recent gas accretion, and {\\it ii}) investigate what this tells us about the formation of SN progenitors.\nWe adopt a redshift of M74 of $z=0.00219$ \\citep{lu93}, a distance of 9.4\\,Mpc, and a corresponding scale of 2.7 kpc arcmin$^{-1}$.\nThis assumes a cosmological model with $H_0=70$ km s$^{-1}$ Mpc$^{-1}$, $\\Omega_\\Lambda=0.7$, and $\\Omega_{\\rm m}=0.3$.\n\n\n\n\n\n\\section{Selection and data}\n\\label{sec:data}\n\n\n\n{The supernova SN\\,2002ap} and its host galaxy M74 were selected as part of a larger study of gas in SN hosts (Gotkiewicz \\& Micha\\l owski, in prep.). We investigated all known SNe up to Aug 2018 with redshifts $z<0.1$ from the Open Supernova Catalog\\footnote{{\\tt https:\/\/sne.space}} \\citep{snespace} and searched the NASA\/IPAC Extragalactic Database (NED) for {\\sc Hi} data for their hosts. {SN\\,2002ap} was the only SN type Ic-BL that satisfied these criteria.\n\nThe archival {\\sc Hi} and CO data for M74 are shown in Figs.~\\ref{fig:lowres} and \\ref{fig:image}, while the rest of the data are shown in Fig.~\\ref{fig:image2}.\n{\\sc Hi} data are from \\citet[][a resolution of $72\\arcsec\\times62\\arcsec$]{kamphuis92} and The {\\sc Hi} Nearby Galaxy Survey \\citep[THINGS;][a resolution of $11.9\\arcsec\\times9.3\\arcsec$ and\n$6.9\\arcsec\\times5.6\\arcsec$]\n{walter08}. The CO(2-1) data are from the HERA CO Line Extragalactic Survey \\citep[HERACLES;][a resolution of $13.4\\arcsec$]{leroy09}. The H$\\alpha$ image is from \\citet{marcum01}. In addition, we use the following continuum data: \nthe {\\it Swift} \\citep{swift,uvot} \nUVW2 0.2\\,{\\mbox{$\\mu$m}} image \\citep{brown14};\n{\\it Spitzer} \\citep{spitzer,irac} \n3.6 and 8.0\\,{\\mbox{$\\mu$m}} images \\citep{dale09},\n {\\it Herschel}\n\n\\citep{herschel,pacs} \n160\\,{\\mbox{$\\mu$m}} image \\citep{kingfish}, and\nNational Science Foundation's (NSF's) Karl G. Jansky Very Large Array (VLA) 3.1\\,GHz image \\citep{mulcahy17}.\n\n\n\n\\section{Results}\n\\label{sec:results}\n\nAccording to \\citet{kamphuis92}, M74 harbours an off-centre asymmetric {\\sc Hi} tail, located \non the south-western outskirts of the galaxy, outside the optical disc\n(Fig.~\\ref{fig:lowres}). The feature is detected over 20{\\arcmin} (55\\,kpc) and contains \n$\\log(M_{\\rm HI}\/\\mbox{$M_\\odot$})=8.95$, or 7.5\\% of the total atomic gas of M74. {SN\\,2002ap} is located \nwhere this feature connects with the symmetric disc of M74.\nThe velocity pattern of the feature is irregular \n(Fig.~\\ref{fig:lowres}).\nThis gas does not follow the overall rotation of the gas disc, as evidenced by both negative and positive velocity residuals from the disc models at this location presented in Figs.~8 and 9 of \\citet{kamphuis92}. \n\nThe higher resolution THINGS data are not sensitive to such large scales, but allow detailed investigation of the local environments of SNe.\nAt this resolution, the position of {SN\\,2002ap} is not associated with any strong concentration of atomic or molecular gas (Fig.~\\ref{fig:image}).\n{SN\\,2002ap} is located $\\sim80\\arcsec$ (3.6\\,kpc) south-west of the main spiral arm running from the south to the west of the galaxy (marked as a curved region on Figs.~\\ref{fig:image} and \\ref{fig:image2}, traced clearly on all images up to the southernmost point, and by the {\\sc Hi} and 3.1\\,GHz images to the west) \nand $\\sim20\\arcsec$ (0.9\\,kpc) from \na bright {\\sc Hi} knot to the north. The main spiral arm in the south is also visible in the {\\sc Hi} velocity map in which \nthe integrated mean velocities show a larger deviation from the systemic velocity in the interarm regions.\n\nMoreover, {SN\\,2002ap} exploded away from regions of significant star formation activity and very little emission is present at its position at any wavelength (Fig.~\\ref{fig:image2}). There is, however, a faint star-forming region visible in the UV $\\sim4\\arcsec$ (180\\,pc) away from the SN position (this is not the object $10\\arcsec$ away mentioned by \\citealt{crowther13}).\n{SN\\,2002ap} is also outside the CO disc.\n\nThe other three type II SNe in M74 exploded along the most prominent spiral arm (running from the east to south of M74; curved regions on Figs.~\\ref{fig:image} and \\ref{fig:image2}), but are displaced from the arm towards the outside \nby $\\sim25\\arcsec$ ($\\sim1$\\,kpc). This is especially evident in the H$\\alpha$, $3.6\\,\\mbox{$\\mu$m},$ and CO images. SN\\,2013ej and 2019krl exploded in interarm regions with very little {\\sc Hi}, 8, 160\\,{\\mbox{$\\mu$m},} and 3.1\\,GHz emission. All three type II SNe exploded in regions with undetectable CO emission.\n\nWe have investigated the large-scale environment of M74. Within 150\\,kpc (55{\\arcmin}) and $\\pm500\\,\\mbox{km\\,s$^{-1}$}$ from M74 (velocity of $627\\,\\mbox{km\\,s$^{-1}$}$), NED lists three galaxies:\nUGC\\,1171,\nUGC\\,1176,\nboth to the east,\nand the much fainter SDSS J013800.30+145858.1\nto the south. \nAll of them are detected in {\\sc Hi} by the Arecibo Legacy Fast ALFA Survey (ALFALFA; \\citealt{haynes18}). In Table~\\ref{tab:other} we list their properties.\n\nIn the ALFALFA catalogue within 200{\\arcsec} (540\\,kpc) of M74 there are in total 13 galaxies. All but one have atomic gas masses $7<\\log(M_{\\rm HI}\/\\mbox{$M_\\odot$})<9$. Only NGC\\,660, 426\\,kpc away, with $\\log(M_{\\rm HI}\/\\mbox{$M_\\odot$})=9.59$ has a mass comparable to that of M74. \nThe positions of these galaxies are shown on Fig.~\\ref{fig:comp}.\n\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=0.5\\textwidth,clip]{companion.eps} \n\\end{center}\n\\caption{Large-scale environment around M74 within $\\pm500\\,\\mbox{km\\,s$^{-1}$}$. The width of the panel is 400{\\arcmin} ($\\sim1$\\,Mpc). Circles show the sizes of larger galaxies: 20{\\arcmin} for M74, 10{\\arcmin} for NGC\\,660, 5{\\arcmin} for UGC\\,1176, and 1.4{\\arcmin} for UGC\\,1171 (a small circle next to UGC\\,1176). Crosses show the positions of additional galaxies detected at the {\\sc Hi} line by ALFALFA \\citep{haynes18}. \n}\n\\label{fig:comp}\n\\end{figure}\n\n\\begin{table*}\n\\caption{Properties of galaxies in the vicinity of M74 from the ALFALFA survey \\citep{haynes18}.}\n \\centering\n \\begin{tabular}{lrccrccccc}\n \\hline\\hline\n Galaxy & ID & RA & Dec & \\multicolumn{2}{c}{Dist$_{\\rm M74}$} & $z$ & $V_{\\rm helio}$ & $f_{HI}$ & $\\log(M_{\\rm HI})$ \\\\ \n & & (deg) & (deg) & ($'$) & (kpc) & & ($\\mbox{km\\,s$^{-1}$}$) & (Jy\\,$\\mbox{km\\,s$^{-1}$}$) & ($\\mbox{$M_\\odot$}$) \\\\\n \\hline\n M74 & 1149 & 24.18500 & 15.79222 & $\\cdots$ & $\\cdots$ & 0.002190 & 657 & $424.30\\pm0.18$ & $9.73\\pm0.1\\phantom{0}$ \\\\\n UGC\\,1171 & 1171 & 24.93708 & 15.89611 & 44.6 & 120 & 0.002463 & 738 & $\\phantom{10}2.07\\pm0.05$ & $7.42\\pm0.09$ \\\\\n UGC\\,1176 & 1176 & 25.03167 & 15.90167 & 50.6 & 137 & 0.002103 & 630 & $\\phantom{1}31.22\\pm0.06$ & $8.78\\pm0.1\\phantom{0}$ \\\\\n SDSSJ013\n & 112503 & 24.50583 & 14.99333 & 51.6 & 139 & 0.002478 & 743 & $\\phantom{10}0.56\\pm0.05$ & $7.14\\pm0.2\\phantom{0}$ \\\\\n NGC\\,660 & 1201 & 25.76167 & 13.64000 & 157.9 & 426 & 0.002830 & 848 & $148.39\\pm0.14$ & $9.59\\pm0.34$ \\\\\n \\hline\n \\end{tabular}\n \\label{tab:other}\n \\tablecomments{The columns show the galaxy name (that of SDSSJ013800.30+145858.1 has been abbreviated), ALFALFA ID, position, projected distance to M74, redshift, heliocentric velocity, {\\sc Hi} flux, and the atomic gas mass.}\n\\end{table*}\n\n\n\n\\section{Discussion}\n\\label{sec:discussion}\n\nThe estimated mass of the progenitor of {SN\\,2002ap} implies that it was formed 3.2--5.6\\,Myr before the explosion, whereas the progenitors of \nSN\\,2003gd and 2013ej\nwere formed 10--55\\,Myr before the explosions \n(assuming a main-sequence lifetime of $10^{10}\\,\\mbox{yr} \\times [M\/\\mbox{$M_\\odot$}\\mbox{$]$} ^{-2.5}$; \\citealt{kippenhahn90}).\nThis lifetime for {SN\\,2002ap} agrees with the single-progenitor estimate of \\citet[][5\\,Myr]{zapartas17}, but is lower than the binary-progenitor estimate (20\\,Myr). Similarly, \\citet{maund18} obtained an age of 15\\,Myr based on the analysis of stars in the vicinity of {SN\\,2002ap}.\n\n\\subsection{Type Ic-BL {SN\\,2002ap}}\n\n{The supernova SN\\,2002ap} is the fourth known explosion of a presumably massive star located close to a concentration of atomic gas. Similarly to {SN\\,2002ap}, GRB\\,060505 and SN\\,2009bb were both located close to {\\sc Hi} extensions, whereas GRB\\,980425 was located close to the most significant {\\sc Hi} concentration \\citep{michalowski15hi,michalowski18,arabsalmani15b,arabsalmani19}.\n\nWe can assess the statistical significance of the associations of the {\\sc Hi} off-centre concentrations with the GRB and SN positions by investigating the probability of four GRBs and SNe exploding by chance in the quadrants of their hosts at which these {\\sc Hi} concentrations are located. For a given explosion this probability is $0.25$, so for four out of four analysed cases this is $(0.25)^4\\sim0.004$. This corresponds to $\\sim3\\sigma$, so the associations are unlikely to be random, but a larger sample is needed to confirm this result.\n\nThe case of {SN\\,2002ap} adds to \nthe hypothesis put forward in \\citet{michalowski15hi} that the progenitors of these explosions are born when atomic gas is accreted from the intergalactic medium. Indeed, \\citet{kamphuis92} concluded that the south-western {\\sc Hi} extension in M74 is a result of an accretion event \nbecause it has not settled yet, and is inconsistent with the rotation of the gas disc. This extension may be the gas flowing in and feeding star formation directly, or distorting gas on the outskirts of the optical disc of the galaxy leading to star formation at the position of {SN\\,2002ap}.\n\nThe asymmetric tail in M74 has a mass of $\\log(M_{\\rm HI}\/\\mbox{$M_\\odot$})=8.95$. This is comparable to the mass of the most massive galaxy within 150\\,kpc (UGC\\,1176) and only a factor of four less than the atomic gas mass of NGC\\,660.\nThe sum of atomic masses of all galaxies within 200{\\arcmin} (540\\,kpc) excluding NGC\\,660 is $\\log(M_{\\rm HI}\/\\mbox{$M_\\odot$})=9.36$.\nHence, the tail in M74 might have come from UGC\\,1176 if it was significantly distorted by the interaction and lost half of its gas, or from NGC\\,660.\nIt could also be a remnant of a galaxy similar to UGC\\,1176 that has been accreted entirely (as postulated by \\citealt{kamphuis92}), or a result of the accretion of intragroup medium.\n\nIn principle this tail could be a remnant of a tidal feature created by interaction with these galaxies, but we found this interpretation unlikely. First, the tail does not resemble recent tidal features, whereas an older feature would wind almost symmetrically around the galaxy. Second, simulations shows that tidal tails are created on both sides of interacting galaxies \\citep{hopkins06b,hayward12,hayward14,pettitt16,oh16}, whereas M74 does not have such feature on the other side.\nWe also note that the asymmetric nature of M74, with the southern arm being stronger than the northern one (Fig.~\\ref{fig:image}), could be a result of interaction with the UGC\\,1176\/1171 pair.\n\n\nThe only possible counter-example of a potential explosion of a massive star without an associated gas concentration is the enigmatic transient AT\\,2018cow, whose host galaxy does not show such off-centre asymmetric {\\sc Hi} features \\citep{michalowski19} and possibly only a gas ring \\citep{roychowdhury19}. \nSuch an {\\sc Hi} ring would also be apparent for M74 if it was further away so the sensitivity and resolution were poorer, because the central part is devoid of atomic gas, likely due to conversion to the molecular phase (Fig.~\\ref{fig:image}).\nTo demonstrate this we smoothed the {\\sc Hi} VLA image with a Gaussian with a full width half maximum of 100{\\arcsec} (4.5\\,kpc; Fig.~\\ref{fig:smooth} in the appendix). At this resolution the spiral structure of M74 resembles an irregular ring, similar to that detected for the AT\\,2018cow host (for which the resolution was around 2\\,kpc).\nHowever, the nature of AT\\,2018cow is not clear, so it may not be connected with the explosion of a massive star\n(\\citealt{prentice18,liu18,kuin19,perley19,soker19,lyutikov19,bietenholz20}, but see \\citealt{prentice18,riverasandoval18,margutti19,fox19,huang19}).\n\nIt is unlikely that the lack of molecular gas or star formation at the position of {SN\\,2002ap} is due to the progenitor being kicked out of a star-forming region. The velocities of runaway stars are up to 200\\,{\\mbox{km\\,s$^{-1}$}} \\citep{blaauw93,%\nhoogerwerf01,%\neldridge11%\n}, which corresponds to 1\\,kpc per 5\\,Myr. This is only $\\sim20\\arcsec$ at the distance of M74, so not sufficient to move the birth place of the {SN\\,2002ap} progenitor to any place of significant star formation or CO concentration.\nThis is true even if the lifetime of the progenitor is three to four times longer (15--20\\,My; \\citealt{zapartas17}, \\citealt{maund18}).\nCO deficiency at GRB positions was also claimed by \\citet{hatsukade14}, \\citet{stanway15}, and \\citet{michalowski16}, but \\citet{perley17}, \\citet{michalowski18co}, and \\citet{arabsalmani18} suggest an alternative. If the lack of molecular gas is confirmed for a larger sample of type Ic-BL SNe, this would support the hypothesis of {\\sc Hi}-fuelled star formation giving rise to the birth of their progenitors \\citep{michalowski15hi}.\n\n\n\nIt is unlikely that the {SN\\,2002ap} progenitor moved to its explosion position due to a random kick.\nAssuming a lifetime of 5\\,Myr, the {SN\\,2002ap} progenitor could not be born in the main arm, as the required velocity is 700\\,{\\mbox{km\\,s$^{-1}$}} to cross 3.6\\,kpc. Even the closest bright {\\sc Hi} knot \nto the north\nis likely too far to be the birthplace, as this would require a velocity of 175\\,{\\mbox{km\\,s$^{-1}$}}. \nSuch velocity kicks are at the high end for runaway stars \\citep{hoogerwerf01}. For the longer lifetime estimates of 15--20\\,Myr, the required velocities from the spiral arm would be 235--175\\,\\mbox{km\\,s$^{-1}$}, so still too high for the {SN\\,2002ap} progenitor to have been born there. However, in such a case it is feasible that it was born in the closest bright {\\sc Hi} knot to the north,\nas this would require velocities of 60--40\\,\\mbox{km\\,s$^{-1}$}.\n\n\\subsection{Type II SN\\,2003gd, 2013ej, and 2019krl}\n \nAll type II SNe in M74 \nare not located close to the {\\sc Hi} extension, so are unlikely connected to gas accretion.\nThey are located at the outside edge of a spiral arm. This can be explained by either of two scenarios: \nby a gas density build-up and shock scenario at the edge of the arm, or by SN progenitors moving away from the arm during their lifetimes.\n\nThe first possibility is that the SN progenitors are born when gas is piling up and shocked at the edge of the arm when gas clouds are being swept up by the arm, as explained by the spiral density wave theory (\\citealt{shu16}). This is similar to the hypothesis presented in \\citet{michalowski14} that GRB progenitors are preferentially born in high-density gas.\nWe note that the amount of gas piling up at the edge of the arm giving rise to the birth of SN progenitors cannot be large, because the concentration is not detected with {\\sc Hi} or CO observations. \n\nThe SNe in M74 are on the outside of the spiral arm, so this scenario is only valid if they are outside the corotation radius (where the orbital velocity is equal to the spiral pattern speed), so the arm is catching up with gas that is moving slower \\citep{shu16}. \n\\citet{aramyan16} found that core-collapse SNe are indeed shifted towards the outside edge of spiral arms as long as they are outside the corotation radius.\nUnfortunately the accuracy of the estimate of the corotation radius for M74 is not sufficient to test this. The corotation radius given by \\citet{scarano13}\\footnote{$4.6\\pm1.2$\\,kpc and their adopted scale of 1.95 kpc arcmin$^{-1}$.} is $(2.4\\pm0.6)\\arcmin$, corresponding to $(6.4\\pm1.7)\\,$kpc with our adopted distance, so it cannot be established whether type II SNe are inside or outside this radius. \n\\citet{karapetyan18} quoted a slightly lower (but consistent within errors) value of the corotation radius\\footnote{The ratio of the corotation and isophotal ($R_{25}=5.52\\arcmin$) radii of $0.34\\pm0.09$.} of $(1.9\\pm0.5)\\arcmin$, concluding that SNe\\,2003gd and 2013ej are indeed outside the corotation radius, supporting the spiral density wave scenario for their formation.\nThis scenario cannot explain the birth of the {SN\\,2002ap} progenitor, because gas should not pile up so far away (3.6\\,kpc; Fig.~\\ref{fig:image}) from the spiral arm.\n\n\nHowever, the region of increased gas density and shocks, predicted by the spiral density wave theory, does manifest itself with increased star formation, and therefore more intense UV and H$\\alpha$ emission. These main sites of star formation are where SN progenitors should be born, not 1\\,kpc away. This scenario does not explain this discrepancy.\n\nThe second possibility is that SN progenitors are in fact born in the spiral arm, but, due to their orbital motions, move away before they explode. \nInside the corotation radius stars (and gas) are moving faster than the spiral pattern, so are constantly drifting towards the outside of the arm. \nThis means that SNe with progenitors with long enough lifetimes should be happening preferentially outside the arm. On the other hand, H$\\alpha$ emission is dominated by stars born very recently (i.e. after the SN progenitors), which have therefore not yet moved away from the arm.\n\nThe lifetimes of type II progenitors imply that they would need to have (reasonable) velocities of $18$--$100\\,\\mbox{km\\,s$^{-1}$}$ with respect to the arm to cross 1\\,kpc from the arm to their current position.\nWe note that this velocity is not due to any random-direction kick, but is the orbital velocity minus the spiral pattern speed.\n\n\n\n\n\nThe orbital migration scenario requires that type II SNe progenitors are inside the corotation radius, so their orbital motion is faster than the spiral pattern and so they can leave it on the outside edge \\citep[e.g.][]{aramyan16}.\nThis effect is also visible in M51 where younger stellar clusters have a distribution that has the strongest correlation with the distribution of star formation, and they are shifted towards the outside of the arm inside the corotation radius \\citep{scheepmaker09}.\n\nThis scenario cannot explain the position of {SN\\,2002ap}, because it is located securely outside the corotation radius, so cannot leave the spiral arm at the outside edge. Instead, as we discuss above, it may\nbe connected with the gas accretion visible in the {\\sc Hi} map of \\citet{kamphuis92}.\n\nThe position of SNe away from the main sites of star formation (spiral arms) is not unique to M74. We investigated galaxies with four or more core-collapse SNe and with well-separated spiral structures from the list of \\citet{thone09}. NGC\\,6946 and 4303 hosted nine and six type II SNe, respectively, and only one (1948B) and two (1999gn and 2006ov) exploded in spiral arms; the rest exploded in interarm regions or outside the detectable stellar disk (Figs.~9 and 11 of \\citealt{anderson13}). \nThis is consistent with the spiral density wave theory and the migration scenario described above.\nIndeed, larger samples of type II SNe show that they do not concentrate in the brightest regions of their hosts \\citep{fruchter06,anderson08,\nleloudas11}.\n\n\\section{Conclusions}\n\\label{sec:conclusion}\n\nWe have used archival {\\sc Hi} and CO data for M74 (not previously investigated in the context of SN positions), together with H$\\alpha$ and continuum images.\n{SN\\,2002ap} is located at the end of an off-centre asymmetric 55\\,kpc-long {\\sc Hi} extension containing 7.5\\% of the total atomic gas of M74. \nIt is the fourth known explosion of a presumably massive star that is located close to the concentration of atomic gas (after GRB\\,980425, 060505, and SN\\,2009bb). \nIt is unlikely that all these associations are random (at a $3\\sigma$ significance), so\nthe case of {SN\\,2002ap} adds to\n the evidence that the birth of the progenitors of type Ic-BL SNe and GRBs is connected with the accretion of atomic gas from the intergalactic medium.\nThe {\\sc Hi} extension could come from tidally disrupted companions of M74, or be a remnant of a galaxy or a gas cloud in the intragroup medium accreted entirely. \n\nThe type II SNe in M74 do not seem to be related to gas accretion.\nThe fact that \nthey\nare located at the outside edge of a spiral arm suggests either that their progenitors are born when gas is piling up there, reaching high density, or that SN progenitors move away from the arm during their lifetimes, due to their orbital motions.\nThis is also similar for NGC\\,6946 and 4303, with eight out of nine and four out of six type II SNe, respectively, located in interarm regions or outside the detectable stellar discs, not in the spiral arms.\n\n\\begin{acknowledgements}\n\n\nWe wish to thank the referee for careful and important suggestions, Joanna Baradziej and Phillip Hopkins for discussion and comments, and Frank Briggs for permission to use the figure from \\citet{kamphuis92}.\n\nM.J.M.~acknowledges the support of \nthe National Science Centre, Poland through the SONATA BIS grant 2018\/30\/E\/ST9\/00208, and of the Polish-U.S. Fulbright Commission.\nJ.H.~was supported by a VILLUM FONDEN Investigator grant (project number 16599).\nP.K.~is partially supported by the BMBF project 05A17PC2 for D-MeerKAT.\nThe National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.\nThis work made use of HERACLES, `The HERA CO-Line Extragalactic Survey'.\nThis research has made use of data obtained from the High Energy Astrophysics Science Archive Research Center (HEASARC), provided by NASA's Goddard Space Flight Center.\nThis work is based on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA.\n{\\it Herschel} is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA.\nThis research has made use of \nthe Open Supernova Catalog (\\url{https:\/\/sne.space});\nNASA\/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration;\nSAOImage DS9, developed by the Smithsonian Astrophysical Observatory \\citep{ds9};\nEdward Wright cosmology calculator \\citep{wrightcalc};\nthe WebPlotDigitizer of Ankit Rohatgi ({\\tt arohatgi.info\/WebPlotDigitizer});\nand NASA's Astrophysics Data System Bibliographic Services.\n\\end{acknowledgements}\n\n\n\\input{ms.bbl}\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nAn \\emph{alternating sign matrix} (ASM) is a square matrix with entries in $\\{0,1,-1\\}$ such that in each row and each column the non-zero entries alternate and sum up to $1$. Robbins and Rumsey introduced alternating sign matrices in the 1980s \\cite{lambda} when studying their \\emph{$\\lambda$-determinant} (a generalization of the classical determinant) and showing that the $\\lambda$-deter\\-mi\\-nant can be expressed as a sum over all alternating sign matrices of fixed size. The classical determinant is obtained from this by setting $\\lambda=-1$, in which case the sum reduces so that it extends only over all ASMs \\emph{without} $-1$'s, i.e., permutation matrices, and the well-known formula of Leibniz is recovered.\nNumerical experiments led Robbins and Rumsey to conjecture that the number of $n \\times n$ alternating sign matrices is given by the surprisingly simple product formula\n\\begin{equation}\n\\label{asm}\n\\prod_{i=0}^{n-1} \\frac{(3i+1)!}{(n+i)!}.\n\\end{equation}\n\n\\medskip\n\nBack then the surprise was even bigger when they learned from Stanley (see \\cite{BrePro99,Bre99}) that this product formula had recently also appeared in Andrews' paper \\cite{And79} on his proof of the weak Macdonald conjecture, which in turn provides a formula for the number of \\emph{cyclically symmetric plane partitions}. As a byproduct, Andrews had introduced \\emph{descending plane partitions}\u00a0and had proven that the number of descending plane partitions (DPPs) with parts at most $n$ is also equal to \\eqref{asm}. Since then the problem of finding an explicit bijection between alternating sign matrices and descending plane partitions has attracted considerable attention from combinatorialists and to many of them it is a miracle that such a bijection has not been found so far. All the more so because Mills, Robbins and Rumsey had also introduced several ``statistics'' on alternating sign matrices and on descending plane partitions for which they had strong numerical evidence that the joint distributions coincide as well, see \\cite{MilRobRum83}.\n\n\\medskip\n\nThere were a few further surprises yet to come. Robbins introduced a new operation on plane partitions, \\emph{complementation}, and had strong numerical evidence that totally symmetric self-complementary plane partitions (TSSCPPs) in a $2n \\times 2n \\times 2n$-box are also counted by \\eqref{asm}. Again this was further supported by statistics that have the same joint distribution as well as certain refinements, see \\cite{MilRobRum86,Kra96,krattsurvey,bianecheballah}. We still lack an explicit bijection between TSSCPPs and ASMs, as well as between TSSCPPs and DPPs.\n\n\\medskip\n\nIn his collection of bijective proof problems (which is available from his webpage) Stanley says the following about the problem of finding all these bijections: ``\\emph{This is one of the most intriguing open problems in the area of bijective proofs.}'' In Krattenthaler's survey on plane partitions \\cite{krattsurvey} he expresses his opinion by saying: ``\\emph{The greatest, still unsolved, mystery concerns the question of what plane partitions have to do with alternating sign matrices.}''\n\n\\medskip\n\nMany of the above mentioned conjectures have since been proved by non-bijective means: Zeilberger \\cite{Zei96a} was the first who proved that $n \\times n$ ASMs are counted by \\eqref{asm}. Kuperberg gave another shorter proof \\cite{Kup96}\u00a0based on the remarkable observation that the \\emph{six-vertex model} (which had been introduced by physicists several decades earlier) with domain wall boundary conditions is equivalent to ASMs, see \\cite{ElkKupLarPro92a,ElkKupLarPro92b}, and he used the techniques that had been developed by physicists to study this model. Andrews enumerated TSSCPPs in \\cite{And94}. The equivalence of certain statistics for ASMs and of certain statistics for DPPs has been proved in \\cite{BehDifZin12,BehDifZin13}, while for ASMs and TSSCPPs see \\cite{Zei96b,FonZin08}, and note in particular that already in Zeilberger's first ASM paper \\cite{Zei96a} he could deal with an important refinement.\nFurther work including the study of \\emph{symmetry classes} has been accomplished; for a more detailed description of this we defer to \\cite{BehFisKon17}. Then, in very recent work, alternating sign triangles (ASTs) were introduced in \\cite{AyyBehFis16}, which establishes a fourth class of objects that are equinumerous with ASMs, and also in this case nobody has so far been able to construct a bijection.\n\n\\medskip\n\nAnother aspect that should be mentioned here is Okada's work \\cite{Oka06}\u00a0(see also \\cite{stro}), which hints at a connection between ASMs and representation theory that has not yet been well understood. He observed that a certain multivariate generating function (a specialization at a root of unity of the partition function that had been introduced by physicists in their study of the six-vertex model) can be expressed---up to a power of $3$---by a single \\emph{Schur polynomial}. Since Schur polynomials are generating functions of semistandard tableaux, this establishes yet another challenging open problem for combinatorialists inclined to find bijections.\n\n\\medskip\n\nThe proofs of the results briefly reviewed above contain rather long and complicated computations, and include hardly any arguments of a combinatorial flavor. In fact it seems that all ASM-related identities for which there exists a bijective proofs are trivial, with the exception of the rotational invariance of fully packed loop configurations. This was proved by Wieland \\cite{wieland} bijectively and is also used in the celebrated proof of the Razumov-Stroganov (ex-)conjecture \\cite{razstrog}.\n\n\n\\medskip\n\nWe come now to the purpose of the current paper. This is the first paper in a planned series that seeks to give the first bijective proofs of several results described so far. The seed of the idea to do so came from a brief discussion of the first author with Zeilberger on the problem of finding such bijections at the AMS-MAA Joint Mathematics Meetings 2019. Zeilberger mentioned that such bijections can be constructed from existing ``computational'' proofs, however, most likely these bijections are complicated. The authors of the current paper agree, in fact the first author gave her ``own'' proof of the ASM theorem in \\cite{Fis06,Fis07,Fis16} and expressed some speculations in this direction in the final section of the last paper. It is also not implausible that a simple satisfactory bijective proof of the ASM theorem does not exist at all. Combinatorialists have failed to find such bijections for decades now, and we may start to ask ourselves why we are not rewarded for these efforts.\n\n\\medskip\n\nThis is how the authors of the current paper decided to work on converting the proof in \\cite{Fis16} into a bijective proof. After having figured out how to actually convert computations and also having shaped certain useful fundamental concepts related to \\emph{signed sets} (see Section~\\ref{sec:ss}), the translation of several steps became quite straightforward; some steps were quite challenging. Then a certain type of (exciting) dynamics evolved, where the combina\\-to\\-rial point of view led to simplifications and other modifications, and after this process the original ``computational'' proof is in fact rather difficult to recognize. For several obvious reasons, we find it essential to check all our constructions with computer code; to name one it can possibly be used to identify new equivalent statistics.\n\n\\medskip\n\nAfter the above mentioned simplifications, it seems that \\emph{signs} seem to be unavoidable. After all, if there would be a simple bijective proof that avoided signs, would it not also be plausible that such a proof could be converted into a simple ``computational'' proof that avoids signs? Such a proof has also not been found so far.\n\n\\medskip\n\nIn the remainder of the introduction we discuss the result that is proved bijectively in this paper, in particular we discuss why signed enumerations seem to be unavoidable from this point of view. We also sketch a few ideas informally before giving rigorous definitions and proofs later on.\n\n\\subsection*{The operator formula}\u00a0\nWe use the well-known correspondence between order $n \\times n$ ASMs and \\emph{monotone triangles} with bottom row $1,2,\\ldots,n$. A \\emph{monotone triangle} is a triangular array $(a_{i,j})_{1 \\le j \\le i \\le n}$ of integers, where the elements are usually arranged as follows\n\\begin{equation}\n\\label{triangle}\n\\begin{array}{ccccccccccccccccc}\n & & & & & & & & a_{1,1} & & & & & & & & \\\\\n & & & & & & & a_{2,1} & & a_{2,2} & & & & & & & \\\\\n & & & & & & \\dots & & \\dots & & \\dots & & & & & & \\\\\n & & & & & a_{n-2,1} & & \\dots & & \\dots & & a_{n-2,n-2} & & & & & \\\\\n & & & & a_{n-1,1} & & a_{n-1,2} & & \\dots & & \\dots & & a_{n-1,n-1} & & & & \\\\\n & & & a_{n,1} & & a_{n,2} & & a_{n,3} & & \\dots & & \\dots & & a_{n,n} & & &\n\\end{array},\n\\end{equation}\nsuch that the integers increase weakly along $\\nearrow$-diagonals and $\\searrow$-diagonals, and increase strictly along rows, i.e.,\n$a_{i,j} \\le a_{i-1,j} \\le a_{i,j+1}$ and $a_{i,j} < a_{i,j+1}$ for all $i,j$ with $1 \\le j < i \\le n$. In order to convert an ASM into the corresponding monotone triangle, add to each entry all the entries that are in the same column above it, and record then row by row the positions of the $1$'s, see Figure~\\ref{ASM-MT} for an example.\n\n\n\\begin{figure}\n$$\n\\left(\n\\begin{matrix}\n0 & 0 & 0 & 1 & 0 & 0\\\\\n0 & 1 & 0 & -1 & 1 & 0 \\\\\n1 & -1 & 0 & 1 & -1 & 1 \\\\\n0 & 1 & 0 & -1 & 1 & 0 \\\\\n0 & 0 & 0 & 1 & 0 & 0 \\\\\n0 & 0 & 1 & 0 & 0 & 0\n\\end{matrix} \\right) \\rightarrow\n\\left(\n\\begin{matrix}\n0 & 0 & 0 & 1 & 0 & 0\\\\\n0 & 1 & 0 & 0 & 1 & 0 \\\\\n1 & 0 & 0 & 1 & 0 & 1 \\\\\n1 & 1 & 0 &0 & 1 & 1 \\\\\n1 & 1 & 0 & 1 & 1 & 1 \\\\\n1 & 1 & 1 & 1 & 1 & 1\n\\end{matrix} \\right) \\rightarrow\n\\begin{array}{ccccccccccc}\n & & & & & 4 & & & & & \\\\\n & & & & 2 & & 5 & & & & \\\\\n & & & 1 & & 4 & & 6 & & & \\\\\n & & 1 & & 2 & & 5 & & 6 & & \\\\\n &1 & & 2 & & 4 & & 5 & & 6 & \\\\\n1 & & 2 & & 3 & & 4 & & 5 & & 6\n\\end{array}\n$$\n\\caption{\\label{ASM-MT} ASM $\\rightarrow$ partial columnsums $\\rightarrow$ monotone triangle}\n\\end{figure}\n\n\\medskip\n\nThe following \\emph{operator formula} for the number of monotone triangles with prescribed bottom row was first proved in \\cite{Fis06} (see \\cite{Fis10,Fis16} for simplifications and generali\\-za\\-tions). Note that we allow arbitrary strictly increasing bottom rows.\n\n\\begin{theorem}\n\\label{operator}\nLet $k_1 < k_2 < \\ldots < k_n$ be a sequence of strictly increasing integers. The number of monotone triangles with bottom row $k_1,\\ldots,k_n$ is\n\\begin{equation}\n\\label{operatorexpr}\n\\prod_{1 \\le p < q \\le n}\u00a0\\left( {\\operatorname{E}}_{k_p} + {\\operatorname{E}}_{k_q}^{-1} - {\\operatorname{E}}_{k_p} {\\operatorname{E}}_{k_q}^{-1} \\right) \\prod_{1 \\le i < j \\le n} \\frac{k_j-k_i+j-i}{j-i},\n\\end{equation}\nwhere ${\\operatorname{E}}_x$ denotes the shift operator, i.e., ${\\operatorname{E}}_x p(x) = p(x+1)$.\\footnote{The formula has to be understood as follows: Take $\\prod_{1 \\le i < j \\le n} \\frac{k_j-k_i+j-i}{j-i}$ and treat the $k_i$'s as indeterminates. Apply $\\prod_{1 \\le p < q \\le n}\u00a0\\left( {\\operatorname{E}}_{k_p} + {\\operatorname{E}}_{k_q}^{-1} - {\\operatorname{E}}_{k_p} {\\operatorname{E}}_{k_q}^{-1} \\right)$ to this polynomial to obtain another polynomial. Only then the $k_i$'s can be specialized to the actual values.}\n\\end{theorem}\n\n\n\nThe purpose of this paper is to provide a bijective proof of Theorem~\\ref{operator}. While the operator formula is an interesting result in its own right, it has also been the main tool for proofs of several results mentioned above. This will be reviewed in the final section of this paper along with indications for future projects on converting also these proofs into bijective proofs.\n\n\\medskip\n\nIn order to be able to construct a bijective proof of Theorem~\\ref{operator}, we need to interpret \\eqref{operatorexpr} combinatorially. Recall that \\emph{Gelfand-Tsetlin patterns} are defined as monotone triangles with the condition on the strict increase along rows being dropped, see \\cite[p.\\ 313]{Sta99} or \\cite[(3)]{gelfand} for the original reference\\footnote{Gelfand-Tsetlin patterns with bottom row $0 \\le k_1 \\le k_2 \\le \\ldots \\le k_n$ are in an easy bijective correspondence with seminstandard tableaux of shape $(k_n,k_{n-1},\\ldots,k_1)$ and entries in $\\{1,2,\\ldots,n\\}$.}. It is well known that the number of Gelfand-Tsetlin patterns with bottom row $k_1 \\le k_2 \\le \\ldots \\le k_n$ is\n\\begin{equation}\n\\label{gelfandtsetlin}\n\\prod_{1 \\le i < j \\le n} \\frac{k_j-k_i+j-i}{j-i},\n\\end{equation}\nwhich is the operand in the operator formula \\eqref{operatorexpr}. Expanding $\\prod_{1 \\le p < q \\le n}\u00a0\\left( {\\operatorname{E}}_{k_p} + {\\operatorname{E}}_{k_q}^{-1} - {\\operatorname{E}}_{k_p} {\\operatorname{E}}_{k_q}^{-1} \\right)$ into $3^{\\binom{n}{2}}$ monomials in ${\\operatorname{E}}^{\\pm 1}_{k_1}, {\\operatorname{E}}^{\\pm 1}_{k_2}, \\ldots, {\\operatorname{E}}^{\\pm 1}_{k_n}$ (keeping a copy for each multiplicity), \\eqref{operatorexpr}\u00a0is a signed enumeration of certain Gelfand-Tsetlin patterns, where each monomial causes a deformation of the bottom row $k_1,\\ldots,k_n$. It is useful to encode these deformations by \\emph{arrow patterns} as defined in Section~\\ref{sec:recursion}, where we choose $\\swarrow$ if we pick ${\\operatorname{E}}_{k_p}$ from ${\\operatorname{E}}_{k_p} + {\\operatorname{E}}_{k_q}^{-1} - {\\operatorname{E}}_{k_p} {\\operatorname{E}}_{k_q}^{-1}$, we choose $\\searrow$ if we pick ${\\operatorname{E}}_{k_q}^{-1}$, while we choose $\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow$ if we pick $-{\\operatorname{E}}_{k_p} {\\operatorname{E}}_{k_q}^{-1}$. Arranging the $\\binom{n}{2}$ arrows in a triangular manner so that the arrows coming from ${\\operatorname{E}}_{k_p} + {\\operatorname{E}}_{k_q}^{-1} - {\\operatorname{E}}_{k_p} {\\operatorname{E}}_{k_q}^{-1}$ are situated in the $p$-th $\\swarrow$-diagonal and the $q$-th $\\searrow$-diagonal, and placing $k_1,\\ldots,k_n$ in the bottom row will allow us to describe the deformation coming from a particular monomial in a convenient way. The combinatorial objects associated with \\eqref{operatorexpr} then consist of a pair of such an arrow pattern and a Gelfand-Tsetlin pattern where the bottom row is a deformation of $k_1,\\ldots,k_n$ as prescribed by the arrow pattern. This will lead directly to the definition of \\emph{shifted Gelfand-Tsetlin patterns}.\n\n\\medskip\n\nA sign comes from picking $- {\\operatorname{E}}_{k_p} {\\operatorname{E}}_{k_q}^{-1}$, but there is also a more subtle appearance. The deformation induced by the arrow pattern can cause a deformation of the increasing bottom row $k_1,k_2,\\ldots,k_n$ into a sequence that is not increasing. Therefore we are in need of an extension of the combinatorial interpretation of \\eqref{gelfandtsetlin} to any sequence $k_1,\\ldots,k_n$ of integers. Such an interpretation was given in \\cite{Fis05} and is repeated below in Section~\\ref{sec:gt}.\n\n\\subsection*{Outline of the bijective proof} Given a sequence $k_1 < \\ldots < k_n$, it suffices to find an injective map from the set of monotone triangles with bottom row $k_1,\\ldots,k_n$ to our shifted Gelfand-Tsetlin patterns associated with $k_1,\\ldots,k_n$ so that the images under this map have positive signs, along with a sign-reversing involution on the set of shifted Gelfand-Tsetlin patterns that are not the image of a monotone triangle.\n\n\\medskip\n\nWe will accomplish something more general, as we will also consider an extension of monotone triangles to all integer sequences $k_1,\\ldots,k_n$, see Section~\\ref{sec:recursion}, along with a sign function on these objects, and prove that the operator formula also holds in this instance. To do that, we will construct a sign-reversing involution on a subset of monotone triangles, another sign-reversing involution on a subset of shifted Gelfand-Tsetlin patterns, and a sign-\\emph{preser\\-ving} bijection between the remaining monotone triangles and the remaining shifted Gelfand-Tsetlin patterns. Note that this is actually equivalent to the construction of a bijection between the (disjoint) union of the ``positive'' monotone triangles and the ``negative'' shifted Gelfand-Tsetlin patterns, and the (disjoint) union of the ``negative'' monotone triangles and the ``positive'' shifted Gelfand-Tsetlin patterns. We call such maps \\emph{sijections} for general signed sets.\n\n\\medskip\n\nThe actual construction here will make use of the recursion underlying monotone triangles. For a monotone triangle with bottom row $k_1,\\ldots,k_n$, the eligible penultimate rows $l_1,\\ldots,l_{n-1}$ are those with\n$$\nk_1 \\le l_1 \\le k_2 \\le l_2 \\le \\ldots \\le l_{n-1} \\le k_n,\n$$\nand $l_1 < l_2 < \\ldots < l_{n-1}$. This establishes a recursion that can be used to construct all monotone triangles. Phrased differently, ``at'' each $k_i$ we need to sum over all $l_{i-1},l_i$ such that $l_{i-1}\u00a0\\le k_i \\le l_i$ and $l_{i-1} < l_{i}$.\\footnote{The degenerate cases $k_1$ and $k_n$ are slightly different.} However, we can split this into the following three cases:\n\\begin{enumerate}\n\\item Consider all $l_{i-1},l_i$ with $l_{i-1} < k_i \\le l_i$.\n\\item Consider all $l_{i-1},l_i$ with $l_{i-1} \\le k_i < l_i$.\n\\item Combining (1) and (2), we have done some double counting, thus we need to subtract the intersection, i.e., all $l_{i-1},l_i$ with $l_{i-1} < k_i < l_i$.\n\\end{enumerate}\nThis can be written as a recursion. The \\emph{arrow rows} in Section~\\ref{sec:recursion} are used to describe this recursion: we choose $\\nwarrow$ ``at'' $k_i$ if we are in Case (1), $\\nearrow$ in Case (2), and $\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$ in Case (3). Our main effort will be to show ``sijectively'' that shifted Gelfand-Tsetlin patterns also fulfill this recursion.\n\n\\subsection*{Outline of the paper} The remainder of this paper is devoted to the bijective proof of Theorem~\\ref{operator} (or rather, the more general version with the increasing condition on $k_1,\\ldots,k_n$ dropped). In Section~\\ref{sec:ss} we lay the groundwork by defining concepts like signed sets and sijections, and we extend known concepts such as disjoint union, Cartesian product and composition for ordinary sets and bijections to signed sets and sijections. The composition of sijections will use a variation of the well-known Garsia-Milne involution principle \\cite{GarsiaMilne2,GarsiaMilne1}. Many of the signed sets we will be considering are signed boxes (Cartesian products of signed intervals) or at least involve them, and we define some sijections on them in Section~\\ref{sec:sb}. These sijections will be the building blocks of our bijective proof later on. In\nSection~\\ref{sec:gt} we introduce the extended Gelfand-Tsetlin patterns and construct some related sijections. In Section~\\ref{sec:recursion}, we finally define the extended monotone triangles as well as the shifted Gelfand-Tsetlin patterns (i.e., the combinatorial interpretation of \\eqref{operatorexpr}), and use all the preparation to construct the sijection between monotone triangles and shifted Gelfand-Tsetlin patterns. In the final section, we discuss further projects.\n\n\\medskip\n\nTo emphasize that we are not merely interested in the fact that two signed sets have the same size, but want to use the constructed signed bijection later on, we will be using a convention that is slightly unorthodox in our field. Instead of listing out results as lemmas and theorems with their corresponding proofs, we will be using the Problem--Construction terminology. See for instance \\cite{Voevodsky} and \\cite{Bauer}.\n\n\\section{Signed sets and sijections} \\label{sec:ss}\n\n\\subsection*{Signed sets}\n\nA \\emph{signed set} is a pair of disjoint finite sets: $\\u S = (S^+,S^-)$ with $S^+ \\cap S^- = \\emptyset$. Equivalently, a signed set is a finite set $S$ together with a sign function $\\sign \\colon S \\to \\{1,-1\\}$. While we will mostly avoid the use of the sign function altogether (with the exception of monotone triangles defined in Section \\ref{sec:recursion}), it is useful to keep this description at the back of one's mind. Note that throughout the paper, signed sets are underlined. We will write $i \\in \\u S$ to mean $i \\in S^+ \\cup S^-$.\n\n\\medskip\n\nThe \\emph{size} of a signed set $\\u S$ is $|\\u S| = |S^+| - |S^-|$. The \\emph{opposite} signed set of $\\u S$ is $- \\u S = (S^-,S^+)$. We have $|{- \\u S} | = -|\\u S|$. The \\emph{Cartesian product} of signed sets $\\u S$ and $\\u T$ is\n$$\\u S \\times \\u T = (S^+ \\times T^+ \\cup S^- \\times T^-,S^+ \\times T^- \\cup S^- \\times T^+),$$\nand we can similarly (or recursively) define the Cartesian product of a finite number of signed sets. We have\n$$|\\u S \\times \\u T| = |S^+| \\cdot |T^+| + |S^-| \\cdot |T^-| - |S^+| \\cdot |T^-| - |S^-| \\cdot |T^+| = |\\u S| \\cdot |\\u T|.$$\n\nThe intersection of signed sets $\\u S$ and $\\u T$ is defined as $\\u S \\cap \\u T = (S^+ \\cap T^+, S^- \\cap T^-)$, while the union $\\u S \\cup \\u T = (S^+ \\cup T^+, S^- \\cup T^-)$ is only defined when $S^+ \\cap T^- = S^- \\cap T^+ = \\emptyset$. Again, we can extend these definitions to a finite family of signed sets.\n\n\\begin{example}\n One of the crucial signed sets is the \\emph{signed interval}\n $$\\u{[a,b]} = \\begin{cases} ([a,b],\\emptyset) & \\mbox{if } a \\leq b \\\\ (\\emptyset,[b+1,a-1]) & \\mbox{if } a > b \\end{cases}$$\n for $a,b \\in {\\mathbb Z}$, {where $[a,b]$ stands for an interval in $\\mathbb{Z}$ in the usual sense}. We have $\\si{b+1}{a-1} = -\\si a b$ and $|\\u{[a,b]}| = b - a + 1$.\\\\\n We will also see many \\emph{signed boxes}, Cartesian products of signed intervals. Note that $S^+ = \\emptyset$ or $S^- = \\emptyset$ for every signed box $\\u S$.\n\\end{example}\n\n{Signed subsets $\\u T \\subseteq \\u S$ are defined in an obvious manner, in particular, for $s \\in \\u S$, we have}\n$$\\u{\\{s\\}} = \\begin{cases} (\\{s\\},\\emptyset) & \\mbox{if } s \\in S^+ \\\\ (\\emptyset,\\{s\\}) & \\mbox{if } s \\in S^- \\end{cases}.$$\n\nThe \\emph{disjoint union} of signed sets $\\u S$ and $\\u T$ is the signed set\n$$\\u S \\sqcup \\u T = (\\u S \\times (\\{0\\},\\emptyset)) \\cup (\\u T \\times (\\{1\\},\\emptyset))$$\nwith elements $(s,0)$ for $s \\in \\u S$ and $(t,1)$ for $t \\in \\u T$. If $\\u S$ and $\\u T$ are signed sets with $(S^+ \\cup S^-) \\cap (T^+ \\cup T^-) = \\emptyset$, we can identify $\\u S \\cup \\u T$ and $\\u S \\sqcup \\u T$.\n\n\\medskip\n\nMore generally, we can define the disjoint union of a family of signed sets $\\u S_t$, where the family is indexed with a signed set $\\u T$:\n$$\\bigsqcup_{t \\in \\u T} \\u S_t = \\bigcup_{t \\in \\u T} (\\u S_t \\times \\u{\\{t\\}}).$$\n{We get $\\bigsqcup_{t \\in \\u{[0,1]}} \\u S_t = S_0 \\sqcup S_1$. For $a,b \\in \\mathbb{Z}$, we may also write $\\bigsqcup_{i=a}^b \\u S_i$ instead of $\\bigsqcup_{i \\in \\si a b} \\u S_i$.}\n{As for the size, we have $$\\left| \\bigsqcup_{t \\in \\u T} \\u S_t \\right|= \\sum_{t \\in T} | \\u{S_t} | \\cdot\n| \\u { \\{ t \\} } |.$$}\n\n\\medskip\n\nThe usual properties such as {associativity $(\\u S \\sqcup \\u T) \\sqcup \\u U = \\u S \\sqcup (\\u T \\sqcup \\u U)$ and} {distributivity}\n$(\\u S \\sqcup \\u T) \\times \\u U = \\u S \\times \\u U \\sqcup \\u T \\times \\u U$ also hold. {Strictly speaking, the $=$ sign here and sometimes later on indicates that there is an obvious and natural sign-preserving bijection between the two signed sets.} We summarize a few more basic properties that will be needed in the following and that are easy to prove.\n\\begin{enumerate}\n\\item $$\\bigsqcup\\limits_{\\q l \\in \\si{a_1}{b_1} \\times \\ldots \\times \\si{a_{n}}{b_{n}}} \\u S_{l_1+c_1,\\ldots,l_n+c_n} =\n\\bigsqcup\\limits_{\\q l \\in \\si{a_1+c_1}{b_1+c_1} \\times \\ldots \\times \\si{a_{n}+c_{n}}{b_{n}+c_{n}}} \\u S_{l_1,\\ldots,l_n}$$\n\\item $$\n\\bigsqcup_{t \\in \\u T} \\bigsqcup_{u \\in \\u U} \\u S_{t,u} = \\bigsqcup_{(u,t) \\in \\u U \\times \\u T}\u00a0\n\\u S_{t,u} = \\bigsqcup_{(t,u) \\in \\u T \\times \\u U}\u00a0\\u S_{t,u} =\n\\bigsqcup_{u \\in \\u U} \\bigsqcup_{t \\in \\u T} \\u S_{t,u}\n$$\n\\item\n$$\n\\bigsqcup_{t \\in \\bigsqcup_{u \\in \\u U} \\u T_u} \\u S_t = \\bigsqcup_{u \\in \\u U} \\bigsqcup_{t \\in \\u T_u} \\u S_t\n$$\n\\item $$\n- \\bigsqcup_{t \\in \\u T} \\u S_t = \\bigsqcup_{t \\in \\u T} - \\u S_t= \\bigsqcup_{t \\in -\\u T} \\u S_t.\n$$\n\\end{enumerate}\n\n\\medskip\n\n\\subsection*{Sijections}\n\nThe role of bijections for signed sets is played by ``signed bijections'', which we call \\emph{sijections}. A sijection $\\varphi$ from $\\u S$ to $\\u T$,\n$$\\varphi \\colon \\u S \\Rightarrow \\u T,$$\nis an involution on the set $(S^+ \\cup S^-) \\sqcup (T^+ \\cup T^-)$ with the property $\\varphi(S^+ \\sqcup T^-) = S^- \\sqcup T^+$, {where $\\sqcup$ refers to the disjoint union for ordinary (``unsigned'') sets.}\n It follows that also $\\varphi(S^- \\sqcup T^+) = S^+ \\sqcup T^-$. {There is an obvious sijection $\\id_{\\u S} \\colon \\u S \\Rightarrow \\u S$.}\n\\medskip\n\nWe can think of a sijection as a collection of a sign-reversing involution on a subset of $\\u S$, a sign-reversing involution on a subset of $\\u T$, and a {sign-preserving} matching between the remaining elements of $\\u S$ with the remaining elements of $\\u T$. When $S^- = T^- = \\emptyset$, the signed sets can be identified with ordinary sets, and a sijection in this case is simply a bijection.\n\n\\medskip\n\n{A sijection is a manifestation of the fact that two signed sets have the same size. Indeed,}\nif there exists a sijection $\\varphi \\colon \\u S \\Rightarrow \\u T$, we have $|S^+| + |T^-| = |S^+ \\sqcup T^-| = |S^- \\sqcup T^+| = |S^-| + |T^+|$ and therefore $|\\u S| = |S^+| - |S^-| = |T^+| - |T^-| = |\\u T|$. A sijection $\\varphi \\colon \\u S \\Rightarrow \\u T$ has an inverse $\\varphi^{-1} \\colon \\u T \\Rightarrow \\u S$ that we obtain by identifying $(T^+ \\cup T^-) \\sqcup (S^+ \\cup S^-)$ with $(S^+ \\cup S^-) \\sqcup (T^+ \\cup T^-)$.\n\n\\medskip\n\n\\comment{A sijection $\\varphi \\colon \\u S \\Rightarrow \\u T$ is \\emph{simple} if $\\varphi(S^+) = T^+$ and $\\varphi(S^-) = T^-$.}\n\n\\medskip\n\nFor a signed set $\\u S$, there is a natural sijection $\\varphi$ from $\\u S \\sqcup (- \\u S)\n$\nto the empty signed set $\\u \\emptyset = (\\emptyset,\\emptyset)$. Indeed, the {involution} should be defined on {$(S^+ \\times \\{0\\} \\cup S^- \\times\\{1\\}) \\cup (S^- \\times \\{0\\} \\cup S^+ \\times \\{1\\})$ and map $S^+ \\times \\{0\\} \\cup S^- \\times\\{1\\}$ to $S^+ \\times \\{1\\} \\cup S^- \\times \\{0\\}$}, and {so} we can take $\\varphi((s,0),0) = ((s,1),0)$, $\\varphi((s,1),0) = ((s,0),0)$. {Note that in general, a sijection from a signed set $\\u S$ to $\\u \\emptyset$ is simply a sign-reversing involution on $\\u S$, in other words, a bijection between $S^+$ and $S^-$.} \\comment{In other words, a sijection $\\varphi \\colon \\u S \\Rightarrow \\u \\emptyset$ is equivalent to a simple sijection $\\varphi \\colon \\u S \\Rightarrow - \\u S$.}\n\n\\medskip\n\n{If we have a sijection $\\varphi \\colon \\u S \\Rightarrow \\u T$, there is a natural sijection $-\\varphi \\colon -\\u S \\Rightarrow -\\u T$ (as a map, it is actually precisely the same).}\n\n\\medskip\n\n{If we have sijections $\\varphi_i \\colon \\u S_i \\Rightarrow \\u T_i$ for $i=0,1$, then there is a natural sijection $\\varphi \\colon \\u S_0 \\sqcup \\u S_1 \\Rightarrow \\u T_0 \\sqcup \\u T_1$.}\nMore interesting ways to create new sijections are described below in Proposition \\ref{prop:sijections}, but we will need this in our first construction {for the special case $\\u S_0 = \\u T_0$ and $\\varphi_0 = \\id_{\\u S_0}$.}\n\n\\medskip\n\nTo motivate our first result, note that if $a \\leq b \\leq c$ or $c < b < a$, then $\\si a c = \\si a b \\cup \\si{b+1}c= \\si a b \\sqcup \\si{b+1}c$. Of course, this does not hold in general; for $a = 1$, $b = 8$, $c = 5$, we have $\\si 1 5 = (\\{1,2,3,4,5\\},\\emptyset)$, $(\\si 1 8 \\sqcup \\si 9 5)^+ = (\\{(1,0),(2,0),(3,0),(4,0),(5,0),(6,0),(7,0),(8,0)\\}$ and $(\\si 1 8 \\sqcup \\si 9 5)^- = \\{(6,1),(7,1),(8,1)\\})$. The following, however, tells us that there is in general a sijection between {$\\si a c$ and\n$\\si a b \\sqcup \\si{b+1}c$.}\nThis map will be the crucial building block for more complicated sijections.\n\n\\begin{problem} \\label{prob:alpha}\n Given $a,b,c \\in {\\mathbb Z}$, construct a sijection\n $$\\alpha = \\alpha_{a,b,c} \\colon \\si a c \\Rightarrow \\si a b \\sqcup \\si{b+1}c.$$\n\\end{problem}\n\\begin{proof}[Construction]\n For $a \\leq b \\leq c$ and $c < b < a$, there is nothing to prove. For, say, $a \\leq c < b$, we have\n $$\\si a b \\sqcup \\si{b+1}c = (\\si a c \\sqcup \\si{c+1} b) \\sqcup \\si{b+1}c = \\si a c \\sqcup (\\si{c+1} b \\sqcup (-\\si {c+1}{b}))$$\n and since there is a sijection $\\si{c+1} b \\sqcup (-\\si {c+1}{b}) \\Rightarrow \\u \\emptyset$, we get a sijection $\\si a b \\sqcup \\si{b+1}c \\Rightarrow \\si a c$. The cases $b < a \\leq c$, $b \\leq c < a$, and $c < a \\leq b$ are analogous. \\comment{Note that in all cases, $\\si a b$ and $\\si{b+1}c$ are either disjoint or one set is contained in the other.}\n\\end{proof}\n\n\\medskip\n\nThe following proposition describes composition, Cartesian product, and disjoint union of sijections. The composition is a variant of the well-known Garsia-Milne involution principle. All the statements are easy to prove{, and the proofs are left to the reader.}\n\n\\begin{prop} \\leavevmode \\label{prop:sijections}\n \\begin{enumerate}\n \\item (Composition) Suppose that we have sijections $\\varphi \\colon \\u S \\Rightarrow \\u T$ and $\\psi \\colon \\u T \\Rightarrow \\u U$. For $s \\in \\u S$ (resp.\\ $u \\in \\u U$), define $\\psi \\circ \\varphi(s)$ {(resp.\\ $\\psi \\circ \\varphi(u)$)} as the last well-defined element in the sequence $s, \\varphi(s), \\psi(\\varphi(s)), \\varphi(\\psi(\\varphi(s))),\\ldots$ (resp.\\ $u, \\psi(u), \\varphi(\\psi(u)), \\psi(\\varphi(\\psi(u))),\\ldots$). Then $\\psi \\circ \\varphi$ is a well-defined sijection from $\\u S$ to $\\u U$. \\comment{If $\\varphi$ and $\\psi$ are simple, so is $\\psi \\circ \\varphi$.}\n \\item (Cartesian product) Suppose we have sijections $\\varphi_i \\colon \\u S_i \\Rightarrow \\u T_i$, $i = 1,\\ldots,k$. Then $\\varphi = \\varphi_1 \\times \\cdots \\times \\varphi_k$, defined by\n$$\\varphi(s_1,\\ldots,s_k) = \\begin{cases} (\\varphi_1(s_1),\\ldots,\\varphi_k(s_k)) & \\mbox{if } \\varphi_i(s_i) \\in \\u T_i \\mbox{ for } i=1,\\ldots,k \\\\ (s_1,\\ldots,s_{j-1},\\varphi_j(s_j),s_{j+1},\\ldots,s_k) & \\mbox{if } \\varphi_j(s_j) \\in \\u S_j, \\varphi_i(s_i) \\in \\u T_i \\mbox{ for } i < j \\end{cases}$$\n{if $(s_1,\\ldots,s_k) \\in \\u S_1 \\times \\dots \\times \\u S_k$ and\n$$\\varphi(t_1,\\ldots,t_k) = \\begin{cases} (\\varphi_1(t_1),\\ldots,\\varphi_k(t_k)) & \\mbox{if } \\varphi_i(t_i) \\in \\u S_i \\mbox{ for } i=1,\\ldots,k \\\\ (t_1,\\ldots,t_{j-1},\\varphi_j(t_j),t_{j+1},\\ldots,t_k) & \\mbox{if } \\varphi_j(t_j) \\in \\u T_j, \\varphi_i(t_i) \\in \\u S_i \\mbox{ for } i < j \\end{cases}$$\nif $(t_1,\\ldots,t_k) \\in \\u T_1 \\times \\dots \\times \\u T_k$,}\n is a well-defined sijection from $\\u S_1 \\times \\cdots \\times \\u S_k$ to $\\u T_1 \\times \\cdots \\times \\u T_k$. \\comment{If $\\varphi_i$ are all simple sijections, so is $\\varphi$.}\n \\item (Disjoint union) {Suppose we have signed sets $\\u T, \\u{\\widetilde T}$ and a sijection $\\psi \\colon \\u T \\Rightarrow \\u{\\widetilde T}$. Further\\-more, suppose that for every $t \\in \\u T \\sqcup \\u{\\widetilde T}$, we have a signed set $\\u S_t$ and a sijection $\\varphi_t \\colon \\u S_t \\Rightarrow \\u S_{\\psi(t)}$ satisfying $\\varphi_{\\psi(t)} = \\varphi_t^{-1}$. Then $\\varphi = \\bigsqcup_{t \\in \\u T \\sqcup \\u{\\widetilde T}} \\varphi_t$, defined by\n$$\\varphi(s_t,t) = \\begin{cases}\n(\\varphi_t(s_t),t) & \\mbox{if } t \\in \\u T \\sqcup \\u {\\widetilde T}, s_t \\in \\u S_t, \\varphi_t(s_t) \\in \\u S_t \\\\\n(\\varphi_t(s_t),\\psi(t)) & \\mbox{if } t \\in \\u T \\sqcup \\u {\\widetilde T}, s_t \\in \\u S_t, \\varphi_t(s_t) \\in \\u {S}_{\\psi(t)} \\\\\n\\end{cases}\n$$\nis a sijection $\\bigsqcup_{t \\in \\u T} \\u S_t \\Rightarrow \\bigsqcup_{t \\in \\u {\\widetilde T}} \\u {\\widetilde S}_t$.} \\comment{If $\\psi$ and $\\varphi_t$ are all simple sijections, so is $\\varphi$.}\n\\end{enumerate}\n\\end{prop}\n\n\n{One} important special case of Proposition \\ref{prop:sijections} (3) is $\\u T = \\u {\\widetilde T}$ and $\\psi = \\id$. We have two sets of signed sets indexed by $\\u T$, $\\u S_{(t,0)} =: \\u{S}^{{0}}_t$ and $\\u S_{(t,1)} =: \\u{S}^{{1}}_t$, and sijections $\\varphi_t \\colon \\u{S}^{{0}}_t \\Rightarrow \\u{S}^{{1}}_t$. By the proposition, these sijections have a disjoint union that is a sijection $\\bigsqcup_{t \\in \\u T} \\u S^{{0}}_t \\Rightarrow \\bigsqcup_{t \\in \\u T} \\u S^{{1}}_t$.\n\n\\medskip\n\nBy the proposition, the relation\n$$\\u S \\approx \\u T \\iff \\mbox{there exists a sijection from } \\u S \\mbox{ to } \\u T$$\nis an equivalence {relation}.\n\n\\subsection*{Elementary signed sets and normal sijections}\n\nOften, we will be interested in disjoint unions of Cartesian products of signed intervals. An element of such a signed set is a pair, consisting of a tuple of integers and an element of the indexing signed set. Intuitively, the first one is ``more important'', as the second one serves just as an index. We formalize this notion in the following definition.\n\n\\begin{definition}\n A signed set $\\u A$ is \\emph{elementary of dimension $n$ and depth $0$} if its elements are in ${\\mathbb Z}^n$. A signed set $\\u A$ is \\emph{elementary of dimension $n$ and depth $d$}, $d \\geq 1$, if it is of the form\n $$\\bigsqcup_{t \\in \\u T} \\u S_t,$$\n where $\\u T$ is a signed set, and $\\u S_t$ are all signed sets of dimension $n$ and depth at most $d-1$, with the depth of at least one of them equal to $d-1$. A signed set $\\u A$ is \\emph{elementary of dimension $n$} if it is an elementary signed set of dimension $n$ and depth $d$ for some $d \\in {\\mathbb N}$.\\\\\n The \\emph{projection map} on an elementary set of dimension $n$ is the map\n $$\\xi \\colon \\u A \\to {\\mathbb Z}^n$$\n defined as follows. If the depth of $\\u A$ is $0$, then $\\xi$ is simply the inclusion map. Once $\\xi$ is defined on elementary signed sets of depth $< d$, and the depth of $\\u A$ is $d$, then $\\u A = \\bigsqcup_{t \\in \\u T} \\u S_t$, where $\\xi$ is defined on all $\\u S_t$. Then define $\\xi(s,t) = \\xi(s)$ for $(s,t) \\in \\u A$.\\\\\n A sijection $\\psi \\colon \\u T \\Rightarrow \\u{\\widetilde T}$ between elementary signed sets $\\u T$ and $\\u{\\widetilde T}$ of the same dimension is \\emph{normal} if $\\xi(\\psi(t)) = \\xi(t)$ for all $t \\in \\u T \\sqcup \\u{\\widetilde T}$.\n\\end{definition}\n\nSimple examples of elementary signed sets are $\\si a c$, $\\si a b \\sqcup \\si {b+1} c$ and $\\si a c \\sqcup (\\si a b \\sqcup \\si {b+1} c)$. They are all of dimension $1$ and depth $0$, $1$ and $2$, respectively.\\footnote{{To avoid ambiguity, we should consider signed intervals in this case to be subsets of ${\\mathbb Z}^1$ ($1$-tuples of integers), not ${\\mathbb Z}$. Otherwise, $\\si{0}{1} \\sqcup \\si{2}{3} = (\\{(0,0),(1,0),(2,1),(3,1)\\},\\emptyset)$, and this can be seen either as an elementary set of dimension $1$ and depth $1$, or as an elementary signed set of dimension $2$ and depth $0$. So the interpretation depends on the ``representation'' of the set as disjoint union. Instead, we should understand $\\si{0}{1} \\sqcup \\si{2}{3}$ to mean $ (\\{((0),0),((1),0),((2),1),((3),1)\\},\\emptyset)$, with dimension $1$ and depth $1$. For coding, the distinction is important, but in the paper we nevertheless think of elements of signed intervals as integers.}} It is easy to see that the sijection $\\alpha_{a,b,c}$ from Problem \\ref{prob:alpha} is normal.\n\nLet us illustrate this with the example $a = 1$, $b = 5$, $c = 3$. We have $\\si a c = (\\{1,2,3\\},\\emptyset)$ and $\\si a b \\sqcup \\si {b+1} c = (\\{(1,0),(2,0),(3,0),(4,0),(5,0)\\},\\{(4,1),(5,1)\\})$. The sijection $\\alpha_{1,5,3}$ is the involution on $\\si 1 3 \\sqcup (\\si 1 5 \\sqcup \\si {6} 3)$ defined by\n$$(1,0) \\leftrightarrow ((1,0),1), \\quad (2,0) \\leftrightarrow ((2,0),1), \\quad (3,0) \\leftrightarrow ((3,0),1),$$\n$$((4,0),1) \\leftrightarrow ((4,1),1), \\quad ((5,0),1) \\leftrightarrow ((5,1),1).$$\nSince $\\xi(i,0) = i$ for $i = 1,2,3$, $\\xi((i,0),1) = i$ for $i = 1,2,3,4,5$ and $\\xi((i,1),1) = i$ for $i=4,5$, $\\alpha_{1,5,3}$ is indeed normal.\n\n\\medskip\n\nOther examples of elementary signed sets appear in the statements of Problems \\ref{prob:beta} and \\ref{prob:gamma} (in both cases, they are of dimension $n-1$).\n\n\\medskip\n\nNormality is preserved under Cartesian product, disjoint union etc. For example, the sijection\n\\begin{multline*}\n \\si{a_1}{c_1} \\times \\si{a_2}{c_2} \\Rightarrow \\\\\n \\si{a_1}{b_1} \\times \\si{a_2}{b_2} \\sqcup \\si{a_1}{b_1} \\times \\si{b_2+1}{c_2} \\sqcup \\si{b_1+1}{c_1} \\times \\si{a_2}{b_2} \\sqcup \\si{b_1+1}{c_1} \\times \\si{b_2+1}{c_2},\n\\end{multline*}\nobtained by using $\\alpha_{a_1,b_1,c_1} \\times \\alpha_{a_2,b_2,c_2}$ and distributivity on disjoint unions, is normal.\n\n\\medskip\n\nThe main reason normal sijections are important is that they give a very natural special case of Proposition \\ref{prop:sijections} (3). Suppose that $\\u T$ and $\\u{\\widetilde T}$ are elementary signed sets of dimension $n$, and that $\\psi \\colon \\u T \\Rightarrow \\u{\\widetilde T}$ is a normal sijection. Furthermore, suppose that we have a signed set $\\u S_{\\q k}$ for every $\\q k \\in {\\mathbb Z}^n$. Then we have a sijection\n$$\\bigsqcup_{t \\in \\u T} \\u S_{\\xi(t)} \\Rightarrow \\bigsqcup_{t \\in \\u{\\widetilde T}} \\u S_{\\xi(t)}.$$\nIndeed, Proposition \\ref{prop:sijections} gives us a sijection provided that we have a sijection $\\varphi_t \\colon \\u S_{\\xi(t)} \\Rightarrow \\u S_{\\xi(\\psi(t))}$ satisfying $\\varphi_{\\psi(t)} = \\varphi_t^{-1}$ for every $t \\in \\u T \\sqcup \\u{\\widetilde T}$. But since $\\xi(\\psi(t)) = \\xi(t)$, we can take $\\varphi_t$ to be the identity.\n\n\\comment{The following observation is crucial: Suppose $\\u T \\approx \\u {\\widetilde T}$, then in general we do not have\n\\begin{equation}\n\\label{indexset_equivalence}\n\\bigsqcup_{t \\in \\u T} S_t \\approx \\bigsqcup_{t \\in \\u {\\widetilde T}} S_t.\n\\end{equation}\nHowever, if $\\u T=\\si{a}{c}$ and $\\u {\\widetilde T}=\\si{a}{b} \\sqcup \\si{b+1}{c}$ (see Problem~\\ref{prob:alpha}), then the identity is true because either $\\si{a}{b}$ and\n$\\si{b+1}{c}$ are disjoint (in which case the two boxes have the same sign) or one set is contained in the other (in which case the two boxes have opposite sign).}\n\n\\section{Some sijections on signed boxes} \\label{sec:sb}\n\nThe first sijection in this section will serve as the base of induction for Problem \\ref{prob:pi}.\n\n\\comment{\n\n\\begin{example} \\label{ex:toempty}\n For $a,b \\in {\\mathbb Z}$, define $\\u T = \\si{a+1}{b+1} \\times \\si a b$. Then $\\psi: \\u T \\Rightarrow \\u T$, defined by $\\psi((l_1,l_2),i) = ((l_2+1,l_1-1),1-i)$ for $l_1 \\in \\si{a+1}{b+1}, l_2 \\in \\si a b, i \\in \\{0,1\\}$, is a (non-normal) sijection. Define $\\u S_{((l_1,l_2),i)} = (-1)^i \\si{l_1}{l_2}$. Since $\\u S_{((l_2+1,l_1-1),i)} = - \\u S_{((l_1,l_2),i)}$ and $\\u S_{((l_1,l_2),1-i)} = - \\u S_{((l_1,l_2),i)}$, we have $\\u S_{\\psi((l_1,l_2),i)} = \\u S_{((l_1,l_2),i)}$. Therefore we can use Proposition \\ref{prop:sijections} with $\\varphi_t = \\id$, and we obtain a sijection\n $$\\bigsqcup_{(l_1,l_2) \\in \\si{a+1}{b+1} \\times \\si a b} \\si{l_1}{l_2} \\Rightarrow \\bigsqcup_{(l_1,l_2) \\in \\si{a+1}{b+1} \\times \\si a b} -\\si{l_1}{l_2},$$\n which is equivalent to a sijection\n $$\\bigsqcup_{(l_1,l_2) \\in \\si{a+1}{b+1} \\times \\si a b} \\si{l_1}{l_2} \\Rightarrow \\u \\emptyset.$$\n\\end{example}}\n\n\\begin{example} \\label{ex:toempty}\n For $a,b \\in {\\mathbb Z}$, we have a normal sijection\n $$\\bigsqcup_{(l_1,l_2) \\in \\si{a+1}{b+1} \\times \\si a b} \\si{l_1}{l_2} \\Rightarrow \\u \\emptyset$$\n defined by $\\varphi((x,(l_1,l_2)),0) = ((x,(l_2+1,l_1-1)),0)$. It is well defined because $(l_1,l_2) \\in \\si{a+1}{b+1} \\times \\si a b$ if and only if $(l_2+1,l_1-1) \\in \\si{a+1}{b+1} \\times \\si a b$, and because $x \\in \\si{l_1}{l_2}$ if and only if $x \\in \\si{l_2+1}{l_1-1}$.\n\\end{example}\n\nNote that the $0$ as the second coordinate in the example comes from the fact that a sijection in question is an involution on the disjoint union\n$$\\left(\\bigsqcup_{(l_1,l_2) \\in \\si{a+1}{b+1} \\times \\si a b} \\si{l_1}{l_2}\\right) \\sqcup \\u \\emptyset = \\left(\\bigsqcup_{(l_1,l_2) \\in \\si{a+1}{b+1} \\times \\si a b} \\si{l_1}{l_2}\\right) \\times \\u{\\{0\\}} \\cup \\u \\emptyset \\times \\u{\\{1\\}}.$$\nWe could be a little less precise and write $\\varphi(x,(l_1,l_2)) = (x,(l_2+1,l_1-1))$ without causing confusion.\n\n\\medskip\n\nThe following generalizes the construction of Problem \\ref{prob:alpha}; indeed, for $n = 2$ the construction gives a sijection from $[a_1,b_1]$ to $[a_1,x] \\sqcup (-[b_1+1,x])$.\n\n\\begin{problem} \\label{prob:beta}\n Given $\\q a =(a_1,\\ldots,a_{n-1}) \\in {\\mathbb Z}^{n-1}$, $\\q b=(b_1,\\ldots,b_{n-1}) \\in {\\mathbb Z}^{n-1}$, $x \\in {\\mathbb Z}$, construct a normal sijection\n $$ \\beta = \\beta_{\\q a,\\q b,x} \\colon \\si{a_1}{b_1} \\times \\cdots \\times \\si{a_{n-1}}{b_{n-1}} \\Rightarrow \\bigsqcup_{\\q (l_1,\\ldots,l_{n-1}) \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\si{l_1}{l_2} \\times \\si{l_2}{l_3} \\times \\cdots \\times \\si{l_{n-2}}{l_{n-1}} \\times \\si{l_{n-1}} x,$$\n where $\\u S_i = (\\{a_i\\},\\emptyset) \\sqcup (\\emptyset,\\{b_i+1\\})$\n\\end{problem}\n\n{Note that $(\\{a_i\\},\\emptyset) \\sqcup (\\emptyset,\\{b_i+1\\})$ can be identified with\n$(\\{a_i\\},\\{b_i+1\\})$ if $a_i \\not= b_i+1$.}\n\n\\begin{proof}[Construction]\n The proof is by induction, with the case $n = 1$ being trivial and the case $n=2$ was constructed in Problem~\\ref{prob:alpha}. Now, for $n \\ge 3$,\n \\begin{multline*}\n \\si{a_1}{b_1} \\times \\cdots \\times \\si{a_{n-1}}{b_{n-1}} \\approx \\si{a_1}{b_1} \\times \\bigsqcup_{\\q (l_2,\\ldots,l_{n-1}) \\in \\u S_2 \\times \\cdots \\times \\u S_{n-1}} \\si{l_2}{l_3} \\times \\cdots \\times \\si{l_{n-2}}{l_{n-1}} \\times \\si{l_{n-1}}x \\\\\n \\approx \\left( \\si{a_1}{b_1} \\times \\bigsqcup_{\\q (l_3,\\ldots,l_{n-1}) \\in \\u S_3 \\times \\cdots \\times \\u S_{n-1}} \\si{a_2}{l_3} \\times \\cdots \\times \\si{l_{n-1}}x \\right) \\\\ \\sqcup \\left( \\si{a_1}{b_1} \\times \\bigsqcup_{\\q (l_3,\\ldots,l_{n-1}) \\in \\u S_3 \\times \\cdots \\times \\u S_{n-1}} (-\\si{b_2+1}{l_3}) \\times \\cdots \\times \\si{l_{n-1}}x \\right),\n\\end{multline*}\nwhere we used induction for the first equivalence, and distributivity and the fact that $S_2 = (\\{a_2\\},\\emptyset) \\sqcup (\\emptyset,\\{b_2+1\\})$ for the second equivalence. By Problem~\\ref{prob:alpha} and Proposition~\\ref{prop:sijections} (2), there exists a sijection from the last expression to\n\\begin{multline*}\n \\left( \\left(\\si{a_1}{a_2} \\sqcup (-\\si{b_1+1}{a_2}) \\right) \\times \\bigsqcup_{\\q (l_3,\\ldots,l_{n-1}) \\in \\u S_3 \\times \\cdots \\times \\u S_{n-1}} \\si{a_2}{l_3} \\times \\cdots \\times \\si{l_{n-1}}x \\right) \\\\\n \\sqcup \\left( \\left( \\si{a_1}{b_2+1} \\sqcup (-\\si{b_1+1}{b_2+1}) \\right) \\times \\bigsqcup_{\\q (l_3,\\ldots,l_{n-1}) \\in \\u S_3 \\times \\cdots \\times \\u S_{n-1}} (-\\si{b_2+1}{l_3}) \\times \\cdots \\times \\si{l_{n-1}}x \\right) \\\\\n \\approx \\bigsqcup_{\\q (l_1,\\ldots,l_{n-1}) \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\si{l_1}{l_2} \\times \\si{l_2}{l_3} \\times \\cdots \\cdots \\si{l_{n-2}}{l_{n-1}} \\times \\si{l_{n-1}} x,\n\\end{multline*}\nwhere for the last equivalence we have again used distributivity. Normality follows from the normality of all the sijections involved in the construction.\n\\end{proof}\n\n\\begin{problem} \\label{prob:gamma}\n Given $\\q k =(k_1,\\ldots,k_n) \\in {\\mathbb Z}^n$ and $x \\in {\\mathbb Z}$, construct a normal sijection\n \\begin{multline*}\n \\gamma = \\gamma_{\\q k,x} \\colon \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{n-1}}{k_n} \\\\\n \\Rightarrow \\bigsqcup_{i = 1}^n \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{x+n-i} \\times \\si{x+n-i}{k_{i+1}} \\times \\cdots \\times \\si{k_{n-1}}{k_n} \\\\\n \\sqcup \\bigsqcup_{i = 1}^{n-2} \\cdots \\times \\si{k_{i-1}}{k_i} \\times \\si{k_{i+1}+1}{x+n-i-1} \\times \\si{k_{i+1}}{x+n-i-2} \\times \\si{k_{i+2}}{k_{i+3}} \\times\\cdots .\n \\end{multline*}\n \\end{problem}\n\n\\begin{proof}[Construction] The proof is by induction with respect to $n$. The case $n=1$ is trivial, and $n=2$ is Problem~\\ref{prob:alpha}. Now take $n > 2$. By the induction hypothesis (for $(k_1,\\ldots,k_{n-1})$ and $x+1$), we have\n\\begin{multline*}\\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{n-1}}{k_n} \\approx \\bigg( \\bigsqcup_{i = 1}^{n-1} \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{x+n-i} \\times \\si{x+n-i}{k_{i+1}} \\times \\cdots \\times \\si{k_{n-2}}{k_{n-1}}\\\\\n \\sqcup \\bigsqcup_{i = 1}^{n-3} \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i+1}+1}{x+n-i-1} \\times \\si{k_{i+1}}{x+n-i-2} \\times \\cdots \\times \\si{k_{n-2}}{k_{n-1}} \\bigg) \\times \\si{k_{n-1}}{k_n}.\n \\end{multline*}\nWe use distributivity. We keep all terms except the one corresponding to $i = n-1$ in the first part. Because\n\\begin{multline*}\n\\si{k_{n-2}}{x+1} \\times \\si{k_{n-1}}{k_n} \\approx \\si{k_{n-2}}{x+1} \\times (\\si{k_{n-1}}x \\sqcup \\si{x+1}{k_n}) \\\\\n\\approx (\\si{k_{n-2}}{k_{n-1}} \\sqcup \\si{k_{n-1}+1}{x+1}) \\times \\si{k_{n-1}}x \\sqcup \\si{k_{n-2}}{x+1} \\times \\si{x+1}{k_n} \\\\\n{\\approx \\si{k_{n-2}}{k_{n-1}} \\times \\si{k_{n-1}}x \\sqcup \\si{k_{n-1}+1}{x+1} \\times \\si{k_{n-1}}x \\sqcup \\si{k_{n-2}}{x+1} \\times \\si{x+1}{k_n}},\n\\end{multline*}\nwe obtain the required Cartesian products for the first term on the right-hand side at $i = n$, the second term at $i = n-2$, and the first term at $i = n-1$. Again, normality follows from the fact that $\\alpha$ is normal.\n\\end{proof}\n\n\n\\section{Gelfand-Tsetlin patterns} \\label{sec:gt}\n\nUsing our definition of a disjoint union of {signed sets}, it is easy to define generalized Gelfand-Tsetlin patterns, or GT patterns for short (compare with \\cite{Fis05}).\n\n\\begin{definition}\n For $k \\in {\\mathbb Z}$, define ${\\GT(k) = (\\{\\cdot\\},\\emptyset)}$, \n and for $\\q k = (k_1,\\ldots,k_n) \\in {\\mathbb Z}^n$, define {recursively}\n $$\\GT(\\q k) = \\GT(k_1,\\ldots,k_n) = \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} \\GT(l_1,\\ldots,l_{n-1}).$$\n\\end{definition}\n\nIn particular, $\\GT(a,b) \\approx \\si a b$.\n\n\\medskip\n\nOf course, one can think of an element of $\\GT(\\q k)$ in the usual way, as a triangular array $A=(A_{i,j})_{1 \\leq j \\leq i \\le n}$ of $\\binom {n+1}2$ numbers,\narranged as\n$$\n\\begin{array}{ccccccccc}\n &&&& A_{1,1} &&&& \\\\\n &&& A_{2,1} && A_{2,2} &&& \\\\\n && A_{3,1} && A_{3,2} && A_{3,3} && \\\\\n & \\iddots & \\vdots & \\ddots & \\vdots & \\iddots & \\vdots & \\ddots & \\\\\nA_{n,1} && A_{n,2} && \\ldots && \\ldots && A_{n,n},\n \\end{array}\n$$\nso that $A_{i+1,j} \\leq A_{i,j} \\leq A_{i+1,j+1}$ or $A_{i+1,j} > A_{i,j} > A_{i+1,j+1}$ for $1 \\leq j \\leq i < n$, and $A_{n,i}=k_i$. The sign of such an array is $(-1)^m$, where $m$ is the number of $(i,j)$ with $a_{i,j} > a_{i,j+1}$.\n\n\\medskip\n\nSome crucial sijections for GT patterns are given by the following constructions.\n\n\\begin{problem} \\label{prob:rho}\n Given $\\q a =(a_1,\\ldots,a_{n-1}) \\in {\\mathbb Z}^{n-1}$, $\\q b=(b_1,\\ldots,b_{n-1}) \\in {\\mathbb Z}^{n-1}$, $x \\in {\\mathbb Z}$, construct a sijection\n $$ \\rho = \\rho_{\\q a,\\q b,x} \\colon \\bigsqcup_{\\q l \\in \\si{a_1}{b_1} \\times \\cdots \\times \\si{a_{n-1}}{b_{n-1}}} \\GT(\\q l) \\Rightarrow \\bigsqcup_{\\q (l_1,\\ldots,l_{n-1}) \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\GT(l_1,\\ldots,l_{n-1},x),$$\n where $\\u S_i = (\\{a_i\\},\\emptyset) \\sqcup (\\emptyset,\\{b_i+1\\})$.\n\\end{problem}\n\\begin{proof}[Construction]\n In Problem \\ref{prob:beta}, we constructed a normal sijection\n $$ \\si{a_1}{b_1} \\times \\cdots \\times \\si{a_{n-1}}{b_{n-1}} \\Rightarrow \\bigsqcup_{\\q (l_1,\\ldots,l_{n-1}) \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\si{l_1}{l_2} \\times \\si{l_2}{l_3} \\times \\cdots \\times \\si{l_{n-2}}{l_{n-1}} \\times \\si{l_{n-1}} x.$$\n By Proposition~\\ref{prop:sijections} (3) (see the comment at the end of Section \\ref{sec:ss}), this gives a sijection\n $$ \\bigsqcup_{\\q l \\in \\si{a_1}{b_1} \\times \\cdots \\times \\si{a_{n-1}}{b_{n-1}}} \\GT(\\q l) \\Rightarrow \\bigsqcup_{\\q m \\in \\bigsqcup_{\\q (l_1,\\ldots,l_{n-1}) \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\si{l_1}{l_2} \\times \\si{l_2}{l_3} \\times \\cdots \\times \\si{l_{n-2}}{l_{n-1}} \\times \\si{l_{n-1}} x} \\GT(\\q m).$$\n By basic sijection constructions, we get that this is equivalent to\n $$\\bigsqcup_{\\q (l_1,\\ldots,l_{n-1}) \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\bigsqcup_{\\q m \\in \\si{l_1}{l_2} \\times \\si{l_2}{l_3} \\times \\cdots \\times \\si{l_{n-2}}{l_{n-1}} \\times \\si{l_{n-1}} x} \\GT(\\q m),$$\n and by definition of $\\GT$, this is equal to $\\bigsqcup_{\\q (l_1,\\ldots,l_{n-1}) \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\GT(l_1,\\ldots,l_{n-1},x)$.\n\\end{proof}\n\nThe result is important because while it adds a dimension to GT patterns, it (typically) greatly reduces the size of the indexing signed set. {In fact, there is an analogy to the fundamental theorem of calculus: instead of extending the disjoint union over the entire signed box, it suffices to consider the boundary; $x$ corresponds in a sense to the constant of integration.}\n\n\\begin{problem} \\label{prob:pi}\n Given $\\q k =(k_1,\\ldots,k_n) \\in {\\mathbb Z}^n$ and $i$, $1 \\leq i \\leq n-1$, construct a sijection\n $$\\pi = \\pi_{\\q k,i} \\colon \\GT(k_1,\\ldots,k_n) \\Rightarrow -\\GT(k_1,\\ldots,k_{i-1},k_{i+1}+1,k_i-1,k_{i+2},\\ldots,k_n).$$\n Given $\\q a =(a_1,\\ldots,a_n) \\in {\\mathbb Z}^n$, $\\q b =(b_1,\\ldots,b_n) \\in {\\mathbb Z}^n$ such that for some $i$, $1 \\leq i \\leq n-1$, we have $a_{i+1} = a_i - 1$ and $b_{i+1} = b_i - 1$, construct a sijection\n $$\\sigma = \\sigma_{\\q a,\\q b,i} \\colon \\bigsqcup_{\\q l \\in \\si{a_1}{b_1} \\times \\cdots \\times \\si{a_n}{b_n}} \\GT(\\q l) \\Rightarrow \\u \\emptyset.$$\n\\end{problem}\n\\begin{proof}[Construction]\n The proof is by induction, with the induction step for $\\pi$ using $\\sigma$ and vice versa. For $n = 1$, there is nothing to prove. For $n=2$ and $i=1$, the existence of $\\pi$ follows from the statement $\\si{k_1}{k_2} = -\\si{k_2+1}{k_1-1}$, and $\\sigma$ was constructed in Example \\ref{ex:toempty}. Assume that $n > 2$ and $1 < i < n-1$. We have\n$$\\GT(k_1,\\ldots,k_n) = \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{k_i} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i+1}}{k_{i+2}} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} -\\GT(l_1,\\ldots,l_{n-1}).$$\nBy using $\\id \\times \\cdots \\times \\id \\times \\alpha_{k_{i-1},k_{i+1}+1,k_i} \\times \\id \\times \\alpha_{k_{i+1},k_i-2,k_{i+2}} \\times \\id \\times \\cdots \\times \\id$ and distributivity, we get a normal sijection\n\\begin{multline*}\n\\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{k_i} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i+1}}{k_{i+2}} \\times \\cdots \\times \\si{k_{n-1}}{k_n} \\Rightarrow \\\\\n\\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{k_{i+1}+1} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i}-1}{k_{i+2}} \\times \\cdots \\times \\si{k_{n-1}}{k_n} \\\\\n\\sqcup \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{k_{i+1}+1} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i+1}}{k_{i}-2} \\times \\cdots \\times \\si{k_{n-1}}{k_n} \\\\\n\\sqcup \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i+1}+2}{k_i} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i}-1}{k_{i+2}} \\times \\cdots \\times \\si{k_{n-1}}{k_n} \\\\\n\\sqcup \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i+1}+2}{k_i} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i+1}}{k_{i}-2} \\times \\cdots \\times \\si{k_{n-1}}{k_n}\n\\end{multline*}\n By Proposition~\\ref{prop:sijections} (3), this gives a sijection\n \\begin{multline*}\n \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{k_i} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i+1}}{k_{i+2}} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} -\\GT(l_1,\\ldots,l_{n-1}) \\Rightarrow \\\\\n \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{k_{i+1}+1} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i}-1}{k_{i+2}} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} - \\GT(l_1,\\ldots,l_{n-1}) \\\\ \\sqcup \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{k_{i+1}+1} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i+1}}{k_{i}-2} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} -\\GT(l_1,\\ldots,l_{n-1}) \\\\ \\sqcup \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i+1}+2}{k_i} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i}-1}{k_{i+2}} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} -\\GT(l_1,\\ldots,l_{n-1})\n \\\\ \\sqcup \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i+1}+2}{k_i} \\times \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i+1}}{k_{i}-2} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} -\\GT(l_1,\\ldots,l_{n-1}).\n \\end{multline*}\n By definition, the first signed set on the right-hand side is $-\\GT(k_1,\\ldots,k_{i-1},k_{i+1}+1,k_i-1,k_{i+2},\\ldots,k_n)$. The other three disjoint unions all satisfy the condition needed for the existence of $\\sigma$ {(for $i$, for $i-1$ and for both, $i-1$ and $i$, respectively)}, and hence we can siject them to $\\u \\emptyset$.\\\\\n If $i = 1$ or $i = n-1$, the proof is similar but easier (as we only have to use $\\alpha$ once, and we get only two factors after using distributivity). Details are left to the reader.\\\\\n Now take $\\q l = (l_1,\\ldots,l_n)$ and $\\q{l'}=(l_1,\\ldots,l_{i-1},l_{i+1}+1,l_i-1,l_{i+2},\\ldots,l_n)$. The sijection $\\sigma$ can then be defined as\n $$\\sigma_{\\q a,\\q b,i}(A,\\q l) = \\begin{cases} (\\pi_{\\q l,i}(A),\\q l) & \\mbox{if }\\pi_{\\q l,i}(A) \\in \\GT(\\q l) \\\\ (\\pi_{\\q l,i}(A),\\q {l'}) & \\mbox{if } \\pi_{\\q {l},i}(A) \\in \\GT(\\q {l'}) \\end{cases}.$$\n It is easy to check that this is a sijection. Compare with Example \\ref{ex:toempty}.\n \\comment{These follow immediately from such sijections on each of the following two signed sets,\n $$\n \\bigsqcup_{(l_{i-1},l_i) \\in \\si{k_{i+1}+2}{k_i} \\times \\si{k_{i+1}+1}{k_i-1}} \\GT(l_1,\\ldots,l_{n-1})\n$$\nand\n$$\n \\bigsqcup_{ (l_i,l_{i+1}) \\in \\si{k_{i+1}+1}{k_i-1} \\times \\si{k_{i+1}}{k_{i}-2}} \\GT(l_1,\\ldots,l_{n-1}),\n$$\nfixing arbitrary integers $l_1,\\ldots,l_{i-2},l_{i+1},\\ldots,l_{n-1}$ in the first case and $l_1,\\ldots,l_{i-1},l_{i+2},\\ldots,l_{n-1}$ in the second case. As for the first case, we let\n$$\nA \\times \\u {\\{ (l_{i-1},l_{i}) \\}} \\in \\bigsqcup_{(l_{i-1},l_i) \\in \\si{k_{i+1}+2}{k_i} \\times \\si{k_{i+1}+1}{k_i-1}} \\GT(l_1,\\ldots,l_{n-1})\n$$\nand map it to\n$$\n\\begin{cases}\n\\,\\, \\pi_{\\q l,i-1} (A) \\times \\u {\\{ (l_{i-1},l_{i}) \\}} & \\mbox{if } \\pi_{\\q l,i-1}(A) \\in \\GT(l_1,\\ldots,l_{n-1}) \\\\\u00a0\n-\\pi_{\\q l,i-1} (A) \\times \\u {\\{ (l_{i}+1,l_{i-1}-1) \\}} & \\mbox{if } \\pi_{\\q l,i-1}(A) \\in -\\GT(l_1,\\ldots,l_{i-2},l_i+1,l_{i-1}-1,l_{i+1},\\ldots,l_{n-1})\n\\end{cases}\n$$\nwhich is feasible since $(l_{i}+1,l_{i-1}-1) \\in \\si{k_{i+1}+2}{k_i} \\times \\si{k_{i+1}+1}{k_i-1}$, and $\\u {\\{ (l_{i-1},l_{i}) \\}}$ and\n$\\u {\\{ (l_{i}+1,l_{i-1}-1) \\}}$ have the same sign in $\\si{k_{i+1}+2}{k_i} \\times \\si{k_{i+1}+1}{k_i-1}$. The second case is}\n\\end{proof}\n\n\\begin{problem} \\label{prob:tau}\n Given $\\q k =(k_1,\\ldots,k_n) \\in {\\mathbb Z}^n$ and $x \\in {\\mathbb Z}$, construct a sijection\n $$\\tau = \\tau_{\\q k,x} \\colon \\GT(k_1,\\ldots,k_n) \\Rightarrow \\bigsqcup_{i = 1}^n \\GT(k_1,\\ldots,k_{i-1},x+n-i,k_{i+1},\\ldots,k_n).$$\n\\end{problem}\n\\begin{proof}[Construction]\n In Problem \\ref{prob:gamma}, we constructed a normal sijection\n \\begin{multline*}\n \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{n-1}}{k_n} \\Rightarrow \\bigsqcup_{i = 1}^n \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{x+n-i} \\times \\si{x+n-i}{k_{i+1}} \\times \\cdots \\times \\si{k_{n-1}}{k_n}\\\\\n \\sqcup \\bigsqcup_{i = 1}^{n-2} \\cdots \\times \\si{k_{i-1}}{k_i} \\times \\si{k_{i+1}+1}{x+n-i-1} \\times \\si{k_{i+1}}{x+n-i-2} \\times \\si{k_{i+2}}{k_{i+3}} \\times\\cdots ,\n \\end{multline*}\nwhich gives a sijection\n\\begin{multline*}\\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} \\GT(\\q l) \\Rightarrow \\bigsqcup_{i = 1}^n \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{x+n-i} \\times \\si{x+n-i}{k_{i+1}} \\times \\cdots \\times \\si{k_{n-1}}{k_n}}\\GT(\\q l) \\\\\n\\sqcup \\bigsqcup_{i = 1}^{n-2} \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{k_i} \\times \\si{k_{i+1}+1}{x+n-i-1} \\times \\si{k_{i+1}}{x+n-i-2} \\times \\si{k_{i+2}}{k_{i+3}} \\times\\cdots \\times \\si{k_{n-1}}{k_n}} \\GT(\\q l).\n\\end{multline*}\nAll disjoint unions in the second term satisfy the conditions for the existence of $\\sigma$ from Problem \\ref{prob:pi}, so we can siject them to $\\u \\emptyset$. This gives a sijection\n $$\\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{n-1}}{k_n}} \\GT(\\q l) \\Rightarrow \\bigsqcup_{i = 1}^n \\bigsqcup_{\\q l \\in \\si{k_1}{k_2} \\times \\cdots \\times \\si{k_{i-1}}{x+n-i} \\times \\si{x+n-i}{k_{i+1}} \\times \\cdots \\times \\si{k_{n-1}}{k_n}}\\GT(\\q l),$$\n which is, by the definition of $\\GT$, a sijection $\\GT(k_1,\\ldots,k_n) \\Rightarrow \\bigsqcup_{i = 1}^n \\GT(k_1,\\ldots,k_{i-1},x+n-i,k_{i+1},\\ldots,k_n)$.\n \\comment{Finally, for each $i$, apply the sijection\n $$\\pi_{(k_1,\\ldots,k_{i-1},k_{i+1}+1,\\ldots,k_{n-1}+1,x+1,k_n),n-1} \\circ \\cdots \\circ \\pi_{(k_1,\\ldots,k_{i-1},k_{i+1}+1,x+n-i-1,k_{i+2},\\ldots,k_n),i+1} \\circ \\pi_{(k_1,\\ldots,k_{i-1},x+n-i,k_{i+1},\\ldots,k_n),i}$$\n from $\\GT(k_1,\\ldots,k_{i-1},x+n-i,k_{i+1},\\ldots,k_n)$ to $(-1)^{n-i} \\GT(k_1,\\ldots,k_{i-1},k_{i+1}+1,\\ldots,k_n+1,x)$.}\n\\end{proof}\n\n\n\\section{Combinatorics of the monotone triangle recursion} \\label{sec:recursion}\n\n\\subsection*{Monotone triangles}\n\nSuppose that $\\q k = (k_1,\\ldots,k_n)$ and $\\q l = (l_1,\\ldots,l_{n-1})$ are two sequences of integers. We say that $\\q l$ \\emph{interlaces} $\\q k$, $\\q l \\prec \\q k$, if the following holds:\n\\begin{enumerate}\n \\item for every $i$, $1 \\leq i \\leq n-1$, $l_i$ is in the closed interval between $k_i$ and $k_{i+1}$;\n \\item if $k_{i-1} \\leq k_i \\leq k_{i+1}$ for some $i$, $2 \\leq i \\leq n-1$, then $l_{i-1}$ and $l_i$ cannot both be $k_i$;\n \\item if $k_i > l_i = k_{i+1}$, then $i \\leq n-2$ and $l_{i+1} = l_i = k_{i+1}$;\n \\item if $k_i = l_i > k_{i+1}$, then $i \\geq 2$ and $l_{i-1} = l_i = k_i$.\n\\end{enumerate}\n\nFor example, if $k_1 < k_2 < \\ldots < k_n$, then $l_i \\in [k_i,k_{i+1}]$ and $l_1 < l_2 < \\ldots < l_{n-1}$.\n\n\\medskip\n\nA \\emph{monotone triangle of size $n$} is a map $T \\colon \\{(i,j) \\colon 1 \\leq j \\leq i \\leq n \\} \\to {\\mathbb Z}$ so that line $i-1$ (i.e.~the sequence $T_{i-1,1},\\ldots,T_{i-1,i-1}$) interlaces line $i$ (i.e.~the sequence $T_{i,1},\\ldots,T_{i,i}$).\n\\begin{example} {The following is a monotone triangles of size $5$:}\n$$\\begin{array}{ccccccccc}\n&&&& 4 &&&& \\\\\n&&& 3 && 5 &&& \\\\\n&& 3 && 4 && 5 && \\\\\n& 3 & & 3 & & 4 & & 5 & \\\\\n5 & & 3 & & 1 & & 4 & & 6\n\\end{array}\n$$\n\\end{example}\n{This notion of (generalized) monotone triangle was introduced in \\cite{Rie13}. Other notions appeared in \\cite{Fis12}.}\u00a0\n\n\n\\medskip\n\nThe \\emph{sign} of a monotone triangle $T$ is $(-1)^r$, where $r$ is the sum of:\n\\begin{itemize}\n \\item the number of strict descents in the rows of $T$, i.e.~the number of pairs $(i,j)$ so that $1 \\leq j < i \\leq n$ and $T_{i,j} > T_{i,j+1}$, and\n \\item the number of $(i,j)$ so that $1 \\leq j \\leq i - 2$, $i \\leq n$ and $T_{i,j} > T_{i-1,j} = T_{i,j+1} = T_{i-1,j+1} > T_{i,j+2}$.\n\\end{itemize}\n{The sign of our example is $-1$.}\n\n\\medskip\n\nWe denote the signed set of all monotone triangles with bottom row $\\q k$ by ${\\MT(\\q k)}$.\n\n\\medskip\n\nIt turns out that $\\MT(\\q k)$ satisfies a recursive ``identity''. Let us define the signed set of \\emph{arrow rows of order $n$} as\n$$\\AR_n = (\\{\\nearrow,\\nwarrow\\},\\{\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow\\})^{n}.$$\nAlternatively, we can think of them as rows of length $n$ with elements $\\nwarrow, \\nearrow, \\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$, where the positive elements are precisely those with an even number of $\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$'s.\n\n\\medskip\n\nThe role of an arrow row $\\mu$ of order $n$ is that it induces a deformation of $\\si{k_1}{k_2} \\times \\si{k_2}{k_3} \\times \\cdots \\times \\si{k_{n-1}}{k_n}$ as follows. Consider\n$$\n\\begin{array}{ccccccccccc}\n & \\si{k_1}{k_2} & & \\si{k_2}{k_3} & & \\ldots & & \\si{k_{n-2}}{k_n-1} & & \\si{k_{n-1}}{k_n} & \\\\\n \\mu_1 & & \\mu_2 & & \\mu_3 & & \\ldots & & \\mu_{n-1} & & \\mu_{n} ,\n \\end{array}\n $$\nand if $\\mu_i \\in \\{\\nwarrow, \\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow\\}$ (that is we have an arrow pointing towards $\\si{k_{i-1}}{k_i}$) then\n$k_i$ is decreased by $1$ in $\\si{k_{i-1}}{k_i}$, while there is no change for this $k_i$ if $\\mu_i=\\nearrow$. If $\\mu_i \\in \\{\\nearrow, \\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow\\}$ (that is we have an arrow pointing towards $[k_i,k_{i+1}]$) then $k_i$ is increased by $1$ in $[k_i,k_{i+1}]$, while there is no change for this $k_i$ if $\\mu_i=\\nwarrow$.\n\n\\medskip\n\nFor a more formal description, we let $\\delta_{\\nwarrow}(\\nwarrow) = \\delta_{\\nwarrow}(\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow) = \\delta_{\\nearrow}(\\nearrow) = \\delta_{\\nearrow}(\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow) = 1$ and $\\delta_{\\nwarrow}(\\nearrow) = \\delta_{\\nearrow}(\\nwarrow) = 0$, and we define\n$$e(\\q k,\\mu) = \\si{k_1+\\delta_{\\nearrow}(\\mu_1)}{k_2-\\delta_{\\nwarrow}(\\mu_2)} \\times \\ldots \\times \\si{k_{n-1}+\\delta_{\\nearrow}(\\mu_{n-1})}{k_n-\\delta_{\\nwarrow}(\\mu_n)}.$$\nfor $\\q k = (k_1,\\ldots,k_n)$ and $\\mu \\in \\AR_n$.\n\n\\begin{problem} \\label{prob:Xi}\n Given $\\q k = (k_1,\\ldots,k_n)$, construct a sijection\n $$\\Xi = \\Xi_{\\q k} \\colon \\MT(\\q k) \\Rightarrow \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\MT(\\q l).$$\n\\end{problem}\n\\begin{proof}[Construction]\n All elements on the left are mapped to the right with $\\Xi$, while there are quite a few cancellations on the right-hand side. More specifically, take a monotone triangle $T$ with bottom row $\\q k$. Then $\\Xi(T) = ((T',\\q l),\\mu)$, where $T'$ is the monotone triangle we obtain from $T$ by deleting the last row, $\\q l$ is the bottom row of $T'$, and $\\mu = (\\mu_1,\\ldots,\\mu_n)$ is the arrow row defined as follows:\n \\begin{itemize}\n \\item $\\mu_1 = \\nwarrow$;\n \\item $\\mu_n = \\nearrow$;\n \\item for $ 1 < i < n$, $\\mu_i$ is determined as follows:\n \\begin{enumerate} \\item if $k_{i-1} \\leq l_{i-1} = k_i$, take $\\mu_i = \\nearrow$; \\item if $k_{i-1} > l_{i-1} = k_i = l_{i} > k_{i+1}$, take $\\mu_i = \\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$; \\item otherwise, take $\\mu_i = \\nwarrow$. \\end{enumerate}\n \\end{itemize}\n It is easy to check that $\\q l$ is indeed in $e(\\q k,\\mu)$.\n Note that in (1) and (2) of the third bullet point, $\\mu_i$ is forced if we require $\\q l \\in e(\\q k,\\mu)$. In (3), $\\mu_i=\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$ would also be possible if and only if $\\mu_i=\\nearrow$ would also be possible.\\\\\n On the other hand, for $((T',\\q l),\\mu)$, define $\\Xi((T',\\q l),\\mu)$ as follows. For the construction it is useful to keep in mind that $\\q l \\in e(\\q k, \\mu)$ implies that conditions (1) and (2) for $\\q l \\prec \\q k$ are satisfied.\n \\begin{itemize}\n \\item if $\\mu_1 \\neq \\nwarrow$, take $\\Xi((T',\\q l),\\mu) = ((T',\\q l),\\mu')$, where we obtain $\\mu'$ from $\\mu$ by replacing $\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$ in position $1$ by $\\nearrow$ and vice versa;\n \\item if $\\mu_1 = \\nwarrow$ and $\\mu_n \\neq \\nearrow$, take $\\Xi((T',\\q l),\\mu) = ((T',\\q l),\\mu')$, where we obtain $\\mu'$ from $\\mu$ by replacing $\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$ in position $n$ by $\\nwarrow$ and vice versa;\n \\item if $\\mu_1 = \\nwarrow$ and $\\mu_n = \\nearrow$, and $\\q l \\not\\prec \\q k$, find the smallest $i$ between $2$ and $n-1$ such that:\n \\begin{itemize}\n \\item condition (3) of $\\q l \\prec \\q k$ is not satisfied at $i$, i.e.\n $k_{i-1} > l_{i-1} = k_{i} \\not= l_{i}$ (which implies $\\mu_i \\in \\{\\nwarrow, \\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow\\}$), or\n \\item condition (4) of $\\q l \\prec \\q k$ is not satisfied at $i$, i.e.\n $l_{i-1} \\not= k_i = l_i > k_{i+1}$ (which implies $\\mu_i \\in \\{\\nearrow, \\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow\\}$).\n\\end{itemize}\nThen take $\\Xi((T',\\q l),\\mu) = ((T',\\q l),\\mu')$, where we obtain $\\mu'$ from $\\mu$ by replacing $\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$ in position $i$ by $\\nwarrow$ and vice versa in the first case, and replacing $\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$ in position $i$ by $\\nearrow$ and vice versa in the second case;\n \\item if $\\mu_1 = \\nwarrow$ and $\\mu_n = \\nearrow$, and $\\q l \\prec \\q k$, find the smallest $i$ for an instance of (3) of the third bullet point in the first paragraph of the proof with $\\mu_i \\not= \\nwarrow$ (if such an $i$ exists). Then take $\\Xi((T',\\q l),\\mu) = ((T',\\q l),\\mu')$, where we obtain $\\mu'$ from $\\mu$ by replacing $\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$ in position $i$ by $\\nwarrow$ and vice versa.\n \\end{itemize}\nIf no such $i$ exists, we take $\\Xi((T',\\q l),\\mu) = T$, where we obtain $T$ from $T'$ by adding $\\q k$ as the last row. It is easy to see that this is a well-defined sijection.\n\\end{proof}\n\n\n\\begin{remark} The previous construction could have been avoided by using alternative extensions of monotone triangles provided in \\cite{Fis12}. However, the advantage of the definition used in this paper is that it is more reduced than the others in the sense that it can obtained from these by cancelling elements using certain sign-reversing involutions.\n\\end{remark}\n\n\\subsection*{Arrow patterns and shifted GT patterns}\n\nDefine the signed set of \\emph{arrow patterns of order $n$} as\n$$\\AP_n = (\\{\\swarrow, \\searrow\\},\\{\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow\\})^{\\binom n 2}.$$\n\n\\medskip\n\nAlternatively, we can think of an arrow pattern of order $n$ as a triangular array $T=(t_{p,q})_{1 \\le p < q \\le n}$ arranged as\n$$T = \\begin{smallmatrix} & & & & t_{1,n} & & & & \\\\ & & & t_{1,n-1} & & t_{2,n} & & & \\\\ & & t_{1,n-2} & & t_{2,n-1}& & t_{3,n} & & \\\\ & \\vstretch{0.35}{\\udots} & \\vstretch{0.35}{\\vdots} & \\vstretch{0.35}{\\ddots} & \\vstretch{0.35}{\\vdots} & \\vstretch{0.35}{\\udots} & \\vstretch{0.35}{\\vdots} & \\vstretch{0.35}{\\ddots} & \\\\ t_{1,2} & & t_{2,3} & & \\ldots & & \\ldots & & t_{n-1,n} \\end{smallmatrix},$$\nwith $t_{p,q} \\in \\{\\swarrow, \\searrow, \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow\\}$, and the sign of an arrow pattern is $1$ if the number of $\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow$'s is even and $-1$ otherwise.\n\n\\medskip\n\nThe role of an arrow pattern of order $n$ is that it induces a deformation of $(k_1,\\ldots,k_n)$, which can be thought of as follows. Add $k_1,\\ldots,k_n$ as bottom row of $T$ (i.e., $t_{i,i}=k_i$), and for each $\\swarrow$ or$ \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow$ which is in the same $\\swarrow$-diagonal as $k_i$ add $1$ to $k_i$, while for each $\\searrow$ or $\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow$ which is in the same $\\searrow$-diagonal as $k_i$ subtract $1$ from $k_i$.\nMore formally, letting $\\delta_{\\swarrow}(\\swarrow) = \\delta_{\\swarrow}(\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow) = \\delta_{\\searrow}(\\searrow) = \\delta_{\\searrow}(\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow) = 1$ and $\\delta_{\\swarrow}(\\searrow) = \\delta_{\\searrow}(\\swarrow) = 0$, we set\n$$c_i(T) = \\sum_{j=i+1}^{n} \\delta_{\\swarrow}(t_{i,j}) - \\sum_{j=1}^{i-1} \\delta_{\\searrow}(t_{j,i}) \\mbox{ and } d(\\q k,T) = (k_1+c_1(T),k_2+c_2(T),\\ldots,k_n+c_n(T))$$\nfor $\\q k = (k_1,\\ldots,k_n)$ and $T \\in \\AP_n$.\n\n\\medskip\n\nFor $\\q k =(k_1,\\ldots,k_n)$ define \\emph{shifted Gelfand-Tsetlin patterns}, or SGT patterns for short, as the following disjoint union of GT patterns over arrow patterns of order $n$:\n$$\n\\SGT(\\q k) = \\bigsqcup_{T \\in \\AP_n} \\GT(d(\\q k,T))\n$$\n\n\n\\medskip\n\nConsidering that $|(\\{\\swarrow, \\searrow\\},\\{\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow\\})| = 1$ and therefore $|\\AP_n| = 1$, the following is not surprising.\n\n\\begin{problem} \\label{prob:Psi}\n Given $n$ and $i$, $1 \\leq i \\leq n$, construct a sijection\n $$\\Psi = \\Psi_{n,i} \\colon \\AP_{n-1} \\Rightarrow \\AP_n.$$\n\\end{problem}\n\\begin{proof}[Construction]\n For $T \\in \\AP_{n-1}$, take $\\Psi(T) = (t'_{p,q})_{1 \\leq p < q \\leq n}$ to be the arrow pattern defined by\n $$t'_{p,q} = \\begin{cases} t_{p,q} & \\mbox{if } p < q < i \\\\ t_{p,q-1} & \\mbox{if } p < i < q \\\\ t_{p-1,q-1} & \\mbox{if } i < p < q \\\\ \\searrow & \\mbox{if } p < q = i \\\\ \\swarrow & \\mbox{if } i = p < q \\end{cases}.$$\n{An example for $n=6$ and $i=4$ is\n$$\n\\begin{array}{ccccccc}\u00a0\n&&& \\searrow &&& \\\\\n&& \\swarrow && \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow && \\\\\n& \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow && \\swarrow && \\swarrow & \\\\\n\\searrow && \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow && \\searrow && \\swarrow\n\\end{array} \\quad \\stackrel{\\Psi}{\\Rightarrow} \\quad\n\\begin{array}{ccccccccc}\u00a0\n&&&& \\searrow &&&& \\\\\n&&& \\swarrow && \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow &&& \\\\\n&& {\\color{red} \\searrow} && \\swarrow && \\swarrow && \\\\\n& \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow && {\\color{red} \\searrow} && \\searrow && {\\color{red} \\swarrow} & \\\\\n\\swarrow && \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow && {\\color{red} \\searrow}\u00a0&& {\\color{red} \\swarrow} && \\swarrow\n\\end{array},\n$$\nwhere the new arrows are indicated in red.}\n If $T \\in \\AP_{n}$, $t_{p,i} = \\searrow$ for $p = 1,\\ldots,i-1$, $t_{i,q} = \\swarrow$ for $q = i+1,\\ldots,n$, take $\\Psi(T) = (t'_{p,q})_{1 \\leq p < q \\leq n-1}$, where\n $$t'_{p,q} = \\begin{cases} t_{p,q} & \\mbox{if } p < q < i \\\\ t_{p,q+1} & \\mbox{if } p < i \\leq q \\\\ t_{p+1,q+1} & \\mbox{if } i \\leq p < q \\end{cases}.$$\nOtherwise, there either exists $p$ so that $t_{p,i} \\neq \\searrow$, or there exists $q$ so that $t_{i,q} \\neq \\swarrow$. In the first case, define $\\Psi(T) = (t'_{p,q})_{1 \\leq p < q \\leq n}$, where $t'_{p,i}=\\swarrow$ if $t_{p,i} = \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow$ and $t'_{p,i}=\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow$ if $t_{p,i} = \\swarrow$, and all other array elements are equal. In the second case, define $\\Psi(T) = (t'_{p,q})_{1 \\leq p < q \\leq n}$, where $t'_{i,q}=\\searrow$ if $t_{i,q} = \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow$ and $t'_{i,q}=\\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow$ if $t_{i,q} = \\searrow$, and all other array elements are equal. It is easy to see that this is a sijection.\n\\end{proof}\n\nThe difficult part of this paper is to prove that $\\SGT$ satisfies the same ``recursion'' as $\\MT$. While the proof of the recursion was easy for monotone triangles, it is very involved for shifted GT patterns, and needs almost {all} the sijections we have constructed in this and previous sections.\n\n\\begin{problem}\nGiven $\\q k = (k_1,\\ldots,k_n) \\in {\\mathbb Z}^n$ and $x \\in {\\mathbb Z}$, construct a sijection\n$$\\Phi = \\Phi_{\\q k,x} \\colon \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\SGT(\\q l) \\Rightarrow \\SGT(\\q k).$$\n\\end{problem}\n\\begin{proof}[Construction]\nTo make the construction of $\\Phi$ a little easier, we will define it as the composition of several sijections. The first one will reduce the indexing sets (from a signed box to its ``corners'') using Problem \\ref{prob:rho}. The second one increases the order of the arrow patterns using the sijection from Problem \\ref{prob:Psi}. The third one further reduces the indexing set (from a signed set with $2^{n-1}$ elements to $\\si{1}{n}$). The last one gets rid of the arrow row and then uses Problem \\ref{prob:tau}.\\\\\nFor $\\mu \\in \\AR_n$, define $\\u S_i = (\\{k_i+\\delta_{\\nearrow}(\\mu_i)\\},\\emptyset) \\sqcup (\\emptyset,\\{k_{i+1}-\\delta_{\\nwarrow}(\\mu_{i+1})+1\\})$. Then $\\Phi$ is the composition of sijections\n\\begin{multline*}\n \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\SGT(\\q l) \\\\\\stackrel{\\Phi_1}\\Longrightarrow \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{T \\in \\AP_{n-1}} \\bigsqcup_{\\q m \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\GT(m_1+c_1(T),\\ldots,m_{n-1}+c_{n-1}(T),x) \\\\\n\\stackrel{\\Phi_2}\\Longrightarrow \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{T \\in \\AP_{n}} \\bigsqcup_{\\q m \\in \\u S_1 \\times \\cdots \\times \\u S_{n-1}} \\GT(m_1+c_1(T),\\ldots,m_{n-1}+c_{n-1}(T),x) \\\\ \\stackrel{\\Phi_3} \\Longrightarrow\n\\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{T \\in \\AP_{n}} \\bigsqcup_{i=1}^n \\GT(\\ldots,k_{i-1}+\\delta_{\\nearrow}(\\mu_{i-1})+c_{i-1}(T), x + n-i,k_{i+1}-\\delta_{\\nwarrow}(\\mu_{i+1})+c_{i}(T),\\ldots) \\\\\n\\stackrel{\\Phi_4}\\Longrightarrow \\SGT(\\q k), \\hspace*{12cm}\n\\end{multline*}\nwhere $\\Phi_1$, $\\Phi_2$, $\\Phi_3$, and $\\Phi_4$ are constructed as follows.\\\\\n\\emph{Construction of $\\Phi_1$.} By definition of $\\SGT$, we have\n$$\\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\SGT(\\q l) = \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\bigsqcup_{T \\in \\AP_{n-1}} \\GT(d(\\q l,T)).$$\nBy switching the inner disjoint unions, we get a sijection to\n$$\\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{T \\in \\AP_{n-1}} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\GT(d(\\q l,T)).$$\nThere is an obvious sijection from this signed set to\n$$\\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{T \\in \\AP_{n-1}} \\bigsqcup_{\\q l \\in d(e(\\q k,\\mu),T)} \\GT(\\q l),$$\n{by abuse of notation setting}\n$${ d(\\si{x_1}{y_1} \\times \\ldots \\si{x_{n-1}}{y_{n-1}},T)=\\si{x_1+c_1(T)}{y_1+c_1(T)} \\times \\cdots \\times \\si{x_{n-1}+c_{n-1}(T)}{y_{n-1}+c_{n-1}(T)}.}$$\nNow for each $\\mu$ and $T$, use the map $\\rho$ from Problem \\ref{prob:rho} for $a_i = k_i+\\delta_{\\nearrow}(\\mu_i)+c_i(T)$, $b_i = k_{i+1}-\\delta_{\\nwarrow}(\\mu_{i+1})+c_i(T)$, and $x$. We get a sijection to\n$$\\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{T \\in \\AP_{n-1}} \\bigsqcup_{\\q m \\in \\u {S}'_1 \\times \\cdots \\u{S}'_{n-1}} \\GT(m_1,\\ldots,m_{n-1},x),$$\nwhere $\\u{S}'_i = (\\{k_i+\\delta_{\\nearrow}(\\mu_i)+c_i(T)\\},\\emptyset) \\sqcup (\\emptyset,\\{k_{i+1}-\\delta_{\\nwarrow}(\\mu_{i+1})+c_i(T)+1\\})$. Finally, there is an obvious sijection from this signed set to\n$$\\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{T \\in \\AP_{n-1}} \\bigsqcup_{\\q m \\in \\u {S}_1 \\times \\cdots \\u{S}_{n-1}} \\GT(m_1+c_1(T),\\ldots,m_{n-1}+c_{n-1}(T),x).$$\n\\emph{Construction of $\\Phi_2$.} In Problem \\ref{prob:Psi}, we constructed sijections $\\Psi_{n,i} \\colon \\AP_{n-1} \\Rightarrow \\AP_n$. We construct $\\Phi_2$ by using Proposition \\ref{prop:sijections}~ (3) for $\\psi = \\Psi_{n,n}$, $\\u T = \\AP_{n-1}$, $\\u{\\widetilde T} = \\AP_n$,\n$$\\u S_T = \\bigsqcup_{\\q m \\in \\u {S}_1 \\times \\cdots \\u{S}_{n-1}} \\GT(m_1+c_1(T),\\ldots,m_{n-1}+c_{n-1}(T),x) \\quad \\mbox{for } T \\in \\AP_{n-1} \\sqcup \\AP_n$$\nand $\\varphi_T = \\u\\id$. This is well defined because $c_i(T) = c_i(\\Psi_{n,n}(T))$ for $T \\in \\AP_{n-1} \\sqcup \\AP_n$ and $i=1,\\ldots,n-1$.\\\\%Since $x(\\Phi(T)) = x(T)$ for all $T \\in \\AP_{n-1} \\sqcup \\AP_n$, this is well defined. \\\\\n\\emph{Construction of $\\Phi_3$.} Let $\\eta$ be the involution that maps $\\swarrow \\leftrightarrow \\nearrow, \\searrow \\leftrightarrow \\nwarrow, \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\leftrightarrow \\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$. The elements of the signed set $\\u S = \\u {S}_1 \\times \\cdots \\times \\u{S}_{n-1}$ are $(n-1)$-tuples of elements that are either $(k_i+\\delta_{\\nearrow}(\\mu_i),0)$ or $(k_{i+1}-\\delta_{\\nwarrow}(\\mu_{i+1})+1,1)$. Define $\\u S'$ as the subset of $\\u S$ containing tuples of the form $(\\ldots,(m_i,1),(m_{i+1},0),\\ldots)$, i.e.~the ones where we choose $k_{i+1}-\\delta_{\\nwarrow}(\\mu_{i+1})+1$ in position $i$ and $k_{i+1}+\\delta_{\\nearrow}(\\mu_{i+1})$ in position $i+1$ for some $i$. Then we can define a sijection\n$$ \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{T \\in \\AP_{n}} \\bigsqcup_{\\q m \\in \\u S'} \\GT(m_1+c_1(T),\\ldots,m_{n-1}+c_{n-1}(T),x) \\Rightarrow \\u \\emptyset$$\nas follows: given $\\mu \\in \\AR_n$, $T \\in \\AP_n$, $\\q m = (\\ldots,k_{i+1}-\\delta_{\\nwarrow}(\\mu_{i+1})+1,k_{i+1}+\\delta_{\\nearrow}(\\mu_{i+1}),\\ldots)$ (and $i$ is the smallest index where this happens), $A \\in \\GT(m_1+c_1(T),\\ldots,m_{n-1}+c_{n-1}(T),x)$, map $(((A,\\q m),T),\\mu)$ to $(((A',\\q m'),T'),\\mu')$, where:\n\\begin{itemize}\n \\item $A' = \\pi_{i,n}(A)$;\n \\item $T'$ is $T$ if $A'$ has the same bottom row as $A$; otherwise, $T'$ is obtained from $T$ by interchanging {$t_{i,j}$ and $t_{i+1,j}$ for $j>i+1$ as well as $t_{j,i}$ and $t_{j,i+1}$ for $j i$, replace $\\nwarrow$ with $\\nwarrow \\!\\!\\!\\!\\!\\;\\!\\! \\nearrow$ and vice versa in position $p$ to get $\\Lambda_{n,i}(\\mu)$ from $\\mu$. If $\\mu \\neq \\mu'$, $(\\delta_{\\nearrow}(\\mu_1),\\ldots,\\delta_{\\nearrow}(\\mu_{i-1}),\\delta_{\\nwarrow}(\\mu_{i+1}),\\ldots,\\delta_{\\nwarrow}(\\mu_n))$ are unaffected by this sijection, so it induces a sijection\n\\begin{multline*}\n \\bigsqcup_{\\mu \\in \\AR_n} \\GT(\\ldots,k_{i-1}+\\delta_{\\nearrow}(\\mu_{i-1})+c_{i-1}(T), x + n-i,k_{i+1}-\\delta_{\\nwarrow}(\\mu_{i+1})+c_{i}(T),\\ldots) \\\\\n \\Rightarrow \\GT(\\ldots,k_{i-1}+c_{i-1}(T), x + n-i,k_{i+1}+c_{i}(T),\\ldots).\n\\end{multline*}\nWe switch disjoint unions again, and we get\n$$\\bigsqcup_{i=1}^n \\bigsqcup_{T \\in \\AP_{n}} \\GT(k_1+c_1(T),\\ldots,k_{i-1}+c_{i-1}(T), x + n-i,k_{i+1}+c_{i}(T),\\ldots,k_{n} + c_{n-1}(T)).$$\nFor chosen $i$, use Proposition \\ref{prop:sijections} (3) for $\\psi = \\Psi_{n,i} \\circ \\Psi_{n,n}^{-1}$ and $\\varphi_t = \\id$. We get a sijection to\n$$\\bigsqcup_{i=1}^n \\bigsqcup_{T \\in \\AP_{n}} \\GT(k_1+c_1(T),\\ldots,k_{i-1}+c_{i-1}(T), x + n-i,k_{i+1}+c_{i+1}(T),\\ldots,k_{n} + c_{n}(T)).$$\nIf we switch disjoint unions one last time, we can use the sijection $\\tau^{-1}$ (see Problem \\ref{prob:tau}), and we get\n$$\\bigsqcup_{T \\in \\AP_n} \\GT(d(\\q k,T)) = \\SGT(\\q k).$$\nThis completes the construction of $\\Phi_4$ and therefore of $\\Phi$.\n\\end{proof}\n\n\\begin{problem}\n Given $\\q k = (k_1,\\ldots,k_n) \\in {\\mathbb Z}^n$ and $x \\in {\\mathbb Z}$, construct a sijection\n $$\\Gamma = \\Gamma_{\\q k,x} \\colon \\MT(\\q k) \\Rightarrow \\SGT(\\q k).$$\n\\end{problem}\n\\begin{proof}[Construction]\n The proof is by induction on $n$. For $n = 1$, both sides consist of one (positive) element, and the sijection is obvious. Once we have constructed $\\Gamma$ for all lists of length less than $n$, we can construct $\\Gamma_{\\q k,x}$ as the composition of sijections\n $$\\MT(\\q k) \\stackrel{\\Xi_{\\q k}}{\\Longrightarrow} \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\MT(\\q l) \\stackrel{\\sqcup \\sqcup \\Gamma}{\\Longrightarrow} \\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\SGT(\\q l) \\stackrel{\\Phi_{\\q k,x}}{\\Longrightarrow} \\SGT(\\q k),$$\n where $\\sqcup \\sqcup \\Gamma$ means $\\bigsqcup_{\\mu \\in \\AR_n} \\bigsqcup_{\\q l \\in e(\\q k,\\mu)} \\Gamma_{\\q l,x}.$\n\\end{proof}\n\nRunning the code shows that the main sijection $\\Gamma$ indeed depends on the choice $x$. As an example, take $\\q k = (1,2,3)$. In this case, $\\MT(\\q k)$ has $7$ positive elements, and $\\SGT(\\q k)$ has $10$ positive and $3$ negative elements. For $x = 0$, the sijection is given by\n$$\\mtthree 1 1 2 1 2 3 \\leftrightarrow \\left(\\mtthree 1 1 1 1 1 1,\\mttwo \\searrow \\searrow \\searrow\\right) \\qquad \\mtthree 2 1 2 1 2 3 \\leftrightarrow \\left(\\mtthree 2 1 2 1 2 2,\\mttwo \\searrow \\searrow \\swarrow\\right) \\qquad \\mtthree 1 1 3 1 2 3 \\leftrightarrow \\left(\\mtthree 1 1 2 1 2 2,\\mttwo \\searrow \\searrow \\swarrow\\right)$$\n$$\\mtthree 2 1 3 1 2 3 \\leftrightarrow \\left(\\mtthree 2 2 3 2 2 3,\\mttwo \\swarrow \\searrow \\swarrow\\right) \\qquad \\mtthree 3 1 3 1 2 3 \\leftrightarrow \\left(\\mtthree 3 2 3 2 2 3, \\mttwo \\swarrow \\searrow \\swarrow\\right) \\qquad \\mtthree 2 2 3 1 2 3 \\leftrightarrow \\left(\\mtthree 2 2 2 3 1 2, \\mttwo \\swarrow \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\searrow\\right)$$\n$$\\mtthree 3 2 3 1 2 3 \\leftrightarrow \\left(\\mtthree 3 3 3 3 3 3,\\mttwo \\swarrow \\swarrow \\swarrow\\right) \\qquad \\left(\\mtthree 2 2 2 2 2 3, \\mttwo \\swarrow \\searrow \\swarrow\\right) \\leftrightarrow \\left(\\mtthree 2 2 2 2 2 2,\\mttwo \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\searrow \\swarrow\\right)$$\n$$\\left(\\mtthree 2 2 2 2 3 1,\\mttwo \\searrow \\swarrow \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow\\right) \\leftrightarrow \\left(\\mtthree 2 2 2 2 2 2,\\mttwo \\swarrow \\searrow \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow\\right) \\qquad \\left(\\mtthree 2 2 2 1 2 2,\\mttwo \\searrow \\searrow \\swarrow\\right) \\leftrightarrow \\left(\\mtthree 2 2 2 2 2 2,\\mttwo \\searrow \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\swarrow\\right)$$\nwhile for $x = 1$, it is given by\n$$\\mtthree 1 1 2 1 2 3 \\leftrightarrow \\left(\\mtthree 1 1 1 1 1 1,\\mttwo \\searrow \\searrow \\searrow\\right) \\qquad \\mtthree 2 1 2 1 2 3 \\leftrightarrow \\left(\\mtthree 2 2 2 2 2 3,\\mttwo \\swarrow \\searrow \\swarrow\\right) \\qquad \\mtthree 1 1 3 1 2 3 \\leftrightarrow \\left(\\mtthree 1 1 2 1 2 2,\\mttwo \\searrow \\searrow \\swarrow\\right)$$\n$$\\mtthree 2 1 3 1 2 3 \\leftrightarrow \\left(\\mtthree 2 2 3 2 2 3,\\mttwo \\swarrow \\searrow \\swarrow\\right) \\qquad \\mtthree 3 1 3 1 2 3 \\leftrightarrow \\left(\\mtthree 3 2 3 2 2 3,\\mttwo \\swarrow \\searrow \\swarrow\\right) \\qquad \\mtthree 2 2 3 1 2 3 \\leftrightarrow \\left(\\mtthree 2 2 2 1 2 2,\\mttwo \\searrow \\searrow \\swarrow\\right)$$\n$$\\mtthree 3 2 3 1 2 3 \\leftrightarrow \\left(\\mtthree 3 3 3 3 3 3,\\mttwo \\swarrow \\swarrow \\swarrow\\right) \\qquad \\left(\\mtthree 2 1 2 1 2 2,\\mttwo \\searrow \\searrow \\swarrow\\right) \\leftrightarrow \\left(\\mtthree 2 2 2 2 2 2,\\mttwo \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\searrow \\swarrow\\right)$$\n$$\\left(\\mtthree 2 2 2 3 1 2, \\mttwo \\swarrow \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\searrow \\right) \\leftrightarrow \\left(\\mtthree 2 2 2 2 2 2, \\mttwo \\swarrow \\searrow \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\right) \\qquad \\left(\\mtthree 2 2 2 2 3 1,\\mttwo \\searrow \\swarrow \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\right) \\leftrightarrow \\left(\\mtthree 2 2 2 2 2 2, \\mttwo \\searrow \\swarrow \\!\\!\\!\\!\\!\\;\\!\\! \\searrow \\swarrow\\right)$$\n\n\\section{Concluding remarks}\n\n\\subsection*{Future work}\n\nIn this article, we have presented the first bijective proof of the operator formula. The operator formula is the main tool for non-combinatorial proofs of several results where alternating sign matrix objects are related to plane partition objects, or simply for showing that $n \\times n$ ASMs are enumerated by \\eqref{asm}.\n\n\\begin{itemize}\n\\item The operator formula was used in \\cite{Fis07} to show that $n \\times n$ ASMs are counted by \\eqref{asm} and, more generally, to count ASMs with respect to the position of the unique $1$ in the top row.\n\\item While working on this project, we actually realized that the final calculation in \\cite{Fis07} also implies that ASMs are equinumerous with DPPs without having to use Andrews' result \\cite{And79} on the number of DPPs; more generally, we can even obtain the equivalence of the refined count of $n \\times n$ ASMs with respect to the position of the unique $1$ in the top row and the refined count of DPPs with parts no greater than $n$ with respect to the number of parts equal to $n$. This was conjectured in \\cite{MilRobRum83} and first proved in \\cite{BehDifZin12}.\n\\item In \\cite{Fis19a}, the operator formula was used to show that ASTs with $n$ rows are equinumerous with TSSCPPs in a $2n \\times 2n \\times 2n$-box. Again we do not rely on Andrews' result \\cite{And94} on the number of TSSCPPs and we were actually able to deal with a refined count again (which has also the same distribution as the position of the unique $1$ in the top row of an ASM).\n\\item In \\cite{Fis19b}, we have considered alternating sign trapezoids (which generalize ASTs) and, using the operator formula, we have shown that they are equinumerous with objects generalizing DPPs. These objects were already known to Andrews and he actually enumerated them in \\cite{And79}. Later Krattenthaler \\cite{Kra06} realized that these more general objects are (almost trivially) equivalent to cyclically symmetric lozenge tilings of a hexagon with a triangular hole in the center. Again we do not rely on Andrews' enumeration of these generalized DPPs, and in this case we were able to include three statistics.\n\\end{itemize}\n\nWe plan to work on converting the proofs just mentioned into bijective proofs. For those mentioned in the first and second bullet point, this has already been worked out.\nThe attentive reader will have noticed that working out all of them will link all four known classes of objects that are enumerated by \\eqref{asm}.\n\n\\subsection*{Computer code}\n\nAs mentioned before, we consider computer code for the constructed sijections an essential part of this project. The code (in python) is available at \n\\begin{center}\n\\url{https:\/\/www.fmf.uni-lj.si\/~konvalinka\/asmcode.html}.\n\\end{center}\n All the constructed sijections are quite efficient. If run with pypy, checking that $\\Gamma_{(1,2,3,4,5),0}$ is a sijection between $\\MT(1,2,3,4,5)$ (with $429$ positive elements and no negative elements) and $\\SGT(1,2,3,4,5)$ (with $18913$ positive elements and $18484$ negative elements) takes less than a minute. Of course, the sets involved can be huge, so checking that $\\Gamma_{(1,2,3,4,5,6),0}$ is a sijection between $\\MT(1,2,3,4,5,6)$ (with $7436$ positive elements and no negative elements) and $\\SGT(1,2,3,4,5,6)$ (with $11167588$ positive elements and $11160152$ negative elements) took almost 20 hours.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction} \nGiven a complex non-K\\\"ahler $n$-dimensional manifold $(\\rm{M},\\rm{J})$ it is a natural and meaningful problem to find special Hermitian metrics which might help in understanding the geometry of $\\rm{M}$. Great effort has been spent in the last decades in this research topic and among special metrics the pluriclosed and the balanced conditions have shown to be highly significant.\\par \nThe balanced condition can be defined saying that the fundamental form $\\o=h(\\cdot,\\rm{J}\\cdot)$ of a Hermitian metric $h$ satisfies the non-linear condition $d\\o^{n-1}=0$ or equivalently, $-\\rm{J} \\theta = \\d\\o=0$, where $\\d$ denotes the codifferential and $\\theta$ the torsion $1$-form (see e.g. \\cite{Ga2}).\nWhile this concept appears in \\cite{Ga1} under the name of semi-K\\\"ahler (see also \\cite{Gra}), in \\cite{Mi} the balanced condition was started to be thoroughly investigated, highlighting also the duality with the K\\\"ahler condition and establishing necessary and sufficient conditions for the existence of these metrics in terms of currents. While K\\\"ahler metrics are obviously balanced and share with these the important relation among Laplacians $\\Delta_{\\partial}=\\Delta_{\\overline\\partial}=\\frac 12 \\Delta$ (see \\cite{Ga1}), there are many examples of non-K\\\"ahler manifolds carrying balanced metrics. Basic examples are given by compact complex parallelizable manifolds, which are covered by complex unimodular Lie groups $\\rm{G}$ and every left invariant Hermitian metric turns out to be balanced (see \\cite{AG}\\cite{Ga1}\\cite{Gr}). Further examples of balanced metrics are provided by any Hermitian invariant metric on a compact homogeneous flag manifold (see also \\cite{FGV} for a characterization of compact homogeneous complex manifolds carrying balanced metrics) as well as by twistor spaces of certain self-dual $4$-manifolds (\\cite{Mi}) and more generally (\\cite{To}) by twistor spaces of compact hypercomplex manifolds (see also \\cite{Fo} for other examples on toric bundles over hyperk\\\"ahler manifolds). Contrary to the K\\\"ahlerness condition, being balanced is a birational invariant (see \\cite{AB1}, so that e.g. Moishezon manifolds are balanced) and compact complex manifolds $X$ which can be realized as the base of a holomorphic proper submersion $f:Y\\to X$ inherit the balanced condition whenever $Y$ has it (\\cite{Mi}), while the balanced property is not stable under small deformations of the complex structure (see \\cite{AB2},\\cite{FuY}, \\cite{AU}). On the other hand, the balanced condition is obstructed, as on compact manifolds with balanced metrics no compact complex hypersurface is homologically trivial, so that for instance Calabi-Eckmann manifolds do not carry balanced metrics. This is in contrast with the fact that Gauduchon metrics, which statisfy the weaker condition $\\partial\\bar\\partial\\o^{n-1}=0$, always exist on a compact complex manifold. \\par \nIn more recent years, the rising interest in the Strominger System (see \\cite{GF} and \\cite{FeY} for the case of invariant solutions on complex Lie groups) has given balanced metrics a really central role in non-K\\\"ahler geometry, as the equivalence between the dilatino equation (i.e. one of the equations of the system) and the conformally balanced equation requires the solutions of the system to be necessarily balanced. We refer also to the work \\cite{FLY}, where new examples of balanced metrics are constructed on some Calabi Yau non-K\\\"ahler threefolds, as well as to the results in \\cite{BV}, where a new balanced flow is introduced and investigated. \\par \nThe main goal of this paper is to search for invariant special Hermitian, in particular balanced, metrics in the class of semisimple real non-compact Lie groups and on their compact (non-K\\\"ahler) quotients by a cocompact lattice; actually it appears that, despite invariant complex structures on semisimple (reductive) Lie algebras being fully classified in \\cite{Sn} (after the special case of compact Lie algebras had been considered by Samelson (\\cite{Sam}) and later in \\cite{Pi}), they have never been deeply investigated from this point of view. In contrast, the case of $\\K$ compact is fully understood, as in such a case it is very well known that every invariant complex structure can be deformed to an invariant one for which the opposite of the Cartan-Killing form is a pluriclosed Hermitian metric $h$, i.e. it satisfies $dd^c\\o_h=0$. Moreover it has been proved in \\cite{FGV} that $\\K$ does not carry {\\it any} balanced metric at all, fueling the conjecture (\\cite{FV}) that a compact complex manifold carrying two Hermitian metrics, one balanced and the other pluriclosed, must be actually K\\\"ahler.\\par\nMore specifically, in this work we focus on a large class of simple non-compact real Lie algebras $\\gg_o$ of even dimension, namely those which are of inner type, i.e. when the maximal compactly embedded subalgebra $\\mathfrak{k}$ in a Cartan decomposition of $\\gg_o$ contains a Cartan subalgebra. In these algebras we construct standard invariant complex structures (regular in \\cite{Sn}) and write down the balanced condition for invariant Hermitian metrics. A careful analysis of the resulting equation together with some general argument on root systems allows us to show the existence of a suitable invariant complex structure and a corresponding Hermitian metric satisfying the balanced equation. By Borel's Theorem, every semisimple Lie group $\\rm{G}_o$ admits a cocompact lattice $\\Gamma$ so that the compact quotient $\\rm{G}_o\/\\Gamma$ inherits the invariant balanced structure from $\\rm{G}_o$. We note here that the resulting metrics come in families and moreover the same kind of arguments can be applied to show the existence of balanced structures on quotients $\\rm{G}_o\/\\S$, where $\\rm{G}_o$ is any simple non-compact Lie group of inner type of any dimension and $\\S$ is a suitable abelian closed subgroup.\\par \nWe are also able to prove that the compact quotients $\\rm{M}=\\rm{G}_o\/\\Gamma$, endowed with the invariant complex structure that allows the existence of balanced metrics, do not carry any pluriclosed metric. This result is in accordance with the conjecture by Fino and Vezzoni and in some sense reflects a kind of duality between the compact and non-compact case, switching the existence of balanced\/pluriclosed Hermitian metrics. In the last section, we prove that these balanced manifolds $\\rm{M}$, despite having vanishing first Chern class, carry no non trivial holomorphic $(n,0)$-forms; furthermore we prove that they have vanishing Chern scalar curvature. This last property may allow to better understand the geometry of these manifolds, according to some more recent results concerning the implications of vanishing Chern-scalar curvature on some geometric features (see \\cite{Y}). \\par\nThe paper is structured as follows. In Section 2, we review basic facts on simple real non-compact Lie algebras with invariant complex structures and we consider a class of invariant Hermitian metrics for which we write down the balanced condition in terms of roots. In section 3 we state our main result, namely Theorem \\ref{main}, and we prove it by means of several steps. We first rewrite the balanced equation in terms of simple roots and then the key Lemma \\ref{L1} allows us to select an invariant complex structure so that the relative balanced equation admits solutions. In section 4 we prove that the complex manifolds that we constructed in the previous section and that admit balanced metrics, do not carry {\\it any} pluriclosed metric. In the last section, we show in Theorem 5.1 that these complex compact manifolds $(\\rm{M},\\rm{J})$ have trivial first Chern class and that the balanced metrics we have constructed have vanishing Chern scalar curvature; as a consequence we show that the Kodaira dimension $\\kappa(\\rm{M})=-\\infty$.\n\n\n\\par \n\\vspace{0.5cm}\n{\\bf Aknowledgements.} The second author was supported by GNSAGA of INdAM and by the project PRIN 2017 ``Real and Complex Manifolds: Topology, Geometry and Holomorphic Dynamics'', n. 2017JZ2SW5.\\par \nThe authors would like to thank Daniele Angella for valuable conversations. \n\n\\section{Preliminaries}\n\nLet $\\gg_o$ be a real simple $2n$-dimensional Lie algebra. It is well known that either the complexification $\\gg_o^c$ is a complex simple Lie algebra (and in this case $\\gg_o$ is called absolutely simple) or $\\gg_o$ is the realification $\\gg_\\mathbb R$ of a complex simple Lie algebra $\\gg$ (see e.g. \\cite{He}). \\par \nWhen $\\gg_o$ is even dimensional, it is known (\\cite{Mo}, see also \\cite{Sas}) that $\\gg_o$ admits an invariant complex structure, namely an endomorphism $\\rm{J}\\in \\End(\\gg_o)$ with $\\rm{J}^2=-\\rm{Id}$ and vanishing Nijenhuis tensor or, equivalently, such that \n$$\\gg_o^c = \\gg_o^{10}\\oplus \\gg_o^{01},\\qquad [\\gg_o^{10},\\gg_o^{10}]\\subseteq \\gg_o^{10}.$$\nIf $\\rm{G}_o$ is any Lie group with Lie algebra $\\gg_o$, then the endomorphism $\\rm{J}$ defines a (left)-invariant complex structure on $\\rm{G}_o$. Moreover, thanks to a result due to Borel (\\cite{Bo}), there exists a discrete, torsionfree cocompact lattice $\\Gamma$ so that $\\rm{M}:= \\rm{G}_o\/\\Gamma$ is compact and the left-invariant complex structure $\\rm{J}$ on \n$\\rm{G}_o$ descends to a complex structure $\\rm{J}$ on $\\rm{M}$.\\par \nWe recall that when $\\rm{G}_o$ is compact and even-dimensional, i.e. $\\gg_o$ is of compact type, the existence of an invariant complex structure was already established by Samelson (\\cite{Sam}), while in \\cite{Pi} it was shown that every invariant complex structure on $\\rm{G}_o$ is obtained by means of Samelson's construction. \\par\nIf we now consider an even-dimensional $\\rm{G}_o$ and a compact quotient $\\rm{M}$ endowed with an invariant complex structure $\\rm{J}$, we are interested in the existence of special Hermitian metrics $h$. The following proposition states a known fact, namely the non-existence of (invariant) K\\\"ahler structures.\n\\begin{prop}\\label{invK} The group $\\rm{G}_o$ does not admit any invariant K\\\"ahler metric and the compact quotient $\\rm{M}=\\rm{G}_o\/\\Gamma$ is not K\\\"ahler.\n\\end{prop} \n\\begin{proof} The first assertion is contained in \\cite{Ch}, but we give here an elementary proof. If $\\o$ is an invariant symplectic form \non $\\gg_o$, then the closedness condition $d\\o=0$ can be written as follows for $x,y,z\\in\\gg_o$ \n\\begin{equation}\\label{closed}\\o([x,y],z)+\\o([z,x],y) + \\o([y,z],x)=0.\\end{equation}\nIf $B$ denotes the non-degenerate Cartan-Killing form of $\\gg_o$, then we can define the endomorphism $F\\in\\End(\\gg_o)$ by $B(Fx,y)=\\o(x,y)$ ($x,y\\in\\gg_o$) so that $F$ turns out to be a derivation by \\eqref{closed}. As $\\gg_o$ is semisimple, there exists a unique $z\\in \\gg_o$ with $F=\\ad(z)$, so that $z\\in \\ker\\o$, a contradiction. \\par\nWe now suppose that the compact manifold $\\rm{M}$ has a K\\\"ahler metric with K\\\"ahler form $\\o$. Using $\\o$ and a symmetrization procedure that goes back to \\cite{Be}, we now construct an {\\it invariant} K\\\"ahler form on $\\rm{G}_o$, obtaining a contradiction. We fix a basis $x_1,...,x_{2n}$ of $\\gg_o$ and we extend each vector as a left invariant vector fields on $G_o$; these vector fields can be projected down to $M$ as vector fields $x_1^*,\\ldots,x_{2n}^*$ that span the tangent space $TM$ at each point. As $\\rm{G}_o$ is semisimple, we can find a biinvariant volume form $d\\mu$, that also descends to a volume form on $\\rm{M}$. We now define a left-invariant non-degenerate $2$-form $\\phi$ on $G_o$ by setting\n$$\\phi_e(x_{i},x_j) := \n\\int_M \\o(x_{i}^*,x_{j}^*)\\ d\\mu.$$\nAs $\\mathcal L_{x_k^*}d\\mu = 0$ for every $k$, we have for every $i,j,k=1,\\ldots,2n$\n$$\\int_M x_k^*\\o(x_{i}^*,x_{j}^*)\\ d\\mu = \\int_M \\mathcal L_{x_k^*}( \\o(x_{i}^*,x_{j}^*)\\ d\\mu) = 0$$ by Stokes' theorem and therefore we obtain that \n$$d\\phi(x_{i},x_j,x_k) = \n\\int_M d\\o(x_{i}^*,x_j^*,x_{k}^*)\\ d\\mu = 0.$$\nThis implies that $\\phi$ is a symplectic form and the proof is concluded.\\end{proof}\nTherefore we are interested in the existence of special Hermitian metrics on the complex manifold ($\\rm{M},\\rm{J}$), in particular balanced and pluriclosed metrics, when the group $\\rm{G}_o$ is of non-compact type. \\par \nThe case of a simple Lie algebra $\\gg_o$ which is the realification of a complex simple Lie algebra $\\gg$ can be easily treated and will be dealt with in subsection 2.3.\\par\nWe will now focus on some subclasses of simple real algebras, namely those which are absolutely simple and of inner type. \\par\n\\subsection{Simple Lie algebras of inner type}\\label{simple} Let $\\gg_o$ be an absolutely simple real algebra of non-compact type. It is well-known that $\\gg_o$ admits a Cartan decompositon \n$$\\gg_o = \\mathfrak{k} + \\mathfrak{p},$$\nwhere $\\mathfrak{k}$ is a maximal compactly embedded subalgebra and \n$$[\\mathfrak{k},\\mathfrak{p}]\\subseteq \\mathfrak{p},\\quad [\\mathfrak{p},\\mathfrak{p}]\\subseteq \\mathfrak{k},$$\nso that $(\\gg_o,\\mathfrak{k})$ is a symmetric pair. Moreover the algebra $\\gg_o$ is said to be {\\it of inner type} when the symmetric pair $(\\gg_o,\\mathfrak{k})$ is of inner type, i.e. when a Cartan subalgebra $\\mathfrak{t}$ of $\\mathfrak{k}$ is a Cartan subalgebra of $\\gg_o$, i.e. its complexification $\\mathfrak{t}^c$ is a Cartan subalgebra of $\\gg_o^c$. Using the notation as in \\cite{He}, p.~126, we obtain the list of all inner symmetric pairs $(\\gg_o,\\mathfrak{k})$ of non-compact type with $\\gg_o$ simple and even dimensional (Table 1). \\par\n\\begin{table}[ht]\\label{T1}\n\t\\centering\n\t\\renewcommand\\arraystretch{1.1}\n\t\\begin{tabular}{|c|c|c|c|}\n\t\t\\hline\n\t\t{\\mbox{Type}}\t\t\t\t& \t$\\gg$\t\t\t\t\t&\t$\\mathfrak{k}$\t& \t{\\mbox{conditions}}\t\t\t \t\\\\ \\hline \\hline\n\t$A$ &\t$\\mathfrak{su}(p,q)$ & $\\mathfrak{su}(p) + \\mathfrak{su}(q) +\\mathbb R$ & $p\\geq q\\geq 1$, \\ $p+q\\ {\\rm{odd}}$\t\t\t\t\\\\ \\hline\n\t$B$ & $\\mathfrak{so}(2p+1,2q)$ & $\\mathfrak{so}(2p+1) + \\mathfrak{so}(2q)$ & $p\\geq 0,q\\geq 1$, \\ $p+q\\ {\\rm{even}}$\t\t\t\\\\ \\hline\n\t$C$ & $\\mathfrak{sp}(2n,\\mathbb R)$ & $\\mathfrak{su}(2n)+\\mathbb R$ & $n\\geq 1$\t\t\t\\\\ \\hline\n\t$C$ & $\\mathfrak{sp}(p,q)$ & $\\mathfrak{sp}(p) + \\mathfrak{sp}(q)$ & $p,q\\geq 1$, \\ $p+q\\ {\\rm{even}}$\t\t\t\\\\ \\hline\n\t$D$ & $\\mathfrak{so}(4n)^*$ & $\\mathfrak{su}(2n)+\\mathbb R$ & $n\\geq 2$\t\t\t\\\\ \\hline\n\t$D$ & $\\mathfrak{so}(2p,2q)$ & $\\mathfrak{so}(2p) + \\mathfrak{so}(2q)$ & $p,q\\geq 1,p+q\\ \\rm{even}\\ \\geq 4$\t\t\t\\\\ \\hline\n\t$G$ & $\\gg_{2(2)}$ & $\\mathfrak{su}(2)+\\mathfrak{su}(2)$ & \t\t\t\\\\ \\hline\n\t$F$ & $\\mathfrak{f}_{4(-20)}$ & $\\mathfrak{so}(9)$ & \t\t\t\\\\ \\hline\n\t$F$ & $\\mathfrak{f}_{4(4)}$ & $\\mathfrak{su}(2)+\\mathfrak{sp}(3)$ & \t\t\t\\\\ \\hline\n\t$E$ & $\\mathfrak{e}_{6(2)}$ & $\\mathfrak{su}(2)+\\mathfrak{su}(6)$ & \t\t\t\\\\ \\hline\n\t$E$ & $\\mathfrak{e}_{6(-14)}$ & $\\mathfrak{so}(10)+\\mathbb R$ & \t\t\t\\\\ \\hline\n\t$E$ & $\\mathfrak{e}_{8(8)}$ & $\\mathfrak{so}(16)$ & \t\t\t\\\\ \\hline\n\t$E$ & $\\mathfrak{e}_{8(-24)}$ & $\\mathfrak{su}(2)+\\mathfrak{e}_7$ & \t\t\t\\\\ \\hline\n\t\\end{tabular}\n\t\\vspace{0.1cm}\n\t\\caption{Inner symmetric pairs $(\\gg,\\mathfrak{k})$ of non-compact type with $\\gg$ simple and even dimensional.}\\label{table}\n\\end{table}\n\n\\subsection{Invariant complex structures} In this section we will describe how to construct invariant complex structures on even-dimensional absolutely simple non-compact Lie algebras $\\gg_o$. \\par\nWe fix a maximal abelian subalgebra $\\mathfrak{t}\\subseteq \\mathfrak{k}$, so that $\\mathfrak{h}:= \\mathfrak{t}^c$ is a Cartan subalgebra of $\\gg:= \\gg_o^c$. Note that if $\\gg_o$ is even dimensional , the same holds for $\\mathfrak{t}$. The corresponding root system is denoted by $R$ and we have the following decompositions \n$$\\mathfrak{k}^c = \\mathfrak{t}^c \\oplus \\bigoplus_{\\a\\in R_\\mathfrak{k}}\\gg_\\a,\\quad \\mathfrak{p}^c = \\bigoplus_{\\a\\in R_\\mathfrak{p}}\\gg_\\a,$$\nwhere a root $\\a$ will be called {\\it compact} (resp. {\\it non-compact}), when $\\gg_\\a\\subseteq \\mathfrak{k}^c$ (resp. $\\gg_\\a\\subseteq \\mathfrak{p}^c$) and the set of all compact (resp. non-compact) roots is denoted by $R_\\mathfrak{k}$ (resp. $R_\\mathfrak{p}$). It is a standard fact that $\\mathfrak{u}:= \\mathfrak{k} + i\\mathfrak{p}\\subseteq \\gg$ is a compact real form of $\\gg$ and that we can choose a basis $\\{E_\\a\\}_{\\a\\in R}$ of root spaces so that \n$$\\tau(E_\\a) = -E_{-\\a}, \\qquad B(E_\\a,E_{-\\a}) = 1,\\qquad [E_\\a,E_{-\\a}] = H_\\a$$\nwhere $\\tau$ denotes the anticomplex involution defining $\\mathfrak{u}$, $B$ is the Cartan Killing form of $\\gg$ and $H_\\a$ is the $B$-dual of $\\a$ (see e.g. \\cite{He}). If $\\sigma$ is the involutive anticomplex map defining $\\gg_o$, we then have that \n$$\\sigma(E_\\a) = -E_{-\\a},\\quad \\a\\in R_\\mathfrak{k},$$\n$$\\sigma(E_\\a) = E_{-\\a},\\quad \\a\\in R_\\mathfrak{p}.$$\nIf we fix an ordering , namely a splitting $R = R^+\\cup R^-$ with $R^-=-R^+$ and $(R^++R^+)\\cap R \\subseteq R^+$, we can define a subalgebra \n$$\\mathfrak{q} := \\mathfrak{h}_1 \\oplus \\bigoplus_{\\a\\in R^+}\\gg_\\a,$$\nwhere $\\mathfrak{h}_1\\subset \\mathfrak{h}$ is a subspace so that $\\mathfrak{h}_1\\oplus \\sigma(\\mathfrak{h}_1)= \\mathfrak{h}$. The so defined subalgebra $\\mathfrak{q}\\subset \\gg$ satisfies \n$$\\gg = \\mathfrak{q} \\oplus \\sigma(\\mathfrak{q})$$\nand therefore it defines a complex structure $\\rm{J}$ on $\\gg_o$ with the property that $\\mathfrak{q} = \\gg_o^{10}$. This complex structure depends on the arbitrary choice of $\\mathfrak{h}_1$, i.e. on the arbitrary choice of a complex structure on $\\mathfrak{t}$. \\par \nWe remark that the complex structure $\\rm{J}$ enjoys the further property of being $\\ad(\\mathfrak{t})$-invariant, namely \n$$[\\ad(x),\\rm{J}] = 0,\\quad x\\in\\mathfrak{t}.$$\nTherefore if $\\rm{G}_o$ is a Lie group with Lie algebra $\\gg_o$, then $\\rm{J}$ extends to a left-invariant complex structure on $\\rm{G}_o$ and it will be also right-invariant with respect to right translations by elements $h\\in {\\rm{T}}:= \\exp(\\mathfrak{t})$ (note that ${\\rm{T}}$ might be non-compact, unless $\\rm{G}_o$ has finite center). \\par\nWe will call such an invariant complex structure {\\it standard}.\n\\begin{remark} In \\cite{Sn} the class of (simple) real Lie algebras of inner type is called ``Class I'' and it is then proved that {\\it every} invariant complex structure in these algebras are standard, with respect to a suitable choice of a Cartan subalgebra (such complex structures are called regular in \\cite{Sn}).\n\\end{remark}\n\n\\subsection{Invariant metrics and the balanced condition}\\label{inv}\nLet $\\rm{M}$ be a compact complex manifold of the form $\\rm{G}_o\/\\Gamma$, endowed with a complex structure $\\rm{J}$ which is induced by a standard invariant complex structure $\\rm{J}$ on $\\rm{G}_o$, as in the previous section. It is clear that any left invariant $\\rm{J}$-Hermitian metric $h$ on $\\rm{G}_o$ induces an Hermitian metric $\\bar h$ on $\\rm{M}$ and $\\bar h$ is balanced or pluriclosed if and only if $h$ is so. For the converse, we prove the following\n\\begin{prop} If $(\\rm{M},\\rm{J})$ admits a balanced (pluriclosed) Hermitian metric, there exists a left invariant and right $\\T$-invariant Hermitian metric on $\\rm{G}_o$ which is balanced (pluriclosed resp.). \\end{prop}\n\\begin{proof} Suppose we have a balanced metric $h$ on $M$ with associated fundamental form $\\o$. Then using the same notation and arguments as in the proof of Prop.\\ref{invK}, we define a left-invariant positive $(n-1,n-1)$-form $\\phi$ on $G_o$ as follows\n$$\\phi_e(x_{i_1},\\ldots,x_{i_{2n-2}}) := \n\\int_M \\o^{n-1}(x_{i_1}^*,\\ldots,x_{i_{2n-2}}^*)\\ d\\mu.$$\nAs $d\\o^{n-1}=0$, we obtain that also $d\\phi=0$. Therefore, we\ncan find an unique $(1,1)$-form $\\hat \\o$ so that $\\hat\\o^{n-1} = \\phi$ (see \\cite{Mi}) and the metric given by $\\hat \\o$ is balanced. As $\\phi$ is left invariant, so is $\\hat \\omega$ by uniqueness. Now, the group $\\Ad(\\T)$ is compact and using a standard avaraging process we can make $\\phi_e$ also $\\Ad(\\T)$-invariant. This means that $\\phi$ is also invariant under right $\\T$-translations. Again, by the uniqueness, the same will hold true for $\\hat\\omega$.\\par As for the pluriclosed condition, the lifted metric from $\\rm{M}$ to $\\rm{G}_o$ is clearly pluriclosed and can be made $T$-invariant by a standard averaging. \\end{proof}\n\\begin{remark} We can now deal with the case when $\\gg_o$ is the realification of a simple Lie algebra $\\gg$. In this case the complex structure $\\rm{J}$ commutes with $\\ad(\\gg_o)$ and $\\gg_o = \\mathfrak{u} + i\\mathfrak{u}$ is a Cartan decomposition, where $\\mathfrak{u}$ is a compact real form of $\\gg$. Let $\\rm{G}_o$ be a real group with algebra $\\gg_o$ and let $\\U$ be the compact subgroup with algebra $\\mathfrak{u}$. Then the metric $h$ which coincides with $-B$ on $\\mathfrak{u}$, with $B$ on $i\\mathfrak{u}$ and such that $h(\\mathfrak{u},i\\mathfrak{u})=0$ is a Hermitian metric which is balanced. Indeed, $h$ is $\\Ad(\\U)$-invariant and therefore the corresponding $\\d\\o$ is $\\Ad(\\U)$-invariant $1$-form, hence it vanishes identically. This is consistent with the fact that complex parallelizable manifolds carry balanced metrics as they carry Chern-flat metrics, as noted in \\cite{Ga1}, p. 121 (see also \\cite{AG},\\cite{Gr}). \\par \nOn the other hand, $\\rm{G}_o$ admits no invariant pluriclosed metric. Indeed, any such metric $h$ can be avaraged to produce an $\\Ad(\\U)$-invariant pluriclosed metric, which would be balanced by the previous argument. This is not possible, as a metric which is balanced and pluriclosed at the same time has to be K\\\"ahler (see e.g. \\cite{AI}), contrary to Prop \\ref{invK}.\\end{remark}\n\nWe now focus on the case where $\\gg_o$ is absolutely simple of inner type, endowed with an invariant complex structure. \nWe fix a Cartan subalgebra $\\mathfrak{t}\\subseteq \\mathfrak{k}$ with corresponding root system $R=R_\\mathfrak{k} \\cup R_\\mathfrak{p}$ as in section \\ref{simple} and we consider an ordering \n$R = R^+\\cup R^-$ giving an invariant complex structure $\\rm{J}_o$ on $\\gg_o\/\\mathfrak{t}$. We extend $\\rm{J}_o$ to an invariant complex structure $\\rm{J}$ on $\\gg_o$. \\par \nWe also fix a basis of a complement of $\\mathfrak{t}$ in $\\gg_o$\n$$v_\\a := \\frac 1{\\sqrt 2}(E_\\a-E_{-\\a}),\\ \nw_\\a := \\frac i{\\sqrt 2}(E_\\a+E_{-\\a}),\\ \\a\\in R_\\mathfrak{k}^+,$$\n$$v_\\a := \\frac 1{\\sqrt 2}(E_\\a+E_{-\\a}),\\ \nw_\\a := \\frac i{\\sqrt 2}(E_\\a-E_{-\\a}),\\ \\a\\in R_\\mathfrak{p}^+,$$\nso that $v_\\a,w_\\a\\in \\gg_o$ for every $\\a\\in R^+$ and moreover \n$$Jv_\\a = w_\\a, \\ Jw_\\a = -v_\\a,$$\n$$[H,v_\\a] = -i\\a(H)w_\\a,\\ H\\in\\mathfrak{h},$$ \n$$[v_\\a,w_\\a] = iH_\\a,\\ \\a\\in R_\\mathfrak{k}^+,$$\n$$[v_\\a,w_\\a] = -iH_\\a,\\ \\a\\in R_\\mathfrak{p}^+.$$\n\n\nWe now construct invariant Hermitian metrics $h$ on $\\gg_o$. First, we define $h$ on $\\mathfrak{t}$ by choosing a $J$-Hermitian metric $h_\\mathfrak{t}$ on $\\mathfrak{t}$. If we set $\\mathfrak{m}_\\a := {\\rm{Span}}\\{v_\\a,w_\\a\\}_{\\a\\in R^+}$, we define for $\\a\\neq \\b\\in R^+$\n$$h(\\mathfrak{t},\\mathfrak{m}_\\a)= 0,\\quad h(\\mathfrak{m}_\\a,\\mathfrak{m}_\\b)= 0,$$\n$$h(v_\\a,v_\\a) = h(w_\\a,w_\\a) = h_\\a^2,\\quad h(v_\\a,w_\\a) = 0$$\nfor $h_a\\in \\mathbb R^+$.\\par\nIn particular we are interested in constructing balanced Hermitian metrics, namely Hermitian metrics whose associated $(1,1)$-form $\\o=h(\\cdot,\\rm{J}\\cdot)$ satisfies $d\\o^{n-1}=0$ or equivalently $\\d\\o=0$, where $\\d$ denotes the codifferential. \\par \nWe use the expression \n$$\\d\\o (x) = -{\\rm{Tr}}\\nabla_{\\cdot}\\o(\\cdot,x) = - \\sum_{i}^{2n}\\nabla_{e_i}\\o(e_i,x) = $$\n$$= \\sum_i \\o(\\nabla_{e_i}e_i,x) + \\o(e_i,\\nabla_{e_{i}}x),$$\nwhere $\\nabla$ denotes the Levi Civita connection of $h$ and $\\{e_i\\}$ is an orthonormal basis of $\\gg_o$ w.r.t. $h$. Note that both $h$ and $\\rm{J}$ are $\\ad(\\mathfrak{t})$-invariant and therefore $\\d\\o$, which is also $\\ad(\\mathfrak{t})$-invariant, does not vanish only when evaluated on elements $x\\in \\mathfrak{t}$.\\par We have the following expression for the Levi Civita connection, namely for $x,y,z\\in \\gg_o$ \n$$2h(\\nabla_xy,z) = h([x,y],z) + h([z,x],y) + h([z,y],x).$$\nThen for every $x\\in\\mathfrak{t}$, $y\\in \\gg_o$ \n$$h(\\nabla_{y}y,x) = h([x,y],y) = 0.$$\nTherefore for $x\\in\\mathfrak{t}$ we have\n\\begin{equation}\\label{comp}\\d\\o(x) = \\sum_i\\o(e_i,\\nabla_{e_i}x) = -\\sum_ih(Je_i,\\nabla_{e_i}x) = \\end{equation}\n$$=-\\frac 12\\left( h([e_i,x],Je_i)+ \n h([Je_i,e_i],x) + h([Je_i,x],e_i)\\right).$$\nWe now observe that $J$ is $\\ad(\\mathfrak{t})$-invariant and therefore \n$h([Je_i,x],e_i) = -h([e_i,x],Je_i)$ for every $i=1,\\ldots,2n$, \nso that \\eqref{comp} can be written as \n$$-\\d\\o(x) = \\frac 12 \\sum_ih([Je_i,e_i],x)= $$\n$$= \\frac 12 \\cdot 2 \\left(\\sum_{\\a\\in R_\\mathfrak{k}^+}\\frac 1{h_\\a^2} h([w_\\a,v_\\a],x)+ \n\\sum_{\\a\\in R_\\mathfrak{p}^+}\\frac 1{h_\\a^2}h([w_\\a,v_\\a],x)\\right)=$$\n$$= \\sum_{\\a\\in R_\\mathfrak{k}^+}\\frac 1{h_\\a^2}h(-iH_\\a,x) + \n\\sum_{\\a\\in R_\\mathfrak{p}^+}\\frac 1{h_\\a^2}h(iH_\\a,x),$$\nso that \n$\\d\\o|_{\\mathfrak{t}} =0$ if and only if \n$$-\\sum_{\\a\\in R_\\mathfrak{k}^+}\\frac 1{h_\\a^2}H_\\a + \\sum_{\\a\\in R_\\mathfrak{p}^+}\\frac 1{h_\\a^2}H_\\a=0.$$\nSumming up, the metric $h$ is balanced when the following equation is satisfied\n\\begin{equation}\\label{eq}\\sum_{\\a\\in R_\\mathfrak{k}^+}\\frac 1{h_\\a^2}\\a = \\sum_{\\a\\in R_\\mathfrak{p}^+}\\frac 1{h_\\a^2}\\a.\\end{equation}\\par\\medskip\nNote that this does {\\it not} depend on the choice of the metric along the toral part $\\mathfrak{t}$.\n\n\\section{Main result}\\label{proof}\nIn this section we will prove our main result\n\\begin{theorem}\\label{main} Every non-compact simple Lie group $\\rm{G}_o$ of even dimension and of inner type admits an invariant complex structure $\\rm{J}$ and an invariant balanced $\\rm{J}$-Hermitian metric.\\end{theorem}\nNote that by Borel's Theorem, we can use a cocompact latice $\\Gamma\\subset\\rm{G}_o$ to obtain compact quotients $\\rm{M}=\\rm{G}_o\/\\Gamma$, which will inherit the same balanced structure. \\par \n\n\nWe start noting that equation \\eqref{eq} involves the unknowns $\\{h_\\a\\}_{\\a\\in R^+}$ and also a choice of positive roots, i.e. an ordering or equivalenty a complex structure on $\\gg_o$. We will always fix a complex structure on $\\mathfrak{t}$ once for all. It is known that giving an ordering on the root system $R$ is equivalent to the choice of a system of simple roots $\\Pi$ and that two systems of simple roots are conjugate under the action of the Weyl group $W$. We may fix a system of simple roots $\\Pi = \\{\\a_1,\\ldots,\\a_r\\}$ and put \n$\\Pi = \\Pi_c \\cup \\Pi_{nc}$, where $\\Pi_{c\/nc}$ denotes the set of simple roots which are compact or noncompact. We set $\\Pi_c=\\{\\phi_1,\\ldots,\\phi_k\\}$, $\\Pi_{nc}=\\{\\psi_1,\\ldots,\\psi_l\\}$, $k+l=r = {\\rm{rank}}(\\gg_o)$.\nEach root $\\a\\in R^+$ can be written as \n$$\\a = \\sum_{i=1}^k n_i(\\a)\\phi_i + \\sum_{j=1}^l m_j(\\a)\\psi_j$$\nfor $n_i(\\a),m_j(\\a)\\in\\mathbb N$ nonnegative integers. If we set $g_\\a:= \\frac 1{h_\\a^2}$ and $g_j:= g_{\\phi_j}, h_j := g_{\\psi_j}$, equation \\eqref{eq} can be written as \n$$\\sum_{\\a\\in R_\\mathfrak{k}^+,\\a\\not\\in\\Pi} g_\\a\\left(\\sum n_j(\\a)\\phi_j + \\sum_j m_j(\\a)\\psi_j\\right) + \\sum_j g_j \\phi_j = $$\n$$= \\sum_{\\a\\in R_\\mathfrak{p}^+,\\a\\not\\in\\Pi} g_\\a\\left(\\sum n_j(\\a)\\phi_j + \\sum_j m_j(\\a)\\psi_j\\right) + \\sum_j h_j \\psi_j,$$\nand therefore \n\\begin{equation}\\label{sys1}\n\\left\\{\\begin{aligned}\ng_j &= \\sum_{\\a\\in R_\\mathfrak{p}^+,\\ \\a\\not\\in\\Pi} g_\\a n_j(\\a) &- \\sum_{\\a\\in R_\\mathfrak{k}^+,\\ \\a\\not\\in\\Pi} g_\\a n_j(a),{}\\ \\ &j=1,\\ldots,k,\\\\\nh_j &= \\sum_{\\a\\in R_\\mathfrak{k}^+,\\ \\a\\not\\in\\Pi} g_\\a m_j(\\a) &- \\sum_{\\a\\in R_\\mathfrak{p}^+,\\ \\a\\not\\in\\Pi} g_\\a m_j(a),{}\\ \\ &j=1,\\ldots,l.\n\\end{aligned}\\right.\\end{equation}\n\\begin{remark} If we consider for instance the case $\\gg_o=\\mathfrak{su}(p,q)$ ($p+q$ even, $p,q\\geq 2$) and the standard system of simple roots $\\Pi=\\{\\epsilon_1-\\epsilon_2,\\epsilon_2-\\epsilon_3,\\ldots,\\epsilon_{p-1}-\\epsilon_p,\\epsilon_p-\\epsilon_{p+1},\\ldots,\\epsilon_{p+q-1}-\\epsilon_{p+q}\\}$ of $\\sl(p+q,\\mathbb C)$, then $\\Pi_{nc}=\\{\\epsilon_p-\\epsilon_{p+1}\\}$ and $\\Pi_c$ gives a system of simple roots for the semisimple part $\\mathfrak{k}_{ss}$ of $\\mathfrak{k}$. This means that every root $\\a\\in R_\\mathfrak{k}^+,\\a\\not\\in \\Pi$\nis a linear combination of roots in $\\Pi_c$ and therefore the righthandside of the last equation in \\eqref{sys1} is non-positive, so that \\eqref{sys1} has no solution. This shows that the choice of the invariant complex structure might not be straightforward. \n\\end{remark}\n\nThe following lemmata provide key tools in our argument.\n\\begin{lemma}\\label{L1} For each symmetric pair $(\\gg_o,\\mathfrak{k})$ as in Table 1, $(\\gg_o,\\mathfrak{k})\\not\\cong (\\mathfrak{so}(1,2n),\\mathfrak{so}(2n))$ and given a Cartan subalgebra $\\mathfrak{t}\\subseteq \\mathfrak{k}$ with corresponding root system $R$, there exists an ordering of the roots, hence a system of simple roots $\\Pi$, such that \n\\begin{equation}\\label{prop} \\forall \\psi \\in \\Pi_{nc}\\ \\exists \\psi'\\in \\Pi_{nc}\\ {\\rm{with}}\\ \\psi+\\psi'\\in R.\\end{equation} \n\\end{lemma}\nThis implies that, if $\\Pi_{nc}=\\{\\psi_1,\\ldots,\\psi_l\\}$, then for every $\\psi_j\\in \\Pi_{nc}$ there exists $\\a\\in R_\\mathfrak{k}^+$ with $m_j(\\a)\\neq 0$ and $\\a\\in {\\rm{Span}}\\{\\Pi_{nc}\\}$.\\par\\medskip\n\\noindent {\\bf Remark} Note that $\\sp(1,1)\\cong\\mathfrak{so}(1,4)$ is also not admissible in the above Lemma. In general, for $\\gg_o = \\mathfrak{so}(1,2n)$ we have the standard system $\\Pi=\\{\\epsilon_i-\\epsilon_{i+1},\\epsilon_n,\\ i=1,\\ldots,n-1\\}$ with \n$\\Pi_{nc}= \\{\\epsilon_n\\}$. As $R_c$ consists precisely of all the short roots, it is clear that for any element $\\sigma$ of the Weyl group $W\\cong \\mathbb Z_2^n\\ltimes \\mathcal S_n$ we have that $\\sigma(\\Pi)_{nc}$ consists of one element. We will deal with this case later on.\n\\begin{proof} We first deal with the classical case. We start with the standard system of simple roots $\\Pi$, following the notation as in \\cite{He}. It is immediate to check that in this case $\\Pi_{nc}$ consists of a single root $\\psi$. \\par \nWe first deal with the case where $\\psi$ is a short root. Let $\\Lambda$ be the set of all simple roots which are connected to $\\psi$ in the Dynkin diagram relative to $\\Pi$. If $s\\in W$ denotes the reflection around $\\psi$, then $s$ leaves every element $\\Pi\\setminus \\Lambda$ pointwise fixed. We observe that $\\Lambda$ consists of either at most three short roots or it contains a long root. In the first case, $s(\\Lambda) = \\{\\psi + \\lambda|\\ \\l\\in\\Lambda\\}\\subseteq R_\\mathfrak{p}$ so that $s(\\Pi)_{nc} = \\{-\\psi,s(\\Lambda)\\}$ and therefore the system of simple roots $s(\\Pi)$ satisfies \\eqref{prop}. If $\\Lambda$ contains a long root, then it also contains a short root, unless $(\\gg_o,\\mathfrak{k})=(\\mathfrak{so}(2,3),\\mathbb R + \\mathfrak{so}(3))$, that is isomorphic to $(\\sp(2),\\mathfrak{u}(2))$; this case will be dealt with in the second part of the proof. Therefore $\\Lambda = \\{\\phi_1,\\phi_2\\}$ with $\\phi_1$ short and $\\phi_2$ long. Again the reflection $s$ around $\\psi$ gives $s(\\phi_1) = \\psi+\\phi_1$ and $s(\\phi_2) = \\phi_2+2\\psi\\in R_\\mathfrak{k}$ or $s(\\phi_2) = \\psi+\\phi_2\\in R_\\mathfrak{p}$. This implies that the system of simple roots $s(\\Pi)$ has $s(\\Pi)_{nc} = \\{-\\psi,\\psi+\\phi_1\\}$ or $\\{-\\psi,\\psi+\\phi_1,\\psi+\\phi_2\\}$ and in both cases it satisfies \\eqref{prop}.\\par \nWe are left with the case where $\\psi$ is a long root, namely the case where $\\gg_o=\\sp(2n,\\mathbb R)$ and $\\mathfrak{k} = \\mathfrak{u}(2n)$. A standard system of simple roots is given by $\\Pi=\\{\\epsilon_1-\\epsilon_2,\\epsilon_2-\\epsilon_3,\\ldots,\\epsilon_{2n-1}-\\epsilon_{2n},2\\epsilon_{2n}\\}$ and $\\Pi_{nc}= \\{\\psi=2\\epsilon_{2n}\\}$. Again using $s_{\\b}$, we see that $s_\\b(\\Pi)_{nc}=\\{-2\\epsilon_{2n},\\epsilon_{2n-1}+\\epsilon_{2n}\\}$ so that condition \\eqref{prop} is satisfied.\\par\nWe may now deal with the exceptional cases. Starting with the standard system of simple roots $\\Pi$, we list the set $\\Pi_{nc}$, that turns out to consist of a single root $\\b$. For each case, using the symmetry $s_\\b$ we obtain the system of simple roots $\\Pi':=s_\\b(\\Pi)$ that satisfies condition \\eqref{prop}.\\par \n\\noindent (1)\\ $(\\gg_o,\\mathfrak{k}) = (\\gg_2,\\mathfrak{su}(2)+\\mathfrak{su}(2))$. Here $\\Pi=\\{\\a,\\b\\}$, with $\\b$ long. We have $\\Pi_{nc}=\\{\\b\\}$ and $\\Pi'=\\{-\\b,\\a+\\b\\}$.\\par\n\\noindent (2)\\ $(\\gg_o,\\mathfrak{k}) = (\\mathfrak{f}_{4(-20)},\\mathfrak{so}(9))$. According to \\cite{He}, the standard system of simple roots is $\\Pi=\\{\\a_1=\\epsilon_2-\\epsilon_3,\\a_2=\\epsilon_3-\\epsilon_4,\\a_3=\\epsilon_4,\\a_4=\\frac 12(e_1-\\epsilon_2-\\epsilon_3-\\epsilon_4)\\}$ so that \n$\\Pi_{nc}=\\{\\a_4\\}$ and therefore $\\Pi'_{nc}=\\{-\\a_4,\\a_4+\\a_3\\}$.\\par \n\\noindent (3)\\ $(\\gg_o,\\mathfrak{k}) = (\\mathfrak{f}_{4(4)},\\mathfrak{su}(2)+\\mathfrak{sp}(3))$. In this case $\\Pi_{nc}=\\{\\a_1\\}$ and therefore $\\Pi'_{nc}=\\{-\\a_1,\\a_1+\\a_2\\}$. \\par\n\\noindent\\ (4)\\ $(\\gg_o,\\mathfrak{k})=(\\mathfrak{e}_{8(8)},\\mathfrak{so}(16))$. For $\\mathfrak{e}_8$ we have the standard system of simple roots \n$$\\a_1=\\frac 12(\\epsilon_1+\\epsilon_8)-\\frac 12(\\epsilon_2+\\epsilon_3+\\epsilon_4+\\epsilon_5+\\epsilon_6+\\epsilon_7), \\a_2=\\epsilon_1+\\epsilon_2, $$ \n$$\\a_j=\\epsilon_{j-1}-\\epsilon_{j-2},\\ j=3,\\ldots,8.$$\nThen $\\Pi_{nc}=\\{\\a_1\\}$ and $\\Pi'_{nc}=\\{-\\a_1,\\a_1+\\a_3\\}$.\\par \n\\noindent\\ (5)\\ $(\\gg_o,\\mathfrak{k})=(\\mathfrak{e}_{8(-24)},\\mathfrak{su}(2)+\\mathfrak{e}_7)$. Keeping the same notation for simple roots as above, we have $\\Pi_{nc} = \\{\\a_8\\}$ and $\\Pi'_{nc}=\\{-\\a_8,\\a_8+\\a_7\\}$.\\par\n\\noindent\\ (6)\\ $(\\gg_o,\\mathfrak{k})=(\\mathfrak{e}_{6(2)},\\mathfrak{su}(2)+\\mathfrak{su}(6))$. As the system root system $\\Pi$ can be taken to be composed of the simple roots $\\{\\a_1,\\ldots,\\a_6\\}$ of $\\mathfrak{e}_8$, we have \n$\\Pi_{nc}=\\{\\a_2\\}$ and $\\Pi'_{nc}=\\{-\\a_2,\\a_2+\\a_4\\}$.\\par \n\\noindent\\ (7)\\ $(\\gg_o,\\mathfrak{k})=(\\mathfrak{e}_{6(-14)},\\mathbb R+\\mathfrak{so}(10))$. We have $\\Pi_{nc}=\\{\\a_1\\}$ and $\\Pi'_{nc}=\\{-\\a_1,\\a_1+\\a_3\\}$.\\end{proof}\n\n\n\\begin{lemma}\\label{L2} For every system of simple roots $\\Pi = \\Pi_{c}\\cup \\Pi_{nc}$ \n with $\\Pi_{c}=\\{\\phi_1,\\ldots,\\phi_k\\}$ we have \n$$\\forall\\ j=1,\\ldots,k,\\ \\exists\\ \\a\\in R_\\mathfrak{p}^+,\\ \\a\\not\\in \\Pi : \\ n_j(\\a)\\neq 0,$$\n where $n_j(\\a)$ denotes the coordinate of $\\a$ along the root $\\phi_j$.\n\\end{lemma}\n\\begin{proof} We start noting that the centralizer $C_{\\mathfrak{k}^c}(\\mathfrak{p}^c) = C_{\\mathfrak{k}}(\\mathfrak{p})^c = \\{0\\}$. It then follows that $[E_{\\phi_j},\\mathfrak{p}^c]\\neq \\{0\\}$, hence there exists $\\gamma\\in R_\\mathfrak{p}$ with $[E_{\\phi_j},E_\\gamma]\\neq 0$, i.e. $\\phi_j+\\gamma \\in R_\\mathfrak{p}$. Now, if $\\gamma>0$, then $\\a:= \\phi_j+\\gamma\\in R_\\mathfrak{p}^+\\setminus\\Pi$ and $n_j(\\a)\\geq 1$. Suppose now $\\gamma<0$. We write $\\gamma=c_j\\phi_j + \\sum_{\\theta\\in \\Pi\\setminus{\\phi_j}} c_\\theta\\theta$ for some nonpositive integers $c_j,c_\\theta$. As $\\gamma\\neq -\\phi_j$, there exists at least one negative coefficient $c_\\theta<0$, for some $\\theta\\in \\Pi,\\theta\\neq\\phi_j$. Therefore the root $\\gamma+\\phi_j$ must be negative and $1+c_j\\leq 0$, i.e. $\\a:=-\\gamma\\in R_\\mathfrak{p}^+\\setminus\\Pi$ and $n_j(\\a)=-c_j\\geq 1$.\n\\end{proof}\n\n\nWe now fix a system of simple roots $\\Pi$ as in Lemma \\ref{L1}. In order to solve the corresponding system of equations \\eqref{sys1} for the positive unknowns $\\{g_i,h_j,g_\\a\\}$, we will show how to choose the positive values $\\{g_\\a\\}_{\\a\\in R^+\\setminus\\Pi}$ in such a way to guarantee that the constants $\\{g_i,h_j\\}$,\ndefined to satisfy \\eqref{sys1}, are positive.\\par \nWe set \n$$\\Sigma_\\mathfrak{k} := \\{\\a\\in R_\\mathfrak{k}^+|\\ \\a\\not\\in \\Pi, \\a\\in {\\rm{Span}}\\{\\Pi_{nc}\\}\\},\\qquad A_\\mathfrak{k} = (R_\\mathfrak{k}\\setminus \\Pi_c) \\setminus \\Sigma_\\mathfrak{k}.$$\nThen the system of equations \\eqref{sys1} can be written as \n\\begin{equation}\\label{sys2}\n\\left\\{\\begin{aligned}\ng_j &= \\sum_{\\a\\in R_\\mathfrak{p}^+,\\ \\a\\not\\in\\Pi} g_\\a n_j(\\a) - \\sum_{\\a\\in A_\\mathfrak{k}} g_\\a n_j(a),{}\\ \\ &j=1,\\ldots,k,\\ &(1)\\\\\nh_j &= \\sum_{\\a\\in R_\\mathfrak{k}^+,\\ \\a\\not\\in\\Pi} g_\\a m_j(\\a) - \\sum_{\\a\\in R_\\mathfrak{p}^+,\\ \\a\\not\\in\\Pi} g_\\a m_j(a),{}\\ \\ &j=1,\\ldots,l.\\ &(2)\n\\end{aligned}\\right.\\end{equation}\nWe start assigning $g_\\a=1$ for every $\\a\\in A_\\mathfrak{k}$. \\par \nThen, for every $j=1,\\ldots,k$, we use Lemma \\ref{L2} selecting a root $\\a\\in R_\\mathfrak{p}^+$ with $n_j(\\a)\\neq 0$, $\\a\\not\\in \\Pi$. This root $\\a$, which depends on $j$, contributes to the first sum in the righthandside of equation (1) in \\eqref{sys2} and the value $g_\\a$ can be chosen big enough so that $g_j$ is strictly positive. Summing up, we can assign values \n$\\{g_\\a\\}_{\\a\\in R_\\mathfrak{p}^+\\setminus \\Pi_{nc}}$ so that all $g_j$, $j=1,\\ldots,k$ can be defined as in \\eqref{sys2}, (1), and are strictly positive. \\par \nWe now turn to equation \\eqref{sys2}-(2), which can now be written as \n\\begin{equation}\\label{2}h_j= \\sum_{\\a\\in \\Sigma_\\mathfrak{k}} g_\\a m_j(\\a) +\\sum_{\\a\\in A_\\mathfrak{k}} m_j(\\a) - \\sum_{\\a\\in R_\\mathfrak{p}^+,\\ \\a\\not\\in\\Pi} g_\\a m_j(a),\\end{equation}\nwhere in the righthandside the last two sums have a fixed value. Now, by Lemma \\ref{L1}, we know that for every $j=1,\\ldots,l$, we can find $\\a\\in \\Sigma_\\mathfrak{k}$ with $m_j(\\a)\\neq 0$. These roots can be used to choose the coefficients $g_\\a$ big enough to guarantee that $h_j>0$, when defined to satisfy \\eqref{2}, is strictly positive.\\par \nIn order to complete the proof of our main result Theorem \\eqref{main}, we are left with the case $(\\gg_o,\\mathfrak{k})= (\\mathfrak{so}(1,2n),\\mathfrak{so}(2n))$ with standard system of simple roots $\\Pi=\\{\\epsilon_i-\\epsilon_{i+1},\\epsilon_n,\\ i=1,\\ldots,n-1\\}$, $\\Pi_{nc}=\\{\\epsilon_n\\}$. We see that \n$$R_\\mathfrak{k}^+=\\{\\epsilon_i\\pm\\epsilon_j,\\ i< j\\},\\quad R_\\mathfrak{p} =\\{\\epsilon_1,\\ldots,\\epsilon_n\\}.$$\nNow, we use equation \\eqref{eq} and search for positive real numbers $\\{x,y,z_i,\\ i=1,\\ldots,n\\}$ so that \n$$x\\cdot\\sum_{iy>0$.\n\\begin{remark} We can consider the metric $h_o$ which coincides with $-B$ on the compact part $\\mathfrak{k}$, with $B$ on $\\mathfrak{p}$ and such that $h_o(\\mathfrak{k},\\mathfrak{p})=0$. This metric is easily seen to depend only on $\\gg_o$ and {\\it not} on the Cartan decomposition $\\gg_o=\\mathfrak{k}+\\mathfrak{p}$. We could then ask whether there exists a suitable complex structure such that the metric $h_o$ turns out to be balanced. The resulting equation has been already treated in \\cite{AP} and has a solution if and only if $\\gg_o = \\mathfrak{su}(p,p+1)\\cong \\mathfrak{su}(p+1,p)$ for $p\\geq 1$. \n \\end{remark}\n\\section{Non-existence of pluriclosed metrics}\nIn this section we prove the following non-existence result\n\\begin{proposition} The compact complex manifolds ($\\rm{M},\\rm{J}$), wheer $\\rm{M}=\\rm{G}_o\/\\Gamma$, do not admit any pluriclosed metric.\n \\end{proposition}\nNote that in the above statement $\\rm{J}$ is the complex structure we have exhibited in section \\ref{proof}. \\par \nNow, if $h$ is any such metric, we can obtain a pluriclosed invariant metric $h$ on $\\rm{G}_o$ which is also invariant under right $\\T$-translations. It follows that on $\\gg$ we have \n$$h(\\gg_\\a,\\gg_\\b) = 0 \\quad {\\rm{if}}\\quad \\b\\neq-\\a.$$\nIn order to write down the condition $dd^c\\o=0$, where $\\o$ is the fundamental form of $h$, we recall the Koszul's formula for the differential of invariant forms. If $\\phi$ is any invariant $k$-form on $\\rm{G}_o$ or equivalently on $\\gg_o$, then for every $v_o,\\ldots,v_{k}$ in $\\gg_o$ \n$$d\\phi(v_o,\\ldots,v_{k}) = \\sum_{i 0, \\qquad \\a\\in R_\\mathfrak{p}^+,$$\n$$h(H_\\a,H_\\b) = -h(iH_\\a,iH_\\b)\\in \\mathbb R,\\qquad \nh(H_\\a,H_\\a) < 0.$$\n\nNow, we recall that the existence of the complex structure $\\rm{J}$, which we constructed in section \\ref{proof}, relies on Lemma \\ref{L1}. In particular, when $\\gg_o\\neq \\mathfrak{so}(1,2n)$, we have the existence of two simple roots $\\psi_1,\\psi_2\\in \\Pi_{nc}$ with $\\psi_1+\\psi_2=\\phi\\in R_\\mathfrak{k}$. The following lemma is elementary.\n\\begin{lemma} Either $\\psi_1+2\\psi_2\\not\\in R$ or $\\psi_2+2\\psi_1\\not\\in R$.\\end{lemma}\n\\begin{proof} As $\\psi_1,\\psi_2$ are simple, we have $\\pm(\\psi_1-\\psi_2)\\not\\in R$. Now, $\\psi_i+n\\psi_j\\in R$ if and only if $0\\leq n\\leq q_j$ with \n$q_j = -2\\frac{\\langle \\psi_1,\\psi_2\\rangle}{||\\psi_j||^2}\\in\\mathbb N$ for $i\\neq j$. It is then clear that $q_1,q_2\\geq 2$ is impossible, as $\\psi_1\\neq \\psi_2$ implies $q_1\\cdot q_2 < 4$.\\end{proof}\nSuppone then that $\\phi+\\psi_1 = \\psi_2+2\\psi_1\\not \\in R$. We now apply \\eqref{eq1} with two possible choices for $\\a,\\b$, namely:\\par \\medskip\n\\noindent (1)\\ $\\a=\\psi_1,\\b=\\psi_2$. Then \n$$h(H_{\\psi_1},H_{\\psi_2}) = N_{\\psi_1,\\psi_2}^2(a_\\phi-a_{\\psi_1}-a_{\\psi_2}).$$\n(2)\\ $\\a=\\phi,\\b=\\psi_1$. Then \n$$h(H_{\\phi},H_{\\psi_2}) = N_{\\phi,-\\psi_1}^2(a_{\\psi_2}+a_{\\psi_1}-a_{\\phi}).$$\nSubtracting (1) from (2) we get \n$$h(H_{\\psi_2},H_{\\psi_2}) = \\left(N_{\\phi,-\\psi_1}^2+N_{\\psi_1,\\psi_2}^2\\right)(a_{\\psi_2}+a_{\\psi_1}-a_{\\phi}).$$\nThis is a contradiction, as $h(H_{\\psi_2},H_{\\psi_2})<0$, while \n$a_{\\psi_i}>0$ for $i=1,2$ and $a_\\phi<0$.\\par \nWe are left with the case $\\gg_o=\\mathfrak{so}(1,2n)$, that we have dealt with separately in section \\ref{proof}. In this case the complex structure $\\rm{J}$ is defined by the standard system of positive roots, namely $R^+=\\{\\epsilon_i\\pm\\epsilon_j, \\epsilon_i,\\ 1\\leq i\\neq j\\leq n\\}$. In particular $R_\\mathfrak{k}^+ =\\{\\epsilon_i\\pm\\epsilon_j\\}_{i\\neq j}$ and $R_\\mathfrak{p}^+=\\{\\epsilon_i\\}_{i=1,\\ldots,n}$. We now consider $\\psi_i=\\epsilon_i$, $i=1,2$, $\\phi_1=\\psi_1+\\psi_2\\in R_\\mathfrak{k}^+$ and $\\phi_2=\\psi_1-\\psi_2\\in R_\\mathfrak{k}^+$. We apply \\eqref{eq1} in two different ways: \\par\n\\noindent (1)\\ $\\a=\\psi_1,\\b=\\psi_2$. Then \n$$h(H_{\\psi_1},H_{\\psi_2}) = N_{\\psi_1,\\psi_2}^2(a_\\phi-a_{\\psi_1}-a_{\\psi_2}) + N_{\\psi_1,-\\psi_2}^2(a_{\\phi_2}+a_{\\psi_2}-a_{\\psi_1}).$$\n(2)\\ $\\a=\\phi_1,\\b=\\psi_2$. Note that $\\phi_1+\\psi_1\\not\\in R$. Then \n$$h(H_{\\phi_1},H_{\\psi_1}) = N_{\\phi_1,-\\psi_1}^2(a_{\\psi_2}+a_{\\psi_1}-a_{\\phi_1}).$$\nTherefore \n$$h(H_{\\psi_1},H_{\\psi_1}) = (N_{\\phi_1,-\\psi_1}^2+N_{\\psi_1,\\psi_2}^2)(a_{\\psi_2}+a_{\\psi_1}-a_{\\phi_1})+ N_{\\psi_1,-\\psi_2}^2(a_{\\psi_1} -a_{\\phi_2}-a_{\\psi_2})$$\nWe now recall that, if $\\gamma,\\d\\in R$, then $N_{\\gamma,\\d}^2= \\frac{q(1-p)}{2}||\\gamma||^2$, where $\\d+n\\gamma$, $p\\leq n\\leq q$, is the $\\gamma$-series containing $\\d$ (see \\cite{He}, p.176). We then immediately see that $N_{\\psi_1,\\psi_2}^2 = N_{\\psi_1,-\\psi_2}^2$ and noting furthermore that $N_{\\phi_1,-\\psi_1}^2 = N_{\\psi_1,\\psi_2}^2$, we can write \n$$h(H_{\\psi_1},H_{\\psi_1}) = N_{\\psi_1,\\psi_2}^2(a_{\\psi_2}+3 a_{\\psi_1}-2a_{\\phi_1}- a_{\\phi_2}), $$\ngiving the contradiction $h(H_{\\psi_1},H_{\\psi_1}) >0$.\n\n\n\n\\section{Geometric properties}\nIn this section, we prove the following result, which may contribute to shed to some light on the geometry of the complex balanced manifolds we have constructed in the previous sections. \n\\begin{theorem} If $(\\rm{M},\\rm{J},h)$ is a balanced $n$-dimensional manifold, where $\\rm{M}=\\rm{G}_o\/\\Gamma$, $\\rm{J}$ is a standard invariant complex structure and $h$ is a balanced Hermitian metric, then the metric $h$ has vanishing Chern scalar curvature.\\par \nMoreover $c_1(\\rm{M})=0$ and the Kodaira dimension $\\kappa(M) = -\\infty$.\n \\end{theorem}\n\nWe consider a standard complex structure $\\rm{J}$ on a manifold $\\rm{M} = \\rm{G}_o\/\\Gamma$. We denote by $D$ the Chern connection relative to a Hermitian metric $h$ which is induced by an invariant metric on $\\rm{G}_o$, again denoted by $h$. We can moreover suppose that $h$ is invariant by the right $\\T$-translations. \\par \nIf $x\\in\\gg_o$ and if we still denote by $x$ the induced left-invariant vector field on $\\rm{G}_o$, we consider $D_x\\in\\End(\\gg_o)$ the endomorphism of $\\gg_o$ which assigns to every $y\\in\\gg_o$ the element $D_xy$ corresponding to the left invariant vector field $D_xy$. Clearly $D_x\\in \\mathfrak{so}(\\gg_o,h)$ and $[D_x,\\rm{J}]=0$. Moreover \n\\begin{equation}\\label{chern}D_xy = [x,y]^{10},\\qquad \\forall x\\in \\gg_o^{01},\\ y\\in \\gg_o^{10},\\end{equation}\nthat follows from the fact that $T^{1,1}=0$, where $T$ is torsion of $D$.\\par \nIf $R$ denote the curvature, where $R_{xy} = [D_x,D_y]- D_{[x,y]}$, we are interested in the first Ricci tensor $\\rho$ given by\n$$\\rho(x,y) = -\\frac 12{\\rm{Tr}}(J\\circ R_{xy}).$$\nAs the complex structure and the metric are both invariant under the adjoint action of the group $T = \\exp(\\mathfrak{t})$, we see that \n$$\\rho(\\mathfrak{t},E_\\a) = 0,\\ \\forall \\a\\in R,$$\n$$\\rho(E_\\a,E_\\b)\\neq 0\\ {\\rm{implies}}\\ \\b=-\\a,\\ \\a,\\b\\in R.$$\nTherefore we can compute \n$$\\rho(E_\\a,E_{-\\a}) = \\frac 12 {\\rm{Tr}}(JD_{H_\\a}).$$\n\\begin{lemma} For every $x\\in \\mathfrak{h}$\n$$D_x = \\ad(x).$$\n\\end{lemma}\n\\begin{proof} We use similar arguments as in \\cite{Po}. It will suffice to consider the case where $x\\in \\mathfrak{h}^{10}$; then for every $\\a\\in R^+$ we have \n$$D_xE_{-\\a} = [x,E_{-\\a}]^{01} = [x,E_{-\\a}],\\quad D_x\\mathfrak{h}^{01} = 0$$\nby \\eqref{chern}. Then if $\\b\\in R^+$ we have \n$$h(D_xE_\\a,E_{-\\b}) = - h(E_\\a,D_xE_{-\\b}) = -\\b(x)h(E_\\a,E_{-\\b}) = 0 \\qquad {\\rm{if}}\\ \\a\\neq\\b,$$\nso that $D_xE_\\a=\\a(x)E_\\a = [x,E_\\a]\\ ({\\rm{mod}}\\ \\mathfrak{h})$. As $h(D_xE_\\a,\\mathfrak{h}^{01}) = -h(E_\\a,D_x\\mathfrak{h}^{01}) = 0$, we conclude that \n$$D_xE_\\a = [x,E_\\a].$$\nFinally, $h(D_x\\mathfrak{h}^{10},\\mathfrak{h}^{01})=0$ and $h(D_x\\mathfrak{h}^{10},E_{-\\a}) = \n-h(\\mathfrak{h}^{10},[x,E_{-\\a}]) = 0$, so that $D_x\\mathfrak{h} = 0 = [x,\\mathfrak{h}]$.\\end{proof}\n\nIt follows that \n$$\\rho(\\mathfrak{h},\\mathfrak{h})=0$$ \nand \n$$\\rho(E_\\a,E_{-\\a}) = \\frac 12\\left(2\\sum_{\\beta\\in R^+}i\\beta(H_\\a)\\right) = B(H_\\a,\\delta),$$\nwhere \n$$\\delta = \\sum_{\\beta\\in R^+} iH_\\b\\in \\mathfrak{t} .$$\nThis means that for every $\\a,\\b\\in R$ we have\n$$\\rho(E_\\a,E_\\b) = - B(E_\\a,[\\d,E_\\b]) = B([E_\\a,E_\\b],\\d)$$\nand therefore for every $x,y\\in \\gg_o$\n\\begin{equation} \\label{rho}\\rho(x,y) = B([x,y],\\delta).\\end{equation}\nThis means that $\\rho = d\\phi$, where $\\phi$ is the left-invariant $1$-form that is given by $\\phi(v) = B(v,\\delta)$. Then clearly $c_1(M)=0$.\\par\nWe now show that the tensor powers $K_{\\rm{M}}^{\\otimes k}$ are holomorphically non trivial for every $m\\geq 1$. Indeed, the metric $h$ induces a Hermitian metric on the line bundles $K_{\\rm{M}}^{\\otimes m}$ with curvature form $m\\rho$. If $\\Omega$ is a nowhere vanishing holomorphic section of $K_{\\rm{M}}^{\\otimes k}$ , then $m\\rho = \n-i\\partial\\overline\\partial \\ln(||\\Omega||^2)$. \nIf we denote by\\, $\\widehat{}$\\, the result of the symmetrization process, which commutes with the operators $\\partial$ and $\\overline\\partial$, we obtain on $\\rm{G}_o$ that $\\widehat{\\rho} =-i \\partial\\overline\\partial\\widehat{\\ln(||\\Omega||^2)} = 0$. As $\\rho$ is invariant, $\\widehat\\rho=\\rho=0$ and we get a contradiction as $\\d\\ne 0$.\\par \nWe now compute the Chern scalar curvature $s^{Ch}$ of the metric $h$ using formula \\eqref{rho}. We use the orthonormal frame $e_1,\\ldots,e_{2n}$. Then \n$$s^{Ch} = \\sum_i \\rho(Je_i,e_i) = \n-2\\sum_{\\a\\in R^+} \\frac{1}{h_\\a^2} \\rho(v_\\a,w_\\a) = $$\n$$= 2iB\\left(\\sum_{\\a\\in R_\\mathfrak{p}^+}\\frac{1}{h_\\a^2} H_\\a-\\sum_{\\a\\in R_\\mathfrak{k}^+}\\frac{1}{h_\\a^2} H_\\a,\\d\\right) = 0$$\nif we consider the system of positive roots satisfying equation \\eqref{eq}.\nThe claim $\\kappa(\\rm{M})=-\\infty$ now follows from \nThm. 1.4 in \\cite{Y}.\\par \n\n\n\n\n\n\\begin{remark} Note that also for a compact group $\\K$ endowed with an invariant complex structure we have $h^{n,0}(\\K)=0$, see \\cite{Pi}, Prop. 3.7.\\par\nWe finally remark here that the balanced condition implies that the two scalar curvatures that one can obtain tracing the Chern curvature tensor coincide (see \\cite{Ga3}, p. 501).\\end{remark}\n\n\n\n\n\n\n\n\n\n\\vspace{2cm}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbzrx b/data_all_eng_slimpj/shuffled/split2/finalzzbzrx new file mode 100644 index 0000000000000000000000000000000000000000..2765fdbf8e817de42d800bc75fb5417dd74a508b --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbzrx @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nIn mechanical and structural systems the knowledge of all possible\nsolutions is crucial for safety and reliability. In devices modelled\nby linear ordinary differential equations we can predict the existing\nsolutions using analytical methods \\cite{rao1995mechanical,nayfeh2008linear}.\nHowever, in case of complex, nonlinear systems analytical methods\ndo not give the full view of system's dynamics \\cite{warminski2003approximate,he2004homotopy,belendez2006analytical,nayfeh2011introduction}.\nDue to nonlinearity, for the same set of parameters more then one\nstable solution may exist \\cite{feudel1998dynamical,ChudzikPSK11,Yanchuk2011,gerson2012design,brzeski2012dynamics,menck2013basin,Stability_threshold}.\nThis phenomenon is called multistability and has been widely investigated\nin all types of dynamical systems (mechanical, electrical, biological,\nneurobiological, climate and many more). The number of coexisting\nsolutions strongly depends on the type of nonlinearity, the number of degrees\nof freedom and the type of coupling between the subsystems. Hence, usually\nthe number of solutions vary strongly when values of system's parameters changes. \n\nAs an example, we point out the classical tuned mass absorber\n\\cite{arnoldfr19955,Cartmell1994173,Alsuwaiyan2002791,fischer2007wind,BVG2008,Ikeda2010,Chung2012,Brzeski2014298}.\nThis device is well known and widely used to absorb energy and mitigate\nunwanted vibrations. However, the best damping ability is achieved\nin the neighbourhood of the multistability zone \\cite{brzeski2012dynamics}.\nAmong all coexisting solutions only one mitigates oscillations effectively.\nOther solutions may even amplify an amplitude of the base system.\nSo, it is clear that only by analyzing all possible solutions we can\nmake the device robust. \n\nSimilarly, in systems with impacts one solution can ensure correct\noperation of a machine, while others may lead to damage or destruction\n\\cite{Brzeski_bells,blazejczyk1998co,de2001basins,qun2003coexisting,de2004controlling}.\nThe same phenomena is present in multi-degree of freedom systems where\ninteractions between modes and internal resonances play an important\nrole \\cite{bux1986non,haquang1987non,cartmell1988simultaneous,orlando2013influence}. \n\nPractically, in nonlinear dynamical systems with more then one degree\nof freedom it is impossible to find all existing solutions without\nhuge effort and using classical methods of analytical and numerical\ninvestigation (path-following, numerical integration, basins of attractions),\nespecially in cases when we analyse a wider range of system's parameters and we\ncannot precisely predict the initial conditions. Moreover, solutions\nobtained by integration may have meager basins of attraction and it\ncould be hard or even impossible to achieve them in reality. That is why\nwe propose here a new method basing on the idea of basin stability \\cite{menck2013basin}.\nThe classical basin stability method is based on the idea of Bernoulli\ntrials, i.e., equations of system's motion are integrated $N$ times\nfor randomly chosen initial conditions (in each trial they are different).\nAnalyzing the results we asses the stability of each solution. If\nthere exist only one solution the result of all trials is the same.\nBut, if more attractors coexist we can estimate the probability of their\noccurrence for a chosen set of initial conditions. In mechanical and\nstructural systems we want to be sure that a presumed solution is stable\nand has the dominant basin of attraction in a given range of system's parameters.\nTherefore, we build up a basin stability method by drawing values of\nsystem's parameters. We take into account the fact that values of\nparameters are measured or estimated with some finite precision and\nalso that they can slightly vary during normal operation. \n\nThe paper is organized as follows. In Section 2 we introduce simple\nmodels which we use to demonstrate the main idea of our approach.\nIn the next section we present and describe the proposed method. Section\n4 includes numerical examples for systems described in Section 2.\nFinally, in Section 5 our conclusions are given.\n\n\n\\section{Model of systems\\label{sec:Model-of-systems}}\n\nIn this section we present systems that we use to present our method.\nTwo models are taken from our previous papers \\cite{brzeski2012dynamics,czolczynski2012synchronization}\nand the third one was described by Pavlovskaia et. al.\n\\cite{pavlovskaia2010complex}. We deliberately picked models whose\ndynamics is well described because we can easily evaluate the correctness\nand efficiency of the method we propose. \n\n\n\\subsection{Tuned mass absorber coupled to a Duffing oscillator}\n\nThe first example is a system with a Duffing oscillator and a tuned\nmass absorber. It was investigated in \\cite{brzeski2012dynamics}\nand is shown in Figure \\ref{fig:Duffing1}. The main body consists\nof mass $M$ fixed to the ground with nonlinear spring (hardening\ncharacteristic $k_{1}+k_{2}y^{2}$) and a viscous damper (damping coefficient\n$c_{1}$). The main mass is forced externally by a harmonic excitation\nwith amplitude $F$ and frequency $\\omega$. The absorber is modelled\nas a mathematical pendulum with length $l$ and mass $m$. A small\nviscous damping is present in the pivot of the pendulum. \n\n\\begin{figure}[H]\n\\begin{centering}\n\\includegraphics{Figure1}\n\\par\\end{centering}\n\n\\caption{The model of the first considered system. Externally forced Duffing\noscillator with attached pendulum (tuned mass absorber). \\label{fig:Duffing1}}\n\\end{figure}\n\n\nThe equations of the system's motion are derived in \\cite{brzeski2012dynamics},\nhence we do not present their dimension form. Based on the following\ntransformation of coordinates and parameters we reach the dimensionless\nform: \\foreignlanguage{english}{$\\omega_{1}^{2}=\\frac{k_{1}}{M+m}$,\n$\\omega_{2}^{2}=\\frac{g}{l}$, $a=\\frac{m}{M+m}$, $b=\\left(\\frac{\\omega_{2}}{\\omega_{1}}\\right)^{2}$,\n$\\alpha=\\frac{k_{2}l^{2}}{(M+m)\\omega_{1}^{2}}$, $f=\\frac{F}{(M+m)l\\omega_{1}^{2}},$\n$d_{1}=\\frac{c_{x}}{(M+m)\\omega_{1}}$, $d_{2}=\\frac{c_{\\varphi}}{ml^{2}\\omega_{2}}$,\n$\\mu=\\frac{\\omega}{\\omega_{1}}$, $\\tau=t\\omega_{1}$, $x=\\frac{y}{l}$,\n$\\dot{x}=\\frac{\\dot{y}}{\\omega_{1}l}$, $\\ddot{x}=\\frac{\\ddot{y}}{\\omega_{1}^{2}l}$,\n$\\gamma=\\varphi,$ $\\dot{\\gamma}=\\frac{\\dot{\\varphi}}{\\omega_{2}},$\n$\\gamma=\\frac{\\ddot{\\varphi}}{\\omega_{2}^{2}}$.} \n\nThe dimensionless equations are as follows:\n\n\\begin{equation}\n\\begin{array}{c}\n\\ddot{x}-ab\\ddot{\\gamma}\\sin\\gamma-ab\\dot{\\gamma}^{2}\\cos\\gamma+x+\\alpha x^{3}+d_{1}\\dot{x}=f\\cos\\mu\\tau,\\\\\n\\\\\n\\ddot{\\gamma}-\\frac{1}{b}\\ddot{x}\\sin\\gamma+\\sin\\gamma+d_{2}\\dot{\\gamma}=0,\n\\end{array}\\label{eq:row bez}\n\\end{equation}\n where $\\mu$ is the frequency of the external forcing and we consider it\nas controlling parameter. The dimensionless parameters have the following\nvalues: $f=0.5$, $a=0.091$, $b=3.33$, $\\alpha=0.031$, $d_{1}=0.132$\nand $d_{2}=0.02$. Both subsystems (Duffing oscillator and the pendulum)\nhave a linear resonance for $\\mu=1.0$. \n\n\n\\subsection{System with impacts}\n\nAs the next example we analyse a system with impacts \\cite{pavlovskaia2010complex}.\nIt is shown in Figure \\ref{fig:Impact} and consists of mass\n$M$ suspended by a linear spring with stiffness $k_{1}$ and a viscous\ndamper with the damping coefficient $c$ to harmonically moving frame.\nThe frame oscillates with amplitude $A$ and frequency $\\Omega$.\nWhen amplitude of mass $M$ motion reaches the value $g$, we observe soft\nimpacts (spring $k_{2}$ is much stiffer than spring $k_{1}$). \n\n\\begin{figure}\n\\begin{centering}\n\\includegraphics{Figure2}\n\\par\\end{centering}\n\n\\caption{The model of the second considered system. Externally forced oscillator with\nimpacts. \\label{fig:Impact}}\n\\end{figure}\n\n\nThe dimensionless equation of motion is as follow (for derivation\nsee \\cite{pavlovskaia2010complex}) :\n\n\\[\n\\ddot{x}+2\\xi\\dot{x}+x+\\beta\\left(x-e\\right)\\mathrm{H}\\left(x-e\\right)=a\\omega^{2}\\sin\\left(\\omega\\tau\\right)\n\\]\n\n\nwhere $x=\\frac{y}{y_{0}}$ is the dimensionless vertical displacement\nof mass $M$, $\\tau=\\omega_{n}t$ is the dimensionless time, $\\omega_{n}=\\frac{k_{1}}{M}$,\n$\\beta=\\frac{k_{2}}{k_{1}}$ the stiffness ratio, $e=\\frac{g}{y_{0}}$\n the dimensionless gap between equilibrium of mass $M$ and the\nstop suspended on the spring $k_{2}$, $a=\\frac{A}{y_{0}}$ and $\\omega=\\frac{\\Omega}{\\omega_{n}}$\nare dimensionless amplitude and frequency of excitation, $\\xi=\\frac{c}{2m\\omega_{n}}$\nis the damping ratio, $y_{0}=1.0\\:[\\mathrm{mm}]$ and $\\mathrm{H}(\\cdot)$\n the Heaviside function. In our calculations we take the following\nvalues of system's parameters: $a=0.7$, $\\xi=0.01$, $\\beta=29$,\n$e=1.26$. As a controlling parameter we use the frequency of excitation\n$\\omega$. \n\n\n\\subsection{Beam with suspended rotating pendula}\n\nThe last considered system consists of a beam which can move in\nthe horizontal direction and $n$ rotating pendula. The beam has the mass\n$M$ and supports $n$ rotating, excited pendula. Each pendulum has\nthe same length $l$ and masses $m_{i}$ $(i=1,\\:2,\\ldots,\\:n)$.\nWe show the system in Figure \\ref{fig:Beam_model} \\cite{czolczynski2012synchronization}.\nThe rotation of the $i$-th pendulum is given by the variable $\\varphi_{i}$\nand its motion is damped by the viscous friction described by the damping\ncoefficient $c_{\\varphi}$. The forces of inertia of each pendulum\nacts on the beam causing its motion in the horizontal direction (described\nby the coordinate $x$). The beam is considered as a rigid body, so\nwe do not consider the elastic waves along it. We describe the phenomena\nwhich take place far below the resonances for longitudinal oscillations\nof the beam. The beam is connected to a stationary base by a light\nspring with the stiffness coefficient $k_{x}$ and viscous damper with\na damping coefficient $c_{x}$. The pendula are excited by external\ntorques proportional to their velocities: $N_{0}-\\dot{\\varphi}_{i}N_{1}$,\nwhere $N_{0}$ and $N_{1}$ are constants. If no other external forces\nact on the pendulum, it rotates with the constant velocity $\\omega=N_{0}\/N_{1}$.\nIf the system is in a gravitational field (where $g=9.81\\:[\\mathrm{m\/s^{2}}]$\nis the acceleration due to gravity), the weight of the pendulum causes\nthe unevenness of its rotation velocity, i.e., the pendulum slows\ndown when the centre of its mass goes up and accelerates when the\ncentre of its mass goes down. \n\n\\begin{figure}\n\\begin{centering}\n\\includegraphics{Figure3}\n\\par\\end{centering}\n\n\\caption{The model of the third considered system. Horizontally moving beam\nwith attached pendulums. \\label{fig:Beam_model}}\n\\end{figure}\n\n\nThe system is described by the following set of dimensionless equations:\n\n\\begin{equation}\nm_{i}l^{2}\\ddot{\\varphi}_{i}+m_{i}\\ddot{x}l\\cos{\\varphi_{i}}+c_{\\varphi}\\dot{\\varphi}_{i}+m_{i}gl\\sin{\\varphi_{i}}=N_{0}-\\dot{\\varphi}_{i}N_{1}\\label{eq:Beam1}\n\\end{equation}\n\n\n\\begin{equation}\n\\left({M+\\sum\\limits _{i=1}^{n}{m_{i}}}\\right)\\ddot{x}+c_{x}\\dot{x}+k_{x}x=\\sum\\limits _{i=1}^{n}{m_{i}l\\left({-\\ddot{\\varphi}_{i}\\cos\\varphi_{i}+\\dot{\\varphi}_{i}^{2}\\sin\\varphi_{i}}\\right)}\\label{eq:Beam2}\n\\end{equation}\nIn our investigation we analyze two cases: a system with two pendula\n(where $n=2$ and $i=1,\\:2$) and with $20$ pendula ($n=20$ $i=1,\\:2,\\:...,n$)..\nThe values of the parameters are as follows: $m_{i}=\\frac{2.00}{n}$,\n$l=0.25$, $c_{\\varphi}=\\frac{0.02}{n}$, $N_{0}=5.0$0, $N_{1}=0.50$,\n$M=6.00$, $g=9.81$, $c_{x}=\\frac{\\ln\\left(1.5\\right)}{\\pi}\\sqrt{k_{x}\\left(M+\\sum\\limits _{i=1}^{n}{m_{i}}\\right)}$and\n$k_{x}$ is a controlling parameter. The derivation of the system's equations\ncan be found in \\cite{czolczynski2012synchronization}. We present the\ntransformation to a dimensionless form in Appendix A. \n\n\n\\section{Methodology}\n\nIn \\cite{menck2013basin} Authors present a ``basin stability''\nmethod which let us estimate the stability and number of solutions\nfor given values of system parameters. The idea behind basin stability\nis simple, but it is a powerful tool to assess the size of complex\nbasins of attraction in multidimensional systems. For fixed values\nof system's parameters, $N$ sets of random initial conditions are\ntaken. For each set we check the type of final attractor. Based on\nthis we calculate the chance to reach a given solution and determine\nthe distribution of the probability for all coexisting solutions. This\ngives us information about the number of stable solutions and the\nsizes of their basins of attraction.\n\nWe consider the dynamical system $\\dot{\\mathbf{x}}=f\\,(\\mathbf{x},\\,\\omega)$,\nwhere $\\mathbf{x}\\in\\mathtt{\\mathbb{R}^{n}}$ and $\\omega\\in\\mathbb{R}$\nis the system's parameter. Let ${\\cal B\\subset\\mathbb{\\mathtt{\\mathbb{R}^{n}}}}$\nbe a set of all possible initial conditions and ${\\cal C}\\subset\\mathbb{\\mathtt{\\mathbb{R}}}$\na set of accessible values of system's parameter. Let us assume that an\nattractor ${\\cal A}$ exists for $\\omega\\in{\\cal C_{A}}\\subset{\\cal C}$\nand has a basin of attraction $\\beta({\\cal A})$. Assuming random\ninitial conditions the probability that the system will reach attractor\n${\\cal A}$ is given by $p\\left({\\cal A}\\right)$. If this probability\nis equal to $p\\left({\\cal A}\\right)=1.0$ this means that the considered\nsolution is the only one in the taken range of initial conditions\nand given values of parameters. Otherwise other attractors coexist.\nThe initial conditions of the system are random from set ${\\cal B_{A}}\\subset{\\cal B}$.\nWe can consider two possible ways to select this set. \n\n\\begin{description}\n \\item[I] The first ensures that set the ${\\cal B_{A}}$ includes values of initial \nconditions leading to all possible solutions. This approach is appropriate if \nwe want to get a general overview of the system's dynamics. \n \\item[II] In the second approach we use a narrowed set of initial conditions that \ncorresponds to practically accessible initial states.\\ldots\n\\end{description}\n\nIn our method we chose the second approach because it let us take into \naccount constrains imposed on the system and because in engineering we \nusually know or expect the initial state of the system with some finite \nprecision. \n\nIn the classical approach of Menck et. al. \\cite{menck2013basin} the\nvalues of system's parameters are fixed and do not change during calculations.\nThe novelty of our method is that we not only draw initial conditions\nbut also values of some selected parameters of the system. We assume\nthat the initial conditions and some of the system's parameters are chosen\nrandomly. Then using $N$ trials of numerical simulations we estimate\nthe probability that the system will reach a given attractor ${\\cal A}$\n($p\\left({\\cal A}\\right)$). The idea is to take into consideration\nthe fact that the values of system's parameters are measured or estimated\nwith some finite accuracy which is often hard to determine. Moreover\nvalues of parameters can vary during normal operation. Therefore drawing\nvalus of parameters we can describe how a mismatch in their values influences\nthe dynamics of the system and estimate the risk of failure. In many\npractical applications one is interested in reaching only one presumed\nsolution ${\\cal A}$, and the precise description of other coexisting\nattractors is not necessary. We usually want to know the probability\nof reaching the expected solution $p\\left({\\cal A}\\right)$ and the chance\nthat the system behave differently. If $p\\left({\\cal A}\\right)$ is\nsufficiently large, we can treat the other attractors as an element\nof failure risk. \n\nIn our approach we perform the following steps:\n\\begin{description}\n\t\\item[I] We pick values of system's parameters from the set\n\t\t${\\cal C_{A}}\\subset{\\cal C}$. \n\t\\item[II]We select the set ${\\cal C_{A}}$ so that\n\t\tit consists of all practically accessible values of system's parameters\n\t\t$\\omega$ . This let us ensure that a given solution indeed exists in a practically\n\t\taccessible range (taking into account the mismatch in parameters).\n\t\\item[III] We subdivide the set $C_{A}$ in to $m=1,2,\\dots M$ equally spaced\n\t\tsubsets. The subsets ${\\cal C}_{A}^{m}$ do not overlap and the relation\n\t\t$\\bigcup_{m=1\\dots M}{\\cal C}_{A}^{m}=C_{A}$ is always fulfilled.\n\t\\item[IV] Then for each subset ${\\cal C}_{A}^{m}$ we randomly pick $N$ sets\n\t\tof initial conditions and value of the considered parameter. For each\n\t\tset we check the final attractor of the system. \n\t\\item[V] After a suficient number\n\t\tof trials we calculate the probability of reaching a presumed solution\n\t\tor solutions. \n\t\\item[VI]Finally we describe the relation between the\n\t\tvalue of the system's parameter and the ``basin stability'' of reachable\n\t\tsolutions.\\ldots\n\\end{description}\n\nIn our calculations for each range of parameter values\n(subset ${\\cal C}_{A}^{m}$) we draw from $N=100$ up to $N=1000$\nsets of initial conditions and parameter. The value of $N$ strongly depends\non the complexity of the analysed system. Also the computation time for a\nsingle trial should be adjusted for each system independently\nsuch that it can reach the final attractor. In general, we recommend that\nin most cases $N$ should be at least 100.\n\n\n\\section{Numerical results}\n\n\n\\subsection{Tuned mass absorber coupled to a Duffing oscillator}\n\nAt the beginning we want to recall the results we present in our previous\npaper \\cite{brzeski2012dynamics}. As a a summary we show Figure \\ref{fig:Two-paramters_colour}\nwith a two dimensional bifurcation diagram obtained by the path-following\nmethod. It gives bifurcations for varying amplitude $f$ and frequency\n$\\mu$ of the external excitation (see Eq. \\ref{eq:row bez}). Lines shown\nin the plot correspond to different types of bifurcations (period\ndoubling, symmetry breaking, Neimark-Sacker and resonance tongues).\nWe present these lines in one style because the structure is too complex\nto follow bifurcation scenarios and we do not need that data (details\nare shown in \\cite{brzeski2012dynamics}). We mark areas where we\nobserve the existence of one solution (black colour), or the coexistence of two (grey) and three\n(hatched area) stable solutions. The remaining part of the diagram\n(white area) corresponds to situations where there are four or more\nsolutions. Additionally, by white colour we also mark areas where\nonly the Duffing system is oscillating in 1:1 resonance with the frequency\nof excitation and the pendulum is in a stable equilibrium position,\ni.e., HDP (hanging down pendulum) state. In this case the dynamics\nof the system is reduced to the oscillations of summary mass ($M+m$).\n\nThe detailed analysis of system \\ref{eq:row bez} is time consuming\nand creation of Figure \\ref{fig:Two-paramters_colour} was preceded\nby complex analysis done with large computational effort. Additionally,\nthe obtained results give us no information about the size of the basins\nof attraction of each solution - which practically means that some\nof the solutions may occur only very rarely in the real system (i.e. due to not accessible\ninitial conditions). Nevertheless, such analysis gives us an in-depth\nknowledge about the bifurcation structure of the system. As we can see, the\nrange where less then three solutions exist is rather small, especially\nfor $\\mu<2.0$. To illustrate our method of analysis, we focus on three\nsolutions: $2:1$ oscillating resonance, HDP and $1:1$ rotating resonance\nassuming that only they have some practical meaning.\n\n\\begin{figure}[H]\n\\begin{centering}\n\\includegraphics{Figure4}\n\\par\\end{centering}\n\n\\caption{Two-parameter bifurcations diagram of the system (1) in the plane $\\left(f,\\,\\mu\\right)$\nshowing periodic oscillations and rotations of the pendulum. Black colour\nindicates one attractor, grey colour shows two coexisting attractors\n(the same as for black but with a coexisting stable steady state of\nthe pendulum). In the hatched area we observe the coexistence of stable\nrotations and a stable steady state of the pendulum. A detailed analysis\nis presented in \\cite{brzeski2012dynamics}. \\label{fig:Two-paramters_colour}}\n\\end{figure}\n\n\nTo show our results obtained with integration, we compute bifurcation diagrams\nfor $f=0.5$ in the range $\\mu\\in[0.1,\\:3.0]$ (see Figure \\ref{fig:tma_bif}).\nIn Figure \\ref{fig:tma_bif}(a) we increase $\\mu$ from $0.1$ to\n$3.0$ and in Figure \\ref{fig:tma_bif}(b) we decrease $\\mu$ from $3.0$\nto $0.1$. As the initial conditions we take the equilibrium position\n($x_{0}=\\dot{x}_{0}=0.0$ and $\\gamma_{0}=\\dot{\\gamma}_{0}=0.0$).\nIn both panels we plot the amplitude of the pendulum $\\gamma$. Ranges where the \ndiagrams differ we mark by grey rectangles. It is easy to see that there\nare two dominating solutions: HDP and $2:1$ internal resonance. Near\n$\\mu=1.0$ we observe a narrow range of $1:1$ and $9:9$ resonances\nand chaotic motion (for details see Figure 6 in \\cite{brzeski2012dynamics}).\nBased on previous results we know that we detected all solutions existing\nin the considered range, however we do not have information about the size of\ntheir basins of attraction and coexistence. Hence the analysis with the \nproposed method should give us new important information about the system's\ndynamics. Contrary to the bifurcation diagram obtained by path-following\nin Figure \\ref{fig:tma_bif}, we do not observe rotating solutions\n(the other set of initial conditions should be taken).\n\n\\begin{figure}[H]\n\\begin{centering}\n\\includegraphics{Figure5}\n\\par\\end{centering}\n\n\\caption{Bifurcation diagram showing the behaviour of the pendulum suspended\non the Duffing oscillator. For subplot (a) the value of the bifurcation parameter\n$\\mu$ was increased, while for subplot (b) we decreased the value of $\\mu$.\nGray rectangles mark the range of the bifurcation parameter $\\mu$ for\nwhich different attractors coexist. A detailed analysis is presented\nin \\cite{brzeski2012dynamics}. \\label{fig:tma_bif}}\n\\end{figure}\n\n\nIn Figure \\ref{fig:Duff_prob} we show the probability of reaching the\nthree aforementioned solutions obtained using the proposed method.\nThe initial conditions are random numbers drawn from the following\nranges: $x_{0}\\in[-2,\\:2]$, $\\dot{x}_{0}\\in[-2,\\:2]$, $\\gamma_{0}\\in[-\\pi,\\:\\pi]$\nand $\\dot{\\gamma}_{0}\\in[-2.0,\\:2.0]$ (ranges there selected basing\non the results from \\cite{brzeski2012dynamics}). The frequency of excitation\nis within a range $\\mu\\in[0,\\:3.0]$ (Figure \\ref{fig:Duff_prob}(a,c)\n), then we refine it to $\\mu\\in[1.25,\\:2.75]$ (Figure \\ref{fig:Duff_prob}(b,d)\n). In both cases we take $15$ equally spaced subsets of $\\mu$ and\nin each subset we calculate the probability of reaching a given solution.\nFor each subset we calculate $100$ trials each time drawing initial\nconditions of the system and a value of $\\mu$ from the appropriate\nrange. Then we plot the dot in the middle of the subset which indicate\nthe probability of reaching a given solution in each considered range.\nLines that connect the dots are shown just to ephasize the tendency. For\neach range we take $N=1000$ because we want to estimate the probability\nof a solution with small a basin of stability (1:1 rotating periodic solution).\n\nAs we can see in Figure \\ref{fig:Two-paramters_colour}, the $2:1$\nresonance solution exists in the area marked by black colour around\n$\\mu=2.0$ and coexists with HDP in the neighbouring grey zone. In Figure\n\\ref{fig:Duff_prob} we mark the probability of reaching the $2:1$ resonance\nusing blue dots. As we expected, for $\\mu<1.4$ and $\\mu>2.2$ the solution\ndoes not exist. In the range $\\mu\\in[1.4,\\:2.2]$ the maximum value of\nprobability $p(2:1)=0.971$ is reached in the subset $\\mu\\in[1.8,\\:2.0]$\nand outside that range the probability decreases. To check if we can\nreach $p(2:1)=1.0$, we decrease the range of parameter's values to\n$\\mu\\in[1.25,\\:2.75]$ and the size of subset to $\\Delta\\mu=0.1$\n(we still have 15 equally spaced subsets). The results are shown in\nFigure \\ref{fig:Duff_prob}(b) similarly in blue colour. In the range\n$\\mu\\in[1.95,\\:2.05]$ the probability $p(2:1)$ is equal to unity\nand in the range $\\mu\\in[1.85,\\:1.95]$ it is slightly smaller $p(2:1)=0.992$.\nHence, for both subsets we can be nearly sure that the system reaches the $2:1$\nsolution. This gives us indication of how precise we have to set the\nvalue of $\\mu$ to be sure that the system will behave in a presumed\nway.\n\nA similar analysis is performed for HDP. The values of probability\nis indicated by the red dots. As one can see for $\\mu<0.8$, $\\mu\\in[1.2,\\:1.4]$\nand $\\mu\\in[2.6,\\:2.8]$, the HDP is the only existing solution. The\nrapid decrease close to $\\mu\\approx1.0$ indicates the $1:1$ resonance\nand the presence of other coexisting solutions in this range (see\n\\cite{brzeski2012dynamics}). In the range $\\mu\\in[1.2,\\:1.4]$ the probability\n$p(\\mathrm{HDP})=1.0$ which corresponds to a border between solutions\nborn from $1:1$ and $2:1$ resonance. Hence, up to $\\mu=2.0$ the\nprobability of the HDP solution is a mirror refection of $p(2:1)$. The\nsame tendency is observed in the narrowed range as presented in Figure\n\\ref{fig:Duff_prob}(b). Finally, for $\\mu>2.0$ the third considered\nsolution comes in and we start to observe an increase of probability\nof the rotating solution $S(\\mu,\\:\\mathrm{HDP})$ as shown in Figure \\ref{fig:Duff_prob}(c).\nHowever, the chance of reaching the rotating solution remains small and never\nexceeds $p(\\mathrm{1:1})=8\\times10^{-3}$. We also plot the probability\nof reaching the rotating solution in the narrower range of $\\mu$ in Figure\n\\ref{fig:Duff_prob}(d). The probability is similar to the one presented\nin Figure \\ref{fig:Duff_prob}(c) - it is low and does not exceed $p(\\mathrm{1:1})=8\\times10^{-3}$.\nNote that the results presented in Figure \\ref{fig:Duff_prob}(a,b) and\nFigure \\ref{fig:Duff_prob}(c,d) are computed for different sets of\nrandom initial conditions and parameter values; hence the obtained\nprobability can be slightly different. \n\n\\begin{figure}[H]\n\\begin{centering}\n\\includegraphics{Figure6}\n\\par\\end{centering}\n\n\\caption{Probability of reaching given solutions in (1) system with tuned mass\nabsorber. Subplots (a,b) present solutions with $2:1$ periodic oscillations\n(blue) and without motion of the pendulum (red). Subplots (c,d) present the\nprobability of reaching $1:1$ rotations (black). (Please note that\nin both cases (a,b) and (c,d) the initial conditions and parameter\nare somehow random, hence the results may slightly differ). \\label{fig:Duff_prob}}\n\\end{figure}\n\n\n\n\\subsection{System with impacts}\n\nIn this subsection we present our analysis of different periodic solutions\nin the system with impacts. A discontinuity usually increases the number\nof coexisting solutions. Hence, in the considered system we observe\na large number of different stable orbits and their classification\nis necessary. In Figure \\ref{fig:ImpactBif} we show two bifurcation\ndiagrams with $\\omega$ as controlling parameter. Both of them start\nwith initial conditions $x_{0}=0.0$ and $\\dot{x}_{0}=0.0$. In panel\n(a) we increase $\\omega$ from $0.801$ to $0.8075$; while in panel\n(b) we decrease $\\omega$ in the same range. We select the range of\n$\\omega$ basing on the results presented in \\cite{pavlovskaia2010complex}.\nAs one can see, both diagrams differ in two zones marked by grey colour.\nHence, we observe a coexistence of different solutions, i.e., in the range\n$\\omega\\in[0.8033,\\:0.8044]$ solutions with period-3 and -2 are present,\nwhile in the range $\\omega\\in[0.8068,\\:0.8075]$ we detected solutions\nwith period-2 and -5. As presented in \\cite{pavlovskaia2010complex}\nsome solutions appear from a saddle-node bifurcation and we are not\nable to detect them with the classical bifurcation diagram. The proposed\nmethod solves this problem and shows all existing solutions in\nthe considered range of excitation frequency. \n\n\\begin{figure}\n\\begin{centering}\n\\includegraphics{Figure7}\n\\par\\end{centering}\n\n\\caption{Bifurcation diagram showing the behaviour of impacting oscillator (2).\nFor subplot (a) the value of the bifurcation parameter $\\omega$ was increased\nwhile for subplot (b) we decreased the value of $\\omega$. Grey rectangles\nmark the range of the bifurcation parameter $\\omega$ for which different\nattractors coexist. Further analysis can be found in \\cite{pavlovskaia2010complex}.\n\\label{fig:ImpactBif}}\n\\end{figure}\n\n\nWe focus on periodic solutions with periods that are not longer than\neight periods of excitation. We observe periodic solutions with higher\nperiods in the narrow range of $\\omega$ but the probability that\nthey will occur is very small and we can neglect them. All non-periodic\nsolutions are chaotic (quasiperiodic solutions are not present in\nthis system). The results of our calculations are shown in Figure\n\\ref{fig:Impact_prob}(a,b). We take initial conditions from the following\nranges $x_{0}\\in[-2,\\:2]$, $\\dot{x}_{0}\\in[-2,\\:2]$. The controlling\nparameter $\\omega$ is changed from $0.801$ to $0.8075$ with step\n$\\Delta\\omega=0.0005$ in Figure \\ref{fig:Impact_prob}(a) and from\n$0.806$ to $0.8075$ with the step $\\Delta\\omega=0.0001$ in Figure \\ref{fig:Impact_prob}(b)\n(in each subrange of excitation's frequency we pick the exact value of\n$\\omega$ randomly from this subset). The probability of periodic\nsolutions is plotted by lines with different colours and markers.\nWe detect the following solutions: period-1, -2, -3, -5 (two different\nattractors with large and small amplitude), -6 and -8. The dot lines\nindicate the sum of all periodic solutions' probability (also with\nperiod higher then eight). Hence, when its value is below $1$, chaotic solution exist. Dots are drawn for mean value i.e, middle\nof the subset. For each range we take $N=200$ and we increase the calculation\ntime because the transient time is sufficiently larger than in the\nprevious example due to the piecewise smooth characteristic of spring's\nstiffness.\n\nAs we can see, the chance of reaching a given solution strongly depends\non $\\omega$. Hence, in the sense of basin stability we can say that\nstability of solutions rely upon the $\\omega$ value. In Figure \\ref{fig:Impact_prob}(a)\nthe probability of a single solution is always smaller than one. Nevertheless,\nwe observe two dominant solutions: period-5 with large amplitude in\nthe first half of the considered $\\omega$ range and period-2 in the second\nhalf of the range. The maximum registered value of probability is $p(\\mathrm{period-2})=0.92$\nand it refers to the period-2 solution for $\\omega\\approx0.80675$. To\ncheck if we can achieve even higher probability we analyse a narrower\nrange of $\\omega$ and decrease the step (from $\\Delta\\omega=0.0005$\nto $\\Delta\\omega=0.0001$). In Figure \\ref{fig:Impact_prob}(b) we\nsee that in range $\\omega\\in[0.8069,\\:0.807]$ the probability of reaching\nthe period-2 solution is equal to $1$. Hence, in the sense of basin stability\nit is the only stable solution. Also in the range $\\omega\\in[0.8065,\\:0.8072]$\nthe probability of reaching this solution is higher then $0.9$ and\nwe can say that its basin of attraction is strongly dominant. \n\nOther periodic solutions presented in Figure \\ref{fig:Impact_prob}(a)\nare: period-1 is present in the range $\\omega\\in[0.801,\\:0.8025]$ with the\nhighest probability $p(\\mathrm{period-1})=0.4$, period-3 exists in the\nrange $\\omega\\in[0.803,\\:0.805]$ with the maximum probability $p(\\mathrm{period-3})=0.36$,\nperiod-2 is observed in two ranges $\\omega\\in[0.8025,\\:0.8035]$ and\n$\\omega\\in[0.804,\\:0.8045]$ with the highest probability equal to $0.18$\nand $0.12$ respectively. Solution with period-5 (small amplitude's\nattractor) exists also in two ranges $\\omega\\in[0.8055,\\:0.8065]$\nand $\\omega\\in[0.807,\\:0.8075]$ with the highest probability equal to\n$0.14$ and $0.4$3 respectively.\n\n\\begin{figure}\n\\begin{centering}\n\\includegraphics{Figure8}\n\\par\\end{centering}\n\n\\caption{Probability of reaching given solutions in the impacting system. Subplots\n(a,b) present different periodic solutions and the summary probability\nof reaching any periodic solution. In Subplot (a) we analyze $\\omega\\in[0.801,\\:0.8075]$\nwith the step $\\Delta\\omega=0.0005$, and in subplot (b) we narrow\nthe range $\\omega\\in[0.806,\\:0.8075]$ and decrease the step size\n$\\Delta\\omega=0.0001$. (Please note that in cases (a) and (b) the\ninitial conditions and parameter are somehow random, hence the results\nmay slightly differ).\\label{fig:Impact_prob}}\n\\end{figure}\n\n\n\n\\subsection{Beam with suspended rotating pendula}\n\nThe third considered system consists of a beam that can move horizontally\nwith two ($n=2$) or twenty ($n=20$) pendula suspended on it. As\na control parameter we use $k_{x}$ which describes the stiffness\nof the beam's support. For the considered range of $k_{x}\\in[100,\\,5000]$\ntwo stable periodic attractors exist in that system. One corresponds\nto complete synchronization of the rotating pendula. The second one is called anti-phase synchronization and refers to the state when\nthe pendula rotate in the same direction but are shifted in phase by $\\pi$.\n\nIn Figure \\ref{fig:pendulaBif} we show four bifurcation diagrams\nwith $k_{x}$ as the controlling parameter and a Pioncare map of rotational\nspeed of the pendula. The subplots (a,b) refer to the system with two\npendula ($n=2$). We start with zero initial conditions: $x_{0}=0.0$,\n$\\dot{x}_{0}=0.0$, $\\varphi_{10}=0.0$, $\\dot{\\varphi}_{10}=0.0$,\n$\\varphi_{20}=0.0$, $\\dot{\\varphi}_{20}=0.0$ and take $k_{x}\\in[100,\\,5000]$.\nThe parameter $k_{x}$ is increasing in subplot (a) and decreasing in\n(b). We see that in the range marked by grey rectangle both complete\nand anti-phase synchronization coexist. In subplots (c,d) we present\nresults for twenty pendula ($n=20$). We start the integration from initial\nconditions that refer to anti-phase synchronization (two clusters\nof 10 pendula shifted by $\\pi$) i.e. $x_{0}=0.1$, $\\dot{x}_{0}=0.00057$,\n$\\varphi_{k0}=0.0$, $\\dot{\\varphi}_{k0}=9.81$, $\\varphi_{j0}=3.09$,\n$\\dot{\\varphi}_{j0}=9.784$ where: $k=1,2,\\ldots10$ and $j=11,12,\\ldots20$.\nThe value of $k_{x}$ is increasing in subplot (c) and decreasing in (d).\nSimilarly as in the two pendula case, we observe the region ($k_{x}\\in[100,\\,750]$)\nwhere two solutions coexist: anti-phase synchronization and non-synchronous\nstate. To further analyse multistability in that system we use \nproposed method.\n\n\\begin{figure}\n\\begin{centering}\n\\includegraphics{Figure9}\n\\par\\end{centering}\n\n\\caption{Bifurctaion diagram showing the behaviour of two (a,b) and twenty\n(c,d) pendula suspended on the moving beam. For subplots (a,c) the value\nof the bifurcation parameter $k_{x}$ was increased, while for subplots\n(b,d) we decreased the value of $k_{x}$. Grey rectangles mark the\nranges of the bifurcation parameter $k_{x}$ for which different attractors\ncoexist. Further analysis of number of solutions can be found in \\cite{czolczynski2012synchronization}.\n\\label{fig:pendulaBif}}\n\\end{figure}\n\n\nIn Figure \\ref{fig:Pendula_prob} we present how the probability of\nreaching a given solution depends on the parameter $k_{x}$ . In subplot\n(a) we show the results for the system with 2 pendula, while in subplot\n(b) results obtained for the system with 20 pendula suspended\non the beam are given. In both cases we consider $k_{x}\\in[0,\\:5000]$ and assume\nthe following ranges of initial conditions: $x_{0}\\in[-0.15,\\:0.15]$,\n$\\dot{x}_{0}\\in[-0.1,\\:0.1]$, $\\varphi_{i0}\\in[-\\pi,\\:\\pi]$, $\\varphi_{20}\\in[-\\pi,\\:\\pi]$,\n$\\dot{\\varphi}_{10}\\in[-3.0,\\:3.0]$ and $\\dot{\\varphi}_{20}\\in[-3.0,\\:3.0]$\nin Figure \\ref{fig:Pendula_prob}(a) and $x_{0}\\in[-0.15,\\:0.15]$,\n$\\dot{x}_{0}\\in[-0.1,\\:0.1]$, $\\varphi_{i0}\\in[-\\pi,\\:\\pi]$, $\\dot{\\varphi}_{i0}\\in[-\\pi,\\:\\pi]$\nwhere $i=1\\dots\\:20$ in Figure \\ref{fig:Pendula_prob}(b). We take\n$20$ subsets of parameter $k_{x}$ values with the step equal to\n$\\Delta k_{x}=250$ and mark their borders with vertical lines. For\neach set we run $N=100$ simulations; each one with random initial\nconditions and $k_{x}$value drawn from the respective subset. Then,\nwe estimate the probability of reaching given solution. The dots in\nFigure \\ref{fig:Pendula_prob} indicate the probability of reaching a\ngiven solution in the considered range (dots are drawn for mean value,\ni.e, middle of subset). Contrary to both already presented systems,\nthis one has a much larger dimension of phase space (six and forty two),\nhence we decide to decrease number of the trials to $N=100$ in order\nto minimise the time of calculations. \n\nIn Figure \\ref{fig:Pendula_prob}(a) we show the results for 2 pendula.\nWhen $k_{x}\\in[0,\\:250]$ only anti-phase synchronization is possible.\nThen, with the increase of $k_{x}$ we observe a sudden change\nin the probability and for $k_{x}\\in[750,\\:1750]$ only complete synchronization\nexists. For $k_{x}>2000$ a probability of reaching both solutions fluctuates\naround $p(\\mathrm{complete})=0.7$ for complete and $p(\\mathrm{\\mathrm{anti-phas}e})=0.3$\nfor anti-phase synchronization. Further increase of $k_{x}$\ndoes not introduce any significant changes. \n\nIn Figure \\ref{fig:Pendula_prob}(b) we show the results for twenty\npendula. For $k_{x}\\in[0,\\:250]$ the system reaches solutions different\nfrom the two analysed (usually chaotic). Then, the probability of reaching\ncomplete synchronization drastically increases and for $k_{x}\\in[750,\\:5000]$\nit is equal to $p(complete)=1.0$ which means that the pendula always\nsynchronize completely. We also present the magnification of the plot\nwhere we see that in fact for $k_{x}\\in[715,\\:5000]$ we will always\nobserve complete synchronization of the pendula. Please note that\nfor calculating both plots we use random initial conditions and $k_{x}$\nvalue hence, the results for a narrower range may differ. Anti-phase\nsynchronization was never achieved with randomly chosen initial conditions.\nThis means that even though this solution is stable for $k_{x}\\in[100,\\:750]$\n(see Figure \\ref{fig:pendulaBif}(c)) it has a much smaller basin of attraction\nand is extremely hard to obtain in reality. The results presented in Figure\\ref{fig:Pendula_prob}\nprove that by proper tuning of the parameter $k_{x}$ we can control the\nsystems behaviour even if we can only fix the $k_{x}$ value with finite\nprecision. \n\n\\begin{figure}[H]\n\\begin{centering}\n\\includegraphics{Figure10}\n\\par\\end{centering}\n\n\\caption{Probability of reaching given solutions in the system with rotating\npendula. Subplot (a) refers to the case with two pendula and (b) with\ntwenty pendula. (Please note that on plot (b) and its magnification\nthe initial conditions and parameter are somehow random, hence the\nresults may slightly differ). \\label{fig:Pendula_prob}}\n\\end{figure}\n\n\n\n\\section{Conclusions}\n\nIn this paper we propose a new method of detection of solutions' in \nnon-linear mechanical or structural systems. The method allows\nto get a general view of the system's dynamics and estimate the risk that\nthe system will behave behave differently than assumed. To achieve\nthis goal we extend the method of basin stability \\cite{menck2013basin}.\nWe build up the classical algorithm and draw not only initial conditions\nbut also values of system's parameters. We take this into account\nbecause the identification of parameters' values is quite often not\nvery precise. Moreover values of parameters often slowly vary during\noperation. Whereas in practical applications we usually need certainty\nthat the presumed solution is stable and its basin of stability is large\nenough to ensure its robustness. Hence, there is a need to describe\nhow small changes of parameters' values influence the behaviour of\nthe system. Our method provides such a description and allows us to estimate\nthe required accuracy of parameters values and the risk of unwanted\nphenomena. Moreover it is relatively time efficient and does not require\nhigh computational power.\n\nWe show three examples, each for a different class of systems: a tuned\nmass absorber, a piecewise smooth oscillator and a multi-degree of freedom\nsystem. Using the proposed method we can estimate the number of existing\nsolutions, classify them and predict their probability of appearance.\nNevertheless, in many cases it is not necessary to distinct all solutions\nexisting in a system but it is enough to focus on an expected solution,\nwhile usually other periodic, quasi-periodic and chaotic solutions\nare classified as undesirable. Such a strategy simplifies the analysis and\nreduces the computational effort. We can focus only on probable solutions\nand reduce the number of trials omitting a precise description of solutions\nwith low probability. \n\nThe proposed method is robust and can be used not only for mechanical\nand structural systems but also for any system given by differential\nequations where the knowledge about existing solutions is crucial. \n\n\n\\section*{Acknowledgement}\n\n\nThis work is funded by the National Science Center Poland based on\nthe decision number DEC-2015\/16\/T\/ST8\/00516. PB\nis supported by the Foundation for Polish Science (FNP).\n\n\n\\section*{Appendix A}\n\nThe motion of the system presented in Figure \\ref{fig:Beam_model}\nis described by the following set of two second order ODEs:\n\n\\begin{equation}\nm_{iD}l_{D}^{2}\\ddot{\\varphi'}_{i}+m_{iD}\\ddot{x'}l_{D}\\cos{\\varphi'_{i}}+c_{\\varphi D}\\dot{\\varphi'}_{i}+m_{iD}g_{D}l_{D}\\sin{\\varphi'_{i}}=N_{0D}-\\dot{\\varphi'}_{i}N_{1D}\\label{eq:Beam1-1-1}\n\\end{equation}\n\n\n\\begin{equation}\n\\left({M_{D}+\\sum\\limits _{i=1}^{n}{m_{iD}}}\\right)\\ddot{x'}+c_{xD}\\dot{x'}+k_{xD}x'=\\sum\\limits _{i=1}^{n}{m_{iD}l_{D}\\left({-\\ddot{\\varphi'}_{i}\\cos\\varphi'_{i}+\\dot{\\varphi'}_{i}^{2}\\sin\\varphi'_{i}}\\right)}\\label{eq:Beam2-1-1}\n\\end{equation}\n\n\nThe values of parameters and their dimensions are as follow: $m_{iD}=\\frac{2.00}{n}\\,[kg]$,\n$l_{D}=0.25\\,[m]$, $c_{\\varphi D}=\\frac{0.02}{n}\\,[Nms]$, $N_{0D}=5.00\\,[Nm]$,\n$N_{1D}=0.50\\,[Nms]$, $M_{D}=6.00\\,[kg]$, $g_{D}=9.81\\,[\\frac{m}{s^{2}}]$,\n$c_{x_{D}}=\\frac{\\ln\\left(1.5\\right)}{\\pi}\\sqrt{k_{x}\\left(M+\\sum\\limits _{i=1}^{n}{m_{i}}\\right)}\\,[\\frac{Ns}{m}]$\nand $k_{xD}\\,[\\frac{N}{m}]$ is controlling parameter. The derivation\nof the above equations can be found in \\cite{czolczynski2012synchronization}.\n\\foreignlanguage{english}{We perform a transformation to a dimensionless\nform in a way that enables us to hold parameters' values. It is because\nwe want to present new results in a way that thay can be easily compared\nto results of the investigation presented in }\\cite{czolczynski2012synchronization}\\foreignlanguage{english}{.\nWe introduce dimensionless time $\\tau=t\\omega_{0}$, where $\\omega_{0}=1\\,\\mathrm{[Hz]}$,\nand unit parameters $m_{0}=1.0\\,[kg]$, }$l_{0}=1.0\\,[m]$\\foreignlanguage{english}{\nand reach the dimensionless equations:}\n\n\\begin{equation}\nm_{i}l^{2}\\ddot{\\varphi}_{i}+m_{i}\\ddot{x}l\\cos{\\varphi_{i}}+c_{\\varphi}\\dot{\\varphi}_{i}+m_{i}gl\\sin{\\varphi_{i}}=N_{0}-\\dot{\\varphi}_{i}N_{1}\\label{eq:Beam1-1}\n\\end{equation}\n\n\n\\begin{equation}\n\\left({M+\\sum\\limits _{i=1}^{n}{m_{i}}}\\right)\\ddot{x}+c_{x}\\dot{x}+k_{x}x=\\sum\\limits _{i=1}^{n}{m_{i}l\\left({-\\ddot{\\varphi}_{i}\\cos\\varphi_{i}+\\dot{\\varphi}_{i}^{2}\\sin\\varphi_{i}}\\right)}\\label{eq:Beam2-1}\n\\end{equation}\n\n\n\\selectlanguage{english}%\nwhere: $x=\\frac{x'}{l_{0}}$, $\\dot{x}=\\frac{\\dot{x'}}{l_{0}\\omega_{0}}$,\n$\\ddot{x}=\\frac{\\ddot{x'}}{l_{0}\\omega_{0}^{2}}$, $\\varphi_{i}=\\varphi'_{i}$,\n$\\dot{\\varphi}_{i}=\\frac{\\dot{\\varphi'}_{i}}{\\omega_{0}}$, $\\ddot{\\varphi}_{i}=\\frac{\\ddot{\\varphi'}_{i}}{\\omega_{0}^{2}}$,\n$m_{i}=\\frac{m_{iD}}{m_{0}}$, $l=\\frac{l_{D}}{l_{0}}$, $c_{\\varphi}=\\frac{c_{\\varphi D}}{m_{0}l_{o}^{2}\\omega_{0}}$,\n$N_{0}=\\frac{N_{0D}}{m_{0}l_{o}^{2}\\omega_{0}^{2}}$, $N_{1}=\\frac{N_{1D}}{m_{0}l_{o}^{2}\\omega_{0}}$,\n$M=\\frac{M_{D}}{m_{0}}$, $g=\\frac{g_{D}}{l_{o}\\omega_{0}^{2}}$,\n$c_{x}=\\frac{c_{xD}}{m_{0}\\omega_{0}}$ and dimensionless control\nparameter $k_{x}=\\frac{k_{xD}}{m_{0}\\omega_{0}^{2}}$. Dimensionless\nparameters have the following values: {$m_{i}=\\frac{2.0}{n}$,\n$l=0.25$, $c_{\\varphi}=\\frac{0.02}{n}$, $N_{0}=5.0$, $N_{1}=0.5$,\n$M=6.0$, $g=9.81$.}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction\nThough up to now there is no experimental indication why quantum evolution may be nonlinear, it has been traditionally considered both as a possible way out to the measurement problem\\cite{Wig62a} or as matter of theoretical considerations to be contrasted with high-finesse experiments\\cite{Wei89a}. One of the most remarkable consequences of these considerations\\cite{Gis90a} was the possibility, under the nonlinearity assumption, of supraluminal communication between two spatially separated parties. This soon led some authors to conclude that any nonlinear quantum evolution would necessarily entail the possibility of such a communication\\cite{Gis89a,GisRig95a} and even to consider the relativistic postulate of 'no-faster-than-light' phenomena as the theoretical basis for the quantum evolution to be linear\\cite{SimBuzGis01a}. Recently\\cite{FerSalSan03b} we have proven that this implicaction is not strict, i.e.\\ that there exist possible nonlinear quantum evolutions not implying this fatal supraluminal communication.\\\\\n\nHere we extend our previous result to finite quantum systems of arbitrary dimensions. We formulate the 'no-signaling' condition for these systems and show a full-flegded infinity of examples fulfilling this condition. Everything is expressed in Bloch space language\\cite{Kim03a,ByrKha03a}, i.e.\\ the states of quantum systems are expressed as \n\n\\begin{equation}\n\\rho(t)=\\frac{1}{N}\\left(\\mathbb{I}_{N}+\\mathbf{r}(t)\\cdot\\mathbf{\\sigma}\\right)\n\\end{equation}\n\n\\noindent and orthogonal projectors as\n\n\\begin{equation}\nP=P_{0}\\mathbb{I}_{N}+\\mathbf{P}\\cdot\\mathbf{\\sigma}\n\\end{equation}\n\\noindent where $\\mathbf{r}(t)$ is a time-dependent so-called Bloch vector belonging to a particular convex subset of $\\mathbb{R}^{N^{2}-1}$, $\\mathbf{\\sigma}\\equiv\\left(\\sigma_{1},\\dots,\\sigma_{N^{2}-1}\\right)$ are the traceless orthogonal generators of $SU(N)$ and $(P_{0},\\mathbf{P})\\equiv(P_{0},P_{1},\\dots,P_{N^{2}-1})$ are real numbers subjected to certain restrictions (cf.\\ \\cite{Kim03a} for the details).\n\n\n\\section{The 'no-signaling' condition}\n\n\nAs remarked in \\cite{FerSalSan03b}, the impossibility of communication through the projection postulate, i.e.\\ at a speed faster than that of light, is obtained only after imposing that \\emph{the \\textbf{probability distribution} of any observable of one subsystem \\textbf{only} depends on its own reduced state}. The mathematical translation of this criterion is straightforward provided one is familiar with the preceding language. Let us consider a two-partite system of subsystems $1$ and $2$, which have dimensions $N_{1}$ and $N_2$, respectively. Their common density matrix, using a tensor product basis, will be given by\n\n\\begin{equation}\n\\rho_{12}=\\frac{1}{N_{1}N_{2}}\\left(\\mathbb{I}_{N_{1}N_{2}}+\\mathbf{r}^{(1)}\\cdot\\mathbf{\\sigma}\\otimes\\mathbb{I}_{N_{2}}+\\mathbb{I}_{N_{1}}\\otimes\\mathbf{r}^{(2)}\\cdot\\lambda+\\sum_{ij}r_{ij}^{(12)}\\sigma_{i}\\otimes\\lambda_{j}\\right)\n\\end{equation}\n\n\\noindent and an orthogonal projector for each of them by\n\n\\begin{equation}\nP^{(1)}=P_{0}^{(1)}\\mathbb{I}_{N_{1}}+\\mathbf{P}^{(1)}\\cdot\\sigma\\quad P^{(2)}=P_{0}^{(2)}\\mathbb{I}_{N_{2}}+\\mathbf{P}^{(2)}\\cdot\\lambda\n\\end{equation}\n\n\\noindent respectively, where $\\sigma$ ($\\lambda$) stands for the traceless orthogonal generators of $SU(N_{1})$ ($SU(N_{2})$) and $\\mathbf{P}^{(1)}$ ($\\mathbf{P}^{(2)})$ is a $(N^{2}_{1}-1)$($(N^{2}_{2}-1)$)-dimensional vector restricted to some given subset\\footnote{Namely, $P_{0}=P_{0}^{2}+\\mathbf{P}\\cdot\\mathbf{P}$ and $2P_{0}P_{n}+z_{ijn}P_{i}P_{j}=P_{n}$, where $z_{ijk}\\equiv g_{ijk}+if_{ijk}$, the latter denoting the completely symmetric and antisymmetric tensors of the Lie algebra $\\mathfrak{su}(N_{j})$, respectively.}.\\\\\nSuppose now that an orthogonal projector $(u_{0},\\mathbf{u})$ is measured upon subsystem $2$. Then $N_{2}$ possible outcomes $(u_{0}^{(k)},\\mathbf{u}^{(k)})$ will result with probabilites $p_{k}=u_{0}^{(k)}+\\mathbf{u}^{(k)}\\cdot\\mathbf{r}^{(2)}$ given by the trace rule. Also, the projection postulate allows us to conclude that after such a measurement, the reduced density operator for its partner, subsystem $1$ will be given by\n\n\\begin{equation}\n\\rho_{k}^{(1)}(0)=\\frac{1}{N_{1}}\\left(\\mathbb{I}_{N_{1}}+\\mathbf{r}^{(1;k)}\\cdot\\sigma\\right)\n\\end{equation} \n\n\\noindent where $\\mathbf{r}^{(1;k)}$ is an $(N^{2}_{1}-1)$-dimensional vector ($k=1,\\dots,N_{2}$ possible outcomes) dependent on the joint state $\\mathbf{r}^{(1)},\\mathbf{r}^{(2)},r_{ij}^{(12)}$ and on the measured observable $(u_{0},\\mathbf{u})$:\n\n\\begin{equation}\n\\mathbf{r}^{(1;k)}_{j}=\\frac{u_{0}^{(k)}r_{j}^{(1)}+\\sum_{n=1}^{N_{2}^{2}-1}r_{jn}^{(12)}u_{n}^{(k)}}{u_{0}^{(k)}+\\mathbf{u}^{(k)}\\cdot\\mathbf{r}^{(2)}}\\equiv r^{(1;k)}_{j}(0)\n\\end{equation}\n\nIn these conditions, the probability distribution $\\mathbb{P}$ of an arbitrary orthogonal projector $(v_{0},\\mathbf{v})$ with $p=1,\\dots,N_{1}$ possible outcomes $(v_{0}^{(p)},\\mathbf{v}^{(p)})$ at time $t$ of subsystem $1$ will be given by \n\n\\begin{equation}\n\\mathbb{P}^{(1)}(t;v^{(p)})=\\sum_{k=1}^{N_{2}}(u_{0}^{(k)}+\\mathbf{r}^{(2)}\\cdot\\mathbf{u}^{(k)})(v_{0}^{(p)}+\\mathbf{v}^{(p)}\\cdot\\mathbf{r}^{(1)}(t;\\mathbf{r}^{(1;k)}(0))\n\\end{equation}\n\n\\noindent where $\\mathbf{r}^{(1)}(t;\\mathbf{r}^{(1;k)}(0))$ denotes the Bloch vector of subsystem $1$ at time $t$ with initial condition $\\mathbf{r}^{(1;k)}(0)$.\\\\\n \nThe 'no-signaling' condition can then be easily formulated. The independece with respect to other partners' reduced state and their mutual correlations will be expressed as\n\n\\begin{eqnarray}\\label{NoSig1}\n\\frac{\\partial\\mathbb{P}^{(1)}(t;v^{(p)})}{\\partial r_{k}^{(2)}}&=&0\\\\\n\\label{NoSig2}\\frac{\\partial\\mathbb{P}^{(1)}(t;v^{(p)})}{\\partial r_{ij}^{(12)}}&=&0\n\\end{eqnarray}\n\n\\noindent Finally, the independence with respect to observables to be measured in spatially separated subsystems will be expressed as\n\n\\begin{equation}\\label{NoSig3}\n\\frac{\\partial\\mathbb{P}^{(1)}(t;v^{(p)})}{\\partial u^{(k)}_{\\mu}}=0\\quad\\mu=0,1,\\dots,N_{1}^{2}-1\n\\end{equation}\n\nThese three conditions are the mathematical translation of the previously formulated 'no-signaling' condition. The reader may check for himself that, as expected, the usual linear quantum evolution fulfills each of them (see also below).\n\n\\section{Consequences}\n\nOne of the main consequences of eqs.\\ (\\ref{NoSig1}), (\\ref{NoSig2}) and (\\ref{NoSig3}) arises after noticing that they must be valid for any particular value of the parameters involved, which implies $\\mathbf{r}^{(i)}(t;\\mathbf{r}_{k})=A^{(i)}(t)\\mathbf{r}_{k}$, where $A^{(i)}(t)$ is a time-dependent matrix. In other words, the reduced dynamics in absence of interactions (spatial separation) must be linear. Note that this does not exhaust the possibility of having nonlinear joint evolution. Indeed reduced linearity in absence of interactions entails neither joint linearity nor even reduced unitarity. Expressing this in Bloch vector language, if $(\\mathbf{r}^{(1)}(t),\\mathbf{r}^{(2)}(t),r_{ij}^{(12)}(t))$ denotes the Bloch vector of a two-partite system and if $H=H_{0}\\mathbb{I}_{N_{1}N_{2}}+\\mathbf{H}\\cdot\\sigma_{12}$ ($\\mathbf{H}=(\\mathbf{H}^{(1)},\\mathbf{H}^{(2)},H^{(12)})$ and $\\sigma_{12}=(\\sigma\\otimes\\mathbb{I}_{N_{2}},\\mathbb{I}_{N_{1}}\\otimes\\lambda,\\sigma\\otimes\\lambda)$) denotes its joint Hamiltonian, then any evolution given by\n\n \\begin{eqnarray}\n\\mathbf{r}^{(1)}(t)&=&\\mathbf{F}_{1}(t;H,\\mathbf{r}(0))\\\\\n\\mathbf{r}^{(2)}(t)&=&\\mathbf{F}_{2}(t;H,\\mathbf{r}(0))\\\\\nr^{(12)}(t)&=&F_{12}(t;H,\\mathbf{r}(0))\n\\end{eqnarray}\n\n\\noindent such that in absence of interactions ($H^{(12)}=0$) satisfies\n\n\\begin{eqnarray}\n\\label{RedLin1}\\mathbf{r}^{(1)}(t)&=&M^{(1)}(t;\\mathbf{H}^{(1)})\\mathbf{r}^{(1)}(0)\\\\\n\\label{RedLin2}\\mathbf{r}^{(2)}(t)&=&M^{(2)}(t;\\mathbf{H}^{(2)})\\mathbf{r}^{(2)}(0)\n\\end{eqnarray}\n\n\n\\noindent where $M^{(k)}(t;\\mathbf{H}^{(k)})$ denotes a time-dependent matrix depending only on the Hamiltonian of the $k$th subsystem, is free of supraluminal communication.\\\\\n\nIt should be clear that this nonlinearity only affects the evolution and never the static structure of the theory, i.e.\\ the principle of superposition of quantum states at a given instant of time is still valid, only the evolution of these states is affected.\\\\\n\nAlternatively, one can express these nonlinearities through the evolution equations:\n\n\\begin{eqnarray}\n\\frac{dr^{(1)}_{i}}{dt}&=&\\left(\\sum_{m,n=1}^{N_{1}^{2}-1}f_{imn}^{(1)}H^{(1)}_{m}r^{(1)}_{n}+\\sum_{j,m,n=1}^{N_{1}^{2}-1}f_{ijm}^{(1)}H^{(12)}_{jn}r^{(12)}_{mn}\\xi_{i;jn}^{(1)}(\\mathbf{r}^{(1)},\\mathbf{r}^{(2)},r^{(12)})\\right)\\nonumber\\\\\n&&\\\\\n\\frac{dr^{(2)}_{i}}{dt}&=&\\left(\\sum_{m,n=1}^{N_{2}^{2}-1}f^{(2)}_{imn}H^{(2)}_{m}r^{(2)}_{n}+\\sum_{j,m,n=1}^{N_{2}^{2}-1}f^{(2)}_{ijm}H^{(12)}_{jn}r^{(12)}_{nm}\\xi_{i;jn}^{(2)}(\\mathbf{r}^{(1)},\\mathbf{r}^{(2)},r^{(12)})\\right)\\\\\n\\frac{dr^{(12)}_{pq}}{dt}&=&2\\left(\\sum_{i,j=1}^{N_{1}^{2}-1}f^{(1)}_{jip}H^{(1)}_{j}r^{(12)}_{iq}+\\sum_{i,j=1}^{N_{2}^{2}-1}f^{(2)}_{jip}H^{(1)}_{j}r^{(12)}_{qi}+\\right.\\nonumber\\\\\n&+&\\sum_{i,j=1}^{N_{1}^{2}-1}\\sum_{m,n=1}^{N_{2}^{2}-1}\\textrm{Im}\\left[z_{ijp}^{(1)}z_{mnq}^{(2)}\\right]H_{im}^{(12)}r_{jn}^{(12)}\\xi_{pq;im}(\\mathbf{r}^{(1)},\\mathbf{r}^{(2)},r^{(12)})+\\nonumber\\\\\n&+&\\left.\\sum_{i,j=1}^{N_{1}^{2}-1}f_{ijp}^{(1)}H^{(12)}_{iq}r^{(1)}_{j}\\xi_{pq;iq}^{(12)}(\\mathbf{r}^{(1)},\\mathbf{r}^{(2)},r^{(12)})+\\sum_{i,j=1}^{N_{2}^{2}-1}f_{ijp}^{(1)}H^{(12)}_{qi}r^{(2)}_{j}\\xi_{pq;iq}^{(12)}(\\mathbf{r}^{(1)},\\mathbf{r}^{(2)},r^{(12)})\\right)\\nonumber\\\\\n\\end{eqnarray}\n\n\\noindent where the functions $\\xi$ are completely arbitrary. Notice that in absence of interactions ($H^{(12)}=0$), one recovers the usual well-known quantum evolution.\n\n\\section{Conclusions\n\nThe main two conclusions to be drawn are that (i) nonlinear evolution does not necessarily imply the possibility of supraluminal communication between two arbitrary finite quantum systems, and (ii) non linear terms, in order to fulfill the no-signaling condition, must be necessarily associated to interactions.\\\\\n\nThis reopens a door, originally suggested by Wigner, to explore possible solutions to the measurement problem without contradicting other well contrasted theories.\\\\\n\nA third generalization of this approach can be undertaken by focusing on non-projective measurements, but on generalized measurements, i.e.\\ on POVM's \\cite{Per93a}.\n\n\n\\section*{Acknowledgements}\nWe acknowlegde financial support from Spanish Ministry of Science and Techmology through project no.\\ FIS2004-01576. M.F.\\ also acknowledges financial support from Oviedo University (ref.\\ no.\\ MB-04-514).\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Conclusion}\nWe propose a novel FL method (CoFED) that is simultaneously compatible with heterogeneous tasks, heterogeneous models, and heterogeneous training processes. Compared with the traditional method, CoFED is more suitable for CS-FL settings with fewer participants but higher heterogeneity. CoFED decouples the models and training processes of different participants, thereby enabling each participant to train its independently designed model for its unique task via its optimal training methods. In addition, CoFED protects private data, models and training methods of all participants under FL settings. CoFED enables participants to share multiparty knowledge to increase their local model performance. The method produces promising results under non-IID data settings for models with heterogeneous architectures, which is more practical but is usually difficult to handle in existing FL methods. Moreover, the CoFED method is efficient since training can be performed in only one communication round.\n\nThe CoFED method may be limited by the availability of public unlabeled datasets. Although we conduct numerous experiments to demonstrate that CoFED has low requirements for public datasets and that the use of irrelevant or randomly generated datasets is still effective, some failure scenarios may still occur; this is a problem that we hope to address in the future.\n\\section{Experiments}\n\nIn this section, we execute the CoFED method under different FL settings to explore the impacts of different conditions and compare it with existing FL methods. The source code can be found at https:\/\/github.com\/flcao\/CoFED.\n\n\n\n\n\n\n\\subsection{Model Settings}\nThe CoFED method enables participants to independently design different models. For example, some participants use CNNs as classifiers, while others use support vector machine (SVM) classifiers. We use randomly generated 2-layer or 3-layer CNNs with different architectures as the models for the different participants in image classification tasks to illustrate that CoFED is compatible with heterogeneous models. Ten of the 100 employed model architectures are shown in Table \\ref{netStruct}.\n\n\\begin{table}[ht]\n\\caption{Network Architectures}\n\\label{netStruct}\n\\centering\n\\begin{tabular}{cccc}\n\\toprule\nModel & \\begin{tabular}[c]{@{}c@{}}1st \\\\ conv layer\\end{tabular} & \\ \\begin{tabular}[c]{@{}c@{}}2nd \\\\ conv layer\\end{tabular} & \\ \\begin{tabular}[c]{@{}c@{}}3rd \\\\ conv layer\\end{tabular} \\\\ \\midrule\n1 & 24 3x3 & 40 3x3 & none \\\\\n2 & 24 3x3 & 32 3x3 & 56 3x3 \\\\ \n3 & 20 3x3 & 32 3x3 & none \\\\ \n4 & 24 3x3 & 40 3x3 & 56 3x3 \\\\ \n5 & 20 3x3 & 32 3x3 & 80 3x3 \\\\ \n6 & 24 3x3 & 32 3x3 & 80 3x3 \\\\ \n7 & 32 3x3 & 32 3x3 & none \\\\ \n8 & 40 3x3 & 56 3x3 & none \\\\ \n9 & 32 3x3 & 48 3x3 & none \\\\ \n10 & 48 3x3 & 56 3x3 & 96 3x3 \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\nTo demonstrate that CoFED is applicable to broader ranges of models and task types than other approaches, we choose four types of classifiers as the participant models in the Adult dataset experiment: decision trees, SVMs, generalized additive models, and shallow neural networks.\n\n\\subsection{CIFAR Dataset Experiment}\nWe set the number of participants to 100 in our CIFAR experiments. For each participant, we randomly select a few of the 20 superclasses of CIFAR100 \\cite{krizhevsky2009learning} as its label space. Since we try to study the effect of the proposed method on heterogeneous tasks, the label spaces of different participants are generally different, but some overlap may occur. The local dataset of each participant consists of the samples in its label space.\n\nThe distributions of each superclass possessed by the different participants who own this superclass sample may encounter two situations. In the first case, we assume that the samples of each participant with the superclass are uniformly and randomly sampled from all samples of the superclass in CIFAR100 (that is, the IID setting). In this case, each participant usually has samples belonging to all 5 subclasses of each superclass in its label space. In the second case, we assume that each participant who owns the superclass only has samples belonging to some of the subclasses of its superclass (in our experiment, 1 or 2 subclasses), that is, the non-IID setting. The details of the experimental data settings are as follows.\n\n\\textbf{IID Data Setting}: Each participant is randomly assigned 6 to 8 superclasses in CIFAR100 as its label space. For the local training sets, each participant has 50 instances from each superclass in the CIFAR100 training set, and these 50 samples are evenly sampled from the samples of this superclass in the training set of CIFAR100. No overlap occurs between the training sets of any participants. For the test sets, all instances in the test set of CIFAR100 are used, whereby each participant's test set has 500 instances for each superclass.\n\n\\textbf{Non-IID Data Setting}: This setting is almost the same as the IID setting; the difference is that the sample of a superclass of each participant is only randomly acquired from 1 to 2 subclasses of the superclass in the CIFAR100 training set. The configuration of the test set is exactly the same as that used with the IID setting. The non-IID data setting is often regarded as more difficult than the IID one since a model that learns only 1 or 2 subclasses of a superclass during the training process is required to recognize all 5 subclasses included in the test set.\n\nIn this experiment, the public unlabeled dataset used in CoFED is the training set of CIFAR10 \\cite{krizhevsky2009learning}. Since CoFED is compatible with heterogeneous training processes, we conduct a grid search to determine the optimal training parameter settings for each participant task. The training configuration optimized for each participant (including the trainer, learning rate, and learning rate decay) is used in the initial local training phase, and the update training settings in the final step are adjusted based on these parameters. We always use a minibatch size of 50 for the local training process and 1000 for the update training process.\n\n\\begin{figure}[t]\n \\centering\n \\begin{subfigure}{.5\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/iidcifar_cifar10.eps}\n \\caption{IID setting}\n \\end{subfigure}%\n \n \\begin{subfigure}{.5\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/noniidcifar_cifar10.eps}\n \\caption{Non-IID setting}\n \\end{subfigure}%\n \\caption{Results of the CIFAR experiment. The X-axis value is the relative test accuracy, which is the ratio of the CoFED test accuracy to the local test accuracy.}\n \\label{cifar_cifar10}\n\\end{figure}\n\n\nWe test our proposed CoFED method separately under the IID setting and non-IID setting, and the hyperparameter $\\alpha$ is set to 0.3 for both settings. We compare the test classification accuracies of the models trained by CoFED with those of the models utilizing local training, and the results are shown in Fig. \\ref{cifar_cifar10}(a) and Fig. \\ref{cifar_cifar10}(b). Under the IID setting, CoFED method improves the relative test accuracy of each participant model by 10\\%-32\\% with an average of 17.3\\%. This demonstrates that CoFED can increase participant model performance even when the divergences of models are not large (such as those under the IID data setting). For the non-IID setting, CoFED can lead to greater model performance gains due to the greater divergence of data distributions that make locally trained models more divergent. In this experiment, CoFED achieves a relative average test accuracy improvement of 35.6\\%, and for each participant, the improvement ranges from 14\\% to 67\\%. The performance boost under the Non-IID setting is better than the IID setting, which indicates that CoFED suffers less from statistical heterogeneity.\n\n\n\\subsection{FEMNIST Dataset Experiment}\nFEMNIST dataset is a handwritten character dataset of LEAF \\cite{caldas2018leaf} which is a benchmark framework for FL. It consists of samples of handwritten characters from different users, and we select the 100 users with the most samples of FEMNIST as participants. Forty percent of selected samples are used as training set, and the rest are test set. \n\nThe architectures of participant models are the same as those in the CIFAR experiment, and the local training hyperparameters are tuned in similarly to those in the CIFAR experiment. In this experiment, we use random crops of the images in the Chars74k-Kannada dataset \\cite{de2009character} to construct an unlabeled public dataset with nearly 50,000 items. Chars74k-Kannada contains handwritten character images of English and Kannada, and only the handwritten Kannada characters are used as unlabeled public dataset in our experiment. The hyperparameter $\\alpha$ is set to 0.01, and the results are shown in Fig. \\ref{fem_kan}. CoFED improves the relative test accuracies of the models for almost all participants, with an average improvement of 15.6\\%.\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[width=0.5\\columnwidth]{Figures\/fem_kan.eps}\n\\caption{Results of the FEMNIST experiment.}\n\\label{fem_kan}\n\\end{figure}\n\n\n\\subsection{Public Unlabeled Dataset Availability}\nOne of the major concerns regarding the CoFED method concerns whether a public dataset is available. The role of the public dataset is to express and share the knowledge that the participant models need to learn. Generally, the participants in FL cannot learn knowledge from samples generated entirely by random values, but this does not mean that we must use samples that are highly related to the participants' local data to construct a public dataset.\n\nFor example, in the CIFAR experiment, we use the samples of CIFAR100 to construct the local data of the participants, but we use CIFAR10 as the public dataset. The categories of the data samples contained in CIFAR10 and CIFAR100 only overlap slightly. Therefore, we can regard CIFAR10 as a dataset composed of pictures randomly obtained from the Internet without considering the similarity between its contents and the samples of participants (from CIFAR100). A large morphology difference between English characters and Kannada characters is also observed in the FEMNIST experiment. However, CoFED can effectively improve the performance of almost all participant models in both experiments, which makes us want to know how different participant models use public datasets that are not relevant to them to share knowledge. Therefore, we review the results of pseudolabel aggregation and check how these models classify the irrelevant images. Fig. \\ref{pseudo} shows a partial example of the pseudolabel aggregation results of the 10 participant models (out of 100 participants).\n\n\\begin{figure*}[ht]\n\\includegraphics[width=\\linewidth]{Figures\/aggregating.eps}\n\\caption{The results of pseudolabel aggregation. The images obtained from the training set of CIFAR10 are scattered across the superclasses of CIFAR100. Ten images per superclass are randomly selected.}\n\\label{pseudo}\n\\end{figure*}\n\nFirst, we notice that some trucks and automobiles are correctly classified into the vehicles\\_1 category. This indicates that the unlabeled instances whose categories are contained in the label spaces of the participant models are more likely to be assigned to the correct category. However, exceptions occur; some automobiles are assigned to the {\\it{flowers}} and {\\it{fruit and vegetables}} categories. A common feature possessed by these automobile images is that they contain many red parts, which may be regarded as a distinctive feature of {\\it{flowers}} or {\\it{fruit and vegetables}} by the classifiers. In addition, the unlabeled samples that are not included in the label spaces of any classifier are also classified into the groups that match their visual characteristics. For example, the corresponding instances of aquatic\\_mammals and fish usually have blue backgrounds, which resemble water. Another interesting example is the people category. Although almost no human instances are contained in the training set of CIFAR10, some of the closest instances are still given, including the person on a horse.\n\nMoreover, we also try to replace the public dataset in the CIFAR experiment with the ImageNet dataset \\cite{van2016pixel} with almost the same size as that of CIFAR10. The CoFED method can still achieve considerable performance improvements, as shown in Fig. \\ref{cifar_imagenet}(a) and Fig. \\ref{cifar_imagenet}(b).\n\n\\begin{figure}[t]\n \\centering\n \\begin{subfigure}{.5\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/iidcifar_imagenet.eps}\n \\caption{IID setting}\n \\end{subfigure}%\n \n \\begin{subfigure}{.5\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/noniidcifar_imagenet.eps}\n \\caption{Non-IID setting}\n \\end{subfigure}%\n \\caption{Results of the CIFAR experiment obtained by using the ImageNet dataset as an unlabeled public dataset.}\n \\label{cifar_imagenet}\n\\end{figure}\n\n\n\\subsection{Unlabeled Dataset Size}\nAccording to (\\ref{13}), (\\ref{14}) and (\\ref{15}), when an existing classifier has a higher generalization accuracy and a larger labeled training set, a larger unlabeled dataset is needed to improve its accuracy. This suggests that in the CoFED method, a larger unlabeled dataset can produce a more obvious performance improvement for a given group of participant models. We redo the CIFAR experiment with 10 participants and vary the size of the unlabeled dataset between 500 and 50,000 samples in the training set of CIFAR10. The experimental results are shown in Fig. \\ref{pubsize}.\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[width=0.6\\columnwidth]{Figures\/pubsize.eps}\n\\caption{Size of the unlabeled public dataset vs. the mean relative test accuracy.}\n\\label{pubsize}\n\\end{figure}\n\nThe experimental results are consistent with the theoretical analysis. This shows that the strategy of increasing the size of the unlabeled dataset can be used to boost the performance improvements exhibited by all participant models. Considering that the difficulty of collecting unlabeled data is much lower than that of collecting labeled data in general, this strategy is feasible in many practical application scenarios.\n\n\n\\subsection{Hyperparameter $\\alpha$}\nFrom the theoretical analysis, increasing the reliability of the pseudolabeling process is likely to bring more significant performance improvements, which is also very intuitive. Therefore, we use a hyperparameter $\\alpha$ to improve the reliability of pseudolabel aggregation. A larger $\\alpha$ requires a higher percentage of participants to agree to increase the reliability of the pseudolabels, but this may also reduce the number of available pseudolabel instances. In particular, when the participants disagree greatly, requiring excessive consistency across the results of different participant models may stop the spread of multiparty knowledge.\n\nIn this section, we repeat the CIFAR experiment for 10 participants with different $\\alpha$ and record the changes in the total number of samples generated by pseudolabel aggregation when different $\\alpha$ values are taken in Fig. \\ref{idxSiz}. The changes in the test accuracies of all participant models in the CoFED method are shown in Fig. \\ref{alphasize}.\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[width=0.6\\columnwidth]{Figures\/idxSize.eps}\n\\caption{$\\alpha$ vs. the size of the pseudolabel aggregation results. For $\\alpha=1$, the total numbers of IID and non-IID data are both 0, which cannot be shown with the log scale.}\n\\label{idxSiz}\n\\end{figure}\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[width=0.6\\columnwidth]{Figures\/alphasize.eps}\n\\caption{$\\alpha$ vs. the mean relative test accuracy.}\n\\label{alphasize}\n\\end{figure}\n\nThe results show that when the value of $\\alpha$ changes, its impact on CoFED is not monotonic. Although an excessively large value of $\\alpha$ can increase the reliability of the generated pseudolabels, this also greatly reduces the number of samples in the pseudolabel aggregation results, which may degrade the training results. In the FEMNIST experiment, we find that a larger $\\alpha$ value may greatly reduce the number of samples in the pseudolabel aggregation results, so we set the $\\alpha$ value to 0.01.\n\nAt the same time, an excessively large $\\alpha$ value makes the pseudolabel aggregation process more inclined to choose the samples that most participants agree on. Since these sample are approved by most participants, it is almost impossible to bring new knowledge to these participants. In addition, we find that a larger $\\alpha$ value has a more severe impact on the non-IID data setting, where performance degradation is more significant than in the IID cases. This is because the differences between the models trained on the non-IID data are greater, and the number of samples that most participants agree on is smaller. Therefore, when $\\alpha$ increases, the number of available samples decreases faster than in the IID case, as shown in Fig. \\ref{idxSiz}, which causes greater performance degradation under the non-IID setting.\n\nOn the other hand, an $\\alpha$ that is too small decreases the reliability of the pseudolabel aggregation results, which may introduce more mislabeled samples, making the CoFED method less effective. In addition, an excessively small $\\alpha$ value may cause a large increase in the number pseudolabel aggregation samples, resulting in increased computation and communication overheads.\n\nIn summary, the effectiveness of the CoFED method can be affected by the value of the hyperparameter $\\alpha$, and adopting an appropriate $\\alpha$ value can yield greater performance improvements and avoid unnecessary computation and communication costs.\n\n\\subsection{Adult Dataset Experiment}\nAdult dataset\\cite{kohavi1996scaling} is a census income dataset to be used to classify a person's yearly salary based on their demographic data. We set the number of participants to 100 in our Adult experiments. For each participant, we randomly select 200 samples from the training set of the Adult dataset without replacement. Since we try to study the effect of the proposed method on heterogeneous models, 4 types of classifiers are chosen: decision trees, SVMs, generalized additive models, and shallow neural networks. The number of utilized classifiers of each type is 25. The size of unlabeled public dataset in this experiment is 5000, and each sample is randomly generated with valid values for the input properties used by the classifiers. The Adult test set is used to measure the performance of each participant's classifier.\n\nThe results are shown in Fig. \\ref{4c}. The proposed method improves the test accuracies of most classifiers, with an average of 8.5\\%, and the performance of the classifier that benefits the most increases by more than 25\\%. This demonstrates that the proposed method is applicable not only to image classification tasks involving CNN models but also to nonimage classification tasks with traditional machine learning models.\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[width=0.5\\columnwidth]{Figures\/4classifiers.eps}\n\\caption{Results of the Adult experiment.}\n\\label{4c}\n\\end{figure}\n\n\\subsection{Comparison with Other FL Methods}\nTo the best of our knowledge, CoFED is the first FL method that tries to be compatible with heterogeneous models, tasks, and training processes simultaneously. Therefore, FL methods are available for comparison under HFMTL settings. We use two well-known comparison methods. One is the personalized FedAvg method, which represents the classic parameter aggregation-based FL strategy that is not compatible with heterogeneous models with different architectures; the other is the FedMD method \\cite{li2019fedmd}, which supports different neural network architectures and is used to evaluate the performance of CoFED under heterogeneous models.\n\nTo enable the personalized FedAvg and FedMD methods to handle heterogeneous classification tasks, we treat each participant's local label space $\\mathcal{Y}_i$ as the union of the spaces $\\mathcal{Y}$ in (\\ref{union}). In this way, we can handle heterogeneous tasks with the idea of personalized FedAvg and FedMD. The main steps are as follows.\n\\begin{enumerate}\n \\item Use the FedAvg or FedMD algorithm to train a global model whose label space is the union of the label spaces of all participants.\n \\item The global model is fine-tuned on each participant's local dataset for a personalized model for their local task..\n\\end{enumerate}\n\nIn this comparison experiment, we use the data configuration presented in Section 4.3. Considering that the personalized FedAvg method can not be used for models with different architectures, 100 participants share the same architecture neural network model. In the FedMD comparison experiment, we use the same setup as that in Section 4.3; that is, we select 100 participants with different neural network architectures.\n\nWe try a variety of different hyperparameter settings to achieve better performance in the personalized FedAvg experiment. We use $E=20$ as the number of local training rounds in each communication iteration, $B=50$ is set as the local minibatch size used for the local updates, and participants also perform local transfer learning to train their personalized models in each communication iteration. In the FedMD experiment, We use the similar settings of \\cite{li2019fedmd}, and it should be noted that FedMD uses the labeled data of CIFAR10 as the public dataset, which is different from the unlabeled data used in CoFED.\n\n\\begin{table}[]\n\\caption{Comparison Results}\n\\label{iidtb}\n\\centering\n\\begin{threeparttable}\n\\begin{tabular}{@{}c@{}c@{}ccc}\n\\hline\n & & \\begin{tabular}[c]{@{}c@{}}Personalized\\\\ FedAvg\\end{tabular} & FedMD & CoFED \\\\ \\hline\n\\multirow{2}{*}{IID} & Accuracy & \\textbf{1.06} & 0.87 & 1.00\\tnote{*} \\\\\n & Rounds & 100 & 8\\tnote{\\dag} & \\textbf{1} \\\\ \\hline\n\\multirow{2}{*}{Non-IID} & Accuracy & 0.94 & 0.94 & \\textbf{1.00}\\tnote{*} \\\\\n & Rounds & 150 & 17\\tnote{\\dag} & \\textbf{1} \\\\ \\hline\n\\end{tabular}\n\\begin{tablenotes}\n\\item[*] Reference value.\n\\item[\\dag] The round with the best performance.\n\\end{tablenotes}\n\\end{threeparttable}\n\\end{table}\n\n\\begin{figure}[t]\n \\centering\n \\begin{subfigure}{.5\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/fedavg_iid.eps}\n \\caption{IID setting}\n \\end{subfigure}%\n \n \\begin{subfigure}{.5\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/fedavg_noiid.eps}\n \\caption{Non-IID setting}\n \\end{subfigure}%\n \\caption{Personalized FedAvg method vs. CoFED. To facilitate the comparison, the test accuracy of the CoFED model is used as the reference accuracy, and the value of the Y-axis is the ratio of the comparison algorithm's test accuracy to the reference accuracy. Since each participant has a corresponding ratio, the blue line represents the average of the ratios of all participants corresponding to the given number of iterations.}\n \\label{fedavg}\n\\end{figure}\n\n\\begin{figure}[t]\n \\centering\n \\begin{subfigure}{.5\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/fedmd_iid.eps}\n \\caption{IID setting}\n \\end{subfigure}%\n \n \\begin{subfigure}{.5\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/fedmd_noiid.eps}\n \\caption{Non-IID setting}\n \\end{subfigure}%\n \\caption{FedMD vs. CoFED.}\n \\label{fedmd}\n\\end{figure}\n\nThe results of the comparison between FedAvg and CoFED are shown in Fig. \\ref{fedavg}. The relative test accuracy is calculated as the average of the ratios of all participants to the CoFED test accuracy. Under the IID setting, FedAvg reaches the performance level of CoFED after 42 communication rounds and is finally 6\\% ahead of COFED in Fig. \\ref{fedavg}(a). Under the non-IID setting, FedAvg stabilizes after 150 communication rounds. At this time, CoFED still leads FedAvg by 6\\% in Fig. \\ref{fedavg}(b). The comparing results of personalized FedAvg and CoFED are shown in Fig. \\ref{fedmd}. For both data settings, CoFED outperforms FedMD, and CoFED leads by 14\\% under the IID setting and 35\\% under the non-IID one.\n\nIn terms of communication cost, if we do not consider the communication overhead required to initially construct the public dataset, CoFED achieves better performance with lower communication overheads in all cases because CoFED only needs to pass through the label data (not the sample itself) during the training process, and iterating for multiple rounds is not required. This assumption is not unrealistic because the construction of public datasets may not require the central server to distribute data to the participants; the participants can instead obtain data from a third party, which does not incur communication costs between the participants and the central server. Even if we include that paradigm, CoFED still achieves better performance with lower communication costs except under the IID and identical architecture model settings. In fact, the IID setting used in the FedAvg comparison experiment is not the scenario that is considered most by CoFED because the model architectures are the same and can be shared under that setting.\n\\section{Introduction}\nFederated learning (FL) allows different participants to collaborate in solving machine learning problems under the supervision of a center without disclosing participants' private data \\cite{kairouz2021advances}. The main purpose of FL is to achieve improved model quality by leveraging the multiparty knowledge from participants' private data without the disclosure of data themselves.\n\nFL was originally proposed for training a machine learning model across a large number of users' mobile devices without logging their private data to a data center \\cite{konevcny2015federated}. In this scenario, a center orchestrates edge devices to train a global model that serves a global task. However, some new FL settings have emerged in many fields, including medicine \\cite{rieke2020future}, \\cite{xiao2021federated}, \\cite{raza2022designing}, \\cite{courtiol2019deep}, \\cite{adnan2022federated}, finance \\cite{li2020preserving}, \\cite{gu2021privacy}, and network \\cite{zhang2021survey}, \\cite{nguyen2021federated}, \\cite{ghimire2022recent}, \\cite{regan2022federated}, where the participants are likely companies or organizations. Generally, the terms \\textit{cross-device federated learning} (CD-FL) and \\textit{cross-silo federated learning} (CS-FL) can refer to the above two FL settings \\cite{kairouz2021advances}. However, the majority of these studies' contributions concern training reward mechanisms \\cite{tang2021incentive}, topology designs \\cite{marfoq2020throughput}, data protection optimization approaches \\cite{zhang2020batchcrypt}, etc., and for their core CS-FL algorithms, they simply follow the idea of gradient aggregation used in CD-FL. Such studies ignore the fact that organizations or companies, as participants, may be more heterogeneous than device participants.\n\nOne of the most important heterogeneities to address under the CS-FL setting is model heterogeneity; that is, models may have different architecture designs. Different devices in CD-FL typically share the same model architecture, which is given by a center. As a result, the models obtained through local data training on different devices differ only in terms of their model parameters, allowing the center to directly aggregate the gradients or model parameters uploaded by the participating devices. However, participants in a CS-FL scenario are usually independent companies or organizations, and they are capable of designing unique models. They prefer to use their own independently designed model architectures rather than sharing the same model architecture with others. At this time, the strategy used in CD-FL cannot be applied to CS-FL models with different architectures. Furthermore, model architectures may also be intellectual properties that need to be protected, and companies or organizations that own these properties do not want them to be exposed to anyone else, which makes model architecture sharing hard for CS-FL participants to accept. An ideal CS-FL method should treat each participant's model as a black box, without the need for its parameters or architecture.\n\nThe heterogeneity among models results in the need for training heterogeneity. Under the CD-FL setting, participants usually train their models according to the configuration of a center, which may include training algorithms (such as stochastic gradient descent) and parameter settings (such as the learning rate and minibatch size). However, when participants like companies or organizations use their independently designed model architectures in CS-FL, they need to choose different local training algorithms that are suitable for their models and exert full control over their local training processes. Decoupling the local training processes of different participants not only enables them to choose suitable algorithms for their models but also prevents the leakage of their training strategies, which may be their intellectual properties.\n\nIn addition, CS-FL is more likely to face task heterogeneity than CD-FL. In CD-FL scenarios, all devices usually share the same target task. In terms of classification tasks, the task output categories of all devices are exactly the same. Under the CS-FL setting, because the participating companies or organizations are independent of each other and have different business needs, their tasks may be different. Of course, we must assume that there are still similarities between these tasks. In terms of classification tasks, the task output categories of different participants in CS-FL may be different. For example, an autonomous driving technology company and a home video surveillance system company both need to complete their individual machine learning classification tasks. Although both of them need to recognize pedestrians, the former must also recognize vehicles, whereas the latter must recognize indoor fires. Therefore, the task output categories of the autonomous driving technology company include pedestrians and vehicles without indoor fires, whereas the task output categories of the home video surveillance system company include pedestrians and indoor fires without vehicles. It is easy to see that, in contrast to the complete task consistency of CD-FL, CS-FL participation by independent companies or organizations is more likely to encounter situations in which the different participants possess heterogeneous tasks.\n\n\\begin{figure}[t]\n \\centering\n \\begin{subfigure}{\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/fig0.eps}\n \\caption{CS-FL setting without heterogeneity}\n \\end{subfigure}%\n \\par\\bigskip\n \\begin{subfigure}{\\columnwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Figures\/fig1.eps}\n \\caption{CS-FL setting with heterogeneity (HFMTL)}\n \\end{subfigure}%\n \\caption{(b) is an example of an HFMTL setting with 3 participants. A central server needs to coordinate 3 participants to solve their classification tasks via federated training. Compared with (a), which has no heterogeneity, (b) also contains different machine learning model architectures and training optimization algorithms, and the tasks of participants are distinct from one another(i.e., different label spaces).}\n \\label{fig1}\n\\end{figure}\n\nAlthough the number of participants in CS-FL is much smaller than that in CD-FL in general, the heterogeneity of the participants, including model heterogeneity, training heterogeneity and task heterogeneity, may bring more challenges. Overall, we use the term \\textit{heterogeneous federated multitask learning} (HFMTL) to refer to the FL settings that contain the above three heterogeneity requirements, and Fig. \\ref{fig1}(b) shows an example of the HFMTL setting with three participants. Different participants may have different label spaces. For example, Participant $B$ has a label space $\\left\\{0, 1, 2, 3\\right\\}$. This space can be different from those of other participants, e.g., Participant $C$ has $\\left\\{1, 4, 5\\right\\}$. The classification task of Participant $B$ is to classify inputs with the labels in $\\left\\{0, 1, 2, 3\\right\\}$, while Participant $C$ aims to classify inputs with the labels in $\\left\\{1, 4, 5\\right\\}$. Therefore, they have different tasks and usually have different categories of local training data.\n\nOur main motivation in this paper is to address the needs of \\textit{model heterogeneity}, \\textit{training heterogeneity}, and \\textit{task heterogeneity} in CS-FL settings. Aiming at the HFMTL scenario with these heterogeneities, we propose a novel FL method. The main contributions of this paper are as follows.\n\n\\begin{itemize}\n\\item We propose an FL method that is simultaneously compatible with heterogeneous tasks, models, and training processes, and it boost the performance of each participant's model.\n\\item Compared with the existing FL methods, the proposed method not only protects private local data but also protects the architectures of private models and private local training methods.\n\\item Compared with these other methods, the proposed method achieves more performance improvements for the non-independent and identically distributed (non-IID) data settings in FL and can be completed in one round.\n\\item We conduct comprehensive experiments to corroborate the theoretical analysis conclusions and the impacts of different settings on the suggested method.\n\\end{itemize}\n\nThe rest of this paper is structured as follows. Section 2 provides a brief overview of relevant works. Section 3 contains the preliminaries of our method. Section 4 describes the proposed method in detail. Section 5 offers the experimental results and analysis. Section 6 summarizes the paper.\n\\section*{Acknowledgment}\nThis research was partially supported by the National Key Research and Development Program of China (2019YFB1802800), PCL Future Greater-Bay Area Network Facilities for Large-scale Experiments and Applications (PCL2018KP001).\n\n\\bibliographystyle{IEEEtran}\n\n\\section{Methodology}\n\\subsection{The Overall Steps of CoFED}\nThe main incentive of FL is that it improves the performance of participant models. The poor performance of locally trained models is mainly due to insufficient local training data, so these models fail to learn enough task expertise. Hence, to increase the participant model performance, it is vital to allow them to learn from other participants. The most popular and straightforward methods of sharing knowledge are shared models or data, however both are forbidden by FL settings, thus we must devise alternative methods.\n\nA research \\cite{wang2007analyzing} discovered that a sufficiently enough amount of variety across the models is necessary to increase classification model performance. Generally, it is difficult to generate models with large divergences under single-view settings. However, under FL settings, The architectures of the participant models may be highly varied, and they may have been trained on distinct local datasets that are very likely to be distributed differently. All of these may increase the diversity of different participants' models. Therefore, if enough unlabeled data are obtained, we can regard the federated classification problem as a semisupervised learning problem, and it is very suitable to adopt cotraining-like techniques due to the high diversity between different participant models. We provide the overall steps of CoFED as follows.\n\n\\begin{enumerate}\n \\item \\textbf{Local training}: Each participant independently trains a local model on its private dataset.\n \\item \\textbf{Pseudolabeling}: Each participant pseudolabels an unlabeled dataset, which is public to all participants, with its locally trained model.\n \\item \\textbf{Pseudolabel aggregation}: Each participant uploads its pseudolabeling results to a central server, and the center votes for the pseudolabeled dataset with high confidence for each category based on the category overlap statuses of different participants and the pseudolabeling results. After that, the center sends all pseudolabels to each participant.\n \\item \\textbf{Update training}: Each participant trains its local model on the new dataset created by combining the local dataset with the received pseudolabeled dataset.\n\\end{enumerate}\n\nIt can be seen that there are some differences between CoFED and the cotraining process under single-view settings. First, in cotraining, the training sets used by different classifiers are the same, while the training sets of the different classifiers in CoFED come from different participants, so they are usually different. Second, the target tasks of different classifiers in cotraining are the same; that is, they have the same label space. However, in CoFED, the label spaces of different classifiers are different, and the pseudolabeling of unlabeled samples need to be performed according to the pseudolabel aggregation results of the overlapping classification process. Furthermore, cotraining completes training by repeating the above process, while the process is performed only once in CoFED.\n\n\\subsection{Analysis}\n\nSuppose that we are given two binary classification models $f$ and $g$ from a hypothesis space $\\mathcal{H}: \\mathcal{X} \\rightarrow \\mathcal{Y}, |\\mathcal{H}| < \\infty$, and an oracle model $h \\in \\mathcal{H}$ whose generalization error is zero. We can define the generalization disagreement between $h_1 \\in \\mathcal{H}$ and $h_2 \\in \\mathcal{H}$ as:\n\\begin{equation}\n \\begin{aligned}\n d(h_1,h_2)&=d(h_1,h_2|\\mathcal{X}) \\\\\n &=\\mathbf{Pr}(h_1(x) \\ne h_2(x) | x \\in \\mathcal{X})\n \\end{aligned}\n\\end{equation}\n\nTherefore, the generalization errors of $f$ and $g$ can be computed as $d(f,h)$ and $d(g,h)$, respectively. Let $\\varepsilon$ bound the generalization error of a model, and let $\\delta>0$; a learning process generates an approximate model $h'$ for $h$ with respect to $\\varepsilon$ and $\\delta$ if and only if:\n\\begin{equation}\n \\mathbf{Pr}(d(h', h) \\ge \\varepsilon) \\le \\delta\n\\end{equation}\n\nSince we usually have only a training dataset $X \\subset \\mathcal{X}$ containing finite samples, the training process minimizes the disagreement over $X$:\n\\begin{equation}\n \\mathbf{Pr}(d(f,h|X) \\ge \\varepsilon) \\le \\delta\n\\end{equation}\n\n\\textbf{Theorem 1.} \\textit{\nGiven that $f$ is a probably approximately correct (PAC) learnable model trained on $L \\subset \\mathcal{D}$, $g$ is a PAC model trained on $L_{g} \\subset \\mathcal{D}$, and $\\varepsilon_f < \\frac{1}{2}$, $\\varepsilon_g < \\frac{1}{2}$. $f$ and $g$ satisfy that the following:\n\\begin{align}\n \\label{9}\n & \\mathbf{Pr}(d(f, h) \\ge \\varepsilon_f) \\le \\delta \\\\\n \\label{10}\n & \\mathbf{Pr}(d(g, h) \\ge \\varepsilon_g) \\le \\delta\n\\end{align}\nIf we use $g$ to pseudolabel an unlabeled dataset $X_u \\subset \\mathcal{X}$, we generate a pseudolabeled dataset\n\\begin{equation}\n P=\\left \\{ (x,y)|x \\in X_u, y=g(x) \\right \\}\n\\end{equation}\nand combine $P$ and $L$ into a new training dataset $C$. After that, $f'$ is trained on $C$ by minimizing the empirical risk. Moreover,}\n\\begin{align}\n \\label{13}\n & |L| \\varepsilon_f < \\sqrt[|P|\\varepsilon_g]{(|P|\\varepsilon_g)!} \\hspace{1mm} e-|P|\\varepsilon_g \\\\\n \\label{14}\n & \\varepsilon_{f'} = \\max \\left \\{\\varepsilon_f + \\frac{|P|}{|L|}(\\varepsilon_g-d(g,f')), 0 \\right \\}\n\\end{align}\n\\textit{where $e$ is the base for natural logarithms; then,}\n\\begin{equation}\n \\label{15}\n \\mathbf{Pr}(d(f',h) \\ge \\varepsilon_{f'}) \\le \\delta\n\\end{equation}\n\nTheorem 1 has been proven in \\cite{wang2007analyzing}. Assume that $f$ and $g$ are 2 models from different participants that satisfy (\\ref{9}) and (\\ref{10}). The right side of (\\ref{13}) monotonically increases as $|P|\\varepsilon_g \\in (0, \\infty)$, which indicates that a larger pseudolabeled dataset $P$ enables a larger upper bound of $|L| \\varepsilon_f$. That is, if $f$ is a model trained on a larger training dataset with higher generalization accuracy, a larger unlabeled dataset is required to further improve the generalization accuracy of $f$.It can be seen from (\\ref{14}) and (\\ref{15}) that when $d(g,f')$ is larger, the lower bound of the generalization error of $f'$ under the same confidence is smaller, which is because that $f'$ is trained on $L$ and $P$, and $P$ is generated by $g$, $d(g,f')$ mainly depends on the degree of divergence between $f$ and $g$, i.e., the difference between the training dataset of $f$ and $g$. In FL settings, substantial diversity across different local training datasets is fairly prevalent, hence the performance improvement requirement is generally satisfied. The same conclusion applies to the boost version $g'$ obtained by switching $f$ and $g$ due to symmetry.\n\nOn the other hand, if $|P|$ is sufficiently large since $P$ is generated by $g$, the $f'$ trained on $C$ can be treated as proximal to $g$. That is, if we repeat the above process on $f'$ and $g$, $f'$ and $g$ may be too similar to improve them. Therefore, we utilize a large unlabeled dataset and only perform the above process once instead of iterating for multiple rounds; this technique can also avoid the computational cost increase caused by multiple training iterations.\n\nAn intuitive explanation for the CoFED method is that when the different participant models have great diversity, the differences in the knowledge possessed by the models are greater. As a result, the knowledge acquired by each participant model from others contains more knowledge that is unknown to itself. Therefore, more distinctive knowledge leads to greater performance gains. In addition, when mutual learning between different models is sufficient, the knowledge difference between them will almost disappear, and it is difficult for mutual learning to provide any participant's model with distinctive knowledge.\n\n\\subsection{Pseudolabel Aggregation}\n\nAfter each participant uploads its pseudolabeling results to the public unlabeled dataset, the center exploits their outputs to pseudolabel the unlabeled dataset. In this subsection, we explain the implementation details of this step.\n\nAssume that the union of the label spaces of all participants' tasks is\n\\begin{equation}\n\\label{union}\n\\mathcal{Y}=\\bigcup_{i=1}^N{\\mathcal{Y}_i=\\left\\{ c_k \\right\\} , k=1,2,\\cdots,n_c}\n\\end{equation}\nwhere $n_c$ is the number of elements in the whole label space $\\mathcal{Y}$, and each category $c_k$ exists in the label space of one or multiple participants' label spaces:\n\\begin{equation}\n c_k\\in \\bigcap_{j=1}^{m_k}{\\mathcal{Y}_{i_j}, 1\\leq i_1 < i_2 < \\cdots< i_{m_k}\\leq N}\n\\end{equation}\n$m_k$ is the number of participants who possess category $c_k$. At the same time, we define the pseudolabeled dataset $P_k$ for category $c_k$, and $P_k$ is used to store the indices of the instances in the public dataset corresponding to each category $c_k$ after pseudolabel aggregation.\n\nFor each category $c_k$ existing in $\\mathcal{Y}_{i_j}$, the model $f_{i_j}$ classifies the instances in the public unlabeled dataset $D^{pub}$ as belonging to category $c_k$, and the set of these instances can be defined as\n\\begin{equation}\n S_j=\\left\\{ x|f_{i_j}\\left( x \\right) \\equiv c_k,x\\in D^{pub} \\right\\} , j=1,2,\\cdots,m_k\n\\end{equation}\n\nFor an instance $x\\in D^{pub}$, if we regard the outputs of different participant models on $x$ for category $c_k$ as the outputs of a two-class (belonging to category $c_k$ or not) ensemble classifier $g$, since (\\ref{14}) suggests that a lower generalization error bound $\\varepsilon_g$ is helpful, we can set a hyperparameter $\\alpha$ to make the results more reliable. That is, if\n\\begin{equation}\n \\frac{|\\left\\{ S_j|x\\in S_j \\right\\} |}{m_k} > \\alpha, 0 \\le \\alpha \\le 1,\n\\end{equation}\nan $\\alpha$ value of 0 means that whether $x$ is marked as belonging to category $c_k$ requires only one participant to agree, while an $\\alpha$ of 1 means that the consent of all participants is required. After that, we can put the index of $x$ into $P_k$. After all $P_k$ are generated, the central server sends the corresponding pseudolabeled dataset to each participant's task based on its label space $\\mathcal{Y}_i$. For the participants whose label space is $\\mathcal{Y}_i$, the corresponding pseudolabeled dataset received from the center is:\n\\begin{equation}\n R_i = \\{(P_k, c_k)|c_k \\in \\mathcal{Y}_i\\}\n\\end{equation}\nAssuming that $C_i[index]$ stores the results of $f_i(D^{pub}[index])$, $\\mathcal{P}=\\{P_k|c_k \\in \\mathcal{Y}\\}$ and $M=|D^{pub}|$, Algorithm 1 describes the above process.\n\\begin{algorithm}[!t]\n\\DontPrintSemicolon\n \\caption{Pseudolabel aggregation}\n \\KwIn{$\\{C_i\\}$, $\\mathcal{Y}_i$, $\\mathcal{Y}$, $\\alpha$, $M$}\n \\KwOut{$\\{P_k\\}$}\n \\SetKwBlock{Begin}{function}{end function}\n \\Begin(\\text{Aggregation} {$(\\{C_i\\}$, $\\mathcal{Y}_i$, $\\mathcal{Y}$, $\\alpha$, $M)$})\n {\n \\tcp*[l]{Initialization}\n $TOTAL$ = empty dict\\;\n $\\mathcal{P}$ = empty set\\;\n \n \\tcp*[l]{Counting $c_k$}\n \\ForAll {$c_k \\in \\mathcal{Y}$}\n {\n $TOTAL[c_k] = |\\{i|c_k \\in \\mathcal{Y}_i\\}|$\\;\n $\\mathcal{P}[c_k]$ = empty set\\;\n }\n \n \\tcp*[l]{Label aggregating}\n \\ForAll {$index = 1$ \\textbf{to} $M$}\n {\n $COUNT$ = empty dict with default value 0\\;\n \\ForAll {$i = 1$ \\textbf{to} $N$}\n {\n $COUNT[C_i[index]] = COUNT[C_i[index]] + 1$\\;\n }\n \\ForAll {$c_k \\in keys(COUNT)$}\n {\n \\uIf {$\\frac{COUNT(c_k)}{TOTAL(c_k)}>\\alpha$}\n {\n add $index$ to $P_k$\\;\n }\n \\Else\n {\n continue\\;\n }\n \n }\n }\n\n \\Return{$\\{P_k\\}$}\n }\n \\end{algorithm}\n\nIt should be pointed out that some indices of $x \\in D^{pub}$ may exist in multiple $P_k$ because all $\\mathcal{Y}_i$ are different from each other. Therefore, the different $P_k$ in $R_i$ may overlap, resulting in contradictory labels. To build a compatible pseudolabeled dataset $R_i$, the indices contained by different $P_k$ should be removed from $R_i$.\n\n\\subsection{Unlabeled Dataset}\nTo perform the CoFED method, we need to build a public unlabeled dataset for all participants. Although an unlabeled dataset that is highly relevant to the classification tasks is preferred, we find that even less relevant datasets can yield sufficient results in our experiments. For the image classification tasks that we focus on, the almost unlimited image resources that are publicly accessible on the Internet can be built into an unlabeled dataset. Another benefit of utilizing resources that each participant can independently obtain is that this strategy can prevent the distribution of unlabeled datasets by a central server, thereby saving the limited communication resources in an FL scenario.\n\nThe reason why less relevant datasets work is that even though different objects may have commonality, we can use this commonality as the target of the tasks involving different objects. For example, if someone asks you what an apple looks like when you have nothing else but a pear, you might tell the person that it looks similar to a pear. This may give him or her some incorrect perceptions about apples, but it is better than nothing, and this person will at least be less likely to recognize a banana as an apple. That is, although you have not been able to tell him or her exactly what an apple looks like, the process still improves his or her ability to recognize apples. For a similar reason, even if a less relevant unlabeled dataset is used to transfer knowledge, it can also yield improved model performance in FL scenarios.\n\n\\subsection{Training Process}\nIn the CoFED method, each participant needs to train its model twice, i.e., local training and update training. Both training processes are performed locally, where no exchange of any data with other participants or the central server is necessary. The benefits of this approach are as follows.\n\\begin{enumerate}\n \\item It prevents the leakage of participant data, including the local data and models.\n \\item It avoids the loss of stability and performance through communication.\n \\item It decouples the training processes of different participants so that they can independently choose the training algorithms and training configurations that are most suitable for their models.\n\\end{enumerate}\n\n\\subsection{Different Participant Credibility Levels}\nIn practical applications, the problem of different participant credibility levels may be encountered, resulting in uneven pseudolabel quality for different participants. Credibility weights can be assigned to the pseudolabels provided by different participants. Accordingly, Algorithm 1 can be modified to calculate $TOTAL(c_k)$ and $COUNT(c_k)$ by adding the weights of different participant, and the unmodified version of Algorithm 1 is equivalent to the case in which the weight of each participant is 1.\n\nDifferent bases can be used for setting the weights. For example, since the quality of the model trained on a larger training set is generally higher, weights based on the size of the utilized local dataset may be helpful for the unbalanced data problem. The test accuracy can also be used as a basis for the weights, but the participants may be reluctant to provide this information. Therefore, we can make decisions according to the actual scenario.\n\n\\subsection{Non-IID Data Settings for Heterogeneous Tasks}\nData can be non-IID in different ways. We have pointed out that the heterogeneous task setting itself is an extreme non-IID case of the personalized task setting. However, under the heterogeneous task setting, the instance distributions of a single category in the local datasets of different participants can still be IID or non-IID. The IID case means that the instances of this category owned by different participants have the same probability distribution, while in the extreme non-IID case, each participant may only have the instances of one subclass of this category, and different participants have different subclasses. A non-IID example is a case in which pets are contained in the label spaces of two participants, but all pet instances in the local training set of one participant are dogs, while the other participant only has cat images in its local dataset.\n\nThe existing FL methods based on parameter aggregation usually work well for IID data but suffer when encountering non-IID data. Zhao \\textit{et al.} \\cite{zhao2018federated} showed that the accuracy of convolutional neural networks (CNNs) trained with the FedAvg algorithm could be significantly reduced, by up to 55\\%, with highly skewed non-IID data. Since non-IID data cannot be prevented in practice, addressing them has always been regarded as an open challenge in FL \\cite{kairouz2021advances}, \\cite{li2020federated}. Fortunately, in the CoFED method, where model diversity is helpful for improving performance, a non-IID data setting is usually beneficial. This is because models trained on non-IID data generally have more divergences than models trained on IID data.\n\\section{Related Work}\nThe federated average (FedAvg) algorithm was originally proposed by McMahan \\textit{et al.} \\cite{mcmahan2017communication} to solve machine learning federated optimization problems on mobile devices. The core idea of FedAvg method is to pass model parameters instead of private data from different data sources, and use a weighted average of model parameters from different data sources as a global model. In each round of communication in the FedAvg method, the central server broadcasts the global model to participants, and then the participants who received the global model continue to train the global model using local private data. Following the local training phase, participants submit trained models to the center which utilizes the weighted average of different participant models as the global model for the next round of communication.\n\n\nInspired by the FedAvg algorithm, many FL methods \\cite{wang2019adaptive}, \\cite{li2020federatedhn} based on the aggregation of model parameters have been proposed. These methods are mainly suitable for federated training under the CD-FL setting where the tasks and model architectures are usually published by a central server, and all participants (i.e. devices) sharing the same model architecture makes aggregation of model parameters an effective knowledge sharing strategy. In addition, under the CD-FL setting, it is also practical for the central server to control the local training process and parameters (such as learning rate, epoch number, and mini-batch size, etc.). However, under the CS-FL setting where the model architectures and tasks of different participants may be different, and the training process and parameters are reluctant to be controlled by the center, these FL methods based on aggregation of model parameters are generally unable to cope with these heterogeneity challenges. FedProx proposed by Li \\textit{et al.} \\cite{li2020federatedhn} introduced a proximal term to overcome statistical heterogeneity and systematic heterogeneity (i.e., stragglers), but FedProx is unable to cope with heterogeneous model architectures due to model parameter aggregation.\n\nIn recent years, personalized federated learning has been proposed to address the personalized needs of participants. Smith \\textit{et al.} \\cite{smith2017federated} proposes a multitask FL framework MOCHA which clusters different tasks according to their relevance by an estimated matrix. Khodak \\textit{et al.} \\cite{khodak2019adaptive} proposed an adaptive meta-learning framework utilizing online learning ARUBA, which has the advantage of eliminating the need for hyperparameter optimization during personalized FL. Lin \\textit{et al.} \\cite{lin2020meta} and Fallah \\textit{et al.} \\cite{fallah2020personalized} proposed a model-agnostic meta-learning method and their variants to achieve personalized federated learning. Dinh \\textit{et al.} \\cite{dinh2020personalized} proposed a personalized FL method based on meta-learning by Moreau envelope, which leverages the $l2$-norm regularization loss to balance the personalization performance and generalization performance.\n\n\nIt should be pointed out that personalized tasks are different from heterogeneous tasks. Tasks with personalized settings always have the same label spaces, while tasks with heterogeneous settings have different label spaces. A typical example of personalized tasks that can be presented from \\cite{smith2017federated} is to classify users' activities by using their mobile phone accelerometer and gyroscope data. For each user, the model needs to provide outputs from the same range of activity categories, but the classification for each user is regarded as a personalized task due to the differences in their heights, weights, and personal habits. Despite these differences, from the data distribution perspective, the data of heterogeneous classification tasks can be regarded as non-IID sampling results from an input space $\\mathcal{X}$, which contains all instances of all labels in a label space $\\mathcal{Y}$, and $\\mathcal{Y}$ is the union of the label spaces of all participants. Each participant only has instances of some categories in $\\mathcal{Y}$ since its label space is a subset of $\\mathcal{Y}$. Therefore, an FL method that is compatible with personalized tasks can also be used for heterogeneous tasks.\n\nHowever, the core idea of all these methods is model parameter aggregation, which requires the model architectures of all participants to be consistent or only partially different. However, under CS-FL settings, participants as companies or organizations usually need to use their own independently designed model architectures. Li \\textit{et al.} \\cite{li2019fedmd} proposed a FL method FedMD leveraging model distillation to transfer the knowledge of different participant's model by aligning the output of neural networks models on a public dataset. Although FedMD participants are allowed to use neural network models of different architectures, they must be consistent in the logit output layer, and FedMD is not compatible with participants using non-neural network models. Also, FedMD needs a large number of labeled data for participant model alignment, which raises the bar for its application.\n\nModel parameter aggregation also leads to the leakage of participant models. Many studies try to overcome model leakage using various secure computing methods including differential privacy \\cite{wei2020federated}, \\cite{hu2020personalized}, \\cite{adnan2022federated}, secure multiparty computing \\cite{yin2020fdc}, \\cite{liu2020secure}, homomorphic encryption \\cite{jia2021blockchain}, \\cite{fang2021privacy}, \\cite{zhang2020batchcrypt}, blockchain \\cite{li2022blockchain}, \\cite{otoum2022federated}, and trusted execution environments \\cite{mo2021ppfl}, \\cite{chen2020training}, However, these technologies still have certain drawbacks, such as high computational costs or hardware specific.\n\\section{Preliminaries}\nWe first formulate the HFMTL problem and introduce the cotraining method that inspires us to propose the communication-efficient FL (CoFED) method.\n\\subsection{HFMTL}\nAn FL setting contains $N$ participants, and each of them has its own classification task $T_i$, input space $\\mathcal{X}_i$, output space $\\mathcal{Y}_i$, and $\\mathcal{D}_i$ consisting of all valid pairs $(\\boldsymbol{x}, y)$, where $\\boldsymbol{x} \\in \\mathcal{X}_i, y \\in \\mathcal{Y}_i$. Since we indicate that $T_i$ is a classification task, $\\mathcal{Y}_i$ is the label space of $T_i$. Each participant trains its machine learning model with supervised learning to perform a classification task, so each participant has its own local data $D_i \\subset \\mathcal{D}_i$.\n\nSince the HFMTL settings contain heterogeneous tasks and heterogeneous models $f$, we can assume that for the general $i \\ne j$:\n\\begin{equation}\n\\mathcal{Y}_i\\ne \\mathcal{Y}_j, f_i\\ne f_j\n\\end{equation}\nOn the other hand, the classification tasks of each participant should have commonality with the tasks of other participants. In our setting, this commonality manifests as the overlap between the label spaces. This means that:\n\\begin{equation}\n \\forall \\mathcal{Y}_i\\rightarrow \\left\\{ j|\\mathcal{Y}_i\\cap \\mathcal{Y}_j\\ne \\oslash , i\\ne j \\right\\} \\ne \\oslash \n\\end{equation}\n\nAssuming that a model $f_i$ has been trained for $T_i$, its generalization accuracy can be defined as:\n\\begin{equation}\n GA(f_i) = \\mathbf{E}_{(\\boldsymbol{x}, y) \\sim \\mathcal{D}_i}[\\mathbf{I}\\left( f_i\\left( \\boldsymbol{x} \\right) \\equiv y \\right)]\n\\end{equation}\nwhere $\\mathbf{E}[\\cdot]$ is the expectation and $\\mathbf{I}(\\cdot)$ is an indicator function: $\\mathbf{I}(\\text{TRUE}) \\equiv 1 $ and $\\mathbf{I}(\\text{FALSE}) \\equiv 0 $.\n\nThe goal of this paper is to propose an HFMTL method that is compatible with heterogeneous tasks, models, and training processes. This method should help to improve the performance of each participant model without sharing the local private datasets $D_i$ and private models $f_i$ of these participants:\n\\begin{equation}\n f_i^{fed} = \\underset{f_i}{arg\\max}\\,\\,GA\\left( f_i \\right) \n\\end{equation}\nIn addition, assuming that the model locally trained by the participant is $f_{i}^{loc}$, we expect that the model $f_{i}^{fed}$ trained with our FL method yields better performance for each participant:\n\\begin{equation}\n GA(f_{i}^{fed}) > GA(f_{i}^{loc}), i=1,2,\\cdots,N\n\\end{equation}\n\n\\subsection{Cotraining}\nIn HFMTL settings, many tricky problems come from the differences between participant models and tasks. However, cotraining is an effective method for training different models. Therefore, ideas that are similar to cotraining can be used to solve problems under HFMTL settings.\n\nCotraining is a semi-supervised learning technique proposed by Blum \\textit{et al.} \\cite{blum1998combining} that utilizes unlabeled data from different views. The \\textit{view} means a subset of instance attributes, and two classifiers that can be obtained by training on different view data. Cotraining lets the two classifiers jointly give some pseudo-labels for unlabeled instances, and then retraining the two classifiers on the original training set and the pseudo-labeled dataset can improve the classifier performance. Wang \\textit{et al.} \\cite{wang2007analyzing} pointed out that cotraining is still effective for a single view, and applying cotraining can bring greater classifier performance improvement when the divergence of different classifiers is larger. In FL, local models of different participants are trained from their local data. Under the HFMTL setting, due to differences in model architecture and data distribution, models of different parties are likely to have large divergences, which is beneficial for applying Cotraining.","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Abstract}\n\n\tWe studied the properties of the MSSM Higgs bosons, h and H through the decay into b-quarks in associated production with a top-quark pair. There was used the tree-level Higgs sector described by two parameters M$_A$ and tan$\\beta$ and found their optimal values according to experimental data of ATLAS detector. Using the restricted parameter space we calculated cross sections of associated $t\\bar{t}h(H)$ production at 13 and 14 TeV, the corresponding kinematical cuts, mass distributions and Branching Ratios of h and H decays into $b\\bar{b}$ quark pair. \n\n\\section{Introduction}\n\n Supersymmetry searches are the most attractive in the aspect of searching for new physics beyond the standard model. The search for an extended sector of Higgs bosons is especially urgent, since they are the lightest candidates for supersymmetric particles and information on their production cross sections and decay widths provides additional knowledge about the Yukawa coupling constants. The dependences on these couplings of the cross section and Higgs branching ratios have been studied intensively and it was found, that there are indirect constraints from experimental data on the scalar and \npseudoscalar H-top couplings k$_t$ and $\\tilde{k}$, and these constraints are relatively weak, \\cite {1.} \n\t\n\tOne of the important channels of such searches is the $t\\bar{t}H$ Higgs boson production channel. The search strategy for the $t\\bar{t}H$ process has been studied in various Higgs decay modes: $b\\bar{b}$, \\cite{2.}, $\\tau\\bar{\\tau}$, \\cite{3.} and WW$^*$, \\cite{4.}. Furthermore, from experimental point of view the $H\\rightarrow b\\bar{b}$ decay mode is more prefferable due to the possibility of the reconstruction of the Higgs boson kinematics, which allows to extract the information about the top\u2013Higgs interaction. \n As the decay of Higgs boson into two b-quarks ($H\\rightarrow b\\bar{b}$) is the most probable, \\cite{5.} our paper is devoted to the consideration of this decay channel. Higgs boson decay into b-quarks in associated production with a top-quark pair is connected with\ntesting the predictions of the Standard Model (SM) and\nvery sensitive to effects of physics beyond the SM (BSM). So,\nwe used Minimal Supersymmetric Standard Model (MSSM) as base theory for futher calculations. Our purpose was to calculate production cross sections $\\sigma(t\\bar{t}h),\\ \\sigma(t\\bar{t}H)$ at the centre-of-mass energy of $\\sqrt{s}$ = 13 TeV and to compare obtained data with experimental data. We also found p$_T$ and rapidity distributions, parameter space and mass distributions, which corresponds to the best fit with experimental data.\n\n\\section{Search channels and parameter cuts of Higgs boson production}\n\tATLAS \\cite{6.} and CMS \\cite{7.} have searched for the process of Higgs boson production ($t\\bar{t}H(b\\bar{b})$) intensively using the 8 TeV data set. Later new data collected in proton\u2013proton collisions at the LHC between 2015 and 2018 at a centre-of-mass energy of $\\sqrt{s}$ = 13 TeV were analysed, corresponding to an integrated luminosity of 139 fb$^{-1}$, \\cite{8.}. The measured signal strength, defined as the ratio of the measured signal yield to that predicted by the SM, \n\\[ \\mu = 0.35\\pm0.20 (stat.)^{+0.30}_{-0.28}(syst.)=0.35^{+0.36}_{-0.34} \\ ,\\]\ncorresponds to an observed (expected) significance of 1.0 (2.7) standard deviations. The measured 95$\\%$ confidence level (CL) cross-section upper limits in each bin for simplified template cross-sections (STXS) formalism are shown in Fig.1\n\\begin{center}\n\\includegraphics[width=0.4\\textwidth]{1.eps}\\\\\n\\emph{\\textbf{Fig.1}} {\\emph{The measured 95$\\%$ CL cross-section upper limits with the theoretical uncertainty \nconnected with signal scale and PDF uncertainties.}}\n\\end{center}\n\n \n The Higgs sector of the Minimal Supersymmetric extension of the SM \\cite{9.} consists of five physical Higgs bosons, two neutral CP-even bosons, h, H, one neutral CP-odd boson, A, and a charged Higgs pair, H$^{\\pm}$. \nAs there is the experimental deviation from the SM, we used BSM model - MSSM and studied the properties of two Higgs bosons: the lightest Higgs boson, h and CP-even Higgs boson, H. Our analysis of Higgs boson production in association with a pair of top quarks and decaying into a pair of b-quarks ($t\\bar{t}H(b\\bar{b})$) is presented by Feynman diagrams in Fig.2.\n\\begin{center}\n\\includegraphics[width=0.5\\textwidth]{2.eps}\\\\\n\\emph{\\textbf{Fig.2}} {\\emph{Representative tree-level Feynman diagrams for the production of a Higgs boson in association with a\ntop-quark pair ($t\\bar{t}H$) in (a) the t-channel and (b) the s-channel and the subsequent decay of the Higgs boson into $b\\bar{b}$, from \\cite{8.}.\n}}\n\\end{center}\nThe tree-level Higgs sector, can be described by two parameters, the mass of the CP-odd Higgs boson, M$_A$, and the ratio of the two vacuum expectation values of the two Higgs doublets, tan$\\beta = v_2\/v_1$. So, our purpose was to chooose the optimal value of the correspomding parameters M$_A$ and tan$\\beta$. We calculated Branching ratio (BR) of h and H using FeynHiggs program \\cite{10.} as the function of M$_A$ at 13 TeV (Fig. 3) as well as production cross section as the function of tan$\\beta$ at M$_A$=200 GeV (Fig.4).\n\\begin{center}\n\\includegraphics[width=0.5\\textwidth]{3.eps}\\\\\n\\emph{\\textbf{Fig.3}} {\\emph{Branching ratio (BR) of h and H decays using FeynHiggs program as the function of M$_A$ at 13 TeV.}}\n\\end{center}\n\\begin{center}\n\\includegraphics[width=0.5\\textwidth]{4.eps}\\\\\n\\emph{\\textbf{Fig.4}} {\\emph{Production cross section of Higgs bosons h and H for two search channels as the function of tan$\\beta$ at M$_A$=200 GeV at 13 TeV.}}\n\\end{center}\n \nFrom the obtained data we came to the conclusion about the optimum parameters of M$_A$=200 GeV and tan$\\beta$ =2.\n\n\\section{Results of calculations}\n As gluon-gluon and quark-antiquark processes are most preferable for the Higgs boson production we have considered the following search channels using Pythia program \\cite{11.}, presented in Table 1 and Table 2\n\n\\begin{center}\n{\\it\\normalsize Table 1. Search channels and production cross sections of h and H bosons at the centre-of-mass energy of $\\sqrt{s}$ = 13 TeV at M$_A$ = 200 GeV and tan$\\beta$ =2}\\\\\n\\vspace*{3mm}\n\\begin{tabular}{|c|c|} \\hline \nSearch channels& Production cross sections (pb) \\\\ \n& (with stat. err.) \\\\ \\hline \\hline\ngg $\\rightarrow$ $ht\\bar{t}$ & 2.609e-01 +\/- 5.084e-03 \\\\ \\hline\n$q\\bar{q}$ $\\rightarrow$ $ht\\bar{t}$ & 1.034e-01 +\/- 1.287e-03\n \\\\ \\hline\ngg $\\rightarrow$ $Ht\\bar{t}$\u00a0\u00a0\u00a0 & 2.017e-02 +\/- 5.618e-04 \\\\ \\hline\n$q\\bar{q}$ $\\rightarrow$ $Ht\\bar{t}$ & 7.524e-03 +\/- 3.862e-04\n \\\\ \\hline\n gg $\\rightarrow$ $Ht\\bar{t}$ (SM)\u00a0\u00a0 & 2.530e-1\u00a0+\/- 2.704e-3 \\\\ \\hline\n$q\\bar{q}$ $\\rightarrow$ $Ht\\bar{t}$ (SM)\u00a0\u00a0\u00a0 & 1.041e-1 +\/-\u00a0 2.572e-3\n \\\\ \\hline\n\\end{tabular}\n\\end{center}\n\n\\begin{center}\n{\\it\\normalsize Table 2. Search channels and production cross sections of h and H bosons at the centre-of-mass energy of $\\sqrt{s}$ = 14 TeV at M$_A$ = 200 GeV and tan$\\beta$ =2}\\\\\n\\vspace*{3mm}\n\\begin{tabular}{|c|c|} \\hline \nSearch channels& Production cross sections (pb) \\\\ \n& (with stat. err.)\\\\ \\hline \\hline\ngg $\\rightarrow$ $ht\\bar{t}$\u00a0 & 3.134e-01 +\/- 4.095e-04 \\\\ \\hline\n$q\\bar{q}$ $\\rightarrow$ $ht\\bar{t}$ & 1.171e-01 +\/- 1.660e-04 \\\\ \\hline\ngg $\\rightarrow$ $Ht\\bar{t}$\u00a0\u00a0\u00a0 & 2.514e-02 +\/- 6.099e-05 \\\\ \\hline\n$q\\bar{q}$ $\\rightarrow$ $Ht\\bar{t}$ & 9.221e-03 +\/- 5.913e-05 \\\\ \\hline\n gg $\\rightarrow$ $Ht\\bar{t}$ (SM)\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 & 2.530e-1\u00a0+\/- 2.704e-3 \\\\ \\hline\n$q\\bar{q}$ $\\rightarrow$ $Ht\\bar{t}$ (SM)\u00a0\u00a0\u00a0 & 1.041e-1\u00a0+\/- 2.572e-3 \\\\ \\hline\n\\end{tabular}\n\\end{center}\n\nThe kinematical cuts on h boson corresponding to the production cross section are presented in Fig. 5.\n\n\\begin{center}\n\\includegraphics[width=0.4\\textwidth]{5.eps}\\\\\n\\emph{\\textbf{Fig.5}} {\\emph{Transverse momentum (up) and rapidity distributions (down) of h boson at the energy of 13 TeV.}}\n\\end{center}\nThe kinematical cuts on H boson corresponding to the production cross section are presented in Fig. 6.\n\\begin{center}\n\\includegraphics[width=0.4\\textwidth]{6.eps}\\\\\n\\emph{\\textbf{Fig.6}} {\\emph{Transverse momentum (up) and rapidity distributions (down) of H boson at the energy of 13 TeV.}}\n\\end{center}\nFrom the obtained data we came to the conclusion about the most suitable range of transverse momentum variation of h boson (50; 150) GeV and (100; 300) GeV of H boson. As for rapidity distributions of both Higgs bosons, their maximum region is (-1,1), which signals about the angle range along the longitudinal (beam) direction ~ 45-90$^{\\circ}$ of the Higgs bosons. \n\t\n As for the mass detemination of both Higgs bosons, we received the mass distributions, presented in Fig. 7\n\\begin{center}\n\\includegraphics[width=0.4\\textwidth]{7.eps}\\\\\n\\emph{\\textbf{Fig.7}} {\\emph{Mass distributions of h (up) and H (down) Higgs bosons obtained at 13 TeV.}}\n\\end{center}\nFrom Fig.7 we see, that the mass of h boson is about 126 GeV, which coincides with the mass of the SM Higgs boson. As for the H boson, it's mass is approximately 330 GeV. We also calculated kinematical and mass distributions of both Higgs bosons at 14 TeV and didn't find a significant difference with previous calculations at 13 TeV.\n \\section{Conclusions}\n\nThe study of the properties of the Higgs boson is an urgent task, as evidenced by the experimental data of the ATLAS and CMS collaborations. We have presented the actual experimental data of the cross sections of Higgs boson decay into b-quarks in associated production with a top-quark pair in pp collisions at $\\sqrt{s}$ = 13 TeV and an integrated luminosity of 139 fb$^{-1}$ with the ATLAS detector. This result corresponds to an observed (expected) significance of 1.0 (2.7) standard deviations. To clarify the ambiguities, we decided to consider the minimal extension of SM \u2013 MSSM model. We used the tree-level Higgs sector which can be described by two parameters M$_A$ and tan$\\beta$. The calculation of BR($h\\rightarrow b\\bar{b}$) as the function of M$_A$ and production cross section of both Higgs bosons as the function of tan$\\beta$ gives us the possibility to choose the optimal parameter space corresponding to the maximum values of BR and the production cross section. Using received parameters (M$_A$ = 200, tan$\\beta$=2) we calculated cross sections of associated $t\\bar{t}h(H)$ production at 13 and 14 TeV and the corresponding kinematical cuts on transverse momentum and rapidity. We found out the value of the mass of h (126 GeV) and H (330 GeV) at the chosen parameters from the constructed mass distribution. The values of BR($h\\rightarrow b\\bar{b}$) and BR($H\\rightarrow b\\bar{b}$) are equal to 0.85 and 0.05 correspondingly.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec:intro}\n\nIt is well known that pulsars have considerably steeper spectral indices than the background population of radio sources. Their flux density (S$_\\nu$) can be described by a single power-law with slope $\\alpha$ (i.e. S$_\\nu\\propto{\\nu}^\\alpha$). Observationally-derived spectral indices have been determined variously to be in the range of $-1.6\\pm{0.3}$ \\citep{lylg95} to $-1.8\\pm{0.2}$ \\citep{mkkw00}. \\citet{blv13} attempted to remove pulsar survey biases to derive an intrinsic spectral index of $-1.4\\pm{1.0}$. There have been claims that millisecond pulsars have more shallow spectral indices on average than ``normal\" (i.e. non recycled) pulsars \\citep{kll+99}, but this may be due to an observational bias \\citep{blv13}. \n\nDeviations from this pure power-law behavior have been seen at both high and low frequencies, with some fraction (10\\%) having evidence for flat spectra and spectral steepening above several GHz \\citep{mkkw00}. Low frequency turnovers first seen by \\cite{sie73}, have now been measured for both normal and millisecond (MSP) pulsars, typically below 100 MHz \\citep[e.g.][]{drt+13,kvl+15}. External free-free absorption, either in the immediate environment of the pulsar or along the line of sight, gives a good explanation for the origin of the high frequency turnover pulsars \\citep{lrkm15,rla16}. The origin of the low frequency turnovers, is not so clear. They could be telling us something fundamental about the energy distribution of the coherent emitting electrons, or the turnover could be due to absorption, occurring either within the pulsar magnetosphere or along the line of sight.\n\nProgress in understanding these low frequency behaviors and their dependence (if any) on known pulsar parameters has been slow, owing to a shortage of flux density measurements below 1 GHz \\citep{mgj+94}. The best efforts to date are those of \\citet{mms00} who made measurements of 235 pulsars at 102.5 MHz, while 30 MSPs were observed at 102 and 110 MHz \\citep{kl01}. Fortunately, the situation is changing with new instruments such as the LOw-Frequency ARray (LOFAR) and the Long Wavelength Array (LWA). There are more recent LWA measurements of 44 pulsars from 10-88 MHz \\citep{srb+15} and LOFAR has now observed large samples of normal and MSPs at 110-188 MHz and \\citep{bkk+15, kvh+16}. \n \nIn this paper we use the recently completed GMRT Sky Survey (TGSS ADR) to study known radio and gamma-ray pulsar populations at 150 MHz. This paper is arranged as follows. In \\S\\ref{survey} we briefly describe the TGSS ADR survey while in \\S\\ref{method} we outline our search methods for both the radio-loud and radio-quiet samples. The results are discussed in \\S\\ref{results} where we derive estimates of the spectral index distribution of the TGSS ADR pulsars, and we compare the derived flux densities and the detection statistics with previously published samples. Our conclusions and suggestions for future work are given in \\S\\ref{sec:conclude}.\n\n\\section{The TGSS ADR Survey}\\label{survey}\n\nThe Giant Metrewave Radio Telescope (GMRT) was used to carry out a radio continuum survey at a frequency of 150 MHz using a total of 2000 hrs of observations. The entire sky was surveyed in over 5000 partially overlapping pointings from $-55$\\degr\\, declination to the northern polar cap covering 37,000 deg${^2}$.\n\nThe entirety of these data have recently been re-processed \\citep[TGSS ADR;][]{int16} creating high-quality images of approximately 90\\% of the entire sky. The TGSS ADR achieves a median rms noise level of \\mjybeam{3.5} and an angular resolution of \\asec{25} for Dec.$>19^{\\circ}$, and \n\\asec{25}$\\times$\\asec{25}\/cos(DEC-19$^\\circ$) for more southern declinations. In the final catalog there are some 0.62 million radio sources down to the 7$\\sigma$ level. Compared to existing meter-wavelength surveys \\citep{lcv+14,hpo+15,wlb+15}, the TGSS ADR represents a significant improvement in terms of number of radio sources, sensitivity and angular resolution. The improved angular resolution in particular allows accurate matching of radio sources with counterparts at other wavelengths. The capabilities of the TGSS ADR are well-matched to existing surveys \\citep{bwh95,ccg+98,bls+99} and provide a large frequency leverage arm for spectral index measurements. For more details on this survey and how to obtain the publicly available mosaic images and source catalog, see \\citet{int16}.\n\nThe observing bandwidth and integration time of the survey are especially relevant to the detection of the phase-averaged emission from pulsars. The original data were recorded with 256 frequency channels across 16.7 MHz of total bandwidth centered on 147.5 MHz. \nThe GMRT visibility data from the archive were saved as 16.1 s averages. Typically each (pointing) direction on the sky was observed as a series of short snapshots 3 to 5 times over the course of a single night's observing. The total integration time was 15 minutes per pointing on average.\nDuring imaging of the pointings, these data were combined in time and frequency to create a single Stokes-I image.\nThe full duration spent on any given point on the sky is more difficult to quantify. The final data products used for the analysis are 25 deg$^2$ image mosaics, formed by combining overlapping 7.6 deg$^2$ pointing images.\nNote that some pointings were observed repeatedly during multiple observing sessions, imaged separately, and combined in creating the mosaics.\nAs a result, many sources have been observed more than once, sometimes separated by days or even months.\nAs the survey's pointing centers followed the FIRST survey hexagonal grid strategy \\citep{bwh95}, the TGSS ADR will have similar duration statistics \\citep[see][]{thw+11}.\n\n\\section{Methods\\label{method}}\n\n\\subsection{Radio Loud Pulsars}\\label{sec:loud}\n\nWhile pulsars are typically detected by their pulsed, periodic emissions, they can also be identified in interferometric images as phase-averaged continuum point sources. \\citet{kcac98} were the first to employ a wide-field radio survey for this purpose. They used the 1.4 GHz NRAO VLA Sky Survey \\citep[NVSS;][]{ccg+98} to identify 79 known pulsars from the total intensity alone, while \\citet{ht99} added in the polarized intensity to identify 97 pulsars from the same survey. The 325 MHz Westerbork Northern Sky Survey \\citep[WENSS;][]{rtb+97} was used by \\citet{kou00} to find radio emission toward 25 known pulsars. For this project we employ the TGSS ADR at 150 MHz, using a version of the source catalog that was formed by running the source extraction algorithm PyBDSM \\citep{mr15}\\footnote{http:\/\/www.astron.nl\/citt\/pybdsm\/} with its default parameters searching the mosaicked images down to a 5$\\sigma$ detection threshold. For our list of known pulsars we used the HEASARC 27 December 2015 version of the ATNF Pulsar Catalog \\citep{mhth05}. A total of 1238 pulsars were selected with dec.$\\geq-52^\\circ$ and having known positions with $\\Delta$dec.$<\\pm{3^{\\prime\\prime}}$ or $\\Delta$R.A.$<0.35^s$. {A history file listing the contents and any changes to the ATNF database as of December 2015 is at their web site\\footnote{http:\/\/www.atnf.csiro.au\/research\/pulsar\/psrcat\/catalogueHistory.html}. Up to approximately November 2015, our sample included all well-localized normal pulsars from the \\emph{Fermi} sample at Stanford University as well as the MSP sample at the University of West Virginia.} The pulsar positions were corrected for proper motion using the mean epoch of the TGSS ADR of 11 January 2011 (MJD\\,55579.0). \n\nFollowing \\citet{hwb15}, we searched for matches between the PSR and TGSS ADR catalogs out to a radius of 30\\% of the FWHM of the 25$^{\\prime\\prime}$ beam, or 7.5$^{\\prime\\prime}$. We find 200 known pulsars, or 16\\% of the sample, are associated with TGSS ADR sources. We list these detections in Table \\ref{tab:bright} along with some basic pulsar parameters (period and dispersion measure) along with the total flux density and peak flux from the TGSS ADR. {The ratio of the total flux density and the peak flux can be a useful proxy in helping decide whether the radio emission is extended or unresolved, and therefore likely phase-averaged pulsar emission. We return to this point, as well as discussing the rates of false positives in \\S\\ref{ids}.} For all matches, we derived two-point spectral indices between the TGSS ADR total flux densities at 150 MHz and the 1400 MHz values from the pulsar catalog. The 400 MHz flux density was used in those few cases where the 1400 MHz values were missing. When no values were provided in the pulsar catalog database, we obtained flux densities from the original literature.\n\nThe search method above was supplemented with an image-based approach for pulsars below the 5$\\sigma$ limit of the catalog. For all of the original 1238 well-localized pulsars, we measured the peak flux in the TGSS ADR images at the pixel corresponding to the pulsar position, along with an estimate of the rms noise in immediate vicinity. We did not attempt to search over some radius and fit a Gaussian since below 5$\\sigma$ such a approach would likely lead to many more false positives. The positive identifications are defined as having S$_p$\/$\\sigma_{rms}\\geq$2.5. Our justification for this choice of threshold is shown in Fig. \\ref{fig:noise}. We make an estimate of the shape of the signal-to-noise distribution as an estimate of the blank sky in the vicinity of these pulsars (solid line). The positive S\/N peaks are strongly skewed above that expected from Gaussian noise. From the ratio of the levels of the positive and negative 2.5$\\sigma$ bins we estimate that 4\\% of the detections at this level will be false positives, or about 2 sources.\n\nWith this image-based approach we find significant emission towards another 88 pulsars. For each of these we inspected the mosaic images to verify that the emission was coming from a point centered on the pulsar position and was not due to a nearby extended source or an image artifact. Table \\ref{tab:faint} lists the peak flux and rms toward all 88 pulsars, along with some basic pulsar parameters identical to those in Table \\ref{tab:bright}. Given the lower significance, we are not as confident in these identifications as we are with those in Table \\ref{tab:bright}.\n\n\n\n\\subsection{Radio Quiet Pulsars}\\label{quiet}\n\nMotivated by recent claims of the detection of pulsed radio emission from the ``radio-quiet'' PSR\\,J1732$-$3131 \\citep{mad12}, we carried out a search for emission at 150 MHz. In the Second \\emph{Fermi} Large Area Telescope (LAT) Pulsar Catalog \\citep[2PC;][]{aaa+13} there are 35 PSRs that are radio quiet, defined as having a phase-averaged flux density S(1.4 GHz)$\\leq 30$ $\\mu$Jy. In the meantime the sample has grown; an updated list of radio quiet pulsars is available on the LAT team web site\\footnote{\\url{https:\/\/confluence.slac.stanford.edu\/display\/GLAMCOG\/Public+List+of+LAT-Detected+Gamma-Ray+Pulsars}}. For accurate pulsar positions we began with the recent compilation of \\citet{krj+15}, which has a table of positions for normal pulsars and MSPs obtained from both timing and multi-wavelength observations. All positions from \\citet{krj+15} are computed at the epoch MJD 55555 (25 Dec 2010). This is useful when comparing to the TGSS ADR which was observed around the same epoch. Additional X-ray positions are taken from \\citet{mmd+15}. \n\nThe final list consisted of 30 radio quiet pulsars in the declination range of TGSS ADR with localizations of an arcsecond or better (Table \\ref{tab:unknown}). For all pulsars we extracted image cutouts and looked for faint point sources at the pulsar position. We then made a final stacked image of all 30 pointings weighted by the inverse square of the local rms noise for each image. As the image pixel size is the same \\asec{6.2} in all images, this ensures accurate image stacking. No source is detected. The rms noise is \\mjybeam{0.7} and the max\/min on the image is approximately $\\pm$\\mjybeam{2.3}. \n\n\\section{Results and Discussion\\label{results}}\n\n\\subsection{Radio Loud Pulsars}\n\n\\subsubsection{Identifications}\\label{ids}\n\nThe majority of the emission that was detected at 150 MHz is likely due to phase-averaged pulsar emission. In support of this we note that the distribution of PSR-TGSS ADR offsets follows the expected Rayleigh distribution, with 95\\% of the identifications matched within a \\asec{4.5} radius. This is consistent with the astrometric accuracy (68\\% confidence) derived for the TGSS ADR of \\asec{1.55} \\citep{int16}. Given the source density of the TGSS ADR at the completeness limit of 17.6 source\/deg$^2$, and a search of 1238 positions each of radius \\asec{7.5} \n(see \\S\\ref{sec:loud}), we expect less than one false positive (i.e. a background radio source not associated with a pulsar).\n\nFurther support for pulsar identifications comes from Fig.\\ref{fig:ff}, in which we the show all pulsars (crosses) with published 400 MHz and 1400 MHz flux densities in the ATNF catalog. Those pulsars with TGSS ADR detections are indicated by circles. This figure shows that the TGSS ADR associations are well-correlated with the brightest pulsars, and thus the number of false positives are likely to be low. Furthermore, the number of associations drop off sharply for S$_{1400}<0.6$ mJy and S$_{400}<5$ mJy, as would be expected for steep spectrum pulsars given that the median noise of the TGSS ADR is \\mjybeam{3.5}. The two outliers in the bottom left corner of Fig.\\ref{fig:ff} are PSR\\, J2229+6114 and the LOFAR-identified PSR\\, J0613+3731. Their flux densities at 150 MHz appear to be dominated a pulsar wind nebula called `The Boomerang\" \\citep{kru06} in the first case and some unidentified extended emission in the second case. \n\nAs the above example illustrates, not all the matches in the TGSS ADR are from phase-averaged pulsar emission. Some radio emission is due an associated nebula (e.g. Crab), or is an from an ensemble of pulsars in a globular cluster. {In \\citet{int16} we derive an empirical formula to help decide when a radio source is unresolved. In the high signal--to-noise case this reduces to S$_t\/$S$_p\\leq 1.13$. However, some caution is needed in applying this criteria to pulsars since they can show strong time-variability during an integration time, violating one of the central assumptions of the van Cittert-Zernike theorem upon which radio interferometric imaging is based. This can lead to deviations in the Gaussian fitted beam, or in especially strong cases, diffraction spikes around the pulsar. A visual inspection of the images is required to be sure since this same condition is likely met by strongly scintillating pulsars like PSR\\,B1937+21. We examined the images of {\\it all} of the detections in Table \\ref{tab:bright} and find that likely non-pulsar candidates are those entries for which the total flux density exceeds the peak flux (i.e. S$_t>$S$_p$) by more than 50\\%.} For those small number of TGSS ADR detections (11) that we suspect are contaminated in this way, we add a comment in Table \\ref{tab:bright} and we do not derive a spectral index.\n\n\\subsubsection{Spectral Index Distribution}\n\nThe distribution of the two-point spectral indices of the TGSS ADR sample from Table \\ref{tab:bright} is shown in Fig. \\ref{fig:spec_histo}. For comparison we have plotted the more comprehensive sample of 329 pulsars from the ATNF Pulsar catalog with non-zero spectral indices. As expected, the two histograms are in reasonable agreement with each other, both in terms of the width and the median of the two distributions. \n\nIf we order the spectral index values by pulsar period (Fig. \\ref{fig:alpha}) an unusual feature of our 150 MHz sample appears. The steep spectrum tail of the $\\alpha$ distribution measured at low frequencies is dominated by short period pulsars. This effect is not seen in the ATNF pulsar catalog. We have detected many of the fastest rotating MSPs at 150 MHz, and these pulsars show a marked preference for steeper spectral index values. Of the 16 pulsars with $\\alpha<-2.5$, all but four are MSPs. Of these MSPs, all except one has been detected by the \\emph{Fermi} gamma-rays mission including several eclipsing MSPs such as PSR\\,J1816+4510, with the steepest spectral index in Fig. \\ref{fig:alpha}. The 18 MSPs in Table \\ref{tab:faint} do not have ultra-steep spectra. \\citet{kvl+15} were the first to note a tendency for the gamma-ray MSPs to be steep-spectrum outliers based on a smaller sample. \n\nSince the values in Table \\ref{tab:bright} and Fig. \\ref{fig:alpha} are two-point values, we suspected measurement error as the source of these large values. As a first step we re-calculated the spectral index of all pulsars with $\\alpha<-2.5$ using the flux density and observing frequency taken from the original references. If no rms noise was given we assumed a fractional error of 50\\% for the flux density when estimating the uncertainty on $\\alpha$.\n\nThere are several useful compilations of flux density measurements and spectral indices we can use to cross check our measurements \\citep{tbm+98,kxl+98,kvl+15}. We find reasonable agreement in the $\\alpha$ values for all of the MSPs within the errors. For PSR\\,J1816+4510, the pulsar with the steepest 2-point spectral index in our sample, we re-fit our 150 MHz measurement along with a value at 74 MHz \\citep{kvl+15} and flux densities at 350 and 820 MHz \\citep{slr+14}. The latter two measurements were estimated from the radiometer equation so we have taken typical errors of $\\pm$50\\% on these two values. The mean spectral index is $-3.46\\pm0.10$ in agreement with a preliminary value from \\citet{kvl+15}.\n\nSince there is no evidence that the distribution of MSP spectral indices is steeper than the general population \\citep{tbm+98,kll+99}, we suspect this trend is the result of some low frequency bias. Most of the steep spectrum MSPs in Table \\ref{tab:bright}, were discovered in low frequency searches \\citep[e.g.][]{fst+88,bhl+94,hrm+11,slr+14}. Two pulsars (B1937+21 and J0218+4232) had such steep spectral indices that they were initially identified in imaging data \\cite[e.g.][]{nbf+95}. Thus it is reasonable to expect that the TGSS ADR survey at 150 MHz would be sensitive to steep-spectrum radio sources, with a similar bias as low-frequency searches for pulsations \\citep{blv13}. This explanation, however, does not account for the preponderance of gamma-ray pulsars among our sample, nor for the unusually large fraction of (eclipsing) binaries. We know of no intrinsic property of the MSP population that would produce such an effect. {\\citet{ckr+15} noted that the nearby MSPs were susceptible to deep flux density variations at decimeter wavelengths, with strong expoential statistics such that the measured median flux density is less than the mean, skewing the spectral index to steeper values.} Since many of these systems have been found within the error ellipses of \\emph{Fermi} unassociated sources \\citep[e.g.][]{krj+15}, a more prosaic explanation may be that the \\emph{Fermi} mission has been such a prolific source of MSPs that they are over-represented in any sample. \n\n\\subsubsection{Comparison with the LOFAR Sample}\n\nIt is illustrative to compare the TGSS ADR and LOFAR samples. While both surveys were undertaken at the same frequency, they were observed in very different ways. Thus a comparison could give us some insight into the different biases of each survey. LOFAR has carried out a search for pulsed emission from all northern radio pulsars \\citep{bkk+15,kvh+16}. This census was primarily conducted with the LOFAR high-band antennas (HBA) between 110 and 188 MHz, with 400 channels each of 0.195 MHz in width, or a bandwidth of 78 MHz. Each pulsar was observed once for at least 20 minutes, although long period (P$>3$ s) normal pulsars and faint MSPs were observed up to 60 minutes in duration. Pulsed emission from a total of 158 normal pulsars and 48 MSPs were detected. The GMRT observing method is summarized in \\S\\ref{survey} and the pulsar yield is given in \\S\\ref{ids}. We find 92 pulsars commonly detected in both the LOFAR and TGSS ADR surveys (Tables \\ref{tab:bright} and \\ref{tab:faint}).\n\nFigure \\ref{fig:compare} (left) is a flux-flux plot of LOFAR and TGSS measured flux densities, while the same figure (right) shows a flux ratio plot of the same sample. The flux densities of the LOFAR and TGSS ADR pulsars do not agree. On average, the LOFAR pulsars are about two times brighter than the the TGSS ADR. The result persists even if we use only the bright pulsars in common (i.e. Table \\ref{tab:bright}). There are some significant outliers, dominated by bright, scintillating MSPs such as PSR\\,B1937+21 and PSR\\,J0218+4232, but the overall trend is clear. \n\nWe can immediately rule out frequency-dependent effects for this difference in the flux density scales since the surveys were performed at similar frequencies. Spectral curvature was the most likely explanation offered by \\citet{kvh+16} for why the LOFAR flux densities for one third of their MSPs are {\\it lower} than the predicted values based on an extrapolation from higher frequencies. Diffractive scintillation, while clearly important for the outliers, is not the likely origin for the systematic difference. The large observing bandwidths and the long integration times relative to the scintillation values for both the LOFAR and GMRT observations (\\S\\ref{survey}) suggest modulation of the flux density is not widespread; see \\S\\ref{sec:missing} and Appendix A of \\citet{bkk+15}. There is one important difference: the typical 20-min LOFAR integration time is a single integration, while the 15-min GMRT observations are typically subdivided into 3--5 short observations taken over a night of observing. The later is a more optimal detection strategy when there are intensity variations caused by the phase fluctuations in the interstellar medium \\citep{cl91}. If this effect is important, however, it would result in the LOFAR flux densities being {\\it lower} on average that the GMRT values, the opposite of what is seen. Temporal scattering can also reduce the measured flux density for pulsed surveys but as an imaging survey, the TGSS ADR is not sensitive to pulse smearing caused by interstellar scattering. While the LOFAR surveys are sensitive to such effects, they would also act to {\\it lower} the measured flux density. \n\nWe are left with instrumental effects associated with gain calibration. The TGSS ADR flux density scale is good to about 10\\% over the full sky.\nTaken in interferometric imaging mode, the data each day were calibrated back to several low frequency primary flux density calibrators (3C\\,48, 3C\\,147, 3C\\,286 and 3C\\,468.1). After calibration of the full survey, the accuracy of the flux density scale was cross-checked against other sky surveys such as 7C \\citep{hrw+07} and the LOFAR Multi-frequency Snapshot Survey \\citep[MSSS;][]{hpo+15} and they were found to agree at the $\\sim$5\\% level. On the other hand, the flux density calibration for the LOFAR pulsar survey was done directly using the radiometer equation for direction-dependent estimates of the antenna gain and the sky system temperature. The calibration was cross-checked with regular observations of a sample of normal pulsars and MSPs with well-determined spectra. Variations at a level 2--4 times larger than expected from scintillation alone were seen to occur and thus the resulting flux density scale was quoted with errors of $\\pm$50\\%. \n\nWe tentatively suggest that our TGSS ADR pulsar sample shows that there remains an unaccounted gain error in the LOFAR pulsar observing system that results in an overestimate of the flux density scale by about a factor of two.\n\n\\subsubsection{The Missing Pulsars}\\label{sec:missing}\n\nDespite the high yield, there are also a number pulsars in Fig. \\ref{fig:ff} with large {decimeter} flux densities but with no TGSS ADR counterpart in Table \\ref{tab:bright}. Likewise, we failed to detect several bright pulsars which had been found in previous low frequency pulsation surveys \\citep[e.g.][]{kl01,bkk+15}. To investigate the origin of these missing pulsars, we defined a radio-bright sample from the original 1238 well-localized pulsars in \\S\\ref{sec:loud} as having 400 and 1400 MHz flux densities greater than 21 mJy and 1.8 mJy, respectively. For a canonical pulsar spectral index these flux densities extrapolate at 150 MHz to the completeness limit of the TGSS ADR \\citep{int16}. There are 232 such pulsars. Of this sample, 70\\% are detected and are listed in Tables \\ref{tab:bright} and \\ref{tab:faint}.\n\nWe can identify three possible reasons that about one third of this radio-bright sample of pulsars would not be detected in the TGSS ADR. The local rms noise may be too high, the pulsar spectrum may be flat or turn over at 150 MHz, or the signal may be reduced due to interstellar scintillation. It may be possible that one of these effects dominant or they are working in tandem. We will look at each of these in turn.\n\nAt low radio frequencies the synchrotron and thermal emission from the Galactic plane makes a non-negligible contribution to the system temperature of the receivers. The frequency dependence of the brightness temperature goes approximately at T$_b\\propto\\nu^{-2.6}$ \\citep{hks+81} so unless the pulsar spectrum is steeper than this value, they become increasingly more difficult to detect. While the increased brightness temperature affects pulsed and imaging searches equally, the later also suffers from increased rms due to confusion and reduced image fidelity in the presence of bright Galactic HII regions or supernova remnants. The pulsar B\\,2319+60 is a good example of a bright pulsar confused by nearby bright, extended emission. We looked at the rms noise statistics of the detected and non-detected samples, following up the large rms cases with a visual inspection of the TGSS ADR image data at the PSR positions. We find evidence that the rms noise of the images has some influence on the detectability of the pulsars. The median rms noise for the detections is \\mjybeam{3.5}. while for the non-detections it is nearby twice this value (\\mjybeam{6.9}).\n\nThe intrinsic spectral shape of the pulsar emission will also affect the detectability at low frequencies. The mean pulsar spectral index, while steep, has a wide scatter (\\S\\ref{sec:intro}). Likewise, for approximately 10\\% of known pulsars there is evidence of a low-frequency spectral turnover, typically around 100 MHz (\\S\\ref{sec:intro}). Our 150 MHz sample has a number of pulsars with known spectral turnovers including PSR\\,J2145-0750 \\citep{drt+13}. We lack a large public database of accurate pulsar flux densities that would be sufficient to look for a turnover frequency for our non-detections, but fortunately most of them have single power-law measurements in the ATNF pulsar catalog. The median spectral index for the detections is $\\alpha=-1.9$ and there are no pulsars in this sample as shallow as $\\alpha\\leq-0.5$. The non-detections have a much flatter median spectral index of $\\alpha=-1.3$. At least one third of our non-detections have spectral indices that are so flat that we do not expect to detect them at 150 MHz based on an extrapolation of their 400 or 1400 MHz catalog flux densities. \n\nDensity fluctuations in the ionized interstellar medium of our Galaxy can induce intensity fluctuations that may depress the flux density of a pulsar during an integration time. The characteristic time and frequency scale depends on many factors including the distance of the pulsar, the turbulent properties of the gas along the line of sight, and the relative velocities of the pulsar and the ionized gas \\citep{cwf+91}. To estimate the magnitude of strong scattering on the phase-averaged pulsar flux densities we followed the method of \\citet{kcac98}. We first estimated the scattering bandwidth and scattering time at 150 MHz for each pulsar using the NE2001 model of \\citet{cl02}. Typical scintillation timescales and bandwidths at these frequencies are small, of order 1 minute and below 1 MHz, respectively. Our values are similar to the values estimated at the same frequency by \\citet{bkk+15}. We then estimate the number of ``scintles'' that are averaged over the observed bandwidth and the duration of the observation. The intensity modulation is equal to the square root of this value. The observed bandwidth is given in \\S\\ref{survey} as 16.7 MHz. The duration of the GMRT observations are more difficult to estimate. The total integration on source is 15 minutes but it is split into 3--5 short snapshots spaced over a full night's observing. As an added complication, the image mosaics are additions of many overlapping fields and so it is possible that a single pixel may contain observations from more than one night. This sampling has the effect of smoothing out any large intensity modulations, so as a (pessimistic) estimate we take the duration as 15 min but we recognize that there may be additional temporal smoothing. Our results by and large suggest that the TGSS ADR pulsar flux densities are only being weakly modulated by scintillation in most cases. There are pulsars that are predicted to be undergoing strong diffractive scintillation at this frequency (e.g. PSR\\,B0950+08 and PSR\\,1929+10) and there are diffraction spikes centered on the MSP PSR\\,B1937+12, likely caused by intensity variations on timescales comparable to the dump time. However, we can find no systematic trend for the non-detected pulsars to have greater predicted modulations from scattering.\n\nSummarizing, we find that the bright cataloged pulsars with no TGSS ADR counterpart may be due to a combination of effects. There is evidence that the non-detections at 150 MHz have more shallow spectral indices than average, and that some of the non-detections are caused by high rms and confusion in the image plane. Strong intensity variations by interstellar scintillation is undoubtedly occurring for some pulsars but we cannot show that the non-detections differ from the detections in their scattering properties. The difficulty in estimating the true GMRT integration time for each pulsar may be masking this effect. \n\n\n\n\\subsection{Radio Quiet Pulsars}\n\nOur search did not find any significant radio emission at 150 MHz toward individual radio quiet pulsars, nor in a weighted stack of all 30 pulsars. The peak of the stacked image in Fig. \\ref{fig:stack} is 0.1$\\pm$0.7 mJy beam$^{-1}$, with upper limit (peak + 2$\\sigma$) of $<$1.5 mJy beam$^{-1}$. Recall from \\S\\ref{quiet} that ``radio-quiet\" pulsars are observationally defined as having a phase-averaged flux density at 1.4 GHz S$_\\nu<$30 $\\mu$Jy. The simplest hypothesis is that radio quiet pulsars are beamed away from the line of sight \\citep{ckr+12}. \n\nRadio quiet gamma-ray pulsars, with Geminga as the prototype, are expected, given what we know about the structure of neutron star magnetospheres \\citep{car14}. The radio emission is thought to originate further down the poles than the gamma-rays, and thus the radio will be beamed into a narrowing opening angle, increasing the probability that the beam sweeps out away from the observer's line of sight. However, it is well-known that both the radio pulse width and the component separation are frequency dependent \\citep{thor91,mr02}. As noted by \\citet{mad12}, this widening of radio beams at low frequencies might be used to detect radio quiet, gamma-ray pulsars. Such pulsars would be recognized in the image plane as having a spectral index that is much steeper than the canonical value. PSR\\,B1943+10 may be thought of the prototype of such systems, bright at 400 MHz and below but weak at 1.4 GHz, with a spectral index $\\alpha$ steeper than $-3.0$ \\citep[see Table \\ref{tab:bright};][]{wcl+99}. The lower limit estimate on spectral index that we derive from the weighted stack at 150 MHz, assuming the defining radio-quiet 1.4 GHz flux density of 30 $\\mu$Jy, gives $\\alpha>-1.75\\pm0.20$. This is a spectral index limit that is well within the canonical value for normal pulsars. Thus we find no evidence that these gamma-ray pulsars have radio beams that sweep close to our line of sight.\n\n\n\n\n\n\\section{Conclusion}\\label{sec:conclude}\n\nWe have identified nearly 300 pulsars at 150 MHz based in their phase-averaged emission on all-sky images. This imaging approach is complementary to pulsation studies since it is not affected by {pulse scatter} broadening or dispersion, making it sensitive to both normal and millisecond pulsars equally. Our sample includes many southern pulsars which are being detected at low radio frequencies for the first time. We anticipate that these 150 MHz flux densities will be used to study large numbers of pulsar over a wider frequency range than has hitherto been possible, and to addresses questions about the incidence and origins of low-frequency spectral turnovers. Accurate calibration between telescopes remains an important issue and we have identified a discrepancy between the flux densities of pulsars in common between GMRT and LOFAR. We suggest that the LOFAR sample may be overestimating the flux density scale by about a factor of two. It should be straightforward to test this hypothesis by observing a sample of pulsars with LOFAR in both imaging and phase-binning modes, calibrating the interferometric data in the standard way to allow proper comparison with each other and with the GMRT.\n\nWe have carried out a preliminary spectral index study of our sample. Generally there is good agreement with past work, except that we find a curious preponderance of gamma-ray MSPs with unusually steep spectral indices ($\\alpha\\leq-2.5$). Regardless of its origins, this suggests a possible way to identify new MSP candidates in \\emph{Fermi} unassociated sources on the basis of their unusually steep spectrum at low radio frequencies. Such pulsars may have been missed in radio pulsation searches due to propagation effects caused by the interstellar medium or they may be in binary systems and thus more difficult to discover. In such cases, imaging \\emph{Fermi} error regions with LOFAR and the GMRT could provide accurate enough positions to enable blind gamma-ray searches for pulsations.\n\n\\acknowledgments\n\nThis research has made use of data and\/or software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA\/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. DAF thanks T. Readhead and S. Kulkarni for their hospitality at Caltech while this work was being written up.\nHTI acknowledges financial support through the NL-SKA roadmap project funded by the NWO. We thank A. Bilous and J. Hessels for sharing their knowledge of LOFAR pulsar flux density calibration.\n\n{\\it Facilities:} \\facility{GMRT}, \\facility{Fermi (LAT)}.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzctgf b/data_all_eng_slimpj/shuffled/split2/finalzzctgf new file mode 100644 index 0000000000000000000000000000000000000000..2a99bd3facde1c6bcc695724053b921f4e162db9 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzctgf @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nIn the composite fermion (CF) picture of the fractional quantum Hall\n(FQH) effect, fundamental interactions are taken into account at the\nmean field level by mapping the system of strongly interacting 2D\nelectrons in magnetic field into a system of weakly interacting\nComposite Fermions (CF) moving in a reduced effective magnetic field\n\\cite{Jain}. The reduction in magnetic field follows from the\nbinding of $\\phi$ flux quanta to electrons, so that effective\nmagnetic field experienced by CF quasiparticles is $B^*=\\pm B\/(\\phi\np\\pm 1)$, where $p$ is an integer that enumerates members of a\nparticular sequence and $\\phi$ is a even integer that labels\ndifferent sequences. In this picture, the FQH effect can be\nunderstood by the emergence of CF Landau levels with cyclotron\nfrequency: $\\omega_{CF}=\\frac{eB^*}{cm^{*}}$, where $m^{*}$ is an\neffective CF mass. Evidence for a spin split Landau level structure\nof CF for the $\\phi=2$ sequence has been provided by\nmagnetotransport experiments in tilted magnetic fields at filling\nfactors near $\\nu=3\/2$ \\cite{Du} and by inelastic light scattering\nstudies of spin excitations in the range $1\/3<\\nu<2\/5$ \\cite{Irene}.\nFor the $\\phi=4$ sequence (i.e. $\\nu\\lesssim1\/3$), however, direct\nevidence for such CF Landau level structure is lacking. Studies of\nthe $\\phi=4$ sequence are more difficult because of the higher\nmagnetic fields that are required and, compared to the $\\phi=2$\nsequence, the smaller energy scales in the excitations. Insight on\nthe energy scales for excitations were revealed by activated\ntransport measurements \\cite{Pan} and by the recent observations of\n$\\phi=4$ quasiparticle excitations in light scattering experiments\n\\cite{Cyrus}.\n\nIn this work, we present a resonant inelastic light scattering study\nof spin excitations for $\\nu\\lesssim1\/3$. The excitations are spin\nwaves (SW) and spin-flip (SF) modes. The SF excitations involve a\nchange in both the spin orientation and CF Landau level quantum\nnumber. We monitor the evolution of these spin excitations below and\naway from $\\nu=1\/3$ when the population of the excited CF Landau\nlevel increases. Our results reveal the existence of spin split CF\nLandau levels in the $\\phi=4$ sequence. The SW-SF splitting is\nlinear in magnetic field. This determination suggests an effective\nmass significantly larger than the activation mass of CF with\n$\\phi=4$.\n\\section{Sample and Experiment}\nThe 2D electron (2DES) system studied here is a GaAs single quantum\nwell of width $w=330~\\AA$. The electron density at small magnetic\nfields is n=5.5$\\times$10$^{10}$~cm$^{-2}$ and the low temperature\nmobility is $\\mu$=7.2$\\times$10$^6$\/Vs. The sample is mounted in a\nbackscattering geometry, making an angle $\\theta$ between the\nincident photons and the normal to the sample surface. The magnetic\nfield perpendicular to the sample is B=B$_T$cos$\\theta$, where B$_T$ is\nthe total applied field. The results reported here have been\nobtained at $\\theta$=50$\\pm$2 degrees. Similar results have been seen at 30 degrees \\cite{Cyrus-thesis}.The ingoing and outgoing\npolarizations were chosen to be orthogonal (depolarized spectra)\nsince excitations which involve a change in the spin degrees of\nfreedom are stronger in this configuration. The sample was cooled in\na dilution refrigerator with windows for optical access. All the\nmeasurements were performed at the base temperature T=23~mK and the\npower density was kept below 10$^{-5}$W\/cm$^2$ to avoid heating the\nelectron system. The energy of the incident photons was tuned to be\nin resonance with the excitonic optical transitions of the 2DES in\nthe FQH regime \\cite{Goldberg,Bar-joseph,Cyrus2}.\n\\section{Results and discussion}\n\\begin{figure}\n\\centering \\epsfig{figure=spectres.eps, width=0.79\\linewidth, clip=}\n\\caption{Low energy spectra of spin excitations in the filling\nfactor range 0.31$<\\nu<$0.33. The most intense peak is the long\nwavelength spin-wave at the Zeeman energy E$_z$ while the peak its\nlow energy side is assigned to a spin-flip transition (see text and\nfigure \\ref{levels}). The left inset shows the result of a\ntwo-gaussian fitting procedure for the two peaks to extract their\nrespective energies. The right inset shows the backscattering\nconfiguration} \\label{spectres}\n\\end{figure}\nFigure \\ref{spectres} shows the evolution of the low energy spectrum\nfor $\\nu\\lesssim1\/3$. $\\nu=1\/3$ corresponds to a perpendicular field\nof 6.5T and the filling factor range studied corresponds to the\nrange 0.31$<\\nu<$0.33. Close to $\\nu=1\/3$, the spectra are dominated\nby the long wavelength SW at the 'bare' Zeeman energy\nE$_z=g\\mu_BB_T$, where g=0.44 is the Lande factor for electrons in\nGaAs and $\\mu_B$ is the Bohr magneton. For filling factors away from\n$\\nu=1\/3$ an excitation emerges on the low energy side of E$_z$. We\nassign this excitation to a SF mode linked to transitions in the CF\nframework that involve the first excited CF Landau level as depicted\nin figure \\ref{levels} . At $\\nu=1\/3$ the first CF Landau\n($\\ket{0,\\uparrow}$) level is fully occupied while for $\\nu=2\/7$ the\nfirst two CF Landau levels ($\\ket{0,\\uparrow}$ and\n$\\ket{1,\\uparrow}$) are occupied. In between the two incompressible\nstates, the first excited Landau level is partially populated and SF\ntransitions between $\\ket{1,\\uparrow}$ and $\\ket{0,\\downarrow}$\nstarting from the partially filled level become possible. Thus the\nstudy of the SF excitations in the filling factor range\n2\/7$<\\nu<$1\/3 probe directly the CF level structure for $\\phi=4$.\n\\begin{figure}\n\\centering \\epsfig{figure=levels.eps, width=0.7\\linewidth, clip=}\n\\caption{Structure of spin split CF Landau levels for 2\/7$<\\nu<$1\/3 ($\\phi=4$).\nTwo $^4$CF spin transitions are possible. The large $q$ spin wave is\nat E$^*_z$=E$_z$+E$^{\\uparrow\\downarrow}$ where\nE$^{\\uparrow\\downarrow}$ is the spin reversal energy. The spin-flip\nexcitation at E$_{SF}$. The spin-flip excitation emerges when the\n$\\ket{1,\\uparrow}$ level is populated.} \\label{levels}\n\\end{figure}\nFor small occupation of the $\\ket{1,\\uparrow}$ excited level and\nwhen the coupling between the excited quasiparticle and its\nquasihole is negligible, the SF transition energy can be written as\nin the $\\phi=2$ case:\n\\begin{equation}\nE_{SF}=E_z+E^{\\uparrow\\downarrow}-\\hbar\\omega_c\n\\label{ESF}\n\\end{equation}\nwhere E$^{\\uparrow\\downarrow}$ is the spin reversal energy which is\na measure of the residual interactions between $\\phi=4$ CF\nquasiparticles \\cite{Pinczuk,Longo,Aoki,Mandal,Irene}.\n\\begin{figure}\n\\centering \\epsfig{figure=energies.eps, width=0.65\\linewidth, clip=}\n\\caption{Magnetic field dependence of the Zeeman (E$_z$) and\nspin-flip excitation energies for 0.31$<\\nu<$0.33. Also shown is the\nevolution of the splitting E$_z$-E$_{SF}$.} \\label{energies}\n\\end{figure}\nThe energy E$_{SF}$ was extracted for each filling factor by\nperforming a simple analysis of the low energy spectra using a\ntwo-gaussian fitting procedure as shown in the inset of figure\n\\ref{spectres}. Figure \\ref{energies} displays the corresponding\nenergies, E$_z$ and E$_{SF}$ as a function of filling factor. The\nstrong dependence of E$_{SF}$ confirms our assignment of the peak as\nexcitation involving spin degrees of freedom. More importantly, the\nspacing between E$_z$ and E$_{SF}$ is not constant and decreases\nwith the filling factor. From equation \\ref{ESF}, we easily see that\nthis spacing is directly related to the CF cyclotron energy so that\nthe splitting between the two spin excitations is\nE$_z$-E$_{SF}$=$\\hbar\\omega_c$-E$^{\\uparrow\\downarrow}$.\nThe magnetic field dependence of the E$_z$-E$_{SF}$ spacing is set\nby the effective field B$^*$. For the $\\phi=2$ sequence, $^2$CF\nemanate from the $\\nu=1\/2$ state and the effective field has its\norigin at B$_{1\/2}$. For the $\\phi=4$ sequence however, $^4$CF\nemanate from the $\\nu$=1\/4 state and the origin is at B$_{1\/4}$. and\nthe effective magnetic field should then decrease when going from\n0.33 to 0.31. This is indeed consistent with our data and to the\nexistence of $^4$CF or $\\phi=4$ spin-flip excitations below\n$\\nu$=1\/3. Our results support the CF Landau level picture shown figure\n\\ref{levels} for the $\\phi=4$ sequence.\n\nThe linear decrease of E$_z$-E$_{SF}$ with the effective magnetic\nfield makes very tempting the evaluation of an effective mass by using\na slope that is simply given by $\\frac{\\hbar e}{m^{*}c}$ in the\nframework of equation \\ref{ESF} for E$_{SF}$. Our data between\nfilling factors 0.33 and 0.31 give m$^{*}$=1.5($\\pm 0.1$)~m$_e$\nwhere m$_e$ is the bare electron mass. Previous determinations using\nthe activation gap values at $\\nu$=2\/7, 3\/11 and 4\/15 on samples\nwith similar densities yield values around 0.9~m$_e$ \\cite{Pan}. We\nnote that while our determination of m$^{*}$ is performed between\n$\\nu$=0.33 and $\\nu$=0.31, i.e. in between the incompressible states\nat 1\/3 and 2\/7, the effective mass determined via transport\nmeasurements comes from a linear scaling of the activation gap\nvalues at the incompressible states. The high\neffective mass obtained in our analysis may be linked to the onset\nof significant CF interactions in the partially populated CF Landau\nlevel.\n\\begin{figure}\n\\centering\n\\epsfig{figure=intensities.eps, width=0.65\\linewidth, clip=}\n\\caption{Evolution of the SF integrated intensity for 0.31$<\\nu<$0.33.}\n\\label{intensities}\n\\end{figure}\nAdditional insights can be obtained by tracking the evolution of the\nintensity of the SF excitation when the population of the first\nexcited CF level increases. This is done in Fig. \\ref{intensities}\nwhere the integrated spectral weight of the SF excitation is plotted\nas a function of filling factor. As expected, the SF intensity\nincreases when the population of $\\ket{1,\\uparrow}$ increases but\ndisplays an intriguing saturation around below $\\nu$=0.32. As\nalready mentioned for the effective mass, the saturation may result\nfrom increasing impact of CF residual interactions. These\ninteractions could possibly lead to further condensation into higher\norder CF in the partially populated level. Recent transport\nmeasurements have indeed shown the possible existence of such higher\norder states even for the $\\phi$=4 sequence \\cite{Pan2}.\n\\section{Conclusion}\nIn this study, we have shown the existence of spin-flip excitations\nof $^4$CF quasiparticle below $\\nu$=1\/3. The results indicate the\nexistence of a spin-split CF Landau level structure for the $\\phi=4$\nsequence of the fractional quantum Hall effect that is similar to\nthe one found for the $\\phi=2$ sequence. The evolution of the SF\nenergy with effective magnetic field yields an effective mass\nof 1.5m$_e$. The evolution of the SF intensity with filling factor\nmight signal the onset of significant CF-CF interactions that could\npossibly lead to further CF condensation.\n\nThis work is supported by the National Science Foundation under Grant No. NMR-0352738, by the Department of Energy under Grant No. DE-AIO2--04ER46133, and by a research grant from the W.M. Keck Foundation.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nRare flavour-changing neutral-current (FCNC) $B^0_{(s)} \\rightarrow \\mu^+ \\mu^-$ and $b \\rightarrow s \\mu^+ \\mu^-$ decays\nare considered among the most promising probes\nof the standard model (SM) and its extensions.\nThe precise measurement of several observables (total and differential\nbranching ratios, angular distributions of the decay products) of these decays\nmight provide interesting clues for new physics (NP) phenomena,\nif any sizable deviation from the SM predictions is observed.\nIn this paper we review the current status of these indirect searches at the Collider \nDetector at Fermilab (CDF II), which\nreached a sensitivity very close to SM predictions after a \ndecade of Tevatron leadership in the exploration of $B^0_s$ dynamics.\nThe Tevatron $p \\bar{p}$ collider, whose operations ended in October 2011 after 20 years of operation, \nprovided excellent opportunities to study $B$ physics.\nCDF II is a multipurpose detector, consisting of a central charged particle tracking system, \nsurrounded by calorimeters and muon chambers. It collected a final data set corresponding\nto about 10 fb$^{-1}$ of integrated luminosity. \n\nIn the search for rare $B$ decays,\nthe experimental challenge is to reject a huge background while keeping the signal efficiency high.\nA dedicated dimuon trigger has been used to select \nevents with a pair of muons in the final state in the pseudorapidity region $|\\eta|<$1.1.\nIn the reconstruction and analysis of $B$ hadron decays, CDF takes advantage of\nan excellent transverse momentum resolution, \n$\\frac{\\sigma_{p_T}}{p_T} = 0.07\\%\\, p_T$ (GeV\/c),\nwhich implies a resolution on the invariant\ndimuon mass of 24 MeV in the $B^0_{(s)} \\rightarrow \\mu^+ \\mu^-$ decay, \na vertex resolution of about 30 $\\mu$m in the transverse plane, and \nparticle identification (PID) capability, \nbased on multiple measurements of the ionization per unit of path length ($dE\/dx$) in the drift chambers. \n\\section{Search for $B^0_{(s)} \\rightarrow \\mu^{+} \\mu^{-}$}\n$B_{(s)}^0 \\rightarrow \\mu^{+} \\mu^{-}$ decays are mediated by FCNC and thus forbidden at first order in the SM. \nMoreover they are further suppressed by helicity factors $(m_{\\mu}\/m_B)^2$ in the final dimuon state.\nThey can only occur at second order through penguin and box diagrams. \nThe SM predicts very low rates for these processes: $\\ensuremath{\\mathcal B}(B_{s}^0 \\rightarrow \\mu^{+} \\mu^{-}) = \\left(3.2 \\pm 0.2\\right)\\times 10^{-9}$ \nand $\\ensuremath{\\mathcal B}(B^0 \\rightarrow \\mu^{+} \\mu^{-}) = \\left(1.0 \\pm 0.1\\right)\\times 10^{-10}$ \\cite{SM1,SM2}.\nHowever, a wide variety of beyond the standard model (BSM) theories predict enhancement of their branching ratios\n by several order of magnitudes, \nmaking these decays one of the most sensitive probes in indirect searches for NP. \n\nIn 2011, CDF observed an intriguing $\\sim$2.5$\\sigma$ excess over background in\n$B_{s}^0 \\rightarrow \\mu^{+} \\mu^{-}$ using 7 fb$^{-1}$ of data \\cite{CDF}.\nThough it was compatible with other experimental results (LHCb \\cite{LHCb}, CMS \\cite{CMS})\nand the SM prediction, it could be interpreted as the first indication of a signal\nand allowed CDF to set a two-sided bound on the rate \n$\\ensuremath{\\mathcal B}(B_{s}^0 \\rightarrow \\mu^{+} \\mu^{-}) = \\left(1.8^{+1.1}_{-0.9}\\right)\\times 10^{-8}$ .\nTo further investigate the nature of the excess, we repeated the analysis unchanged using the whole\n Run II data set, corresponding to 9.7 fb$^{-1}$ of integrated\nluminosity, about 30\\% more data with respect to the 2011 analysis. Here we report on \nthe final results of this search \\cite{CDF2}.\n\\begin{figure}\n \\centering\n\\subfigure[]{\\label{fig:bmumu1}\n \\includegraphics[width=.85\\textwidth]{bd-8NNbins_poiE_bgE_v12.eps}\n}\n\\subfigure[]{\\label{fig:bmumu2}\n \\includegraphics[width=.85\\textwidth]{bs-8NNbins_poiE_bgE_v12.eps}\n}\n\\caption{\nDimuon mass distributions for the (a) $B^0 \\rightarrow \\mu^+ \\mu^-$ and (b) \n$B_s^0 \\rightarrow \\mu^+ \\mu^-$ signal region in the eight NN bins. \nThe observed data (points) are compared to the total background expectation (light gray\nhistogram). The hatched region is the total uncertainty on the background expectation.\nIn (b) the dark gray histogram represents the SM signal expectation enhanced by a factor 4.1. \n}\n\\label{fig:bmumu}\n\\end{figure}\n\nThe baseline selection requires high quality muon candidates with opposite charge,\n transverse momentum $p_T >$ 2 GeV\/c, and a dimuon invariant mass $m_{\\mu\\mu}$ in the range 4.669-5.969 GeV\/c$^2$. \nThe muon pairs are constrained to originate from a common, well-measured reconstructed decay point. \nA likelihood-based muon identification method, \nis used to suppress contributions \nfrom hadrons misidentified as muons. \nThe branching ratios of $B_{(s)}^0 \\rightarrow \\mu^{+} \\mu^{-}$ are measured\nby normalizing to a sample of 40225$\\pm$ 267\n $B^+ \\rightarrow J\/\\psi (\\rightarrow \\mu^+ \\mu^-)\\,K^+$ candidates, \nselected with the same baseline requirements.\nA Neural Network (NN) classifier is used to improve signal over background separation. \nFourteen variables are used to construct the NN discriminant that ranges between 0 and 1. \nThe six most discriminating variables \ninclude the 3D opening angle between the dimuon momentum and the displacement vector between the\nprimary and secondary vertex; \nthe isolation $I$ \\footnote{$I = |\\vec{p}_T^{\\mu\\mu}|\/(\\sum_i p_T^i + |\\vec{p}_T^{\\mu\\mu}|)$, where $\\vec{p}^{\\mu\\mu}$ is the momentum\nof the dimuon pair; the sum is over all tracks with $\\sqrt{ (\\Delta\\phi)^2 + (\\Delta\\eta)^2} \\le 1$; $\\Delta\\eta$ and $\\Delta\\phi$ are the relative azimuthal angle and pseudorapidity of track $i$ with respect to $\\vec{p}^{\\mu\\mu}$.}\nof the candidate $B^0_{s}$ ;\nthe muon and $B^0_{(s)}$ impact parameters; the $B^0_{(s)}$ decay length significance; the vertex-fit $\\chi^2$.\nThe NN from the 2011 analysis was used with the same training.\nThe final search region in the dimuon invariant mass has a half width of about 60 MeV corresponding to 2.5 times the dimuon \nmass resolution. \nThe NN was validated with signal and background events.\nCareful checks for possible mass-biases of the NN output and overtraining show no anomalies. \n\nExtensive and detailed background estimates and checks have been performed.\nBackground is due to both combinatorial and peaking contributions in the signal region. \nCombinatorial background is estimated by fitting the sidebands to linear functions,\nafter blinding the signal region in the dimuon mass distribution.\nThe peaking background is due to $B \\rightarrow h^+ h^{'-}$ decays where the hadrons \n($h,h'$ stand for $\\pi$ or $K$) are misidentified as muons. \nThis has been estimated from both MC and data. \nThe misidentification probability is parametrized as a function of the track transverse momentum\n using $D^*$-tagged $D^0 \\rightarrow \\pi^+ K^-$ events. \nIt turned out that the peaking background is about 10\\% of the combinatorial background \nin $B_s^0 \\rightarrow \\mu^+ \\mu^-$ and about 50\\% of the total background in \nthe $B^0 \\rightarrow \\mu^+ \\mu^-$ channel.\nBackground estimates have been cross-checked using independent background-dominated control samples, \nin which the muons have the same measured charge or the reconstructed\ndimuon candidate lifetime is negative. \nNo significant discrepancies between the expected and observed number of events in the control samples have been found.\\\\\nThe data are divided into 8 bins of the NN discriminant to exploit the improved background suppression at high NN values, \nand five bins of mass in the search region.\nIn the $B^0$ search region data are consistent with the background \nprediction (Fig.~\\ref{fig:bmumu1}) and yield the limit of\n $\\mathcal{B}(B^0 \\rightarrow \\mu^+ \\mu^-) < 3.8 (4.6)\\times 10^{-9}$ at 90\\% (95\\%) C.L.. \nThe significance of the background-only hypothesis expressed as a p-value, estimated from an \nensemble of background-only pseudo-experiments, is 41\\%.\n\nIn the $B^0_s$ search region, a moderate excess in the highest NN bins ($>$0.97)\nis observed (Fig.~\\ref{fig:bmumu2}).\nThe p-value for the\nbackground-only hypothesis is 0.94\\%. We also\nproduce an ensemble of simulated experiments that includes\na $B^0_s \\rightarrow \\mu^+ \\mu^-$ contribution at the expected SM\nbranching fraction which yields a p-value of 7.1\\%.\nWith respect to the previous CDF result with 7 fb$^{-1}$ of data \\cite{CDF}, the excess in the third significant NN bin\n(0.97-0.987) softened, as expected for a statistical fluctuation.\nThough the 2011 hint of signal is not reinforced by the new data,\nit is still present and remains $>$2$\\sigma$ significant over background.\nAssuming the observed\nexcess in the $B^0_s$ region is due to signal, CDF finds \n$\\mathcal{B}(B_s^0 \\rightarrow \\mu^+ \\mu^-) = \\left(1.3^{+0.9}_{-0.7}\\right)\\times 10^{-8}$, \nwhich is still compatible with both\n the SM expectation and the latest constraints from LHC experiments \\cite{LHCbmumu, LHCb2}.\n\n\\begin{figure}[!h]\n \\centering\n\\subfigure[]\n \\includegraphics[width=4.5cm]{fit_bmass_kmm_data_prl.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{fit_bmass_kstmm_data_prl.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{fit_bmass_phimm_data_prl.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{fit_bmass_kstmm_kspi_data_prl.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{fit_bmass_ksmm_data_prl.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{fit_bmass_lmmm_data_prl.eps}\n}\n\\caption{Invariant mass of\n(a) $B^+ \\rightarrow K^+ \\mu^+ \\mu^- $, \n(b) $B^0 \\rightarrow K^{*0} \\mu^+ \\mu^-$,\n(c) $B^0_s \\rightarrow \\phi \\mu^+ \\mu^-$,\n(d) $B^+ \\rightarrow K^{*+} \\mu^+ \\mu^-$,\n(e) $B^0 \\rightarrow K_S \\mu^+ \\mu^-$, \n(f) $\\Lambda_b^0 \\rightarrow \\Lambda \\mu^+ \\mu^-$ with fit results overlaid. }\n\\label{fig:bsmumuyield}\n\\end{figure}\n\\section{$b \\rightarrow s \\mu^{+} \\mu^{-}$ decays}\nRare decays of bottom hadrons mediated by the FCNC process $b \\rightarrow s \\mu^+ \\mu^-$ \nare suppressed at tree level in the SM and must\noccur through higher-order loop amplitudes. Their expected branching ratios are of the order of 10$^{-6}$.\nBecause of their clean experimental signature and the reliable theoretical predictions for their rates, \nthese are excellent channels for NP searches.\n\n\\begin{table}[h]\n\\begin{center} \n \\begin{tabular}{l l}\n \\hline\\hline Decay mode & $\\mathcal{B}(10^{-6})$ \\\\\n \\hline\n $B^+ \\rightarrow K^+ \\mu^+ \\mu^- $ & $0.46 \\pm 0.04 \\pm 0.02 $\\\\\n $B^0 \\rightarrow K^{*0} \\mu^+ \\mu^-$ & $1.02 \\pm 0.10 \\pm 0.06$\\\\\n $B^0_s \\rightarrow \\phi \\mu^+ \\mu^-$ & $1.47 \\pm 0.24 \\pm 0.46$\\\\\n $B^+ \\rightarrow K^{*+} \\mu^+ \\mu^-$ & $0.95 \\pm 0.32 \\pm 0.08$\\\\\n $B^0 \\rightarrow K_S \\mu^+ \\mu^-$ & $0.32 \\pm 0.10 \\pm 0.02$\\\\\n $\\Lambda_b^0 \\rightarrow \\Lambda \\mu^+ \\mu^-$ & $1.73 \\pm 0.42 \\pm 0.55$\\\\\n \\hline\\hline\n \\end{tabular}\n\\caption{Branching ratio of the $H_b\\rightarrow h \\mu^+ \\mu^-$ decays measured by CDF.\nThe first quoted uncertainty is statistical, the second is systematic.}\n \\label{tabBR}\n\\end{center}\n\\end{table}\nCDF has studied the FCNC $H_b\\rightarrow h \\mu^+ \\mu^-$ decays (where $H_b$ and $h$ indicate hadrons \ncontaining a $b$ and $s$ quark, respectively) listed in Table~\\ref{tabBR}, \nusing 6.8 fb$^{-1}$ of data collected with the dimuon trigger \\cite{CDFbsmumuBR}. \nCandidates for each decay have been selected by standard kinematics cuts and a NN optimized for best sensitivity.\nSignal yields are obtained by an unbinned maximum log-likelihood fit to the $b$-hadron mass distributions \n(Fig.~\\ref{fig:bsmumuyield}), modelling \nthe signal peak with a Gaussian, and the combinatorial background with a linear function.\nTo cancel dominant systematic uncertainties, the branching ratio of each rare decay $H_b\\rightarrow h \\mu^+ \\mu^-$ \nis measured relative to the corresponding resonant channel $H_b\\rightarrow J\/\\psi\\, h$,\nused as a normalization and a cross-check of the whole analysis.\nThe results of the total branching ratios are reported in Table~\\ref{tabBR} and include\nthe first observation of the baryonic FCNC decay $\\Lambda_b^0 \\rightarrow \\Lambda \\mu^+ \\mu^-$, \nand the first measurement of the $B^+ \\rightarrow K^{*+} \\mu^+ \\mu^-$ and $B^0 \\rightarrow K_S\\,\\mu^+ \\mu^-$ decays\nat a hadron collider.\n\\begin{figure}[h] \n \\centering\n\\subfigure[]\n \\includegraphics[width=4.5cm]{dbr_kmm.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{dbr_kstmm.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{dbr_phimm.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{dbr_kstmm_kspi.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{dbr_ksmm.eps}\n}\n\\subfigure[]\n \\includegraphics[width=4.5cm]{dbr_lmmm.eps}\n}\n\\caption{\nDifferential branching ratios of \n(a) $B^+ \\rightarrow K^+ \\mu^+ \\mu^- $, \n(b) $B^0 \\rightarrow K^{*0} \\mu^+ \\mu^-$,\n(c) $B^0_s \\rightarrow \\phi \\mu^+ \\mu^-$,\n(d) $B^+ \\rightarrow K^{*+} \\mu^+ \\mu^-$,\n(e) $B^0 \\rightarrow K_S\\,\\mu^+ \\mu^-$, \n(f) $\\Lambda_b^0 \\rightarrow \\Lambda \\mu^+ \\mu^-$.\nThe points are the fit result from data. The solid curves are the SM\nexpectation \\cite{SMexp, SMexp2, SMexp3, SMexp4}. The dashed line in (f) is the SM prediction normalized to our total branching\nratio measurement. The hatched regions are the charmonium veto regions.}\n\\label{fig:bsmumudiff}\n\\end{figure}\n\nRich information about the $b \\rightarrow s \\mu^+ \\mu^-$\ndynamics can be obtained by precise measurements of the differential branching ratio as a function of $q^2 = m_{\\mu\\mu}^2 c^2$\nand the angular distributions of the decay products.\n\\begin{figure}[h] \n \\centering\n\\subfigure[]\n \\includegraphics[width=5cm]{summary_afb_6bin_all_prl.eps}\n}\n\\subfigure[]\n \\includegraphics[width=5cm]{summary_fl_6bin_all_prl.eps}\n}\n\\subfigure[]\n \\includegraphics[width=5cm]{summary_at2_6bin_all_prl.eps}\n}\n\\subfigure[]\n \\includegraphics[width=5cm]{summary_aim_6bin_all_prl.eps}\n}\n\n\\caption{Measurements of angular observables (a) $A_{FB}$, (b) $F_L$, (c) $A_T^{(2)}$, and (d) $A_{im}$ \n as a function of dimuon mass squared $q^2$\nin the combined decay mode $B \\rightarrow K^{*} \\mu^+ \\mu^-$. \nThe points are the fit results from data.\nThe solid and dotted curves represent expectations from the SM \nand a particular BSM scenario, respectively.}\n\\label{fig:angular}\n\\end{figure}\nThe differential branching ratios with respect to $q^2$ have been measured by dividing \nthe signal region into six bins in $q^2$ \nand fitting the signal yield in each bin.\nIn each fit, the mean of the $H_b$ mass and the background slope were fixed to the\nvalue from the global fit, so that only the signal fraction\nwas allowed to vary in the fit.\nThe results are shown in Fig.~\\ref{fig:bsmumudiff}. \nFor $B^0_s \\rightarrow \\phi \\mu^+ \\mu^-$ and $\\Lambda_b^0 \\rightarrow \\Lambda \\mu^+ \\mu^-$ \nthese are the first such measurements. At present no significant discrepancy from SM prediction is found.\n\nThe angular distributions \nof the combined $B^0 \\rightarrow K^{*0} \\mu^+ \\mu^-$\nand $B^+ \\rightarrow K^{*+} \\mu^+ \\mu^-$ decays have been measured and \nparametrized to four angular observables:\nthe muon forward-backward asymmetry $A_{FB}$, \nthe $K^*$ longitudinal polarization fraction $F_L$, \nthe transverse polarization asymmetry $A_T^{(2)}$,\nthe time-reversal-odd charge-and-parity asymmetry $A_{im}$, defined in \\cite{ANG,ANG2}. \n$A_T^{(2)}$ and $A_{im}$ have been measured for the first time by CDF \\cite{CDFbsmumuAfb}.\nThe results for these observables, shown in Fig.~\\ref{fig:angular}, are among the most precise to date and consistent \nwith SM predictions and other experiments, but\nstill statistically limited in providing stringent tests on\nvarious BSM models.\n\\section{Conclusion}\nWe have summarized the recent updates on the searches of rare $b$-hadron decays at CDF.\nThe intriguing excess in $B_s^0 \\rightarrow \\mu^+ \\mu^-$ reported in 2011\nis confirmed with the full data set, though its significance is softened to the level of 2$\\sigma$ over background. \nThe measured $B_s^0 \\rightarrow \\mu^+ \\mu^-$ branching ratio is still compatible with \nthe SM expectation and recent combined results from LHC experiments.\n\nThe dynamics of several rare decays mediated by the FCNC process $b \\rightarrow s \\mu^- \\mu^-$ \nhas been studied in detail extending the reach to new angular observables. \nThe $\\Lambda_b^0 \\rightarrow \\Lambda \\mu^+ \\mu^-$ decay has been observed for the first time. \nAnalyses of the $b \\rightarrow s \\mu^- \\mu^-$ decays \nare still in progress and may yield interesting results in the\nnear future. \n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Background on \\texorpdfstring{$p$}{p}-local compact groups}\\label{Background}\n\nLet $p$ a prime, to remain fixed for the rest of the paper unless otherwise stated. In this section we review all the definitions and results about $p$-local compact groups that we need in this paper. The main references for this section are the works of C. Broto, R. Levi and B. Oliver \\cite{BLO2, BLO3, BLO6}.\n\nRoughly speaking, $p$-local compact groups are abstractions of the fusion data obtained from finite and compact Lie groups. This idea already implies the existence of some sort of \\emph{Sylow $p$-subgroup}. In the finite case this role was played by finite $p$-groups, while in this more general setup we use discrete $p$-toral groups. Let $\\Z\/p^{\\infty} = \\bigcup_{n \\geq 1} \\Z\/p^n$ be the union of the cyclic $p$-groups $\\Z\/p^n$ under the obvious inclusions.\n\n\\begin{defi}\\label{defidiscptor}\n\nA \\emph{discrete $p$-toral group} $P$ is a group that contains a normal subgroup $P_0$ of finite index and which is isomorphic to $(\\Z\/p^{\\infty})^{\\times r}$ for some finite $r \\geq 0$.\n\n\\end{defi}\n\nIn other words, a discrete $p$-toral group is a group $P$ fitting in an exact sequence\n$$\n\\{1\\} \\to P_0 \\Right4{} P \\Right4{} \\pi \\to \\{1\\},\n$$\nwhere $\\pi$ is a finite $p$-group and $P_0 \\cong (\\Z\/p^{\\infty})^{\\times r}$. The \\emph{rank} of $P$, denoted by $\\mathrm{rk}(P)$, is $r$, and the \\emph{order} of $P$ is then defined as the pair $|P| \\stackrel{def} = (\\mathrm{rk}(P), |\\pi(P)|)$, considered as an element of $\\mathbb{N}^2$. This way we can compare the order of two discrete $p$-toral groups, by writing $|Q|\\leq |P|$ if either $\\mathrm{rk}(Q) < \\mathrm{rk}(P)$, or $\\mathrm{rk}(Q) = \\mathrm{rk}(P)$ and $|\\pi(Q)|\\leq |\\pi(P)|$. Given a discrete $p$-toral group $S$ and subgroups $P, Q\\leq S$, define\n$$\n\\mathrm{Hom}_S(P, Q) = \\{f = c_x \\in \\mathrm{Hom}(P, Q) \\,\\, | \\,\\, \\exists x \\in S \\mbox{ such that } x P x^{-1}\\leq Q\\}.\n$$\n\n\\begin{defi}\n\nGiven a discrete $p$-toral group $S$, a \\emph{fusion system} over $S$ is a category $\\mathcal{F}$ with $\\mathrm{Ob}(\\mathcal{F}) = \\{P\\leq S\\}$, and whose morphisms are actual homomorphisms satisfying the following:\n\\begin{enumerate}[(i)]\n\n\\item $\\mathrm{Hom}_S(P,Q) \\subseteq \\mathrm{Hom}_{\\mathcal{F}}(P,Q) \\subseteq \\operatorname{Inj}\\nolimits(P,Q)$ for all $P, Q \\in \\mathrm{Ob}(\\mathcal{F})$; and\n\n\\item every morphism in $\\mathcal{F}$ is the composition of an isomorphism in $\\mathcal{F}$, followed by an inclusion.\n\n\\end{enumerate}\nThe \\emph{rank of $\\mathcal{F}$} is the rank of $S$.\n\n\\end{defi}\n\nThe following notation will be used tacitly throughout the rest of the paper. Let $\\mathcal{F}$ be a fusion system over $S$, and let $P, Q, X\\leq S$. As objects in $\\mathcal{F}$, we say that $P$ and $Q$ are \\emph{$\\mathcal{F}$-conjugate} if they are isomorphic as objects in $\\mathcal{F}$. The notation $P^X$ and $P^{\\mathcal{F}}$, respectively, stands for the $X$-conjugacy and $\\mathcal{F}$-conjugacy classes of $P$. Note also that $\\mathrm{Aut}_{\\mathcal{F}}(P)$ is a group, by definition of fusion system, and that $\\mathrm{Inn}(P)\\leq \\mathrm{Aut}_{\\mathcal{F}}(P)$. Thus, it is reasonable to define\n$$\n\\mathrm{Out}_S(P) \\stackrel{def} = \\mathrm{Aut}_S(P)\/\\mathrm{Inn}(P) \\qquad \\mbox{and} \\qquad \\mathrm{Out}_{\\mathcal{F}}(P) \\stackrel{def} = \\mathrm{Aut}_{\\mathcal{F}}(P)\/\\mathrm{Inn}(P).\n$$\nFinally, we say that $P$ is \\emph{fully $\\mathcal{F}$-centralized}, respectively \\emph{fully $\\mathcal{F}$-normalized}, if $|C_S(P)| \\geq |C_S(Q)|$ for all $Q \\in P^{\\mathcal{F}}$, respectively if $|N_S(P)| \\geq |N_S(Q)|$ for all $Q \\in P^{\\mathcal{F}}$.\n\n\\begin{defi}\\label{defisat}\n\nLet $S$ be a discrete $p$-toral group, and let $\\mathcal{F}$ be a fusion system over $S$. We say that $\\mathcal{F}$ is a \\emph{saturated fusion system} if the following conditions are satisfied.\n\\begin{enumerate}[(I)]\n\n\\item If $P\\leq S$ is a fully $\\mathcal{F}$-normalized subgroup, then it is also fully $\\mathcal{F}$-centralized. Moreover, in this case $\\mathrm{Out}_{\\mathcal{F}}(P)$ is a finite group, and $\\mathrm{Out}_S(P) \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Out}_{\\mathcal{F}}(P))$.\n\n\\item Suppose $P\\leq S$ and $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,S)$ are such that $f(P)$ is fully $\\mathcal{F}$-centralized, and set\n$$\nN_f = \\{g \\in N_S(P) \\,\\, | \\,\\, f \\circ c_g \\circ f^{-1} \\in \\mathrm{Aut}_S(f(P))\\}.\n$$\nThen, there exists $\\4{f} \\in \\mathrm{Hom}_{\\mathcal{F}}(N_f, S)$ such that $\\4{f}|_P = f$.\n\n\\item Let $P_1\\leq P_2\\leq P_3\\leq \\ldots$ be a sequence of subgroups of $S$, and set $P = \\bigcup_{n = 1}^{\\infty} P_n$. If $f \\in \\mathrm{Hom}(P,S)$ is a homomorphism such that $f|_{P_n} \\in \\mathrm{Hom}_{\\mathcal{F}}(P_n,S)$ for all $n$, then $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,S)$.\n\n\\end{enumerate}\n\n\\end{defi}\n\nWe also recall the definition of centric and radical subgroups, which are crucial concepts in the $p$-local group theory.\n\n\\begin{defi}\n\nLet $\\mathcal{F}$ be a saturated fusion system over a discrete $p$-toral group $S$.\n\\begin{itemize}\n\n\\item A subgroup $P\\leq S$ is \\emph{$\\mathcal{F}$-centric} if $C_S(Q) = Z(Q)$ for all $Q \\in P^{\\mathcal{F}}$.\n\n\\item A subgroup $P\\leq S$ is \\emph{$\\mathcal{F}$-radical} if $\\mathrm{Out}_{\\mathcal{F}}(P)$ contains no nontrivial normal $p$-subgroup.\n\n\\end{itemize}\n\n\\end{defi}\n\nGiven a saturated fusion system $\\mathcal{F}$ over a discrete $p$-toral group $S$, we denote by $\\mathcal{F}^c$ and $\\mathcal{F}^r$ the full subcategories of $\\mathcal{F}$ with object sets the collections of $\\mathcal{F}$-centric and $\\mathcal{F}$-radical subgroups, respectively. We also write $\\mathcal{F}^{cr} \\subseteq \\mathcal{F}$ for the full subcategory of $\\mathcal{F}$-centric $\\mathcal{F}$-radical subgroups.\n\nProving that a given fusion system is saturated is a rather difficult task, even when the fusion system is finite, but there are some techniques that may be helpful. One of these techniques, which we will use in later sections, is \\cite[Theorem A]{BCGLO1}, restated as Theorem \\ref{5A} below.\n\n\\begin{defi}\n\nLet $\\mathcal{F}$ be a fusion system over a finite $p$-group $S$, and let $\\mathcal{H} \\subseteq \\mathrm{Ob}(\\mathcal{F})$ be a subset of objects.\n\\begin{itemize}\n\n\\item $\\mathcal{F}$ is \\emph{$\\mathcal{H}$-generated} if every morphism in $\\mathcal{F}$ can be described as a composite of restrictions of morphisms in $\\mathcal{F}$ between subgroups in $\\mathcal{H}$.\n\n\\item $\\mathcal{F}$ is \\emph{$\\mathcal{H}$-saturated} if the saturation axioms hold for all subgroups in the set $\\mathcal{H}$.\n\n\\end{itemize}\n\n\\end{defi}\n\n\\begin{thm}\\label{5A}\n\nLet $\\mathcal{F}$ be a fusion system over a finite $p$-group $S$, and let $\\mathcal{H}$ be a subset of objects of $\\mathcal{F}$ closed under $\\mathcal{F}$-conjugacy and such that $\\mathcal{F}$ is $\\mathcal{H}$-generated and $\\mathcal{H}$-saturated. Suppose further that, for each $\\mathcal{F}$-centric subgroup $P \\notin \\mathcal{H}$, $P$ is $\\mathcal{F}$-conjugate to some $Q$ such that\n$$\n\\mathrm{Out}_S(Q) \\cap O_p(\\mathrm{Out}_{\\mathcal{F}}(Q)) \\neq \\{1\\}.\n$$\nThen $\\mathcal{F}$ is saturated.\n\n\\end{thm}\n\nThe following result will be useful in later sections when checking the condition displayed in the previous theorem. We state it in full generality since in fact it will apply in different situations throughout this paper.\n\n\\begin{lmm}\\label{Kpgp}\n\nLet $\\mathcal{F}$ be a fusion system over a discrete $p$-toral group $S$. Let also $P\\leq S$ be a subgroup, and let $P_0 \\lhd P$ be a normal subgroup such that $f|_{P_0} \\in \\mathrm{Aut}_{\\mathcal{F}}(P_0)$ for all $f \\in \\mathrm{Aut}_{\\mathcal{F}}(P)$. Set\n$$\nK_P \\stackrel{def} = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{F}}(P) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}}(P_0) \\times \\mathrm{Aut}(P\/P_0)).\n$$\nThen, $K_P\\leq O_p(\\mathrm{Aut}_{\\mathcal{F}}(P))$.\n\n\\end{lmm}\n\n\\begin{proof}\n\nBy definition $K_P$ is normal in $\\mathrm{Aut}_{\\mathcal{F}}(P)$, and thus we only have to show that $K_P$ is a discrete $p$-toral subgroup of $\\mathrm{Aut}_{\\mathcal{F}}(P)$ (that is, every element of $K_P$ has order a power of $p$). This in turn follows by \\cite[Theorem 3.2]{Gor}: although the result in \\cite{Gor} is stated for finite groups, the arguments in its proof apply here without modification, since $\\mathrm{Aut}_{\\mathcal{F}}(P)$ is a locally finite group.\n\\end{proof}\n\nThe concept of transporter system associated to a fusion system was first introduced in \\cite{OV} for fusion systems over finite $p$-groups, and then extended to discrete $p$-toral groups in \\cite{BLO6}, with centric linking systems as a particular case. We refer the reader to the aforementioned sources for further details.\n\nLet $G$ be a group and let $\\mathcal{H}$ be a set of subgroups of $G$ that is closed by overgroups, i.e. if $H \\in \\mathcal{H}$ and $K \\geq H$, then $K \\in \\mathcal{H}$, and closed by conjugation in $G$, i.e. if $H \\in \\mathcal{H}$ and $g \\in G$, then $gHg^{-1} \\in \\mathcal{H}$. The transporter category of $G$ with respect to $\\mathcal{H}$ is the category $\\mathcal{T}_{\\mathcal{H}}(G)$ whose object set is $\\mathcal{H}$, and with morphism sets\n$$\n\\mathrm{Mor}_{\\mathcal{T}_{\\mathcal{H}}(G)}(P,Q) = \\{x \\in G \\,\\, | \\,\\, x \\cdot P \\cdot x^{-1}\\leq Q\\}\n$$\nfor each $P,Q \\in \\mathcal{H}$.\n\n\\begin{defi}\\label{defitransporter}\n\nLet $S$ be a discrete $p$-toral group, and let $\\mathcal{F}$ be a fusion system over $S$. A \\emph{transporter system} associated to $\\mathcal{F}$ is a nonempty category $\\mathcal{T}$ whose object set $\\mathrm{Ob}(\\mathcal{T})$ is a subset of $\\mathrm{Ob}(\\mathcal{F})$ that is closed by overgroups and conjugation in $\\mathcal{F}$, together with functors\n$$\n\\mathcal{T}_{\\mathrm{Ob}(\\mathcal{T})}(S) \\Right4{\\varepsilon} \\mathcal{T} \\qquad \\mbox{and} \\qquad \\mathcal{T} \\Right4{\\rho} \\mathcal{F}\n$$\nsatisfying the following conditions.\n\\begin{itemize}\n\n\\item[(A1)] The functor $\\varepsilon$ is the identity on objects and an inclusion on morphism sets, and the functor $\\rho$ is the inclusion on objects and a surjection on morphism sets.\n\n\\item[(A2)] For each $P, Q \\in \\mathrm{Ob}(\\mathcal{T})$, the set $\\mathrm{Mor}_{\\mathcal{T}}(P,Q)$ has a free action of\n$$\nE(P) \\stackrel{def} = \\mathrm{Ker} \\big[\\rho_P \\colon \\mathrm{Aut}_{\\mathcal{T}}(P) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}}(P) \\big]\n$$\nby right composition, and $\\rho_{P,Q}$ is the orbit map of this action. Also, $E(Q)$ acts freely on $\\mathrm{Mor}_{\\mathcal{T}}(P,Q)$ by left composition.\n\n\\item[(B)] Let $P,Q \\in \\mathrm{Ob}(\\mathcal{T})$. Then, the map $\\varepsilon_{P,Q} \\colon N_S(P,Q) \\to \\mathrm{Mor}_{\\mathcal{T}}(P,Q)$ is injective, and\n$$\n(\\rho_{P,Q} \\circ \\varepsilon_{P,Q})(g) = c_g \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q)\n$$\nfor all $g \\in \\mathrm{Mor}_{\\mathcal{T}_{\\mathrm{Ob}(\\mathcal{T})}(S)}(P, Q) = N_S(P, Q)$.\n\n\\item[(C)] For all $P, Q \\in \\mathrm{Ob}(\\mathcal{T})$, for all $\\varphi \\in \\mathrm{Mor}_{\\mathcal{T}}(P,Q)$, and for all $g \\in P$, the following is a commutative diagram in $\\mathcal{T}$.\n$$\n\\xymatrix{\nP \\ar[r]^{\\varphi} \\ar[d]_{\\varepsilon_P(g)} & Q \\ar[d]^{\\varepsilon_Q(\\rho(\\varphi)(g))} \\\\\nP \\ar[r]_{\\varphi} & Q\n}\n$$\n\n\\item[(I)] Each isomorphism class of objects in $\\mathrm{Ob}(\\mathcal{T})$ contains an element $P$ such that\n$$\n\\varepsilon_P(N_S(P)) \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Aut}_{\\mathcal{T}}(P));\n$$\nor, in other words, such that $\\varepsilon(N_S(P))$ has finite index prime to $p$ in $\\mathrm{Aut}_{\\mathcal{T}}(P)$.\n\n\\item[(II)] Let $P, Q \\in \\mathrm{Ob}(\\mathcal{T})$ be isomorphic objects, and let $\\varphi \\in \\mathrm{Iso}_{\\mathcal{T}}(P,Q)$. Let also $\\4{P}\\leq N_S(P)$ and $\\4{Q}\\leq N_S(Q)$ be such that $\\varphi \\circ \\varepsilon_P(\\4{P}) \\circ \\varphi^{-1}\\leq \\varepsilon_Q(\\4{Q})$. Then there is some morphism $\\4{\\varphi} \\in \\mathrm{Mor}_{\\mathcal{T}}(\\4{P}, \\4{Q})$ such that\n$$\n\\4{\\varphi} \\circ \\varepsilon_{P, \\4{P}}(1) = \\varepsilon_{Q, \\4{Q}}(1) \\circ \\varphi.\n$$\n\n\\item[(III)] Let $P_1\\leq P_2\\leq P_3\\leq \\ldots$ be a sequence in $\\mathrm{Ob}(\\mathcal{T})$, and let $\\varphi_n \\in \\mathrm{Mor}_{\\mathcal{T}}(P_n,S)$ be such that $\\varphi_n = \\varphi_{n+1} \\circ \\varepsilon_{P_n, P_{n+1}}(1)$ for all $n \\geq 1$. Then, upon setting $P = \\bigcup_{n \\geq 1} P_n$, there is a morphism $\\varphi \\in \\mathrm{Mor}_{\\mathcal{T}}(P,S)$ such that $\\varphi_n = \\varphi \\circ \\varepsilon_{P_n,P}(1)$ for all $n \\geq 1$.\n\n\\end{itemize}\nThe \\emph{rank of $\\mathcal{T}$} is the rank of $S$. A \\emph{centric linking system} associated to a saturated fusion system $\\mathcal{F}$ is a transporter system $\\mathcal{L}$ such that $\\mathrm{Ob}(\\mathcal{L})$ is the collection of all $\\mathcal{F}$-centric subgroups of $S$ and $E(P) = \\varepsilon(Z(P))$ for all $P \\in \\mathrm{Ob}(\\mathcal{L})$.\n\n\\end{defi}\n\n\\begin{rmk}\\label{rmktransp}\n\nThe above definition of centric linking system is taken from \\cite{BLO6}, and it is seen in \\cite[Corollary A.5]{BLO6} to coincide with the original \\cite[Definition 4.1]{BLO3}. Notice also that axiom (I) above differs from the corresponding axiom for the finite case, see \\cite[Definition 3.1]{OV}, in that condition (I) above seems to be more restrictive than the corresponding condition in \\cite{OV}:\n\\begin{itemize}\n\n\\item[(I')] $\\varepsilon_{S,S}(S) \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Aut}_{\\mathcal{T}}(S))$.\n\n\\end{itemize}\nHowever, \\cite[Proposition 3.4]{OV} implies that both definitions, \\cite[Definition 3.1]{OV} and the above, agree in the finite case.\n\n\\end{rmk}\n\n\\begin{lmm}\\label{epimono}\n\nIn a transporter system, all morphisms are monomorphisms and epimorphisms in the categorical sense.\n\n\\end{lmm}\n\n\\begin{proof}\n\nThis is \\cite[Proposition A.2 (d)]{BLO6}.\n\\end{proof}\n\n\\begin{defi}\n\nA \\emph{$p$-local compact group} is a triple $\\mathcal{G} = (S, \\FF, \\LL)$ formed by a discrete $p$-toral group $S$, a saturated fusion system $\\mathcal{F}$ over $S$, and a centric linking system $\\mathcal{L}$ associated to $\\mathcal{F}$. The \\emph{classifying space} of a $p$-local compact group $\\mathcal{G}$ is the $p$-completed nerve of $\\mathcal{L}$, denoted by $B\\mathcal{G} = |\\mathcal{L}|^{\\wedge}_p$. The \\emph{rank of $\\mathcal{G}$} is the rank of $S$.\n\n\\end{defi}\n\nGeneralizing work of \\cite{Chermak} and \\cite{Oliver}, it is proved in \\cite{Levi-Libman} that every saturated fusion system over a discrete $p$-toral group has an associated centric linking system which is unique up to isomorphism. Thus, from now on we speak of \\emph{the} associated centric linking system for a given saturated fusion system. \n\nFinally, we recall the ``bullet construction'' on a $p$-local compact group.\n\n\\begin{defi}\\label{defibullet}\n\nLet $\\mathcal{F}$ be a saturated fusion system over a discrete $p$-toral group $S$. Let also $T\\leq S$ be the maximal torus, and let $W = \\mathrm{Aut}_{\\mathcal{F}}(T)$. Set the following\n\n\\begin{enumerate}[(i)]\n\n\\item The exponent of $S\/T$ is $e = \\exp(S\/T) = \\min\\{k \\in \\mathbb{N} \\, | \\, x^{p^k} \\in T \\mbox{ for all } x \\in S\\}$.\n\n\\item For each $P\\leq T$, let $I(P) = \\{t \\in T \\, | \\, \\omega(t) = t \\mbox{ for all } \\omega \\in W \\mbox{ such that } \\omega|_P = \\mathrm{Id}_P\\}$, and let $I(P)_0$ denote its maximal torus.\n\n\\item For each $P\\leq S$, set $P^{[e]} = \\{x^{p^e} \\, | \\, x \\in P\\}\\leq T$, and set\n$$\nP^{\\bullet} = P \\cdot I(P^{[e]})_0 = \\{xt \\, | \\, x \\in P, \\, t \\in I(P^{[e]})_0\\}.\n$$\n\n\\item Let $\\mathcal{F}^{\\bullet}$ be the full subcategory of $\\mathcal{F}$ with object set $\\mathrm{Ob}(\\mathcal{F}^{\\bullet}) = \\{P^{\\bullet} \\, | \\, P\\leq S\\}$.\n\n\\end{enumerate}\n\n\\end{defi}\n\nThe following summarizes the main properties of the ``bullet construction''.\n\n\\begin{prop}\\label{3.2BLO3}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group. Then, for each $P, Q \\in \\mathrm{Ob}(\\mathcal{F})$ and each $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q)$ there is a unique $f^{\\bullet} \\in \\mathrm{Hom}_{\\mathcal{F}}(P^{\\bullet}, Q^{\\bullet})$ whose restriction to $P$ is $f$. This way, the ``bullet construction'' makes $P \\mapsto P^{\\bullet}$ into a functor $(-)^{\\bullet} \\colon \\mathcal{F} \\to \\mathcal{F}$ that satisfies the following properties.\n\\begin{enumerate}[(i)]\n\n\\item The set $\\mathrm{Ob}(\\mathcal{F}^{\\bullet}) = \\{P^{\\bullet} \\, | \\, P\\leq S\\}$ contains finitely many $S$-conjugacy classes of subgroups of $S$.\n\n\\item For all $P\\leq S$, $(P^{\\bullet})^{\\bullet} = P^{\\bullet}$.\n\n\\item If $P\\leq Q\\leq S$, then $P^{\\bullet}\\leq Q^{\\bullet}$.\n\n\\item For all $P, Q\\leq S$, $N_S(P, Q) \\subseteq N_S(P^{\\bullet}, Q^{\\bullet})$.\n\n\\item For all $P\\leq S$, $C_S(P) = C_S(P^{\\bullet})$.\n\n\\item The functor $(-)^{\\bullet}$ is a left adjoint to the inclusion of $\\mathcal{F}^{\\bullet}$ as a full subcategory of $\\mathcal{F}$.\n\n\\item All $\\mathcal{F}$-centric $\\mathcal{F}$-radical subgroups of $S$ are in $\\mathcal{F}^{\\bullet}$. In particular, there are only finitely many $\\mathcal{F}$-conjugacy classes of such subgroups.\n\n\\end{enumerate}\nMoreover, if we denote by $\\mathcal{L}^{\\bullet} \\subseteq \\mathcal{L}$ the full subcategory with $\\mathrm{Ob}(\\mathcal{L}^{\\bullet}) = \\{P^{\\bullet} \\, | \\, P \\in \\mathrm{Ob}(\\mathcal{L})\\}$, then there is a unique functor $(-)^{\\bullet} \\colon \\mathcal{L} \\to \\mathcal{L}^{\\bullet}$ such that the following holds.\n\\begin{enumerate}[(a)]\n\n\\item $(-)^{\\bullet} \\circ \\rho = \\rho \\circ (-)^{\\bullet} \\colon \\mathcal{L} \\to \\mathcal{F}$.\n\n\\item For all $P, Q \\in \\mathrm{Ob}(\\mathcal{L})$ and all $\\varphi \\in \\mathrm{Mor}_{\\mathcal{L}}(P,Q)$, we have $\\varepsilon_{Q, Q^{\\bullet}}(1) \\circ \\varphi = \\varphi^{\\bullet} \\circ \\varepsilon_{P, P^{\\bullet}}(1)$.\n\n\\item For all $P, Q \\in \\mathrm{Ob}(\\mathcal{L})$ and all $g \\in N_S(P,Q)$, we have $\\varepsilon_{P,Q}(g)^{\\bullet} = \\varepsilon_{P^{\\bullet}, Q^{\\bullet}}(g)$.\n\n\\item The functor $(-)^{\\bullet} \\colon \\mathcal{L} \\to \\mathcal{L}$ is left adjoint to the inclusion of $\\mathcal{L}^{\\bullet}$ as a full subcategory of $\\mathcal{L}$. In particular, the inclusion $\\mathcal{L}^{\\bullet} \\subseteq \\mathcal{L}$ induces an equivalence $|\\mathcal{L}^{\\bullet}|\\simeq |\\mathcal{L}|$.\n\n\\end{enumerate}\n\n\\end{prop}\n\n\\begin{proof}\n\nThe first part of the statement corresponds to \\cite[Proposition 3.3]{BLO3}. Parts (i), (ii) and (iii) correspond to \\cite[Lemma 3.2 (a), (b) and (c)]{BLO3} respectively. Part (iv) is an easy variation of \\cite[Lemma 3.2 (b)]{BLO3} (details are left to the reader). For part (v), let $P\\leq S$. Since $P\\leq P^{\\bullet}$, we have $C_S(P) \\geq C_S(P^{\\bullet})$. Let $x \\in C_S(P)$. By (iv), we have $x \\in N_S(P^{\\bullet})$. Since $c_x = \\mathrm{Id} \\in \\mathrm{Aut}_{\\mathcal{F}}(P)$ extends uniquely to $c_x = \\mathrm{Id} \\in \\mathrm{Aut}_{\\mathcal{F}}(P^{\\bullet})$, it follows that $x \\in C_S(P^{\\bullet})$. Part (vi) corresponds to \\cite[Corollary 3.4]{BLO3}, and part (vii) corresponds to \\cite[Corollary 3.5]{BLO3}. The last part of the statement, including parts (a), (b) and (c) corresponds to \\cite[Proposition 1.12]{JLL}. Part (d) corresponds to \\cite[Proposition 4.5 (a)]{BLO3}.\n\\end{proof}\n\n\n\\subsection{Isotypical equivalences and unstable Adams operations}\\label{Ssisotyp}\n\nIn this subsection we review the concept of isotypical equivalence, with particular interest on the unstable Adams operations for $p$-local compact groups originally introduced in \\cite{JLL}.\n\n\\begin{defi}\n\nLet $(\\mathcal{T}, \\varepsilon, \\rho)$ be a transporter system associated to a fusion system $\\mathcal{F}$. An automorphism $\\Psi \\colon \\mathcal{T} \\to \\mathcal{T}$ is \\emph{isotypical} if $\\Psi(\\varepsilon_P(P)) = \\varepsilon_{\\Psi(P)}(\\Psi(P))$ for each $P \\in \\mathrm{Ob}(\\mathcal{T})$.\n\n\\end{defi}\n\nWe denote by $\\mathrm{Aut}_{\\mathrm{typ}}^I(\\mathcal{T})$ the group of isotypical automorphisms $\\Psi$ of $\\mathcal{T}$ which in addition satisfy $\\Psi(\\varepsilon_{P,Q}(1)) = \\varepsilon_{\\Psi(P), \\Psi(Q)}(1)$ whenever $P\\leq Q$. Notice that if $\\Psi \\in \\mathrm{Aut}_{\\mathrm{typ}}^{I}(\\mathcal{T})$, then $\\Psi$ induces an automorphism of $S$ by restricting to the object $S \\in \\mathrm{Ob}(\\mathcal{T})$. By abuse of notation, we will denote the induced automorphism by $\\Psi \\in \\mathrm{Aut}(S)$.\n\nNext we review the concept of unstable Adams operations for $p$-local compact groups. Our definition corresponds to the definition of \\emph{normal Adams operation} in \\cite[Definition 3.3]{JLL}, conveniently adapted to our notation. By $(\\Z^\\wedge_p)^{\\times}$ we denote the subgroup of multiplicative units in the ring of $p$-adic integers $\\Z^\\wedge_p$.\n\n\\begin{defi}\\label{uAo}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group. An \\emph{unstable Adams operation of degree $\\zeta \\in (\\Z^\\wedge_p)^{\\times}$} on $\\mathcal{G}$ is an isotypical equivalence $\\Psi \\in \\mathrm{Aut}_{\\mathrm{typ}}^{I}(\\mathcal{L})$ such that the induced automorphism $\\Psi \\in \\mathrm{Aut}(S)$ satisfies\n\\begin{enumerate}[(i)]\n\n\\item the restriction of $\\Psi$ to the maximal torus $T\\leq S$ is the $\\zeta$-power automorphism; and\n\n\\item $\\Psi$ induces the identity on $S\/T$.\n\n\\end{enumerate}\nAn unstable Adams operation is \\emph{fine} if its degree is $\\zeta \\neq 1$, with $\\zeta$ congruent to $1$ modulo $p$.\n\n\\end{defi}\n\nAs proved in \\cite[Theorem 4.1]{JLL}, unstable Adams operations exist for all $p$-local compact groups, and in particular this applies to the existence of fine unstable Adams operations.\n\n\\begin{thm}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group. Then, for some large enough $m \\in \\mathbb{N}$, $\\mathcal{G}$ has unstable Adams operations of degree $\\zeta$, for each $\\zeta \\in 1 + p^m\\Z^\\wedge_p$.\n\n\\end{thm}\n\n\\begin{rmk}\\label{uAo1}\n\nRoughly speaking, the construction of unstable Adams operations in \\cite{JLL} is done by defining $\\Psi$ to fix enough objects and morphisms in $\\mathcal{L}$. More specifically, $\\Psi$ fixes\n\\begin{enumerate}[(a)]\n\n\\item a set $\\mathcal{H}$ of representatives of the $S$-conjugacy classes $\\mathrm{Ob}(\\mathcal{L}^{\\bullet})$; and\n\n\\item for each $P \\in \\mathcal{H}$, a set of representatives $\\mathcal{M}_P \\subseteq \\mathrm{Aut}_{\\mathcal{L}}(P)$ of the classes in $\\mathrm{Aut}_{\\mathcal{L}}(P)\/P \\cong \\mathrm{Out}_{\\mathcal{F}}(P)$.\n\n\\end{enumerate}\nThis properties will be crucial in our constructions in Section \\ref{Sfam}.\n\n\\end{rmk}\n\nLet $S$ be a discrete $p$-toral group, let $\\mathcal{F}$ be a fusion system over $S$ (not necessarily saturated), and let $\\mathcal{T}$ be a transporter system associated to $\\mathcal{F}$. Let also $\\Psi \\in \\mathrm{Aut}_{\\mathrm{typ}}^{I}(\\mathcal{T})$ be an isotypical automorphism. Set also\n\\begin{equation}\\label{fixS}\nC_S(\\Psi) = \\{g \\in S \\, | \\, \\Psi(\\varepsilon_S(g)) = \\varepsilon_S(g)\\}\\leq S,\n\\end{equation}\nthe subgroup of fixed points of $S$ by $\\Psi$. The following result is the main tool in detecting objects and morphisms in $\\mathcal{T}$ that are invariant by $\\Psi$.\n\n\\begin{lmm}\\label{invar1}\n\nThe following holds.\n\\begin{enumerate}[(i)]\n\n\\item Let $P\\leq C_S(\\Psi)$, and let $Q \\in P^S$. Then, $Q\\leq C_S(\\Psi)$ if and only if, for some $x \\in N_S(Q,P)$,\n$$\nx^{-1} \\cdot \\Psi(x) \\in C_S(Q).\n$$\n\n\\item Let $P, P' ,Q, Q'\\leq C_S(\\Psi)$ be such that $P' \\in P^S$ and $Q' \\in Q^S$, and suppose $P, P', Q, Q' \\in \\mathrm{Ob}(\\mathcal{L})$. Let also $x \\in N_S(P',P)$ and $y \\in N_S(Q', Q)$, and let $\\varphi \\in \\mathrm{Mor}_{\\mathcal{T}}(P,Q)$ be such that $\\Psi(\\varphi) = \\varphi$. Set $\\varphi' = \\varepsilon(y^{-1}) \\circ \\varphi \\circ \\varepsilon(x) \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(P', Q')$. Then, $\\Psi(\\varphi') = \\varphi'$ if and only if\n$$\n\\varepsilon(y^{-1} \\cdot \\Psi(y)) \\circ \\varphi' = \\varphi' \\circ \\varepsilon(x^{-1} \\cdot \\Psi(x)).\n$$\n\n\\end{enumerate}\n\n\\end{lmm}\n\n\\begin{proof}\n\nFor part (i), let $P\\leq C_S(\\Psi)$, and let $Q \\in P^S$. Let also $g \\in Q$ and $x \\in N_S(Q,P)$, and set $h = x \\cdot g \\cdot x^{-1} \\in P$. Since $P\\leq C_S(\\Psi)$, we get\n$$\nx \\cdot g \\cdot x^{-1} = h = \\Psi(h) = \\Psi(x \\cdot g \\cdot x^{-1}) = \\Psi(x) \\cdot \\Psi(g) \\cdot \\Psi(x)^{-1}.\n$$\nThus, if $Q\\leq C_S(\\Psi)$ then clearly $x^{-1} \\cdot \\Psi(x) \\in C_S(Q)$, and conversely if $x^{-1} \\cdot \\Psi(x) \\in C_S(Q)$ then $h \\in C_S(\\Psi)$. Since the argument works for any $g \\in Q$ and any $x \\in N_S(Q, P)$, part (i) follows.\n\nFor part (ii), let $P, P' ,Q, Q'\\leq C_S(\\Psi)$, with $P' \\in P^S$ and $Q' \\in Q^S$. Let also $x \\in N_S(P',P)$ and $y \\in N_S(Q', Q)$, and let $\\varphi \\in \\mathrm{Mor}_{\\mathcal{T}}(P,Q)$ be such that $\\Psi(\\varphi) = \\varphi$, with $\\varphi' = \\varepsilon(y^{-1}) \\circ \\varphi \\circ \\varepsilon(x) \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(P', Q')$. We have\n$$\n\\varepsilon(y) \\circ \\varphi' \\circ \\varepsilon(x^{-1}) = \\varphi = \\Psi(\\varphi) = \\Psi(\\varepsilon(y) \\circ \\varphi' \\circ \\varepsilon(x^{-1})) = \\varepsilon(\\Psi(y)) \\circ \\Psi(\\varphi') \\circ \\varepsilon(\\Psi(x)^{-1}),\n$$\nand (ii) follows easily.\n\\end{proof}\n\n\n\\subsection{Normalizers, centralizers, and related constructions}\\label{Squotient}\n\nIn this subsection we review the construction of the centralizer and normalizer $p$-local compact subgroups for a given $p$-local compact group. The main references here are \\cite[Appendix A]{BLO2} and \\cite[Section 2]{BLO6}. For the rest of this subsection, fix a $p$-local compact group $\\mathcal{G} = (S, \\FF, \\LL)$, a subgroup $A\\leq S$, and a subgroup $K\\leq \\mathrm{Aut}(A)$, and define the following:\n\\begin{itemize}\n\n\\item $\\mathrm{Aut}_{\\mathcal{F}}^K(A) = K \\cap \\mathrm{Aut}_{\\mathcal{F}}(A)$;\n\n\\item $\\mathrm{Aut}_S^K(A) = K \\cap \\mathrm{Aut}_S(A)$; and\n\n\\item $N_S^K(A) = \\{x \\in N_S(A) \\, | \\, c_x \\in K\\}$.\n\n\\end{itemize}\nThe subgroup $A$ is \\emph{fully $K$-normalized in $\\mathcal{F}$} if we have $|N_S^K(A)| \\geq |N_S^{^{f}K}(f(A))|$ for each $f \\in \\mathrm{Hom}_{\\mathcal{F}}(A, S)$, where $^{f}K = \\{f \\gamma f^{-1} \\, | \\, \\gamma \\in K\\}\\leq \\mathrm{Aut}(f(A))$.\n\n\\begin{defi}\\label{definorm}\n\nThe \\emph{$K$-normalizer fusion system of $A$ in $\\mathcal{F}$}, is the fusion system $N_{\\mathcal{F}}^K(A)$ over $N_S^K(A)$ with morphism sets\n$$\n\\begin{aligned}\n\\mathrm{Hom}_{N_{\\mathcal{F}}^K(A)}&(P,Q) = \\\\\n & = \\{f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q) \\,\\, | \\,\\, \\exists \\4{f} \\in \\mathrm{Hom}_{\\mathcal{F}}(PA, QA) \\mbox{ with } \\4{f}|_P = f \\mbox{ and } \\4{f}|_A \\in K\\}\n\\end{aligned}\n$$\nfor each $P, Q\\leq N_S^K(A)$.\n\n\\end{defi}\n\nBy \\cite[Theorem 2.3]{BLO6} we know that $N_{\\mathcal{F}}^K(A)$ is a saturated fusion system whenever $A$ is fully $K$-normalized in $\\mathcal{F}$. For this reason, for the rest of this subsection we assume that $A$ satisfies this property.\n\n\\begin{lmm}\\label{centricNFKA}\n\nIf $P\\leq N_S^K(A)$ is $N_{\\mathcal{F}}^K(A)$-centric, then $P \\cdot A$ is $\\mathcal{F}$-centric.\n\n\\end{lmm}\n\n\\begin{proof}\n\nLet $P\\leq N_S^K(A)$ be $N_{\\mathcal{F}}^K(A)$-centric. We have to check that, for each $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}}(P \\cdot A, S)$, there is an inclusion $C_S(\\gamma(P \\cdot A))\\leq \\gamma(P \\cdot A)$. We can apply \\cite[Proposition A.2]{BLO2}, since the proof in \\cite{BLO2} works without modifications in the compact setup, and it follows that the subgroup $A$ is fully centralized in $\\mathcal{F}$, and there is some $f \\in \\mathrm{Hom}_{\\mathcal{F}}(N_S^{^{\\gamma}K}(\\gamma(A)) \\cdot \\gamma(A),S)$ such that $(f \\circ \\gamma)|_A \\in K$. Thus $f \\circ \\gamma$ is a morphism in $N_{\\mathcal{F}}^K(A)$.\n\nNote that $C_S(\\gamma(P \\cdot A))\\leq C_S(\\gamma(A))\\leq N_S^{^{\\gamma} K}(\\gamma(A))$, and we have inclusions\n$$\n\\begin{aligned}\nf(C_S(\\gamma(P \\cdot A))) &\\leq C_S((f \\circ \\gamma)(P \\cdot A)) = C_S((f \\circ \\gamma)(P) \\cdot A)\\leq \\\\\n &\\leq C_S((f \\circ \\gamma)(P)) \\cap C_S(A)\\leq C_S((f \\circ \\gamma)(P)) \\cap N_S^K(A)\\leq (f \\circ \\gamma)(P),\n\\end{aligned}\n$$\nwhere the last inequality holds since $P \\in N_{\\mathcal{F}}^K(A)^c$. Thus,\n$$\nC_S(\\gamma(P \\cdot A))\\leq \\gamma(P)\\leq \\gamma(P) \\cdot \\gamma(A) = \\gamma(P \\cdot A),\n$$\nand this proves that $P \\cdot A \\in \\mathcal{F}^c$.\n\\end{proof}\n\nIn view of the above, we can now define $N_{\\mathcal{L}}^K(A)$ as the category with objects the set of $N_{\\mathcal{F}}^K(A)$-centric subgroups of $N_S^K(A)$ and with morphism sets\n$$\n\\mathrm{Mor}_{N_{\\mathcal{L}}^K(A)}(P,Q) = \\{\\varphi \\in \\mathrm{Mor}_{\\mathcal{L}}(PA, QA) \\,\\, | \\,\\, \\rho(\\varphi)|_P \\in \\mathrm{Hom}_{N_{\\mathcal{F}}^K(A)}(P,Q) \\mbox{ and } \\rho(\\varphi)|_A \\in K\\}.\n$$\nIn general, $N_{\\mathcal{L}}^K(A)$ need not be a transporter system associated to $N_{\\mathcal{F}}^K(A)$, but there are two particular situations where this is indeed the case.\n\n\\begin{lmm}\n\nIf either $K = \\{\\mathrm{Id}\\}$ or $K = \\mathrm{Aut}(A)$, then the category $N_{\\mathcal{L}}^K(A)$ is a centric linking system associated to $N_{\\mathcal{F}}^K(A)$.\n\n\\end{lmm}\n\n\\begin{proof}\n\nThe case $K = \\{\\mathrm{Id}\\}$ corresponds to \\cite[Proposition 2.5]{BLO2} in the finite case, while the case $K = \\mathrm{Aut}(A)$ corresponds to \\cite[Lemma 6.2]{BLO2} for $p$-local finite groups. In both situations, the proof for $p$-local finite groups applies here without modification to show that $N_{\\mathcal{L}}^{\\{\\mathrm{Id}\\}}(A)$ satisfies all the condition of a centric linking system, except perhaps axiom (III), which is easily checked.\n\\end{proof}\n\n\\begin{defi}\\label{rmknorm}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $A\\leq S$.\n\\begin{enumerate}[(a)]\n\n\\item If $A$ is fully $\\mathcal{F}$-centralized, the \\emph{centralizer $p$-local compact group of $A$ in $\\mathcal{G}$} is the triple\n$$\nC_{\\mathcal{G}}(A) = (C_S(A), C_{\\mathcal{F}}(A), C_{\\mathcal{L}}(A)) \\stackrel{def} = (N_S^{\\{\\mathrm{Id}\\}}(A), N_{\\mathcal{F}}^{\\{\\mathrm{Id}\\}}(A), N_{\\mathcal{L}}^{\\{\\mathrm{Id}\\}}(A)).\n$$\n\n\\item If $A$ is fully $\\mathcal{F}$-normalized, the \\emph{normalizer $p$-local compact group of $A$ in $\\mathcal{G}$} is the triple\n$$\nN_{\\mathcal{G}}(A) = (N_S(A), N_{\\mathcal{F}}(A), N_{\\mathcal{L}}(A)) \\stackrel{def} = (N_S^{\\mathrm{Aut}(A)}(A), N_{\\mathcal{F}}^{\\mathrm{Aut}(A)}(A), N_{\\mathcal{L}}^{\\mathrm{Aut}(A)}(A)).\n$$\n\n\\end{enumerate}\nA subgroup $A\\leq S$ is called \\emph{central in $\\mathcal{F}$} if $C_{\\mathcal{G}}(A) = \\mathcal{G}$. Similarly, $A\\leq S$ is called \\emph{normal in $\\mathcal{F}$} if $N_{\\mathcal{G}}(A) = \\mathcal{G}$. Clearly, if $A\\leq S$ is central in $\\mathcal{F}$ then in particular it is normal in $\\mathcal{F}$.\n\n\\end{defi}\n\n\\begin{lmm}\\label{central1}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $P\\leq S$. Then $P$ is fully $\\mathcal{F}$-centralized if and only if $P^{\\bullet}$ is fully $\\mathcal{F}$-centralized. Furthermore, if this is the case then $C_{\\mathcal{G}}(P) = C_{\\mathcal{G}}(P^{\\bullet})$.\n\n\\end{lmm}\n\n\\begin{proof}\n\nSuppose first that $P^{\\bullet}$ is fully $\\mathcal{F}$-centralized. By Proposition \\ref{3.2BLO3} (v), we have $C_S(P) = C_S(P^{\\bullet})$. If $Q \\in P^{\\mathcal{F}}$, then $Q^{\\bullet} \\in (P^{\\bullet})^{\\mathcal{F}}$ by Proposition \\ref{3.2BLO3}, and we have\n$$\n|C_S(Q)| = |C_S(Q^{\\bullet})|\\leq |C_S(P^{\\bullet})| = |C_S(P)|,\n$$\nwhich implies that $P$ is fully $\\mathcal{F}$-centralized.\n\nConversely, suppose that $P$ is fully $\\mathcal{F}$-centralized, and let $R \\in (P^{\\bullet})^{\\mathcal{F}}$ be fully $\\mathcal{F}$-centralized. Choose some $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}}(P^{\\bullet} C_S(P^{\\bullet}), S)$ such that $\\gamma(P^{\\bullet}) = R$, and set $Q = \\gamma(P)$, with $Q^{\\bullet} = \\gamma(P^{\\bullet}) = R$. By Proposition \\ref{3.2BLO3} (v), we have $C_S(P) = C_S(P^{\\bullet})$, and thus\n$$\n\\gamma(C_S(P^{\\bullet})) = \\gamma(C_S(P)) = C_S(Q) = C_S(Q^{\\bullet}) = C_S(R),\n$$\nwhere the leftmost and rightmost equalities hold by Proposition \\ref{3.2BLO3} (v), and the equality in the middle holds since $P$ is fully $\\mathcal{F}$-centralized. It follows that $P^{\\bullet}$ is fully $\\mathcal{F}$-centralized.\n\nTo finish the proof, suppose that $P$ and $P^{\\bullet}$ are fully $\\mathcal{F}$-centralized, and consider $C_{\\mathcal{G}}(P)$ and $C_{\\mathcal{G}}(P^{\\bullet})$, which are $p$-local compact groups with Sylow $C_S(P) = C_S(P^{\\bullet})$. By definition, it is enough to show that $C_{\\mathcal{F}}(P) = C_{\\mathcal{F}}(P^{\\bullet})$. Notice that there is an obvious inclusion $C_{\\mathcal{F}}(P^{\\bullet}) \\subseteq C_{\\mathcal{F}}(P)$. Let $Q, R\\leq C_S(P)$, and let $f \\in \\mathrm{Hom}_{C_{\\mathcal{F}}(P)}(Q,R)$. By definition of $C_{\\mathcal{F}}(P)$, there is some $\\4{f} \\in \\mathrm{Hom}_{\\mathcal{F}}(QP, RP)$ such that $\\4{f}|_Q = f$ and $\\4{f}|_P = \\mathrm{Id}$. Let $\\gamma = \\4{f}$, and consider $\\gamma^{\\bullet} \\in \\mathrm{Hom}_{\\mathcal{F}}((QP)^{\\bullet}, (RP)^{\\bullet})$. Then $\\gamma^{\\bullet}$ restricts to a morphism $\\omega \\in \\mathrm{Hom}_{\\mathcal{F}}(QP^{\\bullet}, RP^{\\bullet})$. Furthermore, by definition of $\\omega$, we have\n$$\n\\omega|_Q = \\gamma^{\\bullet}|Q = \\gamma|Q = f \\qquad \\mbox{and} \\qquad \\omega|_{P^{\\bullet}} = \\gamma^{\\bullet}|_{P^{\\bullet}} = (f|_P)^{\\bullet} = \\mathrm{Id}.\n$$\nThus $f$ is a morphism in $C_{\\mathcal{F}}(P^{\\bullet})$, and $C_{\\mathcal{F}}(P) = C_{\\mathcal{F}}(P^{\\bullet})$.\n\\end{proof}\n\n\\begin{cor}\\label{central2}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group. Then, for each $P\\leq S$ which is fully $\\mathcal{F}$-centralized, there is a sequence of finite subgroups $P_0\\leq P_1\\leq \\ldots$ such that $P = \\bigcup_{n \\geq 0} P_n$ and such that the following conditions hold for all $n \\geq 0$.\n\\begin{enumerate}[(i)]\n\n\\item $P_n$ is fully $\\mathcal{F}$-centralized and $P_n^{\\bullet} = P^{\\bullet}$.\n\n\\item $C_{\\mathcal{G}}(P_n) = C_{\\mathcal{G}}(P)$.\n\n\\end{enumerate}\n\n\\end{cor}\n\n\\begin{proof}\n\nLet $P\\leq S$. Since $S$ is locally finite, so is $P$, and we can find some sequence of finite subgroups $P_0\\leq P_1\\leq \\ldots$ such that $P = \\bigcup_{n \\geq 0} P_n$. Furthermore, there is some $M \\in \\mathbb{N}$ such that $P_n^{\\bullet} = P^{\\bullet}$ for all $n \\geq M$, and we may assume for simplicity that $M = 0$. By Lemma \\ref{central1}, $P^{\\bullet}$ is fully $\\mathcal{F}$-centralized, and then so is $P_n$, for all $n \\geq 0$. Furthermore,\n$$\nC_{\\mathcal{F}}(P_n) = C_{\\mathcal{F}}(P_n^{\\bullet}) = C_{\\mathcal{F}}(P^{\\bullet}) = C_{\\mathcal{F}}(P),\n$$\nand this finishes the proof.\n\\end{proof}\n\nTo finish this section, we recall the construction of the quotient of a transporter system by a $p$-group. This quotient was already explored in \\cite[Appendix A]{Gonza2}, and here we only recall the necessary definitions. Let $(\\mathcal{T}, \\varepsilon, \\rho)$ be a transporter system associated to a fusion system $\\mathcal{F}$, and let $A\\leq S$ be a normal subgroup in $\\mathcal{F}$. If $P, Q\\leq S$ are such that $A\\leq P,Q$, then each morphism $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q)$ restricts to an automorphism of $A$, and hence it also induces a homomorphism $\\mathrm{ind}(f) \\colon P\/A \\to Q\/A$. For a subgroup $P\/A\\leq S\/A$, we will denote by $P\\leq S$ the unique subgroup of $S$ that contains $A$ with image $P\/A$ through the projection $S \\to S\/A$.\n\n\\begin{defi}\\label{quotient1}\n\nLet $A$ is a normal subgroup in $\\mathcal{F}$. The \\emph{quotient} of $\\mathcal{T}$ by $A$ is the transporter system $(\\mathcal{T}\/A, \\3{\\varepsilon}, \\3{\\rho})$ associated to the fusion system $\\mathcal{F}\/A$, where\n\\begin{itemize}\n\n\\item $\\mathcal{F}\/A$ is the fusion system over $S\/A$ with morphism sets\n$$\n\\begin{aligned}\n\\mathrm{Hom}_{\\mathcal{F}\/A}&(P\/A, Q\/A) = \\\\\n & = \\{\\overline{f} \\in \\mathrm{Hom}(P\/A,Q\/A) \\,\\, | \\,\\, \\exists f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q) \\mbox{ such that } \\overline{f} = \\mathrm{ind}(f)\\}.\n\\end{aligned}\n$$\n\n\\item $\\mathcal{T}\/A$ is the category with object set $\\{P\/A\\leq S\/A \\, | \\, A\\leq P \\in \\mathrm{Ob}(\\mathcal{T})\\}$ and morphism sets\n$$\n\\mathrm{Mor}_{\\mathcal{T}\/A}(P\/A,Q\/A) = \\mathrm{Mor}_{\\mathcal{T}}(P,Q)\/\\varepsilon_P(A).\n$$\nThe structural functors $\\3{\\varepsilon}$ and $\\3{\\rho}$ are induced, respectively, by the structural functors $\\varepsilon$ and $\\rho$ of $\\mathcal{T}$.\n\n\\end{itemize}\n\n\\end{defi}\n\n\\begin{rmk}\\label{quotient21}\n\nBy \\cite[Proposition A.2]{Gonza2}, $(\\mathcal{T}\/A, \\3{\\varepsilon}, \\3{\\rho})$ is a transporter system associated to $\\mathcal{F}\/A$. \n\n\\end{rmk}\n\n\\begin{lmm}\\label{quotient22}\n\nLet $S$ be a discrete $p$-toral group, let $\\mathcal{F}$ be a saturated fusion system over $S$, and let $\\mathcal{T}$ be a transporter system associated to $\\mathcal{F}$, such that $\\mathrm{Ob}(\\mathcal{T})$ contains all the centric subgroups of $\\mathcal{F}$. Let also $A\\leq S$ be normal in $\\mathcal{F}$. Then the following holds:\n\\begin{enumerate}[(i)]\n\n\\item the fusion system $\\mathcal{F}\/A$ is saturated; and\n\n\\item the transporter system $\\mathcal{T}\/A$ contains all the $\\mathcal{F}\/A$-centric subgroups of $S\/A$.\n\n\\end{enumerate}\n\n\\end{lmm}\n\n\\begin{proof}\n\nPart (i) corresponds to \\cite[Proposition A.3]{Gonza2}, and part (ii) is easily checked: let $P\/A\\leq S\/A$ be $\\mathcal{F}\/A$-centric, and let $Q\/A$ be $\\mathcal{F}\/A$-conjugate to $P\/A$. If $P, Q\\leq S$ denote the preimages of $P\/A$ and $Q\/A$ in $S$ respectively, then $Q$ is $\\mathcal{F}$-conjugate to $P$ by definition of $\\mathcal{F}\/A$. Moreover,\n$$\nC_S(Q)A\/A\\leq C_{S\/A}(Q\/A)\\leq Q\/A,\n$$\nand thus $C_S(Q)\\leq Q$ (since $A\\leq Q$). It follows that $P$ is $\\mathcal{F}$-centric, and hence an object in $\\mathcal{T}$. This implies that $P\/A \\in \\mathrm{Ob}(\\mathcal{T}\/A)$.\n\\end{proof}\n\n\n\\section{Telescopic transporter systems}\\label{Sbig}\n\nIn this section we describe a general procedure to add new objects to a given transporter system. The constructions in this section play a crucial role in the next section.\n\n\\begin{defi}\n\nLet $S$ be a discrete $p$-toral group, let $\\mathcal{F}$ be a fusion system over $S$ (not necessarily saturated), and let $\\mathcal{T}$ be a transporter system associated to $\\mathcal{F}$. The transporter system $\\mathcal{T}$ is \\emph{telescopic} if it satisfies the following condition.\n\\begin{itemize}\n\n\\item[(T)] For each $P \\in \\mathrm{Ob}(\\mathcal{T})$ there is a sequence $P_0\\leq P_1\\leq \\ldots$ of objects in $\\mathcal{T}$ such that $P = \\bigcup_{i \\geq 0} P_i$, and such that $P_i$ is a finite subgroup of $S$ for all $i \\geq 0$.\n\n\\end{itemize}\n\n\\end{defi}\n\n\\begin{lmm}\\label{equinerv}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $\\widetilde{\\LL}$ be a telescopic transporter system associated to $\\mathcal{F}$. Suppose in addition that $\\widetilde{\\LL}$ contains $\\mathcal{L}$ as a full subcategory. Then, the inclusion $\\mathcal{L} \\subseteq \\widetilde{\\LL}$ induces an equivalence between the corresponding nerves.\n\n\\end{lmm}\n\n\\begin{proof}\n\nThis is an immediate consequence of \\cite[Proposition A.9]{BLO6}.\n\\end{proof}\n\nIn terms of the above definition, in this section we study some situations where we can add objects to a given transporter system to produce a telescopic transporter system, without changing the homotopy type of the nerve of the original transporter system.\n\n\\begin{defi}\\label{compsyst}\n\nLet $S$ be a discrete $p$-toral group, let $\\mathcal{F}$ be a saturated fusion system over $S$, and let $\\mathcal{T}$ be a transporter system associated to $\\mathcal{F}$. Let also $(-)^{\\star}_{\\mathcal{F}} \\colon \\mathcal{F} \\to \\mathcal{F}$ and $(-)^{\\star}_{\\mathcal{T}} \\colon \\mathcal{T} \\to \\mathcal{T}$ be a pair of idempotent functors, and let $\\mathcal{C}^{\\star} \\subseteq \\mathcal{C}$, with $\\mathcal{C} = \\mathcal{F}, \\mathcal{T}$, be the full subcategory with $\\mathrm{Ob}(\\mathcal{C}^{\\star}) = \\{P^{\\star} \\, | \\, P \\in \\mathrm{Ob}(\\mathcal{C})\\}$. The pair $((-)^{\\star}_{\\mathcal{F}}, (-)^{\\star}_{\\mathcal{T}})$ is a \\emph{finite retraction pair} if the following conditions are satisfied.\n\\begin{itemize}\n\n\\item[(1)] For each $P\\leq S$, $P\\leq (P)^{\\star}_{\\mathcal{F}}$. Moreover, if $P \\in \\mathrm{Ob}(\\mathcal{T})$, then $(P)^{\\star}_{\\mathcal{F}} = (P)^{\\star}_{\\mathcal{T}} = P^{\\star}$.\n\n\\item[(2)] For each $P, Q\\leq S$ and each $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q)$, $(f)^{\\star}_{\\mathcal{F}} \\in \\mathrm{Hom}_{\\mathcal{F}}(P^{\\star}, Q^{\\star})$ extends $f$, and it is the unique extension.\n\n\\item[(i)] $\\mathrm{Ob}(\\mathcal{F}^{\\star})$ contains finitely many $S$-conjugacy classes of subgroups of $S$.\n\n\\item[(ii)] For all $P\\leq S$, $P^{\\star} = (P^{\\star})^{\\star}$.\n\n\\item[(iii)] If $P\\leq Q\\leq S$, then $P^{\\star}\\leq Q^{\\star}$.\n\n\\item[(iv)] For all $P, Q\\leq S$, $N_S(P,Q) \\subseteq N_S(P^{\\star}, Q^{\\star})$.\n\n\\item[(v)] For all $P\\leq S$, $C_S(P) = C_S(P^{\\star})$.\n\n\\item[(a)] $(-)^{\\star}_{\\mathcal{F}} \\circ \\rho = \\rho \\circ (-)^{\\star}_{\\mathcal{T}}$.\n\n\\item[(b)] For all $P, Q \\in \\mathrm{Ob}(\\mathcal{T})$ and all $\\varphi \\in \\mathrm{Mor}_{\\mathcal{T}}(P,Q)$, we have $\\varepsilon_{Q, Q^{\\star}}(1) \\circ \\varphi = (\\varphi)^{\\star}_{\\mathcal{T}} \\circ \\varepsilon_{P, P^{\\star}}(1)$.\n\n\\end{itemize}\n\n\\end{defi}\n\nThe above definition is inspired in the pair of ``bullet'' functors described in Proposition \\ref{3.2BLO3}. In particular, conditions (i)-(v) and (a)-(b) are labelled to emphasize the relation with the motivating example. Properties (vi), (vii), (c) and (d) in \\ref{3.2BLO3} are actually consequences of the definition, as we prove below.\n\n\\begin{lmm}\\label{extraprop}\n\nLet $\\mathcal{F}$ be a saturated fusion system over a discrete $p$-toral group $S$, let $\\mathcal{T}$ be a transporter system associated to $\\mathcal{F}$, and let $((-)^{\\star}_{\\mathcal{F}}, (-)^{\\star}_{\\mathcal{T}})$ be a finite retraction pair. Then the following properties hold (where we label the properties according to \\ref{3.2BLO3} to emphasize the correspondence).\n\\begin{itemize}\n\n\\item[(vi)] The functor $(-)^{\\star}_{\\mathcal{F}}$ is left adjoint to the inclusion of $\\mathcal{F}^{\\star}$ as a full subcategory of $\\mathcal{F}$.\n\n\\item[(vii)] All $\\mathcal{F}$-centric $\\mathcal{F}$-radical subgroups of $S$ are in $\\mathcal{F}^{\\star}$.\n\n\\item[(c)] For all $P, Q \\in \\mathrm{Ob}(\\mathcal{T})$ and all $g \\in N_S(P,Q)$, we have $(\\varepsilon_{P,Q}(g))^{\\star}_{\\mathcal{T}} = \\varepsilon_{P^{\\star}, Q^{\\star}}(g)$.\n\n\\item[(d)] The functor $(-)^{\\star}_{\\mathcal{T}}$ is left adjoint to the inclusion of $\\mathcal{T}^{\\star}$ as a full subcategory of $\\mathcal{T}$. In particular, the inclusion $\\mathcal{T}^{\\star} \\subseteq \\mathcal{T}$ induces an equivalence $|\\mathcal{T}^{\\star}| \\simeq |\\mathcal{T}|$.\n\n\\end{itemize}\n\n\\end{lmm}\n\n\\begin{proof}\n\nProperty (vi) follows from condition (2) \\ref{compsyst}, since it implies that, for all $P, Q\\leq S$, the restriction map $\\mathrm{Hom}_{\\mathcal{F}}(P^{\\star}, Q^{\\star}) \\to \\mathrm{Hom}_{\\mathcal{F}}(P, Q^{\\star})$ is a bijection. To prove property (vii), let $P\\leq S$ be $\\mathcal{F}$-centric, and suppose that $P \\notin \\mathrm{Ob}(\\mathcal{F}^{\\star})$. Then, $P \\lneqq P^{\\star}$, which implies that $P \\lneqq N_{P^{\\star}}(P)$. Since every element of $\\mathrm{Aut}_{\\mathcal{F}}(P)$ extends uniquely to an element of $\\mathrm{Aut}_{\\mathcal{F}}(P^{\\star})$, it is not hard to see that $1 \\neq N_{P^{\\star}}(P)\/\\mathrm{Inn}(P)$ is normalized by $\\mathrm{Aut}_{\\mathcal{F}}(P)$, and hence $P$ cannot be $\\mathcal{F}$-radical. Property (c) is an immediate consequence of property (b) in \\ref{compsyst}, applied to $\\varphi = \\varepsilon_{P,Q}(g)$, together with Lemma \\ref{epimono}.\n\nFinally, we prove property (d). For each $P \\in \\mathrm{Ob}(\\mathcal{T})$, set as usual\n$$\nE(P) = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{T}}(P) \\Right2{}\\mathrm{Aut}_{\\mathcal{F}}(P)).\n$$\nWe claim that $E(P) = E(P^{\\star})$ for all $P \\in \\mathrm{Ob}(\\mathcal{T})$. Clearly, restriction from $P^{\\star}$ to $P$ maps $E(P^{\\star})$ to $E(P)$, and this restriction is injective by \\ref{epimono}. Let now $\\varphi \\in E(P)$, and consider $(\\varphi)^{\\star}_{\\mathcal{T}} \\in \\mathrm{Aut}_{\\mathcal{T}}(P^{\\star})$. By assumption, $f = \\rho(\\varphi) = \\mathrm{Id}$, and thus $(f)^{\\star}_{\\mathcal{F}} = \\mathrm{Id} \\in \\mathrm{Aut}_{\\mathcal{F}}(P^{\\star})$ by property (2) in \\ref{compsyst}. By property (a) in \\ref{compsyst} we get\n$$\n\\rho((\\varphi)^{\\star}_{\\mathcal{T}}) = (\\rho(\\varphi))^{\\star}_{\\mathcal{F}} = (\\mathrm{Id})^{\\star}_{\\mathcal{F}} = \\mathrm{Id},\n$$\nand $(\\varphi)^{\\star}_{\\mathcal{T}} \\in E(P^{\\star})$. Using axiom (A2) of transporter systems, together with property (vi) above, it is easy to deduce now that the functor $(-)^{\\star}_{\\mathcal{T}}$ is left adjoint to the inclusion of $\\mathcal{T}^{\\star}$ as a full subcategory of $\\mathcal{T}$, and property (d) follows.\n\\end{proof}\n\n\\begin{defi}\\label{wT}\n\nLet $((-)^{\\star}_{\\mathcal{F}}, (-)^{\\star}_{\\mathcal{T}})$ be a finite retraction pair. Define $\\4{\\mathcal{T}}$ to be the category with object set $\\mathrm{Ob}(\\4{\\mathcal{T}}) = \\{P\\leq S \\, | \\, P^{\\star} \\in \\mathrm{Ob}(\\mathcal{T})\\}$, and with morphism sets\n$$\n\\mathrm{Mor}_{\\4{\\mathcal{T}}}(P,Q) = \\{\\varphi \\in \\mathrm{Mor}_{\\mathcal{T}}(P^{\\star}, Q^{\\star}) \\, | \\, \\varphi \\circ \\varepsilon_{P^{\\star}}(g) \\circ \\varphi^{-1} \\in \\varepsilon_{Q^{\\star}}(Q) \\mbox{, for all } g \\in P\\},\n$$\nfor all $P, Q \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$. Composition in $\\4{\\mathcal{T}}$ is given by composition in $\\mathcal{T}$. Define also functors\n$$\n\\mathcal{T}_{\\mathrm{Ob}(\\4{\\mathcal{T}})}(S) \\Right3{\\4{\\varepsilon}} \\4{\\mathcal{T}} \\qquad \\mbox{and} \\qquad \\4{\\mathcal{T}} \\Right3{\\4{\\rho}} \\mathcal{F}\n$$\nas follows. The functor $\\4{\\varepsilon}$ is the identity on objects, and the functor $\\4{\\rho}$ is injective on objects. For all $P, Q \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$, all $g \\in N_S(P,Q)$, and all $\\varphi \\in \\mathrm{Mor}_{\\4{\\mathcal{T}}}(P,Q)$, define\n$$\n\\4{\\varepsilon}_{P,Q}(g) = \\varepsilon_{P^{\\bullet}, Q^{\\bullet}}(g) \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(P,Q) \\qquad \\mbox{and} \\qquad \\4{\\rho}(\\varphi \\colon P \\to Q) = \\rho(\\varphi \\colon P^{\\bullet} \\to Q^{\\bullet})|_P.\n$$\nThe properties of the functors $(-)^{\\star}_{\\mathcal{F}}$ and $(-)^{\\star}_{\\mathcal{T}}$ imply that both $\\4{\\mathcal{T}}$ and the above functors are well-defined, and that $\\mathcal{T}$ is a full subcategory of $\\4{\\mathcal{T}}$.\n\n\\end{defi}\n\n\\begin{prop}\\label{extendL}\n\nFor each finite retraction pair $((-)^{\\star}_{\\mathcal{F}}, (-)^{\\star}_{\\mathcal{T}})$, the category $\\4{\\mathcal{T}}$, with the functors $\\4{\\varepsilon}$ and $\\4{\\rho}$ defined above, is a telescopic transporter system associated to $\\mathcal{F}$. Furthermore, the functor $(-)^{\\star}_{\\mathcal{T}} \\colon \\mathcal{T} \\to \\mathcal{T}$ extends to a functor $(-)^{\\star}_{\\4{\\mathcal{T}}} \\colon \\4{\\mathcal{T}} \\to \\mathcal{T}$, which is unique satisfying the following properties\n\\begin{enumerate}[(a)]\n\n\\item There is an equality $(-)^{\\star}_{\\mathcal{F}} \\circ \\4{\\rho} = \\rho \\circ (-)^{\\star}_{\\4{\\mathcal{T}}} \\colon \\4{\\mathcal{T}} \\to \\mathcal{F}$.\n\n\\item For all $P, Q \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$ and all $\\varphi \\in \\mathrm{Mor}_{\\4{\\mathcal{T}}}(P,Q)$, we have $\\4{\\varepsilon}_{Q,Q^{\\star}}(1) \\circ \\varphi = (\\varphi)^{\\star}_{\\4{\\mathcal{T}}} \\circ \\4{\\varepsilon}_{P,P^{\\star}}(1)$.\n\n\\end{enumerate}\nIn particular, the inclusion of $\\mathcal{T}$ in $\\4{\\mathcal{T}}$ as a full subcategory induces an equivalence $|\\mathcal{T}| \\simeq |\\4{\\mathcal{T}}|$.\n\n\\end{prop}\n\n\\begin{proof}\n\nBy definition, $\\4{\\mathcal{T}}$ contains $\\mathcal{T}$ as a full subcategory, and the functor $(-)_{\\mathcal{T}}^{\\star} \\colon \\mathcal{T} \\to \\mathcal{T}^{\\star}$ can be extended to a functor $(-)_{\\4{\\mathcal{T}}}^{\\star} \\colon \\4{\\mathcal{T}} \\to \\mathcal{T}^{\\star}$ as follows. On objects, $(P)^{\\star}_{\\4{\\mathcal{T}}} = (P)^{\\star}_{\\mathcal{F}} = P^{\\star}$. On morphisms, $(-)^{\\star}_{\\4{\\mathcal{T}}}$ is defined by the inclusion\n$$\n\\mathrm{Mor}_{\\4{\\mathcal{T}}}(P,Q) \\subseteq \\mathrm{Mor}_{\\mathcal{T}}(P^{\\star}, Q^{\\star})\n$$\ngiven by definition of $\\4{\\mathcal{T}}$. The proof of (a) and (b), as well as the uniqueness of $(-)^{\\star}_{\\4{\\mathcal{T}}}$ satisfying these conditions, is left to the reader as an easy exercise.\n\nNext we show that $\\4{\\mathcal{T}}$ is indeed a transporter system. Conditions (A1), (B) and (C) are clear. Condition (A2) follows from the properties of the functor $(-)^{\\star}_{\\mathcal{F}} \\colon \\mathcal{F} \\to \\mathcal{F}^{\\star}$. Indeed, for each $P \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$ set\n$$\n\\begin{array}{c}\n\\4{E}(P) = \\mathrm{Ker}(\\4{\\rho}_P \\colon \\mathrm{Aut}_{\\4{\\mathcal{T}}}(P) \\to \\mathrm{Aut}_{\\mathcal{F}}(P)) \\\\\nE(P^{\\star}) = \\mathrm{Ker}(\\rho_{P^{\\star}} \\colon \\mathrm{Aut}_{\\mathcal{T}}(P^{\\star}) \\to \\mathrm{Aut}_{\\mathcal{F}}(P^{\\star})).\n\\end{array}\n$$\nIf $\\varphi \\in E(P^{\\star})$, then, by definition of $\\4{\\mathcal{T}}$, together with axiom (C) on $\\mathcal{T}$, it follows that $\\varphi \\in \\4{E}(P)$. Conversely, if $\\varphi \\in \\4{E}(P)$, then by definition $\\varphi \\in \\mathrm{Aut}_{\\mathcal{T}}(P^{\\star})$ is such that $\\rho(\\varphi)|_P = \\mathrm{Id}$. By property (a) on $(-)^{\\star}_{\\4{\\mathcal{T}}}$, we have\n$$\n\\rho(\\varphi) = \\rho((\\varphi)^{\\star}_{\\4{\\mathcal{T}}}) = (\\rho(\\varphi)|_P)^{\\star}_{\\mathcal{F}} = (\\mathrm{Id})^{\\star}_{\\mathcal{F}} = \\mathrm{Id},\n$$\nand thus $\\varphi \\in E(P^{\\star})$. Hence $\\4{E}(P) = E(P^{\\star})$, and the freeness of the action of $\\4{E}(P)$ on $\\mathrm{Mor}_{\\4{\\mathcal{T}}}(P,Q)$ follows from property (A2) in $\\mathcal{T}$. That $\\4{\\rho}_{P,Q}$ is the orbit map of this action follows easily.\n\nNext we check condition (I) for $\\4{\\mathcal{T}}$. Fix $Q \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$. If $Q \\in \\mathrm{Ob}(\\mathcal{T})$ then there is nothing to show, since $\\mathcal{T}$ is a full subcategory of $\\4{\\mathcal{T}}$. Thus, assume that $Q \\notin \\mathrm{Ob}(\\mathcal{T})$. We can choose $Q$ such that $Q^{\\star}$ is fully $\\mathcal{F}$-normalized, so that\n$$\n\\varepsilon_{Q^{\\star}}(N_S(Q^{\\star})) \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Aut}_{\\mathcal{T}}(Q^{\\star})).\n$$\nSet for short $G = \\mathrm{Aut}_{\\mathcal{T}}(Q^{\\star})$ and $K = \\varepsilon_{Q^{\\star}}(N_S(Q^{\\star}))$.\n\nWe claim first that every subgroup $H$ of $G$ has Sylow $p$-subgroups. Notice that $G\/Q^{\\star} \\cong \\mathrm{Out}_{\\mathcal{F}}(Q^{\\star})$, which is a finite group. Thus, $H\/(H \\cap Q^{\\star}) \\cong HQ^{\\star}\/Q^{\\star}\\leq G\/Q^{\\star}$, and thus $H \\cap Q^{\\star}$ is a discrete $p$-toral normal subgroup of $H$ with finite index. The claim follows by \\cite[Lemma 8.1]{BLO3}.\n\nNow, by definition we can consider $H = \\mathrm{Aut}_{\\4{\\mathcal{T}}}(Q)$ as a subgroup of $G$, and in particular the above discussion implies that $H$ has Sylow $p$-subgroups. Fix $R \\in \\operatorname{Syl}\\nolimits_p(H)$ such that $\\varepsilon_Q(N_S(Q))\\leq R$. Since $K \\in \\operatorname{Syl}\\nolimits_p(G)$, there is some $\\varphi \\in G$ such that $\\varphi \\circ R \\circ \\varphi^{-1}\\leq K$. Set $P = \\rho(\\varphi)(Q)\\leq Q^{\\star}$. Note that $P^{\\star}\\leq Q^{\\star}$ by definition of $P$, and $P$ is $\\mathcal{F}$-conjugate to $Q$, which implies that $P^{\\star}$ is $\\mathcal{F}$-conjugate to $Q^{\\star}$ This implies that $P^{\\star} = Q^{\\star}$. Thus,\n$$\n\\mathrm{Aut}_{\\4{\\mathcal{T}}}(P) = \\varphi \\circ H \\circ \\varphi^{-1} = \\varphi \\circ \\mathrm{Aut}_{\\4{\\mathcal{T}}}(Q) \\circ \\varphi^{-1}\\leq G,\n$$\nand $\\varepsilon_P(N_S(P)) \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Aut}_{\\4{\\mathcal{T}}}(P))$. Condition (I) follows.\n\nCondition (II) for $\\4{\\mathcal{T}}$ follows easily from condition (II) for $\\mathcal{T}$. Indeed, let $\\varphi \\in \\mathrm{Iso}_{\\4{\\mathcal{T}}}(P,Q)$, $P \\lhd \\4{P}\\leq S$ and $Q \\lhd \\4{Q}\\leq S$ be such that $\\varphi \\circ \\4{\\varepsilon}_P(\\4{P}) \\circ \\varphi^{-1}\\leq \\4{\\varepsilon}_Q(\\4{Q})$. By applying the functor $(-)_{\\4{\\mathcal{T}}}^{\\star} \\colon \\4{\\mathcal{T}} \\to \\mathcal{T}^{\\star}$, we get $\\varphi^{\\star} \\in \\mathrm{Iso}_{\\mathcal{T}}(P^{\\star}, Q^{\\star})$, and\n$$\nP^{\\star} \\lhd \\widehat{P} \\stackrel{def} = N_{(\\4{P})^{\\star}}(P^{\\star})\\leq S \\qquad \\mbox{and} \\qquad Q^{\\star} \\lhd \\widehat{Q} \\stackrel{def} = N_{(\\4{Q})^{\\star}}(Q^{\\star})\\leq S,\n$$\nsuch that $\\varphi^{\\star} \\circ \\varepsilon_{P^{\\star}}(\\widehat{P}) \\circ (\\varphi^{\\star})^{-1}\\leq \\varepsilon_{Q^{\\star}}(\\widehat{Q})$. Axiom (II) in $\\mathcal{T}$ implies that there exists some $\\widehat{\\varphi} \\in \\mathrm{Mor}_{\\mathcal{T}}(\\widehat{P}, \\widehat{Q})$ such that\n$$\n\\widehat{\\varphi} \\circ \\varepsilon_{P^{\\star}, \\widehat{P}}(1) = \\varepsilon_{Q^{\\star}, \\widehat{Q}}(1) \\circ \\varphi^{\\star}.\n$$\nNote that $\\4{P}\\leq \\widehat{P}$ and $\\4{Q}\\leq \\widehat{Q}$ by property (iv) in \\ref{compsyst}. Thus, we may restrict the morphism $\\widehat{\\varphi}$ to $\\4{P}$, and condition (II) follows.\n\nCondition (III) for $\\4{\\mathcal{T}}$ follows easily by condition (III) for $\\mathcal{T}$, together with the properties of the functor $(-)^{\\star}_{\\4{\\mathcal{T}}} \\colon \\4{\\mathcal{T}} \\to \\mathcal{T}^{\\star}$, since $\\mathcal{T}^{\\star}$ contains finitely many isomorphism classes of objects by property (i) in \\ref{compsyst}.\n\nLet us now prove that that $\\4{\\mathcal{T}}$ is a telescopic transporter system. Let $P \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$. By \\cite[Lemma 1.9]{BLO3}, there is a sequence $P_0\\leq P_1\\leq \\ldots$ of finite subgroups of $P$ such that $P = \\bigcup_{i \\geq 0} P_i$. Since $\\mathcal{F}^{\\star}$ contains finitely many $S$-conjugacy classes of subgroups by property (i) in \\ref{compsyst}, it follows that there is some $M \\in \\mathbb{N}$ such that $(P_i)^{\\star} = P^{\\star}$ for all $i \\geq M$, and we may assume that $M = 0$ for simplicity. This way, since $P \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$, it follows that $P_i \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$ for all $i \\geq 0$. Thus $\\4{\\mathcal{T}}$ is a telescopic transporter system.\n\nFinally, we check that the inclusion of $\\mathcal{T}$ in $\\4{\\mathcal{T}}$ as a full subcategory induces an equivalence between the corresponding nerves. Recall from property (d) in \\ref{extraprop} that the inclusion $\\mathcal{T}^{\\star} \\subseteq \\mathcal{T}$ induces an equivalence $|\\mathcal{T}^{\\star}| \\simeq |\\mathcal{T}|$. Thus, we only need to show that $(-)^{\\star}_{\\4{\\mathcal{T}}} \\colon \\4{\\mathcal{T}} \\to \\mathcal{T}^{\\star}$ is (left) adjoint to the inclusion of $\\mathcal{T}^{\\star}$ as a full subcategory of $\\4{\\mathcal{T}}$. That is, given $P, Q \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$, we have to show that the restriction map\n$$\n\\mathrm{Mor}_{\\4{\\mathcal{T}}}(P^{\\star}, Q^{\\star}) \\Right2{} \\mathrm{Mor}_{\\4{\\mathcal{T}}}(P, Q^{\\star})\n$$\nis a bijection. Let $\\4{E}(P) = E(P^{\\star})$ as above, and recall that there is a bijection between the sets $\\mathrm{Hom}_{\\mathcal{F}}(P^{\\star}, Q^{\\star})$ and $\\mathrm{Hom}_{\\mathcal{F}}(P, Q^{\\star})$, given by the restriction map, by (vi) in \\ref{extraprop}. Thus, by axiom (A2) of transporter systems,\n$$\n\\mathrm{Mor}_{\\mathcal{T}}(P^{\\star}, Q^{\\star})\/E(P^{\\star}) = \\mathrm{Hom}_{\\mathcal{F}}(P^{\\star}, Q^{\\star}) \\cong \\mathrm{Hom}_{\\mathcal{F}}(P,Q) = \\mathrm{Mor}_{\\4{\\mathcal{T}}}(P, Q^{\\star})\/\\4{E}(P),\n$$\nand the claim follows.\n\\end{proof}\n\nWe call $\\4{\\mathcal{T}}$ the \\emph{telescopic transporter system associated to $((-)^{\\star}_{\\mathcal{F}}, (-)^{\\star}_{\\mathcal{T}})$}, or simply the telescopic transporter system associated to $\\mathcal{T}$ if there is no need to specify $((-)^{\\star}_{\\mathcal{F}}, (-)^{\\star}_{\\mathcal{T}})$.\n\n\\begin{prop}\\label{extendL2}\n\nEach $\\Psi \\in \\mathrm{Aut}_{\\mathrm{typ}}^I(\\mathcal{T})$ extends uniquely to some $\\4{\\Psi} \\in \\mathrm{Aut}_{\\mathrm{typ}}^I(\\4{\\mathcal{T}})$.\n\n\\end{prop}\n\n\\begin{proof}\n\nLet $\\Psi \\in \\mathrm{Aut}_{\\mathrm{typ}}^I(\\mathcal{T})$ and let $\\psi \\in \\mathrm{Aut}(S)$ be the automorphism induced by $\\Psi$. Then $\\Psi$ extends to $\\4{\\mathcal{T}}$ by the formulas\n$$\n\\4{\\Psi}(P) = \\psi(P) \\qquad \\mbox{and} \\qquad \\4{\\Psi}(\\varphi \\colon P \\to Q) = \\Psi(\\varphi^{\\star}_{\\4{\\mathcal{T}}} \\colon P^{\\star} \\to Q^{\\star}).\n$$\nClearly, this determines an isotypical equivalence $\\4{\\Psi}$ of $\\4{\\mathcal{T}}$. Moreover, since $\\4{\\Psi}$ is isotypical, that is $\\4{\\Psi}(\\4{\\varepsilon}_{P,Q}(1)) = \\4{\\varepsilon}_{\\4{\\Psi}(P), \\4{\\Psi}(Q)}(1)$ for all $P, Q \\in \\mathrm{Ob}(\\4{\\mathcal{T}})$ with $P\\leq Q$, and since morphisms in $\\4{\\mathcal{T}}$ are monomorphisms and epimorphisms in the categorical sense by Lemma \\ref{epimono}, it follows that $\\4{\\Psi}$ is the unique extension of $\\Psi$ to $\\4{\\mathcal{T}}$.\n\\end{proof}\n\nBelow we analyze some examples which will be of interest in later sections.\n\n\\begin{expl}\\label{expl1}\n\nWe start with the most obvious example. Let $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $(-)^{\\bullet}_{\\mathcal{F}} \\colon \\mathcal{F} \\to \\mathcal{F}$ and $(-)^{\\bullet}_{\\mathcal{L}} \\colon \\mathcal{L} \\to \\mathcal{L}$ be the usual ``bullet'' functors. Then, clearly $((-)^{\\bullet}_{\\mathcal{F}}, (-)^{\\bullet}_{\\mathcal{L}})$ is a finite retraction pair (by \\ref{3.2BLO3}), and this way we obtain a telescopic transporter system $\\widetilde{\\LL}$ which in addition satisfies the following properties.\n\\begin{itemize}\n\n\\item[(1)] For each $P \\in \\mathrm{Ob}(\\widetilde{\\LL})$, we have \n$$\n\\mathrm{Ker}(\\mathrm{Aut}_{\\widetilde{\\LL}}(P) \\to \\mathrm{Aut}_{\\mathcal{F}}(P)) = \\varepsilon_P(C_S(P)) = \\varepsilon_P(Z(P^{\\bullet})).\n$$\n\n\\end{itemize}\nLet $P \\in \\mathrm{Ob}(\\widetilde{\\LL})$, and set $E(P) = \\mathrm{Ker}(\\mathrm{Aut}_{\\widetilde{\\LL}}(P) \\to \\mathrm{Aut}_{\\mathcal{F}}(P))$. If $P \\in \\mathrm{Ob}(\\mathcal{L})$ then $\\mathrm{Aut}_{\\widetilde{\\LL}}(P) = \\mathrm{Aut}_{\\mathcal{L}}(P)$, and there is nothing to prove. Suppose that $P \\notin \\mathrm{Ob}(\\mathcal{L})$. By definition $P^{\\bullet} \\in \\mathrm{Ob}(\\mathcal{L})$, and there is a commutative diagram of group extensions\n$$\n\\xymatrix{\nE(P^{\\bullet}) \\ar[r] & \\mathrm{Aut}_{\\widetilde{\\LL}}(P^{\\bullet}) \\ar[r] & \\mathrm{Aut}_{\\mathcal{F}}(P^{\\bullet}) \\\\\nE(P) \\ar[r] \\ar[u] & \\mathrm{Aut}_{\\mathcal{L}}(P) \\ar[r] \\ar[u] & \\mathrm{Aut}_{\\mathcal{F}}(P) \\ar[u]\n}\n$$\nwhere all the vertical arrows are inclusions. Thus, we have\n$$\nE(P)\\leq E(P^{\\bullet}) = \\varepsilon_{P^{\\bullet}}(Z(P^{\\bullet})) = \\varepsilon_{P^{\\bullet}}(C_S(P^{\\bullet})) = \\varepsilon_P(C_S(P)),\n$$\nwhere $C_S(P^{\\bullet}) = Z(P^{\\bullet})$ since $P^{\\bullet}$ is $\\mathcal{F}$-centric, and $C_S(P^{\\bullet}) = C_S(P)$ by property (v) in \\ref{compsyst}. The inclusion $\\varepsilon_P(C_S(P))\\leq E(P)$ is clear. This proves (1). In particular, every object in $\\widetilde{\\LL}$ is \\emph{quasicentric} (that is $C_{\\mathcal{F}}(P)$ is the fusion system of $C_S(P)$ for all $P \\in \\mathrm{Ob}(\\widetilde{\\LL})$), and in this sense $\\widetilde{\\LL}$ is a \\emph{quasicentric linking system}.\n\\begin{itemize}\n \n\\item[(2)] There is an isomorphism $\\mathrm{Aut}_{\\mathrm{typ}}^{I}(\\mathcal{L}) \\cong \\mathrm{Aut}_{\\mathrm{typ}}^{I}(\\widetilde{\\LL})$.\n\n\\end{itemize}\nThis follows by Proposition \\ref{extendL2}, together with the observation that every isotypical automorphism of $\\widetilde{\\LL}$ must restrict to an isotypical automorphism of $\\mathcal{L}$, since $\\mathrm{Ob}(\\mathcal{L})$ is the set of all $\\mathcal{F}$-centric subgroups of $S$. Moreover, this restriction is injective as a consequence of Lemma \\ref{epimono}, and since every morphism in $\\widetilde{\\LL}$ is the restriction of some morphism in $\\mathcal{L}$.\n\n\\end{expl}\n\n\\begin{expl}\\label{expl3}\n\nThe following is a less obvious example. Again, let $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, let $((-)^{\\bullet}_{\\mathcal{F}}, (-)^{\\bullet}_{\\mathcal{L}})$ be the finite retraction pair in \\ref{expl1}, and let $\\widetilde{\\LL}$ be the telescopic transporter system associated to $\\mathcal{L}$. Let also $A\\leq S$ be a fully $\\mathcal{F}$-normalized subgroup such that $N_S(A)$ has finite index in $S$ (for example $A\\leq T$ a subgroup of the maximal torus of $S$).\n\nLet $N_{\\mathcal{G}}(A) = (N_S(A), N_{\\mathcal{F}}(A), N_{\\mathcal{L}}(A))$ be the normalizer $p$-local compact group of $A$, as defined in \\ref{rmknorm}. Again, in general $N_{\\mathcal{L}}(A)$ is not a telescopic linking system. In this case, since $N_{\\mathcal{G}}(A)$ is a $p$-local compact group, one could apply Example \\ref{expl1} to produce a telescopic transporter system associated to $N_{\\mathcal{L}}(A)$. However, this way there is no obvious correspondence between the telescopic transporter systems for $N_{\\mathcal{L}}(A)$ and $\\mathcal{L}$, mainly because usually the ``bullet'' functors in $N_{\\mathcal{F}}(A)$ and in $\\mathcal{F}$ do not agree, that is $(P)^{\\bullet}_{\\mathcal{F}} \\neq (P)^{\\bullet}_{N_{\\mathcal{F}}(A)}$ in general.\n\nInstead, we propose a different construction. Set for short $N = N_S(A)$, $\\mathcal{E} = N_{\\mathcal{F}}(A)$ and $\\mathcal{T} = N_{\\mathcal{L}}(A)$. We define a finite retraction pair $((-)^{\\star}_{\\mathcal{E}}, (-)^{\\star}_{\\mathcal{T}})$ as follows. For each $P\\leq N$, notice that $(P)^{\\bullet}_{\\mathcal{F}}\\leq N_S(A)$, since $N_S(A)$ already contains the maximal torus of $S$. Thus, we can define\n$$\nP^{\\star} = P^{\\bullet}.\n$$\nOn morphisms, let $P, Q\\leq N$, and let $f \\in \\mathrm{Hom}_{\\mathcal{E}}(P,Q)$. By definition of $\\mathcal{E}$, the morphism $f$ extends to some $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}}(PA, QA)$ such that $\\gamma|_A \\in \\mathrm{Aut}_{\\mathcal{F}}(A)$. Applying the functor $(-)^{\\bullet}_{\\mathcal{F}}$ to the commutative square\n$$\n\\xymatrix{\nPA \\ar[r]^{\\gamma} & QA\\\\\nP \\ar[u]^{\\mathrm{incl}} \\ar[r]_{f} & Q \\ar[u]_{\\mathrm{incl}}\n}\n$$\nwe see that $(f)^{\\bullet}_{\\mathcal{F}}$ extends to $(\\gamma)^{\\bullet}_{\\mathcal{F}}$, and the latter restricts in turn to a morphism $\\5{\\gamma} \\in \\mathrm{Hom}_{\\mathcal{F}}((P)^{\\bullet}_{\\mathcal{F}}A, (Q)^{\\bullet}_{\\mathcal{F}}A)$ such that $\\5{\\gamma}|_A = \\gamma|_A \\in \\mathrm{Aut}_{\\mathcal{F}}(A)$. We define\n$$\n(f)^{\\star}_{\\mathcal{E}} = (f)^{\\bullet}_{\\mathcal{F}}.\n$$\nProperties (1)-(2) and (i)-(v) in \\ref{compsyst} for $(-)^{\\bullet}_{\\mathcal{F}}$ imply that $(-)^{\\star}_{\\mathcal{E}}$ also satisfies these conditions.\n\nOn $\\mathcal{T}$, define $(-)^{\\star}_{\\mathcal{T}}$ as follows. Let $P, Q \\in \\mathrm{Ob}(\\mathcal{T})$, and let $\\varphi \\in \\mathrm{Mor}_{\\mathcal{T}}(P,Q)$. By definition, $\\varphi$ is a morphism in $\\mathrm{Mor}_{\\mathcal{L}}(PA, QA)$ such that $\\rho(\\varphi)|_P \\in \\mathrm{Mor}_{\\mathcal{E}}(P,Q)$, and $\\rho(\\varphi)|_A \\in \\mathrm{Aut}_{\\mathcal{F}}(A)$. Clearly,\n$$\nP^{\\bullet}A\\leq (PA)^{\\bullet} \\qquad \\mbox{and} \\qquad Q^{\\bullet}A\\leq (QA)^{\\bullet},\n$$\nand thus $(\\varphi)^{\\bullet}_{\\mathcal{L}}$ restricts to a morphism $\\4{\\varphi} \\in \\mathrm{Mor}_{\\mathcal{L}}(P^{\\bullet}A, Q^{\\bullet}A)$ such that $\\rho(\\4{\\varphi})|_{P^{\\bullet}} \\in \\mathrm{Hom}_{\\mathcal{E}}(P^{\\bullet}, Q^{\\bullet})$ and $\\rho(\\4{\\varphi})|_A \\in \\mathrm{Aut}_{\\mathcal{F}}(A)$. Define $(\\varphi)^{\\star}_{\\mathcal{T}} = \\4{\\varphi}$. It is not difficult to check that $(-)^{\\bullet}_{\\mathcal{T}}$ satisfies properties (a)-(b) in \\ref{compsyst}.\n\nLet $\\widetilde{\\LL}$ and $\\4{\\mathcal{T}}$ be the associated telescopic transporter systems for $\\mathcal{L}$ and $\\mathcal{T}$, respectively. In general, $\\mathcal{T}$ is not a subcategory of $\\mathcal{L}$, and neither is $\\4{\\mathcal{T}}$ a subcategory of $\\widetilde{\\LL}$. Let $\\mathcal{T}_{\\geq A} \\subseteq \\mathcal{T}$ be the full subcategory of subgroups that contain $A$, and let $\\4{\\mathcal{T}}_{\\geq A} \\subseteq \\4{\\mathcal{T}}$ be the full subcategory of subgroups $P$ such that $A\\leq P^{\\bullet}$. Then the following is easily checked.\n\\begin{enumerate}[(a)]\n\n\\item $\\mathcal{T}_{\\geq A}$ is a subcategory of $\\mathcal{L}$, and $\\4{\\mathcal{T}}_{\\geq A}$ is a subcategory of $\\widetilde{\\LL}$.\n\n\\item $\\mathcal{T}_{\\geq A}$ contains all the centric radical subgroups of $\\mathcal{E}$.\n\n\\item The functor $(-)^{\\star}_{\\4{\\mathcal{T}}}$ coincides with the functor $(-)^{\\bullet}_{\\widetilde{\\LL}}$ on the subcategory $\\4{\\mathcal{T}}_{\\geq A}$.\n\n\\end{enumerate}\nThis example, including the above remarks, will be very useful in the next section, when we have to compare certain constructions on a $p$-local compact group $\\mathcal{G} = (S, \\FF, \\LL)$ and on the normalizer $N_{\\mathcal{G}}(A) = (N_S(A), N_{\\mathcal{F}}(A), N_{\\mathcal{L}}(A))$ of a certain subtorus $A\\leq S$.\n\n\\end{expl}\n\n\n\\section{Families of unstable Adams operations}\\label{Sfam}\n\nIn this section we prove Theorem \\ref{thmA}, restated as Theorem \\ref{fix6} below: every $p$-local compact group can be approximated by $p$-local finite groups. Roughly speaking, given a $p$-local compact group we produce an \\emph{approximation of $\\mathcal{G}$ by $p$-local finite groups} (defined below) by considering \\emph{fixed point subcategories} of a telescopic transporter system associated to $\\mathcal{L}$ by iterations of a given unstable Adams operation on $\\mathcal{G}$. \n\nEssentially, we follow the same lines as \\cite{Gonza1}. However, the introduction of telescopic transporter systems means a great deal of simplification, and it is actually thanks to this that we are finally able to prove Proposition \\ref{fix5}, basically the missing step in \\cite{Gonza1} in proving Theorem \\ref{thmA}. We have opted for reproving here every property that we need from \\cite{Gonza1} for the sake of completeness as well as for correcting mistakes: while working on \\ref{fix5} below, the author realized that the statement of \\cite[Lemma 2.11]{Gonza1} is false. Nevertheless, this does not affect neither the main results of \\cite{Gonza1} nor the results that we present in this paper, and this comment is just intended as a warning to the interested reader.\n\n\\begin{defi}\\label{defiapprox}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $\\widetilde{\\LL}$ be a telescopic transporter system associated to $\\mathcal{F}$ and containing $\\mathcal{L}$ as a full subcategory. An \\emph{approximation of $\\mathcal{G}$ by $p$-local finite groups} is a family $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ satisfying the following conditions.\n\\begin{enumerate}[(i)]\n\n\\item $S = \\bigcup_{i \\geq 0} S_i$.\n\n\\item For each $i \\geq 0$, $S_i$ is a finite $p$-group, $\\mathcal{F}_i$ is a saturated fusion system over $S_i$, and $\\mathcal{L}_i$ is a linking system associated to $\\mathcal{F}_i$. Furthermore, $\\mathrm{Ob}(\\mathcal{F}_i^{cr}) \\subseteq \\mathrm{Ob}(\\mathcal{L}_i)$, and there are inclusions $\\mathcal{L}_i \\subseteq \\mathcal{L}_{i+1}$ and $\\mathcal{L}_i \\subseteq \\widetilde{\\LL}$.\n\n\\item For each $P, Q \\in \\mathrm{Ob}(\\widetilde{\\LL})$ and each $\\varphi \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(P,Q)$ there exists some $M \\in \\mathbb{N}$ such that, for all $i \\geq M$, there are objects $P_i, Q_i \\in \\mathrm{Ob}(\\mathcal{L}_i)$ and morphisms $\\varphi_i \\in \\mathrm{Mor}_{\\mathcal{L}_i}(P_i, Q_i)$, such that $P = \\bigcup_{i \\geq M} P_i$ and $Q = \\bigcup_{i \\geq M} Q_i$, and $\\4{\\varepsilon}_{Q_i, Q}(1) \\circ \\varphi_i = \\varphi \\circ \\4{\\varepsilon}_{P_i, P}(1)$.\n\n\\end{enumerate}\n\n\\end{defi}\n\nAlthough condition (i), or at least a weaker version of it, can be deduced from condition (iii) applied to $P = Q = S$ and to any $\\varphi \\in \\mathrm{Aut}_{\\mathcal{L}}(S)$, we prefer to include (i) in the definition for the sake of clarity. We now show some basic properties of approximations of $p$-local compact groups by $p$-local finite groups.\n\n\\begin{lmm}\\label{finmorph}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be a finite approximation of $\\mathcal{G}$ by $p$-local finite groups with respect to some telescopic transporter system $\\widetilde{\\LL}$ satisfying the conditions in \\ref{defiapprox}. Then, for every finite subgroup $P\\leq S$ and every $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,S)$, there exists some $M \\in \\mathbb{N}$ such that, for all $i \\geq M$, $P\\leq S_i$, and $f$ is the composition of a morphism $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}_i}(P, S_i)$ with the inclusion $S_i\\leq S$.\n\n\\end{lmm}\n\n\\begin{proof}\n\nLet $Q = f(P)$, which is also a finite subgroup of $S$, and let $\\gamma \\in \\mathrm{Iso}_{\\mathcal{F}}(P,Q)$ be the restriction of $f$ to its image. By property (i) in \\ref{defiapprox}, it is clear that there is some $M_0 \\in \\mathbb{N}$ such that $P, Q\\leq S_i$ for all $i \\geq M_0$. By Alperin's Fusion Theorem \\cite[Theorem 3.6]{BLO3}, there exist $W_0 = P, W_1, \\ldots, W_n = Q\\leq S$, $U_1, \\ldots, U_n \\in \\mathrm{Ob}(\\mathcal{L}) \\subseteq \\mathrm{Ob}(\\widetilde{\\LL})$, and morphisms $\\varphi_j \\in \\mathrm{Aut}_{\\widetilde{\\LL}}(U_j)$, for $j = 1, \\ldots, n$, such that, for each $j$\n$$\nW_{j-1}, W_j\\leq U_j \\qquad \\mbox{and} \\qquad \\rho(\\varphi_j)(W_{j-1}) = W_j,\n$$\nand $\\gamma = \\rho(\\varphi_n) \\circ \\ldots \\circ \\rho(\\varphi_1)$. Combining properties (i) and (iii) in \\ref{defiapprox}, we see that for each $j = 1, \\ldots, n$ there exists some $M_j \\in \\mathbb{N}$ such that, for all $i \\geq M_j$, there exist $U_{j,i}, V_{j, i} \\in \\mathrm{Ob}(\\mathcal{L}_i)$, together with an isomorphism $\\varphi_{j.i} \\in \\mathrm{Iso}_{\\mathcal{L}_i}(U_{j,i}, V_{j,i})$, such that\n$$\nU_j = \\bigcup_{i \\geq M_j} U_{j,i} = \\bigcup_{i \\geq M_j} V_{j,i} \\qquad \\mbox{and} \\qquad \\4{\\varepsilon}_{V_{j,i}, U_j}(1) \\circ \\varphi_{j,i} = \\varphi_j \\circ \\4{\\varepsilon}_{U_{j,i}, U_j}(1).\n$$\nMoreover, since $W_{j-1}, W_j$ are finite subgroups, we may assume without loss of generality that $W_{j-1}\\leq U_{j,i}$ and $W_j\\leq V_{j, i}$ for all $i \\geq M_j$. Let $M = \\max\\{M_0, \\ldots, M_n\\}$. Then, for all $i \\geq 0$, it follows that\n$$\n\\gamma = \\rho_i(\\varphi_{n,i}) \\circ \\ldots \\circ \\rho_i(\\varphi_{1,i}) \\in \\mathrm{Mor}(\\mathcal{F}_i),\n$$\nand this finishes the proof.\n\\end{proof}\n\n\\begin{lmm}\\label{Quill}\n\nLet $\\mathcal{C}$ be a nonempty category that satisfies the following conditions:\n\\begin{enumerate}[(i)]\n\n\\item given objects $a_1, a_2$, there is an object $b$ and morphisms $a_1 \\Right1{g_1} b \\Left1{g_2} a_2$; and\n\n\\item given morphisms $g_1, g_2 \\colon a \\to b$, there is $h \\colon b \\to c$ such that $h \\circ g_1 = h \\circ g_2$.\n\n\\end{enumerate}\nThen, the nerve of $\\mathcal{C}$ is contractible.\n\n\\end{lmm}\n\n\\begin{proof}\n\nThis is \\cite[Corollary 2]{Quillen}.\n\\end{proof}\n\n\\begin{lmm}\\label{approx-2}\n\nLet $\\mathcal{C}$ be a small category all of whose morphisms are epimorphisms and monomorphisms in the categorical sense, and let $\\mathcal{C}_0 \\subseteq \\mathcal{C}_1 \\subseteq \\ldots $ be a sequence of subcategories such that $\\mathcal{C} = \\bigcup_{i \\geq 0} \\mathcal{C}_i$. Then, $|\\mathcal{C}| \\simeq \\mathrm{hocolim \\,} |\\mathcal{C}_i|$.\n\n\\end{lmm}\n\n\\begin{proof}\n\nLet $I$ be the category of natural ordinals, with objects $\\mathrm{Ob}(I) = \\{i \\in \\mathbb{N}\\}$, and where the morphism set $\\mathrm{Mor}_I(i,j)$ is $\\{\\sigma_{i,j}\\}$ if $i\\leq j$, or empty otherwise. Define a functor\n$$\n\\Theta \\colon I \\Right3{} \\curs{\\mathbf{Cat}}\n$$\nby $\\Theta(i) = \\mathcal{C}_i$ and $\\Theta(\\sigma_{i,j}) = \\mathrm{incl} \\colon \\mathcal{C}_i \\to \\mathcal{C}_j$. The Grothendieck construction on $\\Theta$, namely $G(\\Theta)$, is the category with object set\n$$\n\\{(i, X) \\, | \\, i \\in \\mathrm{Ob}(I) \\mbox{ and } X \\in \\mathrm{Ob}(\\mathcal{C}_i)\\}.\n$$\nThe morphism sets $\\mathrm{Mor}_{G(\\Theta)}((i,X), (j, Y))$ are empty whenever $j < i$. Otherwise they consist of the pairs $(\\sigma_{i,j}, \\varphi)$, with $\\varphi \\in \\mathrm{Mor}_{\\mathcal{C}_j}(X,Y)$. By \\cite[Theorem 1.2]{Thomason}, we have an equivalence $\\mathrm{hocolim \\,} |\\mathcal{C}_i| \\simeq |G(\\Theta)|$.\n\nConsider now the projection functor $\\tau \\colon G(\\Theta) \\to \\mathcal{C}$ that sends an object $(i, X)$ to $\\tau(i, X) = X$, and a morphism $(\\sigma_{i,j}, \\varphi)$ to $\\tau(\\sigma_{i,j}, \\varphi) = \\varphi$. We claim that this functor induces an equivalence between the corresponding nerves. For each $X \\in \\mathcal{C}$, let $\\tau\/X$ be the category with object set\n$$\n\\mathrm{Ob}(\\tau\/X) = \\{((j, Y), \\varphi) \\,\\, | \\,\\, (j, Y) \\in \\mathrm{Ob}(G(\\Theta)) \\mbox{ and } \\varphi \\in \\mathrm{Mor}_{\\mathcal{C}}(Y, X)\\}.\n$$\nA morphism in $\\tau\/X$ from $((j,Y), \\varphi)$ to $((k, Z), \\psi)$ is $(\\sigma_{i,j}, \\gamma) \\in \\mathrm{Mor}_{\\mathcal{C}}((j,Y), (k, Z))$ such that $\\varphi = \\psi \\circ \\gamma$. By \\cite[Theorem A and Corollary 2]{Quillen}, it is enough to check that $\\tau\/X$ satisfies the conditions of Lemma \\ref{Quill}. Clearly, $\\tau\/X$ is nonempty, since $((i, X), 1_X) \\in \\tau\/X$ for some $i \\in \\mathbb{N}$ big enough. Let $((j,Y), \\varphi), ((k,Z), \\psi) \\in \\mathrm{Ob}(\\tau\/X)$, and let $m = \\max\\{i,j,k\\}$. Then condition (i) in Lemma \\ref{Quill} holds with\n$$\n((j,Y), \\varphi) \\Right3{(\\sigma_{j,m},\\varphi)} ((m, X), 1_X) \\Left3{(\\sigma_{k,m}, \\psi)} ((j,Y), \\psi).\n$$\nRegarding condition (ii), notice that $\\mathrm{Mor}_{\\tau\/X}(((j,Y), \\varphi), ((k, Z), \\psi))$ is either empty, or contains a single morphisms, since morphisms in $\\mathcal{C}$ are all epimorphisms and monomorphisms in the categorical sense. Thus, $|\\tau\/X|$ is contractible for all $X \\in \\mathcal{C}$, and the claim follows.\n\\end{proof}\n\n\\begin{lmm}\\label{approx0}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and suppose $\\mathcal{G}$ admits an approximation by $p$-local finite groups $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ with respect to some telescopic transporter system $\\widetilde{\\LL}$ satisfying the conditions in \\ref{defiapprox}. Then, there is an equivalence $B\\mathcal{G} \\simeq (\\mathrm{hocolim \\,} |\\mathcal{L}_i|)^{\\wedge}_p$.\n\n\\end{lmm}\n\n\\begin{proof}\n\nBy \\ref{defiapprox}, $\\widetilde{\\LL}$ contains $\\mathcal{L}$ as a full subcategory, and thus by \\ref{equinerv} the inclusion $\\mathcal{L} \\subseteq \\widetilde{\\LL}$ induces an equivalence $|\\mathcal{L}| \\simeq |\\widetilde{\\LL}|$. It is enough to show that $\\mathrm{hocolim \\,} |\\mathcal{L}_i| \\simeq |\\widetilde{\\LL}|$.\n\nSet $\\mathcal{L}^{\\circ} \\stackrel{def} = \\bigcup_{i \\geq 0} \\mathcal{L}_i \\subseteq \\widetilde{\\LL}$. By Lemma \\ref{epimono}, all morphisms in $\\widetilde{\\LL}$ are epimorphisms and monomorphisms in the categorical sense, and thus the same applies to $\\mathcal{L}^{\\circ}$. By Lemma \\ref{approx-2} it follows that\n$$\n\\mathrm{hocolim \\,} |\\mathcal{L}_i| \\simeq |\\mathcal{L}^{\\circ}|.\n$$\nThus, to finish the proof it is enough to show that the inclusion functor $\\iota \\colon \\mathcal{L}^{\\circ} \\to \\widetilde{\\LL}$ induces an equivalence of nerves. For each $P \\in \\mathrm{Ob}(\\widetilde{\\LL})$, the undercategory $\\iota\/P$ has object set\n$$\n\\mathrm{Ob}(\\iota\/P) = \\{(Q, \\varphi) \\, | \\, Q \\in \\mathrm{Ob}(\\mathcal{L}^{\\circ}) \\mbox{ and } \\varphi \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(Q,P)\\}.\n$$\nA morphism in $\\iota\/P$ from $(Q, \\varphi)$ to $(R, \\psi)$ is a morphism $\\gamma \\in \\mathrm{Mor}_{\\mathcal{L}^{\\circ}}(Q,R)$ such that $\\varphi = \\psi \\circ \\gamma$.\n\nWe show that $\\iota\/P$ satisfies the conditions of Lemma \\ref{Quill}, which implies that $|\\iota\/P|$ is contractible. Clearly, $\\iota\/P$ is nonempty. Let $(Q, \\varphi), (R, \\psi) \\in \\mathrm{Ob}(\\iota\/P)$. By property (iii) in Definition \\ref{defiapprox}, there is some $X \\in \\mathrm{Ob}(\\mathcal{L}^{\\circ})$, with $X\\leq P$, such that $\\varphi$ and $\\psi$ restrict to morphisms $\\varphi \\colon Q \\to X$ and $\\psi \\colon R \\to X$ in $\\mathcal{L}^{\\circ}$. Thus, condition (i) of \\ref{Quill} is satisfied with\n$$\n(Q, \\varphi) \\Right3{\\varphi} (X, \\varepsilon(1)) \\Left3{\\psi} (R, \\psi).\n$$\nRegarding condition (ii) in \\ref{Quill}, the set $\\mathrm{Mor}_{\\iota\/P}((Q, \\varphi), (R, \\psi))$ is either empty or contains a single morphism. Since the argument works for all $P \\in \\mathrm{Ob}(\\mathcal{L})$, it follows that $|\\mathcal{L}^{\\circ}| \\simeq |\\widetilde{\\LL}|$.\n\\end{proof}\n\n\\begin{rmk}\\label{approx-1}\n\nSuppose the $p$-local compact group $\\mathcal{G} = (S, \\FF, \\LL)$ admits an approximation by $p$-local finite groups $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$. For each $i$, the fusion system $\\mathcal{F}_i$ is saturated, and we may consider its associated centric linking system $\\mathcal{T}_i$. Let also $\\mathcal{H}_i = \\mathrm{Ob}(\\mathcal{L}_i) \\cap \\mathrm{Ob}(\\mathcal{T}_i)$, and let $\\mathcal{L}_{\\mathcal{H}_i} \\subseteq \\mathcal{L}_i$ be the full subcategory with object set $\\mathcal{H}_i$. Since both $\\mathrm{Ob}(\\mathcal{L}_i)$ and $\\mathrm{Ob}(\\mathcal{T}_i)$ contain $\\mathrm{Ob}(\\mathcal{F}_i^{cr})$, it follows that $\\mathrm{Ob}(\\mathcal{F}_i^{cr}) \\subseteq \\mathcal{H}_i$. Moreover, there is a commutative diagram\n$$\n\\xymatrix@R=1.2cm{\n\\ldots & \\mathcal{T}_i & \\mathcal{T}_{i+1} & \\ldots \\\\\n\\ldots \\ar[r] & \\mathcal{L}_{\\mathcal{H}_i} \\ar[u]^{\\iota_i} \\ar[d]_{\\mathrm{incl}} \\ar[r]^{\\mathrm{incl}} & \\mathcal{L}_{\\mathcal{H}_{i+1}} \\ar[u]_{\\iota_{i+1}} \\ar[d]^{\\mathrm{incl}} \\ar[r] & \\ldots \\\\\n\\ldots \\ar[r] & \\mathcal{L}_i \\ar[r]_{\\mathrm{incl}} & \\mathcal{L}_{i+1} \\ar[r] & \\ldots\n}\n$$\nwhere all the vertical arrows induce homotopy equivalences between the realizations of the corresponding nerves, by \\cite[Theorem B]{BCGLO1}. Thus, if we denote $B\\mathcal{G}_i = |\\mathcal{T}_i|^{\\wedge}_p$, then Lemma \\ref{approx0} implies that $B\\mathcal{G} \\simeq (\\mathrm{hocolim \\,} B\\mathcal{G}_i)^{\\wedge}_p$.\n\n\\end{rmk}\n\n\n\\subsection{Preliminary constructions}\\label{Sprelim}\n\nIn this subsection we establish the notation and basic facts necessary for the proof that every $p$-local compact group has an approximation by $p$-local finite groups, in the next subsection. \n\n\\begin{hyp}\\label{hyp1}\n\nFor the rest of this subsection, let $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $((-)^{\\star}_{\\mathcal{F}}, (-)^{\\star}_{\\mathcal{L}})$ be a finite retraction pair. Let also $\\widetilde{\\LL}$ be the associated telescopic transporter system, and let $\\Psi$ be a fine unstable Adams operation on $\\mathcal{L}$ (in the sense of \\ref{uAo}). By a slight abuse of notation, we denote by $\\Psi$ the corresponding extension of $\\Psi$ to $\\widetilde{\\LL}$ (see \\ref{extendL2}), which is again a fine unstable Adams operation. Set $\\Psi_0 = \\Psi$, and for all $i \\geq 0$, define\n\\begin{enumerate}[(a)]\n\n\\item $S_i = C_S(\\Psi_i) = \\{x \\in S \\, | \\, \\Psi_i(x) = x\\}$; and\n\n\\item $\\Psi_{i+1} = (\\Psi_i)^p$.\n\n\\end{enumerate}\n\n\\end{hyp}\n\n\\begin{lmm}\\label{SiS}\n\nThe following properties hold.\n\\begin{enumerate}[(i)]\n\n\\item $S = \\bigcup_{i \\geq 0} S_i$.\n\n\\item There is some $M_a \\in \\mathbb{N}$ such that $(S_i)^{\\star} = S$ for all $i \\geq M_a$.\n\n\\end{enumerate}\n\n\\end{lmm}\n\n\\begin{proof}\n\nLet $T\\leq S$ be the maximal torus of $S$, and set $T_i = T \\cap S_i$. Notice that by definition $T_i$ is the subgroup of $T$ of elements fixed by $\\Psi_i$. By hypothesis, $\\Psi = \\Psi_0$ has degree $\\zeta \\in 1 + p^m \\Z^{\\wedge}_p$ for some $m > 0$, and $\\Psi_{i+1} = (\\Psi_i)^p$. Thus, we have $T_i \\lneqq T_{i+1}$ for all $i \\geq 0$, and $T = \\bigcup_{i \\geq 0} T_i$. By \\cite[Lemma 2.6]{JLL}, there is a subgroup $H\\leq S_0$ such that $S = H \\cdot T$. Thus, $H\\leq S_i$ for all $i$, and we get\n$$\nS = H \\cdot T = \\bigcup_{i \\geq 0} H \\cdot T_i \\subseteq \\bigcup_{i \\geq 0} S_i.\n$$\nThis proves part (i). To prove part (ii), suppose otherwise that no such $M_a \\in \\mathbb{N}$ exists, that is, $(S_i)^{\\star} \\lneqq S$ for all $i \\geq 0$. Since $\\mathrm{Ob}(\\mathcal{F}^{\\star})$ contains only finitely many conjugacy classes of elements, this means that there exist some $R \\in \\mathrm{Ob}(\\mathcal{F}^{\\star})$ and some $M \\in \\mathbb{N}$ such that $R \\lneqq S$ and such that $(S_i)^{\\star} = R$ for all $i \\geq M$. Notice that this contradicts part (i): if $R \\lneqq S$, then there is some $x \\in S \\setminus R$. On the other hand, by part (i) we have $x \\in S_i$ for $i$ big enough, and thus $x \\in (S_i)^{\\star} = R$, hence a contradiction.\n\\end{proof}\n\nFor simplicity we may assume that $M_a = 0$. In particular, $S_i \\in \\mathrm{Ob}(\\widetilde{\\LL})$ for all $i \\geq 0$.\n\n\\begin{defi}\\label{Li}\n\nWith the conventions above, for each $i \\geq 0$ define $\\mathcal{L}_i$ as the category with object set $\\mathrm{Ob}(\\mathcal{L}_i) = \\{P\\leq S_i \\, | \\, P \\in \\mathrm{Ob}(\\widetilde{\\LL})\\}$, and with morphism sets\n$$\n\\mathrm{Mor}_{\\mathcal{L}_i}(P,Q) = \\{\\varphi \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(P,Q) \\, | \\, \\Psi_i(\\varphi) = \\varphi\\}.\n$$\nDefine also $\\mathcal{F}_i$ as the fusion system over $S_i$ generated by the restriction of $\\4{\\rho} \\colon \\widetilde{\\LL} \\to \\mathcal{F}$ to $\\mathcal{L}_i$ (i.e. $\\mathcal{F}_i$ is $\\mathrm{Ob}(\\mathcal{L}_i)$-generated). Finally, define functors\n$$\n\\mathcal{T}_{\\mathrm{Ob}(\\mathcal{L}_i)}(S_i) \\Right3{\\varepsilon_i} \\mathcal{L}_i \\qquad \\mbox{and} \\qquad \\mathcal{L}_i \\Right3{\\rho_i} \\mathcal{F}_i\n$$\nas the obvious restrictions of the structural functors $\\4{\\varepsilon} \\colon \\mathcal{T}_{\\mathrm{Ob}(\\widetilde{\\LL})}(S) \\to \\widetilde{\\LL}$ and $\\4{\\rho} \\colon \\widetilde{\\LL} \\to \\mathcal{F}$.\n\n\\end{defi}\n\nDespite its simplicity, the following example illustrates why it is necessary to work with a telescopic linking system rather than a centric linking system.\n\n\\begin{expl}\\label{expl0}\n\nLet $T$ be a discrete $p$-torus, i.e. $T \\cong (\\Z\/p^{\\infty})^r$ for some $r \\geq 1$. Let also $\\mathcal{G} = (S, \\FF, \\LL)$ be the \\emph{trivial} $p$-local compact group associated to $T$. That is, $S = T$ and $\\mathcal{F} = \\mathcal{F}_T(T)$ is the fusion system over $T$ whose only morphisms are inclusions (since $T$ is abelian). This fusion system is obviously saturated, and has only one centric object, namely $T$ itself. Thus, $\\mathcal{L}$ has a single object, $T$, with $\\mathrm{Aut}_{\\mathcal{L}}(T) = T$. On the other hand, since $T = S$, we have $P^{\\bullet} = T$ for all $P\\leq T$, and the telescopic linking system $\\widetilde{\\LL}$ associated to $\\mathcal{L}$ in \\ref{expl1} is the actual transporter category of the group $T$. That is, $\\mathrm{Ob}(\\widetilde{\\LL}) = \\{P\\leq T\\}$, and $\\mathrm{Mor}_{\\widetilde{\\LL}}(P,Q) = N_T(P,Q) = T$ for all $P, Q\\leq T$. Let now $\\Psi$ be an unstable Adams operation as fixed in \\ref{hyp1}. An easy computation reveals that $C_T(\\Psi)$ must be a finite subgroup of $T$, and thus is not an object in $\\mathcal{L}$. In particular, without replacing $\\mathcal{L}$ by $\\widetilde{\\LL}$, the subcategories $\\mathcal{L}_i$ defined above would be empty for all $i \\geq 0$.\n\n\\end{expl}\n\n\\begin{prop}\\label{fix1}\n\nThe following holds.\n\\begin{enumerate}[(i)]\n\n\\item For each $P \\in \\mathrm{Ob}(\\mathcal{F}^{\\star})$ there exists some $M_P \\in \\mathbb{N}$ such that $(P \\cap S_i)^{\\star} = P$ for all $i \\geq M_P$.\n\n\\item For each $\\varphi \\in \\mathrm{Mor}(\\widetilde{\\LL})$ there exists some $M_{\\varphi} \\in \\mathbb{N}$ such that $\\Psi_i(\\varphi) = \\varphi$ for all $i \\geq M_{\\varphi}$.\n\n\\item For each $i \\geq 0$ and each $x \\in S$, $x^{-1} \\cdot \\Psi_i(x) \\in T$.\n\n\\end{enumerate}\n\n\\end{prop}\n\n\\begin{proof}\n\nTo prove part (i), notice that $P^{\\star} = P = \\bigcup_{i \\geq 0} P \\cap S_i$ by Lemma \\ref{SiS} (i). Suppose that $(P \\cap S_i)^{\\star} \\lneqq P$ for all $i \\geq 0$. Since $\\mathcal{F}^{\\star}$ contains finitely many conjugacy classes of objects, this means that there is some $R \\in \\mathrm{Ob}(\\mathcal{F}^{\\star})$ such that $(P \\cap S_i)^{\\star} = R \\lneqq P$ for all $i$ big enough, contradicting the identity $P = \\bigcup_{i \\geq 0} P \\cap S_i$. Part (iii) follows immediately by definition of unstable Adams operation, since the $\\Psi_i \\in \\mathrm{Aut}(S)$ induces the identity on $S\/T$.\n\nFinally, part (ii) follows by construction of the unstable Adams operations $\\Psi_i$. More specifically, as stablished in \\ref{hyp1}, the unstable Adams operation $\\Psi$ satisfies the following property (see Remark \\ref{uAo1}, or the proof of \\cite[Theorem 4.1]{JLL} for a more detailed explanation): there is a (finite) set $\\mathcal{M}$ of morphisms in $\\mathcal{L}^{\\star}$ such that the following holds\n\\begin{enumerate}[(1)]\n\n\\item $\\Psi(\\varphi) = \\varphi$ for all $\\varphi \\in \\mathcal{M}$; and\n\n\\item every morphism $\\psi$ in $\\mathcal{L}$ (and hence in $\\widetilde{\\LL}$ by definition) decomposes as $\\psi = \\varepsilon(g) \\circ \\varphi$, where $\\varphi$ is (the restriction of) a morphism in $\\mathcal{M}$, and $g$ is an element of $S$.\n\n\\end{enumerate}\nMoreover, these properties depend only on $\\mathcal{L}^{\\star}$ containing finitely many $S$-conjugacy classes, and on $\\mathcal{L}$ being a centric linking system, but not on the functor $(-)^{\\star}_{\\mathcal{L}}$. Fix $\\psi \\in \\mathrm{Mor}(\\widetilde{\\LL})$, and let $\\psi = \\varepsilon(g) \\circ \\varphi$ be the corresponding decomposition, as described above. By assumption, $\\Psi(\\varphi) = \\varphi$, and thus $\\Psi_i(\\varphi) = \\varphi$ for all $i \\geq 0$. Also, $S = \\bigcup_{i \\geq 0} S_i$ by Lemma \\ref{SiS} (i), and thus there exists some $M_{\\varphi} \\in \\mathbb{N}$ such that $g \\in S_i$ for all $i \\geq M_{\\varphi}$. It follows that $\\Psi_i(\\psi) = \\Psi_i(\\varepsilon(g)) \\circ \\varphi = \\varepsilon(g) \\circ \\varphi = \\psi$, and part (ii) follows.\n\\end{proof}\n\n\\begin{prop}\\label{fix2}\n\nThere exists some $M_b \\in \\mathbb{N}$ such that, for all $i \\geq M_b$, the triple $(\\mathcal{L}_i, \\varepsilon_i, \\rho_i)$ is a transporter system associated to the fusion system $\\mathcal{F}_i$.\n\n\\end{prop}\n\n\\begin{proof}\n\nWe have to check the axioms in Definition \\ref{defitransporter}. Notice that $\\mathcal{L}_i$ is a finite category, and thus we do not have to deal with axiom (III). Axioms (A1), (B) and (C) follow immediately by definition of $\\mathcal{L}_i$ as a subcategory of $\\widetilde{\\LL}$. We deal with the remaining axioms of transporter systems in separate steps for the reader's convenience.\n\nFor each $P \\in \\mathrm{Ob}(\\mathcal{L}_i)$, set\n$$\nE_i(P) = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{L}_i}(P) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}_i}(P)).\n$$\nNote that, via the inclusion $\\mathrm{Aut}_{\\mathcal{L}_i}(P) \\to \\mathrm{Aut}_{\\widetilde{\\LL}}(P^{\\star})$, and by property (a) in Definition \\ref{compsyst} of the functor $(-)^{\\star}_{\\mathcal{L}}$, the subgroup $E_i(P)$ is mapped to a subgroup of $E(P^{\\star}) = \\mathrm{Ker}(\\mathrm{Aut}_{\\widetilde{\\LL}}(P^{\\star}) \\to \\mathrm{Aut}_{\\mathcal{F}}(P^{\\star}))$. Indeed, if $\\varphi \\in E_i(P)$, then $f = \\rho_i(\\varphi) = \\mathrm{Id}$. Thus, $\\4{\\rho}((\\varphi)^{\\star}_{\\mathcal{L}}) = (\\mathrm{Id})^{\\star}_{\\mathcal{F}} = \\mathrm{Id}$.\n\n\\textbf{Step 1.} Axiom (A2) is satisfied: for each $P, Q \\in \\mathrm{Ob}(\\mathcal{L}_i)$, the group $E_i(P)$ acts freely on $\\mathrm{Mor}_{\\mathcal{L}_i}(P,Q)$ by right composition, and $\\rho_i \\colon \\mathrm{Mor}_{\\mathcal{L}_i}(P,Q) \\to \\mathrm{Hom}_{\\mathcal{F}_i}(P,Q)$ is the orbit map of this action. Also, $E_i(Q)$ acts freely on $\\mathrm{Mor}_{\\mathcal{L}_i}(P,Q)$ by left composition.\n\nThe freeness of both the left action by $E_i(P)$ and the right action of $E_i(Q)$ follows by Lemma \\ref{epimono}, which states that morphisms in $\\widetilde{\\LL}$ (and in particular in $\\mathcal{L}_i$ by definition) are monomorphisms and epimorphisms in the categorical sense. That $\\rho_i$ is the orbit map of the left conjugation action of $E_i(P)$ is now immediate.\n\n\\textbf{Step 2.} Axiom (I) is satisfied. In fact, since $\\mathcal{L}_i$ is a finite category, it is enough to show that there exists some $M \\in \\mathbb{N}$ such that, for all $i \\geq M$, $\\mathcal{L}_i$ satisfies axiom (I') in Remark \\ref{rmktransp}: $\\varepsilon_i(S_i) \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Aut}_{\\mathcal{L}_i}(S_i))$ or, equivalently, the group $\\mathrm{Out}_{\\mathcal{F}_i}(S_i)$ has trivial Sylow $p$-subgroup.\n\nFix a set $\\mathcal{N} \\subseteq \\mathrm{Aut}_{\\widetilde{\\LL}}(S)$ of representatives of the elements of $\\mathrm{Out}_{\\mathcal{F}}(S) = \\mathrm{Aut}_{\\widetilde{\\LL}}(S)\/S$. Then there exists some $M_b \\in \\mathbb{N}$ such that, for all $i \\geq M_b$, $(S_i)^{\\star} = S$ and $\\mathcal{N} \\subseteq \\mathrm{Aut}_{\\widetilde{\\LL}}(S_i)$ (by abuse of notation we consider $\\mathcal{N}$ as the restriction of its elements to $S_i$). Furthermore, by Proposition \\ref{fix1} (ii) we can assume that $\\mathcal{N} \\subseteq \\mathrm{Aut}_{\\mathcal{L}_i}(S_i)$ for all $i \\geq M_b$. Thus, there is a commutative diagram of group extensions\n$$\n\\xymatrix{\n\\4{\\varepsilon}_S(S)^{\\Psi_i} \\ar[r] \\ar[d]_{\\mathrm{res}} & \\mathrm{Aut}_{\\widetilde{\\LL}}(S)^{\\Psi_i} \\ar[r] \\ar[d]^{\\mathrm{res}} & \\mathrm{Out}_{\\mathcal{F}}(S) \\ar[d] \\\\\n\\varepsilon_i(S_i) \\ar[r] & \\mathrm{Aut}_{\\mathcal{L}_i}(S_i) \\ar[r] & \\mathrm{Out}_{\\mathcal{F}_i}(S_i)\n}\n$$\nwhere $G^{\\Psi_i} = \\{g \\in G \\, | \\, \\Psi_i(g) = g\\}$, for $G = \\varepsilon_S(S)$ or $G = \\mathrm{Aut}_{\\mathcal{L}}(S)$. Furthermore, note that the restrictions $\\mathrm{res} \\colon \\4{\\varepsilon}_S(S)^{\\Psi_i} \\to \\varepsilon_i(S_i)$ and $\\mathrm{res} \\colon \\mathrm{Aut}_{\\widetilde{\\LL}}(S)^{\\Psi_i} \\to \\mathrm{Aut}_{\\mathcal{L}_i}(S_i)$ are isomorphisms by definition. Thus, for all $i \\geq M_b$ we have $\\mathrm{Out}_{\\mathcal{F}_i}(S_i) \\cong \\mathrm{Out}_{\\mathcal{F}}(S)$, and axiom (I') follows since $\\{1\\} \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Out}_{\\mathcal{F}}(S))$.\n\n\\textbf{Step 3.} Axiom (II) is satisfied: let $\\varphi \\in \\mathrm{Iso}_{\\mathcal{L}_i}(P,Q)$, $P \\lhd \\4{P}\\leq S_i$, and $Q \\lhd \\4{Q}\\leq S_i$ be such that $\\varphi \\circ \\varepsilon_i(\\4{P}) \\circ \\varphi^{-1}\\leq \\varepsilon_i(\\4{Q})$; then there is some $\\4{\\varphi} \\in \\mathrm{Mor}_{\\mathcal{L}_i}(\\4{P}, \\4{Q})$ such that $\\4{\\varphi} \\circ \\varepsilon_i(1) = \\varepsilon_i(1) \\circ \\varphi$.\n\nFix some $i \\geq 0$, and let $\\varphi \\in \\mathrm{Iso}_{\\mathcal{L}_i}(P,Q)$, $P \\lhd \\4{P}\\leq S_i$, and $Q \\lhd \\4{Q}\\leq S_i$ be as above, and notice that in this case we have $\\varepsilon_i(1) = \\4{\\varepsilon} \\colon X \\to \\4{X}$, where $X = P,Q$. Since $\\widetilde{\\LL}$ is a transporter system, there is some $\\4{\\varphi} \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(\\4{P}, \\4{Q})$ such that $\\4{\\varphi} \\circ \\varepsilon_i(1) = \\varepsilon_i(1) \\circ \\varphi$. Applying $\\Psi_i$ to this equation we get\n$$\n\\4{\\varphi} \\circ \\varepsilon_i(1) = \\varepsilon_i(1) \\circ \\varphi = \\Psi_i(\\varepsilon_i(1) \\circ \\varphi) = \\Psi_i(\\4{\\varphi}) \\circ \\varepsilon_i(1),\n$$\nand thus Lemma \\ref{epimono} implies that $\\Psi_i(\\4{\\varphi}) = \\4{\\varphi}$.\n\\end{proof}\n\nAgain, we may assume that $M_b = 0$ for simplicity.\n\n\\begin{cor}\\label{fix3}\n\nFor all $i \\geq 0$, the fusion system $\\mathcal{F}_i$ is $\\mathrm{Ob}(\\mathcal{L}_i)$-generated and $\\mathrm{Ob}(\\mathcal{L}_i)$-saturated.\n\n\\end{cor}\n\n\\begin{proof}\n\nThe fusion system $\\mathcal{F}_i$ is $\\mathrm{Ob}(\\mathcal{L}_i)$-generated by definition, and the $\\mathrm{Ob}(\\mathcal{L}_i)$-saturation follows by \\cite[Proposition 3.6]{OV}.\n\\end{proof}\n\n\n\\subsection{Existence of finite approximations}\\label{Ssapprox}\n\nWe are ready to prove that every $p$-local compact group has an approximation by $p$-local finite groups. In fact, we prove something stronger: every fine unstable Adams operation (in the sense of \\ref{uAo}) defines an approximation by $p$-local finite groups.\n\n\\begin{hyp}\\label{hyp2}\n\nFor the rest of this section, let $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $((-)^{\\bullet}_{\\mathcal{F}}, (-)^{\\bullet}_{\\mathcal{L}})$ be the finite retraction pair of \\ref{expl1}. Let also $\\widetilde{\\LL}$ be the associated telescopic linking system, and let $\\Psi$ be a fine unstable Adams operation on $\\widetilde{\\LL}$ (that is, $\\Psi$ is an unstable Adams operation whose degree $\\zeta \\neq 1$ is congruent to $1$ modulo $p$). Let also $\\{\\Psi_i\\}_{i \\geq 0}$ and $\\{S_i\\}_{i \\geq 0}$ be as defined in \\ref{hyp1}, and let $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be the associated family of finite transporter systems defined in \\ref{Li}. Finally, for all $i \\geq 0$ let\n\\begin{enumerate}[(a)]\n\n\\item $\\Gamma_i =\\{P\\leq S_i \\, | \\, P^{\\bullet} \\notin \\mathrm{Ob}(\\mathcal{L}^{\\bullet}) \\mbox{ and } P^{\\bullet} \\cap S_i = P\\}$; and\n\n\\item $\\Omega_i = \\{R \\in \\mathrm{Ob}(\\mathcal{F}^{\\bullet})\\setminus \\mathrm{Ob}(\\mathcal{L}^{\\bullet}) \\, | \\, (R \\cap S_i)^{\\bullet} = R\\}$.\n\n\\end{enumerate}\nNote that $\\Psi_i$ is a fine unstable Adams operation for all $i$. Also, for each $i \\geq 0$ the sets $\\Gamma_i$ and $\\Omega_i$ are in one-to-one correspondence with each other for all $i \\geq 0$. The bijection is given in one direction by $P \\mapsto P^{\\bullet}$, and in the reverse direction by $R \\mapsto R \\cap S_i$. Also note that $\\Gamma_i \\cap \\mathrm{Ob}(\\mathcal{L}_i) = \\emptyset$ for all $i \\geq 0$.\n\n\\end{hyp}\n\n\\begin{prop}\\label{fix2-1}\n\nFor all $i \\geq 0$, $\\mathcal{L}_i$ is a linking system, and $P$ is $\\mathcal{F}_i$-centric for each $P \\in \\mathrm{Ob}(\\mathcal{L}_i)$.\n\n\\end{prop}\n\n\\begin{proof}\n\nBy definition of $\\mathcal{L}_i$, for all $P \\in \\mathrm{Ob}(\\mathcal{L}_i)$ we have $\\mathrm{Ker}(\\mathrm{Aut}_{\\widetilde{\\LL}}(P) \\to \\mathrm{Aut}_{\\mathcal{F}}(P)) = \\varepsilon_P(Z(P^{\\bullet}))$ by property (1) in \\ref{expl1}, and thus $\\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{L}_i}(P) \\to \\mathrm{Aut}_{\\mathcal{F}_i}(P)) = \\varepsilon_P(Z(P^{\\bullet}) \\cap S_i) = \\varepsilon_i(Z(P))$. Furthermore, we have $\\varepsilon_i(C_{S_i}(P))\\leq \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{L}_i}(P) \\to \\mathrm{Aut}_{\\mathcal{F}_i}(P))$ by axiom (C) of transporter systems on $\\mathcal{L}_i$, and the statement follows immediately.\n\\end{proof}\n\n\\begin{prop}\\label{fix4}\n\nLet $i \\geq 0$ and let $P\\leq S_i$ be such that $P \\lneqq P^{\\bullet} \\cap S_i$. Then\n$$\n\\mathrm{Out}_{S_i}(P) \\cap O_p(\\mathrm{Out}_{\\mathcal{F}_i}(P)) \\neq 1.\n$$\nIn particular, $P$ is not $\\mathcal{F}_i$-centric $\\mathcal{F}_i$-radical.\n\n\\end{prop}\n\n\\begin{proof}\n\nSuppose $P$ is $\\mathcal{F}_i$-centric. Since $P \\notin \\Gamma_i$, we have $P \\lneqq P^{\\bullet} \\cap S_i \\stackrel{def} = Q$. Notice that the functor $(-)^{\\bullet}$ induces an inclusion $\\mathrm{Aut}_{\\mathcal{F}_i}(P)\\leq \\mathrm{Aut}_{\\mathcal{F}_i}(Q)$. Consider the subgroup $A = \\{c_x \\in \\mathrm{Aut}_{\\mathcal{F}_i}(P) \\, | \\, x \\in N_Q(P)\\}$. Via the above inclusion, we have $A = \\mathrm{Aut}_{\\mathcal{F}_i}(P) \\cap \\mathrm{Inn}(Q)$. Since $P \\lneqq Q$, it follows that $P \\lneqq N_Q(P)$, and hence $\\mathrm{Inn}(P) \\lneqq A$, since $P$ is $\\mathcal{F}_i$-centric by hypothesis. The group $\\mathrm{Aut}_{\\mathcal{F}_i}(P)$, seen as a subgroup of $\\mathrm{Aut}_{\\mathcal{F}_i}(Q)$, normalizes $\\mathrm{Inn}(Q)$, and thus $A \\lhd \\mathrm{Aut}_{\\mathcal{F}_i}(P)$, and\n$$\n\\{1\\} \\neq A\/\\mathrm{Inn}(P)\\leq \\mathrm{Out}_{S_i}(P) \\cap O_p(\\mathrm{Out}_{\\mathcal{F}_i}(P)).\n$$\nThis finishes the proof.\n\\end{proof}\n\n\\begin{prop}\\label{fix5}\n\nAssume Hypothesis \\ref{hyp2}. Then, there exists some $M \\in \\mathbb{N}$ such that, for all $i \\geq M$, the following holds: if $P \\in \\Gamma_i$, then either $P$ is not $\\mathcal{F}_i$-centric or $P$ is $\\mathcal{F}_i$-conjugate to some $Q\\leq S_i$ such that\n$$\n\\mathrm{Out}_{S_i}(Q) \\cap O_p(\\mathrm{Out}_{\\mathcal{F}_i}(Q)) \\neq 1.\n$$\n\n\\end{prop}\n\n\\begin{proof}\n\nWe start with some general observations, after which we deduce a certain condition ($\\ddagger$) which will imply the statement. The rest of the proof consists of a series of steps to show that ($\\ddagger$) holds.\n\nBy Proposition \\ref{3.2BLO3} (i), the set $\\mathrm{Ob}(\\mathcal{F}^{\\bullet})$ contains finitely many $\\mathcal{F}$-conjugacy classes, and the same applies to $\\mathrm{Ob}(\\mathcal{F}^{\\bullet}) \\setminus \\mathrm{Ob}(\\mathcal{L}^{\\bullet})$. Let $\\mathcal{P} = \\{X_1, \\ldots, X_n\\}$ be a set of representatives of the $\\mathcal{F}$-conjugacy classes in $\\mathrm{Ob}(\\mathcal{F}^{\\bullet}) \\setminus \\mathrm{Ob}(\\mathcal{L}^{\\bullet})$. By Proposition \\ref{fix1} (i), there exists some $M' \\in \\mathbb{N}$ such that $\\mathcal{P} \\subseteq \\Omega_i$ for all $i \\geq M'$, and we can assume that $M' = 0$ without loss of generality.\n\nFor each $X \\in \\mathcal{P}$, fix also a set $\\mathcal{H}_X = \\{Y_1, \\ldots, Y_m\\}$ of representatives of the $T$-conjugacy classes in $X^{\\mathcal{F}}$. Again, there exists some $M'' \\in \\mathbb{N}$ such that $\\mathcal{H}_X \\subseteq \\Omega_i$ for all $i \\geq M''$, and once more we can assume $M'' = 0$ for simplicity.\n\nLet $i \\geq 0$, and let $Q\\leq S_i$. Then, $T_{Q^{\\bullet}} \\cap Q \\lhd Q$, and every automorphism in $\\mathcal{F}_i$ of $Q$ restricts to an automorphism of $T_{Q^{\\bullet}} \\cap Q$ (by the properties of $(-)^{\\bullet}$, see \\ref{3.2BLO3}). Set\n$$\nK_Q = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{F}_i}(Q) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}_i}(T_{Q^{\\bullet}} \\cap Q) \\times \\mathrm{Aut}(Q\/(T_{Q^{\\bullet}} \\cap Q))),\n$$\nwhich is a normal $p$-subgroup of $\\mathrm{Aut}_{\\mathcal{F}_i}(Q)$ by Lemma \\ref{Kpgp}. Since $\\Gamma_i$ and $\\Omega_i$ are in one-to-one correspondence (see \\ref{hyp2}), in order to prove the statement it is enough to prove the following, slightly stronger statement for each $X \\in \\mathcal{P}$ and each $Y \\in \\mathcal{H}_X$.\n\\begin{itemize}\n\n\\item[($\\ddagger$)] There exists some $M_Y \\in \\mathbb{N}$ such that the following holds for all $i \\geq M_Y$: if $R \\in Y^T \\cap \\Omega_i$, then either $R \\cap S_i$ is not $\\mathcal{F}_i$-centric, or $R \\cap S_i$ is $\\mathcal{F}_i$-conjugate to some $Q\\leq S_i$ such that $K_Q \\cap \\mathrm{Aut}_S(Q)$ contains some element which is not in $\\mathrm{Inn}(Q)$.\n\n\\end{itemize}\n\nIndeed, suppose that ($\\ddagger$) holds for all $X \\in \\mathcal{P}$ and all $Y \\in \\mathcal{H}_X$. We claim that the statement follows with $M = \\max\\{M_Y \\, | \\, X \\in \\mathcal{P} \\mbox{ and } Y \\in \\mathcal{H}_X\\}$. To prove this, let $P \\in \\Gamma_i$, and let $R = P^{\\bullet} \\in \\Omega_i$. Then, there exist some $X \\in \\mathcal{P}$ and some $Y \\in \\mathcal{H}_X$ such that $R \\in Y^T \\cap \\Omega_i$. Thus, ($\\ddagger$) applies to $Y$, and it follows that either $P = R \\cap S_i$ is not $\\mathcal{F}_i$-centric, or $P = R \\cap S_i$ is $\\mathcal{F}_i$-conjugate to some $Q\\leq S_i$ such that $K_Q \\cap \\mathrm{Aut}_S(Q)$ contains some element which is not in $\\mathrm{Inn}(Q)$, in which case we have\n$$\n\\mathrm{Out}_{S_i}(Q) \\cap O_p(\\mathrm{Out}_{\\mathcal{F}_i}(Q)) \\neq 1.\n$$\n\nFor the rest of the proof, fix $X \\in \\mathcal{P}$ and $\\mathcal{H}_X$ as above. Since this proof is rather long, we have divided it into several steps, for the reader's convenience. We also include a brief summary of the steps in the proof. In Step 1, we give a general tool to deduce that ($\\ddagger$) holds in some cases. In Step 2 we show that we may reduce to prove that ($\\ddagger$) holds for all $Y \\in \\mathcal{H}_X$ with $T_Y$ fully $\\mathcal{F}$-normalized. In Step 3 we justify the reduction to assume that $A = T_X$ is normal in $\\mathcal{F}$. In Step 4 we show some properties regarding the quotient $\\mathcal{G}\/A = (S\/A, \\mathcal{F}\/A, \\mathcal{L}\/A)$. In Step 5 we introduce a certain subgroup $Z_Y$ for each $Y \\in \\mathcal{H}_X$, related to $C_{T\/A}(Y\/A)$, and prove some of its properties. In Step 6, we show that ($\\ddagger$) holds for all $Y \\in \\mathcal{H}_X$ such that $Z_Y \\not\\leq Y$. In Step 7 we prove that we may reduce to prove ($\\ddagger$) for all $Y \\in \\mathcal{H}_X$ such that $Z_Y$ is fully $\\mathcal{F}$-normalized. In Step 8 we show that ($\\ddagger$) holds for all $Y \\in \\mathcal{H}_X$ such that $C_S(Y) \\not\\leq Y$ (in particular, this applies to $Y \\in \\mathcal{H}_X$ such that $C_S(Y) \\not\\leq Y$ and such that $Z_Y$ is fully $\\mathcal{F}$-normalized). In step 9 we prove a technical property necessary for the last step of the proof. Finally, in Step 10 we show that ($\\ddagger$) holds for all $Y \\in \\mathcal{H}_X$ such that $Z_Y$ is fully $\\mathcal{F}$-normalized.\n\n\\textbf{Step 1.} Let $Y, Y' \\in \\mathcal{H}_X$, and suppose that ($\\ddagger$) holds for $Y$. Suppose in addition that there exists some $f \\in \\mathrm{Hom}_{\\mathcal{F}}(Y'T, YT)$ such that $f(Y') = Y$. Then ($\\ddagger$) holds for $Y'$.\n\nLet $f \\in \\mathrm{Hom}_{\\mathcal{F}}(Y'T, YT)$ as above. By Alperin's Fusion Theorem \\cite[Theorem 3.6]{BLO3}, there exist subgroups $A_0 = Y'T, A_1, \\ldots, A_n = YT\\leq S$, objects $B_1, \\ldots, B_n \\in \\mathrm{Ob}(\\mathcal{L}^{\\bullet})$, and automorphisms $\\phi_k \\in \\mathrm{Aut}_{\\widetilde{\\LL}}(B_k)$, for $k = 1, \\ldots, n$, such that\n$$\nA_{k-1}, A_k\\leq B_k \\qquad \\mbox{and} \\qquad \\rho(\\phi_k)(A_{k-1}) = A_k\n$$\nfor each $k = 1, \\ldots, n$, and such that $f = \\rho(\\phi_n) \\circ \\ldots \\rho(\\phi_1)$. By Proposition \\ref{fix1} (i) and (ii), there exists some $M_1 \\in \\mathbb{N}$ such that, for all $i \\geq M_1$ and all $k = 1, \\ldots, n$,\n$$\nB_k \\cap S_i \\in \\mathrm{Ob}(\\mathcal{L}_i) \\qquad \\mbox{and} \\qquad (\\phi_k)|_{B_k \\cap S_i} \\in \\mathrm{Aut}_{\\mathcal{L}_i}(B_k \\cap S_i).\n$$\nMoreover, since $T\\leq A_0$, it follows that $T\\leq A_k, B_k$ for all $k = 1, \\ldots, n$. Note that for each $k$, there exists some $t_k \\in T$ such that $t_k A_k t_k^{-1} \\in \\mathcal{H}_X$ (in particular, $t_0 = t_n = 1$). Since $T\\leq B_k$ for each $k$, we may replace $\\phi_k$ by $\\varepsilon(t_k) \\circ \\phi_k \\circ \\varepsilon(t_{k-1})$, and this way we may assume that $A_k \\in \\mathcal{H}_X$ for each $k$.\n\nSuppose now that ($\\ddagger$) holds for $Y$. That is, there exists some $M_Y \\in \\mathbb{N}$ (we may choose $M_Y \\geq M_1$) such that, for all $i \\geq M_Y$, the following holds: if $R \\in Y^T \\cap \\Omega_i$, then $R \\cap S_i$ satisfies the conclusion of ($\\ddagger$). Let $i \\geq M_Y$, and let $K \\in (Y')^T \\cap \\Omega_i$, and let $H = f(K)$. Since $f \\colon Y'T \\to YT$, it follows that $H \\in Y^T$. Moreover, $f(K \\cap S_i) = H \\cap S_i$ (since each $\\phi_k$ above is fixed by $\\Psi_i$). Thus, $K \\cap S_i$ is $\\mathcal{F}_i$-conjugate to $H \\cap S_i$, and ($\\ddagger$) holds for $Y'$.\n\n\\textbf{Step 2.} We show that if ($\\ddagger$) holds for all $Y \\in \\mathcal{H}_X$ such that $T_Y$ is fully $\\mathcal{F}$-normalized, then ($\\ddagger$) holds for all $Y \\in \\mathcal{H}_X$.\n\nIndeed, let $Y' \\in \\mathcal{H}_X$, and suppose that $T_{Y'}$ is not fully $\\mathcal{F}$-normalized. Then, there exists some $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}}(N_S(T_{Y'}), S)$ such that $\\gamma(T_{Y'})$ is fully $\\mathcal{F}$-normalized. Since $Y'\\leq N_S(T_{Y'})$, we may define $Y = \\gamma(Y')$. Since $\\mathcal{H}_X$ contains representatives of all the $T$-conjugacy classes in $X^{\\mathcal{F}}$, there is some $t \\in T$ such that $tYt^{-1} \\in \\mathcal{H}_X$. Thus, upon replacing $\\gamma$ by $c_t \\circ \\gamma$, we may assume that $Y \\in \\mathcal{H}_X$. Note that $T_{Y} = \\gamma(T_{Y'})$. Notice also that $T\\leq N_S(T_{Y'})$, and thus $\\gamma$ restricts to some $f \\in \\mathrm{Hom}_{\\mathcal{F}}(Y'T, YT)$ such that $Y = \\gamma(Y')$. The claim follows by Step 1.\n\n\\textbf{Step 3.} Let $\\mathcal{M} = \\{A = T_Y\\leq T \\, | \\, Y \\in \\mathcal{H}_X \\mbox{ and $T_Y$ is fully $\\mathcal{F}$-normalized}\\}$. Since $\\mathcal{H}_X$ is finite, so is $\\mathcal{M}$. For each $A \\in \\mathcal{M}$, let $\\mathcal{H}_{X,A} = \\{U \\in \\mathcal{H}_X \\, | \\, T_U = A\\} \\subseteq \\mathcal{H}_X$. To prove the statement of \\ref{fix5}, it is enough to prove that ($\\ddagger$) holds for all $U \\in \\mathcal{H}_{X,A}$, for a fixed $A \\in \\mathcal{M}$ at a time. Fix $A \\in \\mathcal{M}$, and let $\\mathcal{H}_{X,A}$ be as above. The main goal of this step is to justify the reduction to the case where $A$ is normal in $\\mathcal{F}$.\n\nSince $A$ is fully $\\mathcal{F}$-normalized, to prove that ($\\ddagger$) holds for all $U \\in \\mathcal{H}_{X,A}$, we can reduce to work on the normalizer $p$-local compact group $N_{\\mathcal{G}}(A) = (N_S(A), N_{\\mathcal{F}}(A), N_{\\mathcal{L}}(A))$ defined in \\ref{rmknorm}, instead of the whole $\\mathcal{G} = (S, \\FF, \\LL)$. Set for short $N = N_S(A)$, $\\mathcal{E} = N_{\\mathcal{F}}(A)$, and $\\mathcal{T} = N_{\\mathcal{L}}(A)$, and note that $T\\leq N$ since $A\\leq T$. Let $((-)^{\\bullet}_{\\mathcal{F}}, (-)^{\\bullet}_{\\mathcal{L}})$ be the finite retraction pair for $\\mathcal{G}$ fixed in \\ref{hyp2}, and let $((-)^{\\star}_{\\mathcal{E}}, (-)^{\\star}_{\\mathcal{T}})$ be the finite retraction pair for $N_{\\mathcal{G}}(A)$ described in \\ref{expl3}. Recall that $P^{\\star} = P^{\\bullet}$ for all $P\\leq N$. Let also $\\widetilde{\\LL}$ and $\\4{\\mathcal{T}}$ be the corresponding associated telescopic transporter systems.\n\nWe start by stating and proving several general properties.\n\\begin{itemize}\n\n\\item[(3-a)] Let $U \\in \\mathcal{H}_{X,A}$ and let $R \\in U^T$. Then, $T_R = A$, $N_S(T_R) = N_S(A)$, and $N_S(R)\\leq N_S(A)$. Moreover, every automorphism of $R$ preserves $T_R$, and thus $\\mathrm{Aut}_{\\mathcal{F}}(R) = \\mathrm{Aut}_{\\mathcal{E}}(R)$.\n\n\\item[(3-b)] Since $A\\leq T$ is a subtorus, it follows that $\\Psi_i(A) = A$ for all $i \\geq 0$. Thus, each $\\Psi_i$ restricts to a fine unstable Adams operation (see \\ref{uAo}) on $N_{\\mathcal{G}}(A)$, which extends to $\\4{\\mathcal{T}}$ by \\ref{extendL2}. Let us denote by $\\Psi_i$ the resulting unstable Adams operation.\n\n\\end{itemize}\nConsider the family of transporter systems $\\{(N_i, \\mathcal{E}_i, \\mathcal{T}_i)\\}_{i \\geq 0}$ associated to $(N, \\mathcal{E}, \\4{\\mathcal{T}})$ in \\ref{Li}. As pointed out in \\ref{expl3}, $\\4{\\mathcal{T}}$ is not a subcategory of $\\widetilde{\\LL}$, and thus it is hard to compare the fusion systems $\\mathcal{E}_i$ and $\\mathcal{F}_i$. However, if we restrict to the full subcategory $\\4{\\mathcal{T}}_{\\geq A} \\subseteq \\4{\\mathcal{T}}$ of subgroups $P\\leq N$ such that $A\\leq P^{\\star} = P^{\\bullet}$, then $\\4{\\mathcal{T}}_{\\geq A}$ is a subcategory of $\\widetilde{\\LL}$, by \\ref{expl3} (a). In particular we have the following.\n\\begin{itemize}\n\n\\item[(3-c)] For all $i \\geq 0$, there is an inclusion $\\4{\\mathcal{T}}_{\\geq A} \\cap \\mathcal{T}_i \\subseteq \\mathcal{L}_i$.\n\n\\end{itemize}\nFinally, note that $A^{\\bullet} = A$, since $A$ is the maximal torus of $U$, for some $U \\in \\mathcal{H}_{X,A}$, and $U = U^{\\bullet}$ by hypothesis. The following holds.\n\\begin{itemize}\n\n\\item[(3-d)] For each $i \\geq 0$, let $A_i = A \\cap S_i$. By \\ref{fix1} (i), there exists some $M_2 \\in \\mathbb{N}$ such that $(A_i)^{\\bullet} = A$ for all $i \\geq M_2$. For simplicity we may assume that $M_2 = 0$.\n\n\\item[(3-e)] For each $U \\in \\mathcal{H}_{X,A}$ and each $R \\in U^T \\cap \\Omega_i$, we have $R \\cap N_i = R \\cap S_i$ and $\\mathrm{Aut}_{N_i}(R \\cap S_i) = \\mathrm{Aut}_{S_i}(R \\cap S_i)$. This follows since $N_S(R \\cap S_i)\\leq N_S(R)\\leq N_S(A) = N$ by (3-a).\n\n\\item[(3-f)] Let $H,K\\leq N_i$ be such that $A\\leq H^{\\bullet}, K^{\\bullet}$. Then, for all $f \\in \\mathrm{Hom}_{\\mathcal{E}}(H,K)$, we have $(f)^{\\star}_{\\mathcal{E}} = (f)^{\\bullet}_{\\mathcal{F}}$. Similarly, if $H, K \\in \\mathrm{Ob}(\\4{\\mathcal{T}}_{\\geq A})$ and $\\varphi \\in \\mathrm{Mor}_{\\4{\\mathcal{T}}}(H,K)$, then $H, K \\in \\mathrm{Ob}(\\widetilde{\\LL})$, and $(\\varphi)^{\\star}_{\\4{\\mathcal{T}}} = (\\varphi)^{\\bullet}_{\\widetilde{\\LL}}$. The first part follows by definition of $(-)^{\\star}_{\\mathcal{E}}$ in \\ref{expl3}, and the second part follows by \\ref{expl3} (c).\n\n\\end{itemize}\nIn fact, we deduce more. Let $W\\leq N_i$ be such that $A\\leq W^{\\bullet}$. Then, the following holds.\n\\begin{itemize}\n\n\\item[(3-g)] $\\mathrm{Hom}_{\\mathcal{E}_i}(W, N_i) \\subseteq \\mathrm{Hom}_{\\mathcal{F}_i}(W, N_i)$.\n\n\\end{itemize}\nLet $f \\in \\mathrm{Hom}_{\\mathcal{E}_i}(W, N_i)$. Since $\\mathcal{E}_i$ is $\\mathrm{Ob}(\\mathcal{T}_i)$-generated, there exist objects $H_k, H_k' \\in \\mathrm{Ob}(\\mathcal{T}_i)$ and morphisms $\\gamma_k \\in \\mathrm{Hom}_{\\mathcal{T}_i}(H_k, H'_k)$, for $k = 1, \\ldots, n$, and such that, upon taking the necessary restrictions,\n$$\nf = \\4{\\rho}_i(\\gamma_n) \\circ \\ldots \\circ \\4{\\rho}_i(\\gamma_1).\n$$\nNotice that $(A_i)^{\\bullet} = A\\leq W^{\\bullet}$. Thus, $A\\leq W^{\\bullet}\\leq (H_1)^{\\bullet}$, and $A = \\4{\\rho}(\\gamma_1)(A)\\leq (H_1')^{\\bullet}$ since $\\gamma_1 \\in \\mathrm{Mor}(\\4{\\mathcal{T}})$. Inductively, it follows that $A\\leq (H_k)^{\\bullet}, (H'_k)^{\\bullet}$ for all $k$, and thus $H_k, H_k' \\in \\mathrm{Ob}(\\4{\\mathcal{T}}_{\\geq A})$. By (3-c) above, for all $k = 1, \\ldots, n$ we have $H_k, H'_k \\in \\mathcal{L}_i$, and $\\gamma_k \\in \\mathrm{Mor}(\\mathcal{L}_i)$. Thus $f \\in \\mathrm{Mor}(\\mathcal{F}_i)$, and (3-g) follows. In particular, if $U \\in \\mathcal{H}_{X,A}$, and $P \\in U^{\\mathcal{E}} \\cap \\Omega_i$, then\n$$\n\\mathrm{Hom}_{\\mathcal{E}_i}(P \\cap S_i, N_i) \\subseteq \\mathrm{Hom}_{\\mathcal{F}_i}(P \\cap S_i, N_i).\n$$\n\nLet $U \\in \\mathcal{H}_{X,A}$. For each $P \\in U^{\\mathcal{E}} \\cap \\Omega_i$, note that $A \\cap S_i \\lhd P \\cap S_i$, and every automorphism of $P \\cap S_i$ in $\\mathcal{F}_i$ restricts to an automorphism of $A \\cap S_i$. Let $\\3{P} = P\/A$. Note that $P = (P \\cap S_i)A$ (since $P \\in \\Omega_i$), and thus we have $(P \\cap S_i)\/(A \\cap S_i) \\cong \\3{P}$. Set for short $Q = P \\cap S_i$, and let\n$$\nK_Q = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{F}_i}(Q) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}_i}(A \\cap S_i) \\times \\mathrm{Aut}(\\3{P}));\n$$\n$$\nK'_Q = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{E}_i}(Q) \\Right2{} \\mathrm{Aut}_{\\mathcal{E}_i}(A \\cap S_i) \\times \\mathrm{Aut}(\\3{P})).\n$$\nBy Lemma \\ref{Kpgp}, we have $K_Q\\leq O_p(\\mathrm{Aut}_{\\mathcal{F}_i}(Q))$ and $K'_Q\\leq O_p(\\mathrm{Aut}_{\\mathcal{E}_i}(Q))$. Moreover, by (3-g) there is an inclusion $\\mathrm{Aut}_{\\mathcal{E}_i}(Q)\\leq \\mathrm{Aut}_{\\mathcal{F}_i}(Q)$, and it follows that $K'_Q\\leq K_Q$. We claim that, in order to prove that ($\\ddagger$) holds for $U \\in \\mathcal{H}_{X,A}$, it is enough to prove that the following version of ($\\ddagger$) in terms of $\\mathcal{E}_i$ holds.\n\\begin{itemize}\n\n\\item[(3-h)] There exists some $M_U \\in \\mathbb{N}$ such that, for all $i \\geq M_U$ and all $R \\in U^T \\cap \\Omega_i$, either $R \\cap S_i$ is not $\\mathcal{E}_i$-centric, or $R \\cap S_i$ is $\\mathcal{E}_i$-conjugate to some $Q$ such that $K'_{Q} \\cap \\mathrm{Aut}_{N_i}(Q)$ contains some element that is not in $\\mathrm{Inn}(Q)$.\n\n\\end{itemize}\nIndeed, suppose that (3-h) holds for $U$, and let $i \\geq M_U$ and $R \\in U^T \\cap \\Omega_i$. Clearly, if $R \\cap S_i$ is not $\\mathcal{E}_i$-centric, then it is not $\\mathcal{F}_i$-centric by (3-g). Suppose that $R \\cap S_i$ is $\\mathcal{E}_i$-centric. Then, $R \\cap S_i$ is $\\mathcal{E}_i$-conjugate to some $Q$ such that $K'_Q \\cap \\mathrm{Aut}_{N_i}(Q)$ contains some element that is not in $\\mathrm{Inn}(Q)$. Then, $R \\cap S_i$ is $\\mathcal{F}_i$-conjugate to $Q$ by (3-g), and ($\\ddagger$) holds for $U$ since $K'_Q \\cap \\mathrm{Aut}_{N_i}(Q)\\leq K_Q \\cap \\mathrm{Aut}_{S_i}(Q)$.\n\nThe above just shows that, in order to prove ($\\ddagger$) for $U \\in \\mathcal{H}_{X, A}$ with respect to $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$, it is enough to prove that the conclusion ($\\ddagger$) holds for $U$ with respect to $\\{(N_i, \\mathcal{E}_i, \\mathcal{T}_i)\\}_{i \\geq 0}$. For the rest of the proof, we assume that $A = T_X$ is normal in $\\mathcal{F}$, so $\\mathcal{H}_{X, A} = \\mathcal{H}_X$ (and $T_R = A$ for all $R \\in X^{\\mathcal{F}}$). Moreover, notice that we are only concerned about subgroups $H\\leq S$ such that $A\\leq H^{\\bullet}$, and for these subgroups there is no difference between the finite retraction pairs $((-)^{\\bullet}_{\\mathcal{F}}, (-)^{\\bullet}_{\\mathcal{L}})$ and $((-)^{\\star}_{\\mathcal{E}}, (-)^{\\star}_{\\mathcal{T}})$, by (3-f). Thus, we may assume also that we are still working with $((-)^{\\bullet}_{\\mathcal{F}}, (-)^{\\bullet}_{\\mathcal{L}})$.\n\nTo finish this step, we prove the following.\n\\begin{itemize}\n\n\\item[(3-i)] If $A = T$ then ($\\ddagger$) holds for all $Y \\in \\mathcal{H}_X$.\n\n\\end{itemize}\nIndeed, in this case we have $Y^T = \\{Y\\}$ for each $Y \\in \\mathcal{H}_X$. Since $\\mathcal{H}_X$ is finite, it is not hard to check in this case that in fact there exists some $M_X \\in \\mathbb{N}$ such that, for all $i \\geq M_X$ and all $Y \\in \\mathcal{H}_X$, the subgroup $Y \\cap S_i$ is not $\\mathcal{F}_i$-centric.\n\n\\textbf{Step 4.} Since $A = T_X$ is normal in $\\mathcal{F}$, consider the quotient $(S\/A, \\mathcal{F}\/A, \\widetilde{\\LL}\/A)$. By Lemma \\ref{quotient22}, $\\mathcal{F}\/A$ is a saturated fusion system over $S\/A$, and $\\widetilde{\\LL}\/A$ is a transporter system associated to $\\mathcal{F}\/A$ which contains all the centric subgroups of $\\mathcal{F}\/A$. In this step we prove several properties relating $(S, \\mathcal{F}, \\widetilde{\\LL})$ to $(S\/A, \\mathcal{F}\/A, \\widetilde{\\LL}\/A)$, which we will need in later steps.\n\nSet for short $\\3{S} = S\/A$, $\\3{\\mathcal{F}} = \\mathcal{F}\/A$ and $\\3{\\mathcal{T}} = \\widetilde{\\LL}\/A$. Let also $\\widetilde{\\LL}_{\\geq A} \\subseteq \\widetilde{\\LL}$ be the full subcategory of subgroups that contain $A$, and let $\\tau \\colon \\widetilde{\\LL}_{\\geq A} \\to \\3{\\mathcal{T}}$ be the projection functor. By a slight abuse of notation, we also write $\\tau \\colon S \\to \\3{S}$ for the projection homomorphism. We adopt the notation $\\3{P}, \\3{Q}, \\ldots$ to denote subgroups of $\\3{S}$, $\\3{f}, \\3{f}', \\ldots$ to denote morphisms in $\\3{\\mathcal{F}}$, and $\\3{\\varphi}, \\3{\\psi}, \\ldots$ to denote morphisms in $\\3{\\mathcal{T}}$. In particular, $T\/A = \\3{T}\\leq \\3{S}$ denotes the maximal torus of $\\3{S}$. Also, for each $P\\leq S$ that contains $A$, we will write $\\3{P}$ instead of $\\tau(P) = P\/A$, unless there is risk of confusion.\n\nThe following is easily check to hold since $A\\leq T$ is normal in $\\mathcal{F}$.\n\\begin{itemize}\n\n\\item[(4-a)] Let $P, Q\\leq S$ be such that $A\\leq P,Q$. Then, $Q \\in P^{\\mathcal{F}}$ if and only if $\\3{Q} \\in \\3{P}^{\\3{\\mathcal{F}}}$. Similarly, $Q \\in P^T$ if and only if $\\3{Q} \\in \\3{P}^{\\3{T}}$, since $A\\leq T$.\n\n\\end{itemize}\n\nBy property (3-b), $\\Psi_i(A) = A$ for all $i \\geq 0$. By (3-i) we may assume that $A \\lneqq T$, and thus, for each $i \\geq 0$, the unstable Adams operation $\\Psi_i$ induces a fine unstable Adams (see \\ref{uAo}) operation $\\3{\\Psi}_i$ on $\\3{\\mathcal{T}}$. By definition, if $\\varphi \\in \\mathrm{Mor}(\\widetilde{\\LL})$ is such that $\\Psi_i(\\varphi) = \\varphi$ for some $i$, then $\\3{\\Psi}_i(\\3{\\varphi}) = \\3{\\varphi}$. The following is some sort of converse of this statement.\n\\begin{itemize}\n\n\\item[(4-b)] Let $P, Q \\in \\mathrm{Ob}(\\widetilde{\\LL})$ be such that $A\\leq P, Q$, and let $\\3{\\varphi} \\in \\mathrm{Mor}_{\\3{\\mathcal{T}}}(\\3{P}, \\3{Q})$ be such that $\\3{\\Psi}_i(\\3{\\varphi}) = \\3{\\varphi}$. Then, there exists some $\\varphi \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(P,Q)$ such that $\\tau(\\varphi) = \\3{\\varphi}$ and such that $\\Psi_i(\\varphi) = \\varphi$. Similarly, let $\\3{x} \\in \\3{S}$ be such that $\\3{\\Psi}_i(\\3{x}) = \\3{x}$. Then there exists some $x \\in S$ such that $\\Psi_i(x) = x$ (i.e. $x \\in S_i$) and $\\tau(x) = \\3{x}$.\n\n\\end{itemize}\nLet $\\3{\\varphi}$ be as above, and let $\\varphi \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(P,Q)$ be such that $\\tau(\\varphi) = \\3{\\varphi}$. Since $\\tau(\\varphi) = \\3{\\varphi} = \\3{\\Psi}_i(\\3{\\varphi}) = \\tau(\\Psi_i(\\varphi))$, it follows that $\\Psi_i(\\varphi) = \\varphi \\circ \\varepsilon_P(a)$, for some $a \\in A$ (see Definition \\ref{quotient1}). Consider the map $A \\to A$ defined by $t \\mapsto t^{-1} \\Psi_i(t)$. Since $A$ is abelian, this turns out to be a group homomorphism, which is in fact surjective, since $\\mathrm{Ker}(A)$ is the subgroup of fixed points of $A$, and this is a finite subgroup of $A$ for all $i \\geq 0$. In particular, there exists some $t \\in A$ such that $t^{-1} \\Psi_i(t) = a^{-1}$, and we get\n$$\n\\Psi_i(\\varphi \\circ \\varepsilon_P(t)) = \\Psi_i(\\varphi) \\circ \\varepsilon_P(\\Psi_i(t)) = \\varphi \\circ \\varepsilon_P(a) \\circ \\varepsilon_P(\\Psi_i(t)) = \\varphi \\circ \\varepsilon_P(t).\n$$\nA similar argument shows that, for each $\\3{x} \\in \\3{S}$ such that $\\3{\\Psi}_i(\\3{x}) = \\3{x}$, there is some $x \\in S_i$ such that $\\tau(x) = \\3{x}$.\n\nFor each $i \\geq 0$, let $\\3{S}_i\\leq \\3{S}$ be the subgroup of fixed points by $\\3{\\Psi}_i$. By (4-b), we deduce that $S_iA\/A = \\3{S}_i$, and thus $S_i\/(S_i \\cap A) \\cong \\3{S}_i$. Let $U \\in \\mathcal{H}_X$. By (4-a), $R \\in U^T$ if and only if $\\3{R} \\in \\3{U}^{\\3{T}}$.\n\\begin{itemize}\n\n\\item[(4-c)] For all $i \\geq 0$ and all $R \\in U^{T}$, $R \\in \\Omega_i$ if and only if $\\3{R}\\leq \\3{S}_i$.\n\n\\end{itemize}\nSuppose first that $R \\in \\Omega_i$, and let $P = R \\cap S_i$. Then, $R = PA$ (since $A = T_R$), and $\\3{R} = PA\/A\\leq S_iA\/A = \\3{S}_i$. Conversely, let $R \\in U^{T}$ be such that $\\3{R}\\leq \\3{S}_i$. Then, it follows by (4-b) that $P = R \\cap S_i$ contains representatives of all the elements in $\\3{R}$. Since $A_i = A \\cap S_i\\leq R \\cap S_i = P$ and $(A_i)^{\\bullet} = A$, it follows that $P^{\\bullet} \\geq R$. The inclusion $P^{\\bullet}\\leq R$ follows from $P = R \\cap S_i\\leq R$ and the fact that $R^{\\bullet} = R$. Thus $R \\in \\Omega_i$.\n\n\\textbf{Step 5.} For each $R \\in X^{\\mathcal{F}}$, recall the notation $\\3{R} = R\/A$. Set also\n$$\nZ_{\\3{R}} \\stackrel{def} = C_{\\3{T}}(\\3{R}) \\qquad \\mbox{and} \\qquad Z_R \\stackrel{def} = \\{t \\in N_T(R) \\, | \\, \\tau(t) \\in Z_{\\3{R}}\\}.\n$$\nNote that $A\\leq Z_R$ and $Z_{\\3{R}} = Z_R\/A$. The main goal of this step is to show the following: if $Z_U$ is fully $\\mathcal{F}$-normalized for some $U \\in \\mathcal{H}_X$, then $Z_{\\3{U}}$ is fully $\\3{\\mathcal{F}}$-normalized.\n\nIndeed, let $U \\in \\mathcal{H}_X$ be such that $Z_U$ is fully $\\mathcal{F}$-normalized. Note that $T\\leq N_S(Z_U)$, and\n$$\n\\3{T}\\leq N_{\\3{S}}(Z_{\\3{U}}) = N_S(Z_U)\/A.\n$$\nSuppose that $Z_{\\3{U}}$ is not fully $\\3{\\mathcal{F}}$-normalized, and let $\\3{\\gamma} \\in \\mathrm{Hom}_{\\3{\\mathcal{F}}}(N_{\\3{S}}(Z_{\\3{U}}), \\3{S})$ be such that $\\3{H} = \\3{\\gamma}(Z_{\\3{U}})$ is fully $\\3{\\mathcal{F}}$-normalized, and $|N_{\\3{S}}(Z_{\\3{U}})| < |N_{\\3{S}}(\\3{\\gamma}(Z_{\\3{U}}))|$. Then $\\3{\\gamma}$ lifts to a map $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}}(N_S(Z_U), S)$ such that $|N_S(Z_U)| = |\\gamma(N_S(Z_U))| < |N_S(\\gamma(Z_U))|$, since\n$$\n\\3{T}\\leq N_{\\3{S}}(\\3{\\gamma}(Z_{\\3{U}})) = N_S(\\gamma(Z_U))\/A,\n$$\nand this contradicts the maximality of $|N_S(Z_U)|$.\n\n\\textbf{Step 6.} We show that ($\\ddagger$) holds for all $U \\in \\mathcal{H}_X$ such that $Z_U \\not\\leq U$.\n\nLet $U \\in \\mathcal{H}_X$ be such that $Z_U \\not\\leq U$, and let $a \\in Z_U \\setminus U$. Then, by Lemma \\ref{SiS} there exists some $M_U \\in \\mathbb{N}$ such that $a \\in S_i$ for all $i \\geq M_U$. Fix some $i \\geq M_U$, and let $R \\in U^T \\cap \\Omega_i$. Since $R$ is $T$-conjugate to $U$, we have $Z_R = Z_U$. Let\n$$\nK_{R \\cap S_i} = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{F}_i}(R \\cap S_i) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}_i}(T_R \\cap S_i) \\times \\mathrm{Aut}(\\3{R})).\n$$\nBy Lemma \\ref{Kpgp} we know that $K_{R \\cap S_i}\\leq O_p(\\mathrm{Aut}_{\\mathcal{F}_i}(R \\cap S_i))$.\n\nThe element $a \\in Z_U \\cap S_i$ fixed above satisfies $a \\in Z_R \\cap S_i$, since $Z_R = Z_U$, and in particular $a \\in N_{S_i}(R \\cap S_i)$. Moreover, $c_a \\in K_{R \\cap S_i} \\cap \\mathrm{Aut}_{S_i}(R \\cap S_i)$, since $a \\in T \\cap S_i$. In particular, if $R \\cap S_i$ is $\\mathcal{F}_i$-centric then $c_a \\notin \\mathrm{Inn}(R \\cap S_i)$, and this shows that ($\\ddagger$) holds for $U$.\n\n\\textbf{Step 7.} Suppose ($\\ddagger$) holds for all $U \\in \\mathcal{H}_X$ such that $Z_U$ is fully $\\mathcal{F}$-normalized. Then we claim that ($\\ddagger$) holds for all $U \\in \\mathcal{H}_X$.\n\nLet $U \\in \\mathcal{H}_X$ be such that $Z_U$ is not fully $\\mathcal{F}$-normalized. Then there exists some $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}}(N_S(Z_U), S)$ such that $\\gamma(Z_U)$ is fully $\\mathcal{F}$-normalized. Since $U\\leq N_S(Z_U)$, we may set $V = \\gamma(U)$. Moreover, since $\\mathcal{H}_X$ contains representatives of all the $T$-conjugacy classes in $X^{\\mathcal{F}}$, we may assume that $V \\in \\mathcal{H}_X$. Finally, note that $T\\leq N_S(Z_U)$, since $Z_U\\leq T$. Thus, $Z_V = \\gamma(Z_U)$.\n\nSuppose now that ($\\ddagger$) holds for all $V \\in \\mathcal{H}_X$ such that $Z_V$ is fully $\\mathcal{F}$-normalized, and let $U \\in \\mathcal{H}_X$. By the above discussion, there exists some $f \\in \\mathrm{Hom}_{\\mathcal{F}}(UT, S)$ such that $V = f(U) \\in \\mathcal{H}_X$ is such that $Z_V$ is fully $\\mathcal{F}$-normalized, and the claim follows by Step 1.\n\n\\textbf{Step 8.} We show that ($\\ddagger$) holds for all $U \\in \\mathcal{H}_X$ such that $C_S(U) \\not\\leq U$. \n\nBy Step 6, if $Z_U \\not\\leq U$ then ($\\ddagger$) holds for $U$. Thus we may assume that $Z_U\\leq U$. Also, since $C_S(U) \\not\\leq U$, it follows that $C_{\\3{S}}(\\3{U}) \\not\\leq \\3{U}$. Notice that $C_{\\3{T}}(\\3{U}) = Z_{\\3{U}}\\leq \\3{U}$ and $\\3{U}$ is finite, which implies that $C_{\\3{S}}(\\3{U})$ is a finite group. Thus, there exists some $M_U \\in \\mathbb{N}$ such that $C_{\\3{S}}(\\3{U})\\leq \\3{S}_i$. Moreover, since $U \\in \\Omega_i$ by assumption, we have $\\3{U}\\leq \\3{S}_i$ by (4-c).\n\nLet $i \\geq M_U$ and let $R \\in U^T \\cap \\Omega_i$. Then $C_{\\3{T}}(\\3{R}) = Z_{\\3{U}}$. By (4-a) we have $\\3{R} \\in \\3{U}^{\\3{T}}$, and by (4-c) we know that $\\3{R}\\leq \\3{S}_i$. Thus, by Lemma \\ref{invar1} (i), together with Proposition \\ref{fix1} (iii), we have\n$$\n\\3{x}^{-1} \\cdot \\3{\\Psi}_i(\\3{x}) \\in C_{\\3{T}}(\\3{R}) = Z_{\\3{R}} = Z_{\\3{U}}\n$$\nfor some $\\3{x} \\in N_{\\3{T}}(\\3{R}, \\3{U})$. Fix such $\\3{x} \\in N_{\\3{T}}(\\3{R}, \\3{U})$, and note that $\\3{x}$ conjugates $C_{\\3{S}}(\\3{R})$ to $C_{\\3{S}}(\\3{U})$, and in particular $C_{\\3{S}}(\\3{R}) \\not\\leq \\3{R}$. Moreover, since $Z_{\\3{U}} = C_{\\3{T}}(\\3{R})\\leq \\3{R}$, it follows that\n$$\n\\3{x}^{-1} \\cdot \\3{\\Psi}_i(\\3{x}) \\in C_{\\3{T}}(\\3{R})\\leq \\3{R} \\cap \\3{T}\\leq C_{\\3{T}}(C_{\\3{S}}(\\3{R})).\n$$\nThus, by \\ref{invar1} (i) we deduce that $C_{\\3{S}}(\\3{R})\\leq \\3{S}_i$.\n\nSet $X_R = C_S(R)A$. Then, by (4-b) $X_R \\cap S_i$ contains representatives of all the elements in $C_S(R)A\/A\\leq C_{\\3{S}}(\\3{R})\\leq \\3{S}_i$. Note that $C_S(R)A \\not\\leq R$, and thus $X_R \\cap S_i$ contains elements which are not in $R \\cap S_i$. If $(X_R \\cap S_i)\\setminus (R \\cap S_i)$ contains some element of $C_S(R)$, then clearly $R \\cap S_i$ is not $\\mathcal{F}_i$-centric. Suppose then that $(X_R \\cap S_i) \\cap C_S(R)\\leq R \\cap S_i$, and let\n$$\nK_{R \\cap S_i} = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{F}_i}(R \\cap S_i) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}_i}(T_R \\cap S_i) \\times \\mathrm{Aut}(\\3{R})),\n$$\nwhich is a normal $p$-subgroup of $\\mathrm{Aut}_{\\mathcal{F}_i}(R \\cap S_i)$ by Lemma \\ref{Kpgp}. If $R \\cap S_i$ is $\\mathcal{F}_i$-centric, then, for every $x \\in (X_R \\cap S_i)\\setminus(R \\cap S_i)$, we have $c_x \\in K_{R \\cap S_i} \\cap \\mathrm{Aut}_{S_i}(R \\cap S_i)$ and $c_x \\notin \\mathrm{Inn}(R \\cap S_i)$, and ($\\ddagger$) holds for $U$.\n\n\\textbf{Step 9.} Recall the notation $\\3{\\mathcal{T}} = \\widetilde{\\LL}\/A$, introduced in Step 4, which is a transporter system associated to $\\3{\\mathcal{F}}$. Fix $U \\in \\mathcal{H}_X$ such that $Z_U$ is fully $\\mathcal{F}$-normalized. Then $Z_{\\3{U}}$ is fully $\\3{\\mathcal{F}}$-normalized by Step 5, and in particular it is fully $\\3{\\mathcal{F}}$-centralized. Thus we may consider the centralizer fusion system $\\3{\\mathcal{E}} = C_{\\3{\\mathcal{F}}}(Z_{\\3{U}})$ over $\\3{Z} = C_{\\3{S}}(Z_{\\3{U}})$, and note that $\\3{T}\\leq \\3{Z}$. Since $\\3{\\mathcal{F}}$ is saturated, it follows that $\\3{\\mathcal{E}}$ is also saturated. The main goal of this step is to prove the following property.\n\\begin{itemize}\n\n\\item[(9-a)] Suppose that $Z_U\\leq U$. Then, there exists $V \\in \\mathcal{H}_X$ such that $C_S(V) \\not\\leq V$ and such that $\\3{V} \\in \\3{U}^{\\3{\\mathcal{E}}}$.\n\n\\end{itemize}\n\nFirst, note that the following holds.\n\\begin{itemize}\n\n\\item[(9-b)] For each $\\3{R} \\in \\3{U}^{\\3{T}}$, we have $Z_{\\3{R}} = C_{\\3{T}}(\\3{R}) = Z_{\\3{U}}$.\n\n\\item[(9-c)] Since $Z_{\\3{U}}\\leq \\3{T}$ is abelian, every $\\3{\\mathcal{E}}$-centric subgroup of $\\3{Z}$ must contain $Z_{\\3{U}}$. Thus, by Lemma \\ref{centricNFKA}, every $\\3{\\mathcal{E}}$-centric subgroup of $\\3{Z}$ is also an $\\3{\\mathcal{F}}$-centric subgroup of $\\3{S}$, and hence also an object in $\\3{\\mathcal{T}}$, since $\\3{\\mathcal{T}}$ contains all the $\\3{\\mathcal{F}}$-centric subgroups of $\\3{S}$.\n\n\\end{itemize}\nWe are ready now to prove (9-a). Let $K = \\mathrm{Aut}_U(Z_U)$, and let $N_S^K(Z_U) = \\{x \\in S \\, | \\, c_x \\in K\\}$. Then,\n$$\n\\mathrm{Aut}_S^K(Z_U) = K \\cap \\mathrm{Aut}_S(Z_U) = \\mathrm{Aut}_U(Z_U) = K \\cap \\mathrm{Aut}_{\\mathcal{F}}(Z_U) = \\mathrm{Aut}_{\\mathcal{F}}^K(Z_U).\n$$\nIn particular, $Z_U$ is fully $\\mathcal{F}$-centralized (since it is fully $\\mathcal{F}$-normalized), and clearly $\\mathrm{Aut}_S^K(Z_U) \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Aut}_{\\mathcal{F}}^K(Z_U))$. By \\cite[Lemma 2.2]{BLO6} it follows that $Z_U$ is fully $K$-normalized in $\\mathcal{F}$ (see Section \\ref{Squotient}). Thus, the fusion system $N_{\\mathcal{F}}^K(Z_U)$ over $N_S^K(Z_U)$ (as defined in \\ref{definorm}) is saturated.\n\nNote that $U, T\\leq N_S^K(Z_U) = U C_S(Z_U)$. Since $U$ is not $\\mathcal{F}$-centric and $Z_U\\leq U$, it follows that $U$ is not $N_{\\mathcal{F}}^K(Z_U)$-centric by Lemma \\ref{centricNFKA}. Let $V$ be $N_{\\mathcal{F}}^K(Z_U)$-conjugate to $U$ and such that $C_{N_S^K(Z_U)}(V) \\not\\leq V$. Since $T\\leq N_S^K(Z_U)$, we may choose $V \\in \\mathcal{H}_X$. Then,\n$$\nC_{N_S^K(Z_U)}(V)\\leq C_S(V) \\not\\leq V.\n$$\nAlso, if $f \\in \\mathrm{Hom}_{N_{\\mathcal{F}}^K(Z_U)}(U,V)$, then by definition $f|_{Z_U} \\in \\mathrm{Aut}_U(Z_U)$. Hence, the induced morphism $\\3{f} \\in \\mathrm{Hom}_{\\3{\\mathcal{F}}}(\\3{U}, \\3{V})$ satisfies $\\3{f}|_{Z_{\\3{U}}} = \\mathrm{Id}$, and thus $\\3{V} \\in \\3{U}^{\\3{\\mathcal{E}}}$. Note that in particular $Z_U\\leq Z_V$, although this may not be an equality.\n\n\\textbf{Step 10.} We show that ($\\ddagger$) holds for all $U \\in \\mathcal{H}_X$ such that $Z_U$ is fully $\\mathcal{F}$-normalized.\n\nFix $U \\in \\mathcal{H}_X$ such that $Z_U$ is fully $\\mathcal{F}$-normalized. By Step 6, we may assume that $Z_U\\leq U$, and thus $Z_{\\3{U}}\\leq \\3{U}$. By Step 5, we know that $Z_{\\3{U}}$ is fully $\\3{\\mathcal{F}}$-normalized (and in particular fully $\\3{\\mathcal{F}}$-centralized). Let $\\3{Z} = C_{\\3{S}}(Z_{\\3{U}})$ for short, and let $\\3{\\mathcal{E}} = C_{\\3{\\mathcal{F}}}(Z_{\\3{U}})$ be the centralizer fusion system of $Z_{\\3{U}}$ in $\\3{\\mathcal{F}}$, which is saturated by Step 9. By (9-a), there exists $V \\in \\mathcal{H}_X$ such that $\\3{V} \\in \\3{U}^{\\3{\\mathcal{E}}}$ and $C_S(V) \\not\\leq V$.\n\nNotice that the set $\\{T_K \\, | \\, K \\in \\mathrm{Ob}(\\mathcal{L}^{\\bullet})\\}$ is finite since $\\mathrm{Ob}(\\mathcal{L}^{\\bullet})$ contains finitely many $T$-conjugacy classes. Moreover, since $K^{\\bullet} = K$, it follows that $(T_K)^{\\bullet} = T_K$ for all $K \\in \\mathrm{Ob}(\\mathcal{L}^{\\bullet})$. By Proposition \\ref{fix1} (i) there exists some $M_3 \\in \\mathbb{N}$ such that $(T_K \\cap S_i)^{\\bullet} = T_K = (T_K)^{\\bullet}$ for all $i \\geq M_3$ and all $T_K$ in the set above. Without loss of generality we may assume that $M_3 = 0$.\n\nFix $\\3{f} \\in \\mathrm{Hom}_{\\3{\\mathcal{E}}}(\\3{U}, \\3{V})$. By Alperin's Fusion Theorem \\cite[Theorem 3.6]{BLO3}, there exist sequences of subgroups $\\3{W}_0 = \\3{U}, \\3{W}_1, \\ldots, \\3{W}_n = \\3{V}\\leq\\3{Z}$ and $\\3{K}_1, \\ldots, \\3{K}_n \\in \\mathrm{Ob}((\\3{\\mathcal{E}})^c) \\subseteq \\mathrm{Ob}(\\3{\\mathcal{F}})$, and morphisms $\\3{\\gamma}_j \\in \\mathrm{Aut}_{\\3{\\mathcal{E}}}(\\3{K}_j)$ for each $j = 1, \\ldots, n$, such that\n$$\n\\3{W}_{j-1}, \\3{W}_j\\leq \\3{K}_j \\qquad \\mbox{and} \\qquad \\3{\\gamma}_j(\\3{W}_{j-1}) = \\3{W}_j\n$$\nfor each $j = 1, \\ldots, n$. For each $j = 1, \\ldots, n$, let $\\3{\\varphi}_j \\in \\mathrm{Aut}_{\\3{\\mathcal{T}}}(\\3{K}_j)$ be such that $\\3{\\rho}(\\3{\\varphi}_j) = \\3{\\gamma}_j$. Let also $K_j \\in \\mathrm{Ob}(\\widetilde{\\LL})$ be the preimage of $\\3{K}_j$, and let $\\varphi_j \\in \\mathrm{Aut}_{\\widetilde{\\LL}}(K_j)$ be such that $\\tau(\\varphi_j) = \\3{\\varphi}_j$. Note that $U\\leq K_1$ and $V\\leq K_n$.\n\nWe claim that we may assume that $K_j \\in \\mathrm{Ob}(\\mathcal{L}^{\\bullet})$ for all $j$ without loss of generality. Indeed, we have $\\3{K}_j \\in \\mathrm{Ob}(\\3{\\mathcal{T}})$ by (9-c) (where $\\3{\\mathcal{T}} = \\widetilde{\\LL}\/A$), and thus $K_j \\in \\mathrm{Ob}(\\widetilde{\\LL})$. This implies that $(K_j)^{\\bullet} \\in \\mathrm{Ob}(\\mathcal{L}^{\\bullet})$ by definition of $\\widetilde{\\LL}$. Furthermore, we have $A\\leq (K_j)^{\\bullet}$, and it follows that $(K_j)^{\\bullet}\/A\\leq \\3{Z}$, since $(K_j)^{\\bullet}\\leq K_j T$ and $\\3{T}\\leq \\3{Z}$. By Proposition \\ref{fix1} (i) and (ii), there exists some $M_U \\in \\mathbb{N}$ such that\n$$\nK_j \\cap S_i \\in \\mathrm{Ob}(\\mathcal{L}_i) \\qquad \\mbox{and} \\qquad \\Psi_i(\\varphi_j) = \\varphi_j\n$$\nfor all $i \\geq M_U$ and all $j = 1, \\ldots, n$. In particular, this implies that $U \\cap S_i$ is $\\mathcal{F}_i$-conjugate to $V \\cap S_i$. Note that this also implies that $\\3{\\Psi}_i(\\3{\\varphi}_j) = \\3{\\varphi}_j$ for all $i \\geq M_U$.\n\nLet $i \\geq M_U$, and let $R \\in U^T \\cap \\Omega_i$. By assumption, $\\3{U}\\leq \\3{S}_i$. By (4-a) we have $\\3{R} \\in \\3{U}^{\\3{T}}$, which implies that $C_{\\3{T}}(\\3{R}) = Z_{\\3{R}} = Z_{\\3{U}}$, and by (4-c) we know that $\\3{R}\\leq \\3{S}_i$. Thus, by Lemma \\ref{invar1} (i), together with Proposition \\ref{fix1} (iii), we have\n$$\n\\3{x}^{-1} \\cdot \\3{\\Psi}_i(\\3{x}) \\in C_{\\3{T}}(\\3{R}) = Z_{\\3{U}}\\leq \\3{R}\n$$\nfor some $\\3{x} \\in N_{\\3{T}}(\\3{R}, \\3{U})$. Fix such $\\3{x} \\in N_{\\3{T}}(\\3{R}, \\3{U})$, set $\\3{W}'_0 = \\3{R}$, and for all $j = 1, \\ldots, n$ set\n$$\n\\3{W}'_j = \\3{x}^{-1} \\cdot \\3{W}_j \\cdot \\3{x} \\qquad \\mbox{and} \\qquad \\3{K}'_j = \\3{x}^{-1} \\cdot \\3{K}_j \\cdot \\3{x}.\n$$\nSet also $\\3{\\varphi}_j' = \\3{\\varepsilon}(\\3{x})^{-1} \\circ \\3{\\varphi}_j \\circ \\3{\\varepsilon}(\\3{x}) \\in \\mathrm{Aut}_{\\3{\\mathcal{T}}}(\\3{K}'_j)$. Let also $W_j'$ and $K_j'$ be the corresponding preimages in $S$. The following holds.\n\\begin{itemize}\n\n\\item[(10-a)] Since $Z_{\\3{U}}$ is abelian and $\\3{K}_j, \\3{K}_j'$ are $\\3{\\mathcal{E}}$-centric, it follows that $Z_{\\3{U}}\\leq \\3{K}_j, \\3{K}_j'$ for each $j$.\n\n\\item[(10-b)] Since $\\3{x} \\in \\3{T}\\leq \\3{Z} = C_{\\3{S}}(Z_{\\3{U}})$ and $\\3{K}_j\\leq \\3{Z}$, it follows that $\\3{K}'_j\\leq \\3{Z}$. Moreover, since $\\3{x}^{-1} \\cdot \\3{\\Psi}_i(\\3{x}) \\in Z_{\\3{U}}$ and $Z_{\\3{U}}\\leq C_{\\3{T}}(\\3{K}_j), C_{\\3{T}}(\\3{K}'_j)$, it follows from Lemma \\ref{invar1} (i) that $\\3{x} \\in N_{\\3{T}}(\\3{K}'_j \\cap \\3{S}_i, \\3{K}_j \\cap \\3{S}_i)$.\n\n\\item[(10-c)] Since $Z_{\\3{U}}\\leq \\3{K}_j, \\3{K}'_j$, $\\3{\\varphi}_j \\in \\mathrm{Mor}(\\3{\\mathcal{E}})$, and $\\3{x}^{-1} \\cdot \\3{\\Psi}_i(\\3{x}) \\in Z_{\\3{U}}$, it follows from axiom (C) of transporter systems for $\\3{\\mathcal{T}}$ that $\\3{\\varepsilon}(\\3{x}^{-1} \\Psi_i(\\3{x})) \\circ \\3{\\varphi_j}' = \\3{\\varphi}'_j \\circ \\3{\\varepsilon}(\\3{x}^{-1}\\Psi_i(\\3{x}))$. Thus, $\\3{\\Psi}_i(\\3{\\varphi}'_j) = \\3{\\varphi}'_j$ by Lemma \\ref{invar1} (ii). By (4-b), this implies that there is some $\\varphi'_j \\in \\mathrm{Aut}_{\\widetilde{\\LL}}(K'_j)$ such that $\\Psi_i(\\varphi'_j) = \\varphi'_j$ and $\\tau(\\varphi'_j) = \\3{\\varphi}'_j$.\n\n\\end{itemize}\nLet $\\3{R'} = \\3{W}'_n \\in \\3{V}^{\\3{T}}$, and let $R'$ be its preimage in $S$, and note that $R' \\in V^T$ by (4-a). Note that $R\\leq K'_1$ and $R'\\leq K'_n$. Thus, if $K'_j \\cap S_i \\in \\mathrm{Ob}(\\mathcal{L}_i)$ for each $j$, then $R \\cap S_i$ is $\\mathcal{F}_i$-conjugate to $R' \\cap S_i$.\n\nIt remains to show that $K'_j \\cap S_i \\in \\mathrm{Ob}(\\mathcal{L}_i)$ for each $j$. Recall that, by definition, $K_j' \\cap S_i \\in \\mathrm{Ob}(\\mathcal{L}_i)$ if $(K'_j \\cap S_i)^{\\bullet} \\in \\mathrm{Ob}(\\mathcal{L})$. For each $j = 1, \\ldots, n$, let $T_j$ be the maximal torus of $K_j$. Note that $K_j'$ is $T$-conjugate to $K_j$, so in particular $K_j' \\in \\mathrm{Ob}(\\mathcal{L}^{\\bullet})$. As noted above, we have $(T_j \\cap S_i)^{\\bullet} = T_j$ for all $i \\geq M_U$. Also, $T_j \\cap S_i\\leq K'_j \\cap S_i$ and\n$$\n(T_j \\cap S_i)^{\\bullet} = T_j\\leq (K'_j \\cap S_i)^{\\bullet}.\n$$\nIn particular, this implies that $K'_j \\cap S_i, T_j\\leq (K'_j \\cap S_i)^{\\bullet}$. Since $(K_j \\cap S_i)^{\\bullet} = K_j$, it follows that $K_j \\cap S_i$ contains representatives of all the elements in $\\3{K}_j \\cap \\3{S}_i$. Thus, by (10-b) we deduce that $K_j' \\cap S_i$ contains representatives of all the elements in $\\3{K}'_j \\cap \\3{S}_i$, and thus $K'_j = (K'_j \\cap S_i)T_j\\leq (K'_j \\cap S_i)^{\\bullet}$. It follows that $(K'_j \\cap S_i)^{\\bullet} = K_j'$, and thus $K'_j \\cap S_i \\in \\mathrm{Ob}(\\mathcal{L}_i)$.\n\nBy the above, $R \\cap S_i$ is $\\mathcal{F}_i$-conjugate to $R' \\cap S_i$. Moreover, $R' \\in V^T$, and $C_S(V) \\not\\leq V$ by assumption. By Step 8, ($\\ddagger$) holds for $V$, and thus the conclusion of ($\\ddagger$) holds for $R$ and $R'$. This shows that ($\\ddagger$) holds for $U$ as well. Repeating this step, we deduce that ($\\ddagger$) holds for all $U \\in \\mathcal{H}_X$ such that $Z_U$ is fully $\\mathcal{F}$-normalized, and thus ($\\ddagger$) holds for all $U \\in \\mathcal{H}_X$ by Step 7. This finishes the proof.\n\\end{proof}\n\n\\begin{thm}\\label{fix6}\n\nEvery $p$-local compact group admits an approximation by $p$-local finite groups.\n\n\\end{thm}\n\n\\begin{proof}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $\\widetilde{\\LL}$ and $\\Psi$ be as fixed in \\ref{hyp2}. Let also $\\Psi_0 = \\Psi$, and let $\\{\\Psi_i\\}_{i \\geq 0}$ be such that $\\Psi_{i+1} = \\Psi_i^p$. Let $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be the family of transporter systems defined in \\ref{Li}. For simplicity, we can assume that the degree of $\\Psi$ is high enough so that Lemma \\ref{SiS} and Propositions \\ref{fix2} and \\ref{fix5} hold (otherwise replace $\\Psi$ by an appropriate power of it), and we claim that $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ is an approximation of $\\mathcal{G}$ by $p$-local finite groups.\n\nCondition (i) in \\ref{defiapprox} is satisfied, by Lemma \\ref{SiS} (i). Also, $S_i$ is a finite $p$-group for all $i \\geq 0$, and $\\mathcal{L}_i$ is a linking system associated to $\\mathcal{F}_i$ by Proposition \\ref{fix2-1}, and there are inclusions $\\mathcal{L}_i \\subseteq \\mathcal{L}_{i+1}$ and $\\mathcal{L}_i \\subseteq \\widetilde{\\LL}$ for all $i \\geq 0$. Thus, to show that condition (ii) in \\ref{defiapprox} is satisfied, it remains to check that $\\mathcal{F}_i$ is saturated and $\\mathrm{Ob}(\\mathcal{F}_i^{cr}) \\subseteq \\mathrm{Ob}(\\mathcal{L}_i)$. By Propositions \\ref{fix4} and \\ref{fix5}, if $P\\leq S_i$ is $\\mathcal{F}_i$-centric but $P \\notin \\mathrm{Ob}(\\mathcal{L}_i)$, then $P$ is $\\mathcal{F}_i$-conjugate to some $Q$ such that\n$$\n\\mathrm{Out}_{S_i}(Q) \\cap O_p(\\mathrm{Out}_{\\mathcal{F}_i}(Q)) \\neq 1.\n$$\nMoreover, $\\mathcal{F}_i$ is $\\mathrm{Ob}(\\mathcal{L}_i)$-generated and $\\mathrm{Ob}(\\mathcal{L}_i)$-saturated by Corollary \\ref{fix3}. Thus, the conditions of Theorem \\ref{5A} are satisfied: $\\mathcal{F}_i$ is saturated, and $\\mathrm{Ob}(\\mathcal{L}_i)$ contains all the centric radical subgroups of $\\mathcal{F}_i$. Thus condition (ii) in \\ref{defiapprox} is satisfied.\n\nFinally, we have to check condition (iii): for each $P, Q \\in \\mathrm{Ob}(\\widetilde{\\LL})$ and each $\\varphi \\in \\mathrm{Mor}_{\\widetilde{\\LL}}(P,Q)$, there exists some $M \\in \\mathbb{N}$ such that, for all $i \\geq M$, there are objects $P_i, Q_i \\in \\mathrm{Ob}(\\mathcal{L}_i)$ and morphisms $\\varphi_i \\in \\mathrm{Mor}_{\\mathcal{L}_i}(P_i, Q_i)$, such that $P = \\bigcup_{i \\geq M} P_i$ and $Q = \\bigcup_{i \\geq M} Q_i$, and $\\4{\\varepsilon}_{Q_i, Q}(1) \\circ \\varphi_i = \\varphi \\circ \\4{\\varepsilon}_{P_i, P}(1)$. By Proposition \\ref{fix1} (i) and (ii), there is some $M \\in \\mathbb{N}$ such that $P \\cap S_i, Q \\cap S_i \\in \\mathrm{Ob}(\\mathcal{L}_i)$ for all $i \\geq M$, and the restriction $\\varphi_i = \\varphi|_{P \\cap S_i}$ is a morphism in $\\mathrm{Mor}_{\\mathcal{L}_i}(P \\cap S_i, Q \\cap S_i)$. Since $S = \\bigcup_{i \\geq 0} S_i$ by Lemma \\ref{SiS} (i), it follows that\n$$\nP = \\bigcup_{i \\geq 0} P_i \\qquad \\mbox{and} \\qquad Q = \\bigcup_{i \\geq 0} Q_i.\n$$\nThe condition $\\4{\\varepsilon}_{Q_i, Q}(1) \\circ \\varphi_i = \\varphi \\circ \\4{\\varepsilon}_{P_i, P}(1)$ is easily checked.\n\\end{proof}\n\n\\begin{rmk}\\label{fix7}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $\\mathcal{O}(\\mathcal{F})$ be its \\emph{orbit category}: the category $\\mathcal{O}(\\mathcal{F})$ with object set $\\mathrm{Ob}(\\mathcal{F})$, and with morphism sets\n\\begin{equation}\\label{orbitcat}\n\\mathrm{Mor}_{\\mathcal{O}(\\mathcal{F}_{\\mathcal{H}})}(P,Q) = \\mathrm{Inn}(Q)\\backslash \\mathrm{Hom}_{\\mathcal{F}}(P,Q).\n\\end{equation}\nNotice that the subcategory $\\mathcal{O}(\\mathcal{F}^{\\bullet c}) \\subseteq \\mathcal{O}(\\mathcal{F})$ has a finite (full) subcategory as a skeletal subcategory, which in addition contains a representative of each $\\mathcal{F}$-conjugacy class of centric radical subgroups. Let $\\widetilde{\\LL}$ be the associated telescopic linking system, and let $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be an approximation of $\\mathcal{G}$ by $p$-local finite groups. Denote by $\\mathcal{L}_i^{\\bullet} \\subseteq \\mathcal{L}^{\\bullet}$ the image of $\\mathcal{L}_i$ through the functor $(-)^{\\bullet} \\colon \\widetilde{\\LL} \\to \\mathcal{L}^{\\bullet}$ for each $i \\geq 0$. Then, by Definition \\ref{defiapprox} there is some $M \\in \\mathbb{N}$ such that, for all $i \\geq M$, the category $\\mathcal{L}_i^{\\bullet}$ contains representatives of all the morphisms in $\\mathcal{O}(\\mathcal{F}^{\\bullet c})$ up to $S$-conjugation. In particular, this implies that\n\\begin{enumerate}[(a)]\n\n\\item the fusion system $\\mathcal{F}$ is generated by $\\mathcal{F}_i$ and $\\mathrm{Inn}(S)$; and\n\n\\item the linking system $\\widetilde{\\LL}$ is generated by $\\mathcal{L}_i^{\\bullet}$ and $S$ (and thus so is $\\mathcal{L}$).\n\n\\end{enumerate}\nMore precisely, property (b) means that every object in $\\mathcal{L}^{\\bullet}$ is $S$-conjugate to an object in $\\mathcal{L}_i^{\\bullet}$, and every morphism in $\\mathcal{L}^{\\bullet}$ is the composition of a morphism in $\\mathcal{L}_i^{\\bullet}$ with (the restriction of) a morphism in $\\varepsilon(S)\\leq \\mathrm{Aut}_{\\mathcal{L}}(S)$. Since $\\mathcal{L}^{\\bullet}$ is a deformation retract of $\\widetilde{\\LL}$, we can say that $\\widetilde{\\LL}$ is generated by $\\mathcal{L}_i^{\\bullet}$ and $S$.\n\n\\end{rmk}\n\n\n\\subsection{An example}\\label{Ssexample}\n\nIn this subsection we analyze our constructions in detail on a specific example: the $2$-local compact group associated to $SO(3)$. In particular, this example reveals that there are approximations by $p$-local finite groups that do not appear as fixed points of any (family of) fine unstable Adams operation.\n\nLet us first fix some notation and facts. The reader is referred to \\cite[Example 3.7]{Gonza2} for further details. Let $\\mathcal{G} = (S, \\FF, \\LL)$ be the $2$-local compact group associated to $SO(3)$. Then,\n$$\nS = \\gen{\\{t_n\\}_{n \\geq 1}, \\, x \\, | \\, \\forall n, \\, t_n^{2^n} = x^2 = 1, \\, t_{n+1}^2 = t_n, \\, x \\cdot t_n \\cdot x^{-1} = t_n^{-1}} \\cong D_{2^{\\infty}}.\n$$\nFor each $n \\geq 1$, set $T_n = \\gen{t_n}$, so that the maximal torus of $S$ is $T = \\gen{\\{t_n\\}_{n \\geq 1}}\\leq S$. Set also $V = \\gen{t_1, x} \\cong \\Z\/2 \\times \\Z\/2$. Then $\\mathcal{F}$ is generated by $\\mathrm{Aut}_{\\mathcal{F}}(S) = \\mathrm{Inn}(S)$ and $\\mathrm{Aut}_{\\mathcal{F}}(V) = \\mathrm{Aut}(V) \\cong \\Sigma_3$. Regarding the linking system $\\mathcal{L}$, the only centric radical subgroups (up to $S$-conjugation) are $S$ and $V$, with\n$$\n\\mathrm{Aut}_{\\mathcal{L}}(S) = S \\qquad \\mbox{and} \\qquad \\mathrm{Aut}_{\\mathcal{L}}(V) \\cong \\Sigma_4.\n$$\nLet $\\widetilde{\\LL}$ be the associated telescopic linking system. An easy computation shows that the only new objects in $\\widetilde{\\LL}$ are the subgroups $T_n$, for $n \\geq 2$, with $\\mathrm{Aut}_{\\widetilde{\\LL}}(T_n) = S$ for all $n \\geq 2$.\n\nLet now $\\Psi \\in \\mathrm{Aut}_{\\mathrm{typ}}^{I}(\\mathcal{L})$ be an unstable Adams operation. As usual, set $\\Psi_0 = \\Psi$ and $\\Psi_{i+1} = (\\Psi_i)^2$. Then, for each $i \\geq 0$ we have $S_i = C_S(\\Psi_i) \\cong D_{2^{n_i}}$ for some $n_i \\in \\mathbb{N}$, and we may assume without loss of generality that $V\\leq S_i$ for all $i \\geq 0$. Notice that $S_i$ contains two different $S_i$-conjugacy classes of maximal elementary abelian subgroups, and $V$ is a representative of one of them. Fix a representative $W_i\\leq S_i$ of the other $S_i$-conjugacy class of maximal elementary abelian subgroups. Notice that, after embedding $S_i$ into $S_{i+1}$, the subgroup $W_i$ becomes $S_{i+1}$-conjugate to $V$.\n\nLet also $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be the approximation by $2$-local finite groups associated to $\\{\\Psi_i\\}_{i \\geq 0}$. A careful inspection reveals that, in order to describe $(S_i, \\mathcal{F}_i, \\mathcal{L}_i)$ it is enough to specify the groups $\\mathrm{Aut}_{\\mathcal{L}_i}(S_i)$, $\\mathrm{Aut}_{\\mathcal{L}_i}(V)$ and $\\mathrm{Aut}_{\\mathcal{L}_i}(W_i)$. By construction, we have\n$$\n\\mathrm{Aut}_{\\mathcal{L}_i}(S_i) = S_i \\qquad \\mbox{and} \\qquad \\mathrm{Aut}_{\\mathcal{L}_i}(V) \\cong \\Sigma_4,\n$$\nand we have to determine the group $\\mathrm{Aut}_{\\mathcal{L}_i}(W_i)$. Let $\\varphi \\in \\mathrm{Aut}_{\\mathcal{L}_i}(V)$ be an automorphism of order $3$ that conjugates $t_1$ to $x$. An easy computation in $S$ shows that there is some $t \\in N_T(W_i, V)$ such that $t^{-1} \\cdot \\Psi_i(t) = t_1$. By Lemma \\ref{invar1} (ii), it follows that $\\varepsilon(x)^{-1} \\circ \\varphi \\circ \\varepsilon(x)$ is not fixed by $\\Psi_i$, and thus\n$$\n\\mathrm{Aut}_{\\mathcal{L}_i}(W_i) = N_{S_i}(W_i) \\cong D_8.\n$$\nThese computations imply that, for all $i$, $(S_i, \\mathcal{F}_i, \\mathcal{L}_i)$ is the $2$-local finite group associated to $PGL_2(\\mathbb{F}_q)$, where $q$ is some power of some odd prime $p$.\n\nLet now $(S_i, \\mathcal{E}_i, \\mathcal{T}_i)$ be the $2$-local finite group associated to $PSL_2(\\mathbb{F}_q)$. This $2$-local finite group is determined by $\\mathrm{Aut}_{\\mathcal{T}_i}(S_i) = S_i$ and $\\mathrm{Aut}_{\\mathcal{T}_i}(V) \\cong \\Sigma_4 \\cong \\mathrm{Aut}_{\\mathcal{T}_i}(W_i)$. Clearly, $\\{(S_i, \\mathcal{E}_i, \\mathcal{T}_i)\\}_{i \\geq 0}$ is an approximation of $(S, \\FF, \\LL)$ by $2$-local finite groups, but our computations above show that it cannot be the product of our constructions in (\\ref{Li}).\n\n\n\\section{Stable Elements Theorem for \\texorpdfstring{$p$}{p}-local compact groups}\\label{Sstable}\n\nIn this section we use the approximations constructed in the previous section to prove a version of the Stable Elements Theorem for $p$-local compact groups, which computes the cohomology of the classifying space of a $p$-local compact group with coefficients in a trivial $\\Z_{(p)}$-module as the \\emph{stable} elements of the cohomology of its Sylow $p$-subgroup with the same module of coefficients. The finite version of this result was proved in \\cite[Theorem 5.8]{BLO2} for coefficients in the trivial module $\\mathbb{F}_p$, and then generalized to any trivial $\\Z_{(p)}$-module in \\cite[Lemma 6.12]{BCGLO2}.\n\nLet $S$ be a discrete $p$-toral group, and let $\\mathcal{F}$ be a saturated fusion system. In this section we consider certain contravariant functors $A \\colon \\mathcal{F} \\to \\mathcal{C}$, where $\\mathcal{C}$ is either $\\Z_{(p)}\\mathrm{\\mathbf{-Mod}}$, the category of $\\Z_{(p)}$-modules, or $\\mathrm{\\mathbf{Gr}}-\\Z_{(p)}\\mathrm{\\mathbf{-Mod}}$, the category of graded $\\Z_{(p)}$-modules. We start with a brief discussion of some general properties of such functors. For each $X\\leq S$, let $\\iota_X \\colon X \\to S$ denote the inclusion homomorphism. This way, given a contravariant functor $A \\colon \\mathcal{F} \\to \\mathcal{C}$ and a subcategory $\\mathcal{E} \\subseteq \\mathcal{F}$, we define\n\\begin{equation}\\label{Aee}\nA^{\\mathcal{E}} \\stackrel{def} = \\{z \\in A(S) \\, | \\, A(\\iota_P)(z) = A(\\iota_Q \\circ f)(z), \\, \\forall P, Q \\in \\mathrm{Ob}(\\mathcal{E}) \\mbox{ and } \\forall f \\in \\mathrm{Hom}_{\\mathcal{E}}(P,Q)\\}.\n\\end{equation}\nGiven $\\mathcal{E}_1 \\subseteq \\mathcal{E}_2$ two subcategories of $\\mathcal{F}$, there is an obvious inclusion $A^{\\mathcal{E}_2} \\subseteq A^{\\mathcal{E}_1}$.\n\n\\begin{lmm}\\label{aux34}\n\nLet $S$ be a discrete $p$-toral group, let $\\mathcal{F}$ be a fusion system over $S$, and let $\\{(S_i, \\mathcal{F}_i)\\}_{i \\geq 0}$ be a family of finite fusion subsystems of $\\mathcal{F}$ with $\\mathcal{F}_i \\subseteq \\mathcal{F}_{i+1}$ for all $i$ (in particular, $S_i\\leq S_{i+1}$ are finite subgroups of $S$ for all $i$), and satisfying the following properties:\n\\begin{enumerate}[(i)]\n\n\\item $S = \\bigcup_{i \\geq 0} S_i$; and\n\n\\item for all $P\\leq S$ and for all $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P, S)$ there exists some $M_f \\in \\mathbb{N}$ such that, for all $i \\geq M_f$, $f|_{P \\cap S_i} \\in \\mathrm{Hom}_{\\mathcal{F}_i}(P \\cap S_i, S_i)$.\n\n\\end{enumerate}\nLet also $\\mathcal{C}$ be either $\\Z_{(p)}\\mathrm{\\mathbf{-Mod}}$, the category of $\\Z_{(p)}$-modules, or $\\mathrm{\\mathbf{Gr}}-\\Z_{(p)}\\mathrm{\\mathbf{-Mod}}$, the category of graded-$\\Z_{(p)}$-modules, and let $A \\colon \\mathcal{F} \\to \\mathcal{C}$ be a contravariant functor satisfying the following property:\n\\begin{itemize}\n\n\\item[(\\textasteriskcentered)] For each $P\\leq S$, the natural map $A(P) \\to \\varprojlim_i A(P \\cap S_i)$ is an isomorphism.\n\n\\end{itemize}\nThen, upon setting $\\mathcal{F}^{\\circ} = \\bigcup_{i \\geq 0} \\mathcal{F}_i \\subseteq \\mathcal{F}$, there are equalities\n$$\nA^{\\mathcal{F}} = A^{\\mathcal{F}^{\\circ}} = \\varprojlim_i A^{\\mathcal{F}_i}\n$$\nas subsets of $A(S)$.\n\n\\end{lmm}\n\n\\begin{proof}\n\nWe claim first that $\\mathcal{F}^{\\circ} \\subseteq \\mathcal{F}$ is the full subcategory of $\\mathcal{F}$ whose objects are the finite subgroups of $S$. Indeed, if $P\\leq S$ is a finite subgroup, then there exists some $M_P \\in \\mathbb{N}$ such that $P\\leq S_i$ for all $i \\geq M_P$, since $S = \\bigcup_{i \\geq 0} S_i$. Similarly, if $P, Q\\leq S$ are finite subgroups and $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q)$, then by condition (ii) there exists some $M_f \\in \\mathbb{N}$ such that $P, Q\\leq S_i$, and $f = f|_{P \\cap S_i} \\in \\mathrm{Hom}_{\\mathcal{F}_i}(P,Q)$ for all $i \\geq M_f$.\n\nBy (\\textasteriskcentered), we have $A(S) = \\varprojlim_i A(S_i)$, which implies that $A^{\\mathcal{F}^{\\circ}} = \\varprojlim_i A^{\\mathcal{F}_i}$. The inclusion $\\mathcal{F}^{\\circ} \\subseteq \\mathcal{F}$ implies that $A^{\\mathcal{F}} \\subseteq A^{\\mathcal{F}^{\\circ}}$ by (\\ref{Aee}). To show the reverse inclusion, let $P, Q \\in \\mathrm{Ob}(\\mathcal{F})$ and $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q)$. For $X = P, Q, S$, set $X_i = X \\cap S_i$, and notice that\n$$\nX = \\bigcup_{i \\geq 0} X_i \\qquad \\mbox{and} \\qquad A(X) = \\varprojlim_i A(X_i),\n$$\nby (i) and (\\textasteriskcentered) respectively. By condition (ii), there exists some $M_f \\in \\mathbb{N}$ such that, for all $i \\geq M_f$, $f|_{P_i} \\in \\mathrm{Hom}_{\\mathcal{F}_i}(P_i, Q_i)$. Thus, if $z \\in A^{\\mathcal{F}^{\\circ}}$, then\n$$\nA(\\iota_{P_i})(z) = A(\\iota_{Q_i} \\circ f|_{P_i})(z)\n$$\nfor all $i \\geq M_f$, and thus $A(\\iota_P)(z) = A(\\iota_Q \\circ f)(z)$. Hence $A^{\\mathcal{F}^{\\circ}} \\subseteq A^{\\mathcal{F}}$.\n\\end{proof}\n\n\\begin{rmk}\n\nIf $\\mathcal{G} = (S, \\FF, \\LL)$ is a $p$-local compact group and $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ is an approximation of $\\mathcal{G}$ by $p$-local finite groups, then conditions (i) and (ii) in Lemma \\ref{aux34} follow from condition (i) in \\ref{defiapprox} and \\ref{finmorph}, respectively. Hence, in this case \\ref{aux34} applies to any functor $A \\colon \\mathcal{F} \\to \\mathcal{C}$ as above that satisfies condition (\\textasteriskcentered).\n\n\\end{rmk}\n\n\\begin{prop}\\label{stable1}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $M$ be a (finite) $\\Z_{(p)}$-module with trivial $S$-action. Let also $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be an approximation of $\\mathcal{G}$ by $p$-local finite groups, and let $P\\leq S$. Then, there are isomorphisms\n$$\nH^{\\ast}(BP; M) \\cong \\varprojlim H^{\\ast}(B(P \\cap S_i); M) \\qquad \\mbox{and} \\qquad H^{\\ast}(B\\mathcal{G}; M) \\cong \\varprojlim H^{\\ast}(B\\mathcal{G}_i; M).\n$$\nIn particular, the functor $H^{\\ast}(-; M) \\colon \\mathcal{F} \\to \\mathrm{\\mathbf{Gr}}-\\Z_{(p)}\\mathrm{\\mathbf{-Mod}}$ satisfies condition (\\textasteriskcentered) in Lemma \\ref{aux34}.\n\n\\end{prop}\n\n\\begin{proof}\n\nFix some $P\\leq S$, and set $P_i = P \\cap S_i$ for all $i \\geq 0$. Let $X$ be either $B\\mathcal{G}$ or $BP$, and similarly let $X_i$ be either $B\\mathcal{G}_i$ or $BP_i$, depending on which case we want to prove. Note that the following holds.\n\\begin{enumerate}[(i)]\n\n\\item If $X = BP$, then $X = \\mathrm{hocolim \\,} X_i$, since $P = \\bigcup_{i \\geq 0} P_i$ by hypothesis.\n\n\\item If $X = B\\mathcal{G}$, then $X \\simeq (\\mathrm{hocolim \\,} X_i)^{\\wedge}_p$ by Lemma \\ref{approx0} and Remark \\ref{approx-1}. In particular, $H^{\\ast}(X; M) \\cong H^{\\ast}(\\mathrm{hocolim \\,} X_i; M)$.\n\n\\end{enumerate}\nConsider the homotopy colimit spectral sequence for cohomology \\cite[XII.5.7]{BK}:\n$$\nE^{r,s}_2 = \\varprojlim \\!\\! \\phantom{i}^rH^s(X_i;M) \\Longrightarrow H^{r+s}(X;M).\n$$\nWe will see that, for $r \\geq 1$, $E_2^{r,s} = \\{0\\}$, which, in particular, will imply the statement.\n\nFor each $s$, let $H^s_i = H^s(X_i;M)$, and let $F_i$ be the induced morphism in cohomology (in degree $s$) by the map $|\\Theta_i| \\colon |\\mathcal{L}_i| \\to |\\mathcal{L}_{i+1}|$ induced by the inclusion $\\mathcal{L}_i \\subseteq \\mathcal{L}_{i+1}$. The cohomology ring $H^{\\ast}(X_i;M)$ is noetherian by \\cite[Theorem 5.8]{BLO2}, and in particular $H^s_i$ is finite for all $s$ and all $i$. Thus, the inverse system $\\{H^s_i;F_i\\}$ satisfies the Mittag-Leffler condition \\cite[3.5.6]{Weibel}, and hence the higher limits $\\varprojlim^rH^s_i$ vanish for all $r \\geq 1$. This in turn implies that the differentials in the above spectral sequence are all trivial, and thus it collapses.\n\\end{proof}\n\nWe are ready to prove Theorem \\ref{thmB}, the Stable Elements Theorem for $p$-local compact groups, which we restate below.\n\n\\begin{thm}\\label{stable2}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $M$ be a finite $\\Z_{(p)}$-module $M$ with trivial $S$-action. Then, the natural map\n$$\nH^{\\ast}(B\\mathcal{G}; M) \\Right3{\\cong} H^{\\ast}(\\mathcal{F};M) \\stackrel{def} = \\varprojlim_{\\mathcal{F}} H^{\\ast}(-; M) \\subseteq H^{\\ast}(BS; M)\n$$\nis an isomorphism.\n\n\\end{thm}\n\n\\begin{proof}\n\nLet $\\{\\mathcal{G}_i = (S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be an approximation of $\\mathcal{G}$ by $p$-local finite groups, with respect to some telescopic transporter system $\\widetilde{\\LL}$ satisfying the conditions in \\ref{defiapprox}. By Remark \\ref{approx-1}, the space $B\\mathcal{G}_i \\stackrel{def} = |\\mathcal{L}_i|^{\\wedge}_p$ is the classifying space of a $p$-local finite group, and we can apply the Stable Elements Theorem for $p$-local finite groups: there is a natural isomorphism\n$$\nH^{\\ast}(B\\mathcal{G}_i; M) \\stackrel{\\cong} \\longrightarrow H^{\\ast}(\\mathcal{F}_i;M).\n$$\nBy Proposition \\ref{stable1} there are natural isomorphisms\n$$\nH^{\\ast}(B\\mathcal{G};M) \\cong \\varprojlim_i H^{\\ast}(B\\mathcal{G}_i; M) \\cong \\varprojlim_i H^{\\ast}(\\mathcal{F}_i;M) \\subseteq \\varprojlim_i H^{\\ast}(BS_i; M) \\cong H^{\\ast}(BS;M),\n$$\nand we have to show that $\\varprojlim_i H^{\\ast}(\\mathcal{F}_i;M) \\cong H^{\\ast}(\\mathcal{F};M)$. If we set $\\mathcal{F}^{\\circ} = \\bigcup_{i \\geq 0} \\mathcal{F}_i$, then Lemma \\ref{aux34} and Proposition \\ref{stable1} combined imply that there are isomorphisms\n$$\n\\varprojlim_i H^{\\ast}(\\mathcal{F}_i;M) \\cong H^{\\ast}(\\mathcal{F}^{\\circ};M) \\cong H^{\\ast}(\\mathcal{F};M),\n$$\nand this finishes the proof.\n\\end{proof}\n\n\\begin{rmk}\\label{stable21}\n\nThe reader may have noticed the difference between the original statement of the Stable Elements Theorem for $p$-local finite groups, \\cite[Theorem 5.8]{BLO3}, in terms of the orbit category of $\\mathcal{F}$ defined in (\\ref{orbitcat}), and our statement, in terms of $\\mathcal{F}$. In fact, a formulation of the Stable Elements Theorem in terms of $\\mathcal{F}$, rather than $\\mathcal{O}(\\mathcal{F})$, is already found in \\cite[6.12]{BCGLO2}, as well as in other papers, and it is rather straightforward to justify the equivalence of statements. Consider the projection functor $\\tau \\colon \\mathcal{F} \\to \\mathcal{O}(\\mathcal{F})$, which is the identity on objects. Since conjugation by elements of $S$ induces the identity on cohomology, we have a commutative triangle\n$$\n\\xymatrix{\n\\mathcal{F} \\ar[rrr]^{H^{\\ast}(-)} \\ar[d]_{\\tau} & & & \\mathrm{\\mathbf{Gr}}-\\Z_{(p)}\\mathrm{\\mathbf{-Mod}}\\\\\n\\mathcal{O}(\\mathcal{F}) \\ar[rrru]_{H^{\\ast}(-)} & & &\n}\n$$\nand an induced morphism between the corresponding inverse limits\n$$\n\\varprojlim_{\\mathcal{O}(\\mathcal{F})} H^{\\ast}(-;\\mathbb{F}_p) \\Right2{} \\varprojlim_{\\mathcal{F}} H^{\\ast}(-;\\mathbb{F}_p).\n$$\nThis morphism is easily checked to be an isomorphism upon considering both groups as subgroups of stable elements in $H^{\\ast}(S; \\mathbb{F}_p)$, since every element of $H^{\\ast}(S; \\mathbb{F}_p)$ is stable by any morphism in $\\mathcal{F}_S(S)$.\n\n\\end{rmk}\n\nWe finish this section proving Theorem \\ref{thmD}, restated as Theorem \\ref{thmd} below, which states the existence of a certain spectral sequence associated to a strongly closed subgroup of a given saturated fusion system. We first fix some notation.\n\nLet $\\mathcal{F}$ be a saturated fusion system over a discrete $p$-toral group $S$, let $R\\leq S$ be a strongly $\\mathcal{F}$-closed subgroup, and let $M$ be an $\\Z_{(p)}$-module with trivial $S$-action. For each $X\\leq S$, set $\\overline{X} = X\/(X \\cap R) \\cong XR\/R\\leq S\/R$. Then, each $f \\in \\mathrm{Hom}_{\\mathcal{F}}(P,Q)$ induces a morphism of extensions\n$$\n\\xymatrix{\nP \\cap R \\ar[r] \\ar[d]_{f_0} & P \\ar[d]^f \\ar[r] & \\overline{P} \\ar[d]^{\\overline{f}} \\\\\nQ \\cap R \\ar[r] & Q \\ar[r] & \\overline{Q},\n}\n$$\nand thus also homomorphisms\n$$\n\\gamma(f) \\colon H^n(\\overline{Q}; H^m(Q \\cap R;M)) \\Right2{f_0^{\\ast}} H^n(\\overline{Q}; H^m(P \\cap R; M)) \\Right2{\\overline{f}^{\\,\\ast}} H^n(\\overline{P}; H^m(P \\cap R;M))\n$$\nfor all $n, m \\geq 0$. This defines a contravariant functor\n\\begin{equation}\\label{frakx}\n\\mathfrak{X}^{n,m} \\colon \\mathcal{F} \\Right3{} \\Z_{(p)}\\mathrm{\\mathbf{-Mod}},\n\\end{equation}\nwhich satisfies condition (\\textasteriskcentered) in Lemma \\ref{aux34}, since, for each $P\\leq S$ and each $i \\geq 0$, the group $H^n(\\overline{P \\cap S_i}; H^m(P \\cap S_i \\cap R; M))$ is finite and the Mittag-Leffler Condition \\cite[3.5.6]{Weibel} applies. Set\n$$\nH^n(S\/R; H^m(R; M))^{\\mathcal{F}} = (\\mathfrak{X}^{n,m})^{\\mathcal{F}}\n$$\nin order to match the notation of \\cite{Diaz}\n\n\\begin{thm}\\label{thmd}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, let $R\\leq S$ be a strongly $\\mathcal{F}$-closed subgroup, and let $M$ be a finite $\\Z_{(p)}$-module with trivial $S$-action. Then there is a first quadrant cohomological spectral sequence with second page\n$$\nE^{n,m}_2 = H^n(S\/R; H^m(R;M))^{\\mathcal{F}}\n$$\nand converging to $H^{n+m}(B\\mathcal{G}; M)$.\n\n\\end{thm}\n\n\\begin{proof}\n\nThe spectral sequence of the statement is constructed as an inverse limit of spectral sequences. For the reader's convenience, the proof is divided into smaller steps.\n\n\\textbf{Step 1.} Construction of inverse systems of spectral sequences. Let $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be an approximation of $\\mathcal{G}$ by $p$-local finite groups. Notice that $R_i \\stackrel{def} = R \\cap S_i$ is strongly $\\mathcal{F}_i$-closed for all $i \\geq 0$, since $R$ is strongly $\\mathcal{F}$-closed.\n\nFor each $i \\geq 0$, consider the group extension\n$$\nR_i \\Right2{} S_i \\Right2{} S_i\/R_i,\n$$\nand its associated Lyndon-Hochschild-Serre spectral sequence $\\{E(i)_k^{\\ast, \\ast}, d_k^i\\}_{k \\geq 2}$, with second page $E(i)_2^{n,m} = H^n(S_i\/R_i; H^m(R_i;M))$, and converging to $H^{n+m}(S;M)$. For all $k \\geq 2$ and all $i, n, m \\geq 0$, the inclusion homomorphism $\\iota_{i,i+1} \\colon S_i \\to S_{i+1}$ induces homomorphisms of spectral sequences for each $i \\geq 0$,\n$$\n\\gamma(\\iota_{i, i+1}) \\colon \\{E(i+1)_k^{\\ast,\\ast}, d_k^{i+1}\\}_{k \\geq 2} \\Right3{} \\{E(i)_k^{\\ast,\\ast}, d_k^i\\}_{k \\geq 2}\n$$\nand thus an inverse system of spectral sequences $\\{\\{E(i)_k^{\\ast,\\ast}, d_k^i\\}_{k \\geq 2}, \\gamma(\\iota_{i, i+1})\\}_{i \\geq 0}$.\n\nNote that $R_i$ is strongly $\\mathcal{F}_i$-closed, and thus for each $n, m \\geq 0$ there is a functor\n$$\n\\mathfrak{X}_i^{n,m} \\colon \\mathcal{F}_i \\Right3{} \\Z_{(p)}\\mathrm{\\mathbf{-Mod}},\n$$\ndefined by similar arguments as those used in the definition the functor $\\mathfrak{X}^{n,m}$ in (\\ref{frakx}). Furthermore, we may consider the spectral sequence $\\{\\4{E}(i)_k^{\\ast, \\ast}, \\4{d}_k^i\\}_{k \\geq 2}$ of \\cite[Theorem 1.1]{Diaz}, whose second page is $\\4{E}(i)_2^{n,m} = H^n(S_i\/R_i; H^m(R_i;M))^{\\mathcal{F}_i} = (\\mathfrak{X}_i^{n,m})^{\\mathcal{F}_i}$, and which converges to $H^{n+m}(B\\mathcal{G}_i;M)$. We may see this spectral sequence as the restriction of the spectral sequence $\\{E(i)_k^{\\ast, \\ast}, d_k^i\\}_{k \\geq 2}$. We claim that $\\gamma(\\iota_{i,i+1})$ restricts to a morphism of spectral sequences\n$$\n\\gamma(\\alpha_{i, i+1}) \\colon \\{\\4{E}(i+1)_k^{\\ast,\\ast}, \\4{d}_k^{i+1}\\}_{k \\geq 2} \\Right3{} \\{\\4{E}(i)_k^{\\ast,\\ast}, \\4{d}_k^i\\}_{k \\geq 2}.\n$$\n\nFor each $i,n, m \\geq 0$ and for $X\\leq S_i$, let $A^{n,m}_i(X) = \\mathrm{Hom}_X(\\mathcal{B}^n_{\\overline{X}} \\otimes \\mathcal{B}^m_X, M)$ be the double complex defined in \\cite[Section 3]{Diaz} (although we do not give an explicit description here, notice that the complex itself does not actually depend on $i$). With this notation, for each $k \\geq 2$, the group $\\4{E}(i)_k^{n,m}$ can be seen as $(\\xi(i)^{n,m}_k)^{\\mathcal{F}_i}$, for a certain functor\n$$\n\\xi(i)_k^{n,m} \\colon \\mathcal{F}_i \\Right3{} \\Z_{(p)}\\mathrm{\\mathbf{-Mod}},\n$$\ndefined in terms of the double complexes $A^{n,m}_i(X)$ above. In particular, the functor $\\mathfrak{X}_i^{n,m}$ defined above corresponding to the case $k = 2$ (for the sake of brevity, the reader is referred to \\cite[Section 4]{Diaz} for details). Furthermore, we have a commutative triangle of functors\n$$\n\\xymatrix{\n\\mathcal{F}_i \\ar[rrr]^{\\xi(i)_k^{n,m}} \\ar[d]_{\\mathrm{incl}} & & & \\Z_{(p)}\\mathrm{\\mathbf{-Mod}} \\\\\n\\mathcal{F}_{i+1} \\ar[rrru]_{\\xi(i+1)_k^{n,m}} & & &\n}\n$$\n\nFix some $k \\geq 2$, and some $i, n, m \\geq 0$. For a subgroup $X\\leq S_i\\leq S_{i+1}$, let $\n\\iota_X \\colon X \\to S_i$ and $\\4{\\iota}_X \\colon X \\to S_{i+1}$ be the corresponding inclusion monomorphisms, and note that $\\4{\\iota}_X = \\iota_{i, i+1} \\circ \\iota_X$. Also, recall that $\\4{E}(i+1)_k^{n,m}$ is the subgroup of elements $z \\in E(i+1)_k^{n,m}$ such that\n$$\n\\xi(i+1)_k^{n,m}(\\4{\\iota}_P)(z) = \\xi(i+1)_k^{n,m}(\\4{\\iota}_Q \\circ f)(z)\n$$\nfor all $P,Q\\leq S_{i+1}$ and all $f \\in \\mathrm{Hom}_{\\mathcal{F}_{i+1}}(P,Q)$, and a similar, equation with $\\xi(i)_k^{n,m}$ replacing $\\xi(i+1)_k^{n,m}$, describes the elements of $\\4{E}(i)_k^{n,m}$.\n\nFix some $z \\in E(i+1)_k^{n,m}$, and set $w = \\gamma(\\iota_{i, i+1})(z) \\in E(i)_k^{n,m}$. Then, for all $P, Q\\leq S_i$ and all $f \\in \\mathrm{Hom}_{\\mathcal{F}_i}(P,Q) \\subseteq \\mathrm{Hom}_{\\mathcal{F}_{i+1}}(P,Q)$, the commutativity of the above triangle implies that\n$$\n\\begin{aligned}\n\\xi(i)_k^{n,m}(\\iota_P)(w) & = \\xi(i+1)_k^{n,m}(\\iota_{i, i+1} \\circ \\iota_P)(z) = \\xi(i+1)_k^{n,m}(\\4{\\iota}_P)(z) = \\\\\n & = \\xi(i+1)_k^{n,m}(\\4{\\iota}_Q \\circ f)(z) = \\xi(i+1)_k^{n,m}(\\iota_{i,i+1} \\circ \\iota_Q \\circ f)(z) = \\\\\n & = \\xi(i)_k^{n,m}(\\iota_Q \\circ f)(w).\n\\end{aligned}\n$$\nThus $w \\in \\4{E}(i)_k^{n,m}$ and the claim follows.\n\n\\textbf{Step 2.} The inverse limit spectral sequences and their convergence. Consider the inverse systems of spectral sequences defined in Step 1, $\\{\\{E(i)_k^{\\ast,\\ast}, d_k^i\\}_{k \\geq 2}, \\gamma(\\iota_{i, i+1})\\}_{i \\geq 0}$ and $\\{\\{\\4{E}(i)_k^{\\ast,\\ast}, \\4{d}_k^i\\}_{k \\geq 2}, \\gamma(\\alpha_{i, i+1})\\}_{i \\geq 0}$. For each $k \\geq 2$, and for each $n, m \\geq 0$, define\n$$\nE_k^{n,m} \\stackrel{def} = \\varprojlim_i E_k^{n,m}(i) \\qquad \\qquad \\4{E}_k^{n,m} \\stackrel{def} = \\varprojlim_i \\4{E}_k^{n,m}(i).\n$$\n\nFor each $k \\geq 2$ and each $i, n,m \\geq 0$, the group $E(i)_k^{n,m}$ is finite by definition, and thus the higher limits of $\\{E(i)_k^{n,m}\\}_{i \\geq 0}$ all vanish, by the Mittag-Leffler Condition \\cite[3.5.6]{Weibel}. A similar conclusion applies to $\\{\\4{E}(i)_k^{n,m}\\}_{i \\geq 0}$, since it is a restriction of $\\{E(i)_k^{\\ast, \\ast}\\}_{i \\geq 0}$. Furthermore, the differentials $\\{d_k^i\\}_{i \\geq 0}$ and $\\{\\4{d}_k^i\\}_{i \\geq 0}$ induce differentials $d_k$ (on $E_k^{\\ast, \\ast}$) and $\\4{d}_k$ (on $\\4{E}_k^{\\ast, \\ast}$) respectively, and an easy computation shows that\n$$\n\\begin{array}{c}\n\\mathrm{Ker}(d_k) \\cong \\varprojlim_i \\mathrm{Ker}(d_k^i) \\qquad \\qquad \\Im(d_k) \\cong \\varprojlim_i \\Im(d_k^i)\\\\[4pt]\n\\mathrm{Ker}(\\4{d}_k) \\cong \\varprojlim_i \\mathrm{Ker}(\\4{d}_k^i) \\qquad \\qquad \\Im(\\4{d}_k) \\cong \\varprojlim_i \\Im(\\4{d}_k^i).\n\\end{array}\n$$\nHence, $\\{E_k^{\\ast, \\ast}, d_k\\}_{k \\geq 2}$ and $\\{\\4{E}_k^{\\ast, \\ast}, \\4{d}_k\\}_{k \\geq 2}$ are well defined spectral sequences. Moreover, their corresponding second and infinity pages are, respectively,\n$$\n\\begin{array}{c}\nE_2^{n,m} \\cong \\varprojlim_i H^n(S_i\/R_i; H^m(R_i;M)) \\qquad \\4{E}_2^{n,m} \\cong \\varprojlim_i H^n(S_i\/R_i; H^m(R_i;M))^{\\mathcal{F}_i}\\\\[6pt]\nE_{\\infty}^{n,m} \\cong \\varprojlim_i H^{k}(S_i;M) \\cong H^{k}(S; M) \\qquad \\4{E}_{\\infty}^{n,m} \\cong \\varprojlim_i H^{k}(B\\mathcal{G}_i; M) \\cong H^{k}(B\\mathcal{G};M)\n\\end{array}\n$$\nwhere $k = n+m$, and where the last isomorphism on each position of the bottom row holds by Proposition \\ref{stable1}.\n\nIt remains to show that the corresponding second pages of the limit spectral sequences satisfy, respectively,\n$$\n\\begin{array}{l}\nE_2^{n,m} = \\varprojlim_i H^n(S_i\/R_i; H^m(R_i;M)) \\cong H^n(S\/R; H^m(R;M)) \\\\[2pt]\n\\4{E}_2^{n,m} = \\varprojlim_i H^n(S_i\/R_i; H^m(R_i;M))^{\\mathcal{F}_i} \\cong H^n(S\/R; H^m(R;M))^{\\mathcal{F}}\n\\end{array}\n$$\nBy Proposition \\ref{stable1}, there are isomorphisms \n$$\nH^{\\ast}(R; M) \\cong \\varprojlim H^{\\ast}(R_i;M) \\quad \\mbox{and} \\quad H^{\\ast}(S\/R; H^{\\ast}(R;M))\\cong \\varprojlim H^{\\ast}(S_i\/R_i; H^{\\ast}(R;M)).\n$$\nFurthermore, \\cite[Proposition B.2.3]{Rubin} implies that there are isomorphisms\n$$\n\\begin{aligned}\nH^n(S\/R; H^m(R;M)) & \\cong \\varprojlim_i H^n(S_i\/R_i; H^m(R;M)) \\cong \\\\\n & \\cong \\varprojlim_i \\varprojlim_j H^n(S_i\/R_i; H^m(R_j;M)) \\cong \\varprojlim_i H^n(S_i\/R_i; H^m(R_i;M)),\n\\end{aligned}\n$$\nsince $H^n(S_i\/R_i; H^m(R_j;M))$ is finite for all $i, n, m \\geq 0$. To finish the proof, we have to show that the $H^n(S\/R; H^m(R;M))^{\\mathcal{F}}$ is isomorphic to $\\varprojlim_i H^n(S_iR_i; H^m(R_i;M))^{\\mathcal{F}_i}$, and this follows from Lemma \\ref{aux34}, since the functor $\\mathfrak{X}^{n,m}$ in (\\ref{frakx}) satisfies the required condition (\\textasteriskcentered).\n\\end{proof}\n\n\n\\section{Mapping spaces}\\label{Smap}\n\nIn this section we describe the mapping space $\\operatorname{Map}\\nolimits(BP, B\\mathcal{G})$, where $P$ is a discrete $p$-toral group and $\\mathcal{G}$ is a $p$-local compact group, in terms of centralizers in $\\mathcal{G}$ of subgroups of $S$. Such mapping spaces where described in \\cite{BLO3} when $P\\leq S$ is centric, and in full generality in \\cite{BLO2} when $P$ is a finite $p$-group and $\\mathcal{G}$ is a $p$-local finite group. Our proof follows the same lines as the proof in \\cite[Theorem 6.3]{BLO2}.\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $H^{\\ast}(\\mathcal{F}) \\subseteq H^{\\ast}(BS)$ be the subring of stable elements for $\\mathcal{F}$. Let also $E\\leq S$ be an elementary abelian subgroup which is fully $\\mathcal{F}$-centralized, and let $j_E \\colon H^{\\ast}(\\mathcal{F}) \\to H^{\\ast}(BE)$ be the map induced by inclusion. Let $T_E$ be Lannes' $T$-functor (see \\cite{Lannes}), and let $T_E(H^{\\ast}(\\mathcal{F}); j_E)$ be the component in $T_E(H^{\\ast}(\\mathcal{F}))$ of $j_E \\in T_E^0(H^{\\ast}(\\mathcal{F})) \\cong \\mathrm{Hom}_{\\mathcal{K}}(H^{\\ast}(\\mathcal{F}), H^{\\ast}(BE))$, where $\\mathcal{K}$ is the category of unstable algebras over the mod $p$ Steenrod algebra.\n\n\\begin{lmm}\\label{mapping1}\n\nThere is an isomorphism\n$$\nT_E(H^{\\ast}(\\mathcal{F}); j_E) \\Right3{\\cong} H^{\\ast}(C_{\\mathcal{F}}(E)) \\stackrel{def} = \\varprojlim_{C_{\\mathcal{F}}(E)} H^{\\ast}(-),\n$$\nwhich is the restriction of the homomorphism $T_E(H^{\\ast}(BS);j_E) \\to H^{\\ast}(C_S(E))$ induced by the natural homomorphism $C_S(E) \\times E \\to S$.\n\n\\end{lmm}\n\n\\begin{proof}\n\nLet $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be an approximation of $\\mathcal{G}$ by $p$-local finite groups. For the reader's convenience, we have divided the proof into several shorter steps.\n\n\\textbf{Step 1.} Given a discrete $p$-toral group $X$ and a morphism $\\sigma \\colon E \\to X$, we claim that\n$$\nT_E(H^{\\ast}(BX);\\sigma^{\\ast}) \\cong H^{\\ast}(\\mathrm{Map}(BE, BX)_{B\\sigma}) \\cong H^{\\ast}(BC_X(\\sigma E)).\n$$\nIndeed, since $X$ is discrete $p$-toral, it follows that $BX$ is $p$-good and $H^{\\ast}(BX)$ is of finite type (i.e. finite in every dimension). Moreover, since both $E$ and $X$ are discrete groups, it follows that $\\mathrm{Map}(BE, BX)_{B\\sigma} \\simeq BC_X(\\sigma E)$. The claim follows immediately by \\cite[Proposition 3.4.4]{Lannes}.\n\n\\textbf{Step 2.} Next we claim that there is some $M \\in \\mathbb{N}$ such that $E$ is fully centralized in $\\mathcal{F}_i$, for all $i \\geq M$.\n\nTo prove this, fix representatives $V_0 = E, V_1, \\ldots, V_n\\leq S$ of the different $S$-conjugacy classes in $E^{\\mathcal{F}}$. Note that, since $E$ is abelian, we have $VC_S(V) = C_S(V)$ for each $V \\in E^{\\mathcal{F}}$. We will just write $C_S(V)$ instead of $VC_S(V)$ for simplicity. There is some $M \\in \\mathbb{N}$ such that, for all $i \\geq M$, we have $V_0, \\ldots, V_n\\leq S_i$, and the following holds for each $j = 0, \\ldots, n$:\n\\begin{enumerate}[(a)]\n\n\\item $|C_{S_i}(V_j)|\\leq |C_{S_i}(E)|$; and\n\n\\item $|C_{S_i}(V_j)\/C_{T_i}(V_j)| = |C_S(V_j)\/C_T(V_j)|$.\n\n\\end{enumerate}\nIndeed, since $V_j$ is $\\mathcal{F}$-conjugate to $E$, there exists some $f_j \\in \\mathrm{Hom}_{\\mathcal{F}}(C_S(V_j), C_S(E))$ such that $f_j(V_j) = E$. Moreover, by Lemma \\ref{finmorph}, there is some $M \\in \\mathbb{N}$ such that, for all $i \\geq M$ and all $j = 0, \\ldots, n$, the restriction of $f_j$ to $C_S(V_j) \\cap S_i$ is a morphism in $\\mathcal{F}_i$. Property (a) follows immediately. Property (b) is easily checked, since $S\/T \\cong S_i\/T_i$ for all $i \\geq 0$ by assumption.\n\nFor each $i \\geq M$, set $T_i = T \\cap S_i$ for short. If $V\\leq S_i$ is $\\mathcal{F}_i$-conjugate to $E$, then $V$ is $S$-conjugate to some $V_j$, for some $j \\in \\{1, \\ldots, n\\}$. Fix $x \\in N_S(V, V_j)$. By Lemma \\ref{invar1} (i), together with Proposition \\ref{fix1} (iv), it follows that $x^{-1} \\Psi_i(x) \\in C_T(V)$. Since $T$ is abelian and $T_i\\leq T$, it follows that\n$$\nx^{-1} \\Psi_i(x) \\in C_T(C_{T_i}(V)) = T.\n$$\nThus, $C_{T_i}(V)$ is $S$-conjugate (by $x$) to a subgroup of $C_{T_i}(V_j)$, again by Lemma \\ref{invar1} (i), and a similar argument with $x^{-1}$ instead of $x$ shows that in fact the element $x$ conjugates $C_{T_i}(V)$ onto $C_{T_i}(V_j)$. Moreover, via the inclusion $C_{S_i}(V)\\leq C_S(V)$, the quotient $C_{S_i}(V)\/C_{T_i}(V)$ can be identified with a subgroup of $C_S(V)\/C_T(V)$, and\n$$\n|C_{S_i}(V)\/C_{T_i}(V)|\\leq |C_S(V)\/C_T(V)| = |C_S(V_j)\/C_T(V_j)| = |C_{S_i}(V_j)\/C_{T_i}(V_j)|.\n$$\nSince $C_{S_i}(V)$ and $C_{S_i}(V_j)$ are finite groups, it follows that\n$$\n\\begin{aligned}\n|C_{S_i}(V)| & = |C_{T_i}(V)| \\cdot |C_{S_i}(V)\/C_{T_i}(V)|\\leq \\\\\n &\\leq |C_{T_i}(V_j)| \\cdot |C_{S_i}(V_j)\/C_{T_i}(V_j)| = |C_{S_i}(V_j)|\\leq |C_{S_i}(E)|,\n\\end{aligned}\n$$\nand $E$ is fully centralized in $\\mathcal{F}_i$.\n\n\\textbf{Step 3.} For each $P\\leq S$, set $\\mathcal{T}_P = \\mathrm{Rep}_{\\mathcal{F}}(E,P) = \\mathrm{Inn}(P)\\backslash \\mathrm{Hom}_{\\mathcal{F}}(E,P)$. Notice that $\\mathcal{T}_P$ is finite by \\cite[Lemma 2.5]{BLO3}. Consider the functor $\\4{T}_E \\colon \\mathcal{O}(\\mathcal{F}) \\Right2{} \\mathrm{\\mathbf{Gr}}-\\Z_{(p)}\\mathrm{\\mathbf{-Mod}}$, defined on objects by\n$$\n\\4{T}_E(P) = \\bigoplus_{\\rho \\in \\mathcal{T}_P} T_E(H^{\\ast}(BP);\\rho^{\\ast}).\n$$\nWe claim that there is an isomorphism\n$$\nT_E(H^{\\ast}(\\mathcal{F});j_E) \\cong \\varprojlim_{P \\in \\mathcal{O}(\\mathcal{F})} \\big(\\bigoplus_{\\rho \\in \\mathcal{T}_P} T_E(H^{\\ast}(BP);\\rho^{\\ast}) \\big).\n$$\n\nTo prove this, consider the orbit category $\\mathcal{O}(\\mathcal{F})$, defined in (\\ref{orbitcat}). By \\cite[Lemma 2.5]{BLO3}, all morphism sets in $\\mathcal{O}(\\mathcal{F})$ are finite. Recall also that the full subcategory $\\mathcal{F}^{\\bullet} \\subseteq \\mathcal{F}$ contains only finitely many $\\mathcal{F}$-conjugacy classes by \\cite[Lemma 3.2 (a)]{BLO3}, and thus the full subcategory $\\mathcal{O}(\\mathcal{F}^{\\bullet}) \\subseteq \\mathcal{O}(\\mathcal{F})$ contains a finite skeletal subcategory. Furthermore, $\\mathcal{F}^{\\bullet}$ contains all the $\\mathcal{F}$-centric $\\mathcal{F}$-radical subgroups of $S$ by \\cite[Corollary 3.5]{BLO3}.\n\nFix a finite skeletal subcategory $\\mathcal{O}_{sk}$ of $\\mathcal{O}(\\mathcal{F}^{\\bullet})$. The functor $T_E$ is exact and commutes with direct limits. As a consequence, it also commutes with inverse limits over finite categories, and we have\n$$\n\\begin{aligned}\nT_E(H^{\\ast}(\\mathcal{F})) & = T_E(\\varprojlim_{\\mathcal{O}(\\mathcal{F})} H^{\\ast}(-)) = \\\\\n & = T_E(\\varprojlim_{\\mathcal{O}_{sk}} H^{\\ast}(-)) \\cong \\varprojlim_{\\mathcal{O}_{sk}} T_E(H^{\\ast}(-)) = \\varprojlim_{\\mathcal{O}(\\mathcal{F})} T_E(H^{\\ast}(-)),\n\\end{aligned}\n$$\nRestricting the above to $T_E(H^{\\ast}(\\mathcal{F});j_E)$, we obtain\n$$\n\\begin{aligned}\nT_E(H^{\\ast}(\\mathcal{F});j_E) & \\cong \\varprojlim_{P \\in \\mathcal{O}_{sk}} \\big(\\bigoplus_{\\rho \\in \\mathcal{T}_P} T_E(H^{\\ast}(BP);\\rho^{\\ast}) \\big) \\cong \\\\\n & \\cong \\varprojlim_{P \\in \\mathcal{O}(\\mathcal{F})} \\big(\\bigoplus_{\\rho \\in \\mathcal{T}_P} T_E(H^{\\ast}(BP);\\rho^{\\ast}) \\big) = \\varprojlim_{\\mathcal{O}(\\mathcal{F})} \\4{T}_E(-).\n\\end{aligned}\n$$\n\n\\textbf{Step 4.} Consider the functor $\\4{T}_E \\colon \\mathcal{O}(\\mathcal{F}) \\Right2{} \\mathrm{\\mathbf{Gr}}-\\Z_{(p)}\\mathrm{\\mathbf{-Mod}}$ defined in Step 3. By precomposing with the projection functor $\\tau \\colon \\mathcal{F} \\to \\mathcal{O}(\\mathcal{F})$, we obtain a functor\n$$\n\\mathcal{F} \\Right2{} \\mathrm{\\mathbf{Gr}}-\\Z_{(p)}\\mathrm{\\mathbf{-Mod}},\n$$\nwhich by abuse of notation we also denote by $\\4{T}_E$. This will not lead to confusion since both functors take the same values on objects (as $\\tau$ is the identity on objects), as well as the same value on any two morphisms in $\\mathcal{F}$ representing a given morphism in $\\mathcal{O}(\\mathcal{F})$. Note also that, for each $P \\in \\mathrm{Ob}(\\mathcal{F}) = \\mathrm{Ob}(\\mathcal{O}(\\mathcal{F}))$, the set $\\mathcal{T}_P$ does not depend on the choice of category between $\\mathcal{F}$ and $\\mathcal{O}(\\mathcal{F})$, and an argument similar to that used in Remark \\ref{stable21} implies that\n$$\n\\begin{aligned}\n\\varprojlim_{\\mathcal{O}(\\mathcal{F})} \\4{T}_E(-) & = \\varprojlim_{P \\in \\mathcal{O}(\\mathcal{F})} \\big(\\bigoplus_{\\rho \\in \\mathcal{T}_P} T_E(H^{\\ast}(BP);\\rho^{\\ast}) \\big) \\cong\\\\\n & \\cong \\varprojlim_{P \\in \\mathcal{F}} \\big(\\bigoplus_{\\rho \\in \\mathcal{T}_P} T_E(H^{\\ast}(BP);\\rho^{\\ast}) \\big) = \\varprojlim_{\\mathcal{F}} \\4{T}_E(-).\n\\end{aligned}\n$$\nFrom now on we consider $\\4{T}_E$ as a functor on $\\mathcal{F}$. In this step we prove that the functor $\\4{T}_E$ satisfies condition (\\textasteriskcentered) in Lemma \\ref{aux34}: for each $P\\leq S$, the natural map $\\4{T}_E(P) \\to \\varprojlim_i \\4{T}_E(P \\cap S_i)$ is an isomorphism.\n\nFix $P\\leq S$, and let $\\Omega \\subseteq \\mathrm{Hom}_{\\mathcal{F}}(E,P)$ be a set of representatives of the classes in $\\mathcal{T}_P$. Note that, by definition, there is an equality\n\\begin{equation}\\label{TEP}\n\\4{T}_E(P) \\stackrel{def} = \\bigoplus_{\\rho \\in \\mathcal{T}_P} T_E(H^{\\ast}(BP);\\rho^{\\ast}) = \\bigoplus_{f \\in \\Omega} T_E(H^{\\ast}(BP); f^{\\ast}).\n\\end{equation}\nFor each $i \\geq 0$, set $P_i = P \\cap S_i$. Since $\\Omega$ is a finite set, there exists some $M_P \\in \\mathbb{N}$ such that $f(E)\\leq P_i$ for all $i \\geq M_P$ and all $f \\in \\Omega$. For simplicity we may assume that $M_P = 0$.\n\nFor each $0\\leq i\\leq j$, consider the maps $\\mathcal{T}_{P_i} \\Right1{\\alpha_i} \\mathcal{T}_P$ and $\\mathcal{T}_{P_i} \\Right1{\\beta_{i,j}} \\mathcal{T}_{P_j}$, defined by $\\alpha_i(\\rho) = \\mathrm{incl}_{P_i}^P \\circ \\rho$ and $\\beta_{i,j}(\\rho) = \\mathrm{incl}_{P_i}^{P_j} \\circ \\rho$, respectively. Notice that $\\alpha_i = \\alpha_j \\circ \\beta_{i,j}$ for all $0\\leq i\\leq j$. Moreover, we claim that $\\mathcal{T}_P$ is the colimit of the system $\\{\\mathcal{T}_{P_i}, \\beta_{i,j}\\}$. Indeed, surjectivity of $\\operatornamewithlimits{colim} \\mathcal{T}_{P_i} \\to \\mathcal{T}_P$ follows from the discussion above. To prove injectivity, fix some $i \\geq 0$, and let $\\rho, \\rho' \\in \\mathcal{T}_{P_i}$ be such that $\\alpha_i(\\rho) = \\alpha_i(\\rho')$. Let also $f, f' \\in \\mathrm{Hom}_{\\mathcal{F}}(E, P_i)$ be representatives of $\\rho$ and $\\rho'$ respectively. Then, by definition of $\\mathcal{T}_P$ there exists some $x \\in P$ such that\n$$\n\\mathrm{incl}_{P_i}^P \\circ f' = c_x \\circ \\mathrm{incl}_{P_i}^P \\circ f.\n$$\nSince $P = \\bigcup_{i \\geq 0} P_i$, it follows that $x \\in P_j$ for some $j \\geq i$, and thus $\\beta_{i,j}(\\rho) = \\beta_{i,j}(\\rho') \\in \\mathcal{T}_{P_j}$.\n\nFor each $i \\geq 0$ and each $\\rho \\in \\mathcal{T}_P$, set $\\mathcal{T}_{P_i}^{\\rho} = \\alpha_i^{-1}(\\rho)$. We also fix the following.\n\\begin{enumerate}[(a)]\n\n\\item For each $f \\in \\Omega$, let $f_i \\in \\mathrm{Hom}_{\\mathcal{F}}(E,P_i)$ be the restriction of $f$.\n\n\\item For each $\\rho \\in \\mathcal{T}_P$, fix a set $\\Omega_i^{\\rho} \\subseteq \\mathrm{Hom}_{\\mathcal{F}}(E, P_i)$ of representatives of the classes in $\\mathcal{T}_{P_i}^{\\rho}$. In particular, if $f \\in \\Omega$ represents the class $\\rho \\in \\mathcal{T}_P$, then we choose $f_i$ as representative of its own class in $\\mathcal{T}_{P_i}^{\\rho}$. Let also $\\Omega_i = \\coprod_{\\rho \\in \\mathcal{T}_P} \\Omega_i^{\\rho}$. By definition there is an equality\n\\begin{equation}\\label{TEPi}\n\\4{T}_E(P_i) \\stackrel{def} = \\bigoplus_{\\gamma \\in \\mathcal{T}_{P_i}} T_E(H^{\\ast}(BP_i);\\gamma^{\\ast}) = \\bigoplus_{\\omega \\in \\Omega_i} T_E(H^{\\ast}(BP_i); \\omega^{\\ast}).\n\\end{equation}\n\n\\item For each $\\rho \\in \\mathcal{T}_P$, each $i \\geq 0$, and each $\\omega \\in \\Omega_i^{\\rho}$, fix an element $x_{\\omega} \\in P_{i+1}$ such that $c_{x_{\\omega}} \\circ \\mathrm{incl}_{P_i}^{P_{i+1}} \\circ \\omega \\in \\Omega_{i+1}^{\\rho}$ (such an element must exist since $\\beta_{i,i+1}[\\omega] \\in \\mathcal{T}_{P_{i+1}}^{\\rho}$). In the particular case where $\\omega = f_i$ (see (a) above), we choose $x_{\\omega} = 1$. Although the element $x_{\\omega}$ clearly depends on $i$, we omit this dependence from the notation since it will be clear at all times which $i$ is involved.\n\n\\end{enumerate}\nNote that, since $x_{\\omega} \\in P_{i+1}$, we have\n\\begin{equation}\\label{samemap}\n\\mathrm{Map}(BE, BP_{i+1})_{\\4{\\omega}} = \\mathrm{Map}(BE, BP_{i+1})_{\\sigma},\n\\end{equation}\nwhere $\\4{\\omega} = \\mathrm{incl}_{P_i}^{P_{i+1}} \\circ \\omega$ and $\\sigma = c_{x_{\\omega}} \\circ \\mathrm{incl}_{P_i}^{P_{i+1}} \\circ \\omega$.\n\nFix $\\rho \\in \\mathcal{T}_P$. For each $i \\geq 0$ and each $\\omega \\in \\Omega_i^{\\rho}$, consider the homomorphism\n$$\n\\Gamma_{\\omega} \\colon C_{P_i}(\\omega E) \\times E \\Right2{} P_i,\n$$\ndefined by $\\Gamma_{\\omega}(a,y) = a \\cdot \\omega(y) = \\omega(y) \\cdot a$. Fix $i \\geq 0$ and $\\omega \\in \\Omega_i^{\\rho}$, and let $x_{\\omega}$ be as fixed in (c) above. Let also $\\4{\\omega} = \\mathrm{incl}_{P_i}^{P_{i+1}} \\circ \\omega$ and $\\sigma = c_{x_{\\omega}} \\circ \\mathrm{incl}_{P_i}^{P_{i+1}} \\circ \\omega$. Then there is a commutative diagram\n$$\n\\xymatrix@C=2cm{\nC_{P_i}(\\omega E) \\times E \\ar[d]_{(\\mathrm{incl}, \\mathrm{Id})} \\ar[r]^{\\Gamma_{\\omega}} & P_i \\ar[d]^{\\mathrm{incl}} \\\\\nC_{P_{i+1}}(\\4{\\omega} E) \\times E \\ar[d]^{\\cong}_{(c_{x_{\\omega}}, \\mathrm{Id})} \\ar[r]^{\\Gamma_{\\4{\\omega}}} & P_{i+1} \\ar[d]_{\\cong}^{c_{x_{\\omega}}}\\\\\nC_{P_{i+1}}(\\sigma E) \\times E \\ar[r]_{\\Gamma_{\\sigma}} & P_{i+1}\n}\n$$\nwhich in turn induces the commutative diagram below by first passing to classifying spaces and then applying adjunction.\n\\begin{equation}\\label{diagrams}\n\\vcenter{\n\\xymatrix@C=2cm{\nBC_{P_i}(\\omega E) \\ar[r]^{\\simeq} \\ar[d]_{B\\mathrm{incl}} & \\mathrm{Map}(BE, BP_i)_{\\omega} \\ar[d]^{B\\mathrm{incl}_{\\ast}}\\\\\nBC_{P_{i+1}}(\\4{\\omega} E) \\ar[r]^{\\simeq} \\ar[d]_{Bc_{x_{\\omega}}}^{\\cong} & \\mathrm{Map}(BE, BP_{i+1})_{\\4{\\omega}} \\ar[d]_{\\cong}^{(Bc_{x_{\\omega}})_{\\ast}}\\\\\nBC_{P_{i+1}}(\\sigma E) \\ar[r]_{\\simeq} & \\mathrm{Map}(BE, BP_{i+1})_{\\sigma}\n}\n}\n\\end{equation}\nNote that the horizontal maps in the diagram above are homotopy equivalences by Step 1. Moreover, recall from (\\ref{samemap}) that $\\mathrm{Map}(BE, BP_{i+1})_{\\4{\\omega}} = \\mathrm{Map}(BE, BP_{i+1})_{\\sigma}$.\n\nLet $\\rho \\in \\mathcal{T}_P$, and let $f \\in \\Omega$ be its representative. We claim that there is an isomorphism\n\\begin{equation}\\label{isoTP}\nH^{\\ast}(BC_P(f E)) \\cong \\varprojlim_i \\big( \\bigoplus_{\\omega \\in \\Omega_i^{\\rho}} H^{\\ast}(BC_{P_i}(\\omega E)) \\big),\n\\end{equation}\nwhere the limit is defined by the homomorphisms\n$$\nH_{\\omega} \\colon BC_{P_i}(\\omega E) \\Right2{B\\mathrm{incl}} BC_{P_{i+1}}(\\4{\\omega} E) \\Right2{Bc_{x_{\\omega}}} BC_{P_{i+1}}(\\sigma E).\n$$\nNote that the above limit does not depend on the choice of the elements $x_{\\omega}$ in (c), since a different choice would differ from $x_{\\omega}$ by an element in $C_{P_{i+1}}(\\sigma E)$.\n\nFor each $i \\geq 0$, let $f_i \\in \\mathrm{Hom}_{\\mathcal{F}}(E, P_i)$ be the restriction of $f$ as fixed in (a) above, which is an element of $\\Omega_i^{\\rho}$ by (b), and let $\\3{\\Omega}_i^{\\rho} = \\Omega_i^{\\rho} \\setminus \\{f_i\\}$. For each $\\omega \\in \\Omega_i^{\\rho}$, set for short $A_{\\omega} = H^{\\ast}(BC_{P_i}(\\omega E))$. Then, there are short exact sequences\n$$\n0 \\to \\bigoplus_{\\omega \\in \\3{\\Omega}_i^{\\rho}} A_{\\omega} \\Right2{\\iota_i} \\bigoplus_{\\omega \\in \\Omega_i^{\\rho}} A_{\\omega} \\Right2{\\pi_i} A_{f_i} \\to 0\n$$\nfor all $i \\geq 0$. Let also $\\kappa_{i+1} \\colon \\bigoplus_{\\sigma \\in \\Omega_{i+1}^{\\rho}} A_{\\sigma} \\to \\bigoplus_{\\omega \\in \\Omega_i^{\\rho}} A_{\\omega}$ be the morphism induced by the maps $H_{\\omega}$ described above, and let $\\3{\\kappa}_{i+1}$ be the restriction of $\\kappa_{i+1}$ to $\\bigoplus_{\\sigma \\in \\3{\\Omega}_{i+1}^{\\rho}} A_{\\sigma}$. In order to prove (\\ref{isoTP}) we use the above exact sequences to construct an exact sequence of inverse limits.\n\nRecall that $\\mathcal{T}_P$ is the colimit of the sets $\\mathcal{T}_{P_i}$. Thus, given $i \\geq 0$, there exists some $M \\in \\mathbb{N}$ such that $[\\mathrm{incl}_{P_i}^{P_{i+M}} \\circ \\omega] = [f_{i + M}] \\in \\mathcal{T}_{P_{i+M}}^{\\rho}$ for all $\\omega \\in \\Omega_i^{\\rho}$, where $f_{i+M}$ is the restriction of $f$ to $P_{i+M}$ as fixed in (a) above. For simplicity let us assume that $M = 1$. In terms of mapping spaces, this means that, composing with the inclusion map $BP_i \\to BP_{i+1}$, we have\n$$\n\\coprod_{\\omega \\in \\Omega_i^{\\rho}} \\mathrm{Map}(BE, BP_i)_{\\omega} \\Right2{} \\mathrm{Map}(BE, P_{i+1})_{f_{i+1}},\n$$\nand it follows that the morphism $\\3{\\kappa}_{i+1}$, defined in the previous paragraph, is simply the trivial homomorphism. The morphism $\\kappa_{i+1}$ also induces a morphism $A_{f_{i+1}} \\to A_{f_i}$, which is easily seen to coincide with the restriction homomorphism induced by the inclusion $C_{P_i}(f_iE)\\leq C_{P_{i+1}}(f_{i+1}E)$. Summarizing, we obtain commutative diagrams\n$$\n\\xymatrix@C=1.5cm{\n0 \\ar[r] & \\bigoplus_{\\sigma \\in \\3{\\Omega}_{i+1}^{\\rho}} A_{\\sigma} \\ar[d]_{\\3{\\kappa}_{i+1}= 0} \\ar[r]^{\\iota_{i+1}} & \\bigoplus_{\\sigma \\in \\Omega_{i+1}^{\\rho}} A_{\\sigma} \\ar[r]^{\\pi_{i+1}} \\ar[d]^{\\kappa_{i+1}} & A_{f_{i+1}} \\ar[r] \\ar[d]^{\\mathrm{res}_{i+1}} & 0 \\\\\n0 \\ar[r] & \\bigoplus_{\\omega \\in \\3{\\Omega}_i^{\\rho}} A_{\\omega} \\ar[r]_{\\iota_i} & \\bigoplus_{\\omega \\in \\Omega_i^{\\rho}} A_{\\omega} \\ar[r]_{\\pi_i} & A_{f_i} \\ar[r] & 0\n}\n$$\nThis produces a morphism of inverse systems, and hence, since the inverse limit functor is left exact, there is an exact sequence\n\\begin{equation}\\label{exactlim}\n0 \\to \\varprojlim_i \\big(\\bigoplus_{\\omega \\in \\3{\\Omega}_i^{\\rho}} A_{\\omega} \\big) \\Right1{} \\varprojlim_i \\big( \\bigoplus_{\\omega \\in \\Omega_i^{\\rho}} A_{\\omega} \\big) \\Right2{\\pi} \\varprojlim_i A_{f_i},\n\\end{equation}\nwhere $\\varprojlim_i \\big(\\bigoplus_{\\omega \\in \\3{\\Omega}_i^{\\rho}} H^{\\ast}(BC_{P_i}(\\omega E))\\big) = 0$ since the morphisms in the corresponding inverse system, namely $\\3{\\kappa}_{i+1}$, are trivial. Thus, $\\pi$ is a monomorphism, and it remains to prove the it is also surjective. Let $(x_i)_{i \\in \\mathbb{N}}$ be an element in $\\varprojlim_i H^{\\ast}(BC_{P_i}(f_i E))$. That is, $\\mathrm{res}_{i+1}(x_{i+1}) = x_i$ for all $i \\geq 0$. Let also\n$$\nX_i = \\pi_i^{-1}(\\{x_i\\}) \\subseteq \\bigoplus_{\\omega \\in \\Omega_i^{\\rho}} H^{\\ast}(BC_{P_i}(\\omega E)).\n$$\nRestricting the maps $\\kappa_{i+1}$ to the sets $X_{i+1}$ produces an inverse system of sets, and the resulting inverse limit $X = \\varprojlim_i X_i$ is nonempty by \\cite[Proposition 1.1.4]{RZ}. Moreover, by definition of the maps $\\kappa_i$, any element in $X$ is a preimage of $(x_i)_{i \\in \\mathbb{N}}$, and $\\pi$ is surjective. Thus, we have\n$$\nH^{\\ast}(BC_P(f E)) \\cong \\varprojlim_i H^{\\ast}(BC_{P_i}(f_i E)) \\cong \\varprojlim_i \\big(\\bigoplus_{\\omega \\in \\Omega_i^{\\rho}} H^{\\ast}(BC_{P_i}(\\omega E))\\big),\n$$\nwhere the leftmost isomorphism follows from Proposition \\ref{stable1}, and the rightmost isomorphism corresponds to $\\pi$ in (\\ref{exactlim}). This proves (\\ref{isoTP}).\n\n\nCombining Step 1 and the above discussion, we deduce the following.\n$$\n\\begin{aligned}\n\\varprojlim_i \\4{T}_E(P_i) & = \\varprojlim_i \\big( \\bigoplus_{\\omega \\in \\Omega_i} T_E(H^{\\ast}(BP_i); \\omega^{\\ast}) \\big) \\cong \\varprojlim_i \\big( \\bigoplus_{\\omega \\in \\Omega_i} H^{\\ast}(\\mathrm{Map}(BE, BP_i)_{\\omega}) \\big) \\cong \\\\\n & \\cong \\varprojlim_i \\big( \\bigoplus_{\\omega \\in \\Omega_i} H^{\\ast}(BC_{P_i}(\\omega E)) \\big) \\cong \\bigoplus_{f \\in \\Omega} H^{\\ast}(BC_P(f E)) \\cong \\\\\n & \\cong \\bigoplus_{f \\in \\Omega} H^{\\ast}(\\mathrm{Map}(BE, BP)_{f}) \\cong \\bigoplus_{f \\in \\Omega} T_E(H^{\\ast}(BP); f^{\\ast}) = \\4{T}_E(P).\n\\end{aligned}\n$$\nMore precisely, the first equality holds by (\\ref{TEPi}), while the last equality corresponds to (\\ref{TEP}), the first isomorphism in the first row follows from Step 1, the isomorphism between the last term in the first row and the first term in the second row follows from Step 1 together with the leftmost diagram in (\\ref{diagrams}), the middle isomorphism in the second row holds by (\\ref{isoTP}), and the rest follows again from Step 1.\n\n\n\\textbf{Step 5.} The isomorphism $T_E(H^{\\ast}(\\mathcal{F}); j_E) \\Right3{\\cong} H^{\\ast}(C_{\\mathcal{F}}(E))$. Consider the fusion system $C_{\\mathcal{F}}(E)$ over $C_S(E)$, and consider also the set of fusion subsystems $\\{C_{\\mathcal{F}_i}(E)\\}_{i \\geq 0}$. This setup satisfies the conditions of Lemma \\ref{aux34}: clearly, $C_S(E) = \\bigcup_{i \\geq 0} C_{S_i}(E)$, since $S = \\bigcup_{i \\geq 0} S_i$, and every morphism in $C_{\\mathcal{F}}(E)$ eventually restricts to a morphism in $C_{\\mathcal{F}_i}(E)$ for $i$ big enough, since the fusion systems $\\mathcal{F}_i$ are part of an approximation of $(S, \\FF, \\LL)$ by $p$-local finite groups. It follows that there is a sequence of isomorphisms\n$$\nT_E(H^{\\ast}(\\mathcal{F}); j_E) \\cong \\varprojlim_{\\mathcal{F}} \\4{T}_E(-) \\cong \\varprojlim_i \\big(\\varprojlim_{\\mathcal{F}_i} \\4{T}_E(-) \\big) \\cong \\varprojlim_i H^{\\ast}(C_{\\mathcal{F}_i}(E)) \\cong H^{\\ast}(C_{\\mathcal{F}}(E))\n$$\nwhere the first isomorphism follows from Steps 3 and 4 combined, the second isomorphism is a consequence of Lemma \\ref{aux34}, since we have checked in Step 4 that $\\4{T}_E$ satisfies condition (\\textasteriskcentered), the third isomorphism follows from \\cite[Lemma 5.7]{BLO2}, since we have shown in Step 2 that $E$ can be assumed to be fully $\\mathcal{F}_i$-centralized for all $i$, and the last isomorphism is a consequence of Proposition \\ref{stable1}.\n\\end{proof}\n\n\\begin{rmk}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ be an approximation of $\\mathcal{G}$ by $p$-local finite groups. Let also $E\\leq S$ be an elementary abelian subgroup which is fully centralized, and let $j_E$ be as above. Without loss of generality we may assume that $E\\leq S_i$ for all $i \\geq 0$, and that $E$ is fully $\\mathcal{F}_i$-centralized for all $i \\geq 0$. Let $j_{E,i} \\colon H^{\\ast}(\\mathcal{F}_i) \\to H^{\\ast}(BE)$ be the map induced by the inclusion $E\\leq S_i$, for all $i \\geq 0$. In this situation, we have just proved that\n$$\nT_E(H^{\\ast}(\\mathcal{F}); j_E) \\cong T_E(\\varprojlim_i H^{\\ast}(\\mathcal{F}_i); j_E) \\cong \\varprojlim_i T_E(H^{\\ast}(\\mathcal{F}_i);j_{E,i}).\n$$\nIn other words, the functor $T_E$ commutes with the inverse limit $\\varprojlim_i H^{\\ast}(\\mathcal{F}_i)$. We do not know of any general result about the functor $T_E$ commuting with infinite inverse limits.\n\n\\end{rmk}\n\nThe proof of Theorem \\ref{mapping} below requires the following result involving the quotient of a linking system by a normal discrete $p$-toral subgroup. The following is a generalization of \\cite[Lemma 5.6]{BLO2} to the compact case.\n\n\\begin{lmm}\\label{quotient2}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $\\widetilde{\\LL}$ be the telescopic linking system associated to $\\mathcal{L}$ in \\ref{expl1}. Let also $V\\leq S$ be a central subgroup in $\\mathcal{F}$ of order $p$, and let $(\\widetilde{\\LL}\/V, \\3{\\varepsilon}, \\3{\\rho})$ be the quotient transporter system, associated to the saturated fusion system $\\mathcal{F}\/V$. Finally, let $(\\widetilde{\\LL}\/V)^c \\subseteq \\widetilde{\\LL}\/V$ and $\\widetilde{\\LL}_0 \\subseteq \\widetilde{\\LL}$ be the full subcategories with object sets\n$$\n\\begin{array}{l}\n\\mathrm{Ob}((\\widetilde{\\LL}\/V)^c) = \\{P\/V \\in \\mathrm{Ob}(\\widetilde{\\LL}\/V) \\, | \\, P\/V \\mbox{ is $\\mathcal{F}\/V$-centric}\\} \\\\[2pt]\n\\mathrm{Ob}(\\widetilde{\\LL}_0) = \\{P \\in \\mathrm{Ob}(\\widetilde{\\LL}) \\, | \\, P\/V \\in \\mathrm{Ob}((\\widetilde{\\LL}\/V)^c)\\}\n\\end{array}\n$$\nrespectively. Then the following holds.\n\\begin{enumerate}[(i)]\n\n\\item $(\\widetilde{\\LL}\/V)^c$ is a linking system associated to $\\mathcal{F}\/V$.\n\n\\item $BV \\to |\\widetilde{\\LL}_0|^{\\wedge}_p \\to |(\\widetilde{\\LL}\/V)^c|^{\\wedge}_p$ is a fibration sequence.\n\n\\item The inclusion $\\widetilde{\\LL}_0 \\subseteq \\widetilde{\\LL}$ induces a homotopy equivalence $|\\widetilde{\\LL}_0|^{\\wedge}_p \\simeq B\\mathcal{G}$.\n\n\\end{enumerate}\n\n\\end{lmm}\n\n\\begin{proof}\n\nClearly, all the $\\mathcal{F}$-centric subgroups of $S$ must contain $V$, since $V$ is abelian and $\\mathcal{F}$-central. Thus, for all $P, Q \\in \\mathrm{Ob}(\\widetilde{\\LL})$, it follows by axiom (C) of transporter systems that the left and right actions of $V$ on $\\mathrm{Mor}_{\\widetilde{\\LL}}(P,Q)$ (via composition with $\\varepsilon_Q(V)$ and $\\varepsilon_P(V)$ respectively) are the same. Since the proof is rather long, it is divided into steps for the reader's convenience.\n\n\\textbf{Step 1.} For each $P\\leq S$ which contains $V$, we claim that\n$$\nP \\mbox{ fully $\\mathcal{F}$-normalized } \\Longrightarrow P\/V \\mbox{ fully $\\mathcal{F}\/V$-centralized } \\Longrightarrow \\Gamma_P\\leq \\mathrm{Aut}_S(P).\n$$\n\nLet $P\\leq S$ be such that $V\\leq P$, and note that $\\mathrm{Aut}_{\\mathcal{F}}(V) = \\{\\mathrm{Id}\\}$, since $V$ is central in $\\mathcal{F}$. Thus, the subgroup\n$$\n\\Gamma_P \\stackrel{def} = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{F}}(P) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}\/V}(P\/V))\n$$\nis a discrete $p$-toral normal subgroup of $\\mathrm{Aut}_{\\mathcal{F}}(P)$ by Lemma \\ref{Kpgp}. Since $\\mathcal{F}$ is saturated, every subgroup of $S$ is $\\mathcal{F}$-conjugate to a fully $\\mathcal{F}$-normalized subgroup. Similarly, since $\\mathcal{F}\/V$ is saturated, every subgroup of $S\/V$ is $\\mathcal{F}\/V$-conjugate to a fully $\\mathcal{F}\/V$-centralized subgroup. Thus it is enough to show the following: if $P, Q\\leq S$ are $\\mathcal{F}$-conjugate subgroups such that $P\/V$ is fully $\\mathcal{F}\/V$-centralized and $Q$ is fully $\\mathcal{F}$-normalized, then $Q\/V$ is fully $\\mathcal{F}\/V$-centralized and $\\Gamma_P\\leq \\mathrm{Aut}_S(P)$.\n\nAs show above, the group $\\Gamma_Q$ is a normal discrete $p$-toral subgroup of $\\mathrm{Aut}_{\\mathcal{F}}(Q)$. Furthermore, since $Q$ is fully $\\mathcal{F}$-normalized we have $\\mathrm{Aut}_S(Q) \\in \\operatorname{Syl}\\nolimits_p(\\mathrm{Aut}_{\\mathcal{F}}(Q))$, and thus $\\Gamma_Q\\leq \\mathrm{Aut}_S(Q)$. By axiom (II) of saturated fusion systems, every isomorphism $f \\in \\mathrm{Iso}_{\\mathcal{F}}(P,Q)$ extends to some $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}}(N_f, N_S(Q))$, where\n$$\nN_f = \\{g \\in N_S(P) \\, | \\, f \\circ c_g \\circ f^{-1} \\in \\mathrm{Aut}_S(Q)\\}.\n$$\nSet $N_S^0(P) = \\{g \\in N_S(P) \\, | \\, c_g \\in \\Gamma_P\\}$, and notice that $N_S^0(P)\/V = C_{S\/V}(P\/V)$. We claim that $N_S^0(P)\\leq N_f$. To prove that, fix $g \\in N_S^0(P)$ and $a \\in Q$, and set $b = f^{-1}(a) \\in P$. By definition, $c_g(b) = bv$ for some $v \\in V$, and we have\n$$\n(f \\circ g \\circ f^{-1})(a) = (f \\circ c_g)(b) = f(bv) = f(b) v = a v,\n$$\nwhere $f(bv) = f(b) v$ since $V$ is central in $\\mathcal{F}$ and $V,\\leq P,Q$ (and thus the morphism $f$ restricts to the identity on $V$).\n\nThe above implies that $\\gamma$ restricts to $\\gamma \\in \\mathrm{Hom}_{\\mathcal{F}}(N_S^0(P), N_S^0(Q))$, which in turn factors through a homomorphism\n$$\n\\3{\\gamma} \\in \\mathrm{Hom}_{\\mathcal{F}\/V}(C_{S\/V}(P\/V), C_{S\/V}(Q\/V)).\n$$\nSince $P\/V$ is fully $\\mathcal{F}\/V$-centralized, it follows that $\\3{\\gamma}$ is an isomorphism and $Q\/V$ is also fully $\\mathcal{F}\/V$-centralized. Furthermore, $\\gamma$ must be an isomorphism too, and thus $\\Gamma_P\\leq \\mathrm{Aut}_S(P)$.\n\n\\textbf{Step 2.} We show now that the category $(\\widetilde{\\LL}\/V)^c$ is a centric linking system associated to $\\mathcal{F}\/V$. First notice that $(\\widetilde{\\LL}\/V)^c$ is a transporter system associated to $\\mathcal{F}\/V$ by the above remarks, and thus we only have to check that, for each object $P\/V$ of $(\\widetilde{\\LL}\/V)^c$,\n$$\nE(P\/V) \\stackrel{def} = \\mathrm{Ker}(\\mathrm{Aut}_{(\\widetilde{\\LL}\/V)^c}(P\/V) \\to \\mathrm{Aut}_{\\mathcal{F}\/V}(P\/V)) = \\3{\\varepsilon}_{P\/V}(Z(P\/V)).\n$$\n\nFix $P\/V \\in \\mathrm{Ob}((\\widetilde{\\LL}\/V)^c)$, and consider the following commutative diagram\n$$\n\\xymatrix{\n\\mathrm{Aut}_{\\widetilde{\\LL}}(P) \\ar[rr]^{\\tau_P} \\ar[d]_{\\rho_P} & & \\mathrm{Aut}_{\\widetilde{\\LL}\/V}(P\/V) \\ar[d]^{\\3{\\rho}_{P\/V}} \\\\\n\\mathrm{Aut}_{\\mathcal{F}}(P) \\ar[rr]_{\\omega_P} & & \\mathrm{Aut}_{\\mathcal{F}\/V}(P\/V),\n}\n$$\nwhere $\\rho_{P}$ and $\\3{\\rho}_{P\/V}$ denote the corresponding structural functors in the transporter systems $\\widetilde{\\LL}$ and $\\widetilde{\\LL}\/V$, respectively. By definition, we have\n$$\n\\mathrm{Ker}(\\tau_P) = \\varepsilon_P(V) \\qquad \\qquad \\mathrm{Ker}(\\rho_P) = \\varepsilon_P(C_S(P)) \\qquad \\qquad \\mathrm{Ker}(\\omega_P) = \\Gamma_P,\n$$\nand an easy computation shows then that $E(P\/V) = \\tau_P(\\gen{\\varepsilon_P(C_S(P)), \\varepsilon_P(N_S^0(P))})$. In particular it follows that $E(P\/V)$ is a discrete $p$-toral group. To finish the proof, recall that $P\/V$ is $\\mathcal{F}\/V$-centric, and in particular it is fully $\\mathcal{F}\/V$-centralized. Thus $Z(P\/V) \\in \\operatorname{Syl}\\nolimits_p(E(P\/V))$, which implies that $E(P\/V) = Z(P\/V)$.\n\n\\textbf{Step 3.} $BV \\to |\\widetilde{\\LL}_0|^{\\wedge}_p \\to |(\\widetilde{\\LL}\/V)^c|^{\\wedge}_p$ is a fibration sequence. Using \\cite[Lemma 4.3 (a)]{BLO3} it is easy to check that each undercategory for the projection of $\\widetilde{\\LL}_0$ onto $(\\widetilde{\\LL}\/V)^c$ contains a category equivalent to $\\mathcal{B}(V)$ as a deformation retract. Thus, by Quillen's Theorem B, the map $|\\widetilde{\\LL}_0| \\to |(\\widetilde{\\LL}\/V)^c|$ has homotopy fiber $BV$. By \\cite[II.5.1]{BK}, the fibration sequence $BV \\to |\\widetilde{\\LL}_0| \\to |(\\widetilde{\\LL}\/V)^c|$ is still a fibration sequence after $p$-completion.\n\n\\textbf{Step 4.} The inclusion $\\widetilde{\\LL}_0 \\subseteq \\widetilde{\\LL}$ induces a homotopy equivalence $|\\widetilde{\\LL}_0|^{\\wedge}_p \\simeq B\\mathcal{G}$. Notice that the functor $(-)^{\\bullet}$ restricts to a functor $(-)^{\\bullet}$ on $\\widetilde{\\LL}_0$. Indeed, let $P \\in \\mathrm{Ob}(\\widetilde{\\LL}^{\\bullet})\\setminus \\mathrm{Ob}(\\widetilde{\\LL}_0)$, and let $Q \\in \\mathrm{Ob}(\\widetilde{\\LL})$ be such that $V\\leq Q$ and $Q^{\\bullet} = P$. Then $Q\/V\\leq P\/V$, and since $P\/V$ is not $\\mathcal{F}\/V$-centric by assumption, neither is $Q\/V$. Thus $Q \\notin \\mathrm{Ob}(\\widetilde{\\LL}_0)$. Conversely, if $Q \\in \\mathrm{Ob}(\\widetilde{\\LL}_0)$, then clearly $Q^{\\bullet}\\in \\mathrm{Ob}(\\widetilde{\\LL}_0)$.\n\nAlso, note that if $\\widetilde{\\LL}_0$ contains $\\mathcal{L}^{\\bullet}$ then the claim follows easily. Thus, fix some $P\\leq S$ in $\\mathcal{L}^{\\bullet}$ but not in $\\widetilde{\\LL}_0$. In particular, $P$ is $\\mathcal{F}$-centric, and $V\\leq P$, but $P\/V$ is not $\\mathcal{F}\/V$-centric. By replacing $P$ by a conjugate if necessary, we may assume that $C_{S\/V}(P\/V) \\neq Z(P\/V)$, and thus there is some $gV \\in S\/V$ such that $gV \\notin P\/V$ and $[gV, P\/V] = 1$. Equivalently, there is some $g \\in S$ such that $g \\notin P$ and $[g, P] = V$. Furthermore, $c_g \\in \\mathrm{Aut}_{\\mathcal{F}}(P)$ cannot be an inner automorphism, because if this was the case then $c_g = c_x$ for some $x \\in P$, and $gx^{-1} \\in C_S(P) \\setminus P = \\emptyset$, since $P$ is $\\mathcal{F}$-centric. Thus $c_g$ is a nontrivial element of $\\mathrm{Ker}(\\mathrm{Out}_{\\mathcal{F}}(P) \\to \\mathrm{Out}_{\\mathcal{F}\/V}(P\/V))$, and thus $P$ is not $\\mathcal{F}$-radical. In other words, we have shown that $\\widetilde{\\LL}_0$ contains all $\\mathcal{F}$-centric $\\mathcal{F}$-radical subgroups. By \\cite[Corollary A.10]{BLO6} the inclusion $\\widetilde{\\LL}_0 \\subseteq \\widetilde{\\LL}$ induces a homotopy equivalence $|\\widetilde{\\LL}_0|^{\\wedge}_p \\simeq B\\mathcal{G}$.\n\\end{proof}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, and let $P\\leq S$ be a fully $\\mathcal{F}$-centralized subgroup. Let also $C_{\\mathcal{G}}(P) = (C_S(P), C_{\\mathcal{F}}(P), C_{\\mathcal{L}}(P))$ be the centralizer $p$-local compact group of $P$ defined in \\ref{rmknorm}, with classifying space $BC_{\\mathcal{G}}(P)$, and let $\\mathcal{B}(P)$ be the category with a single object $\\circ_P$ and $P$ as automorphism group. By Lemma \\ref{centricNFKA}, if $Q\\leq C_S(P)$ is $C_{\\mathcal{F}}(P)$-centric then $QP$ is $\\mathcal{F}$-centric. Thus we can define a functor\n$$\n\\Gamma_{\\mathcal{L}, P} \\colon C_{\\mathcal{L}}(P) \\times \\mathcal{B}(P) \\Right3{} \\mathcal{L}\n$$\nby setting $\\Gamma_{\\mathcal{L},P}(Q, \\circ_P) = QP$ for each $C_{\\mathcal{F}}(P)$-centric subgroup of $C_S(P)$. Given a morphism $(\\varphi, g) \\in \\mathrm{Mor}_{C_{\\mathcal{L}}(P) \\times \\mathcal{B}(P)}((Q,\\circ), (R,\\circ))$, the functor $\\Gamma$ is defined as follows\n$$\n\\Gamma_{\\mathcal{L},P}(\\varphi, g) = \\varphi \\circ \\varepsilon_{QP}(g) = \\varepsilon_{RP}(g) \\circ \\varphi,\n$$\nwhere the last equality follows from condition (C) of transporter systems, since the underlying homomorphism of $\\varphi \\in \\mathrm{Mor}_{\\mathcal{L}}(QP, RP)$ restricts to the identity on $P$ by definition of $C_{\\mathcal{L}}(P)$. By first realizing nerves and them $p$-completing, we obtain a map\n$$\nBC_{\\mathcal{G}}(P) \\times (BP)^{\\wedge}_p \\Right3{} B\\mathcal{G}\n$$\n(notice that $(BP)^{\\wedge}_p$ is not necessarily equivalent to $BP$ since $P$ is a discrete $p$-toral group). By taking adjoint first and then precomposing with the natural map $BP \\to (BP)^{\\wedge}_p$, we obtain a map\n$$\n\\Gamma'_{\\mathcal{L},P} \\colon |C_{\\mathcal{L}}(P)|^{\\wedge}_p \\Right3{} \\mathrm{Map}((BP)^{\\wedge}_p, B\\mathcal{G})_{\\mathrm{incl}} \\Right3{} \\mathrm{Map}(BP, B\\mathcal{G})_{\\mathrm{incl}}.\n$$\n\n\\begin{thm}\\label{mapping}\n\nLet $\\mathcal{G} = (S, \\FF, \\LL)$ be a $p$-local compact group, let $P$ be a discrete $p$-toral group, and $\\gamma: P \\to S$ be a group homomorphism such that $\\gamma(P)$ is fully $\\mathcal{F}$-centralized in $\\mathcal{F}$. Then,\n$$\n\\Gamma'_{\\mathcal{L}, \\gamma(P)} \\colon BC_{\\mathcal{G}}(\\gamma(P)) \\Right2{\\simeq} \\mathrm{Map}(BP, B\\mathcal{G})_{B\\gamma}\n$$\nis a homotopy equivalence.\n\n\\end{thm}\n\n\\begin{proof}\n\nOur proof follows the same strategy as the proof of \\cite[Theorem 6.3]{BLO2}. The referee suggested an alternative to the cases 1-3 below, which we briefly discuss after the proof. By \\cite[Proposition 6.2]{BLO3}, for each $\\gamma \\in \\mathrm{Hom}(P, S)$,\n\\begin{equation}\\label{zero}\n\\mathrm{Map}(BP, B\\mathcal{G})_{B\\gamma} \\simeq \\mathrm{Map}(B\\gamma(P), B\\mathcal{G})_{\\mathrm{incl}}.\n\\end{equation}\nThus, it suffices to prove the statement when $P\\leq S$ is fully $\\mathcal{F}$-centralized and $\\gamma$ is the inclusion. The proof is divided into several cases for the reader's convenience.\n\n\\textbf{Case 1.} Suppose that $P$ is elementary abelian. In this case, by Theorem 0.5 \\cite{Lannes}, it is enough to prove that $\\Gamma_{\\mathcal{L},P}$ induces an isomorphism\n$$\nT_P(H^{\\ast}(B\\mathcal{G}); \\mathrm{incl}^{\\ast}) \\Right2{\\cong} H^{\\ast}(BC_{\\mathcal{G}}(P)).\n$$\nBy Theorem \\ref{stable2}, $H^{\\ast}(B\\mathcal{G}) \\cong H^{\\ast}(\\mathcal{F})$, and $H^{\\ast}(BC_{\\mathcal{G}}(P)) \\cong H^{\\ast}(C_{\\mathcal{F}}(P))$, and the above isomorphism follows from Lemma \\ref{mapping1}.\n\n\\textbf{Case 2.} Suppose that $P$ is a normal subgroup of $\\mathcal{F}$, that is, $N_{\\mathcal{F}}(P) = \\mathcal{F}$. Let $\\mathcal{L}_0 \\subseteq \\mathcal{L}$ be the full subcategory whose objects are the subgroups $Q\\leq S$ such that $C_Q(P)$ is $C_{\\mathcal{F}}(P)$-centric.\n\nIn order to prove the statement in this case, we first need to show that the inclusion of $\\mathcal{L}_0$ into $\\mathcal{L}$ induces an equivalence $|\\mathcal{L}_0|^{\\wedge}_p \\simeq |\\mathcal{L}|^{\\wedge}_p$. By \\cite[Corollary A.10]{BLO6}, it is enough to check that $\\mathrm{Ob}(\\mathcal{F}^{cr}) \\subseteq \\mathrm{Ob}(\\mathcal{L}_0)$.\n\nLet $Q \\in \\mathrm{Ob}(\\mathcal{L})$ be an $\\mathcal{F}$-centric subgroup of $S$ which is not an object in $\\mathcal{L}_0$. We claim that $Q$ is not $\\mathcal{F}$-radical. Set $Q_0 = C_Q(P)$, and note that every element of $\\mathrm{Aut}_{\\mathcal{F}}(Q)$ restricts to an automorphism of $Q_0$ since $P$ is normal in $\\mathcal{F}$. Furthermore $Q_0 \\lhd Q$, and we can define\n$$\nK = \\mathrm{Ker}(\\mathrm{Aut}_{\\mathcal{F}}(Q) \\Right2{} \\mathrm{Aut}_{\\mathcal{F}}(Q_0) \\times \\mathrm{Aut}(Q\/Q_0)) \\lhd \\mathrm{Aut}_{\\mathcal{F}}(Q).\n$$\nNote that $K$ is a discrete $p$-toral subgroup of $\\mathrm{Aut}_{\\mathcal{F}}(Q)$ by Lemma \\ref{Kpgp}. In order to prove that $Q$ is not $\\mathcal{F}$-radical, it is enough to check that $1 \\neq K \\not\\leq \\mathrm{Inn}(Q)$.\n\nBy assumption $Q_0 \\notin \\mathrm{Ob}(\\mathcal{L}_0)$, and thus $Q_0$ is not $C_{\\mathcal{F}}(P)$-centric. Thus, we may assume that $C_{C_S(P)}(Q_0) \\not\\leq Q_0$, since otherwise $Q$ can be replaced by an $\\mathcal{F}$-conjugate $R$ such that the corresponding subgroup $R_0 = C_R(P)$ is $C_{\\mathcal{F}}(P)$-conjugate to $Q_0$ and satisfies the desired condition. Set\n$$\nQ_1 \\stackrel{def} = C_S(Q_0P) = C_{C_S(P)}(Q_0).\n$$\nAs discussed above we have $Q_1 \\not\\leq Q_0 = C_Q(P)$, and thus, $Q_1 \\cap Q = C_Q(Q_0P)\\leq Q_0$, and $Q_1 \\not\\leq Q$. Also, $Q\\leq N_S(Q_1)$ by definition, and thus $Q_1Q\\leq S$ is a subgroup. Note that $Q \\lneqq Q_1Q$, and thus $Q \\lneqq N_{Q_1Q}(Q)$. Choose some $x \\in N_{Q_1Q}(Q)$ such that $x \\notin Q$. Then,\n$$\n[x, Q] = \\{xax^{-1}a^{-1} \\,\\, | \\,\\, a \\in Q\\}\\leq Q_1 \\cap Q\\leq Q_0,\n$$\nand hence $c_x \\in K$. Since $x \\notin Q$ and $Q$ is $\\mathcal{F}$-centric, it follows that $c_x \\notin \\mathrm{Inn}(Q)$, and thus $Q$ is not $\\mathcal{F}$-radical.\n\nLet $(\\mathcal{L}_0)_{P, Id}$ be the category with object set the pairs $(Q, \\alpha)$, for $Q$ in $\\mathcal{L}_0$ and $\\alpha \\in \\mathrm{Hom}_{\\mathcal{F}}(P, Q)$ and such that\n$$\n\\mathrm{Mor}_{(\\mathcal{L}_0)_{P, Id}}((Q,\\alpha), (R, \\alpha')) = \\{\\varphi \\in \\mathrm{Mor}_{\\mathcal{L}}(Q,R) \\mbox{ } | \\mbox{ } \\alpha' = \\rho(\\varphi) \\circ \\alpha\\}.\n$$\nThis is equivalent to the component of the object $(P,Id)$ in the category $\\mathcal{L}_0^P$ of \\cite[Proposition 6.2]{BLO3}, and hence there is a homotopy equivalence\n$$\n\\mathrm{Map} (BP, |\\mathcal{L}_0|^{\\wedge}_p)_{\\mathrm{incl}} \\simeq |(\\mathcal{L})_{P, Id}|^{\\wedge}_p.\n$$\n\nAt this point, one can define functors\n$$\n\\xymatrix{\n(\\mathcal{L}_0)_{P,Id} \\ar @< 2pt> [r]^{\\sigma} & C_{\\mathcal{L}}(P) \\ar @< 2pt> [l]^{\\tau}\\\\\n}\n$$\nin the same way as they are constructed in Step 2 of the proof \\cite[Theorem 6.3]{BLO2}, and which are inverse to each other up to natural transformation. This implies that the composite\n$$\nBC_{\\mathcal{G}}(P) \\Right2{\\tau} |(\\mathcal{L}_0)_{P, Id}|^{\\wedge}_p \\Right2{\\simeq} \\mathrm{Map}(BP, |\\mathcal{L}_0|^{\\wedge}_p)_{\\mathrm{incl}} \\Right2{\\simeq} \\mathrm{Map}(BP, B\\mathcal{G})_{\\mathrm{incl}}\n$$\nis a homotopy equivalence, and by construction it is equal to $\\Gamma'_{\\mathcal{L},P}$.\n\n\\textbf{Case 3.} Suppose that $P$ is a finite subgroup of $S$. As an induction hypothesis, we can assume that the statement holds for all maps with source $BP'$, with $|P'| < |P|$, and all $p$-local compact groups.\n\nFix a subgroup $V\\leq P \\cap Z(N_S(P))$ of order $p$, and note that in particular $N_S(P)\\leq C_S(V)$. By \\cite[Lemma 2.2 (b)]{BLO6}, there exists some $\\omega \\in \\mathrm{Hom}_{\\mathcal{F}}(N_S(V), S)$ such that $\\omega(V)$ is fully $\\mathcal{F}$-centralized. Hence, there is an inequality $|N_S(\\omega(P))| \\geq |N_S(P)|$ which is in fact an equality since we are assuming $P$ to be fully $\\mathcal{F}$-normalized.\n\nWe may replace $P$ and $V$ by $\\omega(P)$ and $\\omega(V)$, and assume that $V$ is fully $\\mathcal{F}$-centralized and $P$ is fully $\\mathcal{F}$-normalized. Furthermore, $P$ is fully normalized in the saturated fusion system $C_{\\mathcal{F}}(V)$, since $N_S(P) = N_{C_S(V)}(P)$.\n\nBy Case 1, the map $BC_{\\mathcal{G}}(V) \\to B\\mathcal{G}$, induced by the inclusion $C_{\\mathcal{L}}(V) \\subseteq \\mathcal{L}$, induces a homotopy equivalence $\\mathrm{Map}(BV, BC_{\\mathcal{L}}(V))_{\\mathrm{incl}} \\simeq \\mathrm{Map}(BV, B\\mathcal{G})_{\\mathrm{incl}}$, and hence also a homotopy equivalence\n$$\n\\mathrm{Map}((EP)\/V, BC_{\\mathcal{G}}(V))_{\\mathrm{incl}} \\Right2{\\simeq} \\mathrm{Map}((EP)\/V, B\\mathcal{G})_{\\mathrm{incl}}\n$$\nwhich is $P\/V$-equivariant (where the action of $P\/V$ is the action induced by the original action of $P$ on $EP$). This is still a homotopy equivalence after considering homotopy fixed point sets by \\cite[Remark 10.2]{DW0}, and thus we obtain another homotopy equivalence\n$$\n\\big[\\mathrm{Map}((EP)\/V, BC_{\\mathcal{G}}(V))_{\\mathrm{incl}}\\big]^{h(P\/V)} \\Right2{\\simeq} \\big[\\mathrm{Map}((EP)\/V, B\\mathcal{G})_{\\mathrm{incl}}\\big]^{h(P\/V)}.\n$$\nNotice that $E(P\/V) \\times_{P\/V} (EP)\/V \\simeq BP$ with the given actions (here $P\/V$ is acting diagonally on $E(P\/V) \\times (EP)\/V$). Let $X = BC_{\\mathcal{G}}(V)$ or $B\\mathcal{G}$. By definition of homotopy fixed point sets, we have\n$$\n\\begin{aligned}\n\\big[\\mathrm{Map}((EP)\/V, X)\\big]^{h(P\/V)} & = \\mathrm{Map}_{P\/V}(E(P\/V), \\mathrm{Map}((EP)\/V,X)) \\simeq \\\\\n & \\simeq \\mathrm{Map}_{P\/V}(E(P\/V) \\times (EP)\/V, X),\n\\end{aligned}\n$$\nwhere the rightmost equivalence follows by adjunction. Furthermore, $P\/V$ acts trivially on $X$, and thus it follows that $\\mathrm{Map}_{P\/V}(E(P\/V) \\times (EP)\/V, X) \\simeq \\mathrm{Map}(E(P\/V) \\times_{P\/V} (EP)\/V, X) \\simeq \\mathrm{Map}(BP,X)$. Thus, the equivalence of homotopy fixed point sets above induces the following equivalence\n$$\n\\mathrm{Map}(BP, BC_{\\mathcal{G}}(V))_{\\mathrm{incl}} \\Right2{\\simeq} \\mathrm{Map}(BP, B\\mathcal{G})_{\\mathrm{incl}}.\n$$\nWe can suppose that $\\mathcal{L} = C_{\\mathcal{L}}(V)$, and hence that $V$ is central in $\\mathcal{L}$. Let $\\mathcal{G}\/V$ be the quotient of $\\mathcal{G}$ by $V$, as described in Definition \\ref{quotient1}. In particular, $\\mathcal{F}\/V$ is a saturated fusion system on $S\/V$, and $\\mathcal{L}\/V$ is a transporter system.\n\nConsider the full subcategories $\\mathcal{L}_0 \\subseteq \\mathcal{L}$ and $(\\mathcal{L}\/V)^c \\subseteq \\mathcal{L}\/V$ whose objects are the subgroups $Q\\leq S$, respectively $Q\/V\\leq S\/V$, such that $Q\/V$ is $\\mathcal{F}\/V$-centric. In particular, $(\\mathcal{L}\/V)^c$ determines a centric linking system associated to $\\mathcal{F}\/V$, and there are homotopy equivalences $|\\mathcal{L}_0|^{\\wedge}_p \\simeq B\\mathcal{G}$ and $|(\\mathcal{L}\/V)^c|^{\\wedge}_p \\simeq |\\mathcal{L}\/V|^{\\wedge}_p$.\n\nFinally, let also $\\mathcal{F}' = N_{\\mathcal{F}}(P)$, $\\mathcal{L}' = N_{\\mathcal{L}}(P)$, and define $\\mathcal{L}'\/V$, $\\mathcal{L}_0' \\subseteq \\mathcal{L}'$ in a similar way as done above. It follows by Lemma \\ref{quotient2} that there are fibration sequences\n$$\n\\xymatrix@R=2mm{\nBV \\ar[rr] & & |\\mathcal{L}_0|^{\\wedge}_p \\ar[rr]^{\\Phi} & & |(\\mathcal{L}\/V)^c|^{\\wedge}_p \\\\\nBV \\ar[rr] & & |\\mathcal{L}_0'|^{\\wedge}_p \\ar[rr]^{\\Phi'} & & |(\\mathcal{L}'\/V)^c|^{\\wedge}_p,\\\\\n}\n$$\nand hence also a homotopy pull-back square\n$$\n\\xymatrix{\n\\mathrm{Map}(BP, |\\mathcal{L}_0'|^{\\wedge}_p)_{\\iota} \\ar[rr]^{I_1} \\ar[d]_{\\Phi' \\circ -} & & \\mathrm{Map}(BP, |\\mathcal{L}_0|^{\\wedge}_p)_{\\mathrm{incl}} \\ar[d]^{\\Phi \\circ -} \\\\\n\\mathrm{Map}(BP, |(\\mathcal{L}'\/V)^c|^{\\wedge}_p)_{\\Phi \\circ \\mathrm{incl}} \\ar[rr]_{I_2} & & \\mathrm{Map}(BP, |(\\mathcal{L}\/V)^c|^{\\wedge}_p)_{\\Phi \\circ \\mathrm{incl}},\n}\n$$\nwhere $\\mathrm{Map}(BP, |\\mathcal{L}_0'|^{\\wedge}_p)_{\\iota}$ is the union of the connected components which map to the inclusion in $|\\mathcal{L}_0|^{\\wedge}_p$ and to $\\Phi \\circ \\mathrm{incl}$ in $|(\\mathcal{L}'\/V)^c|^{\\wedge}_p$, and $I_1$, $I_2$ are inclusions.\n\nBy (\\ref{zero}), together with the induction hypothesis, and since $P\/V$ has strictly smaller order than $P$ (recall that $P$ is finite by hypothesis), there are homotopy equivalences\n$$\n\\begin{aligned}\n|C_{(\\mathcal{L}\/V)^c}(P\/V)|^{\\wedge}_p & \\RIGHT6{\\Gamma'_{\\mathcal{L}\/V, P\/V}}{\\simeq} \\mathrm{Map}(B(P\/V), |(\\mathcal{L}\/V)^c|^{\\wedge}_p)_{\\mathrm{incl}} \\Right1{} \\\\\n & \\RIGHT6{- \\circ \\operatorname{proj}\\nolimits}{\\simeq} \\mathrm{Map}(BP, |(\\mathcal{L}\/V)^c|^{\\wedge}_p)_{f \\circ \\mathrm{incl}}\n\\end{aligned}\n$$\nand similarly for maps to $|(\\mathcal{L}'\/V)^c|^{\\wedge}_p$. Since $\\Gamma'_{\\mathcal{L}\/V, P\/V}$ is the composite of $\\Gamma'_{\\mathcal{L}'\/V, P\/V}$ with the inclusion by definition , this shows that the map $I_2$ in the diagram above is a homotopy equivalence, and hence so is $I_1$. In particular, $\\mathrm{Map}(BP, |\\mathcal{L}_0'|^{\\wedge}_p)_{\\iota}$ is connected and contains the component of the inclusion. Thus, Case 3 follows from Case 2 applied to the mapping space $\\mathrm{Map}(BP, |\\mathcal{L}_0'|^{\\wedge}_p)_{\\mathrm{incl}}$.\n\n\\textbf{Case 4.} Suppose that $P$ is an infinite discrete $p$-toral group. By Lemma \\ref{central2}, there is a sequence of subgroups $P_0\\leq P_1\\leq \\ldots$ such that $P = \\bigcup_{n \\geq 0} P_n$, and such that $P_n$ is fully $\\mathcal{F}$-centralized with $C_{\\mathcal{G}}(P_n) = C_{\\mathcal{G}}(P)$ for all $n \\geq 0$. We have a sequence of homotopy equivalences\n$$\n\\begin{aligned}\n\\mathrm{Map}(BP, B\\mathcal{G})_{\\mathrm{incl}} & = \\mathrm{Map}(\\mathrm{hocolim \\,} \\mbox{ } BP_n, B\\mathcal{G})_{\\mathrm{incl}} \\simeq \\\\\n& \\simeq \\operatornamewithlimits{holim} \\mbox{ } \\mathrm{Map}(BP_n, B\\mathcal{G})_{\\mathrm{incl}} \\simeq \\operatornamewithlimits{holim} \\mbox{ } BC_{\\mathcal{G}}(P_n) = BC_{\\mathcal{G}}(P),\n\\end{aligned}\n$$\nwhere the equivalence $\\mathrm{Map}(\\mathrm{hocolim \\,} \\mbox{ } BP_n, B\\mathcal{G})_{\\mathrm{incl}} \\simeq \\operatornamewithlimits{holim} \\mbox{ } \\mathrm{Map}(BP_n, B\\mathcal{G})_{\\mathrm{incl}}$ follows from \\cite[Proposition 2, page 187]{D-F}. This finishes the proof.\n\\end{proof}\n\nThe reader may think of replacing Cases 1 to 3 in the proof above by the following argument. Given a $p$-local compact group $\\mathcal{G} = (S, \\FF, \\LL)$ and a fully $\\mathcal{F}$-centralized finite subgroup $P\\leq S$, consider the centralizer $p$-local compact group $C_{\\mathcal{G}}(P) = (C_S(P), C_{\\mathcal{F}}(P), C_{\\mathcal{L}}(P))$ of $P$ in $\\mathcal{G}$. Set for short $Z = C_S(P)$, $\\mathcal{E} = C_{\\mathcal{F}}(P)$, and $\\mathcal{T} = C_{\\mathcal{L}}(P)$. Given a fine unstable Adams operation (see \\ref{uAo}), we may assume that $\\Psi(P) = P$, since $P$ is a finite subgroup of $S$, and thus $\\Psi$ restricts to a fine unstable Adams operation on $C_{\\mathcal{G}}(P)$.\n\nThis way, $\\Psi$ defines approximations of $\\mathcal{G}$ and $C_{\\mathcal{G}}(P)$ by $p$-local finite groups, namely $\\{(S_i, \\mathcal{F}_i, \\mathcal{L}_i)\\}_{i \\geq 0}$ and $\\{(Z_i, \\mathcal{E}_i, \\mathcal{T}_i)\\}_{i \\geq 0}$ respectively. Moreover, we may assume that $P\\leq S_i$ for all $i \\geq 0$. It is not hard to see that $Z_i = C_{S_i}(P)$ for all $i \\geq 0$, since $Z_i = Z \\cap S_i = C_S(P) \\cap S_i$. The main difficulty of this argument is that it is not clear whether $\\mathcal{E}_i$ corresponds to the centralizer fusion system of $P$ in $\\mathcal{F}_i$ for any $i$. Essentially, the main problem is that the finite retraction pairs for $\\mathcal{G}$ and $C_{\\mathcal{G}}(P)$ given in \\ref{expl1} do not agree with each other in general, and \\ref{expl3} does not apply to this situation, since in general $C_S(P)$ does not have finite index in $S$. One could drop this last condition, but at the price of dealing with a more complicated situation.\n\n\\begin{rmk}\n\nWith the above description of the homotopy type of the mapping spaces $\\operatorname{Map}\\nolimits(BP, B\\mathcal{G})$, one could now generalize the results of C. Broto, N. Castellana, J. Grodal, R. Levi and B. Oliver \\cite{BCGLO1, BCGLO2}. More precisely, \\cite[Theorem A]{BCGLO1} has already been proved for $p$-local compact groups as \\cite[Theorem 4.2]{BLO6}, and \\cite[Theorem B]{BCGLO1} would follow easily now from our result above. Regarding \\cite{BCGLO2}, some results have already been extended to $p$-local compact groups in \\cite[Appendix B]{Gonza2}, and the rest would follow by the same arguments (with some minor modifications). We omit this for the sake of brevity of this paper.\n\n\\end{rmk}\n\n\n\\bibliographystyle{gtart}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section{Introduction}\nAstronomical observations have been traditionally done with visible light. From~the times of Galileo times until now we have been able to expand the observation range to all the electromagnetic spectrum, from~radio to gamma rays.\nApart from photons, the~discovery of other particles has opened the possibility of using them as cosmic messengers to explore the Universe. For~instance, cosmic rays (CRs) were first discovered in the early 1910s. We know that CRs are ionized nuclei of extraterrestrial origin, and~that they are produced and accelerated in a broad energy range, reaching energies above 10$^{20}$ eV. The~spectrum of the CRs detected at Earth decreases with the energy, following approximately a power law E$^{-n}$ with n$\\sim$3. The~Sun is the main source of CRs below GeV energies. Up~to PeV energies, CR are believed to be dominated by Galactic sources, and~then, at~the highest energies, CRs are likely of extragalactic origin~\\cite{ParticleDataGroup:2020ssz}. As CRs are charged, their directionality is lost due to the Galactic magnetic field. This is probably one of the main reasons why the origin of the most energetic CRs is still unknown.\nWe have also detected neutrinos from extraterrestrial origin. First from the Sun~\\cite{Davis:1968cp}, and~then from a nearby supernova explosion in 1987~\\cite{Hirata:1988ad,IMB:1987klg,Baksan:1987} which can be considered as the birth of neutrino astronomy. By~that time, the first project to construct a neutrino telescope was already ongoing~\\cite{DUMAND:1989dxw}. However, only after decades of research and development, neutrino astronomy had its turning point in 2014 with the discovery of a high-energy cosmic neutrino flux by the IceCube collaboration~\\cite{IceCube:2014stg}.\nThe most recent cosmic messengers discovered are gravitational waves (GWs). The~path to the first GW detection was also not easy, and~it took a century from their theoretical prediction to the first confirmation in 2015 by the LIGO and VIRGO collaborations~\\cite{LIGOScientific:2016aoc}.\n\n\nMultimessenger astronomy originates as a consequence of astrophysical neutrino and GW detection techniques reaching maturity. Multimessenger astronomy is based on the observation of four cosmic messengers, namely, photons, CRs, neutrinos, and~GWs. The~detection in coincidence of all, or~some, of~these messengers allows the study of a source in a similar fashion as it has been done with multiwavelength electromagnetic observations. Moreover, the~fact of involving different types of particles adds extra information coming from interactions involving all fundamental forces of~nature.\n\nWe have divided this review into three sections. In~Section~\\ref{Milestones}, we will discuss the two events that marked the start of the multimessenger era, four years ago. In~Section~\\ref{Results}, we will review the most recent results and what we have learned from them. To~conclude, in~Section~\\ref{Future}, we will discuss the future prospects and challenges in the~field.\n \n\\section{Multimessenger Astronomy~Milestones}\n\\label{Milestones}\nExcluding the solar neutrino detection, the~observation by chance of neutrinos coming from the supernova explosion SN1987A is the first astronomical event producing a multimessenger (photon--neutrino) coincidence. However, there are two main events, both happening in 2017, that really marked the birth of a new field in astrophysics, multimessenger~astronomy.\n\nThe first event was the result of two neutron stars merging into a black hole. This produced a GW (GW170817) that was detected by the LIGO and Virgo~\\cite{LIGOScientific:2017vwq} Scientific Collaborations. Less than 2 seconds after the event, a~short gamma-ray burst (GRB) (GRB 170817A) was detected by the Fermi and INTEGRAL satellites. This coincidence triggered a campaign where several observatories followed-up the event in an unprecedented way~\\cite{LIGOScientific:2017ync}.\nThanks to this coincident detection it was possible to determine the location and the type of sources involved, bringing the first experimental evidence of a kilonova~\\cite{Metzger:2017}, a~type of transient event, theoretically predicted more than two decades ago~\\cite{Lixin:1998}, where nucleosynthesis of the heavy elements is produced.\n\nThe second event (IC-170922A) was triggered by a high-energy neutrino, of~about 300 TeV, detected by the IceCube observatory on 22 September 2017. Again, this event was extensively followed up by other observatories. \nIn this case, observations from the Fermi-LAT satellite were able to point out a blazar (TXS0506+056) in active state which was in spatial and temporal coincidence with the neutrino event. The~event was rejected to be produced by background fluctuations at 3$\\sigma$ level~\\cite{IceCube:2018dnn}.\nAfter this detection, archival analysis of IceCube data prior to the IceCube-170922A event unveiled a potential flare in neutrinos~\\cite{IceCube:2018cha}, between~September 2014 and March 2015, with~3.5$\\sigma$ statistical significance and independent of the 2017 neutrino alert. In~this case, no gamma-ray counterpart was observed.\nAn extensive multiwavelength monitoring of TXS0506+056 started after the coincident event in September 2017 showed a low state emission except for December 1st and 3rd, 2018 with a flare comparable to the one in 2017~\\cite{Satalecka:2021}. However, no neutrino excess was observed.\nIt is also important to mention that TXS0506+056 came out as the second most significant source (2.8$\\sigma$ pre-trial) in the point source search analysis done with the ANTARES neutrino telescope using a pre-selected list of sources~\\cite{Illuminati:2021b}, which makes the case of TXS0506+056~stronger.\n\n\\section{Recent~Results}\n\\label{Results}\nOnce it has been well established that a flux of high-energy neutrinos of cosmic origin exist~\\cite{IceCube:2014stg}, the~next step is to disentangle it and identify which are the sources. The~most recent all-sky searches performed by ANTARES~\\cite{Illuminati:2021b} and IceCube~\\cite{IceCube:2019cia} did not reveal any significant detection above the discovery threshold of 5$\\sigma$, with the excess near the galaxy NGC 1068 observed by IceCube being the most interesting spot with a post-trial significance of 2.9$\\sigma$.\nThis type of high-energy searches benefit from multimessenger astronomy thanks to including the sky coordinates and timing information from potential cosmic messenger counterparts. That was how the first evidence of a cosmic neutrino source, TXS0506+056, was~found. \n\nUnderstanding the multimessenger emission from TXS0506+056 has been challenging from the theoretical point of view, as it is difficult to get a good agreement between the observed neutrino signal and other wavelength observations. \nIf one tries to explain it with a single-zone model, i.e.,~both gammas and neutrinos coming from the same region, one finds out that a leptonic scenario with a radiatively subdominant hadronic component provides the only physically consistent single-zone picture~\\cite{Keivani:2018rnh}.\nA higher neutrino flux would be expected if the source hosts two physically distinct emitting regions (see for instance~\\cite{Xue:2019txw}). However, current observations cannot discriminate between single- or multi-zone emission models.\nRelated to this, a~compelling neutrino--radio correlation~\\cite{Plavin:2020mkf} has been recently discovered. The~authors proposed that neutrinos and gamma rays may be indeed produced in different regions~\\cite{Plavin:2020emb}. If~this is actually the case, X-ray and radio may be better wavelengths when looking for photon--neutrino correlations. \nThe correlation with radio blazars is also supported by ANTARES observations~\\cite{ANTARES:2020zng}.\nIn this regard, ANTARES has recently reported the results from an untriggered search from radio blazars with an interesting association coming from J0242+1101~\\cite{Illuminati:2021}.\n\nThanks to the IceCube alert system~\\cite{IceCube:2016cqr}, which is presently providing on the order of 10 (20) gold (bronze) alerts per year\\endnote{Gold (bronze) alerts are neutrino events with >50\\% (>30\\%) probability of being from astrophysical origin.}, more coincidences between neutrino and blazars have been found lately. For~instance, PKS 1502+106 blazar was coincident with a 300~TeV neutrino~\\cite{Rodrigues:2020fbu}. In~this case, the blazar was in a quiescent state at the time of the neutrino alert. However, no more neutrinos were detected. \nAnother example is 3HSP J095507.9, which was also coincident with a high-energy neutrino detected by IceCube~\\cite{Paliya:2020mqm,Giommi:2020viy}. However, for~this event a lot of sources lay around the best position provided by IceCube, preventing the identification of a potential source candidate. This also underscores that sub-degree angular resolution, achievable by future neutrino observatories, will be key when looking for spatial coincidences.\nFrom Fermi-LAT observations we know that blazars are the most abundant extragalactic gamma-ray sources, constituting roughly 80\\% of the entire extragalactic source population~\\cite{Fermi-LAT:2019pir}. However, current predictions based on stacking catalog searches performed with IceCube data estimate that neutrinos emitted by blazars, in~the range between around 10 TeV and 2 PeV, can only contribute up to 27\\% to the total neutrino diffuse flux~\\cite{IceCube:2016qvd}. \nMore multimessenger observations with next generation experiments are required to test current theoretical models, and~therefore provide valuable information to understand the particle production and acceleration in~blazars. \n\nApart from blazars other neutrino candidates have been already identified thanks to multimessenger observations. This is the case of Tidal Disruption Events (TDEs), which are the result of a star being ripped apart when passing next to a supermassive black hole. TDEs were already hypothesized as possible neutrino sources, e.g., in~\\cite{Wang:2011}. However, it was not until 2019 when an IceCube neutrino, with~59\\% probability of being of astrophysical origin (IC-191001A), triggered an alert that was followed-up by the Zwicky Transient Facility~\\cite{Bellm:2019}. In~spatial coincidence with the neutrino alert a TDE (AT2019dsg) was observed. Given that TDEs are rare events, the~chance probability of finding this coincident event was estimated to be less than 0.5\\%~\\cite{Stein:2020xhk}.\nAfter the first neutrino-TDE coincidence, more recently another possible association has been detected (IC200530A with AT2019fdr) which has been considered by some scientists as evidence of an emerging trend. The~chance probability of finding this second event in coincidence was also small.\nBoth events have been followed up by ANTARES, however did not produce any significant neutrino {excess}~\\cite{ANTARES:2021jmp}.\nThe~non detection does not contradict the observation by IceCube, as~the sensitivity of ANTARES was above the neutrino flux prediction.\nOn the other hand, there are preliminary indications of an excess in GVD-Baikal data~\\cite{Allakhverdyan:2021}.\nCurrent estimations predict that TDEs contribute at least 2\\% but not more than 40\\% of the total neutrino flux~\\cite{Stein:2021}. Again, more observations are needed to confirm TDEs as sources of high-energy neutrinos and~determine their precise contribution to the diffuse neutrino~flux.\n\nGRBs are another type of sources that have long been predicted as good candidates to emit high-energy neutrinos, see, for instance, in~\\cite{Waxman:1997ti}. However, so far, all the searches have been unsuccessful~\\cite{Albert:2016eyr,IceCube:2016ipa}.\nRecently, some studies have tried to infer what would be the relative contribution of the different neutrino candidate sources, see, e.g., in~\\cite{Bartos:2021tok}. However, it seems that there is no clear indication of a dominant type of source producing cosmic neutrinos. Interestingly enough, the~same study suggest that there is room for unknown~sources.\n\n\\section{Future Prospects and~Challenges}\n\\label{Future}\n\\unskip\n\\subsection{Future Instruments and Instrument~Upgrades}\n\nRegarding neutrino telescopes there are three major projects that will be operating in the near future.\nKM3NeT~\\cite{KM3Net:2016zxf} is a research infrastructure being built in the Mediterranean sea. KM3NeT is composed of two detectors, first ARCA (Astroparticle Research with Cosmics in the Abyss) which is designed to be sensitive to high-energy neutrinos in the TeV-PeV range, and~therefore with astrophysics studies as the main goal. The~second detector is called ORCA (Oscillation Research with Cosmics in the Abyss) and it is sensitive to GeV neutrinos. The~ORCA detector will primarily be used for the study of neutrino properties.\nBoth instruments will use the same technology and detection principle, i.e.,~array of photomultipliers tubes (PMTs) in sea water, being the main difference the volume covered, and~therefore the PMT density.\nARCA is expected to be fully operational in 2027 and ORCA in~2025.\n\nMoreover, in water there is the GVD-Baikal project that is in construction in the Baikal Lake in Russia. GVD-Baikal is currently operational with 2304 optical modules arranged in eight~clusters of eight strings each. In~the present configuration, it has an effective volume of 0.4~km$^{3}$ for cascades with energy above 100 TeV~\\cite{Baikal:2021}. Current plans are to deploy six~additional clusters for the period from 2022 to 2024 which should provide an additional 0.3~km$^{3}$ effective volume.\n\nThe leading project in neutrino telescopes in the last decade has been IceCube~\\cite{IceCube:2021} which is a neutrino telescope installed in the South Pole.\nAfter 10 years of successful operation there are plans for two major upgrades. One, called IceCube-Gen2~\\cite{IceCube-Gen2:2020qha}, expected to be completed by the early 2030s, will significantly increase the IceCube effective volume and energy range sensitivity. The~other, called IceCube-Upgrade~\\cite{IceCube:2019xdf}, represents a fraction of a larger project called Precision IceCube Next Generation Upgrade (PINGU)~\\cite{IceCube:2016xxt}, which aims to increase the sensitivity to lower energies even more than with IceCube-DeepCore, mainly thanks to a higher string density, and~that will mostly study neutrino~properties.\n\nWe can also add to the list of future intended neutrino telescopes the Pacific Ocean Neutrino Experiment (P-ONE)~\\cite{PONE:2021}. The~P-ONE project is presently in research and development phase. The~goal of the collaboration is to install a multi-cubic-kilometer neutrino telescope in the Pacific Ocean, which is expected to be operational in the next~decade.\n\n\\textcolor{black}{Other neutrino experiments, foreseen to be operational by the end of this decade, like Hyper-Kamiokande~\\cite{Hyper-Kamiokande:2018ofw} and DUNE~\\cite{DUNE:2020lwj}, will be sensitive to lower neutrino energies (MeV-GeV). However, they can still contribute to get the whole picture of some astrophysical events. For~instance, the~explosion of a nearby supernova, where low-energy neutrinos are expected to be produced.}\n\nThere are also very exciting plans for next generation experiments aiming to detect other cosmic messengers. Just to mention a few of them, we have the Cherenkov Telescope Array (CTA)~\\cite{CTA:2021} with two planned sites (Northern and Southern Hemisphere), and~LHAASO~\\cite{LHAASO:2021}, fully operational since July 2021, detecting gamma rays. KAGRA~\\cite{KAGRA:2019}, detecting GWs, will join LIGO and Virgo for the next GW data taking run (O4), which is expected to start in late 2022. Finally, detecting ultra-high-energy CRs, there will be AugerPrime~\\cite{PierreAuger:2016qzd}, the~upgrade of the Pierre Auger Observatory.\nSuch a network of observatories, distributed in different locations around the globe (see Figure~\\ref{fig1}), will provide a full multimessenger coverage of the~sky.\n\n\\subsection{Alert Systems and~Strategies}\n\nConsidering the number of experiments currently being under construction and planned, it seems clear that an efficient communication between collaborations is crucial. Moreover, for~the case of pointing instruments, like CTA, this communication also needs to be fast. \n\\textcolor{black}{Neutrinos are actually very good messengers to trigger alerts because they are able to easily escape from sources. These neutrino alerts can give an early warning to other observatories of an incoming event, allowing for a prompt follow up of transient~phenomena.}\n\nThere are already ways to announce, in~real-time, interesting events to the astrophysics community, e.g., the~GammaRay Coordinates Network (GCN)~\\cite{GCN:2021}. In~addition to these announcements, there are also sites, like the Astronomers Telegram (ATEL)~\\cite{ATEL:2021}, where a brief report about recent observations made by the experiments is posted online.\nStill in beta testing phase there is a project called {Astro-COLIBRI}~\\cite{Reichherzer:2021pfe}\nwhose goal is to act as a central platform where a large set of information coming from different experiments is gathered. This can be accessed via web or smartphone interface. The~data will be immediately available and will contain relevant information such as the visibility of the event for a given observatory, the~false alarm rate, or~the probability of the event to be of astrophysical~origin.\n\n\\begin{figure}[H]\n\\includegraphics[width=0.95\\linewidth]{Figures\/MM_WorldMap.pdf}\n\\caption{Earth map indicating the location of a selection of multimessenger observatories that are either currently operating (circles) or planned (triangles). Satellites are depicted outside to the~map.}\n\\label{fig1}\n\\end{figure} \n\nIn addition to these prompt alert systems across collaborations, there are alert follow-up programs like TaToO~\\cite{ANTARES:2015fce}, where the most promising ANTARES events trigger a prompt optical, radio, and X-ray follow-up on the sky region where the neutrino candidate comes from, looking for any potential transient counterpart within hours, days, and months since the event. This is done using a network of optical telescopes at different locations (at a rate of 25 alerts per year) and, for~the most energetic ones (around 6 alerts per year), the~XRT instrument aboard the Swift satellite and the Murchison Wide field Array radio telescope~\\cite{tatoo:2021}.\n\nAs another example of how data from different experiments is shared, we have the Astrophysical Multimessenger Observatory Network (AMON)~\\cite{AyalaSolares:2019iiy}. One of the main ideas of AMON is to use sub-threshold data from different experiments to exploit the fact that a combined detection is expected to increase the significance of the event. Therefore, an~event that by itself is not enough to claim a detection with a single experiment, and~could have been rejected, can actually become significant when detected in coincidence with other observatories. An~example of a recent analysis done through this network is~\\cite{Hugo:2021}, focused on gamma-ray and neutrino~coincidences.\n\n\\subsection{Future~Challenges}\nConcerning the multimessenger astronomy goals in the next few years, one thing that should be attainable very soon is a firm confirmation of a source of cosmic neutrinos above the discovery threshold of 5$\\sigma$. To~this end, the~selection of the most promising sources, to~reduce the amount of trials in the search, thanks to multimessenger observations will be crucial.\nThis is quite likely to be accomplished by more than one project which will provide an unbiased way of measuring the spectrum of the sources, bringing key information to understand the high-energy neutrino production and acceleration mechanisms in the source. \nAlso combined analyses are possible, as~has been already done in the past~\\cite{ANTARES:2020srt}.\n\nOne multimessenger observation that is most awaited, and~can probably be achieved thanks to the improved sensitivity of the future experiments, is the coincident detection of a GW event with high-energy neutrinos. \nThanks to recent GW observations, we have a better understanding of the link between neutron star mergers and short GRBs, and~the physics involved. We already discussed the particular case of the binary neutron star merger GW170817, which led to the GRB170817A coincidence. This type of event should produce a GW signature together with gamma rays and neutrinos. However, at~present, the~only confirmed coincident detection is the GW-gamma correlation, while no evidence of neutrino emission was found~\\cite{ANTARES:2017bia,Baikal:2019}.\nThe theoretical estimations of neutrino production~\\cite{Kimura:2018vvz} from GW170817 show that the expected flux should be already detectable, with~current neutrino telescopes, under~favorable circumstances, see Figure~\\ref{fig2}.\n\n\\begin{figure}[H]\n\\vspace{-9pt}\n\\includegraphics[width=\\linewidth]{Figures\/GW.pdf}\n\\caption{Fluence upper limits, per flavor, on~the high-energy neutrino emission from experimental data assuming a $\\pm$500s time window around the GW170817 {event}\n~\\cite{ANTARES:2017bia}. Baikal limits are from~\\cite{Baikal:2019}. KM3NeT preliminary sensitivity, computed for an optimal zenith angle, is from~\\cite{Palacios:2021}. Theoretical models for comparison are from~\\cite{Kimura:2018vvz}. Figure adapted from the work in ~\\cite{ANTARES:2017bia}.}\n\\label{fig2}\n\\end{figure}\n\nApart from the detection of high-energy neutrinos, the~detection of the prompt emission in gamma-rays by observatories like HAWC or LHAASO will be essential to understand how these energetic explosions~work.\n\nAnother open question to be addressed by multimessenger observations is the connection between ultra-high-energy CRs, gamma-rays, and~high-energy neutrinos. \nIntensity of gamma-rays, neutrinos, and UHECRs has been shown to be comparable (see \\mbox{Figure~\\ref{fig3}}), suggesting that they may be powered by the same sources. \nAs blazars are the most abundant extragalactic sources, they are by default the most promising candidates. However, blazars do not seem to fit the bill, as they are subdominant in the high-energy neutrino flux~\\cite{Oikonomou:2021}.\n\n\\begin{figure}[H]\n\\includegraphics[width=0.97\\linewidth]{Figures\/DF.pdf}\n\\caption{High-energy fluxes of gamma {rays}~\\cite{Fermi-LAT:2014ryh}, neutrinos~\\cite{IceCube:2020wum}, and~cosmic rays~\\cite{Auger:2017}. Figure adapted from~the work in \\cite{IceCube:2020wum}.}\n\\label{fig3}\n\\end{figure}\n\nApart from the usual suspects (e.g., GRBs and TDEs), the~case for star-forming galaxies as common sources has recently grown in popularity thanks to the work in~\\cite{Roth:2021lvk} where the authors claim that the diffuse gamma-ray flux detected by Fermi-LAT~\\cite{Fermi-LAT:2014ryh} is actually dominated by star-forming galaxies.\nAt the same time, the~data collected in the Pierre Auger Observatory showed an indication of anisotropy at 4.0$\\sigma$ level in the arrival direction of CRs with E > 39 EeV showing an excess from the direction of nearby starburst galaxies~\\cite{PierreAuger:2018qvk}. Claiming that this type of high-rate star-forming galaxies can be the cause of $\\sim$~10\\% of the ultra-high-energy CR flux.\nIt has been also shown that the contribution of the starburst galaxies to the neutrino diffuse flux is sub-dominant and constrained to be at the level of $\\sim$10\\%~\\cite{Lunardini:2019zcf}. Therefore, it remains unanswered for now whether there is a dominant type of source accelerating all cosmic~messengers.\n\n\n\nFinally, if~we restrict the search to our Galaxy, one of the questions that will be solved in the near future thanks to multimessenger observations is what are the Galactic CR sources and how those CR propagate through the Galaxy.\nRegarding this, it has been shown that a sub-PeV diffuse Galactic gamma-ray emission exists~\\cite{TibetASgamma:2021tpz}.\nBased on this result in~\\cite{Fang:2021ylv}, the authors showed that the Galactic neutrino contribution should constitute roughly 5--10$\\%$ of the IceCube diffuse flux and that, in~the 10--100 TeV range, the~expected Galactic neutrino flux should be comparable to the total neutrino diffuse flux. If~so, the~next-generation neutrino telescopes should be sensitive enough to detect it.\nIt is possible that part of the measured gamma-ray diffuse flux comes from individual sources, being the obvious candidates the very-high-energy sources detected by HAWC~\\cite{HAWC:2019tcx} and LHAASO~\\cite{Cao:2021}. \nCombined multimessenger observations should be able to confirm whether this is actually the case.\nIn fact, there have been claims for evidence for such a joint production, of~high-energy neutrinos and gamma rays, in~the Cygnus Coocon region, based on the correlation of a high-energy IceCube neutrino and a high-energy (>300 TeV) photon flare observed by the Carpet\u20132 experiment~\\cite{Carpet-3Group:2021ygp}. \n\n\\section{Outlook}\nMultimessenger astronomy is a new branch of astroparticle physics, for~which neutrinos are expected to play a key role. It took just a few events, detected in coincidence, to~demonstrate that combining different messengers has a great potential for~discoveries.\n\nQuestions like what is the origin of the ultra-high energy cosmic rays are likely to be answered thanks to multimessenger~observations.\n\nConsidering the numerous facilities planned for the near future, or~already taking data (KM3NeT, IceCube-Gen2, GVD-Baikal, P-ONE, CTA, LHAASO, KAGRA, AugerPrime, etc.), multimessenger astronomy has a bright future and is expected to revolutionize our understanding of the Universe in the next~decade. \n\n\n\\vspace{6pt} \n\\authorcontributions{{All authors contributed equally to this article. All authors have read and agreed to the published version of the manuscript.}\n\n\\funding{This work was funded by Generalitat Valenciana, Spain (CIDEGENT\/2018\/034 and CIDEGENT\/2020\/049 grants).}\n\n\\institutionalreview{{Not applicable.}\n\n\\informedconsent{{Not applicable.}\n\n\\dataavailability{{Not applicable.}}\n\n\n\\acknowledgments{The authors thank the Valencia Experimental Group on Astroparticle Physics (VEGA) and the Ministerio de Ciencia, Innovaci\u00f3n, Investigaci\u00f3n y Universidades (MCIU): Programa Estatal de Generaci\u00f3n de Conocimiento (ref. PGC2018-096663-B-C41) (MCIU\/FEDER). }\n\n\\conflictsofinterest{The authors declare no conflict of interest.}\n\n\n\n\\abbreviations{Abbreviations}{\nThe following abbreviations are used in this manuscript:\\\\\n\n\\noindent \n\\begin{tabular}{@{}ll}\nGW & Gravitational Wave\\\\\nCR & Cosmic Ray\\\\\nGRB & Gamma-Ray Burst\\\\\nTDE & Tidal Disruption Event\\\\\nPMT & Photomultipliers tubes\n\n\\end{tabular}}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{\\label{sec:intro}\nIntroduction\n}\n\nThe antiferromagnetic (AF) Ising model on a triangular lattice is one of the most fundamental models for geometrically frustrated systems. \nWhen the interaction is restricted to the nearest-neighbor (NN) pairs, frustration in each triangle prevents the system from forming a long-range order (LRO) down to zero temperature, and the ground state has extensive degeneracy and associated residual entropy~\\cite{Wannier1950,Houtappel1950,Husimi1950}.\nThe degenerate ground state is extremely sensitive to perturbations.\nFor instance, an infinitesimal second-neighbor interaction lifts the degeneracy and induces a LRO in the ground state; a two-sublattice stripe order [Fig.~\\ref{fig:model}(a)] is selected as the ground state when the additional interaction is AF, while a three-sublattice ferrimagnetic (FR) order [Fig.~\\ref{fig:model}(b)] is selected for the ferromagnetic (FM) interaction.\n\nIn such a degenerate situation, thermal fluctuations also play an interesting role. \nIn general, there is a possibility that a high-entropic state is selected out of the ground state manifold by raising temperature ---this is called the order by disorder~\\cite{Villain1977}. \nFor the AF Ising model, a candidate for such an emergent state is a partially disordered (PD) state. \nThe PD state is peculiar coexistence of magnetically ordered moments and thermally-fluctuating paramagnetic moments.\nSuch possibility was first discussed by the mean-field study in the presence of second-neighbor FM interaction~\\cite{Mekata1977}; the mean-field study predicted that a three-sublattice PD phase with an AF ordering on the honeycomb subnetwork and paramagnetic moments at the remaining sites [Fig.~\\ref{fig:model}(c)] was induced at finite temperature from the degenerate manifold in the limit of vanishing second-neighbor interaction. \nAlthough such PD state was experimentally observed in several Co compounds~\\cite{Kohmoto1998,Niitaka2001} and theoretically shown to present in a stacked triangular lattice model~\\cite{Todoroki2004}, Monte Carlo (MC) simulations in two-dimensional triangular lattice models have indicated that PD is fragile and remains at most as a quasi-LRO; namely, in most cases, the PD state is taken over by another peculiar intermediate state, the Kosterlitz-Thouless (KT) state~\\cite{Wada1982,Fujiki1983,Landau1983,Takayama1983,Fujiki1984,Takagi1995}.\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig0v1.eps}\n \\caption{(Color online).\n Schematic pictures of (a) stripe order, (b) ferrimagnetic (FR) order, and (c) partial disorder (PD) on a triangular lattice.\n The arrows show magnetically ordered sites and the open circles are thermally fluctuating paramagnetic sites.\n }\n \\label{fig:model}\n\\end{figure}\n\nOn the other hand, recently, the authors have studied Ising-spin Kondo lattice models on a triangular lattice~\\cite{Ishizuka2012} and kagome lattice~\\cite{Ishizuka2012-3} by MC simulation, and showed the presence of PD state in the purely two-dimensional models.\nIn these models, the interplay between localized moments and itinerant electrons plays a crucial role in the following points. \nFirst, the kinetic motion of electrons induces effective interactions known as the Ruderman-Kittel-Kasuya-Yosida (RKKY) mechanism~\\cite{Ruderman1954,Kasuya1956,Yosida1957}. \nThe long-ranged and oscillating nature of the interactions drives keen competition between different magnetic states.\nFurthermore, the change of magnetic states affects the electronic state in a self-consistent manner through the spin-charge coupling; the system can gain the energy by forming some particular electronic state associated with magnetic ordering. \nIn the previous study, the authors suggested that the PD state is stabilized by the non-perturbative role of itinerant electrons~\\cite{Ishizuka2012}.\n\nIn this contribution, we present our comprehensive numerical results on the magnetic and electronic properties of the Ising-spin Kondo lattice model on a triangular lattice. \nTo further clarify the stabilization mechanism of PD, we analyze the evolution of band structure under the PD type magnetic texture on the basis of a simple mean-field argument. \nThe analysis suggests that the spin-charge coupling can stabilize the PD state by the Slater mechanism.\nBearing this mean-field picture in mind, we present and discuss the results of MC simulation in details. \nWe distinguish the two intermediate-temperature states, PD and KT-like states, from the two-sublattice stripe and three-sublattice FR LRO states, and identify the range of the phases by varying the electron filling and the strength of spin-charge coupling. \nAnalyzing the phase diagram and electronic states in comparison with the mean-field picture, we conclude that the two-dimensional PD state is stabilized through the Slater mechanism.\n\nThe organization of this paper is as follows.\nIn Sec.~\\ref{sec:model_and_method}, we introduce the model and method.\nThe definitions of physical quantities we calculated are also given.\nIn Sec.~\\ref{sec:mft}, we present the mean-field analyses on the band structure in the PD state.\nMC results are presented for magnetic properties in Sec.~\\ref{sec:mc} and for electronic properties in Sec.~\\ref{sec:estruct}.\nSection~\\ref{sec:summary} is devoted to summary.\n\n\\section{\\label{sec:model_and_method}\nModel and Method\n}\n\nIn this section, we introduce the model and method.\nThe model is given in Sec.~\\ref{sec:model} and the MC method is described in Sec.~\\ref{sec:method}.\nIn Sec.~\\ref{sec:pmoment}, we give the definitions of physical quantities that we used to elaborate the phase diagram and thermodynamic properties.\n\n\\subsection{\nModel\n\\label{sec:model}\n}\n\nWe consider a single-band Kondo lattice model on a triangular lattice with localized Ising spin moments.\nThe Hamiltonian is given by\n\\begin{eqnarray}\nH = -t \\! \\sum_{\\langle i,j \\rangle, \\sigma} \\! ( c^\\dagger_{i\\sigma} c_{j\\sigma} + \\text{H.c.} ) + J \\sum_{i}\\sigma_i^z S_i.\n\\label{eq:H}\n\\end{eqnarray}\nThe first term represents hopping of itinerant electrons, where $c_{i\\sigma}$ ($c^\\dagger_{i\\sigma}$) is\nthe annihilation (creation) operator of an itinerant electron with spin $\\sigma= \\uparrow, \\downarrow$ at\n$i$th site, and $t$ is the transfer integral.\nThe sum $\\langle i,j \\rangle$ is taken over nearest-neighbor (NN) sites on the triangular lattice.\nThe second term is the onsite interaction between localized spins and itinerant electrons, where $\\sigma_i^z = c_{i\\uparrow}^\\dagger c_{i\\uparrow} - c_{i\\downarrow}^\\dagger c_{i\\downarrow}$ represents the $z$-component of itinerant electron spin, and $S_i = \\pm 1$ denotes the localized Ising spin at $i$th site; $J$ is the coupling constant (the sign of $J$ does not matter in the present model). \nHereafter, we take $t=1$ as the unit of energy, the lattice constant $a = 1$, and the Boltzmann constant $k_{\\rm B} = 1$.\n\n\\subsection{\nMonte Carlo simulation\n\\label{sec:method}\n}\n\nTo investigate thermodynamic properties of the model (\\ref{eq:H}), we adopted a MC simulation which is widely used for similar models~\\cite{Yunoki1998}.\nThe model belongs to the class of models in which fermions are coupled to classical fields.\nFor this class of models, the partition function is given by\n\\begin{eqnarray}\nZ={\\rm Tr}_f{\\rm Tr}_c \\exp[\\beta(H-\\mu\\hat{N_e})],\n\\end{eqnarray}\nwhere $\\beta=1\/T$ is the inverse temperature, $\\mu$ is the chemical potential, and $\\hat{N_e}$ is the total number operator for fermions.\nHere, ${\\rm Tr}_f$ is the trace over classical degree of freedom (in the current case, Ising spin configurations), and ${\\rm Tr}_c$ is the trace over itinerant fermions. \nIn the MC simulation, ${\\rm Tr}_f$ is calculated by using the Markov-chain MC sampling. \nMC updates are done by the usual single-spin flip on the basis of the standard METROPOLIS algorithm. \nThe MC weight is calculated by taking the fermion trace ${\\rm Tr}_c$ for each configuration of classical variables in the following form, \n\\begin{eqnarray}\nP(\\{S_i \\}) = \\exp[ -S_{\\rm eff}(\\{S_i \\}) ],\n\\label{eq:P}\n\\end{eqnarray}\nwhere $S_{\\rm eff}$ is the effective action calculated as\n\\begin{eqnarray}\nS_{\\rm eff}(\\{S_i \\}) = - \\sum_\\nu \\log[1 + \\exp\\{-\\beta(E_\\nu(\\{S_i \\})-\\mu)\\}].\n\\label{eq:S_eff}\n\\end{eqnarray}\nHere, $E_\\nu(\\{S_i \\})$ are the energy eigenvalues for the configuration $\\{S_i \\}$, which are readily calculated by the exact diagonalization as it is a one-particle problem in a static potential.\n\nThe calculations were conducted for the system sizes $N=12 \\times 12$, $15 \\times 15$, $12 \\times 18$, and $18 \\times 18$ under the periodic boundary conditions.\nThermal averages of physical quantities were calculated for typically 4300-9800 MC steps after 1700-5000 steps for thermalization. \nThe results are shown in the temperature range where the acceptance ratio is roughly larger than 1\\%.\nWe divide the MC measurements into five bins and estimate the statistical errors by the standard deviations among the bins.\n\n\\subsection{\nPhysical quantities\n\\label{sec:pmoment}\n}\n\nAs we will see later, the model (\\ref{eq:H}) exhibits phase transitions to various magnetic states including different types of three-sublattice orders: ferrimagnetic (FR) state [Fig.~\\ref{fig:model}(b)] and partially disordered (PD) state [Fig.~\\ref{fig:model}(c)].\nThese magnetic states, in principle, are distinguishable by the spin structure factor for the Ising spins,\n\\begin{eqnarray}\nS({\\bf q}) = \\frac{1}{N} \\sum_{i,j} \\langle S_i S_j \\rangle \\exp({\\rm i} {\\bf q}\\cdot{\\bf r}_{ij}),\n\\label{eq:Sq}\n\\end{eqnarray}\nwhere the braket denotes the thermal average in the grand canonical ensemble, and ${\\bf r}_{ij}$ is the position vector from $i$ to $j$th site. \nThe PD order is signaled by peaks of $S({\\bf q})$ at ${\\bf q}=\\pm(2\\pi\/3,-2\\pi\/3)$, while the FR order develops a peak at ${\\bf q}=0$ in addition to ${\\bf q}=\\pm(2\\pi\/3,-2\\pi\/3)$.\nNo Bragg peaks develop in the KT state as it is a quasi-LRO.\nHowever, in finite-size calculations, it is difficult to distinguish these phases solely by the structure factor, as the correlation length in the KT state is divergent and easily exceeds the system size at low temperature.\n\nFor distinguishing the FR, PD, and KT instabilities, it is helpful to use the pseudospin defined for each three-site unit cell:\n\\begin{eqnarray}\n\\tilde{\\bf S}_m = \n\\left(\n\\begin{array}{ccc}\n\\frac2{\\sqrt6} & -\\frac1{\\sqrt6} & -\\frac1{\\sqrt6} \\\\\n0 & \\frac1{\\sqrt2} & -\\frac1{\\sqrt2} \\\\\n\\frac1{\\sqrt3} & \\frac1{\\sqrt3} & \\frac1{\\sqrt3} \\\\\n\\end{array}\n\\right)\n\\left(\n\\begin{array}{c}\nS_i \\\\\nS_j \\\\\nS_k \\\\\n\\end{array}\n\\right),\n\\end{eqnarray}\nand its summation \n\\begin{eqnarray}\n\\tilde{\\bf M} = \\frac{3}{N} \\sum_m \\tilde{\\bf S}_m \n\\end{eqnarray}\nwhere $m$ is the index for the three-site unit cells, and $(i,j,k)$ denote the three sites in the $m$th unit cell belonging to the sublattices (A,B,C), respectively~\\cite{Takayama1983,Fujiki1984}.\nThen, the three-sublattice PD state [Fig.~\\ref{fig:model}(c)] is characterized by a finite $\\tilde{\\bf M} = (\\tilde{M}_x,\\tilde{M}_y,\\tilde{M}_z)$ parallel to $(\\sqrt{3\/2},1\/\\sqrt2,0)$, $(0,\\sqrt2,0)$, or their threefold symmetric directions around the $z$-axis.\nOn the other hand, the three-sublattice FR state [Fig.~\\ref{fig:model}(b)] is characterized by a finite $\\tilde{\\bf M}$ along $(\\sqrt{2\/3},\\sqrt2,1\/\\sqrt3)$, $(2\\sqrt{2\/3},0,-1\/\\sqrt3)$, or their threefold symmetric directions around the $z$-axis.\nHence, the two states are distinguished by the azimuth of $\\tilde{\\bf M}$ in the $xy$-plane as well as $M_z$.\nIn the MC calculations, we measure \n\\begin{eqnarray}\nM_{xy} &=& \\langle (\\tilde{M}_x^2 + \\tilde{M}_y^2)^{1\/2} \\rangle, \n\\label{eq:Mxy} \\\\\nM_z &=& \\langle |\\tilde{M}_z| \\rangle,\n\\label{eq:Mz}\n\\end{eqnarray}\nand the corresponding susceptibilities,\n\\begin{eqnarray}\n\\chi_{xy} &=& \\frac{N}{T} (\\langle \\tilde{M}_x^2 + \\tilde{M}_y^2 \\rangle - M_{xy}^2 ), \\\\\n\\chi_z &=& \\frac{N}{T} (\\langle \\tilde{M}_z^2 \\rangle - M_z^2 ).\n\\end{eqnarray}\nWe also introduce the azimuth parameter of $\\tilde{{\\bf M}}$ defined by\n\\begin{eqnarray}\n\\psi = {\\cal M}^3 \n\\cos{6 \\phi_M},\n\\label{eq:psi}\n\\end{eqnarray}\nwhere $\\phi_M$ is the azimuth of $\\tilde{\\bf M}$ in the $xy$ plane and ${\\cal M} = \\frac38 M_{xy}^2$.\nThe parameter $\\psi$ has a negative value and $\\psi \\to -\\frac{27}{64}$ for the perfect PD ordering, while it becomes positive and $\\psi \\to 1$ for the perfect FR ordering; $\\psi=0$ for both paramagnetic and KT phases in the thermodynamic limit $N \\to \\infty$.\n\nIn addition, we calculate the spin entropy to distinguish the three-sublattice orderings.\nThe spin entropy per site is defined by\n\\begin{eqnarray}\n{\\cal S}(T) = -\\frac{1}{N} \\sum_{\\{S_i\\}} P(\\{S_i\\})\\log P(\\{S_i\\}),\n\\label{eq:Sdef}\n\\end{eqnarray}\nwhere $P(\\{S_i\\})$ is the probability for spin configuration $\\{S_i\\}$ to be realized, given in Eq.~(\\ref{eq:P}).\nIn the actual MC calculation, instead of directly calculating Eq.~(\\ref{eq:Sdef}), ${\\cal S}$ is evaluated by calculating its temperature derivative\n\\begin{eqnarray}\n\\frac{\\partial{\\cal S}(T)}{\\partial T} = \\frac{1}{NT^2} \\left\\{ \\langle S_{\\rm eff} H \\rangle - \\langle S_{\\rm eff}\\rangle \\langle H \\rangle \\right\\},\n\\label{eq:delSdelT}\n\\end{eqnarray}\nand integrating it as \n\\begin{equation}\n{\\cal S}(T) = \\int_0^T \\frac{\\partial{\\cal S}(T)}{\\partial T} dT = \\log 2 - \\int_T^\\infty \\frac{\\partial{\\cal S}(T)}{\\partial T} dT. \n\\label{eq:Sint}\n\\end{equation}\nIn Eq.~(\\ref{eq:delSdelT}), $S_{\\rm eff}$ is the effective action in Eq.~(\\ref{eq:S_eff}). \nIn the following calculations, we set the cutoff $T=1$ for the upper limit of the last integral in Eq.~(\\ref{eq:Sint}).\n\nOn the other hand, in order to identify the two-sublattice stripe order [Fig.~\\ref{fig:model}(a)], we calculate the order parameter\n\\begin{eqnarray}\nM_{{\\rm str}} = \\left[ \\sum_{{\\bf q}^{*}_{\\rm str}} \\left\\{ \\frac{S({\\bf q}^{*}_{\\rm str})}{N} \\right\\}^2 \\right]^{1\/2},\n\\label{eq:Mstr}\n\\end{eqnarray}\nand its susceptibility $\\chi_{\\rm str}$. \nHere, the sum is taken for the characteristic wave vectors of the stripe orders running in three different directions, ${\\bf q}^{*}_{\\rm str}= (\\pi,0)$ and $(\\pm\\frac12\\pi,\\frac{\\sqrt3}2\\pi)$.\n\nWe also examine the thermodynamic behavior of electronic states for itinerant electrons.\nThere, we computed the charge modulation defined by\n\\begin{eqnarray}\nn_{\\rm CO} = \\left\\{\\frac{N({\\bf q}^*_{\\rm CO})}{N}\\right\\}^{1\/2}\n\\label{eq:n_CO}\n\\end{eqnarray}\nat ${\\bf q}^*_{\\rm CO}=(-2\\pi\/3,2\\pi\/\\sqrt3)$, which corresponds to the wave numbers for the three-sublattice orders.\nHere, $N({\\bf q})$ is the charge structure factor for itinerant electrons,\n\\begin{eqnarray}\nN({\\bf q}) = \\frac{1}{N} \\sum_{i,j} \\langle n_i n_j \\rangle \\exp({\\rm i} {\\bf q}\\cdot{\\bf r}_{ij}),\n\\end{eqnarray}\nwhere $n_i = \\frac12\\sum_\\sigma c_{i\\sigma}^\\dagger c_{i\\sigma}$.\n\n\\section{\nMean-field band structure\n\\label{sec:mft}\n}\n\nBefore going to the MC results, we here discuss how one particle band structure is modulated by PD ordering in a mean-field picture. \nWe consider a three-sublattice LRO state, in which the localized spins give a mean-field local magnetic field to itinerant electrons.\nNamely, we consider a mean-field Hamiltonian given by\n\\begin{eqnarray}\n{\\cal H}^{\\rm MF} = \\sum_{\\bf k}\n\\begin{pmatrix}\n\\Delta_{\\mathrm{A}}\\sigma^z_\\alpha & \\tau_{\\bf k} & \\tau_{\\bf k}^\\ast \\\\\n \\tau_{\\bf k}^\\ast & \\Delta_{\\mathrm{B}}\\sigma^z_\\alpha & \\tau_{\\bf k} \\\\\n \\tau_{\\bf k} & \\tau_{\\bf k}^\\ast & \\Delta_{\\mathrm{C}}\\sigma^z_\\alpha\n\\end{pmatrix}\\label{eq:mfh}\n.\n\\end{eqnarray}\nHere, three rows correspond to the different sublattices A, B, and C in the three-site unit cell; $\\Delta_\\alpha$ is a mean field given by $J\\langle S_\\alpha\\rangle$ ($\\alpha=\\mathrm{A}, \\mathrm{B}, \\mathrm{C}$).\nThe sum is taken in the first Brillouin zone for the magnetic unit cell for three-sublattice order.\n$\\tau_{\\bf k}$ is the hopping term for itinerant electrons given by\n\\begin{eqnarray}\n\\tau_{\\bf k} = -t[e^{{\\rm i}k_x} + e^{{\\rm i}\\left(-\\frac{k_x}2 + \\frac{\\sqrt3}2 k_y\\right)} + e^{{\\rm i}\\left(-\\frac{k_x}2 - \\frac{\\sqrt3}2 k_y\\right)}]\n\\end{eqnarray}\nand $\\sigma^z_\\alpha$ corresponds to the $z$ component of itinerant electron spin in each sublattice $\\alpha$.\n\nThe band structure for a FR order, $(\\Delta_{\\rm A},\\Delta_{\\rm B},\\Delta_{\\rm C})=( \\Delta, \\Delta, -\\Delta)$, was recently studied by the authors~\\cite{Ishizuka2012-2}.\nThere, it was reported that the electronic structure in the FR order is semimetallic with forming Dirac nodes at the electron filling $n= \\frac{1}{2N}\\sum_{i\\sigma} \\langle c_{i\\sigma}^\\dagger c_{i\\sigma} \\rangle=1\/3$ for $J>t$.\n\n\\begin{figure}\n \\includegraphics[width=0.92\\linewidth]{fig11v1.eps}\n \\caption{(Color online).\n Mean-field band structure calculated by Eq.~(\\ref{eq:mfh}) for the local magnetic field of PD type, $(\\Delta_{{\\rm A}},\\Delta_{{\\rm B}},\\Delta_{{\\rm C}})=(2,0,-2)$.\n Each of the three bands shown is doubly degenerate, and there are totally six bands.\n The gray hexagon on the basal plane shows the first Brillouin zone for the magnetic supercell.\n }\n \\label{fig:band1}\n\\end{figure}\n\nHere, we discuss the band structure for the PD case, $(\\Delta_{\\rm A},\\Delta_{\\rm B},\\Delta_{\\rm C})=(\\Delta,0,-\\Delta)$.\nThe band structure for $\\Delta=2$ is shown in Fig.~\\ref{fig:band1}.\nIn this case, all three bands shown in the figure are doubly degenerate and there are six bands in total.\nThe first Brillouin zone is shown by the gray shade in the bottom surface. \nThe result shows the presence of an energy gap at the Fermi level corresponding to $n=1\/3$, that opens between the lowest energy band and the middle band [see also Fig.~\\ref{fig:band2}(c)].\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig12v3.eps}\n \\caption{(Color online).\n Mean-field band structure along the symmetric lines in the local magnetic field of PD type, $(\\Delta_{\\rm A},\\Delta_{\\rm B},\\Delta_{\\rm C})=(\\Delta,0,-\\Delta)$: (a) $\\Delta=1\/3$, (b) $\\Delta=2\/3$, and (c) $\\Delta=2$.\n The dashed horizontal lines indicate the Fermi level for $n=1\/3$.\n }\n \\label{fig:band2}\n\\end{figure}\n\nWe next look into the conditions for the energy gap formation in the mean-field PD band.\nFigure~\\ref{fig:band2} shows the results of band structure while varying $\\Delta$.\nThe results are plotted along the symmetric line in the Brillouin zone shown in the bottom surface in Fig.~\\ref{fig:band1}.\nFor small $\\Delta$, the system is metallic at $n=1\/3$, as shown in the case of $\\Delta=1\/3$ in Fig.~\\ref{fig:band2}(a); both electron and hole pockets are present at the Fermi level.\nThe pockets shrink as increasing $\\Delta$, and disappear at the same time at $\\Delta=2\/3$, as shown in Fig.~\\ref{fig:band2}(b).\nFor larger $\\Delta$, an energy gap opens between the lowest and middle bands, corresponding to $n=1\/3$, as stated above [Fig.~\\ref{fig:band2}(c)]. \nHence, $\\Delta_c=2\/3$ is the critical point for the metal-insulator transition in this mean-field PD state.\n\n\\begin{figure}\n \\includegraphics[width=0.9\\linewidth]{fig13v2.eps}\n \\caption{(Color online).\n $\\Delta$ dependences of the mean-field energy gap and associated charge modulation $n_{\\rm CO}$ at $n=1\/3$.\n }\n \\label{fig:gap}\n\\end{figure}\n\nFigure~\\ref{fig:gap} shows $\\Delta$ depedences of the energy gap and associated charge modulation $n_{\\rm CO}$ [Eq.~(\\ref{eq:n_CO})] at $n=1\/3$.\nThe charge gap develops for $\\Delta > 2\/3$ and monotonically increases, approaching asymptotically a $\\Delta$-linear form as $\\Delta \\gg t$.\nThe charge modulation is induced by the inhomogeneity of local potential; the local charge density at B sites (the site corresponds to paramagnetic sites) becomes dilute compared to those at A and C sites (the magnetically ordered sites).\nIn the limit of $\\Delta \\gg t$, $n_{\\rm CO}$ approaches $n_{\\rm CO}=1\/\\sqrt{12}\\sim 0.289$.\n\nThe results above suggest a stabilization mechanism of PD which is absent in the localized spin only model.\nIn the previous studies on the Ising spin models~\\cite{Takayama1983,Fujiki1983,Wada1982} and an equivalent classical particle model~\\cite{Landau1983} on a triangular lattice, PD was shown to be unstable against thermal fluctuations and taken over by a KT state.\nIn the case of our model, however, as the KT state lacks a long-range periodic magnetic structure, it is expected that the KT state does not open an energy gap in the electronic state of itinerant electrons.\nTherefore, in contrast to the case of localized spin only models, there is a chance for the current model to stabilize the PD state by the Slater mechanism, that is, by forming an energy gap at the Fermi level with folding the Brillouin zone under a periodic magnetic order.\n\nIn addition, the formation of an energy gap for $\\Delta > 2\/3$ implies that, if the PD state is stabilized by the Slater mechanism, it should appear from a finite $J$, and not remain stable down to $J\\to0$.\nThis is in sharp contrast to magnetic ordering by the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction~\\cite{Ruderman1954,Kasuya1956,Yosida1957}; as the RKKY interaction is given by the second-order perturbation in terms of $J\/t$, if the PD state is stabilized by the RKKY interaction, it should appear for an infinitesimal $J$.\nHence, the phase diagram in the small $J$ region gives an idea on how the PD state is stabilized.\nWe will discuss this point by showing the MC results while changing $J$ in the next section.\n\n\n\\section{\nMonte Carlo simulation\n\\label{sec:mc}\n}\nIn this section, we present the results of MC simulation introduced in Sec.~\\ref{sec:method}.\nWe first show the finite-temperature phase diagrams in Sec.~\\ref{sec:pdiag}, which include four magnetic phases: stripe, PD, FR, and KT-like states.\nThe details of numerical data for the PD state are elaborated in Sec.~\\ref{sec:pd}.\nThe results for stripe, KT-like, and FR states are discussed in Sec.~\\ref{sec:stripe_ferri}.\n\n\\subsection{\nPhase diagrams\n\\label{sec:pdiag}\n}\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig2v3.eps}\n \\caption{(Color online).\n Phase diagrams of the model~(\\ref{eq:H}) while varying $n$ at (a) $J=1$ and (b) $J=2$.\n The symbols show phase boundaries for the four phases: stripe, partially disordered (PD), KT-like (``KT\"), and ferrimagnetic (FR) phases.\n PS represents a phase separation. The lines are guides for the eyes.\n The strips at $T=0$ show the ground states obtained by comparing the energy of stripe and FR states.\n }\n \\label{fig:ndiag}\n\\end{figure}\n\nFigure~\\ref{fig:ndiag}(a) shows the phase diagram around the electron filling $n= 1\/3$ at $J=1$ obtained by MC calculations. \nThere are four dominant ordered phases ---stripe, FR, PD, and KT-like phases, in addition to an electronic phase separation (PS).\nThe strip at the bottom of the figure shows the ground state obtained by variational calculation comparing the ground state energy of the stripe and FR states (the details of variational calculation is given in Appendix~\\ref{sec:pseparation}).\nFor the relatively low filling of $n \\lesssim 0.29$, the stripe order with period two [Fig.~\\ref{fig:model}(a)] develops in the low temperature region.\nOn the other hand, for the higher filling of $n \\gtrsim 0.32$, the system exhibits the three-sublattice FR order at low temperature [Fig.~\\ref{fig:model}(b)]. \nMC data for the stripe and FR orders will be discussed in Sec.~\\ref{sec:stripe_ferri}.\nIn addition to these two states, the numerical results show two intermediate-temperature states depending on the electron filling $n$.\nFor $0.29 \\lesssim n \\lesssim 0.34$, we identify the intermediate phase as the three-sublattice PD state [Fig.~\\ref{fig:model}(c)]. \nThe details will be discussed in Sec.~\\ref{sec:pd}.\nMeanwhile, for $n \\gtrsim 0.34$, we find KT-like behavior similar to the one discussed in the Ising models~\\cite{Takayama1983,Fujiki1984,Wada1982,Fujiki1983,Landau1983}, as presented in Sec.~\\ref{sec:stripe_ferri}.\nIn these intermediate-temperature phases, the numerical data indicate a LRO for PD but a quasi-LRO in the KT-like region.\n\nA similar phase diagram is obtained at $J=2$, as shown in Fig.~\\ref{fig:ndiag}(b).\nIn this case also, the PD phase emerges in the intermediate-temperature region.\nHowever, in contrast to the case with $J=1$ where PD is found widely above the FR state as well as PS, the PD phase dominantly appears above the PS region between the stripe and FR states.\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig3v4.eps}\n \\caption{(Color online).\n Phase diagram of the model~(\\ref{eq:H}) at $n=1\/3$ while varying $J$.\n The notations are common to those in Fig.~\\ref{fig:ndiag}.\n The boundary between PD and PS is difficult to determine by MC calculations, and supposed to be located at lower temperature than indicated by the gray arrows.\n }\n \\label{fig:jdiag}\n\\end{figure}\n\nWe also investigated the phase diagram of the model in Eq.~(\\ref{eq:H}) while varying $J$.\nFigure~\\ref{fig:jdiag} shows the numerically obtained phase diagram at $n=1\/3$.\nThe result shows that the PD state is stable in a wide range of $0.8 \\lesssim J \\lesssim 5.6$.\nThe transition temperature first rapidly increases as increasing $J$, while it turns to a gradual decrease after showing a peak at $J\\sim 2$.\n\nAn important observation in this constant-$n$ phase diagram is that the PD state does not survive down to $J \\to 0$, and it is taken over by the KT-like and FR phases in the small $J$ region.\nThe absence of PD state in the $J\\rightarrow 0$ limit implies that the RKKY interaction in the second-order perturbation theory is insufficient in stabilizing the PD state.\nMoreover, the emergence of PD for $J > J_c \\ne 0$ is consistently understood within the Slater mechanism discussed in Sec.~\\ref{sec:mft}; the MC result of $J_c\\sim 0.8$ is in good accordance with the mean-field argument of the critical value $\\Delta_c = 2\/3$.\nThe result clearly indicates that a non-perturbative effect of itinerant electrons plays a crucial role in stabilizing the PD state.\n\nIn the PD region in Fig.~\\ref{fig:jdiag}, our MC data do not show clear sign of further transition while decreasing temperature before the MC calculations become unstable.\nIn the low temperature region, however, it becomes difficult to determine the chemical potential $\\mu$ for $n=1\/3$.\nThe lowest temperature of MC calculations are shown in the phase diagram by the gray downward arrows. \nOn the other hand, the analysis of the ground state indicates that the ground state for $J \\lesssim 1.68$ is the FR state, while the region for $J \\gtrsim 1.68$ is PS between the stripe and FR states.\nIn addition, we observe the PS instability by carefully investigating the change of $n$ as a function of $\\mu$ at $J=5.4$ (see also Appendix~\\ref{sec:pseparation}). \nFrom these facts, we conclude that the PD for $J \\gtrsim 1.68$ is taken over by PS between the stripe and FR states.\nSince it is tedious to determine the PS boundary from $\\mu$-$n$ plot for all the values of $J$, we merely plot the lowest temperature we reached in our constant-$n$ calculations as the upper limit of temperature for the PS instability.\n\n\\subsection{\nPartial disorder\n\\label{sec:pd}\n}\n\n\\begin{figure*}\n \\includegraphics[width=\\linewidth]{fig42v6.eps}\n \\caption{(Color online).\n MC results for (a1)-(c1) $M_{xy}$, $M_z$, and $\\psi$, (a2)-(c2) $\\chi_{xy}$ and $\\chi_z$, and (a3)-(c3) $\\cal{S}$ and its temperature derivative $\\partial {\\cal S}\/\\partial T$ at $n=1\/3$;\n (a1)-(a3) $J=1$, (b1)-(b3) $J=2$, and (c1)-(c3) $J=4$.\n The calculations were done for the system sizes $N=12\\times 12$, $12\\times 18$, and $18\\times18$.\n $\\cal S$ is calculated from numerical integration of $\\partial {\\cal S}\/\\partial T$ by assuming ${\\cal S}(T=1)=\\log2$.\n }\n \\label{fig:mcpd}\n\\end{figure*}\n\nHere, we present the details of MC data for identifying the PD state.\nFigure~\\ref{fig:mcpd} shows $T$ dependences of MC results for different $J$ at $n=1\/3$.\nTo fix $n$, we tuned $\\mu$ for each temperature; the errors for $n$ at each temperature are controlled within 0.001.\nFigure~\\ref{fig:mcpd}(a1) is the result for the pseudomoments $M_{xy}$ and $M_z$ at $J=1$ [see the definitions in Eqs.~(\\ref{eq:Mxy}) and (\\ref{eq:Mz}), respectively].\n$M_{xy}$ shows two anomalies while decreasing temperature at $T_c^{\\rm (PD)} = 0.086(4)$ and $T_c^{\\rm (FR)} = 0.019(2)$.\nThe critical temperatures are determined by the peaks of the susceptibilities, $\\chi_{xy}$, and $\\chi_z$, as mentioned below.\nAt $T_c^{\\rm (PD)}$, $M_{xy}$ rapidly increases and approaches $\\sqrt{2}$ at lower temperature. \nIn addition, it shows a kink at $T_c^{\\rm (FR)}$ and further increase to $8\/3$ at lower temperature.\nMeanwhile, $M_z$ shows no anomaly at $T_c^{\\rm (PD)}$, while it shows a rapid increase to $1\/\\sqrt3$ at $T_c^{\\rm (FR)}$.\nCorrespondingly, $\\chi_{xy}$ and $\\chi_z$ in Fig.~\\ref{fig:mcpd}(a2) also show divergent peaks increasing with the system size;\npeaks of $\\chi_{xy}$ appear at both $T_c^{\\rm (PD)}$ and $T_c^{\\rm (FR)}$, while $\\chi_z$ shows a peak only at $T_c^{\\rm (FR)}$.\nThese results signal the presence of two successive phase transitions at $T_c^{\\rm (PD)} = 0.086(4)$ and $T_c^{\\rm (FR)} = 0.019(2)$.\nThe error bars are estimated by the range of temperature where the standard deviation of the MC data exceeds the difference of expectation value from the peak value.\nThe transition temperatures and error bars shown in Figs.~\\ref{fig:ndiag} and \\ref{fig:jdiag} are given by this criterion.\nMeanwhile, most of the calculations in Fig.~\\ref{fig:ndiag} were done by fixing $\\mu$ instead of $n$.\nHence, we also give the error bars in terms of $n$, as $n$ changes with $T$ in a fixed $\\mu$ calculation.\n\nTo determine the nature of low temperature phases at $n=1\/3$, we also computed the azimuth parameter $\\psi$ [Eq.~(\\ref{eq:psi})] shown in Fig.~\\ref{fig:mcpd}(a1).\nWhile increasing the system sizes, $\\psi$ apparently deviates from zero to a negative value below $T_c^{\\rm (PD)}$, indicating that the intermediate phase for $T_c^{\\rm (FR)} < T < T_c^{\\rm (PD)}$ has a PD type order.\nOn the other hand, $\\psi$ shows a sign change at $T_c^{\\rm (FR)}$, and rapidly increases to $\\psi=1$ at lower temperature.\nThis is a signature of the FR transition, which will be discussed in detail in Sec.~\\ref{sec:stripe_ferri}.\n\nThe emergence of PD is also seen in the results for the spin entropy $\\cal{S}$ and its temperature derivative [Eqs.~(\\ref{eq:Sint}) and (\\ref{eq:delSdelT}), respectively], as shown in Fig.~\\ref{fig:mcpd}(a3).\nIn the intermediate-temperature region for $T_c^{\\rm (FR)} < T < T_c^{\\rm (PD)}$, $\\cal{S}$ appears to approach $\\frac13\\log 2$ as decreasing temperature, which is the value expected for the ideal PD state where one out of three spins in the magnetic unit cell remains paramagnetic.\nThe remaining entropy is released rapidly at $T_c^{\\rm (FR)}$ and ${\\cal S} \\to 0$ at lower temperature due to the ordering of paramagnetic spins in the FR state.\n\nSimilar phase transitions to the PD state are observed in the wide range of $J$, as shown in Figs.~\\ref{fig:mcpd}(b) and \\ref{fig:mcpd}(c) at $J=2$ and $J=4$, respectively.\nIn these results, however, we could not confirm the presence of another phase transition at a lower temperature in the range of temperature we calculated, in contrast to the FR transition found in the case of $J=1$.\nAs the PD state retains a finite $\\cal{S}$, it is unlikely that this phase survives to $T\\rightarrow0$.\nHence, it is presumably taken over by other ordered phases or PS at a lower temperature. \nAs shown in Fig.~\\ref{fig:jdiag}, the ground state is deduced to be PS for the values of $J$ in Figs.~\\ref{fig:mcpd}(b) and \\ref{fig:mcpd}(c).\nWe, therefore, expect that the PD state is taken over by PS below $T=0.02$ for $J \\gtrsim 2$. \nThe situation is indicated by the gray arrows in the phase diagram in Fig.~\\ref{fig:jdiag}, as discussed in Sec.~\\ref{sec:pdiag}.\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig6v1.eps}\n \\caption{(Color online).\n MC results for $S({\\bf q})$ along the ${\\bf q}=(q_x,0)$ line at $T=0.02$.\n The calculations were done for the system size $N=18\\times18$.\n }\n \\label{fig:sq}\n\\end{figure}\n\nAnother point to be noted is the systematic change in $\\cal{S}$ in the PD state by changing $J$.\nWhile the result at $J=1$ appears to show plateau like behavior at ${\\cal S}\\sim \\frac13 \\log2$, the plateau value of ${\\cal S}$ in the PD state decreases while increasing $J$, as shown in Figs.~\\ref{fig:mcpd}(a3), \\ref{fig:mcpd}(b3), and \\ref{fig:mcpd}(c3).\nThe decrease in ${\\cal S}$ is presumably attributed to the development of spatial correlations between paramagnetic sites in the PD state; \nthe ideal value ${\\cal S} = \\frac13 \\log2$ is for completely uncorrelated paramagnetic spins, and correlations between them reduces the entropy.\nSuch development of correlatins are observed in the spin structure factor $S({\\bf q})$ defined in Eq.~(\\ref{eq:Sq}).\nFigure~\\ref{fig:sq} shows a profile of $S({\\bf q})$ calculated by MC simulation at $T=0.02$.\nThe peaks at ${\\bf q}=(4\\pi\/3,0)$ and $(8\\pi\/3,0)$ indicates that the system is in a three-sublattice ordered phase, while the absence of a sharp peak at ${\\bf q}=(0,0)$ indicates that there is no net magnetic moment; the result is consistent with PD order.\nWhen comparing the results at $J=2$ and $J=4$, the peak corresponding to the three-sublattice order gets sharper for $J=4$, while the height of the peak of $S({\\bf q})$ is almost the same. \nThis indicates that the PD order at $J=2$ shows more spin fluctuations than that at $J=4$, consistent with the trend of the plateau value of ${\\cal S}$.\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig_p1v4.eps}\n \\caption{(Color online).\n MC results for $\\psi$ while varying $n$ at $T=0.08$ and $J=2$.\n The calculations were done for the system sizes $N=12\\times 12$, $12\\times 18$, and $18\\times18$.\n }\n \\label{fig:conT}\n\\end{figure}\n\nThus far, we showed the results at $n=1\/3$.\nNext, we show how the PD evolves while changing $n$.\nFigure~\\ref{fig:conT} shows the MC result of $\\psi$ as a function of $n$ at $T=0.08$ and $J=2$.\n$\\psi$ becomes negative around $n=1\/3$ and takes the lowest value at $n\\simeq1\/3$. \nThe data indicate that $\\psi$ is almost system size independent or rather slightly decreases as the system size increases in the finite range of $n$ around $n=1\/3$. \nHence, the PD state is stabilized not only at $n=1\/3$ but for a finite range of $0.31 \\lesssim n \\lesssim 0.34$ in the thermodynamic limit. \nThe range well agrees with that for the PD phase estimated from the peak of susceptibilities shown in Fig.~\\ref{fig:ndiag}(b).\n\nWith regard to the order of the PD transition, the PD transition in our MC results appears to be continuous, as shown in Fig.~\\ref{fig:mcpd}. \nHowever, it needs careful consideration, as we will discuss here.\nIt is known that the Ising model on a triangular lattice with AF NN interactions is effectively described by a six-state model, in which the low-energy states with three up-up-down and three up-down-down configurations in the three-site unit cell are described by six-state variables. \nThe PD state in our model also retains six low-energy states with different up-down-paramagnetic configurations, and hence, the transition to PD is expected to be classified in the framework of six-state models.\nHowever, from the argument of duality properties, it is prohibited that the six-state models exhibit a single second-order transition for changing temperature~\\cite{Cardy1980}.\nFor instance, a two-dimensional six-state clock model shows two KT transitions at finite temperature, without exhibiting true LRO for $T\\ne0$~\\cite{Jose1977,Challa1986}.\nOn the other hand, a six-state Potts model shows a weak first order transition to LRO, in which the correlation length reaches the order of 1000 sites at the critical point~\\cite{Buffenoir1993}.\nIn our PD case, the apparently second-order transition at $T_c^{\\rm (PD)}$ is not expected to be a single one, but is always followed by another transition to FR or PS at a lower temperature. \nThis appears not to violate the general argument for the six-state models, although it is not clear to what extent the argument applies, as the electronic PS never takes place in the localized spin models.\nHence, the PD transition can be of second order, as indicated in our numerical results. \nOf course, we cannot exclude the possibility of a weak first order transition, similar to that of the Potts model.\nIn this case, due to a long correlation length at the critical temperature, the system sizes used in our calculations are likely to be insufficient to distinguish the first order transition from second order one.\n\n\\subsection{\nOther magnetic orders\n\\label{sec:stripe_ferri}\n}\n\n\\begin{figure}\n \\includegraphics[width=0.9\\linewidth]{fig41v5.eps}\n \\caption{(Color online).\n MC results for (a) $M_{\\rm str}$ and (b) its susceptibility $\\chi_{\\rm str}$ at $J=2$ and $n=0.27$.\n The inset in (b) shows $T_c^{\\rm (str)}$ for different sizes and the solid line is the extrapolation which gives $T_c^{\\rm (str)}=0.051(13)$.\n The calculations were done for the system sizes $N=12\\times 12$, $14\\times 14$, $12\\times 18$, $16\\times 16$, and $18\\times18$.\n }\n \\label{fig:mcstripe}\n\\end{figure}\n\nFigure~\\ref{fig:mcstripe} presents the results for the relatively low filling where the stripe order is stabilized at low temperature.\nFigure~\\ref{fig:mcstripe}(a) shows the order parameter for the stripe order, $M_{\\rm str}$ [Eq.~(\\ref{eq:Mstr})], and Fig.~\\ref{fig:mcstripe}(b) shows the corresponding susceptibility $\\chi_{\\rm str}$ at $J=2$ and $n=0.27$.\nA phase transition to the stripe phase is signaled by a rapid increase of $M_{{\\rm str}}$ and corresponding peak of $\\chi_{\\rm str}$;\nwe determine the transition temperature $T_c^{\\rm (str)}$ by the peak temperature of $\\chi_{\\rm str}$ for each system size, and plot them in the phase diagram in Fig.~\\ref{fig:ndiag}(a). \nThe error bars are estimated in a similar manner to the case of $T^{\\rm (PD)}_c$ and $T^{\\rm (FR)}_c$.\nWe also show the system-size extrapolation of $T_c^{\\rm (str)}$ in the inset of Fig.~\\ref{fig:mcstripe}(b).\nAlthough the data are rather scattered, we fit them by $f(N) = a + b\/N^c$ with fitting parameters $a$, $b$, and $c$. \nThe extrapolation clearly shows that the phase transition takes place at a finite temperature, as expected for the two-dimensional Ising order.\n\nThe stripe ordered phase is a peculiar magnetic state, in which the sixfold rotational symmetry of the lattice is spontaneously broken and reduced to twofold.\nDue to the symmetry breaking, the transport property is expected to show strong spatial anisotropy; e.g., the longitudinal conductivity will be large in the direction along the stripes, while suppressed in the perpendicular direction.\nThis is an interesting topic on the control of transport by magnetism and vice versa.\n\n\\begin{figure}\n \\includegraphics[width=0.76\\linewidth]{fig43v2.eps}\n \\caption{(Color online).\n MC results for (a) $M_{xy}$, $M_z$, and $\\psi$, (b) $\\chi_{xy}$ and $\\chi_z$, and (c) $\\cal{S}$ and its temperature derivative $\\partial {\\cal S}\/\\partial T$ at $n=0.38$ and $J=2$.\n The calculations were done for the system sizes $N=12\\times 12$, $12\\times 18$, and $18\\times18$.\n }\n \\label{fig:mcferri}\n\\end{figure}\n\nFigure~\\ref{fig:mcferri} shows the results for the relatively high filling where the low temperature phase is FR, at $n=0.38$ and $J=2$.\nThe data indicate two successive transitions signaled by the peaks in $\\chi_{xy}$ and $\\chi_{z}$ at different temperature.\nThe peak of $\\chi_z$ corresponding to the increase of $M_z$ signals the phase transition to the FR phase at $T_c^{\\rm (FR)} = 0.098(4)$.\nAt the same time, $\\psi$ becomes finite below $T_c^{\\rm (FR)}$, and approaches 1, as expected for the FR ordering.\nSimilar behavior was observed at $T_c^{\\rm (FR)} = 0.019(2)$ in Figs.~\\ref{fig:mcpd}(a1) and \\ref{fig:mcpd}(a2).\nOn the other hand, at a higher $T_{\\rm KT} = 0.146(4)$, only $M_{xy}$ changes rapidly, and correspondingly, $\\chi_{xy}$ shows a peak.\n$M_{xy}$, however, shows a noticeable system-size dependence even below $T_{\\rm KT}$, in contrast with the results below $T_c^{\\rm (PD)}$. \nSimilar behavior was observed in the KT transition in Ising spin systems~\\cite{Takayama1983,Fujiki1984}.\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig45v2.eps}\n \\caption{(Color online).\n Extrapolation of $\\psi$ to $N\\to\\infty$ at different temperatures.\n The solid lines for $T\\le 0.104$ is the linear fitting of data.\n }\n \\label{fig:psifit}\n\\end{figure}\n\nOn the other hand, $\\psi$ does not show an anomaly at $T_{\\rm KT}$, while it shows a sharp rise around $T_{c}^{{\\rm (FR)}}$, as shown in Fig.~\\ref{fig:mcferri}(a).\nThe value of $\\psi$ extrapolated to large $N$ converges to zero in the intermediate-temperature range.\nFigure~\\ref{fig:psifit} shows the extrapolation of $\\psi$ for $N\\to \\infty$.\nThe results indicate that, $\\psi$ remains to be zero at $N\\to\\infty$ for $T\\gtrsim0.104$, which is far below $T_{\\rm KT}=0.146(4)$.\nOn the other hand, the extrapolated value becomes finite for $T\\lesssim 0.104$, reflecting the FR order; the transition temperature is estimated as $\\tilde{T}_c^{\\rm (FR)} = 0.102(2)$, which is in accordance with $T_c^{\\rm (FR)} = 0.098(4)$.\n\nThe results above indicate that there is no sixfold symmetry breaking in $M_{xy}$ at $T_{\\rm KT}$, as seen in the KT phase in the Ising spin models~\\cite{Takayama1983}.\nHence, we consider that the higher-temperature transition at $T_{\\rm KT}$ is of KT type.\nNamely, the system exhibits two successive transitions from the paramagnetic phase to the KT-like phase at $T_{\\rm KT}$, and the KT-like phase to the low-temperature FR phase at $T_{c}^{{\\rm (FR)}}$. \nHere, we call the intermediate-temperature phase the KT-like phase, as it is difficult to confirm either the KT universality class by critical behavior or the quasi-LRO behavior within the system sizes we reached, as seen below.\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig44v2.eps}\n \\caption{(Color online).\n MC results for the real-space spin correlation function $C(r)$ at $J=2$ and $n=0.38$.\n The results are shown only for the sites with $C(r) > 0$.\n The calculations were done for the system size $N=18\\times18$.\n }\n \\label{fig:mcsk}\n\\end{figure}\n\nThe signature of two successive transitions is also observed in the real-space spin correlation function $C(r)$.\nHere $C(r)$ is the averaged correlations between the Ising spins in distance $r$, defined by \n\\begin{eqnarray}\nC(r) = \\sum_{i,j} \\frac{1}{N_{\\rm p}(r)} \\langle S_i S_j \\rangle \\delta(|{\\bf r}_{ij}| - r),\n\\end{eqnarray}\nwhere $N_{\\rm p}(r) =\\sum_{i,j} \\delta(|{\\bf r}_{ij}| - r)$ is the number of spin pairs with distance $r$, and $\\delta(x)$ is the delta function.\nThe MC data while varying temperature are shown in Fig.~\\ref{fig:mcsk}. \nAlthough the results are not conclusive due to the limitation on accessible system sizes, they appear to be consistent with the two transitions discussed above.\nFor $T \\lesssim T_{c}^{{\\rm (FR)}} = 0.098(4)$, the spin correlation appears to approach constant for large distance, well corresponding to the FR LRO developed in this low temperature region.\nOn the other hand, for $T \\gtrsim T_{\\rm KT} = 0.146(4)$, it becomes concave downward with a steep decrease with respect to the distance, which reflects an exponential decay in the high temperature paramagnetic state.\nIn the intermediate region for $T_c^{\\rm (FR)} \\lesssim T \\lesssim T_{\\rm KT}$, the spin correlation also decays with increasing distance.\nThe decay, however, is much slower and appears to obey an asymptotic power law, which is characteristic to the quasi-LRO in the KT state.\nIn principle, the critical exponents can be estimated from the asymptotic power-law behavior, but it is difficult to be conclusive in the current system sizes.\n\n\\section{\nElectronic structure of partially disordered state\n\\label{sec:estruct}\n}\n\nIn the previous section, we discussed the thermodynamic behavior of the localized spin degree of freedom, with emphasis on the emergence of peculiar PD state.\nIn this section, we focus on the behavior in the charge degree of freedom of itinerant electrons in the PD phase. \n\n\\begin{figure}\n \\includegraphics[width=0.80\\linewidth]{fig5chargev4.eps}\n \\caption{(Color online).\n MC results for $n_{\\rm CO}$ at ${\\bf q}=(2\\pi\/3,-2\\pi\/3)$ at $n=1\/3$ and (a) $J=1$, (b) $J=2$, and (c) $J=4$.\n The calculations were done for the system sizes $N=12\\times 12$, $12\\times 18$, and $18\\times18$.\n }\n \\label{fig:mcnq}\n\\end{figure}\n\nFigure~\\ref{fig:mcnq} shows temperature dependence of the charge modulation $n_{\\rm CO}$ [Eq.~(\\ref{eq:n_CO})] at $n=1\/3$ for different $J$. \nFigure~\\ref{fig:mcnq}(a) is the result at $J=1$ for different system sizes.\nThe result shows an increase of $n_{\\rm CO}$ below $T \\simeq T_c^{\\rm (PD)} =0.086(4)$, indicating that the PD state is accompanied by charge modulation with period three.\nSimilar onsets of charge modulation at $T_c^{\\rm (PD)}$ are observed for larger $J$, as shown in Figs.~\\ref{fig:mcnq}(b) and \\ref{fig:mcnq}(c);\nthe amplitude of the modulation in the PD phase increases monotonically as $J$ increases.\nThe magnitude of the charge modulation is in the same order compared to the mean-field result in Fig.~\\ref{fig:gap}), while the growth is considerably suppressed by a factor of two to four.\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig5dosv2.eps}\n \\caption{(Color online).\n MC results for DOS of itinerant electrons at $n=1\/3$ and $J=2$ for $N=18\\times 18$.\n The Fermi level is set at $\\varepsilon = 0$.\n The statistical errors are comparable to the width of the lines.\n }\n \\label{fig:mcdos}\n\\end{figure}\n\nWe next look into the electronic density of states (DOS) at different temperature. \nFigure~\\ref{fig:mcdos} shows the results for DOS while varying temperature at $J=2$ and $n=1\/3$.\nThe Fermi level is set at $\\varepsilon=0$.\nHere, DOS was calculated by counting the number of energy eigenvalues as the histogram with the energy interval of 0.0375.\nIn the paramagnetic region for $T \\gtrsim T_c^{\\rm (PD)} = 0.130(4)$, DOS is featureless near the Fermi level.\nOn the other hand, below $T_c^{\\rm (PD)}$, an energy gap develops at the Fermi level for $n=1\/3$.\nThe result shows that the PD state is an insulator, which supports the scenario that PD is stabilized by the Slater mechanism described in Sec.~\\ref{sec:mft}.\nSimilarly to the charge modulation, the energy gap in the MC results is largely suppressed compared to that obtained by the mean-field analysis in Fig.~\\ref{fig:gap}.\nThis appears to show the importance of appropriately taking into account of thermal fluctuations.\n\n\\section{\\label{sec:summary}\nSummary\n}\n\nTo summarize, by a combined analysis of the mean-field type calculation and Monte Carlo simulation, we have investigated the origin of the partial disorder in the Ising-spin Kondo lattice model in a two-dimensional triangular lattice. \nIn the mean-field type calculation, we have clarified that a local magnetic field of the partial disorder type induces a metal-insulator transition at 1\/3 filling at a critical value of the field. \nThe result suggests that the three-sublattice partial disorder can give rise to an energy gap, and therefore, it has a chance to be stabilized through the Slater mechanism. \nOn the other hand, in the Monte Carlo simulation, we have provided convincing numerical results on the emergence of partial disorder at finite temperatures where the stripe phase and the ferrimagnetic order compete with each other.\nThe Monte Carlo result shows that the partially disordered state appears above a nonzero value of the spin-charge coupling, and that it is insulating and accompanied by charge disproportionation.\nThe nonzero critical value of the spin-charge coupling and the opening of the charge gap are both qualitatively consistent with the mean-field analysis.\nThe results indicate that the partial disorder is stabilized by the Slater mechanism which is characteristic to itinerant magnets.\nOur results not only clarify the new mechanism of partial disorder in two dimensions but also pave the way for understanding of the interesting physics related to the peculiar coexistence of magnetic order and paramagnetic moments in itinerant electron systems.\n\n\\begin{figure}\n \\includegraphics[width=0.8\\linewidth]{fig8v1.eps}\n \\caption{\n (Color online).\n (a) The grand potetial $\\Omega$ and (b) electron filling $n$ with respect to the chemical potential $\\mu$, numerically calculated by exactly diagonalizing the one-body Hamiltonian for itinerant electrons.\n The results are obtained at $J=2$ with $N_s=24\\times24$ site superlattice of $N=12\\times 12$ site unit cells.\n The strip at the left side of (b) shows the ground state at the corresponding filling.\n }\n \\label{fig:nvc}\n\\end{figure}\n\nAn interesting extension of the current work would be to consider the effect of quantum fluctuation of localized spins.\nIn our result, the partial disorder remains stable down to very low temperature, implying that the paramagnetic spins are largely fluctuating and sensitive to perturbations at low temperatures.\nHence, an interesting possibility is that, by including quantum fluctuations, the partial disorder is further stabilized and remains stable even in the ground state.\nIndeed, a similar partial disorder was found in the ground state of the Kondo lattice model with quantum spins at half filling~\\cite{Motome2010}. \nTherefore, it is intriguing to examine the effect of quantum fluctuations on the present model with Ising spins. \nHowever, it is not straightforwardly calculated by the present Monte Carlo method.\nThe interesting problem is left for future study.\n\n\\begin{acknowledgements}\nThe authors are grateful to G.-W. Chern, H. Kawamura, M. Matsuda, S. Miyashita, and H. Yoshida for fruitful discussions.\nThe authors also thank S. Hayami and T. Misawa for helpful comments.\nPart of the calculations were performed on the Supercomputer Center, Insitute for Solid State Physics,\nUniversity of Tokyo. H.I. is supported by Grant-in-Aid for JSPS Fellows.\nThis research was supported by KAKENHI (No.19052008, 21340090, 22540372, and 24340076), Global COE Program ``the Physical Sciences Frontier\", the Strategic Programs for Innovative Research (SPIRE), MEXT, and the Computational Materials Science Initiative (CMSI), Japan.\n\\end{acknowledgements}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzdcqw b/data_all_eng_slimpj/shuffled/split2/finalzzdcqw new file mode 100644 index 0000000000000000000000000000000000000000..6e64e0a7d86f37afc363adb848953f329208de0b --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzdcqw @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nThe Bernoulli convolution has been around for over seventy years and has surfaced in several different areas of mathematics. This probability measure depends on a parameter $\\beta >1$ and is defined on the interval $\\big[ 0, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1} \\big]$, where $\\lfloor \\beta \\rfloor$ is the largest integer not exceeding $\\beta$. The {\\bf symmetric Bernoulli convolution} is the distribution of $\\sum_{k=1}^{\\infty} \\frac{b_k}{\\beta_k}$ where the coefficients $b_k$ take values in the set $\\{ 0 ,1, \\ldots, \\lfloor \\beta \\rfloor \\}$, each with probability $\\frac{1}{\\lfloor \\beta \\rfloor +1}$. If instead the values $0, 1, \\ldots, \\lfloor \\beta \\rfloor$ are not taken with equal probabilities, then the Bernoulli convolution is called {\\bf asymmetric} or {\\bf biased}. See \\cite{PSS00} for an overview of results regarding the Bernoulli convolution up to the year 2000. Recently attention has shifted to the multifractal structure of the Bernoulli convolution. Jordan, Shmerkin and Solomyak study the multifractal spectrum for typical $\\beta$ in \\cite{JSS11}, Feng considers Salem numbers $\\beta$ in \\cite{Fen12} and Feng and Sidorov look at the Lebesgue generic local dimension of the Bernoulli convolution in \\cite{FS11}. In this paper we are interested in the local dimension function of the Bernoulli convolution and to study the local dimension we use a new approach.\n\nIf a point $x \\in \\big[ 0, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1} \\big]$ can be written as $x = \\sum_{k=1}^{\\infty} \\frac{b_k}{\\beta^k}$ with $b_k \\in \\{0,1, \\ldots, \\lfloor \\beta \\rfloor\\}$ for all $k \\ge 1$, then this expression is called a $\\beta$-expansion of the point $x$. In \\cite{S03} (see also \\cite{DV05}) it is shown that Lebesgue almost every $x$ has uncountably many $\\beta$-expansions. In \\cite{DV05} a random transformation $K$ was introduced that generates for each $x$ all these possible expansions by iteration. The map $K$ can be identified with a full shift which allows one to define an invariant measure $\\nu_{\\beta}$ for $K$ of maximal entropy by pulling back the uniform Bernoulli measure. One obtains the Bernoulli convolution from $\\nu_{\\beta}$ by projection. In this paper we study the local dimension of the measure $\\nu_{\\beta}$. By projection, some of these results can be translated to the Bernoulli convolution. For now, our methods work only for a special set of $\\beta$'s called the generalised multinacci numbers. We have good hopes that in the future we can extend these methods to a more general class of $\\beta$'s.\n\nThe paper is organized as follows. In the first section we will give the necessary definitions. Next we study the local dimension of $\\nu_{\\beta}$. We give a formula for the lower and upper bound of the local dimension that holds everywhere using a suitable Markov shift. Moreover, we show that the local dimension exists and is constant a.e.~and we give this constant. We also show that on the set corresponding to points with a unique $\\beta$-expansion, the local dimension of $\\nu_{\\beta}$ takes a different value. Next we translate these results to a lower and upper bound for the local dimension of the symmetric Bernoulli convolution that holds everywhere. We then use a result from \\cite{FS11} to obtain an a.e.~value for the Bernoulli convolution in case $\\beta$ is a Pisot number. Finally we give the local dimension for points with a unique expansion. In the last section we consider one specific example of an asymmetric Bernoulli convolution, namely when $\\beta$ is the golden ratio. We give an a.e.~lower and upper bound for the local dimension of both the invariant measure for $K$ and the asymmetric Bernoulli convolution. This last section is just a starting point for more research in this direction.\n\n\n\\section{Preliminaries}\n\nThe set of $\\beta$'s we consider in this paper, the generalised multinacci numbers, are defined as follows. On the interval $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1} \\big]$ the {\\bf greedy} $\\beta$-transformation $T_{\\beta}$ is given by\n\\[ T_{\\beta} x = \\left\\{\n\\begin{array}{ll}\n\\beta x \\, (\\text{mod }1), & \\text{if } x \\in \\big[0,1),\\\\\n\\beta x - \\lfloor \\beta \\rfloor, & \\text{if } x \\in \\big[1, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1} \\big].\n\\end{array}\n\\right.\\]\nThe {\\bf greedy digit sequence} of a number $x \\in \\big[ 0, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1} \\big]$ is defined by setting \n\\[ a_1=a_1(x) = \\left\\{\n\\begin{array}{ll}\nk, & \\text{if } x \\in \\big[\\frac{k}{\\beta}, \\frac{k+1}{\\beta} \\big), \\, k \\in \\{0, \\ldots, \\lfloor \\beta \\rfloor -1 \\},\\\\\n\\lfloor \\beta \\rfloor, & \\text{if } x \\in \\big[ \\frac{\\lfloor \\beta \\rfloor}{\\beta}, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1} \\big],\n\\end{array}\n\\right.\\]\nand for $n \\ge 1$, $a_n=a_n(x) = a_1(T_{\\beta}^{n-1} x)$. Then $T_{\\beta}x = \\beta x - a_1(x)$ and one easily checks that $x = \\sum_{n=1}^{\\infty} \\frac{a_n}{\\beta^n}$. This $\\beta$-expansion of $x$ is called its {\\bf greedy $\\beta$-expansion}. A number $\\beta >1$ is called a {\\bf generalised multinacci number} if the greedy $\\beta$-expansion of the number 1 satisfies\n\\begin{equation}\\label{q:genmn}\n1 = \\frac{a_1}{\\beta} + \\frac{a_2}{\\beta^2} + \\cdots + \\frac{a_n}{\\beta^n},\n\\end{equation}\nwith $a_j \\ge 1$ for all $1 \\le j \\le n$ and $n \\ge 2$. (Note that $a_1 = \\lfloor \\beta \\rfloor$.) We call $n$ the {\\bf degree} of $\\beta$.\n\n\\begin{rem}{\\rm\nBetween 1 and 2 the numbers that satisfy this definition are called the multinacci numbers. The {\\bf $n$-th multinacci number} $\\beta_n$ satisfies\n\\[ \\beta^n_n = \\beta^{n-1}_n + \\beta^{n-2}_n + \\cdots + \\beta_n + 1,\\]\nwhich implies that $a_j=1$ for all $1 \\le j \\le n$ in (\\ref{q:genmn}). The second multinacci number is better known as the golden ratio.}\n\\end{rem}\n\n\\vskip .2cm\nFor the Markov shift we will construct later on, we need a suitable partition of the interval $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$. Consider the maps $T_k x = \\beta x -k$, $k =0, \\ldots, \\lfloor \\beta \\rfloor$. For each $x \\in \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$, either there is exactly one $k \\in \\{0, \\ldots, \\lfloor \\beta \\rfloor \\}$ such that $T_k x \\in \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$, or there is a $k$ such that both $T_k x$ and $T_{k+1} x$ are in $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$. In this way the maps $T_k$ partition the interval $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$ into the following regions:\n\\[ \\begin{array}{ll}\n\\displaystyle E_0 = \\Big[0, \\frac{1}{\\beta} \\Big), & \\displaystyle E_{\\lfloor \\beta \\rfloor} = \\Big( \\frac{\\lfloor \\beta \\rfloor}{\\beta(\\beta -1)} +\\frac{\\lfloor \\beta \\rfloor-1}{\\beta}, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1} \\Big],\\\\\n\\\\\n\\displaystyle E_k = \\Big( \\frac{\\lfloor \\beta \\rfloor}{\\beta(\\beta -1)} +\\frac{k-1}{\\beta}, \\frac{k+1}{\\beta}\\Big), & k \\in \\{ 0, 1, \\ldots, \\lfloor \\beta \\rfloor \\},\\\\\n\\\\\n\\displaystyle S_k = \\Big[ \\frac{k}{\\beta}, \\frac{\\lfloor \\beta \\rfloor}{\\beta(\\beta-1)} + \\frac{k-1}{\\beta} \\Big], & k \\in \\{1, \\ldots, \\lfloor \\beta \\rfloor \\}.\n\\end{array}\\]\nSee Figure~\\ref{f:2.5} for a picture of the maps $T_k$ and the regions $E_k$ and $S_k$ in case $2 < \\beta < 3$.\n\\begin{figure}[ht]\n\\centering\n\\includegraphics{figure1kleur}\n\\caption{The maps $T_0 x = \\beta x$, $T_1 x = \\beta x-1$ and $T_2 x = \\beta x -2$ and the intervals $E_0$, $S_1$, $E_1$, $S_2$ and $E_2$ for some $2< \\beta <3$.}\n\\label{f:2.5}\n\\end{figure}\n\n\\vskip .2cm\nWrite $\\Omega = \\{0,1 \\}^{\\mathbb N}$. The {\\bf random $\\beta$-transformation} is the map $K$ from the space $\\Omega \\times \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$ to itself defined as follows.\n\\[ K(\\omega, x) = \\left\\{\n\\begin{array}{ll}\n(\\omega, T_k x), & \\text{if } x \\in E_k, \\, k \\in \\{0, \\ldots, \\lfloor \\beta \\rfloor \\},\\\\\n(\\sigma \\omega, T_{k-1+\\omega_1} x), & \\text{if }x \\in S_k, \\, k \\in \\{ 1, \\ldots, \\lfloor \\beta \\rfloor\\},\n\\end{array}\n\\right.\\]\nwhere $\\sigma$ denotes the left shift on sequences, i.e., $\\sigma (\\omega_n)_{n \\ge 1} = (\\omega_{n+1})_{n \\ge 1}$. The projection onto the second coordinate is denoted by $\\pi$. Let $\\lceil \\beta \\rceil$ denote the smallest integer not less than $\\beta$. The map $K$ is isomorphic to the full shift on $\\lceil \\beta \\rceil$ symbols. The isomorphism $\\phi: \\Omega \\times \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big] \\to \\{0,1, \\ldots, \\lfloor \\beta \\rfloor\\}^{\\mathbb N}$ uses the digit sequences produced by $K$. Let\n\\[b_1(\\omega,x) = \\left\\{\n\\begin{array}{ll}\nk, & \\text{if } x \\in E_k, \\, k \\in \\{0, 1, \\ldots, \\lfloor \\beta \\rfloor \\},\\\\\n& \\ \\text{or if } x \\in S_k \\text{ and } \\omega_1=1, \\, k \\in \\{1, \\ldots, \\lfloor \\beta \\rfloor \\},\\\\\nk-1, & \\text{if } x \\in S_k \\text{ and } \\omega_1=0, \\, k \\in \\{ 1, \\ldots, \\lfloor \\beta \\rfloor \\}\n\\end{array}\n\\right.\\]\nand for $n \\ge 1$, set $b_n(\\omega,x) = b_1\\big(K^{n-1}(\\omega, x)\\big)$. Then\n\\[ \\phi(\\omega,x) = \\big(b_n (\\omega,x) \\big)_{n \\ge 1}.\\]\nThis map is a bimeasurable bijection from the set $Z=\\big\\{ (\\omega, x) \\, : \\, \\pi \\big(K^n (\\omega, x)\\big) \\in S \\, \\, \\text{i.o.} \\big\\}$ to its image. We have $\\phi\\circ K = \\sigma \\circ \\phi$. Let $\\mathcal F$ denote the $\\sigma$-algebra generated by the cylinders and let $m$ denote the uniform Bernoulli measure on $(\\{0,1, \\ldots, \\lfloor \\beta \\rfloor\\}^{\\mathbb N}, \\mathcal F)$. Then $m$ is an invariant measure for $\\sigma$ and $\\nu_{\\beta} = m \\circ \\phi$ is invariant for $K$ with $\\nu_{\\beta}(Z)=1$. The projection $\\mu_{\\beta} = \\nu_{\\beta} \\circ \\pi^{-1}$ is the Bernoulli convolution on $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$. For proofs of these facts and more information on the map $K$ and its properties, see \\cite{DK03} and \\cite{DV05}.\n\n\\vskip .2cm\nWe are interested in the local dimension of the measures $\\nu_{\\beta}$ and $\\mu_{\\beta}$. For any probability measure $\\mu$ on a metric space $(X,\\rho)$, define the {\\bf local lower} and {\\bf local upper dimension} functions by\n\\[ \\underline{d}(\\mu,x) = \\liminf_{r \\downarrow 0} \\frac{\\log \\mu\\big(B_{\\rho}( x,r)\\big)}{\\log r} \\quad \\text{and} \\quad \\overline{d}(\\mu,x) = \\limsup_{r \\downarrow 0} \\frac{\\log \\mu\\big(B_{\\rho}(x,r)\\big)}{\\log r},\\]\nwhere $B_{\\rho}(x,r)$ is the open ball around $x$ with radius $r$. If $\\underline{d}(\\mu,x) = \\overline{d}(\\mu,x)$, then the {\\bf local dimension} of $\\mu$ at the point $x \\in X$ exists and is given by\n\\[ d(\\mu,x)= \\lim_{r \\downarrow 0} \\frac{\\log \\mu\\big(B_{\\rho}(x,r)\\big)}{\\log r}.\\]\nOn the sets $\\{0,1, \\ldots, \\lfloor \\beta \\rfloor\\}^{\\mathbb N}$ and $\\Omega$ we define the metric $D$ by\n\\[ D \\big( \\omega, \\omega' \\big) = \\beta^{-\\min\\{k\\ge 0 \\, :\\, \\omega_{k+1} \\neq \\omega_{k+1}'\\}}.\\]\nWe will define an appropriate metric on the set $\\Omega \\times \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$ later.\n\n\n\n\\section{Local dimension for $\\nu_{\\beta}$}\n\nWe will study the local dimension of the invariant measure $\\nu_{\\beta}$ of the map $K$ for $\\beta$'s that are generalised multinacci numbers. It is proven in \\cite{DV05} that for these $\\beta$'s the dynamics of $K$ can be modeled by a subshift of finite type. So, on the one hand there is the isomorphism of $K$ with the full shift on $\\lceil \\beta \\rceil$ symbols and on the other hand there is an isomorphism to a subshift of finite type. It is this second isomorphism that allows us to code orbits of points $(\\omega, x)$ under $K$ in an appropriate way for finding local dimensions. We give the essential information here.\n\n\\medskip\nWe begin with some notation. We denote the {\\bf greedy map} by $T_{\\beta}$ as before, and the {\\bf lazy map} by $S_{\\beta}$. More precisely, \n\\[ T_{\\beta} x = \\left\\{\n\\begin{array}{ll}\nT_0 x, & \\text{if } x \\in E_0,\\\\\n\\\\\nT_k x, & \\text{if }x \\in S_k\\cup E_k,\\\\\n& \\quad 1 \\le k \\le \\lfloor \\beta \\rfloor,\n\\end{array}\n\\right.\n\\quad \\text{and} \\quad S_{\\beta}x = \\left\\{\n\\begin{array}{ll}\nT_k x, & \\text{if } x \\in E_k\\cup S_{k+1},\\\\\n& \\quad 0 \\le k \\le \\lfloor \\beta \\rfloor-1,\\\\\n\\\\\nT_{\\lfloor \\beta \\rfloor} x, & \\text{if }x \\in E_{\\lfloor \\beta \\rfloor}.\n\\end{array}\n\\right.\\]\n\n\\noindent\nWe will be interested in the $K$-orbit of the points $(\\omega,1)$ and of their `symmetric counterparts' $(\\omega, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1}-1)$.\nProposition 2 (ii) in \\cite {DV05} tells us that the following set $F$ is finite:\n\\begin{multline}\\label{q:finite}\nF=\\Big\\{\\pi \\big(K^n(\\omega, 1)\\big), \\pi \\Big(K^n\\big(\\omega, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1}-1\\big)\\Big) \\, : \\, n \\ge 0,\\, \\omega \\in \\Omega\\Big\\}\\\\\n\\cup \\Big\\{\\frac{k}{\\beta}, \\frac{\\lfloor \\beta \\rfloor}{\\beta(\\beta -1)} + \\frac{k}{\\beta} : k \\in \\{0, \\ldots, \\lfloor \\beta \\rfloor \\} \\Big\\}.\n\\end{multline}\nThese are the endpoints of the intervals $E_k$ and $S_k$ and their forward orbits under all the maps $T_k$. The finiteness of $F$ implies that the dynamics of $K$ can be identified with a topological Markov chain. To find the Markov partition, one starts by refining the partition given by the sets $E_k$ and $S_k$, using the points from the set $F$. Let $\\mathcal C$ be the interval partition consisting of the open intervals between the points from this set. Note that when we say {\\bf interval partition}, we mean a collection of pairwise disjoint open intervals such that their union covers the interval $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$ up to a set of $\\lambda$-measure 0, where $\\lambda$ is the one-dimensional Lebesgue measure. Write\n\\[ \\mathcal C=\\{C_1,C_2,\\ldots , C_L\\}.\\]\nLet $ S=\\bigcup_{1 \\le k \\le \\lfloor \\beta \\rfloor} S_k$. The property {\\bf p3} from \\cite{DV05} says that no points of $F$ lie in the interior of $S$, i.e., each $S_k$ corresponds to a set $C_j$ in the sense that for each $1 \\le k \\le \\lfloor \\beta \\rfloor$ there is a $1 \\le j \\le L$ such that $\\lambda(S_k \\setminus C_j)=0$. Let $s \\subset \\{1, \\ldots, L\\}$ be the set containing those indices $j$. Consider the $L\\times L$ adjacency matrix $A=(a_{i, j})$ with entries in $\\{0,1\\}$ defined by\n\\begin{equation}\\label{matrix}\na_{i,j}\\, =\\, \\left\\{ \\begin{array}{ll}\n1, & {\\mbox{ if }}\\; i\\not \\in s \\text{ and } \\lambda(C_j \\cap T_{\\beta}(C_i))=\\lambda(C_j),\\\\\n0, & \\text{ if }\\; i\\not \\in s \\text{ and } \\lambda(C_i\\cap T_{\\beta}C_j)= 0,\\\\\n1, & \\text{ if }\\; i \\in s \\text{ and } \\lambda(C_j \\cap T_{\\beta} C_i)=\\lambda(C_j) \\text{ or } \\lambda(C_j \\cap S_{\\beta}C_i)=\\lambda(C_j),\\\\\n0, & \\text{ if }\\; i \\in s \\text{ and } \\lambda(C_i\\cap T_{\\beta}C_j)= 0 \\text{ and } \\lambda(C_i\\cap S_{\\beta}C_j)= 0.\n\\end{array}\\right.\n\\end{equation}\n\\noindent Define the partition $\\mathcal P$ of $\\Omega \\times \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$ by\n\\[ \\mathcal P=\\big\\{\\Omega \\times C_j : j \\not \\in s \\big\\} \\cup \\big\\{ \\{\\omega_1=i\\} \\times C_j: i \\in \\{0,1\\}, \\, j \\in s \\big\\}.\\]\nThen $\\mathcal P$ is a Markov partition underlying the map $K$. Let $Y$ denote the topological Markov chain determined by the matrix $A$. That is, $Y=\\{(y_n)_{n \\ge 1}\\in \\{1,\\dots ,L\\}^{\\mathbb N}:a_{y_n, y_{n+1}}=1 \\}$. Let $\\mathcal Y$ denote the $\\sigma$-algebra on $Y$ determined by the cylinder sets, i.e., the sets specifying finitely many digits, and let $\\sigma_Y$ be the left shift on $Y$. We use Parry's recipe (\\cite{Par64}) to determine the Markov measure $Q$ of maximal entropy for $(Y, \\mathcal Y, \\sigma_Y)$. By results in \\cite{DV05} we know that $\\nu_{\\beta}$ is the unique measure of maximal entropy for $K$ with entropy $h_{\\nu_{\\beta}}(K)=\\log \\lceil \\beta \\rceil$. By the identification with the Markov chain we know that $h_Q(\\sigma_Y) = \\log \\lceil \\beta \\rceil$. One then gets that the corresponding transition matrix $(p_{i,j})$ for $Y$ satisfies $p_{i,j}=a_{i,j}\\frac{v_j}{\\lceil \\beta \\rceil v_i}$, where $(v_1,v_2, \\ldots,v_L)$ is the right probability eigenvector of $A$ with eigenvalue $\\lfloor \\beta \\rfloor +1$. From this we see that if $[j_1 \\cdots j_m]$ is an allowed cylinder in $Y$, then\n\\begin{equation}\\label{q:qmeasure}\nQ([j_1 \\cdots j_m])=\\frac{v_{j_m}}{\\lceil \\beta \\rceil^{m-1}}.\n\\end{equation}\nProperty {\\bf p5} from \\cite{DV05} says that for all $i \\in s$, $a_{i,1}=a_{i,L}=1$ and $a_{i,j}=0$ for all other $j$. By symmetry of the matrix $(p_{i,j})$, it follows that\n\\begin{equation}\\label{q:ps1L}\np_{i, 1} = p_{i,L} = \\frac12 \\quad \\text{for all } i \\in s.\n\\end{equation}\nLet\n\\[ X = \\Omega \\times \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big] \\backslash \\big( \\bigcup_{n \\ge 0} K^{-n} F \\big).\\]\nThen $\\nu_{\\beta} (X) =1$. The isomorphism $\\alpha: X \\to Y$ between $(K, \\nu_{\\beta})$ and $(\\sigma_Y, Q)$ is then given by \n$$\\alpha_j(\\omega, x) = k \\mbox{ if } K^{j-1}(\\omega, x) \\in C_k.$$ \nSee Theorem 7 in \\cite{DV05} for a proof of this fact. In Figure~\\ref{f:diagram} we see the relation between the different systems we have introduced so far.\n\\begin{figure}[ht]\n\\centering\n\\includegraphics{figure2}\n\\caption{The relation between the different spaces.}\n\\label{f:diagram}\n\\end{figure}\n\nTo study the local dimension of $\\nu_{\\beta}$, we need to consider balls in $X$ under a suitable metric. Define the metric $\\rho$ on $X$ by\n\\[ \\rho\\big((\\omega,x),(\\omega^{\\prime},x^{\\prime})\\big)=\\beta^{-\\min\\{k\\ge 0\\, : \\, \\omega_{k+1}\\not=\\omega^{\\prime}_{k+1} \\text{ or } \\alpha_{k+1}(\\omega,x)\\not=\\alpha_{k+1}(\\omega^{\\prime},x^{\\prime}) \\}}.\\]\nConsider the ball \n\\[ B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big)=\\{(\\omega^{\\prime},x^{\\prime})\\, : \\, \\omega_i^{\\prime}=\\omega_i, \\mbox{ and } \\, \\alpha_i(\\omega^{\\prime},x^{\\prime})= \\alpha_i(\\omega,x), \\, i=1,\\cdots ,k\\}.\\]\nLet\n\\[ M_k(\\omega,x)=\\sum_{i=0}^{k-1}{\\bf 1}_{X \\cap \\Omega\\times S}\\big(K^i(\\omega,x)\\big) = \\# \\{ 1 \\le i \\le k \\, : \\, \\alpha_i(\\omega,x) \\in s \\}.\\]\nTo determine $\\nu_{\\beta}\\big(B_{\\rho}((\\omega,x),r)\\big)$ for $r \\downarrow 0$ we will calculate $Q \\Big( \\alpha \\Big(B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big) \\Big)\\Big)$. For all points $(\\omega',x')$ in the ball $B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big)$ the $\\alpha$-coding starts with $\\alpha_1(\\omega, x) \\cdots \\alpha_k(\\omega, x)$ and $\\omega'$ starts with $\\omega_1 \\cdots \\omega_k$. From the second part we know what happens the first $k$ times that the $K$-orbit of a point $(\\omega', x')$ lands in $\\Omega \\times S$. Since $M_k(\\omega, x)$ of these values have been used for $\\alpha_1(\\omega, x)\\cdots \\alpha_k(\\omega,x)$, there are $k-M_k(\\omega,x)$ unused values left. Note that $M_k(\\omega', x') = M_k(\\omega, x)=M_k$ for all $(\\omega', x') \\in B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big)$. Define the set\n\\[ Z = X \\cap \\bigcap_{n \\ge 1}\\bigcup_{i \\ge 1} K^{-i} \\big(\\Omega \\times S\\big).\\]\nAll points in $Z$ land in the set $\\Omega \\times S$ infinitely often under $K$. Since $Z$ is $K$-invariant, by ergodicity of $K$ we have $\\nu_{\\beta} (Z) =1$. So, all points $(\\omega', x') \\in B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big) \\cap Z$ make a transition to $S$ some time after $k$. Moreover, after this transition these points move to $C_1$ if $\\omega_{M_k+1} =1$ and to $C_L$ otherwise. The image of a point $(\\omega', x') \\in B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big)$ under $\\alpha$ will thus have the form\n\\begin{multline*}\n\\alpha_1 \\cdots \\alpha_k \\, \\underbrace{a_{k+1} \\cdots a_{m_1-1}}_{\\not \\in s} \\, \\underbrace{a_{m_1}}_{\\in s} \\, \\underbrace{a_{m_1+1} \\cdots a_{m_2-1}}_{\\not \\in s} \\, \\underbrace{a_{m_2}}_{\\in s} \\\\\n\\cdots \\, \\underbrace{a_{m_{N-1}-1}\\cdots a_{m_N-1}}_{\\not \\in s} \\, \\underbrace{a_{m_N}}_{\\in s} \\, \\underbrace{a_{m_N+1}a_{m_N+2} \\cdots}_{\\text{tail}},\n\\end{multline*}\nwhere $a_{m_j+1} \\in \\{1,L\\}$, $m_{j+1}-m_j-2 \\ge 1$ and $N = k-M_k(\\omega, x)$. Note that by the ergodicity of $\\nu_{\\beta}$ we have\n\\[ Q \\Big( \\bigcup_{m \\ge 1} [a_1 \\cdots a_m] \\, : \\, a_m \\in s \\text{ and } a_i \\not \\in s , \\, i < m \\Big) = \\nu_{\\beta} \\Big( X \\cap \\bigcup_{m \\ge 1} K^{-m} (\\Omega \\times S) \\Big)=1.\\]\nSo, the transition from any state to $s$ occurs with probability 1. Then one of the digits $\\omega_j$, $M_k+1 \\le j \\le k$, specifies what happens in this event and by (\\ref{q:ps1L}) both possibilities happen with probability $\\frac12$. To determine the measure of all possible tails of sequences in $\\alpha \\Big(B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big) \\Big)$, note that again by {\\bf p5} of \\cite{DV05} this tail always belongs to a point in $\\Omega \\times C_1$ or $\\Omega \\times C_L$. Since the $\\nu_{\\beta}$-measure of these sets is the same, the $Q$-measure of the set of all possible tails is given by $\\nu_{\\beta}(\\Omega \\times C_1)= \\mu_{\\beta}(C_1)$. Putting all this together gives\n\\begin{equation}\\label{q:Qball}\nQ \\Big( \\alpha \\Big(B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big) \\Big)\\Big) = \\lceil \\beta \\rceil^{-(k-1)} v_{\\alpha_k(\\omega,x)} \\cdot \\underbrace{1 \\cdot \\frac{1}{2} \\cdot 1 \\cdot \\frac{1}{2} \\cdots 1\\cdot \\frac{1}{2}}_{k-M_k(\\omega, x) \\text{ times}} \\cdot \\, \\mu_{\\beta}(C_1),\n\\end{equation}\nand hence,\n\\begin{equation}\\label{q:nuball}\n \\nu_{\\beta}\\big(B_{\\rho}((\\omega,x),\\beta^{-k})\\big) = \\lceil \\beta \\rceil^{-(k-1)}v_{\\alpha_k(\\omega,x)}\\,2^{-(k-M_k(\\omega,x))}\\,\\mu_{\\beta}(C_1).\n\\end{equation}\nThis gives the following theorem.\n\\begin{thm}\\label{t:locdim}\nLet $\\beta >1$ be a generalised multinacci number. For all $(\\omega,x) \\in X$ we have\n\\begin{multline}\\label{q:locdim}\n\\frac{\\log \\lceil \\beta \\rceil}{\\log \\beta} +\\frac{\\log 2}{\\log \\beta}\\Big[ 1-\\limsup_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k} \\Big] \\le \\underline d\\big(\\nu_{\\beta}, (\\omega,x)\\big)\\\\\n\\le \\overline d\\big(\\nu_{\\beta}, (\\omega,x)\\big) \\le \\frac{\\log \\lceil \\beta \\rceil}{\\log \\beta} +\\frac{\\log 2}{\\log \\beta}\\Big[ 1-\\liminf_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k} \\Big].\n\\end{multline}\n\\end{thm}\n\n\\begin{proof}\nLet $\\frac{1}{\\beta^{k+1}} < r \\le \\frac{1}{\\beta^k}$. Set $v_{min} = \\min\\{v_1, \\ldots, v_L\\}$ and $v_{max} = \\max\\{v_1, \\ldots, v_L\\}$. Then, by (\\ref{q:nuball}),\n\\[ \\frac{\\log \\nu_{\\beta}\\big(B_{\\rho}((\\omega,x),r)\\big)}{\\log r} \\le \\frac{(k-1)\\log \\lceil \\beta \\rceil}{k \\log \\beta} + \\frac{(k-M_k(\\omega, x))\\log 2}{k\\log\\beta} - \\frac{\\log \\big(v_{min}\\, \\mu_{\\beta}(C_1)\\big)}{k \\log \\beta}.\\]\nHence,\n\\[ \\overline d\\big(\\nu_{\\beta}, (\\omega,x)\\big) = \\limsup_{k \\to \\infty} \\frac{\\log \\nu_{\\beta}\\big(B_{\\rho}((\\omega,x),r)\\big)}{\\log r} \\le \\frac{\\log \\lceil \\beta \\rceil}{\\log \\beta} + \\frac{\\log 2}{\\log \\beta}\\Big[ 1 - \\liminf_{k \\to \\infty} \\frac{M_k(\\omega,x)}{k} \\Big].\\]\nOn the other hand,\n\\[ \\frac{\\log \\nu_{\\beta}\\big(B_{\\rho}((\\omega,x),r)\\big)}{\\log r} \\ge \\frac{(k-1)\\log \\lceil \\beta \\rceil}{(k+1) \\log \\beta} + \\frac{(k-M_k(\\omega, x))\\log 2}{(k+1)\\log\\beta} - \\frac{\\log \\big(v_{max}\\, \\mu_{\\beta}(C_1)\\big)}{(k+1) \\log \\beta}.\\]\nSince $M_{k+1}(\\omega, x)-1 \\le M_k(\\omega,x) \\le M_{k+1}(\\omega, x)$, we have that\n\\[ \\underline d\\big(\\nu_{\\beta}, (\\omega,x)\\big) = \\liminf_{k \\to \\infty} \\frac{\\log \\nu_{\\beta}\\big(B_{\\rho}((\\omega,x),r)\\big)}{\\log r} \\ge \\frac{\\log \\lceil \\beta \\rceil}{\\log \\beta} + \\frac{\\log 2}{\\log \\beta}\\Big[ 1 - \\limsup_{k \\to \\infty} \\frac{M_k(\\omega,x)}{k} \\Big].\\]\nThis proves the theorem.\n\\end{proof}\n\n\\begin{rem}{\\rm From the proof of the previous theorem it follows that if $\\lim_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k}$ exists, then $d\\big(\\nu_{\\beta}, (\\omega,x) \\big)$ exists and is equal to $\\frac{\\log \\lceil \\beta \\rceil}{\\log \\beta} +\\frac{\\log 2}{\\log \\beta}\\Big[ 1-\\lim_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k}\\Big]$.\n}\\end{rem}\n\n\\begin{cor}\\label{c:locdimnubeta}\nLet $\\beta$ be a generalised multinacci number. The local dimension function $d\\big(\\nu_{\\beta}, (\\omega,x)\\big)$ is constant $\\nu_{\\beta}$-a.e.~and equal to\n\\[d\\big(\\nu_{\\beta}, (\\omega,x)\\big)=\\frac{\\log \\lceil \\beta \\rceil}{\\log \\beta} +\\frac{\\log 2}{\\log \\beta} \\big( 1-\\mu_{\\beta}(S) \\big).\\]\n\\end{cor}\n\n\\begin{proof}\nSince $\\nu_{\\beta}$ is ergodic, the Ergodic Theorem gives that for $\\nu_{\\beta}$-a.e.~$(\\omega, x)$,\n\\[ \\lim_{k \\to \\infty} \\frac{M_k(\\omega, x)}{k} = \\nu_{\\beta}\\big(\\Omega \\times S\\big) = \\mu_{\\beta}(S).\\]\nThis gives the result.\n\\end{proof}\n\n\\medskip\nRecall that $\\phi$ maps points $(\\omega, x)$ to digit sequences $\\big( b_n(\\omega,x) \\big)_{n \\ge 1}$. It is easy to see that $x = \\sum_{n \\ge 1} \\frac{b_n(\\omega,x)}{\\beta^n}$ for each choice of $\\omega \\in \\Omega$. Note that a point $x$ has exactly one $\\beta$-expansion if and only if for all $n \\ge 0$, $\\pi\\big( K^n (\\omega,x) \\big) \\not \\in S$. Let $\\mathcal A_{\\beta} \\subset \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$ be the set of points with a unique $\\beta$-expansion. Then $\\mathcal A_{\\beta} \\neq \\emptyset$, since $0, \\frac{\\lfloor \\beta \\rfloor}{\\beta-1} \\in \\mathcal A_{\\beta}$ for any $\\beta>1$. By Proposition 2 from \\cite{DV05} all elements from $\\cup_{n \\ge 0}K^{-n}F$ will be in $S$ at some point and hence they will have more than one expansion. So, $\\mathcal A_{\\beta} \\subset X$. The next result also follows easily from Theorem~\\ref{t:locdim}. The measure $\\nu_{\\beta}$ is called {\\bf multifractal} if the local dimension takes more than one value on positive Hausdorff dimension sets. \n\n\\begin{cor}\nLet $\\beta$ be a generalised multinacci number. If $x \\in \\mathcal A_{\\beta}$, then $d\\big(\\nu_{\\beta}, (\\omega,x)\\big) =\\frac{\\log \\lceil \\beta \\rceil + \\log 2}{\\log \\beta}$ for all $\\omega \\in \\Omega$. The measure $\\nu_{\\beta}$ is multifractal.\n\\end{cor}\n\n\\begin{proof}\nIf $x \\in \\mathcal A_{\\beta}$, then $M_k(\\omega, x)=0$ for all $\\omega \\in \\Omega$ and $k \\ge 1$. Hence, by (\\ref{q:locdim}), $d\\big(\\nu_{\\beta}, (\\omega,x)\\big) =\\frac{\\log \\lceil \\beta \\rceil + \\log 2}{\\log \\beta}$. From standard results in dimension theory and our choice of metric it follows that $dim_H\\big(\\Omega \\times \\{x\\}\\big)=\\frac{\\log 2}{\\log \\beta}$ for all $x\\in \\mathcal A_{\\beta}$. Hence, $\\nu_{\\beta}$ is a multifractal measure.\n\\end{proof}\n\n\\begin{exa}\\label{r:goldenmean}\nWe give a example to show what can happen on points in $F$. Let $\\beta =\\frac{1+\\sqrt 5}{2}$ be the golden ratio. Then, $1 = \\frac{1}{\\beta} + \\frac{1}{\\beta^2}$. Figure~\\ref{f:gm} shows the maps $T_0$ and $T_1$ for this $\\beta$.\n\\begin{figure}[ht]\n\\centering\n\\includegraphics{figure3kleur}\n\\caption{The maps $T_0 x = \\beta x$ and $T_1 x = \\beta x-1$ for $\\beta = \\frac{1+\\sqrt 5}{2}$. The region $S$ is colored yellow.}\n\\label{f:gm}\n\\end{figure}\nNote that $F = \\{ 0, \\frac{1}{\\beta}, 1, \\beta \\}$. The partition $\\mathcal C$ consists of only three elements and the transition matrix and stationary distribution of the corresponding Markov chain are\n\\[ P = \\left(\n\\begin{array}{ccc}\n1\/2 & 1\/2 & 0\\\\\n1\/2 & 0 & 1\/2\\\\\n0 & 1\/2 & 1\/2\n\\end{array} \\right) \\quad \\text{ and } \\quad v=(1\/3,1\/3,1\/3).\\]\nHence, $\\mu_{\\beta}(S)=1\/3$ and Corollary~\\ref{c:locdimnubeta} gives that for $\\nu_{\\beta}$-a.e.~$(\\omega,x) \\in \\Omega \\times [0, \\beta]$,\n\\[d\\big(\\nu_{\\beta},(\\omega,x)\\big)=\\frac{\\log 2}{\\log \\beta} \\Big[ 2-\\mu_{\\beta}(S) \\Big]=(2-1\/3) \\frac{\\log 2}{\\log \\beta}=\\frac{5\\log 2}{6 \\log \\beta}.\\]\n\n\\vskip .2cm\nNow consider the $\\alpha$-code of the points $(\\overline{10},1)$ and $(\\overline{01},1\/\\beta)$, where the bar indicates a repeating block:\n\\[\\alpha \\big((\\overline{10},1)\\big)=\\alpha \\big((\\overline{01},1\/\\beta)\\big)=(s,s,s,\\cdots),\\]\nwhich is not allowed in the Markov chain $Y$. Then for any point \n\\[(\\omega,x)\\in \\bigcup_{m=0}^{\\infty} K^{-m} \\big(\\{(\\overline{10},1),(\\overline{01},1\/\\beta)\\}\\big),\\]\none has $B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big)$ is a countable set for all $k$ sufficiently large. For the local dimension this implies that\n\\[ d\\big(\\nu_{\\beta},(\\omega,x)\\big) = \\lim_{r \\downarrow 0} \\frac{\\log 0}{\\log r} = \\infty.\\]\n\\end{exa}\n\n\n\\section{Local dimensions for the symmetric Bernoulli convolution}\n\nConsider now the Bernoulli convolution measure $\\mu_{\\beta}=\\nu_{\\beta}\\circ \\pi^{-1}$ on the interval $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$ for generalised multinacci numbers. In \\cite{FS11}, the local dimension of $\\mu_{\\beta}$ with respect to the Euclidean metric was obtained for all Pisot numbers $\\beta$. A {\\bf Pisot number} is an algebraic integer that has all its Galois conjugates inside the unit circle. It is well known that all multinacci numbers are Pisot numbers, but unfortunately not all generalised multinacci numbers are Pisot. In Remark~\\ref{r:pisot}(i) we list some classes of generalised multinacci numbers that are in fact Pisot numbers. Before stating the results from \\cite{FS11}, we introduce more notation. Let\n\\begin{equation}\\label{q:fs}\n\\mathcal N_k (x,\\beta)=\\#\\left\\{(a_1, \\ldots, a_k)\\in \\{0,1, \\ldots, \\lfloor \\beta \\rfloor\\}^k:\\exists (a_{k+n})_{n \\ge 1} \\text{ with } x=\\sum_{m=1}^{\\infty} \\frac{a_m}{\\beta^m} \\right\\}.\n\\end{equation}\nA straightforward calculation (see also Lemma 4.1 of \\cite{Kem12}) shows that\n\\[ \\mathcal N_k (x,\\beta)=\\int_{\\Omega} \\, 2^{M_k(\\omega,x)} \\, dm(\\omega),\\]\nwhere $m$ is the uniform Bernoulli measure on $\\{0,1, \\ldots, \\lfloor \\beta \\rfloor\\}^{\\mathbb N}$ as before. In \\cite{FS11}, it was shown that if $\\beta$ is a Pisot number, then there is a constant $\\gamma=\\gamma(\\beta,m)$ such that \n\\begin{equation}\\label{q:gamma}\n\\lim_{k \\to\\infty}\\frac{\\log \\mathcal N_k(x,\\beta)}{k}=\\gamma\n\\end{equation}\nfor $\\lambda$-a.e.~$x$ in $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$, where $\\lambda$ is the one-dimensional Lebesgue measure. Using this, Feng and Sidorov obtained that for $\\lambda$-a.e.~$x$,\n\\[ d(\\mu_{\\beta},x)=\\frac{\\log \\lceil \\beta \\rceil -\\gamma}{\\log \\beta}\\]\n In fact, the result they obtained was stronger, but we will use their result in this form. We will show that one has the same value for the local dimension when the Euclidean metric on $\\mathbb R$ is replaced by the Hausdorff metric. To this end, consider the metric $\\bar \\rho$ on $\\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$ defined by\n\\[ \\bar \\rho(x,y)=d_H\\big(\\pi^{-1}(x),\\pi^{-1}(y)\\big),\\]\nwhere $d_H$ is the Hausdorff distance given by\n\\begin{multline*}\nd_H \\big(\\pi^{-1}(x),\\pi^{-1}(y)\\big)\\\\\n=\\inf\\Big\\{\\epsilon >0:\\pi^{-1}(y)\\subset \\bigcup_{\\omega \\in \\Omega}B_{\\rho}\\big((\\omega,x),\\epsilon \\big) \\mbox{ and } \\pi^{-1}(x)\\subset \\bigcup_{\\omega \\in \\Omega}B_{\\rho}\\big((\\omega,y),\\epsilon\\big)\\Big\\}.\n\\end{multline*}\n\n\\begin{thm}\nLet $\\beta$ be a generalised multinacci number. Then for all $x \\in \\big[0,\\frac{\\lfloor \\beta \\rfloor}{\\beta-1}\\big]$,\n\\begin{multline*}\n\\frac{1}{\\log \\beta} \\Big[ \\log \\lceil \\beta \\rceil - \\limsup_{k \\to\\infty}\\frac{\\log \\mathcal N_k(x,\\beta)}{k} \\Big] \\le \\underline d(\\mu_{\\beta},x)\\\\\n\\le \\overline d(\\mu_{\\beta},x) \\le \\frac{1}{\\log \\beta} \\Big[ \\log \\lceil \\beta \\rceil - \\liminf_{k \\to\\infty}\\frac{\\log \\mathcal N_k(x,\\beta)}{k} \\Big].\n\\end{multline*}\n\\end{thm}\n\n\\begin{proof}\nLet $B_ {\\bar \\rho}(x,\\epsilon)=\\{y:\\bar \\rho(x,y)<\\epsilon\\}$. We want to determine explicitly the set $\\pi^{-1}\\big(B_{\\bar \\rho}(x,\\beta^{-k}) \\big)$. First note that for any $(\\omega,x)$, and any $k\\ge 0$, one has $(\\omega^{\\prime},y)\\in B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big)$, and $B_{\\rho}\\big((\\omega,x),\\beta^{-k}\\big)=B_{\\rho}\\big((\\omega^{\\prime},y),\\beta^{-k}\\big)$ for any $(\\omega^{\\prime},y)\\in [\\omega_1\\cdots\\omega_k]\\times [\\alpha_1(\\omega,x)\\cdots \\alpha_k(\\omega,x)]$. We denote the common set by $B_{\\rho}\\big(([\\omega_1 \\cdots \\omega_k],x),\\beta^{-k}\\big)$. This implies that\n\\[\\pi^{-1}\\big(B_{\\bar \\rho}(x,\\beta^{-k})\\big)=\\bigcup_{[\\omega_1\\cdots \\omega_k]}B_{\\rho}\\big(([\\omega_1 \\cdots \\omega_k],x),\\beta^{-k}\\big),\\]\nwhere the summation on the right is taken over all possible cylinders of length $k$ in $\\Omega$. Again set $v_{min} = \\min\\{v_1, \\ldots, v_L\\}$ and $v_{max} = \\max\\{v_1, \\ldots, v_L\\}$. Then,\n\\begin{eqnarray*}\n\\mu_{\\beta}\\big(B_{\\bar \\rho}(x,\\beta^{-k})\\big) &=& \\sum_{[\\omega_1 \\cdots \\omega_k]}\\nu_{\\beta}\\big(B_{\\rho}\\big(([\\omega_1 \\cdots \\omega_k],x),\\beta^{-k} \\big)\\big)\\\\\n& \\le & \\sum_{[\\omega_1 \\cdots \\omega_k]}\\lceil \\beta \\rceil^{-(k-1)}v_{\\alpha_k(\\omega,x)}\\,2^{-(k-M_k(\\omega,x))}\\, \\mu_{\\beta}(C_1)\\\\\n& \\le & \\lceil \\beta \\rceil^{-(k-1)}v_{max} \\, \\mu_{\\beta}(C_1) \\sum_{[\\omega_1 \\cdots \\omega_k]} 2^{M_k(\\omega,x)} 2^{-k}\\\\\n& = & \\lceil \\beta \\rceil^{-(k-1)}v_{max}\\, \\mu_{\\beta}(C_1) \\int_{\\Omega} \\, 2^{M_k(\\omega,x)} \\, dm(\\omega)\\\\\n& = & \\lceil \\beta \\rceil^{-(k-1)} \\, v_{max}\\, \\mu_{\\beta}(C_1) \\, {\\mathcal N}_k(x,\\beta).\n\\end{eqnarray*}\nNow taking logarithms, dividing by $\\log \\beta^{-k}$, and taking limits we get\n\\[ \\underline d(\\mu_{\\beta},x)\\ge \\frac{\\log \\lceil \\beta \\rceil}{\\log \\beta} - \\frac{1}{\\log \\beta} \\limsup_{k \\to \\infty} \\frac{\\log \\mathcal N_k(x, \\beta)}{k}.\\]\nSimilarly we get that\n\\[ \\mu_{\\beta}\\big(B_{\\bar \\rho}(x,\\beta^{-k})\\big) \\ge \\lceil \\beta \\rceil^{-(k-1)} \\, v_{min} \\, \\mu_{\\beta}(C_1) \\, {\\mathcal N}_{k}(x,\\beta),\\]\nwhich gives\n\\[ \\overline d(\\mu_{\\beta},x)\\le \\frac{\\log \\lceil \\beta \\rceil}{\\log \\beta} - \\frac{1}{\\log \\beta} \\liminf_{k \\to \\infty} \\frac{\\log \\mathcal N_k(x, \\beta)}{k}. \\qedhere\\]\n\\end{proof}\n\n\\noindent By the results from \\cite{FS11} we have the following corollary.\n\\begin{cor}\nIf $\\beta$ is Pisot, then $d(\\mu_{\\beta},x)$ exists for $\\lambda$-a.e.~$x$ and is equal to $\\frac{\\log \\lceil \\beta \\rceil- \\gamma}{\\log \\beta}$, where $\\gamma$ is the constant from (\\ref{q:gamma}).\n\\end{cor}\n\n\\begin{rem}\\label{r:pisot}{\\rm\n(i) We give some examples of generalised multinacci numbers that are Pisot numbers. The generalised multinacci numbers in the interval $[1,2]$, i.e., the multinacci numbers, are all Pisot. In the interval $[2, \\infty)$ the numbers $\\beta$ that satisfy $\\beta^2 - k\\beta-1=0$, $k \\ge 2$, are all Pisot as well. Recall that if $1=\\frac{a_1}{\\beta} + \\cdots + \\frac{a_n}{\\beta^n}$ with $a_i \\ge 1$ for all $1 \\le i \\le n$, then $n$ is called the degree of $\\beta$. From Theorem 4.2 in \\cite{AG05} by Akiyama and Gjini we can deduce that all generalised multinacci numbers of degree 3 are Pisot numbers. Similarly, from Proposition 4.1 in \\cite{AG05} it follows that all generalised multinacci numbers of degree 4 with $a_4=1$ are Pisot. An example of a generalised multinacci number that is not Pisot is the number $\\beta$ satisfying\n\\[ 1=\\frac{3}{\\beta} + \\frac{1}{\\beta^2} + \\frac{2}{\\beta^3} + \\frac{3}{\\beta^4}.\\]\n(ii) In \\cite{Kem12} it is shown that for all $\\beta > 1$ and $\\lambda$-a.e.~$x$, $\\liminf_{k \\to \\infty} \\frac{\\log \\mathcal N_k(x, \\beta)}{k} \\ge \\mu_{\\beta}(S)\\log 2$, so we get\n\\[ \\overline d(\\mu_{\\beta},x)\\le \\frac{1}{\\log \\beta}\\Big[ \\log \\lceil \\beta \\rceil - \\mu_{\\beta}(S)\\log 2 \\Big].\\]\nKempton also remarks that this lower bound is not sharp.\n}\\end{rem}\n\n\\section{Asymmetric random $\\beta$-transformation: the golden ratio}\n\nIn the previous section, we considered the measure $\\nu_{\\beta}=\\nu_{\\beta,1\/2}$ which is the lift of the uniform Bernoulli measure $m=m_{1\/2}$ under the isomorphism $\\phi(\\omega, x)=\\big(b_n(\\omega,x)\\big)_{n \\ge 1}$. The projection of $\\nu_{\\beta}$ in the second coordinate is the symmetric Bernoulli convolution. In this section, we will investigate the asymmetric Bernoulli convolution in case $\\beta$ is the golden ratio.\n\n\\medskip\nLet $\\beta = \\frac{1+\\sqrt 5}{2}$ and consider the $(p,1-p)$-Bernoulli measure $m_p$ on $\\{0,1\\}^{\\mathbb N}$, i.e., with $m_p([0])=p$ and $m_p([1])=1-p$. Let $\\nu_{\\beta,p}=m_p\\circ \\phi$ on $\\Omega \\times [0, \\beta]$. Since $m_p$ is shift invariant and ergodic, we have that $\\nu_{\\beta,p}$ is $K$-invariant and ergodic. We first show that $\\nu_{\\beta,p}$ is a Markov measure with the same Markov partition as in the symmetric case (see Example~\\ref{r:goldenmean}), but the transition probabilities as well as the stationary distribution are different. This is achieved by looking at the $\\alpha$-code as well. The Markov partition is given by the partition $\\{E_0,S,E_1\\}$, and the corresponding Markov chain has three states $\\{e_0,s,e_1\\}$ with transition matrix\n\\[ P_p=\\left( \\begin{array}{ccc}\np & 1-p & 0 \\\\\np & 0 & 1-p \\\\\n0 & p & 1-p \\end{array} \\right)\\] \nand stationary distribution \n\\[ u=(u_{e_0},u_s,u_{e_1})=\\Big(\\frac{p^2}{p^2-p+1},\\frac{p(1-p)}{p^2-p+1},\\frac{(1-p)^2}{p^2-p+1} \\Big).\\]\nWe denote the corresponding Markov measure by $Q_p$, that is\n\\[ Q_p\\big([j_1 \\cdots j_k]\\big)=u_{j_1}p_{j_1,j_2}\\cdots p_{j_k,j_{k+1}},\\]\nand the space of realizations by \n\\[ Y=\\big\\{(y_1,y_2,\\ldots):y_i\\in \\{e_0,s,e_1\\}, \\mbox{ and } p_{y_i,y_{i+1}}>0\\big\\}.\\]\nConsider the map $\\alpha:\\Omega \\times [0,\\beta]$ of the previous section, namely\n\\[ \\alpha_j(\\omega,x) = \\left\\{ \\begin{array}{ll}\n e_0, & \\mbox{if $K^{j-1}(\\omega,x)\\in \\Omega \\times E_0$};\\\\\n s, & \\mbox{if $K^{j-1}(\\omega,x)\\in \\Omega \\times S$};\\\\\ne_1, & \\mbox{if $K^{j-1}(\\omega,x)\\in \\Omega \\times E_1$}.\\end{array} \\right. \\] \nDefine $\\psi:Y\\to \\{0,1\\}^{\\mathbb N}$ by\n\\[ \\psi(y)_j = \\left\\{ \\begin{array}{ll}\n 0, & \\mbox{if $y_j=e_0$ or $y_jy_{j+1}=se_1$};\\\\\n 1, & \\mbox{if $y_j=e_1$ or $y_jy_{j+1}=se_0$}.\\end{array} \\right. \\] \nIt is easy to see that $\\psi\\circ \\alpha=\\phi$. We want to show that $Q_p\\circ \\alpha =\\nu_{\\beta,p}$. Since $\\nu_{\\beta,p}=m_p\\circ \\phi$, we show instead the following.\n\n\\begin{prop}\nWe have $m_p=Q_p\\circ \\psi^{-1}$.\n\\end{prop}\n\n\\begin{proof}\nIt is enough to check equality on cylinders. To avoid confusion, we denote cylinders in $\\{0,1\\}^{\\mathbb N}$ by $[i_1 \\cdots i_k]$ and cylinders in $Y$ by $[j_1 \\cdots j_k]$. We show by induction that\n\\[ \\psi^{-1}([i_1 \\cdots i_k])=[j_1 \\cdots j_k]\\cup [j^{\\prime}_1,\\ldots, j^{\\prime}_{k+1}],\\]\nwhere $j_k=e_{i_k}$, and $j^{\\prime}_kj^{\\prime}_{k+1}=se_{1-i_k}$, and\n\\[ Q_p([j_1 \\cdots j_k])+Q_p([j^{\\prime}_1 \\cdots j^{\\prime}_{k+1}])=m_p\\big([i_1 \\cdots i_k]\\big) =p^{k-\\sum_{\\ell=1}^k i_{\\ell} }(1-p)^{\\sum_{\\ell=1}^k i_{\\ell}}.\\]\nConsider the case $k=1$. We have $\\psi^{-1}[0]=[e_0]\\cup [se_1]$ and $\\psi^{-1}[1]=[e_1]\\cup [se_0]$. Furthermore,\n\\[ Q_p([e_0]\\big)+Q_p([se_1])=\\frac{p^2}{p^2-p+1}+\\frac{p(1-p)^2}{p^2-p+1}=p=m_p([0]),\\]\nand\n\\[ Q_p([e_1])+Q_p([se_0])=\\frac{(1-p)^2}{p^2-p+1}+\\frac{p^2(1-p)}{p^2-p+1}=1-p=m_p([1])\\]\nas required. Assume now the result is true for all cylinders $[i_1 \\cdots i_k]$ of length $k$, and consider a cylinder $[i_1 \\cdots i_{k+1}]$ of length $k+1$. Then,\n\\[ \\psi^{-1}([i_1 \\cdots i_{k+1}])=[j_1 \\cdots j_{k+1}]\\cup [j^{\\prime}_1 \\cdots j^{\\prime}_{k+2}],\\]\nwhere\n\\[ \\psi^{-1}([i_2 \\cdots i_{k+1}])=[j_2 \\cdots j_{k+1}]\\cup [j^{\\prime}_2 \\cdots j^{\\prime}_{k+2}],\\]\nand \n\\[ j_1, j^{\\prime}_1= \\left\\{ \\begin{array}{ll}\n e_{i_1}, & \\mbox{if $i_1=i_2$ or $i_1\\not= i_2$ and $j_2=s$},\\\\\n s, & \\mbox{if $i_1\\not= i_2$ and $j_2\\not=s$},\\end{array} \\right. \\] \nBy the definition of $Q_p$, we have \n\\[ Q_p([j_1 \\cdots j_{k+1}])=\\frac{u_{j_1} p_{j_1,j_2}}{u_{j_2}} Q_p([j_2 \\cdots j_{k+1}]),\\]\nand\n\\[ Q_p([j^{\\prime}_1 \\cdots j^{\\prime}_{k+2}])=\\frac{u_{j^{\\prime}_1} p_{j^{\\prime}_1,j^{\\prime}_2}}{u_{j^{\\prime}_2}} Q_p([j^{\\prime}_2 \\cdots j^{\\prime}_{k+2}]).\\]\nOne easily checks that\n\\[ \\frac{u_{j_1} p_{j_1,j_2}}{u_{j_2}}=\\frac{u_{j^{\\prime}_1} p_{j^{\\prime}_1,j^{\\prime}_2}}{u_{j^{\\prime}_2}}=p^{1-i_1}(1-p)^{i_1} =\\left\\{ \\begin{array}{ll}\n p, & \\mbox{if }i_1=0;\\\\\n 1-p, & \\mbox{if }i_1=1.\\end{array} \\right. \\] \nBy the induction hypothesis applied to the cylinder $[i_2 \\cdots i_{k+1}]$, we have $j_{k+1}=e_{i_{k+1}}$, and $j^{\\prime}_{k+1}j^{\\prime}_{k+2}=se_{1-i_{k+1}}$, and\n\\[ Q_p([j_2 \\cdots j_{k+1}])+Q_p([j^{\\prime}_2 \\cdots j^{\\prime}_{k+2}])=m_p([i_2\\cdots i_{k+1}])\n=p^{k-\\sum_{\\ell=2}^{k+1} i_{\\ell} }(1-p)^{\\sum_{\\ell=2}^{k+1} i_{\\ell}}.\\]\nThus,\n\\begin{eqnarray*}\nQ_p([j_1 \\cdots j_{k+1}])+Q_p([j^{\\prime}_1 \\cdots j^{\\prime}_{k+2}]) & = &\np^{1-i_1}(1-p)^{i_1} p^{k-\\sum_{\\ell=2}^{k+1} i_{\\ell} }(1-p)^{\\sum_{\\ell=2}^{k+1} i_{\\ell}}\\\\\n& = & p^{k+1-\\sum_{\\ell=1}^{k+1} i_{\\ell} }(1-p)^{\\sum_{\\ell=1}^{k+1} i_{\\ell}}\\\\\n& = & m_p([i_1\\cdots i_{k+1}]).\n\\end{eqnarray*}\nThis gives the result.\n\\end{proof}\n\n\\noindent As before, let $M_k(\\omega,x)=\\sum_{i=0}^{k-1}{\\bf 1}_{\\Omega \\times S}(K_{\\beta}^i(\\omega,x))$.\n\\begin{thm} For $\\nu_{\\beta,p}$-a.e.~$(\\omega,x)$ for which $\\displaystyle\\lim_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k}$ exists, one has\n\\[ d\\big( \\nu_{\\beta,p},(\\omega, x)\\big)=\\frac{H(p)}{\\log \\beta} \\Big(2-\\lim_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k}\\Big),\\]\nwhere $H(p)=-p\\log p -(1-p)\\log(1-p)$.\n\\end{thm}\n\n\\begin{proof} We consider the same metric $\\rho$ as in the previous section, namely\n\\[ \\rho\\big((\\omega,x),(\\omega^{\\prime},x^{\\prime})\\big)=\\beta^{-\\min\\{k\\ge 0 \\, : \\, \n\\omega_{k+1}\\neq \\omega^{\\prime}_{k+1} \\text{ or } \\alpha_{k+1}(\\omega,x)\\neq \\alpha_{k+1}(\\omega^{\\prime},x^{\\prime})\\}}.\\]\nConsider a point $(\\omega,x)$ such that $\\displaystyle\\lim_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k}$ exists. By the same reasoning that led to (\\ref{q:Qball}) we have\n\\begin{multline*}\n\\nu_{\\beta,p}\\big(B_{\\rho}((\\omega,x),\\beta^{-k})\\big)\\\\\n= Q_p([\\alpha_1(\\omega,x),\\ldots,\\alpha_k(\\omega,x)]) p^{(k-M_k(\\omega,x))-\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i} (1-p)^{\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i} u_{e_{1-\\omega_k}}.\n\\end{multline*}\nLet $u_{\\max}=\\max(u_{e_0},u_{e_1})$ and $u_{\\min}=\\min(u_{e_0},u_{e_1})$, then $\\log \\nu_{\\beta,p}\\big(B_{\\rho}((\\omega,x),\\beta^{-k})\\big)$ is bounded from above by\n\\begin{gather*}\n\\log Q_p([\\alpha_1(\\omega,x),\\ldots,\\alpha_k(\\omega,x)]) + \\Big((k-M_k(\\omega,x)-\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i\\Big)\\log p\\\\\n + \\sum_{i=M_k(\\omega,x)+1}^k \\omega_i\\log (1-p) +\\log u_{\\max},\n\\end{gather*}\nand is bounded from below by\n\\begin{gather*}\n\\log Q_p([\\alpha_1(\\omega,x),\\ldots,\\alpha_k(\\omega,x)]) + \\Big((k-M_k(\\omega,x)-\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i\\Big)\\log p\\\\\n+ \\sum_{i=M_k(\\omega,x)+1}^k \\omega_i\\log (1-p) +\\log u_{\\min}.\n\\end{gather*}\nDividing by $-k\\log \\beta$ and taking limits, we have by the Shannon-McMillan-Breiman Theorem that\n\\[ \\lim_{k\\to\\infty}\\frac{\\log Q_p([\\alpha_1(\\omega,x) \\cdots \\alpha_k(\\omega,x)])}{-k\\log \\beta}= \\frac{H(p)}{\\log \\beta},\\]\nand by the Ergodic Theorem we have\n\\[ \\lim_{k\\to\\infty}\\frac{\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i}{-k\\log \\beta}= \\frac{-(1-p)\\big(1-\\lim_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k}\\big)}{\\log \\beta},\\]\nboth for $\\nu_{\\beta}$-a.e.~$(\\omega,x)$. Thus, both the upper and the lower bounds converge to the same value, implying that\n\\[ d\\big(\\nu_{\\beta,p}, (\\omega, x)\\big)=\\frac{H(p)}{\\log \\beta} \\Big(2-\\lim_{k\\to\\infty}\\frac{M_k(\\omega,x)}{k}\\Big). \\qedhere\\]\n\\end{proof}\n\n\\begin{cor}\nFor $\\nu_{\\beta,p}$-a.e.~$(\\omega,x)$ one has \n\\[ d\\big(\\nu_{\\beta,p}, (\\omega, x)\\big)=\\frac{H(p)}{\\log \\beta} \\Big(2-\\nu_{\\beta,p}\\big(\\Omega \\times S\\big)\\Big) =\\frac{H(p)}{\\log \\beta} \\Big(2-\\frac{p(1-p)}{p^2-p+1}\\Big).\\]\n\\end{cor}\n\n\\vskip .5cm\nWe now turn to the study of the local dimension of the asymmetric Bernoulli convolution $\\mu_{\\beta,p}$, which is the projection in the second coordinate of $\\nu_{\\beta,p}$. Let ${\\mathcal N}_k(\\omega,x)$ be as given in equation (\\ref{q:fs}). In the symmetric case, it was shown that\n\\[\\mathcal N_{k}(x,\\beta)=\\int_{\\{0,1\\}^{\\mathbb N}} \\, 2^{M_k(\\omega,x)} \\, dm(\\omega)=\\sum_{[\\omega_1 \\cdots \\omega_k]} 2^{M_k(\\omega,x)} 2^{-k}.\\]\nWe now give a similar formula for the asymmetric case.\n\n\\begin{lem} \\label{counting}\n$\\mathcal N_{k}(x,\\beta)=\\sum_{[\\omega_1 \\cdots \\omega_k]} p^{(k-M_k(\\omega,x))-\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i} (1-p)^{\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i}$.\n\\end{lem}\n\n\\begin{proof} We use a similar argument as the one used for the symmetric case (see \\cite{Kem12}). Define\n\\[ \\Omega(k,x)=\\big\\{\\omega_1 \\cdots \\omega_{M_k(\\omega,x)}: \\omega\\in \\Omega \\big\\}.\\]\nIf $x$ has a unique expansion, then $\\Omega(k,x)$ consists of one element, the empty word. We now have $|\\Omega(k,x)|= \\mathcal N_k(x,\\beta)$, and \n\n\\begin{eqnarray*}\n& &\n \\sum_{[\\omega_1 \\cdots \\omega_k]} p^{(k-M_k(\\omega,x))-\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i} (1-p)^{\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i} \\\\\n& = & \\sum_{[\\omega_1 \\cdots \\omega_k]} \\frac{p^{k-\\sum_{i=1}^k \\omega_i}(1-p)^{\\sum_{i=1}^k \\omega_i}}{p^{M_k(\\omega,x)-\\sum_{i=1}^{M_k(\\omega,x)} \\omega_i}(1-p)^{\\sum_{i=1}^{M_k(\\omega,x)} \\omega_i}}\\\\\n& = & \\int_{\\Omega}\\frac{1}{m_p([\\omega_1 \\cdots \\omega_{M_k(\\omega,x)}])} dm_p(\\omega) = \\sum_{j=0}^k \\int_{\\{\\omega: M_k(\\omega,x)=j\\}}\\frac{1}{m_p([\\omega_1 \\cdots \\omega_j])} dm_p(\\omega)\\\\\n& = & \\sum_{j=0}^k\\,\\,\\sum_{\\omega_1 \\cdots \\omega_j \\in \\Omega(k,x)}\\frac{1}{m_p([\\omega_1 \\cdots \\omega_j])} m_p([\\omega_1 \\cdots \\omega_j]) = |\\Omega(k,x)|=\\mathcal N_k(x,\\beta). \\quad \\quad \\qedhere\n\\end{eqnarray*}\n\\end{proof}\n\n\n\\begin{thm}\\label{bounds}\nFor $\\lambda$-a.e.~$x \\in [0, \\beta]$ one has\n\\[ \\frac{-\\big(\\log (\\max(p,1-p))+\\gamma \\big)}{\\log \\beta}\\le \\underline{d}(\\mu_{\\beta,p},x)\\le \\overline{d}(\\mu_{\\beta,p},x)\\le \\frac{-\\big(\\log (\\min(p,1-p))+\\gamma \\big)}{\\log \\beta},\\]\nwhere $\\displaystyle\\lim_{k \\to\\infty}\\frac{\\log \\mathcal N_k(x,\\beta)}{k}=\\gamma$ is the constant from (\\ref{q:gamma}).\n\\end{thm}\n\n\\begin{proof} We use the same metric $\\bar \\rho$ on $[0,\\beta]$ as in the previous section. Then\n\\begin{multline*}\n\\mu_{\\beta,p}\\big(B_{\\bar \\rho}(x,\\beta^{-k})\\big) = \\sum_{[\\omega_1 \\cdots \\omega_k]}\\nu_{\\beta}(B_{\\rho}\\big([\\omega_1 \\cdots \\omega_k],x),\\beta^{-k})\\big)\\\\ \n= \\sum_{[\\omega_1 \\cdots \\omega_k]}Q_p([\\alpha_1(\\omega,x) \\cdots \\alpha_k(\\omega,x)]) p^{(k-M_k(\\omega,x))-\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i} \\\\\n\\cdot(1-p)^{\\sum_{i=M_k(\\omega,x)+1}^k \\omega_i} u_{e_{1-\\omega_k}}.\n\\end{multline*}\nNow,\n\\[ Q_p([\\alpha_1(\\omega,x) \\cdots \\alpha_k(\\omega,x)])=u_{\\alpha_1(\\omega,x)} p^{L_k(\\omega,x)}(1-p)^{k-L_k(\\omega,x)},\\]\nwhere\n\\[ L_k(\\omega,x)=\\#\\{1\\le j\\le k:\\alpha_j(\\omega,x)=e_0\\}+\\#\\{1\\le j\\le k:\\alpha_j(\\omega,x)\\alpha_{j+1}(\\omega,x)=e_1s\\},\\]\nand hence\n\\begin{multline*}\nk-L_k(\\omega,x)\\\\\n=\\#\\{1\\le j\\le k:\\alpha_j(\\omega,x)=e_1\\}+\\#\\{1\\le j\\le k:\\alpha_j(\\omega,x)\\alpha_{j+1}(\\omega,x)=e_0s\\}.\n\\end{multline*}\nLet $C_1=\\max(u_{e_0},u_s,u_{e_1})$ and $C_2=\\min(u_{e_0},u_s,u_{e_1})$. Then, from Lemma (\\ref{counting}) we have\n\\[ C_2\\big(\\min(p,1-p))\\big)^k \\mathcal N_k(x,\\beta)\\le \\mu_{\\beta,p}(B_{\\bar \\rho}(x,\\beta^{-k}))\\le C_1\\big(\\max(p,1-p))\\big)^k \\mathcal N_k(x,\\beta).\\]\nSince $\\beta$ is a Pisot number, $\\displaystyle\\lim_{k \\to\\infty}\\frac{\\log \\mathcal N_k(x,\\beta)}{k}=\\gamma$ exists $\\lambda$-a.e.~(see \\cite{FS11}) and we have\n\\[ \\frac{-[\\log (\\max(p,1-p))+\\gamma]}{\\log \\beta}\\le \\underline{d}(\\mu_{\\beta,p},x)\\le \\overline{d}(\\mu_{\\beta,p},x)\\le \\frac{-[\\log (\\min(p,1-p))+\\gamma]}{\\log \\beta}. \\qedhere\\]\n\\end{proof}\n\n\\begin{rem}{\\rm (i) If $p=1\/2$, then both sides of the inequality in Theorem (\\ref{bounds}) are equal to $\\displaystyle\\frac{\\log 2-\\gamma}{\\log \\beta}$ leading to \n\\[ d(\\mu_{\\beta,1\/2},x)= \\frac{\\log 2-\\gamma}{\\log \\beta}\\]\na.e.~as we have seen earlier.\\\\\n(ii) We now consider the extreme cases $x\\in \\{0,\\beta\\}$, the only two points with a unique expansion. We begin with $x=\\beta$. In this case\n\\[ Q_p([\\alpha_1(\\omega,\\beta) \\cdots \\alpha_k(\\omega,\\beta)])=Q_p([e_1 \\cdots e_1])=u_{e_1}(1-p)^k,\\]\nand $\\mathcal N_k(\\beta,\\beta)=1$, so that\n\\[ C_2(1-p)^k\\le \\mu_{\\beta,p}(B_{\\bar \\rho}(\\beta,\\beta^{-k}))\\le C_1(1-p)^k.\\]\nHence,\n\\[ d(\\mu_{\\beta,p}, \\beta)=\\frac{-\\log (1-p)}{\\log\\beta}\\]\nfor all $\\omega\\in \\Omega$. A similar argument shows that\n\\[ d(\\mu_{\\beta,p},0)=\\frac{-\\log (p)}{\\log\\beta}\\]\nfor all $\\omega\\in \\Omega$.\n}\\end{rem}\n\n\\bigskip\n\\footnotesize\n\\noindent\\textit{Acknowledgments.}\nThe second author was supported by NWO (Veni grant no. 639.031.140).\n\n\\bibliographystyle{alpha}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{S:0}\nThe weak separation condition (WSC) was introduced by Lau and Ngai \\cite{Lau-Ngai_1999} to study the multifractal formalism of self-similar measures defined by iterated function systems of contractive similitudes with overlaps. Although strictly weaker than the well-known open set condition (OSC), the weak separation condition leads to many interesting results, such as the validity of the multifractal formalism (see, e.g. \\cite{Lau-Ngai_1999,Feng_2003,Feng_2005,Feng-Lau_2009, Shmerkin_2005,Ye_2005}), equality of Hausdorff, box, and packing dimensions of self-conformal sets and the computation of these dimensions \\cite{Deng-Ngai_2011,Ferrari-Panzone_2011,Lau-Ngai-Wang_2009}, and conditions on absolute continuity of self-similar and self-conformal measures \\cite{Lau-Ngai-Rao_2001,Lau-Wang_2004,Lau-Ngai-Wang_2009}. Equivalent forms of the weak separation conditions have also been studied extensive (see, e.g. \\cite{Zerner_1996, Lau-Ngai-Wang_2009}).\n\nThe finite type condition (FTC) was introduced by Ngai and Wang \\cite{Ngai-Wang_2001} to calculate the Hausdorff dimension of self-similar sets with overlaps. It was generalized independently by Jin-Yau \\cite{Jin-Yau_2005} and Lau-Ngai \\cite{Lau-Ngai_2007} to include (OSC). Lau and Ngai proved that (FTC) implies (WSC) for IFSs of contractive similitudes. This result was generalized by Lau \\textit{et al.} \\cite{Lau-Ngai-Wang_2009} to conformal iterated function systems (CIFSs).\n\nIn 2009, Lau \\textit{et al.} \\cite{Lau-Ngai-Wang_2009} formulated (WSC) for conformal iterated function systems on $\\mathbb{R}^{n}$, and proved the equality of the Hausdorff, box and packing dimensions of the associated self-conformal sets. They also studied the absolute continuity of the associated self-conformal measures. The first goal of this paper is to extend results in \\cite{Lau-Ngai-Wang_2009} to Riemannian manifolds with nonnegative Ricci curvature. Our second goal is to generalize the method of computing the Haudorff dimension of self-similar sets in \\cite{Ngai-Wang_2001,Jin-Yau_2005,Lau-Ngai_2007} to Riemannian manifolds that are locally Euclidean.\n\nLet $M$ be a complete $n$-dimensional smooth Riemannian manifold.\nAssume that $U\\subset M$ is open and connected, and $W\\subset U$ is a compact set with $\\overline{W^{\\circ}}=W$, where $\\overline{W^{\\circ}}$ is the closure of the interior of $W$. Assume that $\\{S_{i}\\}_{i=1}^{N}$ is a conformal iterated function system (CIFS) on $U$ defined as in Section \\ref{S:1}. Then there exists a unique nonempty compact set $K\\subset W$, called the \\textit{attractor} or \\textit{self-conformal set}, satisfying $K=\\bigcup_{i=1}^{N}S_{i}(K)$ (see \\cite{Hutchinson_1981}).\n\nGiven a probability vector $(p_{1},\\dots,p_{N})$, i.e., $p_{i}>0$ for any $i\\in\\{1,\\dots,N\\}$ and $\\sum_{i=1}^{N}p_{i}=1$, there exists a unique Borel probability measure $\\mu$, called the \\textit{self-conformal measure}, such that $\\mu=\\sum_{i=1}^{N}p_{i}\\mu\\circ S_{i}^{-1}$ and $K={\\rm supp}(\\mu)$ (see \\cite{Hutchinson_1981}).\n\nLet $\\Sigma:=\\{1,\\dots,N\\}$, where $N\\in\\mathbb{N}$ and $N\\geq2$. Let $\\Sigma^{\\ast}:=\\bigcup_{k\\geq1}\\Sigma^{k}$, where $k\\in\\mathbb{N}$. For $\\mathbf{u}=(u_1,\\dots,u_k)\\in\\Sigma^{k}$, let $\\mathbf{u}^{-}:=(u_{1},\\dots,u_{k-1})$ and write $S_{\\mathbf{u}}:=S_{u_1}\\circ\\cdots\\circ S_{u_k}$, $p_{\\mathbf{u}}:=p_{u_1}\\cdots p_{u_k}$. Define\n$$r_{\\mathbf{u}}:=\\inf_{x\\in W}|\\det S'_{\\mathbf{u}}(x)|^{\\frac{1}{n}},~r:=\\min_{1\\leq i\\leq N}r_{i},~R_{\\mathbf{u}}:=\\sup_{x\\in W}|\\det S'_{\\mathbf{u}}(x)|^{\\frac{1}{n}},~R:=\\max_{1\\leq i\\leq N}R_{i}.$$\nIf $S=S_{\\mathbf{u}}$ for some $\\mathbf{u}\\in\\Sigma^{\\ast}$, we let $R_{S}=R_{\\mathbf{u}}$. For $00$ and any bounded subsets $D\\subset W$ and $A\\subset M$, denote the diameter of $A$ by $|A|$, and let\n$$\\mathcal{A}_{a,A,D}:=\\{S\\in\\mathcal{A}_{a|A|}:S(D)\\cap A\\neq\\emptyset\\},\\quad\\gamma_{a,A}:=\\sup_{A\\subset M}\\#\\mathcal{A}_{a,A,D}.$$\nFor $S\\in\\mathcal{A}_{b}$, let $p_{S}:=\\sum\\{p_{\\mathbf{u}}:S_{\\mathbf{u}}=S,\\mathbf{u}\\in\\mathcal{A}_{b}\\}$.\n\nTheorems \\ref{thm(0.1)}--\\ref{thm(0.4)} generalize analogous results in \\cite{Lau-Ngai-Wang_2009}. In our proofs, the Lebesgue measure in \\cite{Lau-Ngai-Wang_2009} is changed to the more complicated Riemannian volume measure; properties such as the volume doubling property, need not hold. We assume that $M$ is a complete Riemannian manifold with nonnegative Ricci curvature. Under this assumption, the Bishop-Gromov comparison theorem implies that $M$ is a \\textit{doubling space} (see, e.g. \\cite{Baudoin_2011,Berger_2003}), i.e., any $2r$-ball in $M$ can be covered by a finite union of a bounded number of $r$-balls, a property that obviously holds on $\\mathbb{R}^{n}$. Another complication arises on manifolds; unlike Euclidean spaces, it is not easy to calculate the volume of a ball in Riemannian manifolds. For Riemannian manifolds with nonnegative Ricci curvature, we use the Bishop-Gromov inequality (see Lemma \\ref{lem(1.00)}), which says that the Riemannian volume of a ball can be controlled by a ball in $\\mathbb{R}^{n}$ with the same radius. This is crucial in the proof of Theorem \\ref{thm(0.1)}.\n\n\nFor a set $K\\subset M$, let $\\dim_{{\\rm H}}(K),\\dim_{{\\rm B}}(K)$ and $\\dim_{{\\rm P}}(K)$ be the Hausdorff, box and packing dimensions, respectively. Let $\\mathcal{H}^{\\alpha}(K)$ and $\\mathcal{P}^{\\alpha}(K)$ be the Hausdorff and packing measures of $K$, respectively.\n\n\n\n\n\\begin{thm}\\label{thm(0.1)}\nLet $M$ be a complete $n$-dimensional smooth orientable Riemannian manifold with non-negative Ricci curvature, and let $U\\subset M$ be open and connect. Assume that $\\{S_{i}\\}_{i=1}^{N}$ is a CIFS on $U$ satisfying (WSC), and $K$ is the associated attractor. Then $\\alpha:=\\dim_{{\\rm H}}(K)=\\dim_{{\\rm B}}(K)=\\dim_{{\\rm P}}(K)$ and\n$0<\\mathcal{H}^{\\alpha}(K)\\leq\\mathcal{P}^{\\alpha}(K)<\\infty$.\n\\end{thm}\n\nLau \\textit{et al.} \\cite{Lau-Ngai-Rao_2001} formulated a sufficient condition for a self-similar measure defined by an IFS satisfying (WSC) to be singular. Later, Lau and Wang \\cite{Lau-Wang_2004} established the necessary perfected the result on absolute continuity in \\cite{Lau-Ngai-Rao_2001}. We extend these results to manifolds.\n\n\n\n\n\\begin{thm}\\label{thm(0.2)}\nAssume the same hypotheses as in Theorem \\ref{thm(0.1)}. Let $K$ be the attractor\nwith $\\dim_{{\\rm H}}(K)=\\alpha$. Then a self-conformal measure $\\mu$ defined by $\\{S_{i}\\}_{i=1}^{N}$ is singular with respect to $\\mathcal{H}^{\\alpha}|_{K}$ if and only if there exist $0R_{S}^{\\alpha}$.\n\\end{thm}\n\n\n\n\\begin{thm}\\label{thm(0.3)}\nAssume the same hypotheses as in Theorem \\ref{thm(0.1)}. If the self-conformal measure $\\mu$ is absolutely continuous with respect to $\\mathcal{H}^{\\alpha}|_{K}$, then the\nRadon-Nikodym derivative of $\\mu$ is bounded.\n\\end{thm}\n\nIn the proof of Theorem \\ref{thm(0.3)}, we use an analogue of the Lebesgue density theorem in metric spaces (see, e.g. \\cite[Theorem 2.9.8]{Federer_1969} and \\cite[Lemma 2.1(i)]{Bedferd-Fisher_1992}), applying to the collection of Borel sets that forms a Vitali relation (see \\cite{Federer_1969} and a brief summary in Section \\ref{S:2}). By \\cite[Definition 2.8.9 and Theorem 2.8.18]{Federer_1969}, we have a collection of open balls in Riemannian manifolds that forms a Vitali relation.\n\n\n We refer the reader to Section \\ref{S:3} for the definition of (FTC).\n\n\\begin{thm}\\label{thm(0.4)}\nAssume the same hypotheses as in Theorem \\ref{thm(0.1)}, and let $W\\subset U$ be a compact set with $\\overline{W^{\\circ}}=W$. If $\\{S_{i}\\}_{i=1}^{N}$ is a CIFS on $U$ satisfying (FTC) on some open sets $\\Omega\\subset W$,\nthen $\\{S_{i}\\}_{i=1}^{N}$ satisfies (WSC).\n\\end{thm}\n\nDenote the Riemannian distance in $M$ by $d(\\cdot,\\cdot)$. Let $W\\subset M$ be a compact set.\nWe say that $\\{S_{i}\\}_{i=1}^{N}$ is an \\textit{IFS of contractions} on $W$ if for any $i\\in\\{1,\\dots,N\\}$, there exists $\\rho_{i}\\in(0,1)$ such that for any $x,y\\in W$,\n\\begin{equation}\\label{eq:IFS_contraction}\nd(S_{i}(x),S_{i}(y))\\le \\rho_{i}d(x,y).\n\\end{equation}\nIf equality in \\eqref{eq:IFS_contraction} holds for all $i\\in\\{1,\\dots,N\\}$ and all $x,y\\in W$, then\nsay that $\\{S_{i}\\}_{i=1}^{N}$ is an \\textit{IFS of contractive similitudes} on $W$ and call $\\rho_i$ the \\textit{contraction ratio} of $S_{i}$.\n\n\nWe say that a Riemannian manifold $M$ is \\textit{locally Euclidean} if every point of $M$ has a neighborhood which is isometric to an open subset of a Euclidean space (see e.g. \\cite{Kobayashi-Nomizu}). By \\cite[Lemma 2 of Theorem 3.6]{Kobayashi-Nomizu}, contractive similitudes only exist in Riemannian manifolds that are locally Euclidean. In Section \\ref{S:4}, we obtain the following formula for computing the Hausdorff dimension formula of self-similar sets defined by a finite type IFS of contractive similitudes on a locally Euclidean Riemannian manifold, extending a result in \\cite{Jin-Yau_2005} and \\cite{Lau-Ngai_2007} to locally Euclidean Riemannian manifolds (see Theorem \\ref{thm(0.5)}).\n\n\\begin{thm}\\label{thm(0.5)}\nLet $M$ be a complete $n$-dimensional smooth orientable Riemannian manifold that is locally Euclidean, $W\\subseteq M$ be a compact subset, and $\\{S_{i}\\}_{i=1}^{N}$ be an IFS of contractive similitudes on $W$ with attractor $K$. Let $\\lambda_{\\alpha}$ be the spectral radius of the associated weighted incidence matrix $A_{\\alpha}$. If $\\{S_{i}\\}_{i=1}^{N}$ satisfies (FTC), then $\\dim_{{\\rm H}}(K)=\\alpha$, where $\\alpha$ is the unique number such that $\\lambda_{\\alpha}=1$.\n\\end{thm}\n\n\nIn order to compute the Hausdorff dimension of certain attractors on Riemannian manifolds, it is necessary to study graph iterated function systems (see Example~\\ref{exam(5.4)}). We define graph-directed iterated function systems (GIFSs) and graph finite type condition (GFTC) on Riemannian manifolds in Section \\ref{S:41}. We obtain the following result for computing the Hausdorff dimension of graph self-similar sets (see Theorem \\ref{thm(41.1)}), extending a result in \\cite{Ngai-Wang-Dong_2010}.\n\n\n\\begin{thm}\\label{thm(41.1)}\nLet $M$ be a complete $n$-dimensional smooth orientable Riemannian manifold that is locally Euclidean. Assume that $G=(V,E)$ is a GIFS defined on $M$ satisfying (GFTC), and $K$ is the associated graph self-similar set. Let $\\lambda_{\\alpha}$ be the spectral radius of the associated weighted incidence matrix $A_{\\alpha}$. Then $\\dim_{{\\rm H}}(K)=\\alpha$, where $\\alpha$ is the unique number such that $\\lambda_{\\alpha}=1$.\n\\end{thm}\n\n\n\n\nThis paper is organized as follows. Section \\ref{S:1} introduces the definition of CIFSs, some properties of (WSC), and gives the proof of Theorem \\ref{thm(0.1)}. In Section \\ref{S:2}, we study the absolute continuity of self-conformal measures on Riemannian manifolds and prove Theorems \\ref{thm(0.2)} and \\ref{thm(0.3)}. Section \\ref{S:3} is devoted to the proof of Theorem \\ref{thm(0.4)}. In Section \\ref{S:4}, we study finite type IFSs of contractive similitudes and prove Theorem \\ref{thm(0.5)}. Section \\ref{S:41} is devoted to the proof of Theorem \\ref{thm(41.1)}. Finally, we present some examples of CIFSs and GIFSs satisfying (FTC) and (GFTC) on Riemannian manifolds, respectively.\n\n\\section{The weak separation condition}\\label{S:1}\n\nLet $M$ be a complete $n$-dimensional smooth Riemannian manifold, $U\\subset M$ be open and connected, and let $W\\subset U$ be a compact set with $\\overline{W^{\\circ}}=W$.\nRecall that a map $S:U\\longrightarrow U$ is called \\textit{conformal} if $S'(x)$ is a similarity matrix for any $x\\in U$. We say that $\\{S_{i}\\}_{i=1}^{N}$ is a \\textit{conformal iterated function system} (CIFS)\non $U$, if\n\\begin{itemize}\n\\item[$(a)$] for any $i\\in\\Sigma$, $S_{i}:U\\longrightarrow S_{i}(U)\\subset U$ is a conformal $C^{1+\\varepsilon}$ diffeomorphism for some $\\varepsilon\\in(0,1)$;\n\\item[$(b)$] $S_{i}(W)\\subset W$ for any $i\\in\\Sigma$;\n\\item[$(c)$] $0<|\\det S'_{i}(x)|<1$ for any $i\\in\\Sigma$ and $x\\in U$.\n\\end{itemize}\n\nSince $M$ is a manifold, we can find an open and connected set $U_{1}$ such that $\\overline{U_{1}}$ is compact and $W\\subset U_{1}\\subset \\overline{U_{1}}\\subset U$. According to \\cite{Patzschke_1997}, conditions $(a)$--$(c)$ together imply the \\textit{bounded distortion property} (BDP), without assuming any separation condition, i.e., there exists a constant $C_{1}\\geq1$ such that for any $\\mathbf{u}\\in\\Sigma^{*}$ and $x,y\\in U_{1}$,\n\\begin{equation}\\label{eq(1.1)}\nC_{1}^{-n}\\leq\\frac{|\\det S'_{\\mathbf{u}}(x)|}{|\\det S'_{\\mathbf{u}}(y)|}\\leq C_{1}^{n}.\n\\end{equation}\nIt follows that for any $\\mathbf{u}\\in\\Sigma^*$,\n\\begin{equation}\\label{eq:r_R_bound}\nC_{1}^{-1}\\leq\\frac{r_{\\mathbf u}}{R_{\\mathbf u}}\\leq\\frac{R_{\\mathbf u}}{r_{\\mathbf u}}\\leq C_{1}.\n\\end{equation}\nMoreover, there exists a constant $C_{2}\\geq1$ such that for any $\\mathbf{u}\\in\\Sigma^{\\ast}$ and $x,y\\in W$,\n\\begin{equation}\\label{eq(1.2)}\nC_{2}^{-1}R_{\\mathbf{u}}d(x,y)\\leq d(S_{\\mathbf{u}}(x),S_{\\mathbf{u}}(y))\\leq C_{2}R_{\\mathbf{u}}d(x,y).\n\\end{equation}\nLet $\\nu$ be the Riemannian volume measure, and let $A\\subset W$ be a measurable set. Denote the Jacobian determinant of a function $f$ by $\\mathbf{J}f$. Then\n\\begin{equation}\\label{eq(1.02)}\n\\nu\\big(S_{\\mathbf{u}}(A)\\big)=\\int_{A}\\big|\\mathbf{J}\\big(S_{\\mathbf{u}}(x)\\big)\\big|d\\nu=\\int_{A}\\big|\\det S'_{\\mathbf{u}}(x)\\big|d\\nu.\n\\end{equation}\n(see, e.g. \\cite[Proposition 8.1.10]{Abraham-Marsden-Ratiu_2007}).\n\nNote that for any $x\\in W$ and $\\mathbf{u},\\mathbf{v}\\in\\Sigma^{\\ast}$,\n$$|\\det S'_{\\mathbf{u}\\mathbf{v}}(x)|=|\\det S'_{\\mathbf{u}}(S_{\\mathbf{v}}(x))\\cdot S'_{\\mathbf{v}}(x)|=|\\det S'_{\\mathbf{u}}(S_{\\mathbf{v}}(x))|\\cdot|\\det S'_{\\mathbf{v}}(x)|.$$\nHence $R_{\\mathbf{u}\\mathbf{v}}\\leq R_{\\mathbf{u}}R_{\\mathbf{v}}$ and $r_{\\mathbf{u}\\mathbf{v}}\\geq r_{\\mathbf{u}}r_{\\mathbf{v}}$. In particular, for $S=S_{\\mathbf{u}}\\in\\mathcal{A}_{b},\\mathbf{u}=(u_{1},\\dots,u_{k})\\in\\mathcal{W}_{b}$,\n$$\\begin{aligned}\nbr&\\bigg(\\frac{rb}{C_{1}}\\bigg)^{n}\\quad\\text{(by (\\ref{eq(1.3)}))}.\n\\end{aligned}$$\nIt follows that\n$$\\int_{A}b^{n}d\\nu\\geq\\int_{A}|\\det S'_{\\mathbf{u}}(x)|d\\nu\\geq\\int_{A}\\bigg(\\frac{rb}{C_{1}}\\bigg)^{n}d\\nu,$$\nwhich proves $(a)$ by (\\ref{eq(1.02)}).\n\n$(b)$ Making use of part $(a)$, we have\n$$\\nu(S_{\\mathbf{u}}(A))\\leq b^{n}\\nu(A)\\leq\\bigg(\\frac{C_{1}}{r}\\bigg)^{n}\\nu(S_{\\mathbf{v}}(A)).$$\nSimilarly,\n$$\\nu(S_{\\mathbf{v}}(A))\\leq b^{n}\\nu(A)\\leq\\bigg(\\frac{C_{1}}{r}\\bigg)^{n}\\nu(S_{\\mathbf{u}}(A)),$$\nwhich proves $(b)$.\n\\end{proof}\n\nLet $\\mathcal{F}:=\\{S_{\\mathbf{v}}S_{\\mathbf{u}}^{-1}:\\mathbf{u},\\mathbf{v}\\in\\Sigma^{\\ast}\\}$. It is possible that $\\tau=S_{\\mathbf{v}}S_{\\mathbf{u}}^{-1}$ can be simplified to $S_{\\mathbf{v}'}S_{\\mathbf{u}'}^{-1}$. Thus the domain of\n$\\tau$ is $S_{\\mathbf{u}'}(W)$ (containing $S_{\\mathbf{u}}(W)$). Denote the domain of $\\tau$ by ${\\rm Dom}(\\tau)$. The proof of the following lemma is similar to that of \\cite[Lemma 2.2]{Lau-Ngai-Wang_2009} and is omitted.\n\n\\begin{lem}\\label{lem(1.2)}\nAssume the same hypotheses of Lemma \\ref{lem(1.1)}. Then for any $\\mathbf{u},\\mathbf{v}\\in\\Sigma^{\\ast}$ and any $x,y\\in{\\rm Dom}(S_{\\mathbf{v}'}S_{\\mathbf{u}'}^{-1})$, we have\n$$\\frac{|\\det (S_{\\mathbf{v}}S_{\\mathbf{u}}^{-1})'(x)|}{|\\det (S_{\\mathbf{v}}S_{\\mathbf{u}}^{-1})'(y)|}\\leq C_{1}^{2n}.$$\n\\end{lem}\n\n\n\\begin{lem}\\label{lem(1.3)}\nAssume the same hypotheses of Lemma \\ref{lem(1.1)}. Let $\\tau=S_{\\mathbf{v}}S_{\\mathbf{u}}^{-1}=S_{\\mathbf{v}'}S_{\\mathbf{u}'}^{-1}\\in\\mathcal{F}$ with ${\\rm Dom}(\\tau)=S_{\\mathbf{u}'}(W)$. Then the following hold.\n\\begin{itemize}\n\\item[$(a)$] For any measurable subset $A\\subset {\\rm Dom}(\\tau)$,\n$$\\bigg(\\frac{r_{\\mathbf{v}'}}{R_{\\mathbf{u}'}}\\bigg)^{n}\\nu(A)\\leq \\nu(\\tau(A))\\leq\\bigg(\\frac{R_{\\mathbf{v}'}}{r_{\\mathbf{u}'}}\\bigg)^{n}\\nu(A).$$\n\\item[$(b)$] For any $A,B$ belonging to some collection $\\mathcal{C}$ of measurable subsets of $W$, suppose $C\\geq1$ is a constant such that\n$$C^{-1}\\nu(B)\\leq\\nu(A)\\leq C\\nu(B).$$\nThen for any $A,B\\in\\mathcal{C}$ such that $A,B\\subset{\\rm Dom}(\\tau)$,\n$$C^{-1}C_{1}^{-2n}\\nu(\\tau(B))\\leq\\nu(\\tau(A))\\leq CC_{1}^{2n}\\nu(\\tau(B)).$$\n\\end{itemize}\n\\end{lem}\n\\begin{proof}\n$(a)$ For $x\\in {\\rm Dom}(\\tau)$, let $y=S_{\\mathbf{u}'}^{-1}(x)\\in W$. Then\n$$|\\det\\tau'(x)|=|\\det (S_{\\mathbf{v}'}S_{\\mathbf{u}'}^{-1})'(x)|=|\\det S'_{\\mathbf{v}'}(S_{\\mathbf{u}'}^{-1}(x))|\\cdot|\\det (S_{\\mathbf{u}'}^{-1})'(x)|=\\frac{|\\det S'_{\\mathbf{v}'}(y)|}{|\\det S'_{\\mathbf{u}'}(y)|}.$$\nHence\n$$\\bigg(\\frac{r_{\\mathbf{v}'}}{R_{\\mathbf{u}'}}\\bigg)^{n}\\leq\n|\\det\\tau'(x)|\\leq\\bigg(\\frac{R_{\\mathbf{v}'}}{r_{\\mathbf{u}'}}\\bigg)^{n}.$$\nThus,\n$$\\int_{A}\\bigg(\\frac{r_{\\mathbf{v}'}}{R_{\\mathbf{u}'}}\\bigg)^{n}d\\nu\\leq\n\\int_{A}|\\det\\tau'(x)|d\\nu\\leq\\int_{A}\\bigg(\\frac{R_{\\mathbf{v}'}}{r_{\\mathbf{u}'}}\\bigg)^{n}d\\nu,$$\nwhich proves $(a)$ by (\\ref{eq(1.02)}).\n\n$(b)$ Making use of part $(a)$ and (\\ref{eq(1.1)}), we have\n$$\\begin{aligned}\n\\nu(\\tau(A))&\\leq\\bigg(\\frac{R_{\\mathbf{v}'}}{r_{\\mathbf{u}'}}\\bigg)^{n}\\nu(A)\n\\leq C\\bigg(\\frac{R_{\\mathbf{v}'}}{r_{\\mathbf{u}'}}\\bigg)^{n}\\nu(B)\\\\\n&\\leq C\\bigg(\\frac{R_{\\mathbf{v}'}}{r_{\\mathbf{u}'}}\\bigg)^{2n}\n\\nu(\\tau(B))\\quad\\text{(by part $(a)$)}\\\\\n&\\leq CC_{1}^{2n}\\nu(\\tau(B))\\quad\\text{(by (\\ref{eq:r_R_bound}))}.\n\\end{aligned}$$\nOn the other hand,\n$$\\begin{aligned}\n\\nu(\\tau(A))&\\geq\\bigg(\\frac{r_{\\mathbf{v}'}}{R_{\\mathbf{u}'}}\\bigg)^{n}\\nu(A)\n\\geq C^{-1}\\bigg(\\frac{r_{\\mathbf{v}'}}{R_{\\mathbf{u}'}}\\bigg)^{n}\\nu(B)\\\\\n&\\geq C^{-1}\\bigg(\\frac{r_{\\mathbf{v}'}}{R_{\\mathbf{u}'}}\\bigg)^{2n}\n\\nu(\\tau(B))\\quad\\text{(by part $(a)$)}\\\\\n&\\geq C^{-1}C_{1}^{-2n}\\nu(\\tau(B))\\quad\\text{(by (\\ref{eq:r_R_bound}))}.\n\\end{aligned}$$\nThis proves $(b)$.\n\\end{proof}\n\n\n\\begin{lem}\\label{lem(1.30)} (Bishop-Gromov inequality (see, e.g. \\cite{Anderson_1990,Berger_2003,Bishop-Crittenden}))\\label{lem(1.00)}\nLet $M$ be a complete $n$-dimensional Riemannian manifold with non-negative Ricci curvature, and $B_{r}(x)$ be an $r$-ball in $M$. Then\n$$\\nu(B_{r}(x))\\leq c_{n}r^{n},$$\nwhere $c_{n}=\\pi^{\\frac{n}{2}}\/\\Gamma(\\frac{n}{2}+1)$ is the volume of the unit ball in $\\mathbb{R}^{n}$.\n\\end{lem}\n\nThe following proposition generalizes \\cite[Proposition 3.1]{Lau-Ngai-Wang_2009} to manifolds.\n\n\\begin{prop}\\label{prop(1.1)}\nLet $M$ be a complete $n$-dimensional orientable Riemannian manifold with non-negative Ricci curvature. Assume the same hypotheses of Lemma \\ref{lem(1.1)}. Then the following are equivalent:\n\\begin{itemize}\n\\item[$(a)$] $\\{S_{i}\\}_{i=1}^{N}$ satisfies (WSC);\n\\item[$(b)$] there exists $a>0$ and a nonempty subset $D\\subset W$ such that $\\gamma_{a,D}<\\infty$;\n\\item[$(c)$] for any $a>0$ and any nonempty subset $D\\subset W$, $\\gamma_{a,D}<\\infty$;\n\\item[$(d)$] for any $D\\subset W$ there exists $\\gamma=\\gamma(D)$ (depending only on $D$) such that for any $00$ and a nonempty subset $D_{0}\\subset W$ such that $\\gamma_{a_{0},D_{0}}=\\infty$. Hence there exists a sequence $\\{A_{k}\\}_{k=1}^{\\infty}$ of nonempty sets bounded in $M$ such that\n\\begin{equation}\\label{eq(1.5)}\n\\{S\\in\\mathcal{A}_{a_{0}|A_{k}|}:S(D_{0})\\cap A_{k}\\neq\\emptyset\\}\\geq k.\n\\end{equation}\nTo prove $(b)$ fails, we fix an arbitrary $a>0$ and nonempty subset $D\\subset W$. We will show $\\gamma_{a,D}=\\infty$. Let\n$$s:=\\sup_{x\\in D_{0},y\\in D}d(x,y)<\\infty.$$\nWe first claim that for any $S\\in\\mathcal{A}_{a_{0}|A_{k}|}$ and $\\delta_{k}:=s a_{0} C_{2}|A_{k}|$, $S(D_{0})\\cap A_{k}\\neq\\emptyset$ implies $S(D)\\cap (A_{k})_{\\delta_{k}}\\neq\\emptyset$, where $(A_{k})_{\\delta_{k}}=\\{x\\in M:{\\rm dist}(x,A_{k})\\leq\\delta_{k}\\}$ is the closed $\\delta_{k}$-neighborhood of $A_{k}$.\nTo prove the claim, we let $y\\in S(D_{0})\\cap A_{k}$. Then there exists $x\\in D_{0}$ such that $y=S(x)\\in S(D_{0})$. Now let $\\tilde{x}\\in D$ and $\\tilde{y}:=S(\\tilde{x})\\in S(D)$. It follows from (\\ref{eq(1.2)}) that\n$$d(\\tilde{y},y)=d(S(\\tilde{x}),S(x))\\leq C_{2}R_{S}d(\\tilde{x},x)\\leq s C_{2}R_{S}\\leq s a_{0}|C_{2}A_{k}|=\\delta_{k}.$$\nThis proves the claim. Note that $(A_{k})_{\\delta_{k}}$ is a set of diameter $2\\delta_{k}+|A_{k}|=(2sa_{0} C_{2}+1)|A_{k}|$. Since $M$ is a Riemannian manifold with non-negative Ricci curvature, $M$ has the doubling property. Hence we can cover $(A_{k})_{\\delta_{k}}$ by no more than $\\lambda$ sets of diameter $(a_{0}|A_{k}|)\/a$. Note that\n$\\mathcal{A}_{a_{0}|A_{k}|}=\\mathcal{A}_{(a_{0}|A_{k}|)\/a}$. It follows from (\\ref{eq(1.5)}) and the claim in the above that there exists $A_{k}^{\\ast}\\subset M$ with\n$|A_{k}^{\\ast}|=(a_{0}|A_{k}|)\/a$ such that\n$$\\{S\\in\\mathcal{A}_{a_{0}|A_{k}^{\\ast}|}:S(D_{0})\\cap A_{k}^{\\ast}\\neq\\emptyset\\}\\geq \\frac{k}{\\lambda}.$$\nSince $\\lambda$ is independent of $k$, we conclude that $\\gamma_{a,D}=\\infty$.\n\n\n$(c)\\Rightarrow(d)$ Let $D\\subset W$. Then for any $x\\in W$ and $00$ such that for any $0\\frac{b^{\\alpha}}{4C_{3}}\\bigg\\},$$\nwhere $C_{3}$ is as in Corollary \\ref{coro(1.1)}. Then $P(\\Lambda)>1\/2$ implies $P(\\widetilde{\\Lambda})>1\/4$.\n\\end{lem}\n\n\n\n\n\n\n\n\\begin{proof}[Proof of Theorem~\\ref{thm(0.2)}] The proof of Theorem \\ref{thm(0.2)} follows by using Proposition \\ref{prop(1.1)}, Lemma \\ref{lem(2.1)}, and a technique in the proofs of \\cite[Theorem 1.1]{Lau-Ngai-Rao_2001} and \\cite[Theorem 1.1]{Lau-Wang_2004}; we omit the details.\n\\end{proof}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWe first give the definition of Vitali relation (see \\cite[Definition 2.8.16]{Federer_1969}). Let $X$ be a metric space.\nAny subset of\n$$\n\\{(x,A): x\\in A\\subset X\\}\n$$\nis called a \\textit{covering relation}. Let $\\mathbf{C}$ be a covering relation and $Z\\subset X$, we let\n$$\n\\mathbf{C}(Z):=\\{A\\subset X:(x,A)\\in \\mathbf{C}\\text{ for some }x\\in Z\\}.\n$$\nWe say $\\mathbf{C}$ is \\textit{fine at $x$} if\n$$\n\\inf\\{|A|:(x,A)\\in \\mathbf{C}\\}=0.\n$$\nWe say $\\mathbf{C}(Z)$ is \\textit{fine on $Z$} if for any $x\\in Z$, $\\mathbf{C}(x)$ is fine at $x$. Let $\\phi$ be a regular Borel measure on $X$. A covering relation $\\mathbf{V}$ is called a\n$\\phi$ \\textit{Vitali relation} if $\\mathbf{V}(X)$ is a family of Borel sets, $\\mathbf{V}$ is fine on $X$, and moreover, whenever $\\mathbf{C}\\subset \\mathbf{V}, Z\\subset X$, and $\\mathbf{C}$ is fine on $Z$, then $\\mathbf{C}(Z)$ has a countable disjoint subfamily covering $\\phi$ almost all of $Z$.\n\nFor an $n$-dimension Riemannian manifold $M$, by making use of \\cite[Definition 2.8.9 and Theorem 2.8.18]{Federer_1969}, we see that $\\{B_{r}(x):x\\in M,00$, let $A=A(Q):=\\{t\\in K:f(t)>Q\\}$. Then for any $\\delta>0$, by (\\ref{eq(2.4)}), there exist $x\\in K$ and $b>0$ such that\n\\begin{equation}\\label{eq(2.5)}\n\\phi\\big(A\\cap B_{b\\delta}(x)\\big)>\\frac{1}{2}\\phi(B_{b\\delta}(x)).\n\\end{equation}\nThen\n\\begin{equation}\\label{eq(2.6)}\\begin{aligned}\n\\mu(B_{b\\delta}(x))&=\\int_{B_{b\\delta}(x)}f(t)d\\phi(t)\\\\\n&\\geq \\int_{A\\cap B_{b\\delta}(x)}f(t)d\\phi(t)\\\\\n&\\geq Q\\phi(A\\cap B_{b\\delta}(x))\\\\\n&>\\frac{1}{2}Q\\phi(B_{b\\delta}(x))\\quad\\text{(by (\\ref{eq(2.5)}))}.\n\\end{aligned}\\end{equation}\nLet $\\delta:=C_{2}|K|$ in the above inequality. Note that $x\\in K=\\bigcup_{S\\in\\mathcal{A}_{b}}S(K)$, and hence there exists $S\\in\\mathcal{A}_{b}$ such that $x\\in S(K)$.\nMaking use of (\\ref{eq(1.2)}), we have\n$$|S(K)|\\leq C_{2}R_{S}|K|\\leq b\\delta,$$\nwhich implies that $S(K)\\subset B_{b\\delta}(x)$. For $S=S_{u_{1}\\cdots u_{k}}\\in\\mathcal{A}_{b}$, we have $R_{S}=R_{\\mathbf{u}}\\leq b\\bigg(\\frac{r}{C_{1}C_{2}}\\bigg)^{\\alpha}b^{\\alpha}\\phi(K).\n\\end{equation}\nCombining \\eqref{eq(2.6)} and \\eqref{eq(2.8)}, we obtain\n\\begin{equation}\\label{eq(2.9)}\n\\mu(B_{b\\delta}(x))>\\frac{1}{2}Q\\bigg(\\frac{r}{C_{1}C_{2}}\\bigg)^{\\alpha}b^{\\alpha}\\phi(K).\n\\end{equation}\nOn the other hand,\n$$\\begin{aligned}\n\\mu(B_{b\\delta}(x))&=\\sum_{S\\in\\mathcal{A}_{b},S(K)\\cap B_{b\\delta}(x)\\neq\\emptyset}p_{S}\\mu\\circ S^{-1}(B_{b\\delta}(x))\\\\\n&\\leq\\sum_{S\\in\\mathcal{A}_{b},S(K)\\cap B_{b\\delta}(x)\\neq\\emptyset}p_{S}.\n\\end{aligned}$$\nLet $S\\in\\mathcal{A}_{b}$ such that $p_S$ is the maximum among all the summands in the above summation. Then\n\\begin{equation}\\label{eq(2.10)}\n\\mu(B_{b\\delta}(x))\\leq p_{S}\\#\\{S\\in\\mathcal{A}_{b}:S(K)\\cap B_{b\\delta}(x)\\neq\\emptyset\\}\\leq p_{S}\\gamma_{\\frac{1}{2\\delta},K}.\n\\end{equation}\nWe choose $Q$ such that\n\\begin{equation}\\label{eq(2.11)}\n\\frac{1}{2}Q\\bigg(\\frac{r}{C_{1}C_{2}}\\bigg)^{\\alpha}\\phi(K)>\\gamma_{\\frac{1}{2\\delta},K}.\n\\end{equation}\nIt follows from \\eqref{eq(2.9)}--\\eqref{eq(2.11)} that there exists $S\\in\\mathcal{A}_{b}$ such that\n$$b^{\\alpha}\\gamma_{\\frac{1}{2\\delta},K}<\\mu(B_{b\\delta}(x))\\leq p_{S}\\gamma_{\\frac{1}{2\\delta},K}.$$\nHence\n$$R_{S}^{\\alpha}\\leq b^{\\alpha}\\overline{m}_{k}$, there exists $\\mathbf{u}\\in\\mathcal{M}_{k}$ such that $\\mathbf{u}\\preceq\\mathbf{v}$;\n\\item[$(d)$] for any $\\mathbf{v}\\in\\Sigma^{\\ast}$ with $|\\mathbf{v}|<\\underline{m}_{k}$, there exists $\\mathbf{u}\\in\\mathcal{M}_{k}$ such that $\\mathbf{v}\\preceq\\mathbf{u}$;\n\\item[$(e)$] there exists a positive integer $L$, independent of $k$, such that for any $\\mathbf{u}\\in\\mathcal{M}_{k}$ and $\\mathbf{v}\\in\\mathcal{M}_{k+1}$ with\n$\\mathbf{u}\\preceq\\mathbf{v}$, we have $|\\mathbf{v}|-|\\mathbf{u}|\\leq L$.\n\\end{itemize}\n\\end{defi}\n\nNote that $\\mathcal{M}_{k}$ can intersect $\\mathcal{M}_{k+1}$, and $\\{\\Sigma^{k}\\}_{k=0}^{\\infty}$ is an example of a sequence of nested index sets.\nFor each integer $k\\geq0$, let $\\mathcal{V}_{k}$ be the set of \\textit{$k$-th level vertices} defined as\n$$\\mathcal{V}_{0}:=\\{(\\mathbf{u},0)\\}\\quad\\text{and}\\quad\\mathcal{V}_{k}\n:=\\{(S_{\\mathbf{u}},k):\\mathbf{u}\\in\\mathcal{M}_{k}\\}\\text{ for any }k\\geq1.$$\nWe write $\\omega_{{\\rm root}}:=(\\mathbf{u},0)$ and call it the \\textit{root vertex}. Let $\\mathcal{V}:=\\bigcup_{k\\geq0}\\mathcal{V}_{k}$ be the set of all vertices. For\n$\\omega=(S_{\\mathbf{u}},k)$, we define $S_{\\omega}:=S_{\\mathbf{u}}$. Let $W\\subset M$ be a compact set. For an IFS $\\{S_{i}\\}_{i=1}^{N}$ on $W$, let $\\Omega\\subset W$ be a nonempty open set that is invariant under $\\{S_{i}\\}_{i=1}^{N}$. Such a set exists if $\\{S_{i}\\}_{i=1}^{N}$ are contractions on $W$. We say that two $k$-th level vertices $\\omega,\\omega'\\in\\mathcal{V}_{k}$ are \\textit{neighbors} if $S_{\\omega}(\\Omega)\\cap S_{\\omega'}(\\Omega)\\neq\\emptyset$. Let\n$$\\Omega(\\omega):=\\{\\omega':\\omega'\\in\\mathcal{V}_{k}\\text{ is a neighbor of }\\omega\\},$$\nwhich is called the \\textit{neighborhood} of $\\omega$.\n\n\\begin{defi}\\label{defi(3.2)}\nFor any two vertices $\\omega\\in\\mathcal{V}_{k}$ and $\\omega'\\in\\mathcal{V}_{k'}$, let\n$$\\tau:=S_{\\omega'}S_{\\omega}^{-1}:\\bigcup_{\\sigma\\in\\Omega(\\omega)}S_{\\sigma}(W)\\longrightarrow W.$$\nWe say $\\omega$ and $\\omega'$ are \\textit{equivalent}, i.e., $\\omega\\sim\\omega'$, if the following conditions hold\n\\begin{itemize}\n\\item[$(a)$] $\\{S_{\\sigma'}:\\sigma'\\in\\Omega(\\omega')\\}=\\{\\tau S_{\\sigma}:\\sigma\\in\\Omega(\\omega)\\}$;\n\\item[$(b)$] for $\\sigma\\in\\Omega(\\omega)$ and $\\sigma'\\in\\Omega(\\omega')$ such that $S_{\\sigma'}=\\tau S_{\\sigma}$, and for any positive integer $k_{0}\\geq1$,\n$\\mathbf{u}\\in\\Sigma^{\\ast}$, $\\mathbf{u}$ satisfies $(S_{\\sigma}\\circ S_{\\mathbf{u}},k+k_{0})\\in\\mathcal{V}_{k+k_{0}}$ if and only if it satisfies\n$(S_{\\sigma'}\\circ S_{\\mathbf{u}},k'+k_{0})\\in\\mathcal{V}_{k'+k_{0}}$.\n\\end{itemize}\n\\end{defi}\nIt is easy to see that $\\sim$ is an equivalence relation. Denote the equivalent class of $\\omega$ by $[\\omega]$, and call it the \\textit{neighborhood types} of $\\omega$.\n\\par Let $\\omega=(S_{\\mathbf{u}},k)\\in\\mathcal{V}_{k}$ and $\\sigma=(S_{\\mathbf{v}},k+1)\\in\\mathcal{V}_{k+1}$. Suppose that there exists $\\mathbf{w}\\in\\Sigma^{\\ast}$ such that\n$$\\mathbf{v}=(\\mathbf{u},\\mathbf{w}).$$\nThen we connect a \\textit{directed edge} from $\\omega$ to $\\sigma$, and denote this as $\\omega\\stackrel{\\mathbf{w}}{\\longrightarrow}\\sigma$. We call $\\omega$ a \\textit{parent} of $\\sigma$ and $\\sigma$ an \\textit{offspring} of $\\omega$. Define a graph $\\mathcal{G}:=(\\mathcal{V},\\mathcal{E})$, where $\\mathcal{E}$ is the set of all directed edges. We first remove from $\\mathcal{G}$ all but the smallest (in the lexicographic order) directed edges going to a vertex. After that, we remove all vertices that do not have any offspring, together with all vertices and edges leading only to them. The resulting graph is called the \\textit{reduced graph}. Denote it by $\\mathcal{G}_{R}:=(\\mathcal{V}_{R},\\mathcal{E}_{R})$, where $\\mathcal{V}_{R}$ and $\\mathcal{E}_{R}$ are the sets of all vertices and all edges, respectively.\n\nThe proof of the following proposition is similar to that of \\cite[Proposition 2.4]{Lau-Ngai_2007}; we omit the details.\n\n\\begin{prop}\\label{prop(3.1)}\nLet $\\omega$ and $\\omega'$ be two vertices in $\\mathcal{V}$ with offspring $\\mathbf{u}_{1},\\dots,\\mathbf{u}_{m}$ and $\\mathbf{u}'_{1},\\dots,\\mathbf{u}'_{s}$ in $\\mathcal{G}_{R}$, respectively. Suppose $[\\omega]=[\\omega']$. Then\n$$\\big\\{[\\mathbf{u}_{i}]:1\\leq i\\leq m\\big\\}=\\big\\{[\\mathbf{u}'_{i}]:1\\leq i\\leq s\\big\\}$$\ncounting multiplicity. In particular, $m=s$.\n\\end{prop}\n\n\\begin{defi}\\label{defi(3.3)}\nLet $\\{S_{i}\\}_{i=1}^{N}$ be an IFS on $W$ consisting of injective contractions, and let $\\mathcal{V}\/_{\\sim}:=\\{[\\omega]:\\omega\\in\\mathcal{V}\\}$. We say that $\\{S_{i}\\}_{i=1}^{N}$ satisfies the \\textit{finite type condition} (FTC) if there exists a nonempty invariant open set $\\Omega\\subset W$ with respect to some sequence of nested index sets $\\{\\mathcal{M}_{k}\\}_{k=0}^{\\infty}$ and such that\n$$\\#\\mathcal{V}\/_{\\sim}<\\infty.$$\nSuch a set $\\Omega$ is called \\textit{a finite type condition set (FTC set)}.\n\\end{defi}\n\nObviously, if $\\{S_{i}\\}_{i=1}^{N}$ satisfies (OSC), then $\\#\\mathcal{V}\/_{\\sim}=1$, and thus $\\{S_{i}\\}_{i=1}^{N}$ satisfies (FTC). We assume that $\\{S_{i}\\}_{i=1}^{N}$ is a CIFS in the rest of this section.\n\n\\begin{lem}\\label{lem(3.1)}\nLet $\\{S_{i}\\}_{i=1}^{N}$ be a CIFS on a compact subset $W\\subset M$. Assume that $\\{S_{i}\\}_{i=1}^{N}$ satisfies (FTC) with $\\Omega\\subset W$ being an FTC set. Then there\nexists a constant $C_{4}\\geq1$ such that for any two neighboring vertices $\\omega_{1}$ and $\\omega_{2}$, we have\n$$C_{4}^{-1}\\leq\\frac{\\nu(S_{\\omega_{1}}(\\Omega))}{\\nu(S_{\\omega_{2}}(\\Omega))}\\leq C_{4}.$$\n\\end{lem}\n\\begin{proof}\nLet $\\mathcal{T}$ be a neighborhood type, and $\\omega$ be a vertex such that $[\\omega]=\\mathcal{T}$. Let\n$$\\Omega(\\omega)=\\{\\omega_{0},\\omega_{1},\\dots,\\omega_{m}\\},$$\nwhere $\\omega_{0}=\\omega$. Substituting $S_{\\omega_{0}}(\\Omega)=A$ and $S_{\\omega_{i}}S_{\\omega_{0}}^{-1}=\\tau$ into Lemma \\ref{lem(1.3)}$(a)$ and using \\eqref{eq:r_R_bound}, we see that there exists a constant $c_{1}\\geq1$ such that for any $i\\in\\{0,1,\\dots,m\\}$,\n\\begin{equation}\\label{eq(3.1)}\nc_{1}^{-1}\\nu(S_{\\omega_{0}}(\\Omega))\\leq\\nu(S_{\\omega_{i}}(\\Omega))\\leq c_{1}\\nu(S_{\\omega_{0}}(\\Omega)).\n\\end{equation}\nLet $\\omega\\sim\\omega'$, $\\tau=S_{\\omega'}S_{\\omega}^{-1}\\in \\mathcal{F}$, and\n$$\\Omega(\\omega')=\\{\\omega'_{0},\\omega'_{1},\\dots,\\omega'_{m}\\},$$\nwhere $\\omega'_{0}=\\omega'$. Without loss of generality, for any $i\\in\\{0,1,\\dots,m\\}$ we can assume $S_{\\omega'_{i}}=\\tau S_{\\omega_{i}}$. It follows from the definition of $\\tau$ that\n$S_{\\omega_{i}}(\\Omega)\\subset{\\rm Dom}(\\tau)$. Making use of (\\ref{eq(3.1)}) and substituting $S_{\\omega_{i}}(\\Omega)=A$, $S_{\\omega_{0}}(\\Omega)=B$ and $S_{\\omega'_{i}}S_{\\omega_{i}}^{-1}=\\tau$ into Lemma \\ref{lem(1.3)}$(b)$, we see that for any $i\\in\\{0,1,\\dots,m\\}$,\n$$c_{1}^{-1}C_{1}^{-2n}\\nu(S_{\\omega'_{0}}(\\Omega))\\leq\\nu(S_{\\omega'_{i}}(\\Omega))=\\nu(\\tau S_{\\omega_{i}}(\\Omega))\\leq c_{1}C_{1}^{2n}\\nu(S_{\\omega'_{0}}(\\Omega)).$$\nHence the lemma holds for any two neighboring vertices $\\omega_{1},\\omega_{2}$ with one of them being of type $\\mathcal{T}$. Since there are only finitely\nmany distinct neighborhood types, the result follows.\n\\end{proof}\n\n\\begin{lem}\\label{lem(3.2)}\nLet $\\{S_{i}\\}_{i=1}^{N}$ be a CIFS on a compact subset $W\\subset M$. Then for any $\\mathbf{u}\\in\\Sigma^{k}$ and $\\Omega\\subset W$, we have\n\\begin{equation}\\label{eq(3.01)}\nr^{kn}\\leq\\frac{\\nu(S_{\\mathbf{u}}(\\Omega))}{\\nu(\\Omega)}\\leq R^{kn}.\n\\end{equation}\n\\end{lem}\n\\begin{proof}\nLet $x\\in W$. Then by the definition of $R$, we have\n$$\\begin{aligned}\n|\\det S'_{\\mathbf{u}}(x)|&=|\\det S'_{\\mathbf{u}^{-}}(S_{u_{k}}(x))|\\cdot|S'_{u_{k}}(x)|\\\\\n&\\leq R_{\\mathbf{u}^{-}}^{n}R_{u_{k}}^{n}\\\\\n&\\leq R_{u_{1}}^{n}\\cdots R_{u_{k}}^{n}\\\\\n&\\leq R^{kn}.\n\\end{aligned}$$\nFor any set $\\Omega\\subset W$, making use of (\\ref{eq(1.02)}), we have\n$$\\nu(S_{\\mathbf{u}}(\\Omega))=\\int_{\\Omega}|\\det S'_{\\mathbf{u}}(x)|d\\nu(x)\\leq R^{kn}\\nu(\\Omega).$$\nThis proves the right side of (\\ref{eq(3.01)}). On the other hand, if $x\\in W$, then by the definition of $r$, we have\n$$\\begin{aligned}\n|\\det S'_{\\mathbf{u}}(x)|&=|\\det S'_{\\mathbf{u}^{-}}(S_{u_{k}}(x))|\\cdot|S'_{u_{k}}(x)|\\\\\n&\\geq r_{\\mathbf{u}^{-}}^{n}r_{u_{k}}^{n}\\\\\n&\\geq r_{u_{1}}^{n}\\cdots r_{u_{k}}^{n}\\\\\n&\\geq r^{kn}.\n\\end{aligned}$$\nConsequently,\n$$\\nu(S_{\\mathbf{u}}(\\Omega))=\\int_{\\Omega}|\\det S'_{\\mathbf{u}}(x)|d\\nu(x)\\geq r^{kn}\\nu(\\Omega).$$\nThis proves the left side of (\\ref{eq(3.01)}).\n\\end{proof}\n\n\nWe now prove Theorem \\ref{thm(0.4)}.\n\n\\begin{proof}[Proof of Theorem \\ref{thm(0.4)}]\nFor $00$ such that for any $j\\in\\{1,\\dots,m\\}$,\n\\begin{equation}\\label{eq(3.10)}\nc_{3}\\leq R^{|\\mathbf{v}_{j}|}.\n\\end{equation}\nIn particular, we can take $c_{3}=r^{L+1}\/(C_{1}^{3}C_{4}^{1\/n})$. Let $c_{4}:=\\lfloor\\log c_{3}\/\\log R\\rfloor+1$. Then $|\\mathbf{v}_{j}|\\leq c_{4}$. Combining these and (\\ref{eq(3.2)}) yields\n$$\\#\\{S\\in\\mathcal{A}_{b}:x\\in S(\\Omega)\\}\\leq c_{2}N^{c_{4}},$$\nwhich implies that $\\{S_{i}\\}_{i=1}^{N}$ satisfies (WSC).\n\\end{proof}\n\n\n\\section{Hausdorff dimension of self-similar sets}\\label{S:4}\n\nIn this section, we assume that $M$ is a complete $n$-dimensional smooth orientable Riemannian manifold that is locally Euclidean, i.e., every point of $M$ has a neighborhood which is isometric to an open subset of a Euclidean space.\nLet $W\\subset M$ be compact, and let $\\{S_{i}\\}_{i=1}^{N}$ be an IFS satisfying (FTC) of contractive similitudes on some open sets $\\Omega\\subset W$ with attractor $K\\subset W$. Recall that $\\rho_i$ denotes the contraction ratio of $S_{i}$. We define\n$$\\rho:=\\min\\{\\rho_{i}:1\\leq i\\leq N\\}, \\quad \\rho_{\\max}:=\\max\\{\\rho_{i}:1\\leq i\\leq N\\}.$$\nDenote the neighborhood types of $\\{S_{i}\\}_{i=1}^{N}$ by $\\{\\mathcal{T}_{1},\\dots,\\mathcal{T}_{q}\\}$. Fix a vertex $\\omega\\in\\mathcal{V}_{R}$ such that $[\\omega]\\in\\mathcal{T}_{i}$, where $i\\in\\{1,\\dots,q\\}$. Let $\\sigma_{1},\\dots,\\sigma_{m}$ be the offspring of $\\omega$ in $\\mathcal{G}_{R}$, and let $\\mathbf{w}_{k}$ be the unique edge in $\\mathcal{G}_{R}$ connecting $\\omega$ to $\\sigma_{k}$ for $1\\leq k\\leq m$. Define a \\textit{weighted incidence matrix} $A_{\\alpha}=(A_{\\alpha}(i,j))_{i,j=1}^{q}$ as\n$$A_{\\alpha}(i,j):=\\sum_{k=1}^{m}\\{\\rho_{\\mathbf{w}_{k}}^{\\alpha}:\n\\omega\\stackrel{\\mathbf{w}_{k}}{\\longrightarrow}\\sigma_{k},[\\sigma_{k}]=\\mathcal{T}_{j}\\}.$$\nWe remark that the definition of $A_{\\alpha}$ is independent of the choice of $\\omega$ above. We denote by $\\omega\\rightarrow_{R}\\sigma$ if $\\omega,\\sigma\\in\\mathcal{V}_{R}$ and $\\sigma$ is an offspring of $\\omega$ in $\\mathcal{G}_{R}$. We define an (infinite) \\textit{path} in $\\mathcal{G}_{R}$ to be an infinite sequence $(\\omega_{0},\\omega_{1},\\dots)$ such that for any $k\\geq0$,\n$$\\omega_{k}\\in\\mathcal{V}_{k}\\quad\\text{and}\\quad\\omega_{k}\\rightarrow_{R}\\omega_{k+1},$$\nwhere $\\omega_{0}=\\omega_{{\\rm root}}$. Let $\\mathbb{P}$ be the set of all paths in $\\mathcal{G}_{R}$. If the vertices $\\omega_{0}=\\omega_{{\\rm root}},\\omega_{1},\\dots,\\omega_{k}$ are such that\n$$\\omega_{j}\\rightarrow_{R}\\omega_{j+1}\\text{ for }1\\leq j\\leq k-1,$$\nthen we call the set\n$$I_{\\omega_{0},\\omega_{1},\\dots,\\omega_{k}}=\\{(\\sigma_{0},\\sigma_{1},\\dots)\\in\\mathbb{P}:\\sigma_{j}=\\omega_{j}\n\\text{ for any }0\\leq j\\leq k\\}$$\na \\textit{cylinder}. Since the path from $\\omega_{0}$ to $\\omega_{k}$ is unique in $\\mathcal{G}_{R}$, we {\\color{blue}}let\n$$I_{\\omega_{k}}:=I_{\\omega_{0},\\omega_{1},\\dots,\\omega_{k}}.$$\nFor any cylinder $I_{\\omega_{k}}$, where $\\omega_{k}\\in\\mathcal{V}_{k}$ and $[\\omega_{k}]=\\mathcal{T}_{i}$, let\n$$\\hat{\\mu}(\\omega_{{\\rm root}})=a_{1}=1\\quad\\text{and}\\quad\\hat{\\mu}(\\omega_{k})=\\rho_{\\omega_{k}}^{\\alpha}a_{i},$$\nwhere $[a_{1},\\dots,a_{q}]^{T}$ is a $1$-eigenvector of $A_{\\alpha}$, normalized so that $a_{1}=1$. We will show that $\\hat{\\mu}$ is a measure on $\\mathbb{P}$ in the following. Note that for two cylinders $I_{\\omega}$ and $I_{\\omega}'$ with $\\omega\\in\\mathcal{V}_{k},\\omega'\\in\\mathcal{V}_{\\ell}$ and $k\\leq\\ell$, $I_{\\omega}\\cap I_{\\omega}'\\neq\\emptyset$ iff either $\\omega'=\\omega$ in the case $k=\\ell$ or $\\omega'$ is a descendant of $\\omega$ in the case $k<\\ell$. Whatever, $I_{\\omega}'\\subset I_{\\omega}$. Let $\\omega\\in\\mathcal{V}_{R}$ and $\\mathcal{D}:=\\{\\sigma_{k}\\}_{k=1}^{m}$ denote the set of all offspring of $\\omega$ in $\\mathcal{G}_{R}$. For $k\\in\\{1,\\dots,m\\}$, let $\\omega\\stackrel{\\mathbf{w}_{k}}{\\longrightarrow}_{R}\\sigma_{k}$. Then\n$$\\begin{aligned}\n\\sum_{\\sigma\\in\\mathcal{D}}\\hat{\\mu}(I_{\\sigma})\n&=\\sum_{j=1}^{q}\\sum_{\\sigma\\in\\mathcal{D},[\\sigma]=\\mathcal{T}_{j}}\\hat{\\mu}(I_{\\sigma})\\\\\n&=\\sum_{j=1}^{q}\\sum_{\\sigma\\in\\mathcal{D},[\\sigma]=\\mathcal{T}_{j}}\\rho_{\\sigma}^{\\alpha}a_{j}\n\\\\\n&=\\rho_{\\omega}^{\\alpha}\\sum_{j=1}^{q}\\sum_{\\sigma\\in\\mathcal{D},[\\sigma]=\\mathcal{T}_{j}}\n\\rho_{\\mathbf{w}_{k}}^{\\alpha}a_{j}\\\\\n&=\\rho_{\\omega}^{\\alpha}\\sum_{j=1}^{q}A_{\\alpha}(i,j)a_{j}\\\\\n&=\\rho_{\\omega}^{\\alpha}a_{i}=\\hat{\\mu}(I_{\\omega}).\n\\end{aligned}$$\nCombining these with $\\hat{\\mu}(\\mathbb{P})=\\hat{\\mu}(\\omega_{{\\rm root}})=1$ shows that $\\hat{\\mu}$ is indeed a measure on $\\mathbb{P}$. Define $f:\\mathbb{P}\\longrightarrow W$ by letting $f(\\omega_{0},\\omega_{1},\\dots)$ be the unique point in $\\bigcap_{k=0}^{\\infty}S_{\\omega_{k}}(K)$. It is clear that $f(\\mathbb{P})=K$. Let $\\widetilde{\\mu}:=\\hat{\\mu}\\circ f^{-1}$. Then $\\widetilde{\\mu}$ is a measure on $K$.\n\nFor any bounded Borel set $F\\subset M$, let\n\\begin{equation}\\label{eq(4.1)}\n\\mathcal{B}(F):=\\{I_{\\omega_{k}}=I_{\\omega_{k},\\dots,\\omega_{k}}:\n|S_{\\omega_{k}}(\\Omega)|\\leq|F|<|S_{\\omega_{k-1}}(\\Omega)|\\text{ and }F\\cap S_{\\omega_{k}}(\\Omega)\\neq\\emptyset\\}.\n\\end{equation}\n\n\\begin{lem}\\label{lem(4.1)}\nThere exists a constant $C_{5}>0$, independent of $k$, such that for any bounded Borel set $F\\subset M$, we have $\\#\\mathcal{B}(F)\\leq C_{5}$.\n\\end{lem}\n\\begin{proof}\nDefine\n$$\\begin{aligned}\n\\widetilde{\\mathcal{B}}(F):&=\\{\\omega_{k}\\in\\mathcal{V}_{k}:\n|S_{\\omega_{k}}(\\Omega)|\\leq|F|<|S_{\\omega_{k-1}}(\\Omega)|\\text{ and }F\\cap S_{\\omega_{k}}(\\Omega)\\neq\\emptyset\\}\\\\\n&=\\{\\omega_{k}\\in\\mathcal{V}_{k}:\n\\rho_{\\omega_{k}}\\leq|F|\/|\\Omega|<\\rho_{\\omega_{k-1}}\\text{ and }F\\cap S_{\\omega_{k}}(\\Omega)\\neq\\emptyset\\}.\n\\end{aligned}$$\nSince $I_{\\omega_{k}}$ is one-to-one with $\\omega_{k}$, we have $\\#\\mathcal{B}(F)=\\#\\widetilde{\\mathcal{B}}(F)$. Let $b:=|F|\/|\\Omega|$ and $\\omega_{k}\\in\\widetilde{\\mathcal{B}}(F)$. Then there exists a unique $\\mathbf{u}\\in\\mathcal{M}_{k}$ such that $\\omega_{k}=(S_{\\mathbf{u}},k)$. Let $\\mathbf{u}'\\preccurlyeq\\mathbf{u}$ such that $S_{\\mathbf{u}'}\\in\\mathcal{A}_{b}$. Then\n$$\\rho_{\\mathbf{u}'}\\leq b=|F|\/|\\Omega|<\\rho_{\\omega_{k-1}}.$$\nThus $\\mathbf{u}'\\in\\mathcal{M}_{k-1}$ or $\\mathcal{M}_{k}$. Combining these and Definition \\ref{defi(3.1)}$(e)$, we have $|\\mathbf{u}|-|\\mathbf{u}'|\\leq L$.\nNote that $F\\cap S_{\\omega_{k}}(\\Omega)\\neq\\emptyset$ implies that $F\\cap S_{\\mathbf{u}'}(\\Omega)\\neq\\emptyset$. Since $S_{\\mathbf{u}'}\\in\\mathcal{A}_{b}$, we have\n$$|S_{\\mathbf{u}'}(\\Omega)|=\\rho_{\\mathbf{u}'}|\\Omega|\\leq b\\Omega.$$\nLet $\\delta:=2b|\\Omega|$ and fix any $x_{0}\\in F$. Then\n$$S_{\\mathbf{u}'}(\\Omega)\\subset B_{\\delta}(x_{0}).$$\nSince (FTC) implies (WSC), there exists a constant $\\gamma>0$ (independent of $b$) such that for all $x\\in U$,\n$$\\#\\{S\\in\\mathcal{A}_{b}:x\\in S(\\Omega)\\}\\leq\\gamma.$$\nNote that the contraction ratio of $S_{\\mathbf{u}}$ is $\\rho_{\\mathbf{u}}=|\\det S_{\\mathbf{u}}'(x)|^{\\frac{1}{n}}$ for any $x\\in W$. Let $A\\subset W$ be a measurable set. Then by Lemma \\ref{lem(1.1)}, we have\n$$\\nu(S_{\\mathbf{u}}(A))\\geq(b\\rho)^{n}\\nu(A).$$\nCombining these we have\n$$\\begin{aligned}\n(b\\rho)^{n}\\nu(\\Omega)\\#\\{S_{\\mathbf{u}'}:F\\cap S_{\\mathbf{u}'}(\\Omega)\\neq\\emptyset\\}&\\leq\n\\sum\\{\\nu(S_{\\mathbf{u}'}(\\Omega)):F\\cap S_{\\mathbf{u}'}(\\Omega)\\neq\\emptyset\\}\\\\\n&\\leq \\gamma\\nu(B_{\\delta}(x_{0}))\\\\\n&\\leq\\gamma c_{n}\\delta^{n}\\quad(\\text{by Lemma \\ref{lem(1.30)}})\\\\\n&:=\\gamma c_{1}b^{n},\n\\end{aligned}$$\nwhere $c_{n}$ is the volume of the unit ball in $\\mathbb{R}^{n}$ and $c_{1}:=c_{n}2^{n}|\\Omega|^{n}$. Thus,\n$$\\#\\{S_{\\mathbf{u}'}:F\\cap S_{\\mathbf{u}'}(\\Omega)\\neq\\emptyset\\}\\leq\\frac{\\gamma c_{1}}{\\rho^{n}\\nu(\\Omega)}:=c_2.$$\nHence\n$$\\#\\mathcal{B}(F)=\\#\\widetilde{\\mathcal{B}}(F)\\leq\nN^{L}\\#\\{S_{\\mathbf{u}'}:F\\cap S_{\\mathbf{u}'}(\\Omega)\\neq\\emptyset\\}\\leq c_{2}N^{L}.$$\nThe lemma follows by letting $C_{5}:=c_{2}N^{L}$.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Theorem \\ref{thm(0.5)}] Use of Lemma \\ref{lem(4.1)} and the properties of\nmeasures $\\widetilde{\\mu}$ on $K$, as in \\cite[Theorem 1.2]{Lau-Ngai_2007}; we omit the details.\n\\end{proof}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Hausdorff dimension of graph self-similar sets}\\label{S:41}\n\nIn this section, we define graph self-similar sets on Riemannian manifolds, and derive a formula for computing the Hausdorff dimension of such sets. We assume that $M$ is a complete $n$-dimensional Riemannian manifold that is locally Euclidean.\n\nLet $G=(V,E)$ be a graph, where $V=\\{1,\\dots,t\\}$ is the set of vertices and $E$ is the set of all directed edges. We assume that there is at least one edge between two vertices. It is possible that the initial and terminal vertices are same. A \\textit{directed path} in $G$ is a finite string $\\mathbf{e}=e_{1}\\cdots e_{p}$ of edges in $E$ such that the terminal vertex of each $e_{i}$ is the initial vertex of the next edge $e_{i+1}$. For such a path, denote the \\textit{length} of $\\mathbf{e}$ by $|\\mathbf{e}|=p$. For any two vertices $i,j\\in V$, and any positive integer $p$, $E^{i,j}$ be the set of all directed edges from $i$ to $j$, let $E_{p}^{i,j}$ be the set of all directed paths of length $p$ from $i$ to $j$, $E_{p}$ be the set of all directed paths of length $p$, $E^{*}$ be the set of all directed paths, i.e.,\n$$E_{p}:=\\bigcup_{i,j=1}^{p}E_{p}^{i,j}\\quad\\text{and}\\quad E^{*}:=\\bigcup_{p=1}^{\\infty}E_{p}.$$\nFor any edge $e\\in E$, we assume that there corresponds a contractive similitude $S_{e}$ with ratio $\\rho_{e}$ on $M$. For $\\mathbf{e}=e_{1}\\cdots e_{p}\\in E^{*}$, let\n$$S_{\\mathbf{e}}=S_{e_{1}}\\circ\\cdots\\circ S_{e_{p}}\\quad\\text{and}\\quad \\rho_{\\mathbf{e}}=\\rho_{e_{1}}\\cdots \\rho_{e_{p}}.$$\nThen there exists a unique family of nonempty compact sets $K_{1},\\dots,K_{t}$ satisfying\n$$K_{i}=\\bigcup_{j=1}^{t}\\bigcup_{e\\in E^{i,j}}S_{e}(K_{j}),\\quad i\\in\\{1,\\dots,t\\},$$\n(see e.g. \\cite{Mauldin-Williams_1988,Edgar-Mauldin_1992,Ngai-Wang-Dong_2010,Das-Ngai_2004}).\nDefine\n$$K:=\\bigcup_{i=1}^{t}K_{i}.$$\nWe call $K$ the \\textit{graph self-similar set} defined by $G=(V,E)$, and call $G=(V,E)$ the \\textit{graph-directed iterated function system} (GIFS) associated with $\\{S_{e}\\}_{e\\in E}$.\n\nSubstituting $E^{*}$ for $\\Sigma^{*}$ in Definition \\ref{defi(3.1)}, we define a sequence of nested index sets $\\{\\mathcal{F}_{k}\\}_{k=1}^{\\infty}$ of directed paths. Note that $\\{E_{k}\\}_{k=1}^{\\infty}$ is an example of a sequence of nested index sets of directed paths. Fix a sequence $\\{\\mathcal{F}_{k}\\}_{k=1}^{\\infty}$ of nested index sets. For $i,j\\in\\{1,\\cdots,t\\}$, we partition $\\mathcal{F}_{k}$ to $\\mathcal{F}_{k}^{i,j}$ as\n$$\\mathcal{F}_{k}^{i,j}:=\\mathcal{F}_{k}\\cap\\bigg(\\bigcup_{p\\geq1}E_{p}^{i,j}\\bigg)=\\{\\mathbf{e}=e_{1}\\cdots e_{p}\\in \\mathcal{F}_{k}:\\mathbf{e}\\in E_{p}^{i,j}~\\text{for some }p\\geq1\\}.$$\nNote that $\\mathcal{F}_{k}=\\bigcup_{i,j=1}^{t}\\mathcal{F}_{k}^{i,j}$. For $i,j\\in\\{1,\\cdots,t\\}$, $k\\geq1$, let $\\mathbb{V}_{k}$ be the set of \\textit{$k$-th level vertices} defined as\n$$\\mathbb{V}_{k}:=\\{(S_{\\mathbf{e}},i,j,k):\\mathbf{e}\\in\\mathcal{F}_{k}^{i,j},1\\leq i,j\\leq t\\}.$$\nFor $\\mathbf{e}\\in\\mathcal{F}_{k}^{i,j}$, we call $(S_{\\mathbf{e}},i,j,k)$ (or simply $(S_{\\mathbf{e}},k)$) a \\text{vertex}. For a vertex $\\omega=(S_{\\mathbf{e}},i,j,k)\\in\\mathbb{V}_{k}$ with $k\\geq1$, let\n$$S_{\\omega}=S_{\\mathbf{e}}\\quad\\text{and}\\quad \\rho_{\\omega}=\\rho_{\\mathbf{e}}.$$\nLet $\\mathcal{F}_{0}=\\{1,\\dots,t\\}$ and $\\mathbb{V}_{0}=\\{\\omega^{1}_{{\\rm root}},\\dots,\\omega^{t}_{{\\rm root}}\\}$, where $\\omega^{i}_{{\\rm root}}=(I,i,i,0)$ and $I$ is the identity map on $M$. Then we say $\\mathbb{V}_{0}$ is the set of \\textit{root vertices}, and $\\{\\mathcal{F}_{k}\\}_{k=0}^{\\infty}$ is a sequence of nested index sets if $\\{\\mathcal{F}_{k}\\}_{k=1}^{\\infty}$ is. Let $\\mathbb{V}:=\\bigcup_{k\\geq0}\\mathbb{V}_{k}$ be the set of all vertices, and $\\pi:\\bigcup_{k\\geq0}\\mathcal{F}_{k}\\longrightarrow \\mathbb{V}$ be defined as\n$$\\pi(\\mathbf{e}):=\\begin{cases}(S_{\\mathbf{e}},i,j,k),\\quad\\text{if }\\mathbf{e}\\in\\mathcal{F}_{k}^{i,j}, k\\geq1,\\\\\n\\omega^{i}_{{\\rm root}},\\quad\\text{if }\\mathbf{e}=i\\in\\mathcal{F}_{0}.\\end{cases}$$\n\nLet $\\omega\\in\\mathbb{V}_{k}$ and $\\omega'\\in\\mathbb{V}_{k+1}$. Suppose that there exist directed paths $\\mathbf{e}\\in\\mathcal{F}_{k},\\mathbf{e}'\\in\\mathcal{F}_{k+1}$ and $\\mathbf{k}\\in E^{*}$ such that $\\pi(\\mathbf{e})=\\omega$, $\\pi(\\mathbf{e}')=\\omega'$ and $\\mathbf{e}'=\\mathbf{e}\\mathbf{k}$. Then we connect a \\textit{directed edge} $\\mathbf{k}$ from $\\omega$ to $\\omega'$, and denote this as $\\omega\\stackrel{\\mathbf{k}}{\\longrightarrow}\\omega'$. We call $\\omega$ a \\textit{parent} of $\\omega'$ and $\\omega'$ an \\textit{offspring} of $\\omega$.\nDefine a graph $\\mathbb{G}:=(\\mathbb{V},\\mathbb{E})$, where $\\mathbb{E}$ is the set of all directed edges of $\\mathbb{G}$. Let $\\mathbb{G}_{R}:=(\\mathbb{V}_{R},\\mathbb{E}_{R})$ be the \\textit{reduced graph} of $\\mathbb{G}$, defined as in Section \\ref{S:3} similarly, where $\\mathbb{V}_{R}$ and $\\mathbb{E}_{R}$ are the sets of all vertices and all directed edges, respectively.\n\nLet $\\mathbf{\\Omega}=\\{\\Omega_{i}\\}_{i=1}^{t}$, where $\\Omega_{i}\\subset M$ is a nonempty bounded open set for any $i\\in\\{1,\\dots,t\\}$. We say that $\\mathbf{\\Omega}$ is \\textit{invariant} under the GIFS $G=(V,E)$ if\n$$\\bigcup_{e\\in E^{i,j}}S_{e}(\\Omega_{j})\\subset\\Omega_{i},\\quad i,j\\in\\{1,\\cdots,t\\}.$$\nSince $S_{e}$ is a contractive similitude for any $e\\in E^{i,j}$, such a family always exists. Fix an invariant family $\\mathbf{\\Omega}=\\{\\Omega_{i}\\}_{i=1}^{t}$ of $G=(V,E)$. Let $\\omega=(S_{\\mathbf{e}},i,j,k)\\in\\mathbb{V}_{k}$ with $\\mathbf{e}\\in E_{q}^{i,j}$ and $\\omega=(S_{\\mathbf{e}'},i',j',k)\\in\\mathbb{V}_{k}$ with $\\mathbf{e}'\\in E_{s}^{i',j'}$, where $q,s>0$ are integers. We say that two vertices $\\omega$, $\\omega'$ are \\textit{neighbors} (with respect to $\\mathbf{\\Omega}$) if\n$$i=i'\\quad\\text{and}\\quad S_{\\mathbf{e}}(\\Omega_{j})\\cap S_{\\mathbf{e}'}(\\Omega_{j'})\\neq\\emptyset.$$\nLet\n$$\\mathcal{N}(\\omega):=\\{\\omega':\\omega'\\in\\mathbb{V}_{k}\\text{ is a neighbor of }\\omega\\},$$\nwhich is called the \\textit{neighborhood} of $\\omega$ (with respect to $\\mathbf{\\Omega}$).\n\\begin{defi}\\label{defi(41.1)}\nFor any two vertices $\\omega=(S_{\\mathbf{e}_{\\omega}},i_{\\omega},j_{\\omega},k)\\in\\mathbb{V}_{k}$ and $\\omega'=(S_{\\mathbf{e}_{\\omega'}},i_{\\omega'},j_{\\omega'},k')\\in\\mathbb{V}_{k'}$, let $\\sigma=(S_{\\mathbf{e}_{\\sigma}},i_{\\omega},j_{\\sigma},k)\\in\\mathcal{N}(\\omega)$ and $\\sigma'=(S_{\\mathbf{e}_{\\sigma'}},i_{\\omega'},j_{\\sigma'},k')\\in\\mathcal{N}(\\omega')$. Assume that\n$$\\tau=S_{\\omega'}S_{\\omega}^{-1}:\\bigcup_{\\sigma\\in\\mathcal{N}(\\omega)}S_{\\sigma}(\\Omega_{j_{\\sigma}})\n\\longrightarrow \\bigcup_{i=1}^{t}\\Omega_{i}$$\ninduces a bijection $f_{\\tau}:\\mathcal{N}(\\omega)\\longrightarrow\\mathcal{N}(\\omega')$ defined by\n\\begin{equation}\\label{eq(41.1)}\nf_{\\tau}(\\sigma)=f_{\\tau}(S_{\\mathbf{e}_{\\sigma}},i_{\\omega},j_{\\sigma},k)=(\\tau\\circ S_{\\mathbf{e}_{\\sigma'}},i_{\\omega'},j_{\\sigma'},k').\n\\end{equation}\nWe say $\\omega$ and $\\omega'$ are \\textit{equivalent}, i.e., $\\omega\\sim\\omega'$, if the following conditions hold:\n\\begin{itemize}\n\\item[$(a)$] $\\#\\mathcal{N}(\\omega)=\\#\\mathcal{N}(\\omega')$ and $j_{\\sigma}=j_{\\sigma'}$ in \\eqref{eq(41.1)};\n\\item[$(b)$] for $\\sigma\\in\\mathcal{N}(\\omega)$ and $\\sigma'\\in\\mathcal{N}(\\omega')$ such that $f_{\\tau}(\\sigma)=\\sigma'$, and for any positive integer $k_{0}\\geq1$, a directed path $\\mathbf{e}\\in E^{*}$ satisfies $(S_{\\sigma}\\circ S_{\\mathbf{e}},k+k_{0})\\in\\mathbb{V}_{k+k_{0}}$ if and only if it satisfies\n$(S_{\\sigma'}\\circ S_{\\mathbf{e}},k'+k_{0})\\in\\mathbb{V}_{k'+k_{0}}$.\n\\end{itemize}\n\\end{defi}\nIt is easy to check that $\\sim$ is an equivalence relation. Denote the equivalent class of $\\omega$ by $[\\omega]$, and call it the \\textit{neighborhood types} of $\\omega$ (with respect to $\\mathbf{\\Omega}$).\n\nFor a graph $\\mathbb{G}=(\\mathbb{V},\\mathbb{E})$, we can prove that $\\mathbb{G}$ satisfies Proposition \\ref{prop(3.1)} as in \\cite[Propositin 2.5]{Ngai-Wang-Dong_2010}; we omit the details. We now define the graph finite type condition.\n\n\n\\begin{defi}\\label{defi(41.2)}\nLet $M$ be a complete $n$-dimensional Riemannian manifold that is locally Euclidean, let $G=(V,E)$ be a GIFS, and let $\\{S_{e}\\}_{e\\in E}$ be a family of contractive similitudes defined on $M$.\nIf there exists an invariant family of nonempty bounded open sets $\\mathbf{\\Omega}=\\{\\Omega_{i}\\}_{i=1}^{t}$ with respect to some sequence of nested index sets $\\{\\mathcal{F}_{k}\\}_{k=0}^{\\infty}$ such that\n$$\\#\\mathbb{V}\/_{\\sim}<\\infty,$$\nthen we say that $G=(V,E)$ satisfies the \\textit{graph finite type condition} (GFTC).\nWe call such an invariant family $\\mathbf{\\Omega}$ a \\textit{graph finite type condition family} of $G$.\n\\end{defi}\n\n\nBy assuming a GIFS satisfies (GFTC), we get a formula for $\\dim_{{\\rm H}}(K)$.\n\n\\begin{proof}[Proof of Theorem~\\ref{thm(41.1)}] The proof is similar to that of \\cite[Theorem 1.1]{Ngai-Wang-Dong_2010} and the definition of a weighted incidence matrix is the same as that in Section \\ref{S:4}; we omit the details.\n\\end{proof}\n\n\n\\section{Examples}\\label{S:5}\nIn this section, we construct some examples of CIFSs and GIFSs on Riemannian manifolds satisfying (FTC) and (GFTC), respectively.\n\nLet $\\{f_{i}\\}_{i=1}^{N}$ be a contractive CIFS on a compact set $W_0\\subset\\mathbb{R}^{n}$, i.e., $f_{i}$ is $C^{1+\\varepsilon}$ where $0<\\varepsilon<1$, and there exists an open and connected set $U_{0}\\supset W_{0}$ such that for any $i\\in\\{1,\\dots,N\\}$, $f_{i}$ can be extended to an injective conformal map $f_{i}:U_{0}\\longrightarrow U_{0}$. Let $M$ be a complete $n$-dimensional smooth Riemannian manifold. Assume that there exists a diffeomorphism\n$$\\varphi:U\\longrightarrow U_{0},$$\nwhere $U\\subset M$ is open and connected.\nDefine\n\\begin{equation}\\label{eq(5.1)}\nS_{i}:=\\varphi^{-1}\\circ f_{i}\\circ\\varphi:U\\longrightarrow S_{i}(U)\\quad\\text{for any }i\\in\\{1,\\dots,N\\}.\n\\end{equation}\nBy \\cite[Proposition 7.1]{Ngai-Xu_2022}, $\\{S_{i}\\}_{i=1}^{N}$ is a contractive CIFS on $U$.\n\n\nFix a sequence of nested index sets $\\{\\mathcal{M}_{k}\\}_{k=0}^{\\infty}$. Let $\\widetilde{\\mathcal{V}}$ and $\\mathcal{V}$ be the sets of all vertices with respect to $\\{\\mathcal{M}_{k}\\}_{k=0}^{\\infty}$ of $\\{f_{i}\\}_{i=1}^{N}$ and $\\{S_{i}\\}_{i=1}^{N}$, respectively.\nFor $\\tilde{\\omega}=(f_{\\mathbf{u}},k)\\in\\widetilde{\\mathcal{V}}_{k}$ and $\\omega=(S_{\\mathbf{u}},k)\\in\\mathcal{V}_{k}$, where $\\mathbf{u}\\in\\mathcal{M}_{k}$, $k\\geq0$, we define $f_{\\tilde{\\omega}}:=f_{\\mathbf{u}}$ and $S_{\\omega}:=S_{\\mathbf{u}}$. It follows from \\eqref{eq(5.1)} that\n\\begin{equation}\\label{eq(5.01)}\nS_{\\omega}=\\varphi^{-1}\\circ f_{\\tilde{\\omega}}\\circ\\varphi.\n\\end{equation}\n\n\\begin{prop}\\label{prop(5.1)}\nLet $S_{i}$ be defined as in (\\ref{eq(5.1)}), where $i\\in\\{1,\\dots,N\\}$. If $\\{f_{i}\\}_{i=1}^{N}$ satisfies (FTC), then $\\{S_{i}\\}_{i=1}^{N}$ satisfies (FTC).\n\\end{prop}\n\\begin{proof}\nFor any $\\tilde{\\omega},\\tilde{\\omega}'\\in\\widetilde{\\mathcal{V}}$ and $\\tilde{\\omega}\\sim\\tilde{\\omega}'$, by Definition \\ref{defi(3.2)}, there exist $\\tilde{\\sigma}\\in\\Omega(\\tilde{\\omega})$ and $\\tilde{\\sigma}'\\in\\Omega(\\tilde{\\omega}')$ such that\n\\begin{equation}\\label{eq(5.2)}\nf_{\\tilde{\\omega}'}^{-1}\\circ f_{\\tilde{\\sigma}'}=f_{\\tilde{\\omega}}^{-1}\\circ f_{\\tilde{\\sigma}}.\n\\end{equation}\nFor any $\\omega,\\omega'\\in\\mathcal{V}$, $\\sigma\\in\\Omega(\\omega)$ and $\\sigma'\\in\\Omega(\\omega')$, we have\n$$\\begin{aligned}\nS_{\\omega'}^{-1}\\circ S_{\\sigma'}&=\\varphi^{-1}\\circ f_{\\tilde{\\omega}'}^{-1}\\circ\\varphi\\circ\\varphi^{-1}\\circ f_{\\tilde{\\sigma}'}\\circ\\varphi\\quad\\text{(by~(\\ref{eq(5.01)}))}\\\\\n&=\\varphi^{-1}\\circ f_{\\tilde{\\omega}}^{-1}\\circ f_{\\tilde{\\sigma}}\\circ\\varphi\\quad\\text{(by~(\\ref{eq(5.2)}))}\\\\\n&=\\varphi^{-1}\\circ f_{\\tilde{\\omega}}^{-1}\\circ\\varphi\\circ\\varphi^{-1}\\circ f_{\\tilde{\\sigma}}\\circ\\varphi\\\\\n&=S_{\\omega}^{-1}\\circ S_{\\sigma}.\n\\end{aligned}$$\nIt follows that $\\omega\\sim\\omega'$. Since $\\{f_{i}\\}_{i=1}^{N}$ satisfies (FTC),\nwe have $\\#\\widetilde{\\mathcal{V}}\/_{\\sim}<\\infty$. Thus, $\\#\\mathcal{V}\/_{\\sim}<\\infty$. This proves the proposition.\n\\end{proof}\n\nLet\n$$\\mathbb{S}^{n}:=\\left\\{(x_{1},\\dots,x_{n+1})\\in \\mathbb{R}^{n+1}:\\sum_{i=1}^{n+1}x_{i}^{2}=1\\right\\},\\quad\n\\mathbb{D}^{n}:=\\left\\{(x_{1},\\dots,x_{n})\\in \\mathbb{R}^{n}:\\sum_{i=1}^{n}x_{i}^{2}<1\\right\\}.$$\nLet $\\mathbb{S}^{n}_{+}$ be the upper hemisphere of $\\mathbb{S}^{n}$, and define the stereographic projection $\\varphi:\\mathbb{S}^{n}_{+}\\rightarrow \\mathbb{D}^{n}$ as\n$$\n\\varphi(x_{1},\\dots,x_{n+1})=\\frac{1}{1+x_{n+1}}(x_{1},\\dots,x_{n}):=(y_{1},\\dots,y_{n}).\n$$\nThen\n$$\\varphi^{-1}(y_{1},\\dots,y_{n})=\\frac{1}{|\\boldsymbol{y}|^{2}+1}\\big(2y_{1},\\dots,2y_{n},1-|\\boldsymbol{y}|^{2}\\big),$$\nwhere $|\\boldsymbol{y}|^{2}=y_{1}^{2}+\\cdots+y_{n}^{2}$.\n\nFor convenience, we consider the case of $n=2$. We will give some actual examples of Proposition \\ref{prop(5.1)}. The first example below satisfies (OSC), while the other two satisfy (FTC) but not (OSC).\n\n\\begin{exam}\\label{exam(5.1)}\nLet $\\{f_{i}\\}_{i=1}^{3}$ be a Sierpinski gasket on $\\mathbb{R}^{2}$, i.e., for $\\boldsymbol{x}\\in\\mathbb{R}^{2}$,\n$$f_{1}(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}+\\bigg(0,\\frac{1}{2}\\bigg),\\quad\nf_{2}(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}+\\bigg(-\\frac{1}{4},0\\bigg),\\quad\nf_{3}(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{4},0\\bigg).$$\nLet $S_{i}$ be defined as in (\\ref{eq(5.1)}), where $i\\in\\{1,2,3\\}$. Then $\\{S_{i}\\}_{i=1}^{3}$ is a CIFS satisfying (OSC) on $\\mathbb{S}^{2}_{+}$ (see Figure \\ref{fig.1}(a)).\n\\end{exam}\n\n\\begin{exam}\\label{exam(5.2)}\nLet $\\{f_{i}\\}_{i=1}^{4}$ be a CIFS defined as in \\cite{Lau-Ngai_2007} satisfying (FTC), i.e., for $\\boldsymbol{x}\\in\\mathbb{R}^{2}$,\n$$\\begin{aligned}\nf_{1}(\\boldsymbol{x})&=\\rho\\boldsymbol{x}+\\bigg(\\frac{1}{2}\\rho,0\\bigg),\\qquad\nf_{2}(\\boldsymbol{x})=r\\boldsymbol{x}+\\bigg(\\rho-\\rho r+\\frac{1}{2}r,0\\bigg),\\\\\nf_{3}(\\boldsymbol{x})&=r\\boldsymbol{x}+\\bigg(1-\\frac{1}{2}r,0\\bigg),\\qquad\nf_{4}(\\boldsymbol{x})\n=r\\boldsymbol{x}+\\bigg(\\frac{1}{2}r,1-r\\bigg),\n\\end{aligned}$$\nwhere $0<\\rho,r<1$ and $\\rho+2r-\\rho r\\leq1$. Let $S_{i}$ be defined as in (\\ref{eq(5.1)}), where $i\\in\\{1,2,3,4\\}$. By Proposition \\ref{prop(5.1)}, $\\{S_{i}\\}_{i=1}^{4}$ is a CIFS satisfying (FTC) on $\\mathbb{S}^{2}_{+}$ (see Figure \\ref{fig.1}(b)).\n\\end{exam}\n\n\\begin{exam}\\label{exam(5.3)}\nLet $\\{f_{i}\\}_{i=1}^{3}$ be a golden Sierpinski gasket defined as in \\cite{Ngai-Wang_2001} satisfying (FTC), i.e., for $\\boldsymbol{x}\\in\\mathbb{R}^{2}$,\n$$\nf_{1}(\\boldsymbol{x})=\\rho \\boldsymbol{x},\\quad\nf_{2}(\\boldsymbol{x})=\\rho \\boldsymbol{x}+\\big(\\rho^{2},0\\big),\\quad\nf_{3}(\\boldsymbol{x})=\\rho^{2} \\boldsymbol{x}+\\big(\\rho,\\rho\\big),\n$$\nwhere $\\rho=\\big(\\sqrt{5}-1\\big)\/2$. Let $S_{i}$ be defined as in (\\ref{eq(5.1)}), where $i\\in\\{1,2,3\\}$. By Proposition \\ref{prop(5.1)}, $\\{S_{i}\\}_{i=1}^{3}$ is a CIFS satisfying (FTC) on $\\mathbb{S}^{2}_{+}$ (see Figure \\ref{fig.1}(c)).\n\\end{exam}\n\n\n\n\\begin{figure}[htbp]\n\\begin{center}\n \\begin{tikzpicture}[scale=0.2,domain=0:180,>=stealth]\n \\coordinate (org) at (0,0);\n \\draw (0,0) circle[radius=8];\n \\draw (org) ellipse (8cm and 3cm);\n \\draw[help lines,dashed](9.5,0)--(0,0);\n \\draw[help lines,dashed](0,-9.5)--(0,0);\n \\draw[help lines,dashed](5.8,7.25)--(0,0);\n \\draw[white] plot ({8*cos(\\x)},{3*sin(\\x)});\n \\foreach\\i\/\\text in{{-6.8,-8.5}\/y,{-10.5,0}\/x,{0,10.5}\/{}}\n \\draw[help lines,->] (org)node[above right]{}--(\\i)node[above]{$\\text$};\n \\draw[help lines,->]node[left] at (0,10){$z$};\n \\draw[thin,black] (-4,0)--(4,0);\n \\draw[thin,black] (-4,0)--(-2.3,-2.875);\n \\draw[thin,black] (4,0)--(-2.3,-2.875);\n \\draw[thin,black] (-3.15,-1.4375)--(0.85,-1.4375);\n \\draw[thin,black] (0,0)--(0.85,-1.4375);\n \\draw[thin,black] 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\\draw[thin,cyan,densely dashed](0,-8)--(2.74,4.2);\n \\draw[thin,blue] (1.4,1.37) to [out=-11,in=152] (3.5,0.6) to [out=63,in=-126](4.83,2.95) to [out=142,in=-22](2.74,4.2)to[out=-125,in=72](1.4,1.37);\n \\draw[fill=blue!30,opacity=.5](1.4,1.37) to [out=-11,in=152] (3.5,0.6) to [out=63,in=-126](4.83,2.95) to [out=142,in=-22](2.74,4.2)to[out=-125,in=72](1.4,1.37);\n\n \n \n \\draw[thin,cyan,densely dashed](0,-8)--(4.15,4.95);\n \\draw[thin,blue](4.15,4.95) to [out=-32,in=137] (5.565,3.87) to [out=51,in=-135] (6.45,4.75) to [out=127,in=-42] (5.05,6.2) to [out=-132,in=60] (4.15,4.95);\n \\draw[fill=blue!30,opacity=.5](4.15,4.95) to [out=-32,in=137] (5.565,3.87) to [out=51,in=-135] (6.45,4.75) to [out=127,in=-42] (5.05,6.2) to [out=-132,in=60] (4.15,4.95)\n\n \n \n \\draw[thin,cyan,dashed](0,-8)--(1.9,5);\n \\draw[thin,cyan,densely dashed](0,-8)--(4.3,3.9);\n \\draw[thin,blue](3.1,7.375) to [out=-125,in=70] (1.9,5) to [out=-20,in=150] (4.3,3.9) to [out=55,in=-130] (5.65,5.65) to [out=138,in=-21] (3.1,7.375);\n \\draw[fill=blue!30,opacity=.5](3.1,7.375) to [out=-125,in=70] (1.9,5) to [out=-20,in=150] (4.3,3.9) to [out=55,in=-130] (5.65,5.65) to [out=138,in=-21] (3.1,7.375)\n \\foreach \\i\/\\tex in {0\/(b)}\n \\draw(0,-10)node[below]{\\tex};\n \\foreach \\i\/\\tex in {0\/\\text{}}\n \\draw(0,-19)node[below]{\\tex};\n \\end{tikzpicture}\n \\quad\n \\begin{tikzpicture}[scale=.2,domain=0:180,>=stealth]\n \\coordinate (org) at (0,0);\n \\draw (0,0) circle[radius=8];\n \\draw (org) ellipse (8cm and 3cm);\n \\draw[help lines,dashed](9.5,0)--(0,0);\n \\draw[help lines,dashed](0,-9.5)--(0,0);\n \\draw[help lines,dashed](5.8,7.25)--(0,0);\n \\draw[white] plot ({8*cos(\\x)},{3*sin(\\x)});\n \\foreach\\i\/\\text in{{-6.8,-8.5}\/y,{-10.5,0}\/x,{0,10.5}\/{}}\n \\draw[help lines,->] (org)node[above right]{}--(\\i)node[above]{$\\text$};\n \\draw[help lines,->]node[left] at (0,10){$z$};\n \\draw[thin,black] (-8,0)--(-2.3,-2.875);\n \\draw[thin,black] (-2.3,-2.875)--(8,0);\n \\draw[thin,black] (8,0)--(-8,0);\n \\draw[thin,black] (-4.4375,-1.797)--(1.5625,-1.797);\n \\draw[thin,black] (-4.4375,-1.797)--(2,0);\n \\draw[thin,black] (-2,0)--(1.5625,-1.797);\n \\draw[top color=olive!60!white!30,bottom color=red!30,opacity=.5]\n (1.5625,-1.797)--(-2,0)--(8,0)--(1.5625,-1.797);\n \\draw[top color=olive!60!white!30,bottom color=red!30,opacity=.5]\n (-4.4375,-1.797)--(-2.3,-2.875)--(1.5625,-1.797)--(-4.4375,-1.797);\n \\draw[top color=olive!60!white!30,bottom color=red!30,opacity=.5](-8,0)--(-4.4375,-1.797)--(2,0)--(-8,0);\n \\draw[thin,cyan,densely dashed](0,-8)--(-8,0);\n \\draw[thin,cyan,dashed](0,-8)--(-2.3,-2.875);\n \\draw[thin,cyan,densely dashed](0,-8)--(8,0);\n \\draw[thin,blue] (-8,0) to [out=90, in=-180](0,8) to [out=0, in=90] (8,0) to [out=140, in=55] (-2.3,-2.875)\n to [out=125, in=-5] (-8,0);\n \\draw[thin,cyan,densely dashed](0,-8)--(-5.4,-0.51);\n \\draw[thin,cyan,densely dashed](0,-8)--(-3.8,7.05);\n \\draw[thin,cyan,densely dashed](0,-8)--(3.8,7.05);\n \\draw[thin,blue](-5.4,-0.51) to [out=45, in=150] (2.2,0.7);\n \\draw[thin,blue](3.8,7.05) to [out=-175, in=75] (-5.4,-0.51);\n \\draw[thin,blue](-3.8,7.05) to [out=-20, in=110] (2.2,0.7);\n \\draw[fill=blue!30,opacity=.5](-5.4,-0.51) to [out=45, in=150] (2.2,0.7) to [out=-158, in=53] (-2.3,-2.875) to [out=126, in=-19](-5.4,-0.51);\n \\draw[fill=blue!30,opacity=.5](8,0) to [out=90, in=0] (0,8) to [out=-180, in=26] (-3.8,7.05) to [out=-20, in=110] (2.2,0.7) to [out=20, in=140] (8,0);\n \\draw[fill=blue!30,opacity=.5](-8,0) to [out=90, in=180] (0,8) to [out=0, in=154] (3.8,7.05) to [out=-175, in=75] (-5.4,-0.51) to [out=162, in=-5] (-8,0);\n \\foreach \\i\/\\tex in {0\/(c)}\n \\draw(0,-10)node[below]{\\tex};\n \\foreach \\i\/\\tex in {0\/}\n \\draw(0,-19)node[below]{\\tex};\n \\end{tikzpicture}\n \\vspace{-3.2em}\n\\caption{Figures (a), (b) and (c) are respectively the IFSs in Examples \\ref{exam(5.1)}, \\ref{exam(5.2)} and \\ref{exam(5.3)}.} \\label{fig.1}\n\\end{center}\n\\end{figure}\n\n\n\nLet $M$ be a complete $n$-dimensional smooth Riemannian manifold that is locally Euclidean.\nNow, we construct an example of GIFS on $M$ satisfying (GFTC) to illustrate Theorem \\ref{thm(41.1)}. Let $G=(V,E)$ be a GIFS of contractive similitudes defined on $\\mathbb{R}^{n}$, and $\\{O_{i}\\}_{i=1}^{t}$, where $O_{i}\\subset\\mathbb{R}^{n}$, be an invariant family of nonempty bounded open sets under $G=(V,E)$. Let $O:=\\bigcup_{i=1}^{t}O_{i}$. For any edge $e\\in E$, there corresponds a contractive similitude $f_{e}:O\\longrightarrow O$. Assume that there exists a diffeomorphism\n$$\\varphi:O_{i}\\longrightarrow \\Omega_{i}\\quad\\text{for any }i\\in\\{1,\\dots,t\\},$$\nwhere $\\Omega_{i}\\subset M$ is open and connected. Let $\\Omega:=\\bigcup_{i=1}^{t}\\Omega_{i}$.\nFor any edge $e\\in E$, define\n\\begin{equation}\\label{eq(5.3)}\nS_{e}:=\\varphi^{-1}\\circ f_{e}\\circ\\varphi:\\Omega\\longrightarrow \\Omega.\n\\end{equation}\nAs in \\cite[Proposition 7.1]{Ngai-Xu_2022}, $\\{S_{e}\\}_{e\\in E}$ is a family of contractive maps on $M$. If for any $e\\in E$, $S_{e}$ is a similitude, then $G=(V,E)$, along with $\\{\\Omega_{i}\\}_{i=1}^{t}$ and $\\{S_{e}\\}_{e\\in E}$, forms a GIFS on $M$. The proof of the following proposition is similar to that of Proposition \\ref{prop(5.1)}; we omit it.\n\n\n\\begin{prop}\\label{prop(5.2)}\nUse the above notation and setup. Let $M$ be a complete $n$-dimensional smooth Riemannian manifold that is locally Euclidean. Let $G=(V,E)$ be a GIFS defined on $\\mathbb{R}^{n}$ satisfying (GFTC) with $\\{O_{i}\\}_{i=1}^{t}$ being a GFTC-family and $\\{f_{e}\\}_{e\\in E}$ being an associated family of contractive similitudes.\nFor any $e\\in E$, let $S_{e}$ be a similitude defined as in (\\ref{eq(5.3)}). Then the GIFS $G=(V,E)$ defined on $M$ satisfies (GFTC) with $\\{\\Omega_{i}\\}_{i=1}^{t}$ being a GFTC-family and $\\{S_{e}\\}_{e\\in E}$ being an associated family of contractive similitudes. Moreover, for such two GIFSs connected by a diffeomorphism, the neighborhood types, weighted incidence matrices, and the Hausdorff dimension of the corresponding graph self-similar sets are the same.\n\\end{prop}\n\n\n\nAssume that $G=(V,E)$ is a GIFS of contractive similitudes defined on $M$ satisfying (GFTC). Let $\\mathcal{T}_{1},\\dots,\\mathcal{T}_{q}$ be all the distinct neighborhood types with $\\mathcal{T}_{i}=[\\omega_{{\\rm root}}^{i}]$ for any $i\\in\\{1,\\dots,t\\}$. Fix a vertex $\\omega\\in\\mathbb{V}_{R}$ such that $[\\omega]\\in\\mathcal{T}_{i}$, where $i\\in\\{1,\\dots,q\\}$.\nLet $\\sigma_{1},\\dots,\\sigma_{m}$ be the offspring of $\\omega$ in $\\mathbb{G}_{R}$, let $\\mathbf{k}_{\\ell}$ be the unique edge in $\\mathbb{G}_{R}$ connecting $\\omega$ to $\\sigma_{\\ell}$ for $1\\leq \\ell\\leq m$, and let\n$$C_{ij}:=\\{\\sigma_{\\ell}:1\\leq \\ell\\leq m,~[\\sigma_{\\ell}]=\\mathcal{T}_{j}\\}.$$\nNote that for two edges $\\mathbf{k}_{\\ell}$ and $\\mathbf{k}_{\\ell'}$ connecting $\\omega$ to two distinct $\\sigma_{\\ell}$ and $\\sigma_{\\ell'}$ satisfying $[\\sigma_{\\ell}]=[\\sigma_{\\ell}']=\\mathcal{T}_{j}$, the contraction ratios $\\rho_{\\sigma_{\\ell}}$ and $\\rho_{\\sigma_{\\ell'}}$ may be different. We can partition $C_{ij}:=C_{ij}(1)\\cup\\cdots\\cup C_{ij}(n_{ij})$ by using $\\rho_{\\sigma_{\\ell}}$, where for $s=1,\\dots,n_{ij}$,\n$$C_{ij}(s):=\\{\\sigma_{\\ell}\\in C_{ij}:\\rho_{\\sigma_{\\ell}}=\\rho_{ijs}\\},$$\nand the $\\rho_{ijs}$ are distinct. Thus, for any entry $A_{\\alpha}(i,j)$ of the weighted incidence matrix,\n$$A_{\\alpha}(i,j)=\\sum_{s=1}^{n_{ij}}\\#C_{ij}(s)\\rho_{ijs}^{\\alpha}.$$\nMoreover, we can write symbolically\n$$\\mathcal{T}_{i}\\longrightarrow\\sum_{j=1}^{q}\\sum_{s=1}^{n_{ij}}\\#C_{ij}(s)\\mathcal{T}_{j}(\\rho_{ijs}),$$\nwhere the $\\mathcal{T}_{j}(\\rho_{ijs})$ are defined in an obvious way.\nWe say that $\\mathcal{T}_{i}$ generates $\\#C_{ij}(s)$ neighborhoods of type $\\mathcal{T}_{j}$ with contraction ratio $\\rho_{ijs}$.\n\nFor $\\boldsymbol{x}\\in[0,1]\\times[0,1]$, we consider the following iterated function system with overlaps:\n$$\nh_{1}(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}+\\bigg(0,\\frac{1}{4}\\bigg),\\ \\\nh_{2}(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{4},\\frac{1}{4}\\bigg),\\ \\\nh_{3}(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{2},\\frac{1}{4}\\bigg),\\ \\\nh_{4}(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{4},\\frac{3}{4}\\bigg).\n$$\nIterations of $\\{h_i\\}_{i=1}^4$ induce iterations on the $2$-torus $\\mathbb{T}^2=\\mathbb R^2\/\\mathbb Z^2$, generating an attractor on $\\mathbb{T}^2$. We are interested in computing the Hausdorff dimension of the attractor. However, the relations induced by $\\{h_i\\}_{i=1}^4$ on $\\mathbb{T}^2$ are not well-defined functions, making it awkward to apply the theory of IFSs developed in Section~\\ref{S:4}. To overcome this difficulty, we will use the GIFS framework and Theorem~\\ref{thm(41.1)}, as shown in the following example.\n\n\n\\begin{exam}\\label{exam(5.4)}\nLet $\\{h_i\\}_{i=1}^4$ be defined as above, and let $\\mathbb{T}^2=\\mathbb{S}^{1}\\times\\mathbb{S}^{1}$ be a $2$-torus, viewed as $[0,1]\\times[0,1]$ with opposite sides identified. Let $\\mathbb{T}^2$ be endowed with the Riemannian metric induced from $\\mathbb{R}^2$. Consider the IFS $\\{g_{i}\\}_{i=1}^{4}$ on $[0,1]\\times[0,1]$ under the Euclidean metric, where for $\\boldsymbol{x}\\in[0,1]\\times[0,1]$,\n$$\ng_{1}(\\boldsymbol{x})=h_1(\\boldsymbol{x}),\\quad\ng_{2}(\\boldsymbol{x})=h_2(\\boldsymbol{x}),\\quad\ng_{3}(\\boldsymbol{x})=h_3(\\boldsymbol{x}),\n$$\nand\n$$g_{4}(\\boldsymbol{x})=\\begin{cases}\\frac{1}{2}\\boldsymbol{x}+\\big(\\frac{1}{4},\\frac{3}{4}\\big),\\quad \\boldsymbol{x}\\in[0,1]\\times[0,\\frac{1}{2}],\\\\ \\frac{1}{2}\\boldsymbol{x}+\\big(\\frac{1}{4},-\\frac{1}{4}\\big),\\quad \\boldsymbol{x}\\in[0,1]\\times[\\frac{1}{2},1]\\end{cases}$$\n(see Figure \\ref{fig.3}(a)). $\\{g_{i}\\}_{i=1}^{4}$ induces four relations $\\{S_{i}\\}_{i=1}^{4}$ on $\\mathbb{T}^2$. We are interested in the Hausdorff dimension of the attractor generated by $\\{S_{i}\\}_{i=1}^{4}$.\nNote that the image of $S_4$ is a connected rectangle in $\\mathbb{T}^2$, but the image of $g_4$ is divided into two rectangles in $\\mathbb{R}^2$. It is easy to see that $\\{S_{i}\\}_{i=1}^{4}$ are not well-defined functions and are not contractive under the metric of $\\mathbb{T}^{2}$. As a result, we need to use the framework of a GIFS.\nConsider the GIFS $G=(V,E)$ defined on $\\mathbb{R}^{2}$ associated to $\\{g_{i}\\}_{i=1}^{4}$ with $V=\\{1,2\\}$ and $E=\\{e_{1},\\dots,e_{8}\\}$, where $\\mathbf{O}=\\{O_{1},O_{2}\\}$ with $O_{1}=(0,1)\\times(0,1\/2)$ and $O_{2}=(0,1)\\times(1\/2,1)$ is the invariant family, and\n$$e_{1},e_{2},e_{3}\\in E^{1,1},\\quad e_{4}\\in E^{1,2},\\quad e_{5},e_{6},e_{7}\\in E^{2,2},\\quad e_{8}\\in E^{2,1}$$\n(see Figure \\ref{fig.2}). The associated similitudes are defined as\n$$f_{e_{1}}=\\frac{1}{2}\\boldsymbol{x}+\\bigg(0,\\frac{1}{4}\\bigg),~~\nf_{e_{2}}=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{4},\\frac{1}{4}\\bigg),~~\nf_{e_{3}}=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{2},\\frac{1}{4}\\bigg),~~\nf_{e_{4}}=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{4},-\\frac{1}{2}\\bigg),~~\n$$\n$$f_{e_{5}}=\\frac{1}{2}\\boldsymbol{x},~~\nf_{e_{6}}=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{4},0\\bigg),~~\nf_{e_{7}}=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{2},0\\bigg),~~\nf_{e_{8}}=\\frac{1}{2}\\boldsymbol{x}+\\bigg(\\frac{1}{4},\\frac{3}{4}\\bigg).~~\n$$\nLet $\\Omega_{1}$ and $\\Omega_{2}$, viewed as $(0,1)\\times(0,1\/2)$ and $(0,1)\\times(1\/2,1)$, be the lower and upper pieces of the interior of $\\mathbb{T}^{2}$, respectively. Obviously, for $i=1,2$, and any $e\\in E$, there exists a diffeomorphism\n$\\varphi:O_{i}\\longrightarrow \\Omega_{i}$ such that $S_{e}$, defined as in \\eqref{eq(5.3)}, is a contractive similitude.\nLet $K$ and $K_{0}$ be the graph self-similar set of $G=(V,E)$ generated by $\\{S_{e}\\}_{e\\in E}$ and $\\{f_{e}\\}_{e\\in E}$, respectively. Then $\\dim_{{\\rm H}}(K)=\\dim_{{\\rm H}}(K_{0})=\\log(2+\\sqrt{2})\/\\log2=1.77155\\dots$.\n\\end{exam}\n\\begin{figure}[htbp]\n\\begin{center}\n\\tikzset{every picture\/.style={line width=0.75pt}}\n\n\\begin{tikzpicture}[x=0.75pt,y=0.75pt,yscale=-1,xscale=1]\n\n\\draw (170.44,123.61) .. controls (191.13,144.03) and (315.33,147.16) .. 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(364.66,118.71) -- cycle ;\n\\draw (170.11,112.28) .. controls (196.73,89.81) and (320.37,97.44) .. (350.28,111.92) ;\n\\draw [shift={(352.78,113.28)}, rotate = 211.87] [fill={rgb, 255:red, 0; green, 0; blue, 0 } ][line width=0.08] [draw opacity=0] (10.72,-5.15) -- (0,0) -- (10.72,5.15) -- (7.12,0) -- cycle ;\n\\draw (158.78,109.28) .. controls (92.94,41.46) and (229.06,41.9) .. (167.05,107.49) ;\n\\draw [shift={(165.11,109.5)}, rotate = 314.63] [fill={rgb, 255:red, 0; green, 0; blue, 0 } ][line width=0.08] [draw opacity=0] (10.72,-5.15) -- (0,0) -- (10.72,5.15) -- (7.12,0) -- cycle ;\n\\draw (358.44,109.28) .. controls (292.61,41.46) and (428.08,41.9) .. (366.05,107.49) ;\n\\draw [shift={(364.11,109.5)}, rotate = 314.63] [fill={rgb, 255:red, 0; green, 0; blue, 0 } ][line width=0.08] [draw opacity=0] (10.72,-5.15) -- (0,0) -- (10.72,5.15) -- (7.12,0) -- cycle ;\n\\draw (370.27,114.04) .. controls (438.68,48.82) and (437.64,183.9) .. (372.62,121.28) ;\n\\draw [shift={(370.63,119.32)}, rotate = 45.15] [fill={rgb, 255:red, 0; green, 0; blue, 0 } ][line width=0.08] [draw opacity=0] (10.72,-5.15) -- (0,0) -- (10.72,5.15) -- (7.12,0) -- cycle ;\n\\draw (366.67,124.69) .. controls (432.19,192.81) and (297.1,192.37) .. (359.44,127.08) ;\n\\draw [shift={(361.39,125.08)}, rotate = 134.9] [fill={rgb, 255:red, 0; green, 0; blue, 0 } ][line width=0.08] [draw opacity=0] (10.72,-5.15) -- (0,0) -- (10.72,5.15) -- (7.12,0) -- cycle ;\n\\draw (154.9,120.5) .. controls (86.64,185.87) and (87.37,50.79) .. (152.53,113.26) ;\n\\draw [shift={(154.52,115.22)}, rotate = 225.02] [fill={rgb, 255:red, 0; green, 0; blue, 0 } ][line width=0.08] [draw opacity=0] (10.72,-5.15) -- (0,0) -- (10.72,5.15) -- (7.12,0) -- cycle ;\n\\draw (166.01,124.02) .. controls (231.52,192.15) and (96.44,191.7) .. (158.78,126.41) ;\n\\draw [shift={(160.73,124.42)}, rotate = 134.9] [fill={rgb, 255:red, 0; green, 0; blue, 0 } ][line width=0.08] [draw opacity=0] (10.72,-5.15) -- (0,0) -- (10.72,5.15) -- (7.12,0) -- cycle ;\n\n\\draw (122,65.9) node [anchor=north west][inner sep=0.75pt] {$e_{1}$};\n\\draw (82.5,112) node [anchor=north west][inner sep=0.75pt] {$e_{2}$};\n\\draw (122,157) node [anchor=north west][inner sep=0.75pt] {$e_{3}$};\n\\draw (248,82) node [anchor=north west][inner sep=0.75pt] {$e_{4}$};\n\\draw (248,145) node [anchor=north west][inner sep=0.75pt] {$e_{8}$};\n\\draw (390,65.9) node [anchor=north west][inner sep=0.75pt] {$e_{5}$};\n\\draw (428.5,112) node [anchor=north west][inner sep=0.75pt] {$e_{6}$};\n\\draw (390,157) node [anchor=north west][inner sep=0.75pt] {$e_{7}$};\n\\draw (158,132) node [anchor=north west][inner sep=0.75pt] {$\\mathbf{1}$};\n\\draw (358,132) node [anchor=north west][inner sep=0.75pt] {$\\mathbf{2}$};\n\n\\end{tikzpicture}\n\\vspace{-1.1em}\n\\caption{The GIFS in Examples \\ref{exam(5.4)}.} \\label{fig.2}\n\\end{center}\n\\end{figure}\n\\begin{proof}\nFor convenience, we write $f_{e_{i}}=f_{i}$ for any $i\\in\\{1,\\dots,8\\}$. Let $\\mathcal{F}_{k}=E_{k}$ for $k\\geq1$, and let $\\mathcal{T}_{1}$ and $\\mathcal{T}_{2}$ be the neighborhood types of the root neighborhoods $[O_{1}]$ and $[O_{2}]$, respectively. Iterations of the root vertices are shown in Figure \\ref{fig.3}(b, c). All neighborhood types are generated after three iterations.\nTo construct the weighted incidence matrix in the reduced graph $\\mathbb{G}_{R}$, we note that\n$$\\mathbb{V}_{1}=\\{(f_{1},1),\\dots,(f_{8},1)\\}.$$\nDenote by $\\omega_{1},\\dots,\\omega_{8}$ the vertices in $\\mathbb{V}_{1}$ according to the above order. Then $[\\omega_{4}]=\\mathcal{T}_{2}$ and $[\\omega_{8}]=\\mathcal{T}_{1}$. Let $\\mathcal{T}_{3}:=[\\omega_{1}]$, $\\mathcal{T}_{4}:=[\\omega_{2}]$, $\\mathcal{T}_{5}:=[\\omega_{3}]$, $\\mathcal{T}_{6}:=[\\omega_{5}]$, $\\mathcal{T}_{7}:=[\\omega_{6}]$, $\\mathcal{T}_{8}:=[\\omega_{7}]$. Then\n$$\\mathcal{T}_{1}\\longrightarrow\\mathcal{T}_{2}+\\mathcal{T}_{3}+\n\\mathcal{T}_{4}+\\mathcal{T}_{5}$$\nand\n$$\\mathcal{T}_{2}\\longrightarrow\\mathcal{T}_{1}+\\mathcal{T}_{6}\n+\\mathcal{T}_{7}+\\mathcal{T}_{8},$$\nwhere we write $\\mathcal{T}_{i}=\\mathcal{T}_{i}(\\frac{1}{2})$ for convenience. Since $f_{13}=f_{21}$, the edge $e_{1}e_{3}$ is removed in $\\mathbb{G}_{R}$. Hence $\\omega_{1}$ has three offspring, i.e.,\n$$(f_{11},2),(f_{12},2),(f_{14},2)\\in\\mathbb{V}_{2},$$\nwhich are of neighborhood types $\\mathcal{T}_{3},\\mathcal{T}_{4},\\mathcal{T}_{2}$, respectively. Iterating $(f_{1},1)$ gives\n$$\\mathcal{T}_{3}\\longrightarrow\\mathcal{T}_{2}+\\mathcal{T}_{3}+\\mathcal{T}_{4}.$$\nSince $f_{23}=f_{31}$, the edge $e_{2}e_{3}$ is removed in $\\mathbb{G}_{R}$. Hence $\\omega_{2}$ has three offspring, i.e.,\n$$(f_{21},2),(f_{22},2),(f_{24},2)\\in\\mathbb{V}_{2}.$$\nNote that $[(f_{22},2)]=\\mathcal{T}_{4}$ and $[(f_{24},2)]=\\mathcal{T}_{2}$. Let $\\omega_{9}:=(f_{22},2)$ and $\\mathcal{T}_{9}:=[\\omega_{9}]$. Then\n$$\\mathcal{T}_{4}\\longrightarrow\\mathcal{T}_{2}+\\mathcal{T}_{4}+\\mathcal{T}_{9}.$$\nNote that $\\omega_{3}$ has four offspring, i.e.,\n$$(f_{31},2),(f_{32},2),(f_{33},2),(f_{34},2)\\in\\mathbb{V}_{2},$$\nwith $[(f_{32},2)]=\\mathcal{T}_{4}$, $[(f_{33},2)]=\\mathcal{T}_{5}$ and $[(f_{34},2)]=\\mathcal{T}_{2}$. Let $\\omega_{10}:=(f_{31},2)$ and $\\mathcal{T}_{10}:=[\\omega_{10}]$. Then\n$$\\mathcal{T}_{5}\\longrightarrow\\mathcal{T}_{2}+\\mathcal{T}_{4}+\\mathcal{T}_{5}+\\mathcal{T}_{10}.$$\nSince $f_{213}=f_{221}$, the edge $e_{2}e_{1}e_{3}$ is removed in $\\mathbb{G}_{R}$. Hence $\\omega_{9}$ has three offspring, i.e.,\n$$(f_{211},2),(f_{212},2),(f_{214},2)\\in\\mathbb{V}_{3},$$\nwhich are of neighborhood types $\\mathcal{T}_{10},\\mathcal{T}_{4},\\mathcal{T}_{2}$, respectively. Iterating $(f_{22},2)$ gives\n$$\\mathcal{T}_{9}\\longrightarrow\\mathcal{T}_{2}+\\mathcal{T}_{4}+\\mathcal{T}_{10}.$$\nSince $f_{313}=f_{321}$, the edge $e_{3}e_{1}e_{3}$ is removed in $\\mathbb{G}_{R}$. Hence $\\omega_{10}$ has three offspring, i.e.,\n$$(f_{311},2),(f_{312},2),(f_{314},2)\\in\\mathbb{V}_{3},$$\nwhich are of neighborhood types $\\mathcal{T}_{10},\\mathcal{T}_{4},\\mathcal{T}_{2}$, respectively. Iterating $(f_{31},2)$ gives\n$$\\mathcal{T}_{10}\\longrightarrow\\mathcal{T}_{2}+\\mathcal{T}_{4}+\\mathcal{T}_{10}.$$\nLet $\\mathcal{T}_{11}:=[(f_{65},2)]$ and $\\mathcal{T}_{12}:=[(f_{75},2)]$. Using the same argument, it can be checked directly that\n$$\\mathcal{T}_{6}\\longrightarrow\\mathcal{T}_{1}+\\mathcal{T}_{6}+\\mathcal{T}_{7},~~\n\\mathcal{T}_{7}\\longrightarrow\\mathcal{T}_{1}+\\mathcal{T}_{7}+\\mathcal{T}_{11},$$\n$$\\mathcal{T}_{8}\\longrightarrow\\mathcal{T}_{1}+\\mathcal{T}_{7}+\\mathcal{T}_{8}+\\mathcal{T}_{12},~~\n\\mathcal{T}_{11}\\longrightarrow\\mathcal{T}_{1}+\\mathcal{T}_{7}+\\mathcal{T}_{12},~~\n\\mathcal{T}_{12}\\longrightarrow\\mathcal{T}_{1}+\\mathcal{T}_{7}+\\mathcal{T}_{12}.$$\nHence the weighted incidence matrix is\n$$\nA_{\\alpha}=\\Big(\\frac{1}{2}\\Big)^{\\alpha}\\left({\\begin{array}{cccccccccccc}\n0&1&1&1&1&0&0&0&0&0&0&0\\\\\n1&0&0&0&0&1&1&1&0&0&0&0\\\\\n0&1&1&1&0&0&0&0&0&0&0&0\\\\\n0&1&0&1&0&0&0&0&1&0&0&0\\\\\n0&1&0&1&1&0&0&0&0&1&0&0\\\\\n1&0&0&0&0&1&1&0&0&0&0&0\\\\\n1&0&0&0&0&0&1&0&0&0&1&0\\\\\n1&0&0&0&0&0&1&1&0&0&0&1\\\\\n0&1&0&1&0&0&0&0&0&1&0&0\\\\\n0&1&0&1&0&0&0&0&0&1&0&0\\\\\n1&0&0&0&0&0&1&0&0&0&0&1\\\\\n1&0&0&0&0&0&1&0&0&0&0&1\n\\end{array}}\\right)=:\\Big(\\frac{1}{2}\\Big)^{\\alpha}\\widetilde{A}_{\\alpha},\n$$\nand the maximal eigenvalue of $\\widetilde{A}_{\\alpha}$ is $2+\\sqrt{2}$.\nThe GIFS $G=(V,E)$ defined on $\\mathbb{R}^{2}$ satisfies (GFTC) with $\\{O_{i}\\}_{i=1}^{2}$ being a GFTC-family and $\\{f_{e}\\}_{e\\in E}$ being an associated family of contractive similitudes.\nBy Proposition \\ref{prop(5.2)}, the GIFS $G=(V,E)$ defined on $\\mathbb{T}^{2}$ satisfies (GFTC) with $\\{\\Omega_{i}\\}_{i=1}^{2}$ being a GFTC-family and $\\{S_{e}\\}_{e\\in E}$ being an associated family of contractive similitudes.\nBy Theorem \\ref{thm(41.1)}, $\\dim_{{\\rm H}}(K)=\\dim_{{\\rm H}}(K_{0})=\\log(2+\\sqrt{2})\/\\log2=1.77155\\dots$.\n\\end{proof}\n\n\n\\begin{figure}[htbp]\n\\centering\n\\subfigure[]\n{\\includegraphics[width=4.8cm]{0.png}\\label{fig.(a)}}\n\\qquad\\qquad\n\\subfigure[]\n{\\includegraphics[width=4.8cm]{1.png}\\label{fig.(b)}}\n\\\\\n\\subfigure[]\n{\\includegraphics[width=4.8cm]{2.png}\\label{fig.(c)}}\n\\qquad\\qquad\n\\subfigure[]\n{\\includegraphics[width=4.8cm]{G9.png}\\label{fig.(d)}}\n\n\\caption{(a) $\\{g_{i}\\}_{i=1}^{4}$. (b) The first iteration of $G=(V,E)$ on $\\mathbb{R}^{2}$ associated to $\\{g_{i}\\}_{i=1}^{4}$. (c) The second iteration. (d) The corresponding graph self-similar set.}\\label{fig.3}\n\\end{figure}\n\n\n\\begin{rema}\\label{rema(5.1)}\nUnlike $\\mathbb{T}^{2}$, the family of contractive similitudes associated to a GIFS defined on $\\mathbb{R}^{2}$ can be characterized explicitly. In fact, Proposition \\ref{prop(5.2)} is not needed in the proof of Example \\ref{exam(5.4)}. We may use Theorem \\ref{thm(41.1)} and a similar arguement as in the proof of Example \\ref{exam(5.4)} to obtain the same result.\n\\end{rema}\n\n\n\\noindent\\textbf{Acknowledgements} The authors are supported in part by the National Natural Science Foundation of China, grant 11771136, and Construct Program of the Key Discipline in Hunan Province. The first author is also supported in part by a Faculty Research Scholarly Pursuit Funding from Georgia Southern University.\\\\\n\n\n\\noindent\\textbf{Declaration}\\\\\n\n\n\\noindent\\textbf{Competing interests} The authors hereby declare that there is no conflict of interest regarding this work.\n\n\\bigskip\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nNonequilibrium systems are common in nature because they are, in general, subject to thermal gradients, chemical potential gradients or may be triggered by time-dependent forces. Heat transport is one such example of nonequilibrium systems where the heat carriers could be electrons, phonons, magnons, etc. To study heat transport in phononic systems, one considers a finite junction part, which can be an insulator, connected with two heat baths that are maintained at different temperatures. In the past decade, the main focus was on the calculation of the steady state heat current or heat flux flowing through the junction part from the leads \n\\cite{Caroli,Meir,Rego-Kirczenow-1998,Segal,Mingo-Yang-2003,Yamamoto-2006,Dhar1,Wang-prb06,Wang-pre07,WangJS-europhysJb-2008}. For diffusive systems, the answer is given by Fourier's law \\cite{Dhar, Lepri, Lebowitz} which is true only in the linear response regime, i.e., when the temperature difference between the baths is small. However for harmonic or ballistic systems, the heat current is given by a Landauer-like formula \\cite{Rego-Kirczenow-1998,Dhar1,WangJS-europhysJb-2008} which was first derived for electronic transport. Landauer formula on the contrary to the Fourier's law is true for arbitrary temperature differences between the leads. No such explicit expression for current is known for transient states. In recent times, several works \\cite{eduardos-paper,eduardos-paper1} followed to answer what happens to current in the transient regime. This is an important question both from the theoretical and experimental points of view. \n\nMuch attention has been given to phonon transport, in particular on thermal devices and on controlling heat flow \\cite{baowen-review}. With the advent of technology it is now possible to study transport problems and observe a single mode of vibration in small systems with few degrees of freedom \\cite{oconell}. These systems shows strong thermal fluctuations which play an important role because thermal fluctuations can lead to instantaneous heat transfer from colder to hotter lead. It is therefore necessary to talk about the statistical distribution of heat flux for these systems. In the electronic literature the distribution $P(Q_L)$ of the charge $Q_L$, flowing from the left lead to the junction part, was answered by calculating the corresponding CGF, ${\\cal Z}(\\xi)= \\langle e^{i \\xi Q_L} \\rangle$, and is given by the celebrated Levitov-Lesovik formula \\cite{Levitov,Levitov1,Levitov-PRB}. This methodology is also known as the {\\it full counting statistics} \\cite{otherworks,otherworks1,otherworks2,Klich,Pilgram,Bagrets,Gogolin,Urban,Gutman,fluct-theorems}\nin the field of electronic transport. Experimentally the electron counting statistics has been measured in quantum-dot systems \\cite{Flindt-etal-2009,Gustavsson-etal-2006}. However few experiments have been done for phonons \\cite{measurement}. In the phononic case Saito and Dhar \\cite{Saito-Dhar-2007} gave an explicit expression of the CGF. Ren et al.\\ gave a result for two-level systems \\cite{ren-jie}. Full counting statistics of energy fluctuations in a driven quantum resonator is studied by Clerk \\cite{AAClerk-2011}. The main focus in these papers was on the long-time limit and SSFT \\cite{noneq-fluct,fluct-theorem-1,fcs-bijay,kundu,Esposito-review-2009, Hanggi2}. Using the NEGF method \\cite{schwinger-keldysh,rammer86} and two-time measurement \\cite{Esposito-review-2009, Hanggi2,Hanggi3,Hanggi4} concept, Wang~et al.\\ \\cite{fcs-bijay} gave an explicit expression for the CGF which is valid for both transient and steady state regimes. \n\nIn this paper, we extend our previous work in Ref.~\\onlinecite{fcs-bijay} and derive the CGF in a more general scenario, i.e., in the presence of both the temperature difference and the time-dependent driving force. We analyze the cumulants of heat $Q_L$ for three different initial conditions of the density operator and study the effects on both transient and steady state regimes. We also derive the CGF for the joint probability distribution of left and right lead heat $P(Q_L,Q_R)$ which help us to obtain the correlations between $Q_L$ and $Q_R$. By calculating CGF for $P(Q_L,Q_R)$ we can immediately obtain the CGF for the total entropy that flows to the leads. We present analytical expressions of the CGF's in the steady state and discuss the SSFT. Our method can be easily generalized for multiple heat baths.\n\nThe plan of the paper is as follows. We start in Sec.~II by introducing our model. Then in Sec.~III we define current and corresponding quantum heat operator followed by the definition of CGF for $Q_L$ using the two-time measurement concept, in Sec.~IV. In Sec.~V we derive the CGF using Feynman's path integral method and in Sec.~VI we use Feynman's diagrammatic technique to derive the CGF. We discuss the steady state result and fluctuation theorems in Sec.~VII. In Sec. VIII and IX we discuss how to calculate the CGF's numerically in transient regime and give numerical results for one-dimensional (1D) linear chain model, connected with Rubin heat baths, for three different initial conditions of the density operator. Then in Sec.~X we obtain the CGF for joint probability distribution of heat transferred $P(Q_L,Q_R)$ and discuss correlations and total entropy flow. In. Sec.~XI we give the long-time limit expression for the driven part of the full CGF. In Sec.~XII we discuss another definition of generating function due to Nazarov and discuss the corresponding long-time limit. We found that using this definition, the generating function \ndoes not obey the Gallavotti-Cohen (GC) fluctuation symmetry. \nWe conclude with a short discussion in Sec.~XIII. Few appendices give some details of technique nature. In particular, an electron system of a tight-binding model is treated using our method.\n \n\n\\section{The model}\nOur model consists of a finite harmonic junction part, which we denote by $C$, coupled to two heat baths, the left ($L$) and the right ($R$), kept at two different temperatures $T_L$ and $T_R$, respectively. To model the heat baths, we consider an infinite collection of coupled harmonic oscillators. We take the three systems to be decoupled initially and to be described by the Hamiltonians,\n\\begin{equation}\n{\\cal H}_\\alpha = \\frac{1}{2} p_\\alpha^T p_\\alpha + \\frac{1}{2} u_\\alpha^T K^\\alpha u_\\alpha, \\quad \\alpha=L, C, R,\n\\end{equation}\nfor the left, right, and the finite central region. The leads are assumed to be semi-infinite. Masses are absorbed by defining $u = \\sqrt{m}\\,\nx$. $u_\\alpha$ and $p_\\alpha$ are column vectors of coordinates and momenta. $K^\\alpha$ is the spring constant matrix of region $\\alpha$.\nCouplings of the center region with the leads are turned on either adiabatically from time $t=-\\infty$, or switched on abruptly at $t=0$.\nThe interaction Hamiltonian takes the form \n\\begin{equation}\n{\\cal H}_{{\\rm int}} = u_L^T \\, V^{LC} \\,u_{C} + u_R^T\\, V^{RC}\\, u_{C}.\n\\end{equation}\nFor $t>0$, an external time-dependent force is applied only to the center atoms, which is of the form\n\\begin{equation}\n{\\cal V}_C(t) =-f^T(t)\\,u_C,\n\\end{equation}\nwhere $f(t)$ is the time-dependent force vector. The driving force couples only with the position operators of the center. The force can be in the form of electromagnetic field. Coupling of this form helps us to obtain analytical solution for the CGF of heat flux.\nSo the full Hamiltonian for $t>0$ (in the Schr\\\"odinger picture) is\n\\begin{equation}\n{\\cal H}(t) = {\\cal H}(0^{-}) + {\\cal V}_C(t) = {\\cal H}_C + {\\cal H}_L+ {\\cal H}_R+ {\\cal H}_{\\rm int}+ {\\cal V}_C(t).\n\\end{equation}\nIn the next section we will define current operator and the corresponding heat operator based on this Hamiltonian.\n \n\\section{Definition of current and heat operators}\nIt is possible to define the current operator ${\\cal I}$ depending on where we want to measure the current. Here we consider the current flowing from the left lead to the center system and ${\\cal I}_{L}$ is defined (in Heisenberg picture) as\n\\begin{equation}\n{\\cal I}_{L}(t) = - \\frac{d{\\cal H}^{H}_L(t)}{dt}=\\frac{i}{\\hbar}[{\\cal H}^{H}_L(t), {\\cal H}_H(t)]= p_L^{T}(t)\\,V^{LC}\\,u_C(t),\n\\label{current}\n\\end{equation}\nwhere ${\\cal H}_H(t)$ is the (time-dependent) Hamiltonian in the Heisenberg picture at time $t$. The corresponding heat operator can be written down as\n\\begin{equation}\n\\label{eq-hatQ}\n{\\cal Q}_{L}(t)=\\int_0^t {\\cal I}_{L}(t')\\,dt' = {\\cal H}_L(0)-{\\cal H}^{H}_L(t),\n\\end{equation}\nwhere ${\\cal H}_L \\big[={\\cal H}_L(0)\\big]$ is the Schr\\\"odinger operator of the free left lead and \n\\begin{equation}\n{\\cal H}^{H}_L(t)= {\\cal U}(0,t)\\, {\\cal H}_L\\, {\\cal U}(t,0),\n\\end{equation}\nand ${\\cal U}(t,t')$ is the evolution operator corresponding to the full Hamiltonian ${\\cal H}(t)$ and satisfies the Schr\\\"odinger equation\n\\begin{equation}\ni\\hbar \\,{ \\partial {\\cal U}(t,t') \\over \\partial t} = {\\cal H}(t)\\, {\\cal U}(t,t').\n\\end{equation}\nThe formal solution of this equation is (assuming $t \\geq t'$)\n\\begin{equation}\n{\\cal U}(t,t')=T \\exp\\left\\{ - \\frac{i}{\\hbar} \\int_{t'}^t {\\cal H}(\\bar{t})\\, d\\bar{t} \\right\\},\n\\label{eq-unitary}\n\\end{equation}\nwhere $T$ is the time-order operator where time increases from right to left. Also ${\\cal U}^{\\dagger}(t,t')={\\cal U}(t',t)$. $Q$ of non-calligraphic font will be a classical variable. \n\nIn the following section we derive the CGF based on this definition of heat operator and using two-time measurement scheme.\n\n\\section{Definition of the generating function for heat operator}\nOur primary interest here is to calculate the moments or cumulants of the heat energy transferred in a given time interval $t_M$. Hence, it is advantageous to calculate the generating function instead of calculating moments directly. Since ${\\cal Q}_{L}$ is a quantum operator, there are subtleties as to how exactly the generating function should be defined. Naively we may use $\\langle e^{i \\xi {\\cal Q}_{L}} \\rangle $. But this definition fails the fundamental requirement of positive definiteness of the probability distribution.\n\nHere we will give two different definitions that are used to calculate the generating function for such problem. The first definition comes from the idea of two-time measurements and based on this concept the CGF can be written down as\n\\begin{equation}\n{\\cal Z}(\\xi)=\\langle e^{i \\xi {\\cal H}_L} \\, e^{-i \\xi {\\cal H}^{H}_L(t) } \\rangle'\n\\label{eq-Z-two-time}\n\\end{equation}\nwhich we will discuss in great detail in this section.\n\nThe second definition of the CGF is\n\\be\n{\\cal Z}_1(\\xi) = \\langle \\bar{T} e^{i\\xi {\\cal Q}_{L}\/2} T e^{i\\xi {\\cal Q}_{L}\/2} \\rangle,\n\\label{eq-Z1-Nazarov}\n\\ee\nwhere $\\bar{T}$ is the anti-time order operator. The time (or anti-time) order is meant to apply to the integrand when the\nexponential is expanded and ${\\cal Q}_{L}$ is expressed as integral over ${\\cal I}_{L}$ as in Eq.~(\\ref{eq-hatQ}). This definition is used by Nazarov et al.\\ \\cite{otherworks,otherworks1} mostly for the electronic transport case. In the last section we will show how this generating function can be derived starting from ${\\cal Z}(\\xi)$ under a particular approximation and will also give explicit expression for ${\\cal Z}_{1}(\\xi)$ in the long-time limit. \n\nIn the following we will discuss the idea of two-time measurement and derive the corresponding CGF ${\\cal Z}(\\xi)$.\n \n\\subsection{Two-time measurement}\nThe heat operator in Eq.~(\\ref{eq-hatQ}) depends on the left-lead Hamiltonian ${\\cal H}_{L}$ at time 0 and $t$. The concept of two-time measurement implies the measurement of a certain operator (in this case ${\\cal H}_{L})$ at two different times. Here the measurement is in the sense of quantum measurement\nof von Neumann \\cite{neumann}. \n\nLet us first assume that the full system is in a pure state $|\\Psi_0\\rangle$ at $t=0$.\nWe want to do measurement of the energy associated with the operator\n${\\cal H}_L$. According to quantum mechanics, the result of a measurement can\nonly be an eigenvalue of the (Schr\\\"odinger) operator ${\\cal H}_L$ and the wave function\ncollapses into an eigenstate of ${\\cal H}_L$. Let\n\\begin{equation}\n{\\cal H}_L | \\phi_a \\rangle = a | \\phi_a \\rangle,\\quad \\Pi_a = |\\phi_a\\rangle \\langle \\phi_a |,\n\\end{equation}\nwhere $\\Pi_a$ is the projector into the state $|\\phi_a\\rangle$ satisfying $\\Pi_a^2 = \\Pi_a$,\nand $\\sum_a \\Pi_a = 1$. We assume the eigenvalues are discrete (this is always so\nif the lattice system is finite). After the measurement at time $t=0$, the wave function\nis proportional to $\\Pi_a | \\Psi_0 \\rangle$ if the result of the measurement is the energy $a$ and the probability of such event happen is $\\langle \\Psi_0 | \\Pi_a^2 | \\Psi_0 \\rangle$. Let's propagate this state to time $t$ and do a second measurement of the lead\nenergy, finding that the result is $b$. The wave function now becomes proportional to $\\Pi_b \\, {\\cal U}(t,0) \\, \\Pi_a \\, | \\Psi_0 \\rangle$.\nThe joint probability of getting $a$ at time $0$ and $b$ at time $t$ is\nthe norm (inner product) of the above (unnormalized) state. \n\nIf the initial state is in a mixed state, we add up the initial probability classically, i.e., if \n\\begin{equation}\n\\rho(0) = \\sum_k w_k | \\Psi_0^k \\rangle\\langle \\Psi_0^k |,\\quad w_k > 0, \\quad \\sum_k w_k = 1,\n\\end{equation}\nthe joint probability distribution of two-time measurement output is\n\\begin{eqnarray}\nP(b,a) &=& \\sum_k w_k \\langle \\Psi_0^k | \\,\\Pi_a\\, {\\cal U}(0,t) \\,\n\\Pi_b \\,{\\cal U}(t,0) \\,\\Pi_a \\,| \\Psi_0^k \\rangle \\nonumber \\\\\n&=& {\\rm Tr} \\bigl[ \\Pi_a \\,\\rho(0) \\, \\Pi_a \\, {\\cal U}(0,t) \\, \\Pi_b \\, {\\cal U}(t,0)\\bigr].\n\\end{eqnarray}\nBy definition, we see that $P(b,a)$ is a proper probability in the sense that $P(b,a) \\ge 0$ and $\\sum_{a,b} P(b,a) = 1$.\nThen the generating function for $Q_L=a-b$ is defined as\n\\begin{eqnarray}\n{\\cal Z}(\\xi) &=& \\langle e^{i\\xi (a-b)} \\rangle = \\sum_{a,b} e^{i \\xi(a-b)} P(b,a) \\nonumber \\\\\n&=& \\sum_{a,b} e^{i \\xi (a-b)} {\\rm Tr} \\bigl[\\Pi_a \\, \\rho(0) \\, \\Pi_a \\, {\\cal U}(0,t) \\, \\Pi_b \\, {\\cal U}(t,0) \\bigr] \\nonumber \\\\\n& = & \\langle e^{i \\xi {\\cal H}_L} \\, e^{-i \\xi {\\cal H}^{H}_L(t) } \\rangle' \\nonumber \\\\\n\\label{eqZ2-def}\n& = & \\langle e^{i \\xi {\\cal H}_L\/2} \\, e^{-i \\xi {\\cal H}^{H}_L(t) } \\, e^{i \\xi {\\cal H}_L\/2 } \\rangle'.\n\\end{eqnarray}\nwhere we define \\cite{neumann}\n\\begin{equation}\n\\rho'(0) = \\sum_{a} \\Pi_a\\, \\rho(0) \\Pi_a.\n\\label{projected}\n\\end{equation}\nwhich we call as the {\\it projected} density matrix.\n\nIf the initial state at $t=0$ is a product state i.e., $\\rho(0)=\\rho(-\\infty)=\\rho_L \\otimes \\rho_C \\otimes \\rho_R $, where the left, center and right density\nmatrices are in equilibrium distributions corresponding to the \nrespective temperatures:\n$\\rho_\\alpha={e^{-\\beta_\\alpha {\\cal{H}}_\\alpha}}\/{{\\rm Tr} [e^{-\\beta_\\alpha\n {\\cal H}_\\alpha}]}$ for $\\alpha=L,C,R$ and $\\beta_{\\alpha}=1\/(k_{\\rm B}\nT_{\\alpha})$, then the projection operators $\\Pi_a$ do not play any role and $\\langle....\\rangle'={\\rm Tr}\\Bigl[\\rho(-\\infty)\\cdots \\Bigr]=\\langle....\\rangle$. \n\nHere we will derive the CGF for three different initial conditions:\n\\begin{itemize}\n\\item{Product initial state, i.e., $\\rho(-\\infty)$, which corresponds to sudden switch-on of the coupling between the leads and the center.}\n\\item{steady state as the initial state, i.e., $\\rho(0)$, which we can obtain, starting with the decoupled Hamiltonians at $t=-\\infty$, switch on the couplings between the center region and the leads, adiabatically upto time $t=0$.}\n\\item{{\\it projected} density matrix $\\rho'(0)$ considering $\\rho(0)$ as the steady state, i.e., taking the effects of measurements into account.}\n\\end{itemize} \n\nIn the following sections we will analytically show that the CGF's corresponding to different initial conditions reach the same steady state in the long-time limit and hence is independent of initial distributions. However for short time transient behavior depends significantly on initial conditions and also the measurements do play an important role. \n\n\\section{calculation for ${\\cal Z}(\\xi)$ for initial states $\\rho(0)$ and $\\rho'(0)$}\nIn this section we will give detail derivation for ${\\cal Z}(\\xi)$, using Feynman path-integral formalism, for two different initial density operators $\\rho(0)$ and $\\rho'(0)$. \n\n\\subsection{Removing the projection $\\Pi_a$ at $t=0$}\nThe projection by $\\Pi_a$ at $t=0$ Eq.~(\\ref{projected}) to the density matrix creates a problem\nfor formulation in path integrals. We can remove it following Ref.~\\onlinecite{Esposito-review-2009} by putting it into part of an evolution of ${\\cal H}_L$, just like the factor associated with the generating function variable $\\xi$, with a price we have to pay, introducing another integration variable $\\lambda$. The key observation is that we can represent the projector by the Dirac\n$\\delta$ function\n\\begin{eqnarray}\n\\Pi_a & \\propto & \\delta(a-{\\cal H}_L) \\nonumber \\\\\n&=& \\int_{-\\infty}^\\infty \\frac{d\\lambda}{2\\pi} \\,e^{-i\\lambda(a-{\\cal H}_L)}.\n\\label{eq-delta}\n\\end{eqnarray}\nFor this to make sense, we assume the spectrum of the energy of\n${\\cal H}_L$ is continuous, which is valid if we take the large size limit first.\nIdentifying $\\Pi_a$ as $\\delta(a-{\\cal H}_L)$ with a continuous variable\n$a$ introduces an constant proportional to the Dirac $\\delta(0)$ to $\\rho'(0)$, since\n$\\Pi_a$ is now normalized as $\\Pi_a \\Pi_b = \\delta(a-b) \\Pi_a$.\nHowever, this constant can be easily fixed by the condition\n${\\cal Z}(0)=1$. So using $\\Pi_a = \\delta(a -{\\cal H}_L)$ will not cause\ndifficulty. \n\nSubstituting the Fourier integral representation into $\\rho'$ we obtain\n\\begin{eqnarray}\n\\rho'(0) &\\propto&\\int da\\, \\Pi_a\\, \\rho(0) \\Pi_a \\\\\n&=& \\int \\frac{d\\lambda}{2\\pi} e^{i\\lambda {\\cal H}_L} \\rho(0) e^{-i\\lambda {\\cal H}_L}.\n\\end{eqnarray}\nUsing the symmetric form of ${\\cal Z}$, Eq.~(\\ref{eqZ2-def}), we have\n\\begin{eqnarray}\n{\\cal Z}(\\xi)&\\propto& \\int \\frac{d\\lambda}{2\\pi} {\\rm Tr} \\bigl\\{ \n\\rho(0) \\,{\\cal U}_{\\xi\/2-\\lambda}(0,t)\\, {\\cal U}_{-\\xi\/2-\\lambda}(t,0) \\bigr\\} \\nonumber \\\\\n&=& \\int \\frac{d\\lambda}{2\\pi}\\; {\\cal Z}(\\xi, \\lambda),\n\\label{eq-Uxilambda}\n\\end{eqnarray}\nwhere ${\\cal U}_{x}(t,t')$ is the modified evolution operator of an effective Hamiltonian given by\n\\begin{equation}\n{\\cal H}_{x}(t) = e^{i x {\\cal H}_L} {\\cal H}(t) e^{-i x {\\cal H}_L},\n\\end{equation}\nwhere $x$ is a real parameter which in this case is $\\xi\/2 -\\lambda$ and $-\\xi\/2 -\\lambda$. Finally ${\\cal U}_{x}(t,t')$ is given by ($t \\geq t'$)\n\\begin{eqnarray}\n{\\cal U}_{x}(t,t') &=& e^{i x {\\cal H}_L} {\\cal U}(t,t') e^{-i x {\\cal H}_L} \\nonumber \\\\\n&=& \\sum_{n=0}^\\infty \\left(-\\frac{i}{\\hbar} \\right)^n \\int_{t'}^t dt_1 \\int_{t'}^{t_1} dt_2 \\cdots \\int_{t'}^{t_{n-1}}dt_n\n\\nonumber \\\\ \n&& \\times e^{i x {\\cal H}_L} {\\cal H}(t_1) {\\cal H}(t_2) \\cdots {\\cal H}(t_n) e^{-i x {\\cal H}_L} \\nonumber \\\\\n\\label{eq-Ulambda}\n&=& T \\exp\\left\\{ - \\frac{i}{\\hbar} \\int_{t'}^t {\\cal H}_x(t') dt' \\right\\}.\n\\end{eqnarray}\nIt is important to note that substituting $\\lambda=0$ in ${\\cal Z}(\\xi,\\lambda)$ gives us the initial density matrix $\\rho(0)$. \n\nNow we will give an explicit expression of the modified Hamiltonian ${\\cal H}_x$ which helps us to calculate the CGF using path integral.\n\n\n\\subsection{The expression for ${\\cal H}_{x}$}\nThe modified Hamiltonian is the central quantity for calculating CGF. It is the Heisenberg evolution of the full Hamiltonian ${\\cal H}(t)$ (in Schr\\\"odinger picture) with respect to ${\\cal H}_L$. Since ${\\cal H}_L$ commutes with\nevery term $\\tilde{\\cal H}$ where ${\\cal H}(t)= {\\tilde {\\cal H}} + u_L^T V^{LC} u_C$, except the coupling term $u_L^T V^{LC} u_C$, we can write\n\\begin{eqnarray}\n{\\cal H}_{x}(t) &=& e^{i x {\\cal H}_L} {\\cal H}(t) e^{-i x {\\cal H}_L} \\nonumber \\\\\n& = & e^{i x {\\cal H}_L} \\bigl({\\tilde {\\cal H}} + u_L^T V^{LC} u_C \\bigr) e^{-i x {\\cal H}_L} \\nonumber \\\\\n& = & {\\cal H}(t) + \\bigl( u_L(\\hbar x) - u_L\\bigr)^T V^{LC} u_C,\n\\end{eqnarray}\nwhere $u_L(\\hbar x) = e^{i x {\\cal H}_L} u_L e^{-i x {\\cal H}_L}$ is the free left lead\nHeisenberg evolution to time $t = \\hbar x$. $u_L(\\hbar x)$ can be obtained\nexplicitly as\n\\begin{equation}\nu_L(\\hbar x) = \\cos(\\sqrt{K_L} \\hbar x) u_L + \\frac{1}{\\sqrt{K_L}} \\sin( \\sqrt{K_L} \\hbar x) p_L.\n\\end{equation} \nThe matrix $\\sqrt{K_L}$ is well-defined as the matrix $K_L$ is positive definite. $u_L$ and $p_L$ are the initial conditions at $t=0$.\nThe final expression for ${\\cal H}_x(t)$ is \n\\begin{equation}\n{\\cal H}_x(t) = {\\cal H}(t) + \\bigl[ u_L^T {\\cal C}(x) + p_L^T {\\cal S}(x) \\bigr]u_C,\n\\label{modified}\n\\end{equation}\nwhere \n\\begin{eqnarray}\n\\label{eq-C}\n{\\cal C}(x) &=& \\bigl(\\cos(\\hbar x \\sqrt{K_L}) -I\\bigr) V^{LC}, \\\\\n\\label{eq-S}\n{\\cal S}(x) &=& (1\/\\sqrt{K_L}) \\sin( \\hbar x \\sqrt{K_L}) V^{LC}.\n\\end{eqnarray}\nThe effective Hamiltonian now has two additional term with respect to the full ${\\cal H}(t)$. The term $u_L^T {\\cal C}(x)u_C $ is like the harmonic coupling term which modifies the coupling matrix $V^{LC}$. \n\n\nIn the following we calculate the two parameter generating function ${\\cal Z}(\\xi,\\lambda)$ using Eq.~(\\ref{eq-Uxilambda}).\n\n\\subsection{Expression for ${\\cal Z}(\\xi,\\lambda)$}\nThe expression for ${\\cal Z}(\\xi,\\lambda)$ can be written down on the contour as (see Fig.~\\ref{fig1})\n\\begin{equation}\n{\\cal Z}(\\xi,\\lambda) = {\\rm Tr} \\Bigl[ \\rho(0) T_c e^{-\\frac{i}{\\hbar} \\int_C {\\cal H}^{x}(\\tau) d\\tau} \\Bigr].\n\\label{eq-Z-contour} \n\\end{equation} \nwhere $T_c$ is the contour-ordered operator which orders operators according to their contour time argument, earlier contour time places an operator to the right. The contour function $x(\\tau)$ is defined as 0 whenever $t < 0$ or $t>t_M$, and when $0 < t < t_M$, i.e., within the measurement time interval, for upper branch of the contour $x^{+}(t) = -\\xi\/2 - \\lambda$, and for lower branch $x^{-}(t) = \\xi\/2 - \\lambda$. \n \nFor the moment, let us forget about the other lead and concentrate only on the left lead and center.\nThe effect of other lead simply modifies the self-energy of the\nleads additively, according to Feynman and Vernon \\cite{Feynman-Vernon-1963}. Using Feynman path integral technique we can write\n\\begin{equation}\n{\\cal Z}(\\xi,\\lambda)= \\int {\\cal D}[u_C] {\\cal D}[u_L] \\rho(-\\infty) e^{(i\/\\hbar) \n\\int_K d\\tau ( {\\cal L}_C + {\\cal L}_L + {\\cal L}_{LC} ) }.\n\\label{eq-Z-keldysh}\n\\end{equation}\nNote that in Eq.~(\\ref{eq-Z-contour}), the \ncontour $C$ is from 0 to $t_M$ and back, while that in Eq.~(\\ref{eq-Z-keldysh}) is on the\nKeldysh contour $K$, that is, from $-\\infty$ to $t_M$ and back to take into account\nof adiabatic switch on, replacing $\\rho(0)$ by $\\rho(-\\infty)$. Their relation\nis\n\\begin{equation}\n\\rho(0) = {\\cal U}(0, -\\infty) \\rho(-\\infty) {\\cal U}(-\\infty, 0).\n\\end{equation}\nWe can identify the Lagrangian's as \n\\begin{eqnarray}\n{\\cal L} &=& {\\cal L}_L + {\\cal L}_C + {\\cal L}_{LC}, \\nonumber \\\\\n{\\cal L}_L &=& \\frac{1}{2}\\dot{u}_L^2 - \\frac{1}{2} u_L^T K^L u_L, \\nonumber \\\\\n{\\cal L}_C &=& \\frac{1}{2} \\dot{u}_C^{2} +f^T u_C -\\, \\frac{1}{2} u_C^T \\bigl(K^C-{\\cal S}^T{\\cal S}\\bigr) u_C, \\nonumber \\\\\n{\\cal L}_{LC} &=& - \\dot{u}^T_L {\\cal S} u_C - u_L^T \\bigl(V^{LC} + {\\cal C}\\bigr) u_C.\n\\label{Lagrangian}\n\\end{eqnarray}\nFor notational simplicity, we have dropped the argument $\\tau$.\nThe vector or matrices $f$, ${\\cal C}$, and ${\\cal S}$ are parametrically dependent on\nthe contour time $\\tau$. They are zero except on the interval\n$0 < t < t_M$. $f$ is the same on the upper and lower branches,\nwhile ${\\cal C}$ and ${\\cal S}$ take different values depending on $x(\\tau)$.\n\nNow the lead part can be integrated out by performing Gaussian integral \\cite{Feynman-Vernon-1963}. Since the\ncoupling between the lead and the center is linear, it is plausible that the result will be\na quadratic form in the exponential, i.e., another Gaussian. To find exactly\nwhat it is, we convert the path integral back to the\ninteraction picture (with respect to ${\\cal H}_L$) operator form and evaluate the expression by standard perturbative expansion. The only difference is that now the coupling with the center involving both $u_L$ and $\\dot{u}_L$. The result for the influence\nfunctional is given by \\cite{Stockburger}\n\\begin{eqnarray}\nI_L[u^C(\\tau)] &\\equiv & \\int {\\cal D}[u_L] \\rho_L(-\\infty) \ne^{\\frac{i}{\\hbar} \\int d\\tau ({\\cal L}_L + {\\cal L}_{LC}) } \\nonumber \\\\\n&=& {\\rm Tr}\\Bigl[\\frac{e^{-\\beta_L H_L}}{Z_L} T_c e^{ -\\frac{i}{\\hbar} \\int d\\tau {\\cal V}_I(\\tau) } \\Bigr] \\nonumber \\\\\n&=& e^{-\\frac{i}{2\\hbar} \\int d\\tau \\int d\\tau' u_C^T(\\tau) \\Pi(\\tau, \\tau') u_C(\\tau') }.\\\n\\end{eqnarray}\n\n\\begin{figure}[t]\n\\includegraphics[width=0.5\\columnwidth]{fig1.eps\n\\caption{\\label{fig1}The complex-time contour in the Keldysh formalism. The path of the contour begins at time $t_{0}$, goes to time $t_M$, and then goes back to time $t=t_{0}$. $\\tau$ and $\\tau'$ are complex-time variables along the contour. $t_{0}=-\\infty$ and $0$ corresponds to Keldysh contour K and C, respectively.} \n\\end{figure}\nIn the influence functional, the contour function $u_C(\\tau)$ is not a \ndynamical variable but a parametric function. ${\\cal V}_I(\\tau)$ is the interaction picture operator with respect to the Hamiltonian ${\\cal H}_L$ and is given by\n\\begin{eqnarray}\n{\\cal V}_I(\\tau) &=& p_L^T {\\cal S} u_C + u_L^T(V^{LC} + {\\cal C}) u_C+\\frac{1}{2} u_C^T {\\cal S}^T {\\cal S} u_C \\nonumber \\\\\n\\label{eq-SSterm} \n&=&u_L^T\\bigl(\\tau + \\hbar x(\\tau)\\bigr)V^{LC}u_C + \\frac{1}{2} u_C^T {\\cal S}^T {\\cal S} u_C.\n\\end{eqnarray} \n\nThe important influence functional self-energy on contour is given by\n\\begin{eqnarray}\n\\Pi(\\tau,\\tau')= \\Sigma_L^A(\\tau,\\tau') &+& \\Sigma_L(\\tau,\\tau') + {\\cal S}^T{\\cal S}\\,\\delta(\\tau, \\tau'), \\\\\n\\Sigma_L^A(\\tau,\\tau') + \\Sigma_L(\\tau,\\tau') &= & V^{CL} g_L\\bigl(\\tau + \\hbar x(\\tau), \\tau'+\\hbar x(\\tau') \\bigr) V^{LC} \\nonumber \\\\\n\\label{eq-SAL}\n&=& \\Sigma_L\\bigl(\\tau + \\hbar x(\\tau), \\tau'+\\hbar x(\\tau') \\bigr),\n\\label{eq-shifted-self-energy}\n\\end{eqnarray}\nwhere we obtain a shifted self-energy $\\Sigma_L\\big(\\tau+\\hbar x(\\tau),\\tau'+\\hbar x(\\tau')\\big)$ which is the usual self-energy of the lead in contour time with arguments\nshifted by $\\hbar x(\\tau)$ and $\\hbar x(\\tau')$. We define the self-energy $\\Sigma^A_L$ as the difference between the shifted self-energy and the usual one $\\Sigma_L(\\tau,\\tau')$. $\\Sigma^{A}_L$ turns out to be a central quantity for this problem as we will show that, the CGF ${\\cal Z}$ can be concisely expressed in terms of the center Green's function $G_{0}$ and $\\Sigma^{A}_L$.\n\nSubstituting the explicit expression for the influence functionals of both the left and right leads to the path integral expression given in Eq.~(\\ref{eq-Z-keldysh}), we have\n\\begin{eqnarray}\n{\\cal Z}(\\xi,\\lambda)&=& \\int {\\cal D}[u_C] \\rho_C(-\\infty) e^{(i\/\\hbar) \n\\int d\\tau {\\cal L}_C } I_L[u_C] I_R[u_C]\\nonumber \\\\\n&=& \\int {\\cal D}[u_C] \\rho_C(-\\infty) e^{\\frac{i}{\\hbar}S_{\\rm eff}},\n\\end{eqnarray}\nwhere the effective action is given by\n\\begin{eqnarray}\nS_{\\rm eff} &=& \\int d\\tau \\Bigl[ \n\\frac{1}{2} \\dot{u}_C^2 - \\frac{1}{2} u_C^T K^C u_C \n+ f^T u_C \\Bigr] \\\\\n& \\!\\!-\\!& \\frac{1}{2}\\! \\int d\\tau \\!\\int d\\tau'\\! u_C^T(\\tau) \\bigl(\n\\Sigma(\\tau, \\tau') + \\Sigma_L^A(\\tau, \\tau') \\bigr) u_C(\\tau'), \\nonumber\n\\end{eqnarray}\nwhere $\\Sigma = \\Sigma_L +\\Sigma_R$, taking into account the effect of both the leads. The ${\\cal S}^T{\\cal S}$ term in $I_{L}[u_C]$ cancels exactly with\nthe one in ${\\cal L}_C$. We can perform an integration by part on the $\\dot{u}^2$ term, assuming that the surface term\ndoes not matter (since it is at $t=-\\infty$), we can write the expression in a standard quadratic form\n\\begin{eqnarray}\nS_{\\rm eff} &=& \\frac{1}{2} \\int d\\tau \\int d\\tau' u_C^T(\\tau) D(\\tau, \\tau') u_C(\\tau') \\nonumber \\\\\n&& \\qquad + \\int f^T(\\tau) u_C(\\tau) d\\tau. \n\\end{eqnarray}\n$D(\\tau,\\tau')$ is the differential operator and is given by\n\\begin{eqnarray}\nD(\\tau, \\tau') &=& -I \\frac{\\partial^2}{\\partial \\tau^2} \\delta(\\tau, \\tau')\n- K^C \\delta(\\tau, \\tau') \\nonumber \\\\\n&& -\\Sigma(\\tau, \\tau') - \\Sigma_L^A(\\tau, \\tau') \\nonumber \\\\\n&=& D_0(\\tau,\\tau') - \\Sigma^A_L(\\tau,\\tau').\n\\end{eqnarray}\nThe above equation defines the Dyson equation on Keldysh contour. The generating function is obtained by doing another Gaussian integration and is of the following form\n\\begin{equation}\n{\\cal Z} \\propto {\\rm det}(D)^{-1\/2} e^{-\\frac{i}{2\\hbar} f^T D^{-1} f}.\n\\label{eq-Z-det}\n\\end{equation}\n(The meaning of the determinant will be explained in Appendix~C). \nWe define the Green's function $G$ and $G_{0}$ by $DG = 1$, and $D_0 G_0 = 1$, or more precisely\n\\begin{equation}\n\\int D(\\tau, \\tau'') G(\\tau'', \\tau') d\\tau'' = I \\delta(\\tau, \\tau'),\n\\end{equation}\nand similarly for $G_0$. $G$ can be written in terms of $G_{0}$ in the following Dyson equation form\n\\begin{eqnarray}\n\\label{eq-Dyson-full}\nG(\\tau, \\tau') &=& G_0(\\tau,\\tau') \\\\\n&&\\> + \\int \\int d\\tau_1d \\tau_2 \nG_0(\\tau, \\tau_1) \\Sigma_L^A(\\tau_1, \\tau_2) G(\\tau_2, \\tau'). \\nonumber\n\\end{eqnarray}\n\nWe view the differential operator\n(integral operator) $D$ and $D^{-1}$ as matrices that are\nindexed by space $j$ and contour time $\\tau$. $f$ is a column vector. The\nexponential factor term can also be written as a trace, \n$f^T D^{-1} f = {\\rm Tr}_{(j,\\tau)} ( G f f^T)$.\nWe can fix the proportionality constant by noting that\n${\\cal Z}(\\xi=0, \\lambda=0) =1$. Since when $\\xi=0$, $\\lambda=0$, we have\n$x=0$ and thus $\\Sigma_L^A(\\tau,\\tau') = \\Sigma_L(\\tau+x, \\tau'+x') - \\Sigma_L(\\tau, \\tau') = 0$, so $D=D_0$. The properly normalized\nCGF is \n\\begin{equation}\n\\label{eq-Zxilam}\n{\\cal Z}(\\xi, \\lambda) = {\\rm det}\\bigl( D_0^{-1}D)^{-1\/2}e^{-\\frac{i}{2\\hbar} f^T D^{-1} f}.\n\\end{equation}\nWe don't need to do anything for the exponential factor because of the following reason.\nWe note\n\\begin{eqnarray}\nf^T G_0 f &=& \\int \\int d\\tau d\\tau' f(\\tau)^T G_0(\\tau, \\tau') f(\\tau') \\\\\n&=& \\sum_{\\sigma,\\sigma'} \\int \\int \\sigma dt\\, \\sigma' dt' f(t)^T G_0^{\\sigma\\sigma'}(t,t') f(t').\\nonumber\n\\end{eqnarray}\nSince the driven force $f$ does not depend on the branch indices, i.e., \n$f^{+}(t) = f^{-}(t)$, we can take the summation inside and obtain\n\\begin{equation}\n\\sum_{\\sigma\\sigma'} \\sigma\\sigma' G^{\\sigma\\sigma'} = G_0^t + G_0^{\\bar t} - G_0^> - G_0^< = 0.\n\\end{equation}\n\nFinally making use of the formulas for operators or matrices \n${\\rm det}(M) = e^{{\\rm Tr} \\ln M}$, and \n$\\ln (1 - y) = -\\sum_{k=1}^\\infty \\frac{y^k}{k}$ we can write the CGF in terms of $\\Sigma^{A}_L$ for the {\\it projected} initial condition case as,\n\\begin{eqnarray}\n\\ln {\\cal Z}(\\xi)&=& \\lim_{\\lambda \\to \\infty}\\ln {\\cal Z}(\\xi,\\lambda) \\nonumber \\\\\n &=& \\lim_{\\lambda \\to \\infty} \\left\\{-\\frac{1}{2} {\\rm Tr}_{j,\\tau} \\ln ( 1 - G_0 \\Sigma_L^A) - \\frac{i}{2\\hbar} {\\rm Tr}_{j,\\tau}( G f f^T) \\right\\} \\nonumber \\\\\n&=& \\lim_{\\lambda \\to \\infty} \\sum_{n=1}^\\infty \\frac{1}{2n}\n{\\rm Tr}_{(j,\\tau)} \\Bigl[(G_0 \\Sigma_L^A)^n \\Bigr] \\nonumber - \\frac{i}{2\\hbar} f^T G f \\nonumber \\\\\n&=& \\frac{1}{2} {\\rm Tr}_{(j,\\tau)}(G_0 \\Sigma_L^A) + \\frac{1}{4} {\\rm Tr}_{(j,\\tau)}(G_0 \\Sigma_L^A G_0 \\Sigma_L^A) + \\cdots \\nonumber \\\\\n&& -\\frac{i}{2\\hbar} f^T G_0 \\Sigma^A G_0 f + \\cdots.\n\\label{projected_state}\n\\end{eqnarray}\nThis expression for CGF is valid for any transient time $t_M$ present in the self-energy\n$\\Sigma^A_{L}$ and is the starting point for the calculation in transient regime. The notation ${\\rm Tr}_{(j,\\tau)}$ means trace both in\nspace index $j$ and contour time $\\tau$ (see Appendix~C). In order to obtain ${\\cal Z}(\\xi)$ from ${\\cal Z}(\\xi,\\lambda)$ we have to take the limit $\\lambda \\rightarrow \\infty$ because ${\\cal Z}(\\xi, \\lambda)$ approaches a constant as $| \\lambda| \\to \\infty$ and hence\nthe value of the integral is dominated by the value at infinity. Since $\\Sigma^{A}_{L}(\\tau,\\tau')=0$ for $\\xi=0$ we have the correct normalization ${\\cal Z}(0)=1$.\n\nSimilarly, for the steady state initial condition $\\rho(0)$ the CGF is given by \n\\begin{equation}\n\\ln {\\cal Z}(\\xi)=\\lim_{\\lambda \\rightarrow 0} \\ln {\\cal Z}(\\xi,\\lambda)\n\\end{equation}\nThe difference in this two cases is in the matrix $\\Sigma^{A}_L$. \n\nSimilar relations also exist if we want to calculate the CGF for right lead heat operator ${\\cal Q}_R$. In this case one has to do two-time measurement on the right lead corresponding to the Hamiltonian ${\\cal H}_R$. The final formula for the CGF remains the same except $\\Sigma^{A}_L$ should be replaced by $\\Sigma^{A}_R$.\n \nNow in order to calculate the cumulants $\\langle \\langle Q_{\\alpha}^{n} \\rangle \\rangle$ with $\\alpha=L,R$ we need to go to the real time using Langreth's rule \\cite{rammer86}. In this case, it is more convenient to work with a Keldysh rotation (see Appendix~C) for the contour ordered functions while keeping\n${\\rm Tr}(ABC \\cdots D)$ invariant. The effect of the Keldysh rotation is to change any given matrix ${\\cal D}^{\\sigma\\sigma'}(t,t')$,\nwith $\\sigma, \\sigma'=\\pm$ for branch indices, to,\n\\begin{eqnarray}\n\\label{keldysh-rotation}\n\\breve{{\\cal D}} &=& \n\\left( \\begin{array}{cc}\n {\\cal D}^r & {\\cal D}^K \\\\\n {\\cal D}^{\\bar K} & {\\cal D}^a \n\\end{array} \\right) \\\\\n &=&\n\\frac{1}{2} \\left( \\begin{array}{cc}\n {\\cal D}^t - {\\cal D}^< + {\\cal D}^> - {\\cal D}^{\\bar t}, & {\\cal D}^t + {\\cal D}^{\\bar t} + {\\cal D}^< + {\\cal D}^> \\\\\n {\\cal D}^t + {\\cal D}^{\\bar t} - {\\cal D}^< - {\\cal D}^>, & {\\cal D}^< - {\\cal D}^{\\bar t} + {\\cal D}^{t} - {\\cal D}^{>} \n \\end{array} \\right). \\nonumber\n\\end{eqnarray}\nIn this case we define the quantities \n${\\cal D}^r$, ${\\cal D}^a$, ${\\cal D}^K$, and ${\\cal D}^{\\bar{K}}$ as above. In particular,\n${\\cal D}^{K} \\neq {\\cal D}^< + {\\cal D}^>$, as one usually might thought it is. \n\nUsing the above definition for the center Green's function $G_0$ we get \n\\begin{equation}\n\\breve{G_0} = \n\\left( \\begin{array}{cc}\n G_0^r & G_0^K \\\\\n 0 & G_0^a \n\\end{array} \\right). \n\\end{equation}\nThe $G_0^{\\bar K}$ component is 0 due to the standard relation\namong Green's functions. But the $\\bar K$ components are not zero for\n$\\Sigma_L^A$ and $G_0$, as we will compute later. \n\nIt is useful computationally to work in Fourier space even if \nthere is no time translational invariance. We define the two-frequency\nFourier transform by\n\\begin{equation}\n\\breve{A}[\\omega, \\omega'] =\n\\int_{-\\infty}^{+\\infty}\\!\\!\\! dt \\int_{-\\infty}^{+\\infty}\\!\\!\\! dt' \n\\breve{A}(t,t') e^{i(\\omega t + \\omega' t')}.\n\\label{two-time-FT}\n\\end{equation}\nSince $\\breve{G}_0$ is time-translationally invariant then,\n\\begin{equation}\n\\breve{G}_0[\\omega, \\omega'] = 2\\pi \\delta(\\omega+\\omega') \\breve{G}_0[\\omega],\n\\label{G0}\n\\end{equation}\nis ``diagonal'', where the single argument Fourier transform is similarly defined,\n\\begin{equation}\n\\label{eq-Fourier}\nA[\\omega] = \\int_{-\\infty}^{+\\infty} A(t,0) e^{i\\omega t} dt.\n\\end{equation}\n(The expressions for different components of $\\breve{G}_{0}[\\omega]$ and $\\breve{\\Sigma}[\\omega]$ are given in Appendix A). Using $\\breve{G}_0[\\omega]$, we can save one integration due to the $\\delta$ function, and have\n\\begin{eqnarray}\n\\label{eq-Zsteady}\n\\ln {\\cal Z}(\\xi) &=& - \\frac{1}{2} {\\rm Tr}_{j,\\sigma,\\omega}\n\\ln\\Bigl[ 1 - \\breve{G}_0[\\omega] \\breve{\\Sigma_L^A}[\\omega, \\omega'] \\Bigr]\n\\nonumber \\\\\n&& - \\frac{i}{2 \\hbar} {\\rm Tr}_ {j,\\sigma,\\omega} \\Bigl[\\breve{G}[\\omega,\\omega'] \\, \\breve{{\\cal F}}[\\omega', \\omega]\\Bigr],\n\\end{eqnarray}\nwhere $ \\breve{G}_0[\\omega] \\breve{\\Sigma_L^A}[\\omega, \\omega']$ is viewed\n\nas a matrix indexed by $\\omega$ and $\\omega'$. The trace is performed on\nthe frequency as well as the usual space and branch components. (The meaning of trace in frequency domain is discussed in Appendix~C). $\\breve{{\\cal F}}$ is given by\n\\begin{equation}\n\\label{force-matrix}\n\\breve{{\\cal F}}[\\omega,\\omega'] = \n\\left( \\begin{array}{cc}\n 0 & 2 f[\\omega]f[\\omega']^T \\\\\n 0 & 0 \n\\end{array} \\right). \n\\end{equation}\nIn the next section we derive the CGF for the product initial condition using Feynman diagrammatic technique. Because of the special form of the initial density matrix the calculation for the CGF simplifies greatly in this case. \n \n\\section{Product state $\\rho(-\\infty)$ as initial state}\nIn this section, we derive the CGF starting with a product initial state, i.e., the density matrix at time $t=0$ is given\nby $\\rho(-\\infty)=\\rho_C \\otimes \\rho_L \\otimes \\rho_R$. Since this density matrix commutes with the projection operator $\\Pi_a$, the initial projection does not play any role in this case. Working in the interaction picture with respect to the decoupled Hamiltonian ${\\cal H}(-\\infty)= \\sum_i {\\cal H}_i$, the \ninteraction part of the Hamiltonian on the contour $C=[0,t_M]$ is \n\\begin{eqnarray}\n{\\cal V}^{x}_I(\\tau) &=& -f^T(\\tau) u_C(\\tau) + u_R(\\tau) V^{RC} u_C(\\tau) \\nonumber \\\\\n&&+\\, u_L\\bigl(\\tau+\\hbar x(\\tau)\\bigr)V^{LC}u_C(\\tau).\n\\end{eqnarray}\nIn the last term for $u_L$, the argument is shifted by $\\hbar x$ where \n$x^+(t) = - \\xi\/2$, $x^{-}(t) = \\xi\/2$ for $0< t < t_M$.\n\nThe density matrix remains unaffected by the transformation to the interaction picture, because it commutes with ${\\cal H}(-\\infty)$. The CGF can now be written as \n\\begin{equation}\n{\\cal Z}(\\xi) = {\\rm Tr}\\Bigl[ \\rho(-\\infty) T_c \\,e^{-\\frac{i}{\\hbar} \\int_C {\\cal V}^{x}_I(\\tau)\\, d\\tau}\\Bigr].\n\\end{equation}\nExpanding the exponential, we generate various terms of product of\n$u_\\alpha$. These terms can be decomposed in pairs according to\nWick's theorem \\cite{rammer86}. Since the system is decoupled, each type of $u$ comes\nin an even number of times for a non-vanishing contributions because \n$\\langle u_C\\rangle = 0$, $\\langle u_C u_L\\rangle = 0$ and we know\n\\begin{equation}\n-\\frac{i}{\\hbar} \\langle T_C u_{\\alpha}(\\tau) u_{\\alpha'}(\\tau')^T \\rangle_{\\rho(-\\infty)} = \\delta_{\\alpha,\\alpha'} g_\\alpha(\\tau, \\tau').\n\\end{equation}\nWe use Feynman diagrammatic technique to sum the series. since ${\\cal V}_I$ contains only two-point couplings,\nthe graphs are all ring type. The combinatorial factors can be worked\nout as $1\/(2n)$ for a ring containing $n$ vertices.\nWe use a very general theorem which says $\\ln {\\cal Z}$ contains only \nconnected graphs, and the disconnected graphs cancel exactly when we\ntake the logarithm. The final result can be expressed as\n\\begin{equation}\n\\ln {\\cal Z}(\\xi) = - \\frac{1}{2} {\\rm Tr}_{j,\\tau} \\ln \\Big[ 1 - g_C \\Sigma^{\\rm tot} \\Big] \n-\\frac{i}{2\\hbar} f^T G f, \n\\end{equation}\nwhere\n\\begin{equation}\n\\Sigma^{\\rm tot} = \\Sigma_L(\\tau+x,\\tau'+x') + \\Sigma_R(\\tau,\\tau') = \\Sigma + \\Sigma_L^A,\n\\end{equation} \nand $\\Sigma$ is the total self-energy due to both the leads. $G(\\tau,\\tau')$ obeys the following Dyson's equation\n\\begin{eqnarray}\nG(\\tau,\\tau') &=& g_C(\\tau,\\tau') \\\\ \n&&\\> + \\int \\int d\\tau_1 d\\tau_2 g_C(\\tau,\\tau_1) \\Sigma^{\\rm tot}(\\tau_1,\\tau_2) G(\\tau_2,\\tau'). \\nonumber \n\\end{eqnarray} \n\n\nThe above expression for CGF can be written down more explicitly, by\ngetting rid of the vacuum diagrams. Let us define a new type of Dyson's equation\n\\begin{eqnarray}\n\\label{eq-Dyson-product}\nG_0(\\tau, \\tau') &=& g_C(\\tau,\\tau') \\\\\n&&\\> + \\int \\!\\int d\\tau_1d \\tau_2\\, \ng_C(\\tau, \\tau_1) \\Sigma(\\tau_1, \\tau_2) G_0(\\tau_2, \\tau'), \\nonumber\n\\end{eqnarray}\nwhere $g_C$ is the contour ordered Green's function of the isolated center. (The Green's functions for an isolated single \nharmonic oscillator is given is appendix A).\nThis expression looks formally the same as before except that $G_0$\nsatisfies a Dyson equation defined on the contour from 0 to $t_M$ and\nback, while $G$ is defined on the Keldysh contour from $-\\infty$ to\n$t_M$. Using this definition we can write\n\\begin{eqnarray}\n1 - g_C \\Sigma^{\\rm tot} &=& 1 - g_C (\\Sigma + \\Sigma_L^A) \\nonumber \\\\\n&=& (1 - g_C \\Sigma)\\, (1 - G_0 \\Sigma_L^A).\n\\end{eqnarray}\nThe two factors above are in matrix (and contour time) multiplication.\nUsing the relation between trace and determinant, \n$\\ln \\det(M) = {\\rm Tr} \\ln M$, and the fact, $\\det(AB) = \\det(A) \\det(B)$,\nwe find that the two terms give two factors for ${\\cal Z}$, and the factor\ndue to $1 - g_C \\Sigma$ is exactly 1. We have then\n\\begin{equation}\n\\label{eq-Zprod}\n\\ln {\\cal Z}(\\xi) = - \\frac{1}{2} {\\rm Tr}_{j,\\tau} \\ln \\Big[ 1 - G_0 \\Sigma_L^{A} \\Big]\n-\\frac{i}{2\\hbar} f^T G f,\n\\end{equation}\nwhere the $G(\\tau,\\tau')$ can now be expressed in terms of $G_{0}(\\tau,\\tau')$ as\n\\begin{equation}\nG^{-1} = G_0^{-1} - \\Sigma_L^A.\n\\end{equation}\nwhich is similar in form to Eq.~(\\ref{eq-Dyson-full}).\n\nThe expression for $\\ln {\\cal Z}(\\xi)$ is consistent with the earlier result,\nEq.~(\\ref{projected_state}), in the long-time limit. So we can conclude that the long-time limit is the same independent of the initial distributions. \n\nTo compute the cumulants $\\langle \\langle Q^n \\rangle \\rangle$, we\nneed to take derivative with respect to $\\xi$ $n$-times to $\\ln {\\cal Z}$.\nNote that the shifted self-energy for $0 < t < t_M$ is (for all three initial conditions)\n\\begin{eqnarray}\n\\Sigma_A^t (t,t') &=& 0, \\nonumber \\\\\n\\Sigma_A^{\\bar t}(t,t') &=& 0, \\nonumber \\\\\n\\Sigma_A^{<}(t,t') &=& \\Sigma_L^{<}(t-t'-\\hbar \\xi) - \\Sigma_L^{<}(t-t'), \\nonumber \\\\\n\\Sigma_A^{>}(t,t') &=& \\Sigma_L^{>}(t-t'+\\hbar \\xi) - \\Sigma_L^{>}(t-t').\n\\label{shifted-product}\n\\end{eqnarray}\nWe note $\\Sigma_L^A(\\xi=0)=0$. The derivatives at $\\xi=0$ can be obtained as\n\\begin{eqnarray}\n{\\partial^n \\Sigma_A^{<} \\over\n\\partial \\xi^n}\\Big|_{\\xi=0} &=& (-\\hbar)^n \\Sigma_L^{<,(n)}(t-t'), \\nonumber \\\\\n{\\partial^n \\Sigma_A^{>} \\over\n\\partial \\xi^n}\\Big|_{\\xi=0} &=& \\hbar^n \\Sigma_L^{>,(n)}(t-t'),\n\\end{eqnarray}\nwhere the superscript $(n)$ means derivatives with respect to the argument of\nthe function $n$ times. In the following sections we first show the explicit expression of the CGF in the long-time limit and then discuss the steady state fluctuation theorem. \n\n\n\\section{Long-time limit and Steady state fluctuation theorem}\nFor the long-time limit calculation we can use either Eq.~(\\ref{eq-Zsteady}) or Eq.~(\\ref{eq-Zprod}). \nFor convenience of taking the large time limit, i.e., $t_M$ large, we prefer\nto set interval to $(-t_M\/2, t_M\/2)$. In this way, when $t_M \\to \\infty$,\nthe interval becomes the full domain and Fourier transforms to all the\nGreen's functions and self-energy can be performed (where the translational\ninvariance is restored). Applying the convolution theorem to the\ntrace formula in Eq.~(\\ref{eq-Zprod}), we find that there is one more time integral left\nwith integrand independent of $t$. This last one can be set from $-t_M\/2$ to $t_M\/2$, obtaining an overall factor\nof $t_M$ and we have\n\\begin{equation}\n{\\rm Tr}_{(j,\\tau)}(AB \\cdots D) = t_M \n\\int \\frac{d\\omega}{2\\pi}{\\rm Tr} \\Bigl[\\breve {A}(\\omega)\\breve{B}(\\omega) \\cdots \\breve{D}(\\omega)\\Bigr].\n\\end{equation}\n \nIn the long-time limit, the shift given to the argument in $\\Sigma_L^A$ depends on the branches, and the \ntwo arguments $(t,t')$ becomes $t-t'$ and we have \n\\begin{eqnarray}\n\\Sigma_A^{\\sigma\\sigma'}(t,t') &=& \\Sigma^{\\sigma\\sigma'}_L(t\\! +\\!x^\\sigma\\!-\\! t'\\!-\\!x^{\\sigma'}) - \\Sigma^{\\sigma\\sigma'}_L(t\\!-\\!t'), \\\\\n\\Sigma_A^t &=& \\Sigma_A^{\\bar t} = 0,\\nonumber \\\\\n\\Sigma_A^<(t) &=& \\Sigma_L^{<}(t-\\hbar \\xi) -\\Sigma_L^{<}(t),\\\\\n \\Sigma_A^>(t) &=& \\Sigma_L^{>}(t+\\hbar \\xi)-\\Sigma_L^{>}(t). \\nonumber \n\\label{steady}\n\\end{eqnarray}\nFourier transforming the greater and lesser self-energy, we get\n\\begin{eqnarray}\n\\label{eq-a}\n\\Sigma_A^{>}[\\omega] = \\Sigma^{>}_L[\\omega] \\bigl(e^{-i\\hbar \\omega \\xi} - 1 \\bigr) = a, \\\\\n\\label{eq-b}\n\\Sigma_A^{<}[\\omega] = \\Sigma^{<}_L[\\omega] \\bigl(e^{i\\hbar \\omega \\xi} - 1 \\bigr) = b.\n\\end{eqnarray}\nWe note that $\\Sigma_L^A$ is supposed to depend on both $\\xi$ and $\\lambda$.\nHowever in the long-time limit, the $\\lambda$ dependence drops out which makes the steady state result independent of the initial distribution. \n\nFinally, we can express the generating function as\n\\begin{eqnarray}\n\\ln {\\cal Z}(\\xi) &= & - t_M \\int \\frac{d\\omega}{4\\pi} {\\rm Tr} \\ln \\Bigl[1 - \\breve{G}_0 [\\omega]\\breve{\\Sigma}_L^A [\\omega]\\Bigr] \\nonumber \\\\\n&& \\quad - \\frac{i}{\\hbar} \\int \\frac{d\\omega}{4\\pi}\n{\\rm Tr} \\Bigl[ \\breve{G}[\\omega] \\breve{\\cal F}[\\omega,-\\omega]\\Bigr],\n\\label{eq-lnZxi-1}\n\\end{eqnarray}\nwhere $\\breve{G}[\\omega]$ is obtained by solving the Dyson equation\nin frequency domain and in the long-time obeys time-translational invariance. So the full CGF can be written as the sum of contributions due to driving force and due to temperature difference between the leads, i.e.,\n\\begin{equation}\n\\ln {\\cal Z}(\\xi) = \\ln {\\cal Z}^{s}(\\xi) + \\ln {\\cal Z}^{d}(\\xi). \n\\end{equation}\nIn the following and subsequent sections we discuss about ${{\\cal Z}^{s}(\\xi)}$ and we will return to ${{\\cal Z}^{d}(\\xi)}$ in Sec. XI. \n\nIn order to obtain the explicit expression for $\\ln {\\cal Z}^{s}(\\xi)$ we need to compute the matrix product\n\\begin{eqnarray}\n\\breve{G}_0[\\omega] \\breve{\\Sigma}_L^A[\\omega] &=& \n\\frac{1}{2} \\left( \\begin{array}{cc}\nG_0^r & G_0^{K} \\\\\n0 & G_0^a \n\\end{array}\n\\right)\n\\left( \\begin{array}{cc}\na-b & a+b \\\\\n-(a+b) & b-a \n\\end{array}\n\\right).\n\\end{eqnarray}\nTo simplify the expression, we rewrite the term ${\\rm Tr} \\ln (1-M)$ as\na determinant and use the formula (assuming A to be an invertible matrix) \n\\begin{equation}\n{\\rm det} \\left( \\!\\!\\begin{array}{cc}\nA & B \\\\\nC & D \\end{array}\\!\\!\n\\right) = {\\rm det}(A) \\det(D - C A^{-1}B)\n\\end{equation}\nto reduce the dimensions of the determinant matrix by half. \nThe steady state solution for ${{\\cal Z}^{s}(\\xi)}$ is given by\n\\begin{eqnarray}\n\\ln {\\cal Z}^{s}(\\xi)&=&-t_M \\int \\frac{d\\omega}{4\\pi} \\,\\ln \\det \\Bigl\\{ I - G_{0}^r \\Gamma_L \nG_{0}^a \\Gamma_R \\Big[ (e^{i\\xi \\hbar \\omega}\\! -\\! 1) f_L \\nonumber \\\\ \n&&\\!\\!\\!\\!\\!\\!\\!\\!\\!\\!\\!\\!+ ( e^{-i\\xi\\hbar \\omega} \\!-\\! 1) f_R + (\ne^{i\\xi \\hbar \\omega} \\!+\\! e^{-i\\xi\\hbar\\omega} \\!-\\!2 ) f_L f_R \\Big]\\Bigr\\}.\\qquad \n\\label{eq-steady1}\n\\end{eqnarray}\nwith $f_{\\alpha}=1\/(e^{\\beta_{\\alpha} \\hbar \\omega} - 1)$, $\\alpha=L,R,$ the Bose-Einstein distribution function and $\\Gamma_{\\alpha}[\\omega]=i\\big(\\Sigma_{\\alpha}^{r}[\\omega]-\\Sigma_{\\alpha}^{a}[\\omega]\\big)$. If we consider the full system as a one-dimensional linear chain, then because of the special form of $\\Gamma_{\\alpha}$ matrices (only one entry of the $\\Gamma$ matrices are non-zero) it can be easily shown that\n\\begin{equation}\n\\det [I-\\bigl(G_{0}^r \\Gamma_L G_{0}^a \\Gamma_R \\bigr) \\Xi(\\xi)]=1-{\\cal T}[\\omega]\\Xi(\\xi)\n\\end{equation}\nwhere $\\Xi(\\xi)$ is any arbitrary function of $\\xi$ and ${\\cal T}[\\omega]={\\rm Tr}(G_{0}^r \\Gamma_L G_{0}^a \\Gamma_R)$ is known as the transmission function and is given by the Caroli formula \\cite{Caroli,WangJS-europhysJb-2008}. The generating function ${{\\cal Z}^{s}(\\xi)}$ in the steady state obeys the following symmetry \n\\begin{equation}\n{{\\cal Z}^{s}}(\\xi)={{\\cal Z}^{s}}\\big(-\\xi + i\\,{\\cal A}\\big),\n\\end{equation}\nwhere ${\\cal A}=\\beta_R-\\beta_L$ is known as thermodynamic affinity. This relation is also known as Gallavotti-Cohen (GC) symmetry \\cite{noneq-fluct}. The immediate consequence of this symmetry is that the probability distribution for heat transferred $Q_{L}$ which is given by the Fourier transform of the CGF, i.e., $P(Q_L)=\\frac{1}{2 \\pi} \\int_{-\\infty}^{\\infty} d\\xi \\, {\\cal Z}(\\xi) \\, e^{-i \\xi Q_L}$ obeys the following relation in the large $t_M$ limit,\n\\begin{equation}\nP_{t_M}(Q_L)=e^{{\\cal A}Q_L}\\,P_{t_M}(-Q_L).\n\\end{equation} \nThis relation is known as the steady state fluctuation theorem and was first derived by Saito and Dhar \\cite{Saito-Dhar-2007} in the phononic case. This theorem quantifies the ratio of positive and negative heat flux and second law violation. \n\nThe cumulants $\\langle \\langle {Q}^{n} \\rangle \\rangle $ can be obtained by taking derivative of $\\ln {\\cal Z}^{s}(\\xi)$ with respect to $i\\xi$ and setting $\\xi=0$. The first cumulant is given by \n\\begin{equation}\n\\frac{\\langle \\langle Q \\rangle \\rangle}{t_M} = \\int_{-\\infty}^{\\infty} \\frac{d\\omega}{4 \\pi} \\, \\hbar\\, \\omega \\,{\\cal T}(\\omega)(f_L-f_R),\n\\end{equation}\nwhich is known as the Landauer-like formula in thermal transport. Similarly the second cumulant $\\langle \\langle {Q}^{2} \\rangle \\rangle= \\langle {Q}^{2} \\rangle - \\langle {Q}\\rangle ^{2}$, which describes the fluctuation of the heat transferred, can be written as \\cite{Saito-Dhar-2007, Huag, Buttiker},\n\\begin{eqnarray}\n\\frac{\\langle \\langle Q^{2} \\rangle \\rangle}{t_M} &=&\\int_{-\\infty}^{\\infty} \\frac{d\\omega}{4 \\pi} \\, (\\hbar \\omega)^{2} \\Bigl \\{{\\cal T}^{2}(\\omega)\\, (f_L-f_R)^{2} \\nonumber \\\\\n&&+ {\\cal T}(\\omega) \\, (f_L+f_R+2\\,f_L f_R ) \\Bigr\\}.\n\\end{eqnarray}\nOur formalism can be easily generalized for multiple heat baths and for $N$ leads connected with the center $C$, we can generalize the above formula as\n\\begin{eqnarray}\n&&\\ln {\\cal Z}^{s}(\\xi)=\\!-\\!t_M \\!\\int \\frac{d\\omega}{4\\pi} \\,\\ln \\det \\Bigl\\{ I \\!-\\!\\sum_{m} G_{0}^r \\Gamma_L G_{0}^a \\Gamma_m \\Big[(e^{i\\xi \\hbar \\omega}\\! -\\! 1) \\times \\nonumber \\\\\n&& f_L\\!\\!+ ( e^{-i\\xi\\hbar \\omega} \\!-\\! 1) f_m \\!+\\! (\\!\ne^{i\\xi \\hbar \\omega} \\!\\!+\\! e^{-i\\xi\\hbar\\omega}\\! \\!-\\!2 )\\! f_L \\!f_m \\Big]\\Bigr\\}.\\qquad .\n\\end{eqnarray}\nIn the following section we will discuss the how to numerically calculate the CGF in the transient case for projected density matrix $\\rho'(0)$. We also discuss about solving the Dyson equation given in Eq.~(\\ref{eq-Dyson-product}).\n\n\n\\section{Transient Region}\nThe central quantity to calculate the CGF numerically is the shifted self-energy $\\Sigma_L^{A}$ which is given by \n\\begin{equation}\n\\Sigma_L^A(\\tau,\\tau') =\\Sigma_L\\bigl(\\tau + \\hbar x(\\tau), \\tau'+\\hbar x(\\tau') \\bigr)- \\Sigma_L\\big(\\tau,\\tau'\\big).\n\\end{equation}\nHere $\\tau$ is a contour variable which runs over Keldysh contour $K=(-\\infty,\\infty)$ and back, for the initial conditions $\\rho(0)$ and $\\rho'(0)$, whereas for $\\rho(-\\infty)$, $\\tau$ runs over the contour $C=[0,t_M]$ (see Fig.~1). The contour function $x(\\tau)$ is 0 whenever $t < 0$ or $t>t_M$, and for $0 < t < t_M$, $x^{+}(t) = -\\xi\/2 - \\lambda$, and $x^{-}(t) = \\xi\/2 - \\lambda$. \nDepending on the values of $t$, $t'$, and $\\lambda$ ($\\lambda \\rightarrow 0$ and $\\lambda \\rightarrow \\infty$ corresponds to steady state initial state and {\\it projected} initial state, respectively) $\\Sigma_L^A$ will have different functional form. If $ 0 < t,t' < t_M$ then $\\Sigma_L^A$'s are given by Eq.~(\\ref{shifted-product}). This is the region which dominates in the long-time limit and gives steady state result. If both $t$ and $t'$ lies outside the measurement time, i.e., $t,t'<0$ or $t,t'>t_M$ then $\\Sigma_L^A$ is zero. \n\nThe main computational task for a numerical evaluation of the cumulants\nis to compute the matrix series $-\\ln(1-M) = M + \\frac{1}{2} M^2 + \\cdots$.\nIt can be seen due to the nature of $\\Sigma_L^A$ that for the product initial\nstate, exact $n$ terms upto $M^n$ is required for the $n$-th culumants, as the infinite series terminates due to $\\Sigma_L^A(\\xi=0) = 0$.\nNumerically, we also observed for the projected state $\\rho'(0)$, exactly\n$3n$ terms is required (although we don't have a proof) if calculation is\nperformed in time domain. \n\nThe computation can be performed in time as well as in the frequency domain. However for projected and steady state initial condition since $G_{0}[\\omega]$ is time translational invariant it is advantageous to work in the frequency domain. But for the product state there is no such preference as $G_{0}$ in Eq.~(\\ref{eq-Dyson-product}) is not time translational invariant and one has to solve it numerically. \n\nIn the following we first discuss how to calculate $\\Sigma_{L}^{A}[\\omega,\\omega']$ for projected initial state, defined in Eq.~(\\ref{eq-Zsteady}) and then we will discuss how to solve the Dyson equation for the product initial condition case, given in Eq.~(\\ref{eq-Dyson-product}).\n\n\\subsection{calculation of $\\Sigma_L^{A}(\\omega,\\omega')$ }\nTo calculate $\\Sigma_L^{A}(\\omega,\\omega')$ for projected initial state $\\rho'(0)$ we define two types of theta functions $\\theta_{1}(t,t')$ and $\\theta_{2}(t,t')$.\n$\\theta_{1}(t,t')$ is non-zero when \n\\begin{equation}\n0 \\leq t \\leq t_M, \\,\\,{\\rm and} \\quad t' \\leq 0 \\quad {\\rm or} \\quad t'\\geq t_M,\n\\end{equation}\nor \n\\begin{equation}\n0 \\leq t' \\leq t_M, \\,\\,{\\rm and} \\quad t \\leq 0 \\quad {\\rm or} \\quad t \\geq t_M, \n\\end{equation}\nand $\\theta_{2}(t,t')$ is non-zero only in the regime where $ 0 \\le t,t' \\le t_M$ . For the regions where $\\theta_1(t,t')$ is non-zero the expression for $\\Sigma_L^{A}$ after taking the limit $\\lambda \\rightarrow \\infty$ is, (assuming all correlation functions decays to zero as $t \\rightarrow \\pm \\infty$)\n\\begin{equation}\n\\Sigma_{A}^{t,\\bar{t},<,>}(t,t')=-\\Sigma_{L}^{t,\\bar{t},<,>}(t-t').\n\\end{equation}\nSo using theta functions we may write $\\Sigma_L^{A}(t,t')$ in the full t,t' domain as \n\\begin{eqnarray}\n\\Sigma_{A}^{t,\\bar{t}}(t,t')&=&-\\theta_{1}(t,t')\\Sigma_{L}^{t,\\bar{t}}(t-t') \\nonumber \\\\\n\\Sigma_{A}^{<}(t,t')&=&-\\theta_{1}(t,t')\\Sigma_{L}^{<}(t-t')+ \\theta_2 (t,t') \\times \\nonumber \\\\\n&&\\>\\big[\\Sigma_{L}^{<}(t-t'-\\hbar \\xi) - \\Sigma_{L}^{<}(t-t')\\big] \\nonumber \\\\\n\\Sigma_{A}^{>}(t,t')&=&-\\theta_{1}(t,t')\\Sigma_{L}^{>}(t-t')+ \\theta_2 (t,t') \\times \\nonumber \\\\\n&&\\>\\big[\\Sigma_{L}^{>}(t-t'+\\hbar \\xi) - \\Sigma_{L}^{>}(t-t')\\big]\n\\end{eqnarray}\nBy doing Fourier transform it can be easily shown that\n\\begin{equation}\n\\Sigma_{A}^{t,\\bar{t}}[\\omega,\\omega']=-\\int_{-\\infty}^{\\infty}\\!\\!\\! \\frac{d\\omega_{c}}{2 \\pi} \\, \\theta_{1}\\bigl[\\omega\\!-\\!\\omega_{c},\\omega'\\!+\\!\\omega_{c}\\bigr]\\Sigma_{L}^{t,\\bar{t}}(\\omega_{c})\n\\end{equation}\nand \n\\begin{eqnarray}\n\\Sigma_{A}^{>,<}[\\omega,\\omega']&\\!=\\!& \\!-\\!\\int_{-\\infty}^{\\infty} \\!\\!\\!\\frac{d\\omega_{c}}{2 \\pi}\\theta_{1}\\bigl[\\omega\\!-\\!\\omega_{c},\\omega'\\!+\\!\\omega_{c}\\bigr]\\Sigma_{L}^{>,<}(\\omega_{c}) \\\\\n\\!+\\!\\int_{-\\infty}^{\\infty}\\! \\frac{d\\omega_{c}}{2 \\pi}\\!\\!\\!&&\\!\\theta_{2}\\bigl[\\omega\\!-\\!\\omega_{c},\\omega'\\!+\\!\\omega_{c}\\bigr]\\Sigma_{L}^{>,<}(\\omega_{c}) \n(e^{i \\omega_{c}\\eta \\xi}\\!-\\!1), \\nonumber\n\\end{eqnarray}\nwhere $\\eta=\\pm 1$. The positive sign is for $\\Sigma_{A}^{<}$ and negative sign for $\\Sigma_{A}^{>}$.\n\nThe theta functions are now given by\n\\begin{eqnarray}\n\\theta_{1}(\\omega_{a},\\omega_{b})&=&f(\\omega_{a}).g(\\omega_{b})+f(\\omega_{b}).g(\\omega_{a}), \\nonumber \\\\\n\\theta_{2}(\\omega_{a},\\omega_{b})&=&f(\\omega_{a}).f(\\omega_{b}),\n\\end{eqnarray}\nwhere \n\\begin{eqnarray}\nf(\\omega)&=&\\frac{e^{i \\omega t_{M}}-1}{i \\omega}, \\nonumber \\\\\ng(\\omega)&=& \\frac{1}{i \\omega + \\epsilon} -\\frac{e^{i \\omega t_{M}-\\eta t_{M}}}{i \\omega -\\epsilon},\n\\end{eqnarray}\nwith $\\epsilon \\rightarrow 0^{+}$. The theta functions are of immense importance which carries all information about the measurement time $t_{M}$.\n\nIn the limit $t_{M} \\rightarrow \\infty$, the region $ 0 \\le t,t' \\le t_M$ dominates and corresponding theta function, i.e., $\\theta_2(\\omega,\\omega')$ reduces to \n\\begin{equation}\n\\theta_{2}(\\omega-\\omega_{c},\\omega'+\\omega_{c}) \\approx \\delta(\\omega-\\omega_{c}) \\delta(\\omega'+\\omega_{c}),\n\\end{equation}\nand is responsible for obtaining the steady state result.\n\nTo calculate all the cumulants we only need to take derivative of $\\Sigma_{A}(\\omega,\\omega')$ with respect to $i \\xi$ since $G_{0}$ does not have any $\\xi$ dependence. Also $\\Sigma^{A}$ has $\\xi$ dependence only for $ 0 \\le t,t' \\le t_M$ and hence the derivatives are given by \n\\begin{eqnarray}\n\\frac{\\partial^{n}\\Sigma_{A}^{>,<}}{{\\partial}(i \\xi)^{n}}[\\omega,\\omega']&=&\\int_{-\\infty}^{\\infty} \\frac{d\\omega_{c}}{2 \\pi} (\\eta \\hbar \\omega_{c})^{n} \\theta_{2}\\bigl[\\omega-\\omega_{c},\\omega'+\\omega_{c}\\bigr]\\nonumber \\\\\n&&\\Sigma_{L}^{>,<}(\\omega_{c}) e^{i \\omega_{c}\\eta \\xi}.\n\\end{eqnarray} \nHere $n$ refers to the order of the derivative. \n\n\\subsection{Dyson equation on contour C}\n\nLet us now discuss about solving the Dyson's equation for $G_{0}$ given in Eq.~(\\ref{eq-Dyson-product}) for product initial state $\\rho(-\\infty)$. In order to compute the matrix $\\breve{G}_0(t,t')$ we have to calculate two components $G_{0}^{r}$ and $G_{0}^{K}$ which are written in the integral form by applying Langreth's rule \\cite{Huag,rammer86}\n\\begin{eqnarray}\nG_{0}^{r}(t,t')&=&g_{C}^{r}(t\\!-\\!t') \\\\\n&&+\\!\\!\\int_{0}^{t_M}\\!\\!dt_1\\!\\!\\!\\int_{0}^{t_M}\\!\\!dt_2 \\, g_{C}^{r}(t\\!-\\!t_1)\\,\\Sigma^{r}(t_1\\!-\\!t_2)G_{0}^{r}(t_2,t'), \\nonumber \n\\end{eqnarray}\nand \n\\begin{eqnarray}\n&&G_{0}^{K}(t,t')=g_{C}^{K}(t\\!-\\!t') \\\\\n&&+\\!\\!\\int_{0}^{t_M}\\!\\!dt_1\\!\\!\\!\\int_{0}^{t_M}\\!\\!dt_2 \\, g_{C}^{r}(t\\!-\\!t_1)\\,\\Sigma^{r}(t_1\\!-\\!t_2) G_{0}^{K}(t_2,t') \\nonumber \\\\\n&&+\\!\\!\\int_{0}^{t_M}\\!\\!dt_1\\!\\!\\!\\int_{0}^{t_M}\\!\\!dt_2 \\, g_{C}^{r}(t\\!-\\!t_1) \\Sigma^{K}(t_1\\!-\\!t_2) G_{0}^{a}(t_2,t')\\nonumber \\\\\n&&+\\!\\!\\int_{0}^{t_M}\\!\\!dt_1\\!\\!\\!\\int_{0}^{t_M}\\!\\!dt_2 \\, g_{C}^{K}(t\\!-\\!t_1) \\Sigma^{a}(t_1\\!-\\!t_2)G_{0}^{a}(t_2,t'). \\nonumber \n\\end{eqnarray}\n\nNote that the argument for center Green's function $g_{C}$ and lead self-energy $\\Sigma$ are written as time difference $t-t'$ because they are Green's functions for isolated center part and leads respectively and hence are calculated at equilibrium. The analytical expressions for $\\Sigma$ and $g_{C}$ are known in frequency domain and are given in Appendix~A. To determine their time-dependence we numerically calculate their inverse Fourier transforms using trapezoidal rule \\cite{NR}. Then in order to solve above equations for any $t_M$ we discretize the time variable into $N$ total intervals of incremental length $\\Delta t=t_M\/N$ and thus converting the integral into a sum. After discretization, the above equations can be written in the matrix form which are indexed by space $j$ and discrete time $t$, as \n\\begin{eqnarray}\n\\tilde{G}_{0}^{r}&=& \\tilde{g}_C^{r} + \\tilde{g}_C^{r} \\tilde{\\Sigma}^{r} \\tilde{G}_{0}^{r}, \\nonumber \\\\\n\\tilde{G}_{0}^{K}&=& \\tilde{G}_{0}^{r}\\tilde{\\Sigma}^{K}\\tilde{G}_{0}^{a}+ ( I + \\tilde{G}_{0}^{r} \\tilde{\\Sigma}^{r}) \\tilde{g}_C^{K} (I+\\tilde{\\Sigma}^{a} \\tilde{G}_{0}^{a}).\n\\end{eqnarray}\nSo $\\tilde{G}_{0}^{r}$ can be obtained by doing an inverse of the matrix $(I-\\tilde{g}_{C}^{r} \\tilde{\\Sigma}^{r})$ and then multiplying by $\\tilde{g}_{C}^{r}$. $\\tilde{G}_{0}^{r}$ in this case also obeys time-translational invariance, so it can also be obtained by direct inverse Fourier transform. $\\tilde{G}_{0}^{a}$ can be obtained by taking transpose of $\\tilde{G}_{0}^{r}$. Once $\\tilde{G}_{0}^{r}$ and $\\tilde{G}_{0}^{a}$ are obtained we use the second equation to calculate $\\tilde{G}_{0}^{K}$ which is simply multiplying matrices.\n\nSimilarly $\\Sigma_L^{A}$ in Eq.~(\\ref{shifted-product}) are obtained by doing inverse Fourier transforms of the lead self-energy. We follow the same steps independently to calculate the cumulants for $Q_R$. \n\n\\begin{figure}\n\\includegraphics[width=\\columnwidth]{fig2.eps}\n\\caption{(Color online) The cumulants $\\langle \\langle {Q}_{L}^{n} \\rangle \\rangle$ and $\\langle \\langle {Q}_{R}^{n} \\rangle \\rangle$ for $n$=1, 2, 3, and 4 for one-dimensional linear chain connected with Rubin baths, for the projected initial state $\\rho'(0)$. The black and red curves corresponds to $\\langle \\langle {Q}_{L}^{n} \\rangle \\rangle$ and $\\langle \\langle {Q}_{R}^{n} \\rangle \\rangle$ respectively. The temperatures of the left and the right lead are 310 K and 290 K, respectively. The center (C) consists of one particle.}\n\\end{figure}\n\n\\begin{figure}\n\\includegraphics[width=\\columnwidth]{fig3.eps}\n\\caption{(Color online) Same as in Fig.~2 except for product initial state $\\rho(-\\infty)$. The temperatures of the left, the center and the right lead are 310 K, 300 K and 290 K, respectively.}\n\\end{figure}\n\n\n\\begin{figure}\n\\includegraphics[width=\\columnwidth]{fig4.eps}\n\\caption{(Color online) Same as in Fig.~2 except for steady state initial state $\\rho(0)$.}\n\\end{figure}\n\n\\section{Numerical results}\nWe now present some numerical results. In Fig.~2 and 3, we show the results for first four cumulants for both ${\\cal Q}_L$ and ${\\cal Q}_R$ (measurement is on the right lead) for 1D linear chain connected with Rubin baths, starting with the projected initial state $\\rho'(0)$ and product state $\\rho(-\\infty)$ respectively. \nRubin baths \\cite{Rubin, Weiss} mean in our case a uniform linear\nchain with spring constant $K$ and a small onsite $K_0$ for all the atoms.\nOnly one atom is considered as the center. The atoms of the left and right side\nof the center are considered baths. We use $K=1$ eV\/(u\\AA$^2)$ and the onsite potential $K_0=0.1$ eV\/(u\\AA$^2)$ in all our calculations.\nFirst of all, cumulants greater than two are nonzero, which confirms that the distribution for $P(Q_L)$ or $P(Q_R)$ is not Gaussian. The generic features are almost the same in both the cases. However the fluctuations are larger for the product initial state $\\rho(-\\infty)$ as this state corresponds to the sudden switch on of the couplings between the leads and the center and hence the state is far away from the correct steady state distribution. On the contrary, for the initial state $\\rho'(0)$ the fluctuations are relatively small. For $\\rho'(0)$ due to the effect of the measurement, at starting time energy goes into the leads, which is quite surprising. But for $\\rho(-\\infty)$ although initial measurement do not play any role, energy still goes into the leads. This can also be shown analytically (see Appendix B). At the starting time the behavior of both ${\\cal Q}_L$ and ${\\cal Q}_R$ are very similar and can be understood since both the left and right leads are identical and the effect of temperature difference is not present. However at longer times the odd cumulants starts differing and finally grows linearly with time $t_M$ and agrees with the corresponding long-time predictions.\n\nIn Fig.~4 we show the results for the steady state initial condition, i.e., $\\rho(0)$ which can be obtained by mapping the projection operators as identity operator, i.e., taking the limit $\\lambda \\rightarrow 0$. So in this case measurement effect is ignored and the dynamics starts with the actual steady state for the full system. The first cumulant increases linearly from the starting, $\\langle Q \\rangle = t I$ and the slope gives the correct prediction with the Landauer-like formula. However, high order cumulants still have transient behavior. In this case the whole system achieve steady state much faster compared with the other two cases.\n\n\n\\section{correlation between left and right lead heat}\n\\subsection{Product initial state}\nIn this section, we derive the CGF for the joint probability distribution $P(Q_L,Q_R)$ for the product initial state $\\rho(-\\infty)$. In order to calculate the CGF we need to measure both ${\\cal H}_L$ and ${\\cal H}_R$ at time 0 and at time $t_M$. Since the Hamiltonians for the left and the right lead commute at the same instance of time i.e., $\\big[{\\cal H}_L, {\\cal H}_R \\big]=0$, such type of measurements are allowed in quantum mechanics and also Nelson's theorem \\cite{Nelson} gurentee's that $P(Q_L,Q_R)$ is a well-defined probability distribution. The immediate consequence of deriving such CGF is that, the correlations between the left and the right lead heat can be obtained and it is also possible to calculate the CGF for total entropy flow (defined below) to the reservoirs. To calculate the CGF we need two counting fields $\\xi_L$ and $\\xi_R$ and the CGF in this case can be written down as \\cite{Esposito-review-2009}\n\\begin{equation}\n\\mathcal {Z}(\\xi_L,\\xi_R)=\\langle e^{i\\,\\xi_L\\,{\\cal H}_L+i\\,\\xi_R\\,{\\cal H}_R}\\,\\, e^{-i\\,\\xi_L\\,{\\cal H}^{H}_L(t)-i\\,\\xi_R\\,{\\cal H}^{H}_R(t)} \\rangle',\n\\end{equation}\nwhere the average is defined as \n\\begin{equation}\n\\langle \\cdots \\rangle'=\\sum_{a,c}\\Pi^{L}_a \\, \\Pi^{R}_c \\, \\rho(0)\\, \\Pi^{L}_a \\,\\Pi^{R}_c.\n\\end{equation}\n$\\Pi^{L}_a$ and $\\Pi^{R}_c$ are the projectors onto the eigenstates of ${\\cal H}_L$ and ${\\cal H}_R$ with eigenvalues $a$ and $c$ respectively, corresponding to the measurements at $t=0$. Here we will consider only the product state $\\rho(-\\infty)$, then initial projections $\\Pi^{L}_a$ and $\\pi^{R}_c$ do not play any role. We can proceed as before and finally the CGF can be written down as\n\\begin{equation}\n \\ln {\\cal Z}(\\xi_L,\\xi_R) = \\sum_{k=1}^\\infty \\frac{1}{2k}\n{\\rm Tr}_{(j,\\tau)} \\Bigl[\\big( G_{0} (\\Sigma_L^{A}+\\Sigma_R^A) \\big)^k \\Bigr],\n\\label{eq-lead-lead}\n\\end{equation}\ni.e., in this case we need to shift the contour-time arguments for both left and right lead self-energies. In the long-time limit ${\\cal Z}(\\xi_L,\\xi_R)$ becomes a function of difference of counting field $\\xi_L$ and $\\xi_R$, i.e., $\\xi_L-\\xi_R$. The explicit expression for the CGF in the long-time limit is \n\\begin{eqnarray}\n&&\\ln {\\cal Z}(\\xi_L-\\xi_R)=-t_M \\int\\frac{d\\omega}{4\\pi} \\ln \\det \\Bigl\\{ I - G_{0}^r \\Gamma_L \nG_{0}^a \\Gamma_R \\nonumber \\\\ &&\\big[(e^{i(\\xi_L-\\xi_R) \\hbar \\omega}\\! -\\! 1) f_L \n+( e^{-i(\\xi_L-\\xi_R)\\hbar \\omega} \\!-\\! 1) f_R \\nonumber \\\\\n&&+(e^{i(\\xi_L-\\xi_R) \\hbar \\omega} \\!+\\! e^{-i(\\xi_L-\\xi_R)\\hbar\\omega} \\!-\\!2 ) f_L f_R \\big]\\Bigr\\}.\\qquad\n\\label{eq-lnZxi}\n\\end{eqnarray}\nwhere $G_{0}$ obeys the same type of Dyson equation as in Eq.~(\\ref{eq-Dyson-product}). This CGF in the steady state obeys the same type of GC fluctuation symmetry, which in this case is given by \n\\begin{equation}\n{\\cal Z}(\\xi_L-\\xi_R)={\\cal Z}(-\\xi_L+\\xi_R +i {\\cal A}).\n\\end{equation}\nNow performing Fourier transform of the CGF, the joint probability distribution is given by $P(Q_L,Q_R)=P(Q_L)\\,\\delta(Q_L+Q_R)$. The appearance of the delta function is a consequence of the energy conservation in the steady state, i.e., $I_L=-I_R$. In the steady state knowing probability distribution either for ${\\cal Q}_L$ or ${\\cal Q}_R$ is sufficient to know the joint probability distribution.\n\nThe cumulants can be obtained from the CGF by taking derivatives with respect to both $\\xi_L$ and $\\xi_R$, i.e.,$\\langle \\langle {Q}_L^{n} {Q}_R^{m} \\rangle \\rangle =\\partial^{n+m} \\ln {\\cal Z}\/\\partial (i\\xi_L)^n \\partial (i\\xi_R)^m,$ substituting $\\xi_L=\\xi_R=0.$ In the steady state the cumulants obey $\\langle \\langle {Q}_L^{n} {Q}_R^{m} \\rangle \\rangle= (-1)^{m} \\langle \\langle Q_L^{m+n} \\rangle \\rangle = (-1)^{n} \\langle \\langle Q_R^{m+n} \\rangle \\rangle$. The first cumulant give us the left and right lead correlation $\\langle \\langle {Q}_{L} {Q}_{R} \\rangle \\rangle=\\langle {Q}_{L} {Q}_{R} \\rangle - \\langle {Q}_{L} \\rangle \\langle {Q}_{R} \\rangle$ and in the steady state is equal to $-\\langle \\langle {Q}_L^{2} \\rangle \\rangle$.\n\nIn Fig.~5 we plot the first three cumulants for one dimensional linear chain connected with Rubin bath where the center consists of only one atom. Initially the cumulant $\\langle \\langle {Q}_{L} {Q}_{R} \\rangle \\rangle $ is positively correlated as both $Q_L$ and $Q_R$ are negative, however in the longer time since $Q_L=-Q_R$ the correlation becomes negative. We also give plots for $\\langle \\langle {Q}_L^{2} {Q}_R \\rangle \\rangle$ (black online) and $\\langle \\langle {Q}_R^{2} {Q}_L \\rangle \\rangle$ (red online) which are in the long-time limit \nnegative and positively correlated respectively and match with the long-time predictions.\n\n\n\\begin{figure}\n\\includegraphics[width=\\columnwidth]{fig5.eps}\n\\caption{(Color online) First three cumulants of the correlations between left and right lead heat flux for one dimensional linear chain connected with Rubin baths, starting with product initial state $\\rho(-\\infty)$. The left graph corresponds to $\\langle \\langle {Q}_{L} {Q}_{R} \\rangle \\rangle$ and the right graph corresponds to cumulants $\\langle \\langle {Q}_L^{2} {Q}_R \\rangle \\rangle$ (Black curve) and $\\langle \\langle {Q}_R^{2} {Q}_L \\rangle \\rangle$ (Red curve). The left, center and right lead temperatures are 310 K, 290 K and 300 K respectively. The center (C) consists of one particle.}\n\\end{figure}\n\n\\begin{figure}\n\\includegraphics[width=\\columnwidth]{fig6.eps}\n\\caption{The cumulants of entropy production $\\langle \\langle \\sigma^{n} \\rangle \\rangle$ for $n$=1, 2, 3, 4 for one dimension linear chain connected with Rubin baths, for product initial state $\\rho(-\\infty)$. The left, center and right lead temperatures are 510 K, 400 K, and 290 K respectively. The center (C) consists of one particle.}\n\\end{figure}\n\n\n\n\\subsection{Entropy flow to the reservoir}\nFrom the two parameter ($\\xi_L,\\xi_R$) CGF one can also obtain the total entropy that flows into the leads. The total entropy flow to the reservoirs can be defined as \\cite{ep1,ep2}\n\\begin{equation}\n{\\cal \\sigma}=-\\beta_{L}{\\cal Q}_{L} -\\beta_{R}{\\cal Q}_{R}.\n\\end{equation}\nIn order to calculate this CGF we just make the substitutions $\\xi_L \\rightarrow -\\beta_L \\mu$ and $ \\xi_R \\rightarrow - \\beta_R \\mu$ in Eq.~(\\ref{eq-lead-lead}).\nIn the long-time limit the expression for entropy-production is similar to $\\ln {\\cal Z}(\\xi_L,\\xi_R)$ with $\\xi_L-\\xi_R$ replaced by ${\\cal A}$ and becomes an explicit function of thermodynamic affinity $\\beta_R-\\beta_L$ \\cite{fluct-theorems}. The CGF in this case satisfies the following symmetry\n\\begin{equation}\n{\\cal Z}(\\mu)={\\cal Z}(-\\mu + i)\n\\end{equation}\nIn Fig.~6 we give results for the first four cumulants of the entropy flow. All cumulants are positive and in the long-time limit give correct predictions. \n\n\n\n\\section{Long-time result for $\\ln {\\cal Z}^d(\\xi)$}\n\nIn this section we derive the explicit expression for the long-time limit of the CGF $\\ln {\\cal Z}^d(\\xi)$ which is given by (Eq.~\\ref{eq-lnZxi-1})\n\\begin{equation}\n\\ln {\\cal Z}^d(\\xi)=- \\frac{i}{\\hbar} \\int \\frac{d\\omega}{4\\pi}\n{\\rm Tr} \\bigl[ \\breve{G}[\\omega] \\breve{{\\cal F}}[\\omega,-\\omega]\\bigr].\n\\end{equation}\nwhere $G[\\omega]$ obeys the Dyson equation given in Eq.~(\\ref{eq-Dyson-full}). It is possible to write down $\\breve{G}[\\omega]$ in terms of $\\breve{G}_{0}$ and $\\breve{\\Sigma}_L^{A}$ as $\\breve{G}[\\omega]=\\big(I-\\breve{G}_{0}\\breve{\\Sigma}^{A}_L\\big)^{-1} \\breve{G}_{0}[\\omega]$. This equation can be solved analytically. Next we assume that the product of $f(t)$ and $f(t')$ is a time-translationally invariant function, i.e., $f(t)f^{T}(t')=F(t-t')$ in order to get rid of $t+t'$ dependence term. In the Fourier domain this means $f[\\omega]f^{T}[\\omega']=2\\pi F[\\omega] \\delta(\\omega+\\omega')$. So from Eq.~(\\ref{force-matrix}) the matrix element ${\\cal F}_{12}$ is given by $\\breve{{\\cal F}}[\\omega,-\\omega]_{12} \\propto \\delta(0) F[\\omega]$. We write $\\delta(0)=t_M\/2\\pi$. Using these results the CGF can be expressed as\n\n\\begin{equation}\n \\ln {\\cal Z}^{d}(\\xi)= {i t_M} \\int \\frac{d\\omega}{4\\pi \\hbar} \\, \\frac{1} {{\\cal N}(\\xi)}{\\rm Tr} \\Big[G_{0}^{r}[\\omega] (a+b) G_{0}^{a}[\\omega] F[\\omega] \\Big],\n\\end{equation}\nwhere $a$ and $b$ are defined in Eq.~(\\ref{eq-a}) and Eq.~(\\ref{eq-b}). Using the expressions for the self-energy the CGF reduces to \n\\begin{equation}\n \\ln {\\cal Z}^{d}(\\xi)=\\!\\!\\int \\!\\frac{d\\omega}{4 \\pi \\hbar}\\,\\frac{\\cal{K}(\\xi)}{{\\cal N}(\\xi)}\\, {\\rm Tr} \\Big[G_{0}^{r}[\\omega] \\Gamma_{L}[\\omega] G_{0}^{a}[\\omega]F[\\omega] \\Big],\n\\label{eq-driven-CGF}\n\\end{equation}\nwith \n\\begin{equation}\n{\\cal{K}}(\\xi)=(e^{-i\\xi\\hbar \\omega}\\!-\\!1 )+f_L(e^{i\\xi\\hbar\\omega}\\!+\\! e^{-i\\xi\\hbar \\omega}\\!-\\!2),\n\\end{equation} \nand \n\\begin{eqnarray}\n{\\cal {N}}(\\xi)&=& {\\rm det} \\Big[I -\\big(G_{0}^r \\Gamma_L G_{0}^a \\Gamma_R\\big) \\Bigl \\{ (f_L e^{i\\xi \\hbar \\omega}\\!-\\!1) + f_R \\nonumber \\\\ \n&&( e^{-i\\xi\\hbar \\omega}\\!-\\!1) + (e^{i\\xi \\hbar \\omega}\\!+\\! e^{-i\\xi\\hbar\\omega}\\!-\\!2 ) f_L f_R \\Bigr\\}.\n\\end{eqnarray}\nIt is important to note that ${\\cal K}(\\xi)$ depends only on left lead temperature and satisfies the symmetry ${\\cal K}(\\xi)={\\cal K}(-\\xi-i\\beta_L)$. So we can immediately write ${\\cal Z}^{d}(-i\\beta_L)=1$ and this relation is completely independent of the information about the right lead. If we consider the two leads at the same temperature ($\\beta_L=\\beta_R=\\beta$), this form of symmetry is then closely related to the Jarzynski equality (JE) \\cite{JE,talkner2008} and ${\\cal Z}^{d}(-i\\beta)=1$ is one special form of JE. However since ${\\cal N}(\\xi)$ does not satisfy this particular symmetry of $\\xi$ at thermal equilibrium (it obeys the GC summery when the leads are at different temperatures) and the CGF $\\ln{\\cal Z}^{d}(\\xi)$ doesn't satisfy any such symmetry relation and hence JE is not satisfied. This does not violate JE as our definition of ${\\cal Z}^{d}(\\xi)$ is different from the one used to derive JE.\n \nLet us now come back to the general scenario with leads at different temperatures and give the explicit expression of first and second cumulant by taking derivative of $\\ln {\\cal Z}^{d}(\\xi)$ with respect to $i\\xi$. \n\nThe first cumulant or moment is given by \\cite{driven-bijay}\n\\begin{equation}\n\\frac{\\langle \\langle Q_{d} \\rangle \\rangle}{t_M} = - \\int \\frac{d\\omega}{4 \\pi} \\, \\omega \\, {\\cal S}[\\omega],\n\\end{equation}\nwhere we define ${\\cal S}[\\omega]$ as the transmission function for the driven case and is given by\n\\begin{equation}\n{\\cal S}[\\omega]={\\rm{Tr}} \\bigl[G_{0}^{r} \\Gamma_{L} G_{0}^{a} F \\bigr].\n\\end{equation}\nFrom the expression of ${\\cal S}[\\omega]$ it is clear that the average energy current due to driven force is independent of $\\hbar$ and since it contains $G_{0}^{r,a}$ and $\\Gamma_L$, which are independent of temperature we can conclude that the energy current is independent of the temperature of the heat baths in the ballistic transport case. However the second cumulant and similarly the higher ones do depend on temperature of the baths. The second cumulant can be written as\n\\begin{eqnarray}\n\\frac{\\langle \\langle Q_{d}^{2} \\rangle \\rangle}{t_M} &=& \\int \\frac{d\\omega}{4 \\pi \\hbar} \\, (\\hbar \\omega)^{2} \\, {\\cal S}[\\omega] \\Bigl[(1+2\\,f_{L})- \\nonumber \\\\\n&& 2 \\,{\\cal T}(\\omega) (f_L-f_R) \\Bigr].\n\\label{second-driven-cumulant}\n\\end{eqnarray}\nSimilarly all the higher cumulants can be obtained from the CGF and we can conclude that the distribution $P(Q_d)$ is not Gaussian.\n\n\n\\subsection{Classical limit}\nIn this section we will give the classical limit of the steady state expression for the CGF $\\ln {\\cal Z}^{s}(\\xi)$ and $\\ln {\\cal Z}^{d}(\\xi)$ given in Eq.~(\\ref{eq-steady1}) and Eq.~(\\ref{eq-driven-CGF}).\n\nFirst of all we note that retarded and advanced Green's functions, i.e., $G_{0}^{r}$ and $G_{0}^{a}$ are similar both for quantum and classical case, so they stay the same when $\\hbar \\to 0$. We know that in the classical limit $f_{\\alpha} \\rightarrow \\frac{k_{B}T_{\\alpha}}{\\hbar \\omega}$ and also $e^{ix}=1+ix + \\frac{(ix)^{2}}{2} + \\cdots$, where $x=\\xi \\hbar \\omega$. Using this we obtain from Eq.~(\\ref{eq-steady1}) the classical limit of ${\\cal Z}^{s}(\\xi)$. \n\\begin{eqnarray}\n\\ln{\\cal Z}^{s}_{\\rm cls}(\\xi)&=& \\frac{t_M}{4\\pi} \\int d\\omega\\,\\ln \\det \\Big[I-\\big(G_{0}^{r} \\Gamma_{L} G_{0}^{a} \\Gamma_{R}\\big) \\times \\nonumber \\\\\n&& \\, k_B T_L \\, k_B T_R \\,i\\xi (i\\xi +{\\cal A})\\Big].\n\\end{eqnarray}\nThis result reproduces that of Ref.~\\cite{kundu}.\nIn the classical case also the CGF obeys the GC symmetry, i.e., it remains invariant under the transformation $i\\xi \\rightarrow -i\\xi -{\\cal A}$.\n\nLet us now get the classical limit for $\\ln {\\cal Z}^{d}(\\xi)$ using Eq.~(\\ref{eq-driven-CGF}). Following above relations the function ${\\cal K}(\\xi)$ in the limit $\\hbar \\rightarrow 0$ reduces to\n\\begin{equation}\n{\\cal K}_{\\rm cls}(\\xi)= -\\hbar \\omega \\,\\Big(i\\xi + \\frac{\\xi^2}{\\beta_L}\\Big).\n\\end{equation} \nThe transmission function ${\\cal S}[\\omega]$ stays the same as it is independent of temperature and $\\hbar$. So in the classical limit $\\ln {\\cal Z}^{d}(\\xi)$ reduces to \n\\begin{equation}\n\\ln {\\cal Z}^{d}_{\\rm cls}(\\xi)= t_M \\int \\frac{d\\omega}{4 \\pi} \\, \\omega \\, {\\cal S}[\\omega] \\, \\frac{\\Big(i\\xi + \\frac{\\xi^2}{\\beta_L}\\Big)}{{\\cal N}_{\\rm cls}(\\xi)},\n\\end{equation}\nwhere \n\\begin{eqnarray}\n{\\cal N}(\\xi)_{\\rm cls}&=&\\det\\Big[I-\\big(G_{0}^{r} \\Gamma_{L} G_{0}^{a} \\Gamma_{R}\\big) \\,k_B T_L \\, k_B T_R \\nonumber \\\\\n&&i\\xi (i\\xi +{\\cal A})\\Big].\n\\end{eqnarray}\nHere we can easily see that ${\\cal Z}^{d}(-i\\beta_L)=1$.\n\nWe can also derive the fluctuation dissipation theorem from Eq.(\\ref{second-driven-cumulant}) if we assume the leads are at the same temperature, i.e., $\\beta_{L}=\\beta_{R}=\\beta$ then we can write the second cumulant $\\langle \\langle Q_{d}^{2} \\rangle \\rangle$ as \n\\begin{equation}\n\\frac{\\langle \\langle Q_{d}^{2} \\rangle \\rangle}{t_M} = \\int \\frac{d\\omega}{4 \\pi \\hbar} \\, (\\hbar \\omega)^{2} \\,{\\cal S}[\\omega] (1+2\\,f_{L}).\n\\end{equation}\nIn the high-temperature limit using $f_{L} \\rightarrow \\frac{k_{B}T_{L}}{\\hbar \\omega}$ and we obtain\n\\begin{equation}\n\\langle \\langle Q_{d}^{2} \\rangle \\rangle =\\frac{2}{\\beta_{L}} \\langle Q_{d} \\rangle.\n\\end{equation}\n\nIn the next section we discuss Nazarov's generating function and give long-time limit expression. \n\n\\section{Nazarov's definition of generating function}\nIn this section we will derive another definition of CGF given by Eq.~(\\ref{eq-Z1-Nazarov}), starting from the CGF, derived using two-time measurement concept, i.e., Eq.~(\\ref{eq-Z-two-time}). Eq.~(\\ref{eq-Z1-Nazarov}) can be obtained from Eq.~(\\ref{eq-Z-two-time}) in the small $\\xi$ approximation as follows. In the small $\\xi$ approximation the modified Hamiltonian given in Eq.~(\\ref{modified}) takes the following form \n\\be\n{\\cal H}_{x}(t)={\\cal H}(t)+\\hbar x {\\cal I}_L(0),\n\\ee\nbecause $\\lim_{x\\rightarrow 0}{\\cal C}(x)=0$ and $\\lim_{x\\rightarrow 0}{\\cal S}(x)=\\hbar x V^{LC}$. ${\\cal I}_L$ is defined in Eq.~(\\ref{current}).\nSo the modified unitary operator becomes\n\\be\n{\\cal U}_{x}(t,0)=T e^{-\\frac{i}{\\hbar} \\int_{0}^t [{\\cal H}(\\bar{t})+\\hbar x {\\cal I}_L(0)] d\\bar{t}}.\n\\ee\nWe can consider $\\hbar x {\\cal I}_L(0)$ as the interaction Hamiltonian and write the full unitary operator ${\\cal U}_x$ as a product of two unitary operators as following \n\\begin{equation}\n{\\cal U}_{x}(t,0)={\\cal U}(t,0) \\, {\\cal U}_{x}^{I}(t,0),\n\\label{u}\n\\end{equation}\nwhere \n\\begin{eqnarray}\n{\\cal U}(t,0)&=& T e^{-\\frac{i}{\\hbar} \\int_{0}^t {\\cal H}(t') \\, dt'}, \\nonumber \\\\\n{\\cal U}_{x}^{I}(t,0) &=& T e^{-\\frac{i}{\\hbar} \\int_{0}^t \\hbar x {\\cal I}_L(t') dt'},\n\\label{u1}\n\\end{eqnarray}\nwith ${\\cal I}_L(t')={\\cal U}^{\\dagger}(t',0)\\,{\\cal I}_L(0)\\,{\\cal U}(t',0)$ is the current operator in the Heisenberg picture. It is important to note that ${\\cal U}$ is the usual unitary operator which evolves with the full Hamiltonian ${\\cal H}(t)$ in Eq.~(\\ref{eq-unitary}) and has no $\\xi$ dependence.\n\nIf we use product state $\\rho(-\\infty)$ as the initial state the CGF is given by \n\\begin{equation}\n{\\cal Z}(\\xi)={\\rm Tr}\\big[\\rho(-\\infty) \\, {\\cal U}_{\\xi\/2}(0,t)\\, {\\cal U}_{-\\xi\/2}(t,0)\\big].\n\\end{equation} \nIn the small $\\xi$ approximation and using the expressions for ${\\cal U}_{x}$ we can write the CGF as\n\\begin{equation}\n{\\cal Z}_{1}(\\xi)=\\lim_{\\xi \\to 0} {\\cal Z}(\\xi)= {\\rm Tr}\\big[\\rho(-\\infty) \\, {\\cal U}_{\\xi\/2}^{I}(0,t)\\, {\\cal U}_{-\\xi\/2}^{I}(t,0)\\big],\n\\end{equation}\nwhere we use the property of unitary operator, i.e., ${\\cal U}^{\\dagger}(t,0) {\\cal U}(t,0)=1$. Finally using the definition of heat operator ${\\cal Q}_L$ given in Eq.~(\\ref{eq-hatQ}) and the CGF can be written down as \n\\begin{equation}\n{\\cal Z}_{1}(\\xi)=\\Big \\langle {\\bar T} e^{i\\xi {\\cal Q}_{L}(t)\/2} \\, T e^{i\\xi {\\cal Q}_{L}(t)\/2}\\Big \\rangle,\n\\end{equation}\nwhich is the same as in Eq.~(\\ref{eq-Z1-Nazarov}).\n\nIn the following we will give the long-time limit expression for this CGF.\n \nIn order to calculate the CGF, it is important to go to the interaction picture with respect to the Hamiltonian ${\\cal H}_{0}={\\cal H}_L+{\\cal H}_C+ {\\cal H}_R$, as we know how to calculate Green's functions for operators which evolves with ${\\cal H}_{0}$ and treat the rest part as the interaction ${\\cal V}_{x}={\\cal H}_{\\rm int}+ \\hbar x {\\cal I}_{L}(0)$. So the CGF on contour $C=\\big[0,t_M \\big]$ can be written as\n\\begin{equation}\n{\\cal Z}_{1}(\\xi)=\\Big \\langle T_c e^{-\\frac{i}{\\hbar} \\int {\\cal V}_{x}^{I}(\\tau) d\\tau } \\Big \\rangle,\n\\end{equation}\nwhere ${\\cal V}_{x}^{I}(\\tau)$ is now given by \n\\begin{eqnarray}\n{\\cal V}_{x}^{I}(\\tau)&=&u_{L}^T(\\tau) V^{LC} u_{C}(\\tau)+ u_{R}^T(\\tau) V^{RC} u_{C}(\\tau) \\nonumber \\\\\n&&+\\hbar x(\\tau) p_{L}(\\tau) V^{LC} u_{C}(\\tau),\n\\end{eqnarray} \nwhere $p_L=\\dot{u}_L$. The time-dependence $\\tau$ is coming from the free evolution with respect to ${\\cal H}_{0}$. $x(\\tau)$ has the similar meaning as before, i.e., on the upper branch of the contour $x^{+}(t)=-\\xi\/2$ and on the lower branch $x^{-}(t)=\\xi\/2$. Now using the same idea as before, we expand the series, use Wick's theorem and finally the CGF can be expressed as\n\\begin{equation}\n\\ln {\\cal Z}(\\xi)=- \\frac{1}{2} {\\rm Tr}_{j,\\tau} \\ln \\Big[ 1 - G_0 \\Sigma_L^{A} \\Big]. \n\\end{equation}\nHere $G_{0}$ is the same as before and is given by Eq.~(\\ref{eq-Dyson-product}). However the shifted self-energy $\\Sigma_{L}^{A}$ in this case is different and is given by (in contour-time argument) \n\\begin{eqnarray}\n\\Sigma_{L}^{A}(\\tau,\\tau')&=&\\hbar\\, x(\\tau)\\, \\Sigma_{p_L u_L}(\\tau,\\tau')+\\hbar\\, x(\\tau')\\, \\Sigma_{u_L p_L}(\\tau,\\tau') \\nonumber \\\\\n&&+\\hbar^{2}\\, x(\\tau)\\,x(\\tau')\\,\\Sigma_{p_L p_L}(\\tau,\\tau').\n\\end{eqnarray}\nThe notation $\\Sigma_{A B}(\\tau,\\tau')$ means\n\\begin{equation}\n\\Sigma_{A B}(\\tau,\\tau')= \\bigl(-\\frac{i}{\\hbar}\\bigr) V^{CL} \\, \\langle\\, T_{c} A(\\tau) B^{T}(\\tau')\\,\\rangle \\, V^{LC}.\n\\end{equation}\nThe average here is with respect to equilibrium distribution of the left lead. It is possible to express the correlation functions such as $\\Sigma_{p_L u_L}(\\tau,\\tau')$ in terms of the $\\Sigma_{u_L,u_L}(\\tau,\\tau')=\\Sigma_L(\\tau,\\tau')$ correlations. $\\Sigma_{p_L u_L}(\\tau,\\tau')$ and $\\Sigma_{u_L p_L}(\\tau,\\tau')$ is simply related with $\\Sigma_L(\\tau,\\tau')$ by the contour-time derivative whereas for $\\Sigma_{p_L p_L}(\\tau,\\tau')$ the expression is \n\\begin{equation}\n\\Sigma_{p_L p_L}(\\tau,\\tau')= \\frac{\\partial^{2} \\Sigma_{u_L u_L}(\\tau,\\tau')}{\\partial \\tau \\partial \\tau'} + \\delta(\\tau,\\tau') \\Sigma_{L}^{I}.\n\\end{equation}\nWhere $\\Sigma_L^{I}=V^{CL} V^{LC}$. Now in the frequency domain different components of $\\Sigma_L^{A}$ takes the following form\n\\begin{eqnarray}\n\\Sigma_A^{t}[\\omega]&=& \\frac{\\hbar^{2}\\xi^{2}\\omega^{2}}{4} \\Sigma_{L}^{t}[\\omega]+ \\frac{\\hbar^{2}\\xi^{2}}{4}\\Sigma_{L}^{I}, \\nonumber \\\\\n\\Sigma_A^{\\bar{t}}[\\omega]&=& \\frac{\\hbar^{2}\\xi^{2}\\omega^{2}}{4} \\Sigma_{L}^{\\bar{t}}[\\omega]- \\frac{\\hbar^{2}\\xi^{2}}{4}\\Sigma_{L}^{I}, \\nonumber \\\\\n\\Sigma_A^{<}[\\omega]&=& \\big( i \\hbar \\xi \\omega - \\frac{\\hbar^{2}\\xi^{2}\\omega^{2}}{4} \\big) \\Sigma_{L}^{<}[\\omega], \\nonumber \\\\\n\\Sigma_A^{>}[\\omega]&=& \\big(-i \\hbar \\xi \\omega - \\frac{\\hbar^{2}\\xi^{2}\\omega^{2}}{4} \\big) \\Sigma_{L}^{>}[\\omega]. \n\\end{eqnarray}\n\nFinally using the relations between the self-energy (see Appendix A), in the long-time limit the CGF can be written down as,\n\\begin{eqnarray}\n\\ln {\\cal Z}_{1}(\\xi) &=& - t_M \\int \\frac{d\\omega}{4\\pi} \\ln \\Big[1-(i\\xi \\hbar \\omega){\\cal T}[\\omega]\\,(f_L-f_R) \n\\nonumber \\\\ &&- \\frac{(i\\xi \\hbar \\omega)^{2}}{4}\\Big({\\cal T}[\\omega](1+2f_L)(1+2f_R)-G_{0}^{a}\\Sigma_{L}^{r} \\nonumber \\\\\n&&+G_{0}^{r}\\Sigma_{L}^{a} -G_{0}^{r}\\Gamma_{L}G_{0}^{a}\\Gamma_{L}\\Big)+ {\\cal J}(\\xi^{2},\\xi^{4})\\Big],\n\\end{eqnarray}\nwhere ${\\cal J}(\\xi^2,\\xi^4)$ is given by\n\\begin{eqnarray}\n{\\cal J}(\\xi^2,\\xi^4)&=&-\\frac{\\hbar^2 \\xi^2}{4}\\big(G_{0}^{a}+G_{0}^{r}\\big)\\Sigma_{L}^{I} - \\frac{1}{4} \\frac{(i\\xi \\hbar \\omega)^2}{2} \\frac{\\hbar^2 \\xi^2}{2} \\nonumber \\\\ \n&&+\\big(G_{0}^{r}\\Sigma_{L}^{a}G_{0}^{a}\\Sigma_L^{I}+G_{0}^{r}\\Sigma_{L}^{I}G_{0}^{a}\\Sigma_L^{r}\\big) + \\frac{1}{4} \\frac{(i\\xi \\hbar \\omega)^4}{4} \\nonumber \\\\\n&&G_{0}^{r}\\Sigma_{L}^{a}G_{0}^{a}\\Sigma_L^{r} + \\frac{1}{4} \\frac{(\\hbar^{4} \\xi^{4})}{4} G_{0}^{r}\\Sigma_{L}^{I}G_{0}^{a}\\Sigma_L^{I}.\n\\end{eqnarray}\n\nThis CGF does not obey the GC fluctuation symmetry. However it gives the correct first and second cumulant as it should because the definition of first and second cumulant turn out to be the same for both the generating functions ${\\cal Z}(\\xi)$ and ${\\cal Z}_{1}(\\xi)$ and is given by\n\\begin{eqnarray}\n&&\\langle \\langle Q \\rangle \\rangle=\\langle Q \\rangle = \\frac{\\partial \\ln {\\cal Z}(\\xi)}{\\partial {(i\\xi)}}=\\frac{\\partial \\ln {\\cal Z}_1(\\xi)}{\\partial {(i\\xi)}}= \\int_{0}^{t} dt_1 \\langle {\\cal I}_L(t_1) \\rangle, \\nonumber \\\\\n&&\\langle \\langle Q^{2} \\rangle \\rangle =\\langle Q^{2} \\rangle- \\langle Q \\rangle^{2} = \\frac{\\partial^{2} \\ln {\\cal Z}(\\xi)}{\\partial {(i\\xi)^{2}}}=\\frac{\\partial^{2} \\ln {\\cal Z}_1(\\xi)}{\\partial {(i\\xi)^{2}}}\\nonumber \\\\\n&& \\> = \\int_{0}^{t} dt_1 \\int_{0}^{t} dt_2 \\langle {\\cal I}_L(t_1) {\\cal I}_L(t_2) \\rangle-\\Big[\\int_{0}^{t}\\! dt_1\\! \\langle {\\cal I}_L(t_1) \\rangle \\Big]^{2}. \n\\end{eqnarray}\nExpressions for higher cumulants are different for the two generating functions and hence the final expressions for the CGF's are completely different from each other. \n\n\\section{Conclusion}\nIn summary, we present an elegant way of deriving the CGF for heat ${\\cal Q}_{L,R}$ transferred from the leads to the center for driven linear systems using the two-time measurement concept and with the help of the NEGF technique. The CGF is written in terms of the Green's function of the center and the self-energy $\\Sigma_{L}^A$ of the leads. \nThe counting of the energy is related to the shifting in time for the self-energy.\nThis expression is valid in both transient and steady state regimes, where the information about the measurement time $t_M$ is contained in $\\Sigma_{L}^A$. The form of the expression,\n$-(1\/2) {\\rm Tr} \\ln (1 - G_{0} \\Sigma_L^A)$, is the same whether we use a product initial state or a projected initial state, except that the meaning of the Green's function has to be adjusted accordingly. We consider three different initial conditions and show numerically for 1D linear chains connected with Rubin baths, that transient behaviors significantly differs from each other but eventually leads to the same steady state distribution in the long-time limit. We give explicit expressions of the CGF in the steady state invoking the symmetry of translational invariance in time. The CGF obeys the GC symmetry. We also give the steady state expression for the CGF in the presence of time-dependent driving forces. We obtain a two parameter CGF which is useful for calculating the correlations between heat flux and also the total entropy which flows to the leads. Our calculations can be easily generalized to arbitrary dimensions with any number of heat baths. We will show in the appendix that our method can be extended for the electronic calculations where we derive the CGF for a tight-binding model. It will be interesting to derive the CGF by taking magnetic field contribution into the Hamiltonian and also to study the cumulants in the presence of nonlinear interactions such as phonon-phonon interactions or electron phonon interactions.\n\n\n\\section*{Acknowledgments}\nWe are grateful to Juzar Thingna, Meng Lee Leek, Zhang Lifa, and Li Huanan for insightful discussions. This work is supported in part by a URC research grant R-144-000-257-112 of National University of Singapore.\n\n\n\n\\section*{Appendix}\n\\subsection{Expressions for different type of Green's functions}\nHere we give the explicit expressions for the center Green's function $G_{0}[\\omega]$ in the steady state, for a harmonic system which is connected with the leads. These formulas are required to derive the analytical form of the CGF given in Eq.~(\\ref{eq-steady1}). For the basic definitions of different types of Green's functions we refer to Ref.~\\onlinecite{WangJS-europhysJb-2008}. \n\nThe retarded Green's function $G_{0}^{r}[\\omega]$ is given by\n\\begin{equation}\nG_{0}^{r}[\\omega]=\\Big[(\\omega+i\\eta)^{2}-K^{C}-\\Sigma_{L}^{r}[\\omega]-\\Sigma_{R}^{r}[\\omega]\\Big]^{-1}.\n\\end{equation}\nHere $\\eta$ is an infinitesimal positive number which is required to satisfy the condition of causality i.e.,$G_{0}^r(t)=0$ for $t <0 $.\nThe advanced Green's function is $G_{0}^{a}[\\omega]=\\big[G_{0}^{r}[\\omega]\\big]^{\\dagger}$. The Keldysh Green's function $G_{0}^{K}[\\omega]$ can be obtained by solving the corresponding Dyson equation, Eq.~(\\ref{eq-Dyson-product}), and is given by\n\\begin{equation}\nG_{0}^{K}[\\omega]=G_{0}^{r}[\\omega]\\Sigma^{K}[\\omega]G_{0}^{a}[\\omega],\n\\end{equation}\nwhere $\\Sigma^{K}=\\Sigma_L^{K}+\\Sigma_R^{K}$ and $\\Sigma_{\\alpha}^{K}=\\Sigma_{\\alpha}^{<}+ \\Sigma_{\\alpha}^{>}$ with $\\alpha=L,R$. Alternatively, $G_{0}^{K}=G_{0}^< +G_{0}^>$. Another important identity is\n\\begin{equation}\nG_{0}^{r}[\\omega]-G_{0}^{a}[\\omega]=-i\\, G_{0}^{r}[\\omega] \\big(\\Gamma_L[\\omega]+\\Gamma_R[\\omega]\\big)G_{0}^{a}[\\omega],\n\\end{equation}\nwhere $\\Gamma_{\\alpha}[\\omega]=i\\big(\\Sigma_{\\alpha}^{r}[\\omega]-\\Sigma_{\\alpha}^{a}[\\omega]\\big)$, and $\\alpha=L,R$. The self-energy for the leads are given by\n\\begin{eqnarray}\n\\Sigma_{\\alpha}^{<}[\\omega]&=&f_{\\alpha}[\\omega]\\big(\\Sigma_{\\alpha}^{r}[\\omega]-\\Sigma_{\\alpha}^{a}[\\omega]\\big), \\nonumber \\\\\n\\Sigma_{\\alpha}^{>}[\\omega]&=&(1+f_{\\alpha}[\\omega])\\big(\\Sigma_{\\alpha}^{r}[\\omega]-\\Sigma_{\\alpha}^{a}[\\omega]\\big).\n\\end{eqnarray}\nwhere $f_{\\alpha}[\\omega]=1\/\\bigl(e^{\\beta_{\\alpha} \\hbar \\omega_{\\alpha}}-1\\bigr)$ is the Bose distribution function. \n\nExplicit expressions for $G_{0}^{r}[\\omega]$ and $\\Sigma_{L}^{r}[\\omega]$ can be obtained for 1D homogeneous linear chain, with inter particle force constant $K$ and onsite spring constant $K_{0}$ and which is divided into three parts: the center, the left and the right. The classical equation of motion for the atoms in all three regions is\n\\begin{equation}\n\\ddot{u}_j=K u_{j-1} + \\bigl (-2K -K_{0}\\bigr )u_{j} + Ku_{j-1},\n\\end{equation}\nwhere the index $j$ runs over all the atoms in the full system.\n\nThe retarded Green's function $G_{0}^{r}[\\omega]$ can be obtained by solving \\cite{Wang-pre07} $[(\\omega+i\\eta)^{2}-\\tilde{K}]G_{0}^{r}=I$, where matrix $\\tilde{K}$ which is infinite in both directions and is $2K+K_{0}$ on the diagonals and $-K$ on the first off-diagonals. The solution is translationally invariant in space index and is given by \n\\be\nG_{0,jk}^{r}[\\omega]=\\frac{\\lambda^{|j-k|}}{K(\\lambda-\\frac{1}{\\lambda})},\n\\ee \nwith $\\lambda=-\\frac{\\Omega}{2K}\\pm \\frac{1}{2K}\\sqrt{\\Omega^{2}-4K^{2}}$ and $\\Omega=(\\omega+i\\eta)^{2}-2K-K_{0}$, choosing between plus and minus sign by $|\\lambda|\\le 1$. \n\nThe surface Green's function $g_L^{r}[\\omega]$ can be similarly obtained in frequency domain and is given in terms of the self-energy $\\Sigma_{L}^{r}[\\omega]=-K \\lambda$. Since in equilibrium only one Green's function is independent, knowing $\\Sigma_{L}^{r}[\\omega]$ is sufficient to obtain all other Green's functions.\n\nHere we also give the expressions for Green's functions $g_C$ in time and frequency domain for an isolated single harmonic oscillator with frequency $\\omega_{0}$ (we have omitted the subscript $C$ in $g_C$) \\cite{Zeng,Brouwer}\n\\begin{eqnarray}\ng^{r}(t) & = & -\\theta(t) \\, \\frac{\\sin{\\omega_{0}t}}{\\omega_{0}},\\nonumber \\\\\ng^{r}[\\omega] & = & \\frac{1}{(\\omega+i\\eta)^{2}-\\omega_{0}^{2}},\\nonumber \\\\\ng^{<}(t) & = & \\frac{-i}{2\\omega_{0}}\\left[(1+f)e^{i\\omega_{0}t}+fe^{-i\\omega_{0}t}\\right],\\nonumber \\\\\ng^{<}[\\omega] & = & \\frac{-i\\pi}{\\omega_{0}}\\left[\\delta(\\omega+\\omega_{0})(1+f)+\\delta(\\omega-\\omega_{0})f\\right],\n\\end{eqnarray} \nwhere $f=f(\\omega_{0})=\\frac{1}{e^{\\beta\\hbar\\omega_{0}}-1}$. Other components can be obtained by exploiting the symmetry between the Green's functions such as $g^a(-t)=g^r(t)$ for $t > 0$ hence $g^r[\\omega]=g^a[-\\omega]$. The greater component is related with the lesser component via $g^>(t)=g^<(-t)$ which in the frequency domain satisfy $g^>[\\omega]=g^<[-\\omega]$.\n\n\n\n\n\\subsection{Current at short time for product initial state $\\rho(-\\infty)$}\nUsing the definition of current operator given in Eq.~(\\ref{current}) the energy current flowing from the left lead to the center is (here we assume that there is no driving force $f(t)$) \n\\begin{equation}\n\\langle {\\cal I}_{L}(t)\\rangle =-\\langle \\frac{d{\\cal H}_L(t)}{dt} \\rangle =\\frac{i}{\\hbar} \\langle \\big[{\\cal H}_{L}(t),{\\cal H} \\big] \\rangle ,\n\\end{equation}\nwhere the average is with respect to $\\rho(-\\infty)$. If $t$ is small we can expand ${\\cal H}_{L}(t)$ in a Taylor series and is given by ${\\cal H}_{L}(t)={\\cal H}_{L}(0)+t \\dot{{\\cal H}}_{L}(0) +\\cdots $ \n\nNow since $\\big[\\rho(-\\infty), {\\cal H}_{L}(0) \\big]=0$, then it immediately follows that $\\langle \\big[{\\cal H}_{L}(0),{\\cal H}\\big]\\rangle =0$ by using the cyclic property of trace. So in linear order of $t$ the current is given by\n\\begin{equation}\n\\langle {\\cal I}_{L}(t)\\rangle=t \\frac{i}{\\hbar} \\langle \\big[{\\dot{\\cal H}}_{L}(0),{\\cal H}\\big]\\rangle= -t \\frac{i}{\\hbar}\\langle \\big[p_{L}^{T}V^{LC}u_C, {\\cal H}\\big]\\rangle.\n\\end{equation}\nThe only term of full ${\\cal H}$ that will contribute to the is ${\\cal H}_{LC}=u_{L}^T V^{LC} u_{C}$. \n\nNow using the relation that $\\big[p_L,u_L\\big]=-i\\hbar$, for one-dimensional linear chain we can write\n\\begin{equation}\n\\langle {\\cal I}_{L}(t) \\rangle =-t \\, K^{2} \\langle (u^{C}_{1})^{2} \\rangle = -t \\, K^{2} \\frac{\\hbar}{\\omega_{0}} \\Big(f_{C}(\\omega_{0})+\\frac{1}{2}\\Big).\n\\end{equation}\nwhere $u^{C}_{1}$ is the first particle in the center which is connected with the first particle of the left lead with force constant $K$. Now since the average is with respect to $\\rho(-\\infty)$,$\\langle (u^{C}_{1})^{2} \\rangle $ can be easily computed. Here $f_{C}(\\omega_{0})$ is the Bose distribution function of the particle with characteristic frequency $\\omega_{0}$. So we can see that for short time the current is negative, i.e, it goes into the lead. It is now easy to see that similar expression should also hold for $\\langle {\\cal I}_{R}(t)\\rangle$. The negative sign in currents means that the energy flows into the leads initially irrespect to the temperature of the leads. This is consistent with the numerical results obtained by Cuansing et al.\\ \\cite{eduardos-paper,eduardos-paper1}.\n\n\\subsection{\\label{apdC} Convolution, trace, and determinant on Keldysh contour}\nHere we discuss the meaning of convolution, trace and determinant on the Keldysh contour which we used to derive the CGF's for heat flux. We define the convolution on contour in the following way.\n\\begin{eqnarray}\nA B \\cdots D &\\rightarrow& \\sum_{j_2,j_3, \\cdots, j_n}\\int d\\tau_2 \\cdots \\int d\\tau_n A_{j_1,j_2}(\\tau_1, \\tau_2)\\nonumber \\\\\n&& B_{j_2,j_3}(\\tau_2, \\tau_3) \\cdots D_{j_n,j_{n+1}}(\\tau_n, \\tau_{n+1}), \n\\end{eqnarray}\nFrom the convolution we define trace by substituting $\\tau_{n+1}=\\tau_1$, $j_{n+1}=j_1$ and integrate also over $\\tau_1$, sum over $j_1$ i.e., \n\\begin{eqnarray}\n{\\rm Tr}_{j,\\tau}(AB \\cdots D)&=&\\int d\\tau_1 \\int d\\tau_2 \\cdots \\int d\\tau_n \\\\\n&& {\\rm Tr}_j\\bigl[ A(\\tau_1, \\tau_2) B(\\tau_2, \\tau_3) \\cdots D(\\tau_n, \\tau_1) \\bigr], \\nonumber \n\\end{eqnarray}\nChanging from contour to real-time integration from $-\\infty$ to $+\\infty$, i.e., using $\\int d\\tau = \\sum_{\\sigma} \\int \\sigma dt $ we have\n\\begin{eqnarray}\n{\\rm Tr}_{j,\\tau}(AB \\cdots D) = \\!\\!\\!\\sum_{\\sigma_1,\\sigma_2, \\cdots, \\sigma_n}\\!\\!\\! \\int dt_1 \\int dt_2 \\cdots \\int dt_n \\qquad \\\\\n{\\rm Tr}_j \\bigl[ A^{\\sigma_1\\sigma_2}(t_1, t_2) \\sigma_2 B^{\\sigma_2\\sigma_3}(t_2, t_3) \\cdots \\sigma_n D^{\\sigma_n\\sigma_{n+1}}(t_n, t_{1}) \\bigr]. \\nonumber \n\\end{eqnarray}\nLet us absorb the extra $\\sigma$ into the definition of branch components,\ni.e., define\n\\begin{equation}\n\\bar{A}_{\\sigma\\sigma'} = \\sigma A^{\\sigma\\sigma'}, \\quad{\\rm or}\\quad\n\\bar{A} = \\sigma_z A,\n\\end{equation}\nwhere $A$ is viewed as $2\\times 2$ block matrix with the usual\n$+$, $-$ component, \n\\begin{equation}\nA = \\left( \\begin{array}{cc}\n A^{++} & A^{+-} \\\\\n A^{-+} & A^{--} \n \\end{array} \\right) = \n\\left( \\begin{array}{cc}\n A^t & A^< \\\\\n A^> & A^{\\bar t} \n \\end{array} \\right),\n\\end{equation}\nand $\\sigma_z$ is defined as\n\\begin{eqnarray}\n\\sigma_z &=& \\left( \\begin{array}{cc}\n 1 & 0 \\\\\n 0 & -1 \n \\end{array} \\right),\n\\end{eqnarray}\nthen it can be easily seen that \n\\begin{eqnarray}\n{\\rm Tr}_{j,\\tau}(AB \\cdots D)&=& \\int dt_1 \\int dt_2 \\cdots \\int dt_n {\\rm Tr}_{j} \\bigl[{\\bar A}(t_1,t_2) \\nonumber \\\\\n&&{\\bar B}(t_2,t_3) \\cdots {\\bar D}(t_n,t_1) \\bigr] \\nonumber \\\\\n&&={\\rm Tr}_{t,j,\\sigma}(\\bar{A}\\bar{B} \\cdots \\bar {D}).\n\\end{eqnarray}\nThen we can do a rotation, where the rotation matrix is given by \n\\begin{eqnarray}\nO &=& \\frac{1}{\\sqrt{2}} \\left(\n\\begin{array}{cc}\n 1 & 1 \\\\\n -1 & 1 \n\\end{array} \\right), \\quad OO^T = I.\n\\end{eqnarray}\nand we define for any matrix $A$, the rotated matrix as \n\\begin{equation}\n\\breve{A} = O^T \\sigma_z A O = O^{T} \\bar{A} O.\n\\end{equation}\nThis is known as Keldysh rotation. The effect of Keldysh rotation is given in Eq.~(\\ref{keldysh-rotation}). Since this is an orthogonal transformation the trace remains invariant and hence we can write\n\\begin{eqnarray}\n{\\rm Tr}_{t,j,\\sigma}(\\bar{A}\\bar{B} \\cdots \\bar {D})&=& {\\rm Tr}_{t,j,\\sigma}(\\breve{A}\\breve{B} \\cdots \\breve{D}).\n\\end{eqnarray}\nIf we now go to the frequency domain using the definition of two-time Fourier transform given in Eq.~(\\ref{two-time-FT}) \nthen we can compute the trace in frequency domain as \n\\begin{eqnarray}\n{\\rm Tr}_{(j,\\tau)}(AB \\cdots D) &=& \\int\\! \\frac{d\\omega_1}{2\\pi} \\!\n\\int\\! \\frac{d\\omega_2}{2\\pi}\\! \\cdots\n\\int\\! \\frac{d\\omega_n}{2\\pi} \\!\n{\\rm Tr} \\bigl\\{ \\nonumber \\\\\n && \\breve{A}[\\omega_1, -\\omega_2] \n\\breve{B}[\\omega_2, -\\omega_3] \\cdots \\breve{D}[\\omega_n, -\\omega_1] \\bigr\\}\n\\nonumber \\\\ \n&=& {\\rm Tr}_{j,\\sigma,\\omega} ( \\breve{A} \\breve{B} \\cdots \\breve{D} ).\n\\end{eqnarray}\nThe last line above define what we mean by trace over frequency domain given in Eq.~(\\ref{eq-Zsteady}).\nUnlike trace in time domain, the second argument of the each of the variables\nneed a minus sign. \n\nLet us now define what do we mean by 1 on contour. In the sense of convolution we define 1 as\n\\begin{equation}\nA \\, 1\\, D =A \\, D\n\\end{equation}\nwhich means\n\\begin{equation}\n\\int d\\tau_1 \\!\\! \\int d\\tau_2\\, A(\\tau,\\tau_1) I \\delta(\\tau_1,\\tau_2) D(\\tau_2,\\tau')= \\int d\\tau_1 A(\\tau,\\tau_1) D(\\tau_1,\\tau'). \n\\end{equation}\nNote that $\\delta(\\tau,\\tau')$ in the real time has the following form\n\\begin{equation}\n\\delta^{\\sigma,\\sigma'}(t,t')=\\sigma \\delta_{\\sigma,\\sigma'} \\delta(t-t').\n\\end{equation}\nThe inverse on the contour is defined as\n\\begin{equation}\n\\int d\\tau_1 A(\\tau,\\tau_1) B(\\tau_1,\\tau') = I \\delta(\\tau,\\tau'),\n\\end{equation}\nwhere the identity matrix $I$ takes care about the space index. Similar to the above we go to the real time and multiply the above equation with the branch index $\\sigma$ and we can write,\n\\begin{equation} \n\\int dt_1 {\\bar A}(t,t_1) {\\bar B}(t_1,t') = I \\bar{\\delta}(t-t').\n\\end{equation}\nwhere \n\\begin{eqnarray}\n\\bar{\\delta}(t-t')&=&\\sigma \\delta^{\\sigma,\\sigma'}(t,t')=\\sigma^{2} \\delta_{\\sigma,\\sigma'} \\delta(t-t') \\nonumber \\\\\n&&=\\delta_{\\sigma,\\sigma'} \\delta(t-t')\n\\end{eqnarray}\nIf we now discretize the time and write $\\delta(t_i,t_{i'})=\\delta_{i,i'}\/\\Delta t$ with $\\Delta t= |t_i-t_{i'}|$ then we have\n\\begin{equation}\n\\tilde{A} \\tilde{B} =\\tilde{I}.\n\\end{equation}\nwith $\\tilde{A}= A \\Delta t$ and similarly for other matrices. \n\nWith similar notions we can now write different types of Dyson's equation given in Eq.~(\\ref{eq-Dyson-full},\\ref{eq-Dyson-product}) as following. In contour time we have \n\n\\begin{eqnarray}\nG_0(\\tau, \\tau') &=& g_C(\\tau,\\tau') \\\\\n&&\\> + \\int \\!\\int d\\tau_1d \\tau_2\\, \ng_C(\\tau, \\tau_1) \\Sigma(\\tau_1, \\tau_2) G_0(\\tau_2, \\tau'), \\nonumber\n\\end{eqnarray}\nIn real time following the above arguments we write\n\\begin{eqnarray}\n\\bar{G}_{0}(t,t') &=& \\bar{g}_C(t,t') \\\\\n&&\\> + \\int \\!\\int dt_1 dt_2\\, \n{\\bar g}_C(t, t_1) {\\bar \\Sigma}(t_1, t_2) {\\bar G}_0(t_2, t'), \\nonumber\n\\end{eqnarray}\nAfter Keldysh rotation we can write \n\\begin{eqnarray}\n\\breve{G}_{0}(t,t') &=& \\breve{g}_C(t,t') \\\\\n&&\\> + \\int \\!\\int dt_1 dt_2\\, \n{\\breve g}_C(t, t_1) {\\breve \\Sigma}(t_1, t_2) {\\breve G}_0(t_2, t'). \\nonumber\n\\end{eqnarray}\nFinally in the discretize time $t$ we write \n\\begin{equation}\n\\tilde{G}_{0}=\\tilde{g}_C + \\tilde{g}_C \\tilde{\\Sigma} \\tilde{G}_{0},\n\\end{equation}\nwhich is a matrix equation. Similar equations can also be written down for Eq.~(\\ref{eq-Dyson-full}).\n \nNow we define determinant via the relation $\\det(A)=\\exp({\\rm Tr} \\ln A)$, i.e, the determinant is defined in terms of trace. In order for $\\ln A$ to be defined we have to assume a Taylor expansion. For example we can define $\\ln(1+M)=M-M^2\/2 + M^3\/3 + \\cdots $ where 1 means $\\delta_{jj'}\\delta(\\tau,\\tau')$ in contour space.\n\n\\subsection{A quick derivation of the Levitov-Lesovik formula for electrons using NEGF}\nThe generating function for the non-interacting electrons was first derived by Levitov and Lesovik \\cite{Levitov,Levitov1} using Landauer type of wave scattering approach. Klich \\cite{Klich} and Sch\\\"onhammer \\cite{otherworks2} re-derived the formula using a trace and determinant relation to reduce the problem from many-body problem to a single particle Hilbert space problem. Esposito et al.\\ gave an approach using the superoperator nonequilibrium Green's function \\cite{Esposito-review-2009}. A more rigorous treatment is given in Ref.~\\onlinecite{Bernard}.\n\nOur method for calculating CGF can be easily extended for the electron case. Here we will derive the CGF for the joint probability distribution for particle and energy without time-dependent driving force. \nThe Hamiltonian of the whole system can be written as (using tight-binding model)\n\\begin{equation}\n{\\cal H}^{e}=\\sum_{\\alpha=L,C,R} c_{\\alpha}^{\\dagger} h^{\\alpha} c_{\\alpha} + \\sum_{\\alpha=L,R} \\big(c_{\\alpha}^{\\dagger} V_{e}^{\\alpha C} c_{C} + {\\rm h.c.}\\big)\n\\end{equation}\nwhere $c_{\\alpha}$ is a column vector consisting of all the annihilation operator of region $\\alpha$. $c_{\\alpha}^{\\dagger}$ is a row vector of the corresponding creating operators. $h^{\\alpha}$ is the single particle Hamiltonian matrix. $V_{e}^{\\alpha C}$ has similar meaning as $V^{\\alpha C}$ in the phonon Hamiltonian and $V_{e}^{\\alpha C}=(V_{e}^{C\\alpha})^{\\dagger}$. \n\nWe are interested in calculating the generating function corresponding to the particle operator ${\\cal N}_L$ and energy operator ${\\cal H}_L$ of the left-lead where ${\\cal H}_L= c_{L}^{\\dagger} h^{L} c_L$ and ${\\cal N}_L= c_{L}^{\\dagger} c_L$ \\cite{Hanggi1}. One can easily generalize the formula for right lead also as we did in the phonon case. For electrons ${\\cal N}_L$ and ${\\cal H}_L$ can be measured simultaneously because they commute, i.e., $\\big[{\\cal H}_L, {\\cal N}_L \\big]=0$. In order to calculate the CGF we introduce two counting fields $\\xi_p$ and $\\xi_e$ for particle and energy respectively. Here we will consider the product initial state (with fixed temperatures and chemical potentials for the leads) and derive the long-time result.\n\nSimilar to the phonon case we can write the CGF as \n\\begin{equation}\n{\\cal Z}(\\xi_e,\\xi_p)= \\Big \\langle e^{i\\big(\\xi_e {\\cal H}_L + \\xi_p {\\cal N}_L \\big)}\\,e^{-i\\big(\\xi_e {\\cal H}^{H}_L + \\xi_p {\\cal N}^{H}_L \\big)} \\Big \\rangle,\n\\end{equation}\nwhere superscript $H$ means the operators are in the Heisenberg picture at time $t$. In terms of modified Hamiltonian the CGF can be expressed as\n\\begin{equation}\n{\\cal Z}(\\xi_e,\\xi_p)= \\Big \\langle {\\cal U}_{(\\frac{\\xi_e}{2},\\frac{\\xi_p}{2})} (0,t) \\, {\\cal U}_{(-\\frac{\\xi_e}{2},-\\frac{\\xi_p}{2})} (t,0) \\Big \\rangle,\n\\end{equation} \nwhere \n\\begin{eqnarray}\n{\\cal U}_{x,y}(t,0)&=& e^{i x {\\cal H}_L + i y {\\cal N}_L}\\, {\\cal U}(t,0)\\,e^{-i x {\\cal H}_L - i y {\\cal N}_L} \\nonumber \\\\\n&&= e^{-\\frac{i}{\\hbar} {\\cal H}_{x,y} t}\n\\end{eqnarray}\nwith $x=\\xi_e\/2$ and $y=\\xi_p\/2$ and ${\\cal U}(t,0)=e^{-i {\\cal H} t}$. ${\\cal H}_{x,y}$ is the modified Hamiltonian which evolves with both ${\\cal H}_L$ and ${\\cal N}_L$ and is given by\n\\begin{eqnarray}\n{\\cal H}_{x,y}&=& e^{i x {\\cal H}_L + i y {\\cal N}_L}\\, {\\cal H} \\,e^{-i x {\\cal H}_L - i y {\\cal N}_L} \\nonumber \\\\\n&&= {\\cal H}_L + {\\cal H}_C + {\\cal H}_R + \\big(e^{iy} c_{L}^{\\dagger}(\\hbar x) V_{e}^{LC} c_C +{\\rm h.c.}\\big)\\nonumber \\\\\n&& + \\big(c_R^{\\dagger} V_{e}^{RC} c_{C} + {\\rm h.c.}\\big),\n\\end{eqnarray}\nwhere we have used the fact that\n\\begin{eqnarray}\ne^{i x {\\cal H}_L} c_{L}(0) e^{-i x {\\cal H}_L} &=& c_{L}(\\hbar x), \\nonumber \\\\\ne^{i y {\\cal N}_L} c_{L}(0) e^{-i y {\\cal N}_L} &=& e^{-i y} c_{L}.\n\\end{eqnarray}\nSo the evolution with ${\\cal H}_L$ and ${\\cal N}_L$ is to shift the time-argument and produce a phase for $c_{L},c_{L}^{\\dagger}$ respectively. Next we go to the interaction picture of the modified Hamiltonian ${\\cal H}_{x,y}$ with respect to ${\\cal H}_{0}= \\sum_{\\alpha=L,C,R} {\\cal H}_{\\alpha}$ and the CGF then can be written on the contour running from 0 to $t_M$ and back as,\n\\begin{equation}\n{\\cal Z}(\\xi_e,\\xi_p)= {\\rm Tr}\\Big[\\rho(-\\infty) T_{c} e^{-\\frac{i}{\\hbar} \\int d\\tau {\\cal V}_{x,y}^{I}(\\tau)} \\Big],\n\\end{equation} \nwhere ${\\cal V}_{x,y}^{I}(\\tau)$ is written in contour time. \n\\begin{eqnarray}\n{\\cal V}_{x,y}^{I}(\\tau)&=&\\big ( e^{i y} c_{L}^{\\dagger}(\\tau+\\hbar x) V_{e}^{LC} c_C (\\tau)+ {\\rm h.c.} \\big)+ \\nonumber \\\\\n&& \\big(c_{R}(\\tau)^{\\dagger} V_{e}^{RC} c_C(\\tau) + {\\rm h.c.}\\big).\n\\end{eqnarray}\nNow we can expand the exponential in the generating function and use Feynman diagrams to sum the series and finally the CGF can be shown to be\n\\begin{equation}\n\\ln {\\cal Z}(\\xi_e,\\xi_p)={\\rm Tr}_{j,\\tau} \\ln \\Big[1-G^{e}_{0} \\Sigma_{L,e}^{A} \\Big],\n\\end{equation}\nwhere we define the shifted self-energy for the electron case as\n\\begin{equation}\n\\Sigma_{L,e}^{A}(\\tau,\\tau')=e^{i (y(\\tau')-y(\\tau))} \\Sigma_{L,e}(\\tau+\\hbar x, \\tau'+\\hbar x') -\\Sigma_{L,e}(\\tau,\\tau').\n\\end{equation}\nThe counting of the electron number is associated with factor of a phase, while\nthe counting of the energy is related to translation in time.\nNote that the CGF does not have the characteristic $1\/2$ pre-factor as compared to the phonon case because $c$ and $c^{\\dagger}$ are independent\nvariables. In the long-time limit following the same steps as we did for phonons, the CGF can be written down as (after doing Keldysh rotation) \n\\begin{equation}\n\\ln {\\cal Z}(\\xi_e,\\xi_p)=t_M \\int \\frac{dE}{2\\pi \\hbar} {\\rm Tr} \\ln \\big(I- \\breve{G}^{e}_{0}(E)\\breve{\\Sigma}_{L,e}^{A}(E)\\big).\n\\end{equation}\nIn the energy $E$ domain different components of the shifted self-energy are\n\\begin{eqnarray}\n\\Sigma_{A}^{t}(E)&=&\\Sigma_{A}^{\\bar{t}}(E)=0, \\nonumber \\\\\n\\Sigma_{A}^{<}(E)&=& \\big( e^{i (\\xi_p + \\xi_e E)} -1 \\big) \\Sigma_{L}^{<}(E), \\nonumber \\\\\n\\Sigma_{A}^{>}(E)&=& \\big( e^{-i (\\xi_p + \\xi_e E)} -1 \\big) \\Sigma_{L}^{>}(E). \n\\end{eqnarray}\nFinally the CGF can be simplified as\n\\begin{eqnarray}\n\\ln {\\cal Z}&=&t_M \\int \\frac{dE}{2\\pi\\hbar} \\,\\ln \\det \\Bigl\\{ I + G_{0}^r \\Gamma_L \nG_{0}^a \\Gamma_R \\Big[(e^{i\\alpha}\\! -\\! 1)f_L \\nonumber \\\\ \n&&+ ( e^{-i\\alpha} \\!-\\! 1) f_R - (\ne^{i\\alpha} \\!+\\! e^{-i\\alpha} \\!-\\!2 ) f_L f_R \\Big]\\Bigr\\}.\\qquad .\n\\end{eqnarray}\nwhere $\\alpha=\\xi_p+ \\xi_e E$ and $f_\\alpha$ is the Fermi distribution. Note the difference of the signs in the CGF as compared to the phonons. If we replace $\\alpha$ by $(E-\\mu_L) \\xi$, the resulting formula is for the counting of the heat \n${\\cal Q}_L = {\\cal H}_L - \\mu_L {\\cal N}_L$ transferred, where $\\mu_L$ is the chemical potential of the left lead.\nThe CGF obeys the following fluctuation symmetry \\cite{fluct-theorem-1}\n\\begin{equation}\n{\\cal Z}(\\xi_e,\\xi_p)= {\\cal Z}\\big(-\\xi_e + i(\\beta_R-\\beta_L), -\\xi_p -i (-\\beta_R \\mu_R -\\beta_L \\mu_L)\\big).\n\\end{equation}\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n The century-old general relativity (GR) theory still successfully passes \n all local experimental tests. However, there are many reasons to consider \n this theory not as an ultimate theory of gravity but only as a reasonable \n approximation well working in a large but finite range of length and energy \n scales. Among such reasons are the old problem of unifying gravity with other \n physical interactions and the difficulties in attempts to quantize GR. \n Other reasons for dealing with modifications of GR are the well-known problems \n experienced by the theory itself: its prediction of space-time singularities\n in the most physically relevant solutions, actually showing situations where \n the theory does not work any more, and its inability to explain the\n main observable features of the Universe without introducing so far invisible \n forms of matter, dark matter (DM) and dark energy (DE), which \n add up to as much as 95\\,\\% of the energy content of the Universe.\n \n The existing modifications and extensions of classical GR can be divided into \n two large classes. The first one changes the geometric content of the theory\n and includes, in particular, $f(R)$ theories, multidimensional theories and \n non-Riemannian geometries. The second class introduces new fundamental, \n non-geometric fields and includes, in particular, scalar-tensor theories, \n Horndeski theory \\cite{horn} and vector-tensor theories. Of much interest are the \n cases where it is possible to establish connections between different representatives \n of the same class or even different classes of theories (possibly the most well-known \n example of such a connection is the equivalence of $f(R)$ theories with a \n certain subclass of scalar-tensor theories, see, e.g., \\cite{f(R),odin}). In the present paper \n we discuss such an equivalence between large families of models of k-essence theories \n and Rastall's non-conservative theories with a scalar field as a source of gravity. \n\n The k-essence theories, introducing a non-stan\\-dard form of the kinetic term \n of a scalar field \\cite{k1, k2}, evidently belong to the class of theories with\n non-standard fundamental fields coupled to gravity. They proved to be a way of \n obtaining both early inflation and the modern accelerated expansion of the \n Universe \\cite{k2, k3, k4} driven by a scalar kinetic term instead of a \n potential. Notably, a kind of k-essence structure also appears in string theories,\n for example, in the Dirac-Born-Infeld action, where the kinetic term of the \n scalar field has a structure similar to that of the Maxwell-like term in Born-Infeld \n electrodynamics \\cite{dbi}.\n\n Rastall's theory \\cite{rastall} is one more generalization of GR, which relaxes the\n conservation laws expressed by the zero divergence of the stress-energy tensor (SET) \n $T\\mN$ of matter. In this theory, the quantity $\\nabla_\\nu T\\mN$ is linked to the \n gradient of the Ricci scalar, and in this way Rastall's theory may be viewed as \n a phenomenological implementation of some quantum effects in a curved background.\n Rastall's theory leads to results of interest in cosmology, e.g., the evolution of \n small DM fluctuations is the same as in the $\\Lambda$CDM model, but DE is able to \n cluster. This might potentially provide an evolution of DM inhomogeneities in the non-linear \n regime different from the standard CDM model \\cite{cosmo}. The whole success of the \n $\\Lambda$CDM model is reproduced at the background and linear perturbation levels, \n but new effects are expected in the non-linear regime, where the $\\Lambda$CDM model\n faces some difficulties \\cite{cusp, satt}. It has also been shown \n \\cite{oliver} that Rastall's theory with the canonical SET of a scalar field, in the context of \n cosmological perturbations, is only consistent if matter is present. An interesting \n observation in this analysis is that scalar field coupling with gravity leads to equations \n very similar to those in some classes of Galileon theories.\n\n A consideration of static, spherically symmetric solutions in k-essence theories with a power law\n kinetic function \\cite{denis}\n and similar solutions in Rastall's theory in the presence of a free or self-interacting scalar field\n \\cite{we-16} has shown that some exact solutions of these two theories describe quite the same \n geometries, although the properties of the scalar fields are different. \n Also, a no-go theorem concerning the possible emergence of Killing horizons, proved in the \n k-essence framework \\cite{denis}, has its counterpart in the Rastall-scalar field system\n \\cite{we-16}. These similarities indicate a deeper relationship between the two theories,\n to be analyzed in this paper. We will show that in the absence of other matter than the \n (possibly self-interacting) scalar fields, the two theories lead to completely coinciding \n geometries (we will call this {\\sl k-R duality}) under the assumptions that the relevant \n quantities depend on a single spatial or temporal coordinate and that the k-essence theory \n is specified by a power-law function; then, there emerges a simple \n relation between the numerical parameters of the theories. If there are other forms of matter, \n the situation is more involved and depends on how the non-conservation of the SET is \n distributed between different matter contributions in Rastall's theory. We will discuss two \n variants of such non-conservation in the case of isotropic spatially flat cosmological models\n and show that k-R duality generically takes place. \n \n The paper is organized as follows. In the next section we discuss vacuum solutions of \n k-essence and Rastall theories. In Section 3, isotropic cosmological models are analyzed \n with a matter source in the form of a perfect fluid. Some considerations on the speed of \n sound are presented in Section 4, while Section 5 is devoted to some special values of \n the numerical parameters of both theories. Some concrete cosmological configurations \n with dustlike matter are discussed in Section 6. Our conclusions are presented in Section 7.\n\n\\section{Scalar-vacuum space-times}\n\n\\subsection{k-essence}\n\n The k-essence theories can be defined as \\GR\\ with generalized forms \n of scalar fields minimally coupled to gravity. In the absence of matter \n nonminimally coupled to gravity, the most general Lagrangian is\n\\begin{eqnarray} \\lal \t\t\t\\label{L-k}\n\t{\\cal L} = \\sqrt{-g}[R + F(X,\\phi) + L_m],\n\\end{eqnarray}\n with\n\\begin{eqnarray} \\label{X}\n\tX = \\eta\\phi_\\mu\\phi^\\mu,\n\\end{eqnarray}\n where $\\phi_\\mu = \\partial_\\mu \\phi$, $F(X,\\phi)$ is an arbitrary function, and\n $\\eta = \\pm 1$ is used to make $X$ positive since otherwise in the cases\n like general power-law dependence $F$ will be ill-defined for $X < 0$;\n $L_m$ is the Lagrangian density of other kinds of matter having no direct \n coupling to the curvature or the $\\phi$ field.\n We are using the system of units where $c = 8\\pi G =1$\n\n Variation of the Lagrangian (\\ref{L-k}) with respect to the metric and\n the scalar field leads to the field equations\n\\begin{eqnarray} \\lal \t\t \\label{EE}\n\tG\\mN \\equiv R\\mN - {\\fract{1}{2}}\\delta\\mN R = -T\\mN [\\phi] - T\\mN[m],\n\\\\[5pt] \\lal \\label{T-phi}\n\tT\\mN [\\phi] \\equiv \\eta F_X \\phi_\\mu \\phi^\\nu\n\t\t- \\frac{1}{2} \\delta\\mN F ,\n\\\\[5pt] \\lal \t\t\t\t \t\t \\label{eq-phi}\n \\eta\\nabla_\\alpha (F_X \\phi^\\alpha) - {\\fracd{1}{2}}\\, F_\\phi = 0,\n\\end{eqnarray}\n where $G\\mN$ is the Einstein tensor, $F_X = \\partial F\/\\partial X$, $F_\\phi = \\partial F\/\\partial\\phi$,\n and $T\\mN[m]$ is the SET of matter due to $L_m$.\n\n Now, let us make the following assumptions: \\\\[5pt] {}\n{\\bf (i)} the k-essence Lagrangian is\n\\begin{equation} \\label{L-k1}\n F (X, \\phi) = F_0 X^n - 2V(\\phi),\n\\end{equation}\n where $n = {\\rm const} \\ne 0$ and $V(\\phi)$ is an arbitrary function (the potential).\n\\\\[5pt] {}\n{\\bf (ii)} $\\phi = \\phi(u)$, where $u$ is one of the coordinates, which may be temporal or spatial.\n\\\\[5pt] {}\n{\\bf (iii)} The metric has the form \n\\begin{equation} \\label{ds}\n ds^2 = \\eta {\\,\\rm e}^{2\\alpha(u)} du^2 + h_{ik} dx^i dx^k, \n\\end{equation} \n where $i, k$ are the numbers of coordinates other than $u$, and the determinant of $h_{ik}$\n has the factorized structure\n\\begin{equation} \\label{h_ik}\n \\det (h_{ik}) = {\\,\\rm e}^{2\\sigma(u)} h_1(x^i).\n\\end{equation}\n\n In this case, we have $X = {\\,\\rm e}^{-2\\alpha(u)} \\phi_u^2$ (the index $u$ means $d\/du$), \n and the SET of the $\\phi$ field has the following nonzero components:\n\\begin{eqnarray} \\lal \\label{uu-k}\n T^u_u [\\phi] = (n - {\\fract{1}{2}}) F_0 X^n + V,\n\\\\[5pt] \\lal \\label{ii-k}\n T^\\ui_i [\\phi] = - {\\fract{1}{2}} F_0 X^n + V \n\\end{eqnarray}\n (there is no summing over an underlined index). The scalar field equation has the form\n\\begin{eqnarray} \\lal \\label{eq-k1}\n {\\,\\rm e}^{-2n\\alpha} \\phi_u^{2n-2} \\big[(2n-1)\\phi_{uu} + \\sigma_u \\phi_u \n\\nonumber\\\\ \\lal \\hspace*{1cm}\n - (2n-1) \\alpha_u\\phi_u \\big] = - \\frac {1}{nF_0} \\frac {d V}{d\\phi}.\n\\end{eqnarray}\n\n\\subsection{Rastall's theory with a scalar field}\n\n Rastall's theory of gravity is characterized by the following equations \\cite{rastall}:\n\\begin{eqnarray} \\lal \\label{EE-Ra}\n\tR\\mN - \\frac{\\lambda}{2}\\delta\\mN R = - T\\mN,\n\\\\[5pt] \\lal \n\t\\nabla_\\nu T\\mN = \\frac{\\lambda - 1}{2}\\partial_\\mu R,\n\\end{eqnarray}\n where $\\lambda$ is a free parameter and $T\\mN$ is the SET of matter. \n At $\\lambda = 1$, GR is recovered.\n\n These equations can be rewritten as\n\\begin{eqnarray} \\lal \t\\label{EE-R1}\n\tG\\mN = - \\biggr\\{T\\mN - \\frac{b - 1}{2}\\delta\\mN T\\biggl\\} \\equiv - \\tT\\mN,\n\\\\[5pt] \\lal \\hspace*{-0.5em} \\label{cons-R} \n \\nabla_\\nu T\\mN = \\frac{b {-} 1}{2}\\partial_\\mu T,\\quad \n b: = \\frac{3\\lambda - 2}{2\\lambda - 1}, \\quad T = T^\\alpha_\\alpha.\n\\end{eqnarray}\n In this parametrization, GR is recovered if $b = 1$.\n\n Let us consider matter in the form of a minimally coupled scalar field $\\psi$, so that \n\\begin{equation} \\label{SET-psi}\n T\\mN[\\psi] = \\epsilon (\\psi_\\mu \\psi^\\nu - {\\fract{1}{2}} \\delta\\mN \\psi_\\alpha \\psi^\\alpha)\n + \\delta\\mN W(\\psi), \n\\end{equation}\n where $\\epsilon = \\pm 1$, indicating an ordinary ($+1$) or phantom ($-1$) nature \n of the $\\psi$ field, $\\psi_\\mu \\equiv \\partial_\\mu\\psi$, and $W(\\psi)$ is a potential. \n The scalar field equation follows from \\rf{cons-R} and has the form\n\\begin{equation} \\label{e-psi-R}\n \\Box\\psi + (b - 1)\\frac{\\psi^\\mu \\psi^\\nu \\nabla_\\mu \\psi_\\nu}\n {\\psi^\\alpha \\psi_\\alpha} = - \\epsilon (3 - 2b) \\frac {dW}{d\\psi}.\n\\end{equation}\n \n Let us now, in full similarity with what was done for k-essence theory, assume that \n $\\psi = \\psi(u)$ and the metric has the form \\rf{ds}. Then the nonzero components \n of the modified scalar field SET in the right-hand side of \\eqn{EE-R1} are\n\\begin{eqnarray} \\lal \\label{uu-R}\n \\tT^u_u [\\psi] = {\\fract{1}{2}} \\epsilon b \\eta {\\,\\rm e}^{-2\\alpha} \\psi_u^2 + (3-2b) W(\\psi),\n \\\\[5pt] \\lal \\label{ii-R}\n\t \\tT^{\\ui}_i [\\psi] = {\\fract{1}{2}} \\epsilon (b-2) \\eta {\\,\\rm e}^{-2\\alpha} \\psi_u^2 + (3-2b) W(\\psi),\n\\end{eqnarray} \n while the scalar field equation \\rf{e-psi-R} takes the form\n\\begin{equation} \\label{epsi-R2}\n {\\,\\rm e}^{-2\\alpha}\\big[b \\psi_{uu} + \\psi_u (\\sigma_u - b\\alpha_u)\\big]\n = - \\epsilon \\eta (3-2b) W_\\psi,\n\\end{equation}\n where $W_\\psi \\equiv dW\/d\\psi$ and, as before, $\\eta = \\mathop{\\rm sign}\\nolimits g_{uu}$.\n\n\\subsection{Comparison}\n\n We assume that in the k-essence system there is no other matter than the scalar \n field $\\phi$ and in the Rastall system there is no other matter than the scalar $\\psi$. \n Let us find out under which conditions the right-hand sides of the \\EE s \\rf{uu-k}\n and \\rf{ii-k} coincide with those of the effective \\EE s of Rastall's theory,\n \\rf{uu-R} and \\rf{ii-R}. This will guarantee that the solutions for the metric are \n also the same. \n\n To begin with, we identify the potentials:\n\\begin{equation} \\label {VW}\n V(\\phi) = (3-2b) W(\\psi).\n\\end{equation} \n Next, we equate the ratios of the kinetic parts of $T\\mN[\\phi]$ and $\\tT\\mN[\\psi]$,\n to obtain \n\\begin{equation} \\label{nb}\n \\frac{2n-1}{-1} = \\frac b {b-2} \\ \\ \\Rightarrow\\ \\ (2-b) n = 1. \n\\end{equation}\n Then, equating the kinetic parts themselves, we find that\n\\begin{eqnarray} \\lal \\label{phips}\n \\psi^2_u = \\epsilon\\eta n F_0 \\phi_u^{2n}{\\,\\rm e}^{2(1-n)\\alpha}.\n\\end{eqnarray} \n\n Under the three conditions \\rf{VW}--\\rf{phips}, the metric field equations of the \n two theories completely coincide, therefore their sets of solutions are also identical.\n Substituting \\rf{phips} to \\rf{epsi-R2}, one can easily verify that under these conditions \n the scalar field equations \\rf{eq-k1} and \\rf{epsi-R2} are also equivalent.\n\n This general result covers many static symmetries (spherical, plane, cylindrical, etc.),\n homogeneous cosmologies (FRW, all Bianchi types, \\KS) and even inhomogeneous \n ones if their metrics are of the form \\rf{ds}, \\rf{h_ik}.\n\n Here and in most of the paper we consider the generic values of the parameters $n$ and \n $b$ and exclude from consideration their special values that require a separate analysis, \n such as, for example, $b=0$, $b=3\/2$ and $n = 1\/2$. Some remarks on these special cases \n will be made in Section 5. \n\n\\section{Cosmology with matter}\n \n When, besides the scalar field, matter is present, it is better, for evident technical reasons, \n to restrict ourselves from the beginning to a certain type of metrics. We will consider \n cosmological FLRW spatially flat metrics\n\\begin{equation} \\label{ds1}\n\t ds^2 = dt^2 - a(t)^2[dx^2 + dy^2 + dz^2],\n\\end{equation}\n so that in \\rf{ds} and \\rf{h_ik} we have $\\eta=1$, ${\\,\\rm e}^\\alpha =1$, and ${\\,\\rm e}^\\sigma = a(t)^3$.\n Matter will be taken in the form of a perfect fluid, so that \n\\begin{equation} \\label{Tm}\n\t T\\mN [m] = \\mathop{\\rm diag}\\nolimits (\\rho, -p, -p, -p), \n\\end{equation} \n where $\\rho$ is the density and $p$ is the pressure.\n\n\\subsection{k-essence cosmology}\n \n In the FLRW metric \\rf{ds1} and with $\\phi=\\phi(t)$, the field equations \\rf{EE}--\\rf{eq-phi} \n with matter (where we denote $\\rho = \\rho_k,\\ p=p_k$) take the form\n\\begin{eqnarray} \\lal \\label{tt-k1}\n \t3H^2 = {\\fract{1}{2}}(2n - 1) F_0\\dot\\phi^{2n} + V(\\phi) + \\rho_k,\n\\\\[5pt] \\lal \\label{ii-k1}\n 2\\dot H + 3H^2 = - {\\fract{1}{2}} F_0 \\dot\\phi^{2n} + V(\\phi) - p_k,\n\\\\[5pt] \\lal \\label{phi-k1} \n \\dot\\phi^{2n-2} [(2n-1)\\ddot\\phi + 3H\\dot\\phi] = - \\frac{1}{nF_0}V_\\phi,\n\\end{eqnarray}\n where $H =\\dot a\/a$ is the Hubble parameter and $V_\\phi \\equiv dV\/d\\phi$. The SET \n of matter satisfies the conservation law $\\nabla_\\nu T\\mN[m] =0$, whence\n\\begin{equation} \\label{cons}\n\t\\dot\\rho_k + 3H (\\rho_k + p_k) =0.\n\\end{equation} \n \n\\subsection{Rastall cosmology with a scalar field and matter}\n\\def\\trho{{\\widetilde \\rho}}\n\\def\\tp{{\\widetilde p}}\n\n The Rastall equations have the form \\rf{EE-R1} and \\rf{cons-R}, where now \n $T\\mN$ is the total energy-momentum tensor,\n\\begin{eqnarray}\n T\\mN = T\\mN [\\psi] + T\\mN [m],\n\\end{eqnarray}\n with $T\\mN[\\psi]$ given by \\rf{SET-psi} and $T\\mN[m]$ by \\rf{Tm}. \n In Eqs.\\, \\rf{EE-R1}, the modified energy-momentum tensor $\\tT\\mN$ is then a sum\n of $\\tT\\mN[\\psi]$ given by \\rf{uu-R} and \\rf{ii-R} and $\\tT\\mN[m]$ with the components\n\\begin{eqnarray} \\lal \\label {redef}\n\t \\tT^t_t [m] = {\\fracd{1}{2}} [(3-b) \\rho + 3 (b-1) p] \\equiv \\trho,\n\\nonumber\\\\ \\lal \n\t \\tT^\\ui_i [m] = {\\fracd{1}{2}} [(1-b)\\rho + (3b-5)p] \\equiv - \\tp \n\\end{eqnarray} \n (we preserve the notation $\\rho$ and $p$ without indices for matter in Rastall gravity). \n Hence the Rastall equations read\n\\begin{eqnarray} \\lal \\label{tt-Ra}\n 3 H^2 = {\\fracd{1}{2}} \\epsilon b \\dot\\psi{}^2 + (3-2b) W + \\trho,\n\\\\[5pt] \\lal \\label{ii-Ra}\n\t 2\\dot H + 3H^2 = {\\fracd{1}{2}} \\epsilon (b-2) \\dot\\psi{}^2 + (3-2b) W - \\tp,\n\\end{eqnarray}\n while the equation for $\\psi$ depends of further assumptions on how the nonconservation \n of the full SET according to \\rf{cons-R} is distributed between $\\psi$ \n and matter. One can notice that \n\\begin{equation} \\label{NEC-R}\n\t\\trho + \\tp = \\rho + p,\n\\end{equation}\n and \n\\begin{equation} \\label{trace}\n \\trho - \\rho = p - \\tp = \\frac{1-b}{2} (\\rho - 3p). \n\\end{equation}\n\n Let us consider two (of an infinite number of) alternatives in incorporating matter \n to Rastall's theory with a scalar field: \n\\begin{description}\n\\item[R1:] \n The SETs of $\\psi$ and matter obey \\rf{cons-R} each separately, so there is\n no mixing between the two sources of gravity;\n\\item[R2:]\n The SET of matter is conservative, so that \n\\begin{equation} \n \\dot\\rho + 3H (\\rho+p) =0. \n\\end{equation}\n\\end{description}\n\n\\subsection{Case R1: No mixing of scalar field and matter} \n\n In this case we have\n\\begin{eqnarray} \\lal \\label{cons-i1}\n \\nabla_\\nu T\\mN[\\psi] = \\frac{b - 1}{2}\\partial_\\mu T[\\psi],\n\\\\[5pt] \\lal \\label{cons-i2}\n \\nabla_\\nu T\\mN[m] = \\frac{b - 1}{2}\\partial_\\mu T[m],\n\\end{eqnarray}\n The first of these conditions leads to the scalar field equation \\rf{epsi-R2}, which in \n the present case reads\n\\begin{equation} \\label{epsi-i} \n\tb \\ddot\\psi + 3H \\dot\\psi = -\\epsilon (3-2b) W_\\psi.\n\\end{equation}\n With \\rf{NEC-R}, the condition \\rf{cons-i2} has the form \n\\begin{equation} \\label{cons-i}\n\t\\dot\\trho + 3H (\\rho + p) =0.\n\\end{equation} \n The full set of equations consists of \\rf{tt-Ra}, \\rf{ii-Ra}, \\rf{epsi-i}, and \\rf{cons-i}, \n with the definitions \\rf{redef}.\n \n From \\rf{NEC-R} it follows that if matter satisfies the null energy condition (NEC),\n then the same is true for the ``effective'' density and pressure ($\\trho$ and $\\tp$) in \n Rastall's theory. However, from \\rf{redef} it can be verified that the positivity of \n the energy density (or pressure) is not guaranteed in the k-essence case if it is \n imposed in the Rastall theory, and vice versa.\n\n It is easy to see that the right-hand sides of Eqs.\\, \\rf{tt-Ra} and \\rf{ii-Ra} coincide with\n those of \\rf{tt-k1} and \\rf{ii-k1} if, in addition to the relationships \\rf{VW}--\\rf{phips}\n for scalar variables, we identify \n\\begin{equation} \\label{id-m}\n \\rho_k = \\trho, \\qquad p_k = \\tp.\n\\end{equation}\n The correctness of this identification is confirmed by the identity of the conservation \n laws \\rf{cons} and \\rf{cons-i}. Thus, as in the vacuum case, the parameters \n $n$ and $b$ are related by \\rf{nb}, that is, $n(2-b) =1$, and the scalar fields $\\phi$ and \n $\\psi$ are related by \\eqn{phips} which now reads\n\\begin{equation} \\label{psif}\n \\dot\\psi{}^2 = \\epsilon n F_0 \\dot\\phi{}^{2n}.\n\\end{equation}\n \n\\subsection{Case R2: Conservative matter}\n \n We now have $\\nabla_\\nu T\\mN [m] = 0$. This condition is particularly important \n for the structure formation in the universe for the case of a pressureless fluid \n since ordinary matter must agglomerate.\n\n In this case, for the scalar field SET we have \n\\begin{eqnarray}\n \\nabla_\\nu T\\mN [\\psi] ={\\fracd{1}{2}} (b - 1) (\\partial_\\mu T[\\psi] + \\partial_\\mu T[m]),\n\\end{eqnarray}\n which leads to the scalar field equation\n\\begin{eqnarray} \\lal \\label{epsi-ii} \n\t\\dot\\psi(b \\ddot\\psi + 3H \\dot\\psi) = -\\epsilon (3-2b) W_\\psi \\dot\\psi\n\\nonumber\\\\ \\lal \\hspace*{1in}\n\t+ {\\fracd{1}{2}}\\epsilon (b-1)(\\dot\\rho - 3\\dot p). \n\\end{eqnarray}\n The full set of equations consists of \\rf{tt-Ra}, \\rf{ii-Ra}, \\rf{epsi-ii}, and \\rf{cons}, \n with the definitions \\rf{redef}. Note that \\eqn{epsi-ii} mixes the scalar field and the \n matter fluid even though the fluid is conserved as in GR.\n\n This conservation makes us identify the matter SET components in the k-essence\n and Rastall theories: $\\rho = \\rho_k$, $p = p_k$. As a result, identification of the other \n parts of the total SET is only partly the same as in the previous case. \n\n Identifying, as before, the potentials according to \\rf{VW} (that is, $V = (3-2b)W$)\n and comparing the Friedmann-like equations \\rf{tt-Ra} and \\rf{ii-Ra} with \n their k-essence counterparts \\rf{tt-k1} and \\rf{ii-k1}, we obtain, as before, \n\\begin{equation} \\label{psif-ii}\n \\epsilon\\dot\\psi^2 = nF_0\\dot\\phi^{2n}.\n\\end{equation}\n The correctness of this identification is verified by substituting \\rf{psif-ii} into \n the scalar field equation \\rf{epsi-ii}: indeed, since we have now, due to \\rf{trace}, \n\\begin{equation} \\label{nb-ii}\n \\epsilon\\dot\\psi^2 \\Big[b - \\frac{2n-1}{n}\\Big] = (b-1) (\\rho-3p), \n\\end{equation}\n this substitution leads precisely to the scalar field equation \\rf{phi-k1} of the \n k-essence theory.\n\n It is important that in the case of conservative matter, a comparison \n between the two theories does not lead to a direct relationship like \n \\rf{nb} between their numerical parameters $n$ and $b$. Instead, we have \n the equality \\rf{nb-ii}, from which \\rf{nb} is restored only in the special case \n $\\rho=3p$ (zero trace of the matter SET, radiation).\n\n\\subsection{Further consequences of matter conservation}\n\n The relation \\rf{nb-ii} creates a connection between the temporal behavior of $\\psi$ and the \n matter content. Indeed, inserting \\rf{nb-ii} to \\rf{epsi-ii} with zero or constant potential, \n we find\n\\begin{equation}\n \\dot F + \\frac{6n}{2n - 1}HF = 0,\n\\end{equation}\n where $F = \\dot\\psi^2$. This leads to\n\\begin{equation} \\label{sol1}\n \\dot\\psi^2 = \\psi_0a^{-6n\/(2n - 1)},\n\\end{equation}\nwhere $\\psi_0$ is an integration constant.\n\n From Eq.\\, \\rf{nb-ii} it is clear that the matter density and pressure must also evolve by a \n power law as functions of the scale factor. Hence, only an equation of state (EoS) of the type \n $p = w\\rho$, with $w = {\\rm const}$, is possible. In this case, \n\\begin{equation}\n \\rho = \\rho_0 a^{-3(1 + w)}, \\qquad \\rho_0 = {\\rm const}, \n\\end{equation}\n implying the relation between $w$ and $n$\n\\begin{equation} \\label{rela}\n w = \\frac{1}{2n - 1}.\n\\end{equation}\n We see that a substitution of $p = w\\rho$ into the scalar field equation relates the EoS factor\n $w$ with the k-essence power $n$, while the Rastall constant $b$ remains arbitrary. \n Moreover, Eqs.\\, \\rf{tt-k1} and \\rf{ii-k1} show that the pure k-essence scalar field $\\phi$ behaves \n as a perfect fluid with the same EoS factor \\rf{rela} (see also \\cite{carla}).\n\n In other words, assuming a zero or constant potential $V = (3-2b)W$ and conservative matter \n in the Rastall framework, we obtain that {\\sl the k-R duality is only possible if matter \n is a perfect fluid with the linear EoS $p = w\\rho$, coinciding with the effective EoS\n of the scalar field $\\phi$. }\n\n With $p = w\\rho$ \\eqn{nb-ii} gives \n\\begin{eqnarray} \\lal\n \\epsilon \\dot\\psi^2 = k\\rho,\n\\qquad\n\tk = \\frac{n(b- 1)(1 - 3w)}{bn-2n+1}.\n\\end{eqnarray}\n Inserting this to the Friedmann-like equation \\rf{tt-Ra}, we obtain in terms of $n$ or $w$\n\\begin{eqnarray}\n 3H^2 \\al =\\al V + \\frac{\\rho}{2n - 1}\n\t\\biggr\\{\\frac{2nb(b - 1)(n - 2)}{bn -2n + 1}\n\\nonumber\\\\ \\lal \\hspace*{1cm}\\cm\n\t + 2(2b - 3) + 2n(3 - b)\\biggr\\}\n\\\\[5pt] {} \n \\al =\\al V + \\frac{\\rho}2 \\biggl\\{\\frac{b(b-1)(1+w)(1-3w)}{(b-2)(1+w)+2w}\n\\nonumber\\\\ \\lal \\hspace*{1cm}\\cm\n + 3-b + 3w(b-1)\\biggr\\}.\n\\end{eqnarray}\n The right-hand side must be positive. Therefore, given $n$ and $V$ (or alternatively $w$ \n and $V$), we obtain a restriction on $b$. For example, if $V=0$ and $w = 0$ \n (dust, $n \\to \\infty$),\\footnote\n\t{This relation makes sense even if the Lagrangian formulation becomes ill-defined,\n\t see \\cite{carla}.} \n we have either $b < 3\/2$ or $b > 2$ (provided $\\rho > 0$). For $w = 1$ (stiff matter, $n = 1$), \n there is no restriction on $b$, and we obtain $H^2 = V = {\\rm const} >0$, hence a de Sitter \n expansion, $a(t) \\propto{\\,\\rm e}^{Ht}$. In this case, stiff matter precisely cancels the contribution \n from the scalar field $\\psi$ or $\\phi$. \n\n If we introduce a variable potential or a more complex equation of state, the situation becomes \n much more involved. It must be stressed that the EoS $p = w\\rho$ with $w={\\rm const}$\n covers most of the interesting cases in cosmology. Moreover, we expect that the \n perturbative behavior may be very different in the two theories even in this case.\n\n There emerge two more natural questions. First, we have obtained that in k-essence theory\n there are simultaneously a scalar field and a perfect fluid with the same EoS and hence the same \n time evolution of their densities and pressures. Can we unify them by, for example, redefining\n the scalar field? A probable answer is ``no'' because these two kinds of matter are expected to\n behave quite differently at the perturbative level.\n\n Another question is: how is it possible to have a completely definite situation in k-essence \n theory but an arbitrariness in the parameter $b$ in the dual solution of Rastall's theory? An \n answer is that this arbitrariness is compensated by the corresponding non-conservative \n behavior of the scalar field $\\psi$. \n\n\\section{Perturbations and the speed of sound}\n\n A power law k-essence model with $V = 0$ is equivalent in the cosmological \n framework (such that $(\\partial_\\mu \\phi)^2 > 0$) to a perfect fluid with the equation \n of state $p = w \\rho$, where the constant $w$ is related to the power $n$:\n\\begin{equation} \\label{w}\n\tw = \\frac{1}{2n - 1}.\n\\end{equation}\n In a fluid, adiabatic perturbations propagate as a sound with the speed $v_s$ such that\n\\begin{equation} \\label{v-fl}\n\tv_s^2 = dp\/d\\rho = w.\n\\end{equation}\n \n Scalar field perturbations for general Lagrangians of the form $F(X,\\phi)$\n have been treated in detail in \\cite{neven, k3}. It has been shown there that\n a k-essence theory implies \n\\begin{equation}\n v_s^2 = \\frac{F_X}{F_X + 2X F_{XX}},\n\\end{equation}\n and this expression is valid even if there is an arbitrary potential term $V(\\phi)$.\n In particular, for the theory \\rf{L-k1}, where $F(X) = F_0 X^n - 2V(\\phi)$, we find again\n\\begin{equation} \\label{ss1}\n v_s^2 = \\frac{1}{2n - 1}\n\\end{equation}\n in full agreement with \\rf{v-fl}. Thus there is a complete equivalence between \n a perfect fluid and k-essence without a potential not only for a cosmological \n background but even on the perturbative level as far as adiabatic perturbations\n are concerned. In particular, if $w < 0$, corresponding to $n < 1\/2$, the model \n is perturbatively unstable since it implies $v_s^2 < 0$. This is true both for a perfect \n fluid and for k-essence. Moreover, although the presence of a potential \n changes the scalar field dynamics, the propagation speed of its perturbations, coinciding \n with the derived speed of sound \\cite{neven}, is still the same as with $V=0$. \n \n In Rastall's theory things may be different. The speed of sound for a scalar field is \n given by \\cite{oliver, liddle}\n\\begin{equation} \\label{ss2}\n v_s^2 = \\frac{2 - b}{b}.\n\\end{equation}\n In scalar vacuum and in the R1 case (matter obeys the non-conservation equation \n \\rf{cons-i1}), we have the relation \\rf {nb}, $(2-b)n = 1$, which makes (\\ref{ss1}) \n and (\\ref{ss2}) identical. Furthermore, the fluids in the corresponding models \n obey different equations of state, see \\rf{id-m}. However, in the Rastall model we \n can still characterize the fluid by the ``effective'' density and pressure, $\\trho$ and \n $\\tp$, the SET written in their terms is conservative, hence the squared speed of \n sound of the Rastall fluid is equal to $d\\tp\/d\\trho = d p_k\/d\\rho_k$.\n Thus we can conclude, even without performing a complete perturbation analysis, \n that the models belonging to the two theories coincide not\n only at the background level but also at the level of adiabatic perturbations.\n\n In the case R2 (conserved matter), we have another relation \\rf{nb-ii} between the \n parameters $b$ and $n$, without such a simple connection. As a consequence, in principle \n it is possible that an unstable model in a k-essence model may correspond to a stable model \n in Rastall's theory, or vice versa, since, as shown above, Eq.\\, \\rf{nb} does not hold,\n $b$ being now essentially independent of $n$ up to some possible restrictions on their range. \n In fact, in this case, even non-adiabatic perturbations may appear, due to the coupling\n between the scalar field and matter. \n\n\\section{Some special cases} \n\n\\subsection{$n=1\/2$} \n \n In this case, the k-essence scalar field equation takes the form\n\\begin{equation} \\label{n-half}\n\t\t3H = -2 F_0^{-1} V_\\phi. \n\\end{equation}\n Thus if $V={\\rm const}$, we have $H=0$, hence $a = {\\rm const}$, and flat space-time is obtained. \n One can certainly obtain $H=0$ in Rastall gravity under special assumptions, but the question\n of k-R duality looks meaningless in this trivial case. \n \n If $V=V(\\phi)$, the $\\phi$ field has no dynamics of its own, but \\eqn{n-half} expresses it\n in terms of $H$, and the Friedmann equations \\rf{tt-k1} and \\rf{ii-k1} are meaningful.\n \n The Rastall counterpart in the scalar-vacuum and R1 cases is then obtained with $b=0$, \n $V = 3W$ (according to \\rf{nb} and \\rf{VW}) and \n\\begin{equation} \\label{psif-half}\n\t\t2\\epsilon \\dot\\psi{}^2 = F_0 \\dot\\phi. \n\\end{equation}\n\n In the case R2 (conserved matter), $b$ remains arbitrary, but k-R duality still holds.\n Indeed, if we substitute \\rf{psif-half} into \\eqn{epsi-ii} and use the Friedmann-like \n equations \\rf{tt-Ra}, \\rf{ii-Ra} to calculate $\\dot\\rho-3\\dot p$, we obtain \\eqn{n-half}.\n\n The main feature in this case is that nontrivial solutions with $n = 1\/2$ and k-R duality\n are only achieved in the presence of a variable potential.\n\n\\subsection{$b=3\/2$}\n\n With this value of $b$, the potential disappears from Rastall's gravity, hence the k-R duality \n implies $V=0$, and we deal with zero potentials. In other respects, the situation is \n described as in the general case. \n\n A feature of interest is that with $b = 3\/2$ \\eqn{redef} leads to $\\trho = 3\\tp$. \n Therefore, in the scalar-vacuum and R1 cases, the dual k-essence counterpart of \n this Rastall model contains matter with $\\rho_k= 3p_k$ (see \\rf{id-m}). \n Due to \\rf{nb}, in addition, $n=2$, so that the $\\phi$ field also behaves as radiation.\n\n In the case R2 (conserved matter), the relations \\rf{id-m} and \\rf{nb} are no more \n valid, and the general description is applicable. \n \n\\subsection{$b=2$}\n\n Equations \\rf{w} and \\rf{ss2} give zero values of pressure and the speed of sound of\n a scalar field in Rastall's theory. The corresponding expression \\rf{ss1} in k-essence \n theory leads to $n \\to \\infty$ according to the general relation \\rf{nb}.\n \n If there is conserved matter (case R2), then (unless this conserved matter is pure\n radiation, $\\rho = 3p$) \\eqn{nb} is no more valid, so that the speeds of sound \n of scalar fields are different in the two theories. It means that k-R duality does not\n exist for perturbations even though it does exist for the isotropic background.\n\n\\subsection{$b=0$}\n\n In the scalar-vacuum and R1 cases we return to the above description for $n = 1\/2$.\n \n With conserved matter (case R2), the Rastall scalar field takes the form\n\\begin{equation} \\label{psi-b0}\n 3H \\epsilon \\dot\\psi{}^2 = -3 \\dot W -{\\fracd{1}{2}} (\\dot\\rho-3\\dot p),\n\\end{equation}\n looking like a constraint equation since it contains only the first-order derivative.\n However, the k-R duality still works, as before: thus, a substitution of \\rf{psif} \n (what is important, with arbitrary $n \\ne 0$) and $\\rho-3p$ from Eqs.\\, \\rf{tt-Ra}, \n \\rf{ii-Ra} into \\rf{psi-b0} leads to \\rf{phi-k1}, which is a second-order equation \n unless $n = 1\/2$. \n\n\\section{Examples}\n\n Let us now consider some specific examples of the equivalence discussed above,\n assuming a zero or constant potential and dust as a possible matter contribution.\n\n\\subsection{Scalar vacuum}\n\n Consider scalar vacuum with zero potential. The k-essence equations give\n\\begin{eqnarray} \\label{sfe}\n\t\\dot\\phi \\al =\\al \\phi_0 a^{- 3\/(2n - 1)}, \\qquad \\phi_0 = {\\rm const},\n\\\\[5pt] {}\n H^2 \\al =\\al \\frac{2n - 1}{6}F_0 \\phi_0^{2n}a^{-6n\/(2n - 1)}.\n\\end{eqnarray}\n In terms of cosmic time we obtain\n\\begin{eqnarray}\n a \\al =\\al a_0 t^{2\/[3(1 + w)]}, \\qquad a_0 = {\\rm const},\n\\\\[5pt] {}\n\t \\dot\\phi \\al =\\al \\phi_1 t^{- 2w\/(1 + w)},\n\\end{eqnarray}\n where $\\phi_1$ is a combination of the previous constants, and we \n have written $w = 1\/(2n - 1)$, thus identifying the k-essence with a perfect \n fluid with the EoS $p = w\\rho$. \n\n In Rastall's theory, the dual solution contains the same $a(t)$, while the scalar field is given by\n\\begin{equation} \\label {sfr}\n \\dot\\psi \\propto a^{- 3(1 + w)\/2} = a^{-3\/b} \\propto t^{-1},\n\\end{equation}\n where now we should put $w = (2-b)\/b$. We notice that while the k-essence scalar field \n behavior depends on the EoS parameter $w$, the Rastall scalar is simply $\\psi = \\log t + {\\rm const}$.\n\n Addition of a constant potential, equivalent to a cosmological constant, does not change the \n scalar field evolution laws \\rf{sfe} and \\rf{sfr} in terms of $a$ but makes the time \n dependences more complex, not to be considered here.\n\n In the presence of matter, as we saw above, the form of k-R duality depends on how matter \n couples to the scalar field. \n\n\\subsection{Dust and Rastall-R1 models}\n\n Suppose that in k-essence theory, in addition to the scalar field $\\phi$, there is \n pressureless fluid (dust), so that\n\\begin{equation} \\label{dust-k} \n p_k =0, \\qquad \\rho_k = \\rho_1 a^{-3}, \\qquad \\rho_1={\\rm const}.\n\\end{equation}\n then, in the R1 version of Rastall's theory, according to \\rf{id-m}, we have the conditions\n\\begin{eqnarray} \\label{ex1}\n\t {\\fracd{1}{2}}\\Big[(3 - b)\\rho + 3(b-1)p\\Big] \\al =\\al \\trho = \\rho_k,\n\\nonumber\\\\ {}\n {\\fracd{1}{2}} \\Big[(b-1)\\rho + (5-3b)p \\Big] \\al =\\al \\tp = 0,\n\\end{eqnarray}\n leading to the following relations for the density and pressure \n\\begin{eqnarray} \\label{ex2}\n \\rho \\al =\\al \\frac{5-3b}{2(3-2b)} \\rho_k,\n\\nonumber\\\\ {}\n\tp \\al =\\al \\frac{1-b}{5-3b} \\rho = \\frac{1-b}{2(3-2b)} \\rho_k,\n\\end{eqnarray}\n both evolving as $\\rho \\propto p \\propto a^{-3}$. Thus in Rastall cosmology the \n fluid acquires pressure (except for the GR value $b = 1$). The scalar fields in both \n models satisfy the same relations as in the vacuum case, valid for any values of \n $n$ and $b$ such that $n(b-2)=1$. \n\n Adding a constant potential $V = (3-2b)W$ does not change the relations \\rf{ex1}\n and \\rf{ex2} and introduces an effective cosmological constant. We then obtain \n a three-component model with matter, a cosmological constant and a scalar field \n whose behavior is determined by $n$ or, equivalently, by $w = 1\/(2n-1)$. In the \n dual Rastall model, we have an effective pressure even though in the k-essence \n model $p_k=0$. \n\n If, on the contrary, we introduce matter with $p=0$ in Rastall's (R1) theory, \n then in the dual k-essence model we have \n\\begin{equation}\n \\rho_k = \\trho = {\\fracd{1}{2}} (3-b)\\rho, \\qquad p_k = \\tp = {\\fracd{1}{2}} (b-1)\\rho, \n\\end{equation}\n and their evolution law agreeing with \\eqn{cons-i} reads\n\\begin{equation}\n \\rho_k \\propto \\rho \\propto a^{-6\/(3-b)} = a^{-3(1+w_k)}, \n\\end{equation}\n where the EoS parameter $w_k$ of the fluid in the k-essence model is \n\\begin{equation}\n w_k = \\frac{b-1}{3-b} = \\frac{n-1}{n+1} \n\\end{equation}\n (not to be confused with $w = 1\/(2n-1)$ characterizing the $\\phi$\n field behavior); as before, the relation $n(2-b) =1$ holds. The model thus obtained\n is quite different from the one with dust introduced in k-essence theory.\n\n\\subsection{Dust and Rastall-R2 models}\n\n Let us again assume \\eqn{dust-k} but now consider version R2 of Rastall's theory,\n so that now $\\rho \\propto a^{-3}$ and \n\\begin{equation} \\label{nb-ex}\n\t\\epsilon \\dot\\psi^2\\Big[b - \\frac{2n - 1}{n}\\Big] = (b - 1)\\rho \\propto a^{-3}.\n\\end{equation}\n Then for $b \\neq 1$ we find according to \\rf{psif-ii}\n\\begin{eqnarray}\n\t\\dot\\psi &\\propto& a^{- 3\/2},\n\\nonumber\\\\ {}\n \\dot\\phi^n &\\propto& a^{-3\/2} \\ \\ \\ \\Rightarrow\\ \\ \\ \\dot\\phi \\propto a^{-3\/(2n)}.\n\\end{eqnarray}\n Combining this with the relation $\\epsilon \\dot\\psi{}^2 = nF_0 \\dot\\phi{}^{2n}$ and the \n field equation \\rf{phi-k} with $V_\\phi=0$, we find that this situation corresponds to\n the limit $n \\to \\infty$, which is, however, well defined. In this way we obtain \n $a \\propto t^{2\/3}$ as in the pure dust model of GR. \n For the scalar fields it follows in this limit\n\\begin{eqnarray} \\lal \n \\dot\\phi ={\\rm const} \\ \\ \\Rightarrow\\ \\ \\phi \\propto t,\n\\nonumber\\\\ \\lal \n \\dot\\psi \\propto a^{-3\/2} \\propto 1\/t.\n\\end{eqnarray}\n The condition for $b$ is obtained from \\rf{nb-ex}: writing $\\dot\\psi = \\psi_0\/t $ and\n $\\rho = \\rho_0\/t^2$, we arrive at\n\\begin{equation}\n\t\\epsilon\\psi_0^2 (b - 2) = (b - 1)\\rho_0.\n\\end{equation}\n Thus the value of $b$ is determined by the relative contributions of matter and the scalar field. \n Moreover, the speed of sound of the scalar field now does not follow the adiabatic relation \n verified in the R1 case.\n\n A cosmological constant can be easily introduced in the form of $V = (3-2b) W = {\\rm const}$. \n The scalar field again follows the law \\rf{nb-ex}, and the whole configuration reduces to the\n $\\Lambda$CDM model where $\\Lambda$ is given by the constant potential and the matter \n component consists of the scalar field and ordinary matter. All background relations of the \n $\\Lambda$CDM model are preserved in this case, but the degeneracy between the scalar \n field and usual matter is broken at the perturbative level. Due to the fact that the \n $\\Lambda$CDM model is subject to problem at the perturbative level in the non-linear regime \n (see, e.g., \\cite{cusp,satt}), such a more complex configuration in k-essence and Rastall \n models may lead to interesting results, to be studied in the future.\n\n\\section{Conclusion}\n\n We have studied the conditions of equivalence between the k-essence and Rastall theories of \n gravity in the presence a scalar field (k-R duality). These two theories have actually emerged in \n very different contexts, the k-essence theory being based on a generalization of the kinetic term \n of a scalar field, suggested by some fundamental theories, while Rastall's theory is a \n non-conservative theory of gravity which can be seen as a possible phenomenological \n implementation of quantum effects in gravitational theories. Such equivalence has been \n revealed in the case of static spherically symmetric models \\cite{denis, we-16}, and it \n has been more explicitly stated here for all cases where the metric and scalar fields essentially\n depend on a single coordinate, and the k-essence theory is specified by a power-law function \n of the usual kinetic term, to which a potential term can be added. This generalization covers \n diverse static and cosmological models, including all homogeneous cosmologies.\n\n We have discussed cosmological configurations with scalar fields and matter in the form of \n a perfect fluid whose evolution in Rastall's theory can follow one of two possible laws: one (R1) \n assumes no mixing between matter and the scalar field, each of them separately obeying \n the non-conservation law \\rf{cons-R}, and the other (R2) ascribes the whole non-conservation \n to the scalar field while matter is conservative ($\\nabla_\\nu T\\mN[m] =0$). Let us summarize\n the main results obtained in this context:\n\\begin{enumerate}\n\\item \n\tk-R duality has been established for version R1 of Rastall's theory with an arbitrary \n \tEoS of matter. It has been found that the EoS of matter is different in the mutually dual\n \tk-essence and Rastall models; however, it is argued that the respective speeds of \n\tsound are the same. Since the speeds of sound characterizing the scalar fields ($\\phi$ in \n\tk-essence theory and $\\psi$ in Rastall's) also coincide, we conclude that k-R duality\n is maintained not only for the cosmological backgrounds but also for adiabatic \n perturbations.\n\\item \n\tFor version R2 of Rastall's theory, it has been found that k-R duality exists only with \n\tfluids having the EoS $p = w\\rho,\\ w={\\rm const}$, which is the same for k-essence and \n\tRastall models. Moreover, in the k-essence model the scalar field obeys the same \n\teffective EoS. However, on the perturbative level the mutually dual models \n\tbehave, in general, differently. \n\\item \n\tSome special cases have been discussed, showing how there emerge some restrictions \n\ton the free parameters of each theory. \n\\item \n\tAn example has been considered in which a cosmological model completely equivalent \n to the $\\Lambda$CDM model of GR is obtained at the background level, but different \n\tfeatures must appear at the perturbative level. \n\\end{enumerate}\n\n The equivalence between the two theories discussed here is somewhat surprising because of\n their basically different origin. A curious aspect is that the k-essence theory has a Lagrangian\n formulation unlike Rastall's theory. It is possible that the equivalence studied here may lead to\n a restricted Lagrangian formulation of Rastall's theory in the minisuperspace in terms of \n metric functions depending on a single variable. If this is true, it might suggest how to recover \n a complete Lagrangian formulation for Rastall's theory in a more general framework.\n\n\\subsection*{Acknowledgments} \n\n We thank CNPq (Brazil) and FAPES (Brazil) for partial financial support.\n KB thanks his colleagues from UFES for kind hospitality and collaboration.\n The work of KB was performed within the framework of the Center \n FRPP supported by MEPhI Academic Excellence Project \n (contract No. 02.a03. 21.0005, 27.08.2013),\n within the RUDN University program 5-100, and RFBR project No. 16-02-00602. \n\n\\small\n\n\\subsection*{Acknowledgment} #1}\n\n\t\n\n\\def.95{.95}\n\\def.95{.95}\n\\def.95{.95}\n\\def.05{.05}\n\\def.95{.95}\n\\def.95{.95}\n\n\\newcommand{\\EFigure}[2]{\\begin{figure} \\centering \n \\framebox[85mm]{\\epsfxsize=80mm\\epsfbox{#1}}\n \\caption{\\protect\\small #2}\\medskip\\hrule\n\t\\end{figure}}\n\\newcommand{\\REFigure}[2]{\\begin{figure} \\centering\n \\framebox[85mm]{\\epsfysize=80mm\\rotate[r]{\\epsfbox{#1}}}\n \\caption{\\protect\\small #2}\\medskip\\hrule\n\t\\end{figure}}\n\\newcommand{\\WEFigure}[2]{\\begin{figure*} \\centering\n \\framebox[178mm]{\\epsfxsize=170mm\\epsfbox{#1}}\n \\caption{\\protect\\small #2}\\medskip\\hrule\n\t\\end{figure*}}\n\n\t\n\n\\def\\Jl#1#2{#1 {\\bf #2},\\ }\n\n\\def\\ApJ#1 {\\Jl{Astroph. J.}{#1}}\n\\def\\CQG#1 {\\Jl{Class. Quantum Grav.}{#1}}\n\\def\\DAN#1 {\\Jl{Dokl. AN SSSR}{#1}}\n\\def\\GC#1 {\\Jl{Grav. Cosmol.}{#1}}\n\\def\\GRG#1 {\\Jl{Gen. Rel. Grav.}{#1}}\n\\def\\JETF#1 {\\Jl{Zh. Eksp. Teor. Fiz.}{#1}}\n\\def\\JETP#1 {\\Jl{Sov. Phys. JETP}{#1}}\n\\def\\JHEP#1 {\\Jl{JHEP}{#1}}\n\\def\\JMP#1 {\\Jl{J. Math. Phys.}{#1}}\n\\def\\NPB#1 {\\Jl{Nucl. Phys. B}{#1}}\n\\def\\NP#1 {\\Jl{Nucl. Phys.}{#1}}\n\\def\\PLA#1 {\\Jl{Phys. Lett. A}{#1}}\n\\def\\PLB#1 {\\Jl{Phys. Lett. B}{#1}}\n\\def\\PRD#1 {\\Jl{Phys. Rev. D}{#1}}\n\\def\\PRL#1 {\\Jl{Phys. Rev. Lett.}{#1}}\n\n\t\n\n\\def&\\nhq{&\\hspace*{-0.5em}}\n\\def&&\\nqq {}{&&\\hspace*{-2em} {}}\n\\defEq.\\,{Eq.\\,}\n\\defEqs.\\,{Eqs.\\,}\n\\def\\begin{equation}{\\begin{equation}}\n\\def\\end{equation}{\\end{equation}}\n\\def\\begin{eqnarray}{\\begin{eqnarray}}\n\\def\\begin{eqnarray} \\lal{\\begin{eqnarray} &&\\nqq {}}\n\\def\\end{eqnarray}{\\end{eqnarray}}\n\\def\\nonumber \\end{eqnarray}{\\nonumber \\end{eqnarray}}\n\\def\\nonumber\\\\ {}{\\nonumber\\\\ {}}\n\\def\\nonumber\\\\[5pt] {}{\\nonumber\\\\[5pt] {}}\n\\def\\nonumber\\\\ \\lal {\\nonumber\\\\ &&\\nqq {} }\n\\def\\nonumber\\\\[5pt] \\lal {\\nonumber\\\\[5pt] &&\\nqq {} }\n\\def\\\\[5pt] {}{\\\\[5pt] {}}\n\\def\\\\[5pt] \\lal {\\\\[5pt] &&\\nqq {} }\n\\def\\al =\\al{&\\nhq =&\\nhq}\n\\def\\al \\equiv \\al{&\\nhq \\equiv &\\nhq}\n\\def\\sequ#1{\\setcounter{equation}{#1}}\n\n\n\\def\\displaystyle{\\displaystyle}\n\\def\\textstyle{\\textstyle}\n\\def\\fracd#1#2{{\\displaystyle\\frac{#1}{#2}}}\n\\def\\fract#1#2{{\\textstyle\\frac{#1}{#2}}}\n\\def{\\fracd{1}{2}}{{\\fracd{1}{2}}}\n\\def{\\fract{1}{2}}{{\\fract{1}{2}}}\n\n\n\\def{\\,\\rm e}{{\\,\\rm e}}\n\\def\\partial{\\partial}\n\\def\\mathop{\\rm Re}\\nolimits{\\mathop{\\rm Re}\\nolimits}\n\\def\\mathop{\\rm Im}\\nolimits{\\mathop{\\rm Im}\\nolimits}\n\\def\\mathop{\\rm arg}\\nolimits{\\mathop{\\rm arg}\\nolimits}\n\\def\\mathop{\\rm tr}\\nolimits{\\mathop{\\rm tr}\\nolimits}\n\\def\\mathop{\\rm sign}\\nolimits{\\mathop{\\rm sign}\\nolimits}\n\\def\\mathop{\\rm diag}\\nolimits{\\mathop{\\rm diag}\\nolimits}\n\\def\\mathop{\\rm dim}\\nolimits{\\mathop{\\rm dim}\\nolimits}\n\\def{\\rm const}{{\\rm const}}\n\\def\\varepsilon{\\varepsilon}\n\\def\\epsilon{\\epsilon}\n\n\\def\\ \\Rightarrow\\ {\\ \\Rightarrow\\ }\n\\newcommand{\\mathop {\\ \\longrightarrow\\ }\\limits }{\\mathop {\\ \\longrightarrow\\ }\\limits }\n\\newcommand{\\aver}[1]{\\langle \\, #1 \\, \\rangle \\mathstrut}\n\\newcommand{\\vars}[1]{\\left\\{\\begin{array}{ll}#1\\end{array}\\right.}\n\\def\\sum\\limits{\\sum\\limits}\n\\def\\int\\limits{\\int\\limits}\n\n \n\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nGraphs are ubiquitous in the real world, which can easily express various and complex relationships between objectives. In recent years, extensive studies have been conducted on deep learning methods for graph-structured data. There are several approaches on analyzing the graph, including network embedding \\cite{perozzi2014deepwalk, tang2015line, grover2016node2vec}, which only uses the graph structure, and graph neural networks (GNNs), which consider graph structure and node features simultaneously. GNNs have shown powerful ability on modeling the graph-structured data in a variety of graph learning tasks such as node classification \\cite{2018Large, hamilton2017inductive, yang2016revisiting, kipf2016semi, velivckovic2017graph, wu2019simplifying}, link prediction \\cite{zhang2017weisfeiler, zhang2018link, cai2020multi} and graph classification \\cite{gilmer2017neural, lee2019self, ma2019graph, xu2018powerful, ying2018hierarchical, zhang2018end}. GNNs have also been applied to a range of applications, including social analysis \\cite{qiu2018deepinf, li2019encoding}, recommender systems \\cite{ying2018graph, monti2017geometric}, traffic prediction \\cite{guo2019attention, li2019predicting}, drug discovery \\cite{zitnik2017predicting} and fraud detection \\cite{liu2019geniepath}. \n\nGNNs usually have different design paradigms, which include the spectral graph convolutional networks \\cite{bruna2013spectral, defferrard2016convolutional, kipf2016semi}, message passing framework \\cite{gilmer2017neural, hamilton2017inductive}, and neighbor aggregation via recurrent neural networks \\cite{li2015gated, dai2018learning}. By using the idea of message passing framework, GNNs are to design various graph convolutional layers to update each node representation by aggregating the node representations from its neighbors. \n\nHowever, most GNNs only consider the immediate neighbors for each node, which impedes their ability to extract the information of high-order neighbors. More layers usually lead to the performance degradation, which is caused by over-fitting and over-smoothing, of which the former is due to the increasing number of parameters when fitting a limited dataset whereas the latter is the inherent issue of the graph learning. How to make use of the high-order information of neighbors as well as achieving better performance remains a challenge. We need more insights to understand what GCN does and why over-smoothing occurs.\n\\begin{figure*}[t]\n\t\\centering \n\t\\subfigure{\n\t\t\\includegraphics[width=0.12\\textwidth]{original.pdf}}\\hspace{-4mm}\n\t\\subfigure{\n\t\t\\includegraphics[width=0.12\\textwidth]{mlp.pdf}}\\hspace{-2mm}\n\t\\subfigure{\n\t\t\\includegraphics[width=0.12\\textwidth]{GCN-2.pdf}}\\hspace{-2mm}\n\t\\subfigure{\n\t\t\\includegraphics[width=0.12\\textwidth]{GCN-6.pdf}}\\hspace{-2mm}\n\t\\subfigure{\n\t\t\\includegraphics[width=0.12\\textwidth]{GCN-8.pdf}}\\hspace{-2mm}\n\t\\subfigure{\n\t\t\\includegraphics[width=0.12\\textwidth]{GCN+8.pdf}}\\hspace{-2mm}\n\t\\subfigure{\n\t\t\\includegraphics[width=0.12\\textwidth]{GCN+16.pdf}}\\hspace{-2mm}\n\t\\subfigure{\n\t\t\\includegraphics[width=0.12\\textwidth]{GCN+32.pdf}}\n\t\\caption{t-SNE Visualization of learned node representations, which include original features, MLP, different layers of GCN and different hops of GCN+ on \\textit{Cora}. Colors represent node classes.}\n\t\\label{model_vs}\n\\end{figure*}\n\nSeveral studies \\cite{li2018deeper, xu2018representation, klicpera2018predict, chen2020simple, liu2020towards} have noticed over-smoothing, that is after multiple propagations, the final output of vanilla multi-layer GCN converges to a vector which only carries the information of the degree of graph and the node features are indistinguishable. Fig. \\ref{model_vs} shows the node representations of vanilla multi-layer GCN on a small citation network \\textit{Cora}. We can observe that 2-layer GCN learns a meaningful embeddings which distinguish the different classes whereas more layers degrade the performance and lead to indistinguishable features.\n\nDifferent from previous studies, we interpret the current graph convolutional operations from an optimization perspective, and argue that over-smoothing is mainly caused by the naive first-order approximation of the solution to the optimization problem. By solving it and applying the first-order approximation, we get the standard GCN kernel. This suggests that the original GCN kernel can be viewed as a simplified version of the solution. We argue that this simplification loses necessary information which is crucial to tackle the over-smoothing to some extent. Based on this observation, two metrics are proposed to measure the smoothness of connected and disconnected pairwise node features respectively. Furthermore, we set three constraints: (a) the embedding learned by GCNs should not be too far off of the original features; (b) the connected nodes should have similar embeddings; (c) the disconnected nodes are assumed to have different embeddings. \n\nAs a result, we build a universal theoretical framework of GCN from an optimization perspective which smooths the node features and regularizes the (disconnected) node feature simultaneously. We consider two different cases of our framework, where the first case contains the current popular GCN \\cite{kipf2016semi}, SGC \\cite{wu2019simplifying} and PPNP \\cite{klicpera2018predict}, and the second case regularizes the pairwise distance of disconnected nodes.\n\nThe contributions of this work are summarized as follow:\n\\begin{itemize}\n\t\\item We provide a universal theoretical framework of GCN from an optimization perspective where the popular GCNs can be viewed as a special case of it. Furthermore, we derive a novel convolutional kernel named GCN+, which relieves the over-smoothing inherently and has lower parameter amount.\n\t\\item We propose two quantitative metric to measure the smoothness and over-smoothness of the final nodes representations, which provides new insight to analyze the over-smoothing.\n\t\\item We conduct extensive experiments on several public real-world datasets. Our results demonstrate the superior performance of GCN+ over state-of-the-art baseline methods.\n\\end{itemize} \n\n\\section{Notations}\nGiven an undirected graph $G=(V,E,X)$, $V$ is node set with $|V|=n$, $E$ is edge set. Let $A\\in \\mathbb{R}^{n \\times n}$ denote the adjacency matrix, where $A_{ij}=1$ if there is an edge between node $i$ and node $j$ otherwise 0. Let $D\\in \\mathbb{R}^{n \\times n}$ denote the diagonal degree matrix where $D_{ii}=\\sum_{j}A_{ij}$. Each node is associated with $d$ features, and $X \\in \\mathbb{R}^{n\\times d}$ is the feature matrix of nodes. each row of $X$ is a signal defined over nodes. The graph Laplacian matrix is defined as $L=D-A$. Let $\\tilde{A}=A+I$ and $\\tilde{D}=D+I$ denote the adjacency and degree matrices of the self-loop graph respectively. We denote $\\tilde{A}_{\\textit{sym}}=\\tilde{D}^{-1\/2} \\tilde{A} \\tilde{D}^{-1\/2}$ and $\\tilde{A}_{\\textit{rw}}=\\tilde{D}^{-1} \\tilde{A}$. Assume that each node $v_i$ is associated with a class label $y_i \\in Y$ where $Y$ is a set of $c$ classes. Let $N(v)$ denote the neighbors of $v$ in graph, that is $N(v)=\\{u\\in V|\\{u,v\\}\\in E\\}$ and $\\tilde{N}(v)=N(v) \\cup \\{v\\}$.\n$L'$ is the Laplacian matrix of the graph $G'(V',E',X)$, which is the complement of $G$, that means $G'$ has the same nodes as $G$ whereas if $\\{u,v\\}\\in E$, then $\\{u,v\\}\\notin E'$. Let $A'$ and $D'$ denote the corresponding adjacency and degree matrix respectively. We have $A'+A=J_n-I_n$ and $D'+D=(n-1)I$ where $J_n$ is a matrix whose element are all 1. Let $\\text{num}(E)$ and $\\text{num}(E')$ denote the numbers of edges in $G$ and $G'$ respectively, we have $\\text{num}(E)+\\text{num}(E')=\\frac{n(n-1)}{2}$.\n\\section{Perspectives of GCN}\nHere we provide three views to derive or understand the vanilla GCNs.\n\\subsection{Spectral Graph Convolution}\n\\citet{bruna2013spectral} define the spectral convolutions on graph by applying a filter $g_\\theta$ in the Fourier domain to a graph signal. ChebNet \\cite{defferrard2016convolutional} suggests that the graph convolutional operation can be further approximated by the $k$-th order Chebyshev polynomial of Laplacian. \\citet{kipf2016semi} simplify the ChebNet and obtains a reduced version of ChebNet by the renormalization trick:\n\\begin{equation} \\label{gcn}\nH^{(l+1)}\\!\\!=\\!\\!\\sigma(\\!\\tilde{A}_{\\textit{sym}}H^{(l)}W^{(l)})\\!\\!=\\!\\!\\sigma(\\tilde{D}^{-\\frac{1}{2}}\\tilde{A}\\tilde{D}^{-\\frac{1}{2}}H^{(l)}W^{(l)}),\n\\end{equation}\nwhere $\\sigma$ denote the activation function such as ReLU. $W^{(l)}$ is a layer-specific trainable weight matrix. $H^{(l)}$ is the feature matrix of $l$-th layer and $H^{(0)}=X$.\n\n\\subsection{Message Passing}\nMessage passing \\cite{gilmer2017neural} means that a node on the graph aggregates the message from neighbors and update its embedding:\n\\begin{equation} \\label{mpnn}\nh_v^{(l)}=U_l\\bigg(h_v^{(l-1)}, \\sum_{u \\in N(v)}M_l\\big(h_u^{(l-1)}, h_v^{(l-1)}, e_{uv}\\big)\\bigg),\n\\end{equation}\nwhere $M_l(\\cdot)$ and $U_l(\\cdot)$ are message aggregation function and vertex update function, respectively. $h_v^{(l)}$ denotes the hidden state of node $v$ at $l$-th layer, and $e_{uv}$ is the edge features.\n\nIn this way, GCN layer can be decomposed into two steps, including the neighbors' message aggregation and update:\n\\begin{equation} \\label{gcn-mpnn}\nh_v^{(l)}=\\sigma\\bigg(W^{(l)}\\sum_{u\\in \\tilde N (v)} \\frac{h^{(l-1)}_u}{\\sqrt{|N(v)||N(u)|}}\\bigg).\n\\end{equation}\n\nHere a GCN layer can be viewed as a weighted average of all neighbors' message where the weighting is proportional to the inverse of the number of neighbors.\n\n\\subsection{Graph Regularized Optimization} \\label{GRO}\nLet $\\bar{X} \\in \\mathbb{R}^{n\\times d}$ denote the final node embeddings matrix, and $\\bar{x}_i$ is the $i$-th row of $\\bar{X}$. We consider the following optimization problem:\n\\begin{equation} \\label{gcn-opt}\nf = \\min_{\\bar X} \\bigg(\\sum_{i \\in V}\\|\\bar{x}_i -x_i\\|_{\\tilde{D}}^2 + \\alpha \\sum_{\\{i,j\\} \\in E}\\|\\bar{x}_i -\\bar{x}_j\\|_2^2\\bigg),\n\\end{equation}\nwhere $(x,y)_{\\tilde D}=\\sum_{i\\in V}d(i)x(i)y(i)$, if $x=y$, we have$\\quad \\|x\\|_{\\tilde D}=\\sqrt{(x,x)_{\\tilde D}}$.\n\nThe first term in the above optimization problem is the fitting constraint, which means the output features (also called embeddings) should not be too far off of the input features, while the second term is the smoothness constraint, which means the connected nodes should have similar embeddings. $\\alpha > 0$ is a hyperparameter to balance the importance of two objections. It is worth noting that there is no limit to the specific transformation from $X$ to $\\bar{X}$.\n\nBefore solving the optimization problem, we have the following lemma.\n\\begin{lemma}\n\t$\\tilde{A}_{\\textit{rw}}$ and $\\tilde{A}_{\\textit{sym}}$ always have the same eigenvalues $|\\lambda|\\leq1$.\n\\end{lemma}\n\n\\begin{corollary} \\label{inverse}\n\t$(I_n-\\alpha\\tilde{A}_{\\textit{rw}})$ and $(I_n-\\alpha\\tilde{A}_{\\textit{sym}})$ are invertible if $\\alpha \\in[0,1)$. \n\\end{corollary}\n\n\\begin{lemma}\n\tGiven a graph with adjacency matrix $A$, the powers of $A$ give the number of walks between any two vertices.\n\\end{lemma}\n\n\\begin{corollary} \\label{high-order}\n\t$A^k$ includes the information of high-order neighbors.\n\\end{corollary}\nNext, we derive the closed-form solution of Eq. \\ref{gcn-opt}. Specifically, we rewrite Eq. \\ref{gcn-opt} as \n$$\nf=\\min_{\\bar X} \\bigg(\\text{Tr}\\big((\\bar X-X)(\\bar X-X)^T\\tilde{D}\\big) + \\alpha\\text{Tr}({\\bar X}^TL \\bar X)\\bigg).\n$$\nDifferentiating $f$ with respect to $\\bar{X}$, we have \n$$\n\\frac{df}{d\\bar{X}}=\\tilde{D}(\\bar{X}-X)+\\alpha L\\bar{X}=0.\n$$\nNotice Corollary \\ref{inverse}, we have\n$$\n\\bar{X}=(1-\\mu) (I-\\mu\\tilde {A}_{\\textit{rw}})^{-1}X,\n$$\nwhere $\\mu=\\frac{\\alpha}{1+\\alpha}$.\n\nActually, the solution is also the personalized PageRank \\cite{page1999pagerank}' s limiting distribution. If we set $\\mu=0.5$, we get $\\bar X=(2I-\\tilde {A}_{\\textit{rw}})^{-1}X$, \nand $\\tilde {A}_{rw}X$ is the first-order Taylor approximation. \nBy replacing $\\tilde {A}_{rw}$ with $\\tilde {A}_{\\textit{sym}}$, we get standard graph convolution kernel. In other words, we lose the information from high-order neighbors, which is contained in the error series of the Taylor expansion. (See Corollary \\ref{high-order}).\n\nIn a nutshell, we obtains the well-known kernel or resemble form of the graph convolution from different ways. \n\\section{Over-smoothing in Vanilla Deep GCN}\n\nNeural network usually performs better when stack more layers while graph neural network does not benefit from the depth. On the contrary, more layers often result in significant degradation in performance. \n\nPrevious work illustrates the over-smoothing by computing the limiting distribution of $A_{k}$ when $k \\rightarrow \\infty$, Actually, this is not identical with vanilla deep GCN, which contains non-linear transformation among different layers. Litter work considers the non-linearity in multi-layer GCN. \\citet{oono2019graph} extend the linear analysis to the non-linearity firstly, which considers the ReLU activation function. They suggest that the node features of a $k$-layer GCNs will converge to a subspace and incur information loss, which makes the node feature indistinguishable.\n\nAt first, one main reason we introduce the deep architecture in GCN is that we want to use the long-range neighbor's information. We argue that vanilla deep GCN is not the correct way to capture this information. However, It does not mean that deep architecture is useless. \\citet{chen2020simple} and \\citet{liu2020towards} have shown that more layers can boost the performance of GCN on several datasets and tasks.\n\nTo quantify the over-smoothing in vanilla deep GCN, we compute the overall pairwise distance of node embeddings as follows:\n\\begin{equation} \n\\nonumber\n\\begin{aligned}\n\\!M_{\\textit{\\!overall}}&\\!=\\!\\!\\!\\sum_{i,j\\in\\!V}\\!\\!\\|\\bar{x}_i\\!-\\!\\bar{x}_j\\|_2^2\\!=\\!\\!\\!\\!\\sum_{\\{i,j\\}\\in\\!E}\\!\\!\\! \\|\\bar{x}_i\\!-\\!\\bar{x}_j\\|_2^2\\!\\!+\\!\\!\\!\\!\\sum_{\\{i,j\\}\\notin\\!E}\\!\\!\\|\\bar{x}_i\\!-\\!\\bar{x}_j\\|_2^2 \\\\\n&= \\text{Tr}({\\bar X}^TL \\bar X)+ \\text{Tr}({\\bar X}^TL' \\bar X).\\\\\n\\end{aligned}\n\\end{equation}\n\\begin{figure}[t]\n\t\\subfigure[Overall distance]{\n\t\t\\label{overall}\n\t\t\\includegraphics[width=0.22\\textwidth]{m_overall.pdf}}\\hspace{-1mm}\n\t\\subfigure[Fraction of diatsnce]{\n\t\t\\label{fraction}\n\t\t\\includegraphics[width=0.24\\textwidth]{smooth.pdf}}\n\t\\caption{ $M_{\\textit{\\!overall}}, M_{\\textit{\\!smooth}}$ and $M_{\\textit{\\!non-smooth}}$ of the output node embeddings of Vanilla GCN with increasing layers on \\textit{Cora}.}\n\t\\label{boxplot}\n\\end{figure}\n\nFig. \\ref{overall} depicts the pairwise distance distribution of vanilla GCN with increasing layers on \\textit{Cora}. We can see that $M_{overall}$ decreases as the model goes deeper. Revisit the two parts of $M_{overall}$, we propose two fine quantitative metrics to measure the over-smoothing of graph representation.\n\\begin{equation} \n\\begin{aligned}\nM_{\\textit{smooth}} &= D_{\\textit{smooth}}\/D_{\\textit{overall}},\\\\\nM_{\\textit{non-smooth}} &= D_{\\textit{non-smooth}} \/D_{\\textit{overall}},\\\\\n\\end{aligned}\n\\end{equation}\nwhere\n\\begin{equation} \n\\begin{aligned}\nD_{\\textit{smooth}} &= \\text{Tr}({\\bar X}^TL \\bar X)\/\\text{num}(E) ,\\\\\nD_{\\textit{non-smooth}} &= \\text{Tr}({\\bar X}^TL' \\bar X)\/\\text{num}(E'),\\\\\nD_{\\textit{overall}}&=D_{\\textit{smooth}}+D_{\\textit{non-smooth}}.\n\\end{aligned}\n\\end{equation}\n\nHere, $\\text{num}(E)$ and $\\text{num}(E')$ in the denominator are used to eliminate the impact of unbalanced edge numbers in $G$ and $G'$. $M_{\\textit{smooth}}$ measures the smoothness of the graph representation of connected pair nodes while $M_{\\textit{non-smooth}}$ measures the smoothness of the graph representation of disconnected pair nodes. \n\nFig. \\ref{fraction} compares $M_{\\textit{smooth}}$ and $M_{\\textit{non-smooth}}$. We see that $M_{\\textit{smooth}}$ contributes to quite a few parts of the overall distance, which seems counter-intuitive. We will discuss this two metrics of GCN+ in Section \\ref{oversmooth-of-gcn+} again.\n\n\\section{A General Framework of GCN}\nRecall the graph regularized optimization problem, we add a negative term to constrain the sum of distances between disconnected pairs as follow:\n\\begin{equation} \\label{full-opt}\n\\nonumber\n\tf \\!\\!=\\!\\min_{\\bar X}\\!\\!\\bigg(\\!\\sum_{i \\in V}\\|\\bar{x}_i \\!-\\!x_i\\|_{\\tilde{D}}^2 \\!+\\! \\alpha\\!\\!\\!\\!\\! \\sum_{\\{i,j\\} \\in E} \\! \\|\\bar{x}_i \\!-\\!\\bar{x}_j\\|_2^2 \\!-\\!\\beta\\!\\!\\!\\!\\! \\sum_{\\{i,j\\} \\notin E} \\!\\|\\bar{x}_i \\!-\\!\\bar{x}_j\\|_2^2\\!\\bigg)\\!,\n\\end{equation}\nwhere $\\alpha$ and $\\beta$ are hyperparameters to balance the importance of the corresponding terms.\n\n\nWe consider two cases: $\\beta=0$ and $\\beta \\neq0$.\n\\subsection{Case 1:$\\beta=0$}\nIn this situation, $\\bar{X}=(1-\\mu)(I_n-\\mu\\tilde{A}_{\\textit{rw}})^{-1}X$ where $\\mu=\\frac{\\alpha}{1+\\alpha}\\in(0,1)$. Directly calculating such an intractable expression is not only computationally inefficient but also results in a dense $\\mathbb{R}^{n \\times n}$matrix. It would lead to a high computational complexity and memory requirement when we apply such operator on large graphs. We can achieve linear computational complexity via power iteration.\n\nWe use $\\tilde{A}$ to denote $\\tilde{A}_{\\textit{sym}}$ and $\\tilde{A}_{\\textit{rw}}$. Here we consider a more general expression $(1-\\mu)(I_n-\\mu \\tilde{A})^{-1}H$ where $H=H=f_{\\theta}(X)$.\n\\begin{theorem} \\label{theorem1}\n\t$(I_n-\\mu\\tilde{A})$ is invertible. Consider the following iterative scheme\n\t\\begin{equation} \\label{iterative1}\n\t\\begin{aligned}\n\tZ^{(0)}&=H, \\\\\n\tZ^{(k)}&=\\mu\\tilde{A}Z^{(k-1)}+(1-\\mu) H,\n\t\\end{aligned}\n\t\\end{equation} where $\\mu \\in (0,1)$.\n\tWhen $k \\rightarrow \\infty$, \n\t\\begin{equation}\nZ^{(\\infty)}=(1-\\mu)(I_n-\\mu\\tilde{A})^{-1}H.\n\t\\end{equation} \n\\end{theorem}\n\n\\begin{proof}\n\tUsing corollary \\ref{inverse}, we can see that $(I_n-\\mu\\tilde{A})$ is invertible. Combining the two equation of \\ref{iterative1}, we have \n\t$$\n\tZ^{(k)}=\\bigg(\\mu^k \\tilde{A}^k+(1-\\mu) \\sum_{i=0}^{k-1} \\mu^i \\tilde{A}^i\\bigg)H.\n\t$$\n\tNotice that\n\t\\begin{equation} \n\t\\begin{aligned}\n\t\\lim_{k \\rightarrow \\infty}\\mu^k \\tilde{A}^k&=0, \\\\\n\t\\lim_{k \\rightarrow \\infty}\\sum_{i=0}^{k-1} \\mu^i \\tilde{A}^i&=\\big(I-\\mu\\tilde{A}\\big)^{-1}.\n\t\\end{aligned}\n\t\\end{equation}\n\tHence, the proof is finished. \n\n\\end{proof}\n\nActually, the prevalent GCN, SGC and APPNP can be viewed as the special variant of Case 1.\n\\subsection{Case 2:$\\beta \\neq 0$} \n\nIn this situation, $\\bar{X}=Q^{-1}$ if $Q=\\big((1+\\alpha+\\beta)I-(\\alpha+\\beta)\\tilde D^{-1}\\tilde A-\\beta n\\tilde D^{-1}+\\beta \\tilde D^{-1} \\mathbf J_n\\big)$ is invertible when we choose a suitable $\\beta$. We will introduce the conditions later.\n\n\nFirst we use the first-order Taylor approximation of above convolutional kernel ($\\text{GCN}^*$) directly without any tricks such as Batch Normalization \\cite{ioffe2015batch} or residual connection \\cite{he2016deep} on two small citation datasets \\textit{Cora} and \\textit{Citeseer}. We compare the performance of the vanilla deep GCN and $\\text{GCN}^*$ as the model layer increases. Fig. \\ref{figure1} shows the result of GCN and $\\text{GCN}^*$. Dashed lines illustrate the performance of GCN, which shows that deep GCN suffers from performance drop. We can see that the performance decay with $\\text{GCN}^*$ kernel is much slower.\n\n\\citet{oono2019graph} have proved that the node feature of vanilla $k$-layer GCN will converges to an invariant subspace which only carry the information of the connected component and node degree. The convergence speed is proportional to the $\\lambda^k$, where $\\lambda$ is the supremum of eigenvalue of $\\tilde{A}$. In GCN*, $\\lambda>1$(see the proof of Theorem \\ref{theorem2}), which implies that $\\lambda^k$ is large, thus the information loss and over-smoothing are relieved.\n\nAlthough the modified graph kernel relieves over-smoothing to some extent, more layers do not boost the performance, which is not our focus. However the above result demonstrates that it is an efficient way to tackle the over-smoothing issue. We can achieves linear computational complexity via power iteration similar to Case 1.\n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.9\\columnwidth]{cora}\n\t\\caption{Performance comparison of vanilla deep GCN vs. $\\text{GCN}^*$ with increasing layers on two small datasets.}\n\t\\label{figure1}\n\\end{figure}\n\n\\begin{theorem} \\label{theorem2}\n\t$(I_n-\\mu \\hat{A})$ is invertible when $\\beta<\\frac{1}{n}$ where $\\mu=\\frac{\\alpha+\\beta}{1+\\alpha+\\beta}$ and $\\hat{A}=\\tilde{A}+\\frac{\\beta n\\tilde D^{-1}-\\beta \\tilde D^{-1} \\mathbf J_n}{\\alpha+\\beta}$. Consider the following iterative scheme\n\t\\begin{equation} \\label{iterative2}\n\t\\begin{aligned}\n\tZ^{(0)}&=H, \\\\\n\tZ^{(k)}&=\\mu\\hat{A}Z^{(k-1)}+ (1-\\mu)H,\n\t\\end{aligned}\n\t\\end{equation} where $\\mu \\in (0,1)$.\n\tWhen $k \\rightarrow \\infty$, \n\t\\begin{equation}\n\tZ^{(\\infty)}=(1-\\mu)(I_n-\\mu\\hat{A})^{-1}H.\n\t\\end{equation} \n\\end{theorem}\n\\begin{proof}\n\tLet $M=\\frac{\\beta n\\tilde D^{-1}-\\beta \\tilde D^{-1} \\mathbf J_n}{\\alpha+\\beta}=\\frac{\\beta \\tilde D^{-1} }{\\alpha+\\beta}(nI-J_n)$. Note that $(nI-J_n)$ has the largest eigenvalue $n$. Suppose that $\\lambda$ is the eigenvalue of $M$, we have $\\lambda \\leq \\frac{\\beta n}{\\alpha+\\beta} $. Then eigenvalue of $ \\hat{A}$ is less than $1+\\frac{\\beta n}{\\alpha+\\beta}$. \n\t $(I_n-\\mu \\hat{A})$ is invertible iff $\\frac{1}{\\mu}$ is not an eigenvalue of $ \\hat{A}$. Note that $\\frac{1}{\\mu}=\\frac{1+\\alpha+\\beta}{\\alpha+\\beta}=1+\\frac{1}{\\alpha+\\beta}$, when $\\beta<\\frac{1}{n}$ we have $\\frac{1}{\\mu}>1+\\frac{\\beta n}{\\alpha+\\beta}$, hence $\\frac{1}{\\mu}$ cannot be an eigenvalue of $\\hat{A}$ and $(I_n-\\mu \\hat{A})$ is invertible. The proof of the iterative scheme follows the similar procedure of case 1 with a slight difference, as it is trivial, we omit the proof.\n\\end{proof}\n\n\\subsection{Why GCN+ relieve the over-smoothing?}\nWe have no assumptions on the specific transformation from $X$ to $\\bar{X}$. In our implementation, the mathematical expression of GCN+ is defined as \n\\begin{equation} \\label{gcn+}\n\\begin{aligned}\nZ^{(0)}&=H=\\sigma(XW_1), \\\\\nZ^{(k)}&=\\mu\\hat{A}Z^{(k-1)}+ (1-\\mu)H,\\\\\nX_{out}&=\\text{softmax}(Z^{(k)}W_2),\\\\\n\\end{aligned}\n\\end{equation} \nwhere $W_1\\in \\mathbb{R}^{d \\times m}$ and $W_2\\in \\mathbb{R}^{m \\times c}$ are learnable weight matrices, $k$ is the dimension of the hidden layers. \n\n\nWe interpret the anti-oversmoothing of GCN+ from two ways. First, note that in the power iterative scheme, a fraction of initial node features $H$ is always preserved in each iteration, which can be viewed as a flexible version of residual connection. In addition, we can also understand GCN+ from the frequency of graph signal. In Section \\ref{GRO} , we have shown that the original GCN is corresponding to the first-order Taylor approximation of the optimization solution, that means we lost the high frequency part of the signal which contains the high-order information. Actually we omit the error series when we approximate the GCN.\n\n\nRecall the current representative methods: DAGNN and JKNet, which shows promising improvement than the original GCN. The core formulas of them are as follows:\n\\begin{equation} \n\\nonumber\n\\begin{aligned}\n\\text{DAGNN:}&\\\\\n&Z=\\text{stack}(H, Z^{(1)}, ..., Z^{(k)}), \\quad Z^{(k)}=\\hat{A}^{(k)}H,\\\\\n\\text{JKNet:\\quad}&\\\\\n&Z=\\text{Aggr}(Z^{(1)}, ...,Z^{(k)}), \\\\\n\\end{aligned}\n\\end{equation} \nwhere \\text{Aggr} includes \\textit{Concatenation}, \\textit{Max-pooling} and \\textit{LSTM-attention}.\n\nActually, DAGNN and JKNet both make use of the information which from the immediate and high-order neighbors while GCN+ also benefit from this. Moreover, we provide the theoretical and empirical evidence of GCN+.\n\n\\subsection{Parameters Amount}\nIt is worth noting that the power iterative schemes are parameter-free in two versions of GCN+, which is similar to APPNP \\cite{klicpera2018predict}. In particular, GCN+ ($\\beta=0$) adopts the same scheme as APPNP, where we re-implement it and achieve more impressive results. \n\\section{Experiments}\nIn this section, we evaluate the performance of GCN+ on several benchmark datasets against various graph neural networks on semi-supersized node classification tasks.\n\t\n\\subsection{Experimental Setup}\n\\subsubsection{Datasets}\nWe conduct extensive experiments on the node-level tasks on two kinds commonly used networks: Planetoid: \\textit{Cora}, \\textit{CiteSeer}, \\textit{Pubmed} \\cite{sen2008collective} and recent \\textit{Open Graph Benchmark} (OGB) \\cite{hu2020open}:\\textit{ogb-arxiv}, \\textit{ogb-proteins}. The statistics of datasets are summarized in Table \\ref{dataset}. It is worth nothing that OGB includes enormous challenging and large-scale datasets than Planetoid. We refer readers to \\cite{hu2020open} for more details about OGB datasets.\n\\begin{table} \n\t\\small\n\t\\setlength{\\tabcolsep}{1mm}{\n\t\\begin{tabular}{{cccccc}}\n\t\t\\toprule\n\t\tDataset & Nodes & Edges & Classes & Features &Metric\\\\ \n\t\t\\midrule\n\t\tCora & 2708 & 5429 & 7 & 1433 & Accuracy \\\\ \n\t\tCiteseer & 3327 & 4732 & 7 & 2703 & Accuracy \\\\ \n\t\tPubmed & 19717 & 44338 & 3 & 500 & Accuracy \\\\ \n\t\togb-arxiv & 169343 & 1166243 & 40 & 128 & Accuracy \\\\ \n\t\togb-proteins & 132534 & 39561252 & 112 & 8 & ROC-AUC \\\\ \n\t\t\\bottomrule\n\t\\end{tabular}}\n\\caption{Dataset statistics.} \n\\label{dataset}\n\\end{table}\n\\subsubsection{Implementations}\nWe choose the optimizer and hyperparameters of GNN models as follows. We use the Adam optimizer \\cite{kingma2014adam} to train all the GNN models with a maximum of 1500 epochs. We set the number of hidden units to 64 on \\textit{Cora}, \\textit{Citeseer} and \\textit{Pubmed} , 256 on \\textit{ogb-arxiv} and \\textit{ogb-proteins}. For SGC, we vary number of layer in \\{1, 2, ..., 10, 15, ..., 60\\} and for GCN and GAT in \\{2, 4, ..., 10, 15, ..., 30\\}. For $\\alpha$ in APPNP, we search it from \\{0.1, 0.2, 0.3, 0.4, 0.5\\}. For DAGNN and JKNet, we search layers from \\{2, 3, ..., 10\\}. For learning rate, we choose from \\{0.001, 0.005, 0.01\\}. For dropout rate, we choose from \\{0.1, 0.2, 0.3, 0.4, 0.5\\}. We perform a grid search to tune the hyperparameters for other models based on the accuracy on the validation set. We run each experiment 10 times and report the average. \n\nIn practice, we use Pytorch \\cite{paszke2019pytorch} and Pytorch Geometric \\cite{Fey\/Lenssen\/2019} for an efficient GPU-based implementation of GCN+.\nAll experiments in this study are conducted on NVIDIA TITAN RTX 24GB GPU.\n\n\\begin{table*}[t]\n\t\\small\n\t\\centering\n\t\\setlength{\\tabcolsep}{1.5mm}{\n\t\\begin{tabular}{ccccccc}\n\t\t\\toprule\n\t\t\\multirow{2}{*}{model} & \\multicolumn{2}{c}{\\textit{Cora}} & \\multicolumn{2}{c}{\\textit{Citeseer}} & \\multicolumn{2}{c}{\\textit{Pubmed}} \\\\\n\t\t& Fixed & Random & Fixed & Random & Fixed & Random \\\\\n\t\t\\midrule\n\t\tMLP & $61.6\\pm0.6$ & $59.8\\pm2.4$& $61.0\\pm1.0$ & $58.8\\pm2.2$ & $74.2\\pm0.7$ & $70.1\\pm2.4$ \\\\\n\t\tGCN\\cite{kipf2016semi} & $81.3\\pm0.8$ & $79.1\\pm1.8$& $71.1\\pm0.7$ & $68.2\\pm1.6$ & $78.8\\pm0.6$ & $77.1\\pm2.7$ \\\\\n\t\t GAT\\cite{velivckovic2017graph} &$83.1\\pm0.4$ &$80.8\\pm1.6$ &$70.8\\pm0.5$ &$68.9\\pm1.7$ & $79.1\\pm0.4$ &$77.8\\pm2.1$ \\\\\n\t\t SGC\\cite{wu2019simplifying} & $81.1\\pm0.5$ & $80.4\\pm0.3$& $71.9\\pm0.3$ & $71.8\\pm0.3$ & $78.9\\pm0.0$ & $77.8\\pm0.6$ \\\\\n\t\t JKNet\\cite{xu2018representation}& $80.7\\pm0.9$ &$79.2\\pm0.9$ &$70.1\\pm0.6$&$68.3\\pm1.8$ & $78.1\\pm0.6$ & $77.9\\pm0.9$ \\\\\n\t\t APPNP\\cite{klicpera2018predict} &$83.3\\pm0.5$ &$81.9\\pm1.4$ &$71.8\\pm0.4$ &$69.8\\pm1.7$ & $80.1\\pm0.2$ &$79.5\\pm2.2$ \\\\\n\t\t DAGNN\\cite{liu2020towards} &$84.4\\pm0.5$ &$\\textbf{83.7}\\pm\\textbf{1.4}$ &$73.3\\pm0.6$ &$71.2\\pm1.4$ & $\\textbf{80.5}\\pm\\textbf{0.5}$ &$80.1\\pm1.7$ \\\\\n\t\t\\midrule\n\t\t GCN+($\\beta=0$) &$85.2\\pm0.5$ &$83.3\\pm1.1$ &$73.3\\pm0.5$ &$72.3\\pm0.7$ & $80.4\\pm0.6$ &$80.1\\pm0.6$ \\\\\n\t\t GCN+($\\beta \\ne 0$) &$\\textbf{85.6}\\pm\\textbf{0.4}$ &$83.6\\pm1.3$ &$\\textbf{73.5}\\pm\\textbf{0.4}$ &$\\textbf{72.5}\\pm\\textbf{0.9}$ & $80.5\\pm0.6$ &$\\textbf{80.3}\\pm\\textbf{0.7}$ \\\\\n\t\t\\bottomrule \n\t\\end{tabular}}\n\\caption{Summary of classification accuracy(\\%) on Planetoid datasets of semi-supervised node classification.}\n\\label{Plantoid-semi}\n\\end{table*}\n\n\\begin{table}[t]\n\t\\centering\n\t\\setlength{\\tabcolsep}{3.5mm}{\n\t\\begin{tabular}{{ccccc}}\n\t\t\\toprule\n\t\tDataset & \\textit{ogb-arxiv} & \\textit{ogb-proteins} \\\\ \n\t\t\\midrule\n\t\tGCN & $71.74\\pm0.29$ & $72.51\\pm0.35$ \\\\ \n\t\tGraphSAGE & $71.49\\pm0.25$ & $77.68\\pm0.20$ \\\\ \n\t\t\\midrule\n\t\tGCN+($\\beta=0$) & $71.85\\pm0.23$&$78.63\\pm0.28$ \\\\ \n\t\tGCN+($\\beta \\ne0$) &$\\textbf{71.95}\\pm\\textbf{0.28}$ & $\\textbf{79.07}\\pm\\textbf{0.34}$ \\\\ \n\t\t\\bottomrule\n\t\\end{tabular}}\n\t\\caption{Summary of classification performance(\\%) on OGB datasets. For \\textit{ogb-arxiv}, it indicates accuracy and for \\textit{ogb-proteins}, it indicates ROC-AUC.} \n\t\\label{ogb-semi}\n\\end{table}\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=0.9\\columnwidth]{constant}\n\t\\caption{$M_{\\textit{\\!non-smooth}}$ of GCN+ with increasing hops on \\textit{Cora}.}\n\t\\label{oversmooth}\n\\end{figure}\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.9\\columnwidth]{sys_rw}\n\t\\caption{Performance comparison of different propagation matrices $\\tilde{A}_{sym}$ vs. $\\tilde{A}_{rw}$ in GCN+ with increasing hops on \\textit{Cora}.}\n\t\\label{choice}\n\\end{figure}\n\\begin{figure*}[t]\n\t\\centering\n\t\\subfigure[$\\alpha=9$]{\n\t\t\\includegraphics[width=0.24\\textwidth]{alpha_9.pdf}}\\hspace{-1mm}\n\t\\subfigure[$\\alpha=4$]{\n\t\t\\includegraphics[width=0.24\\textwidth]{alpha_4.pdf}}\\hspace{-1mm}\n\t\\subfigure[$\\alpha=2$]{\n\t\t\\includegraphics[width=0.24\\textwidth]{alpha_2.pdf}}\\hspace{-1mm}\n\t\\subfigure[$\\alpha=1$]{\n\t\t\\includegraphics[width=0.24\\textwidth]{alpha_1.pdf}}\\hspace{-1mm}\n\t\\caption{Test accuracy of different propagation steps and $\\alpha$ on \\textit{Cora}.}\n\t\\label{alpha and beta}\n\\end{figure*}\n\n\n\\subsection{Comparison with SOTA}\nThe evaluate metric of various datasets are listed in Table \\ref{dataset}. Actually it is commonly used to evaluate the model by the community.\n\\subsubsection{Planetoid}\nWe use standard fixed and random training\/validation\/testing splits. Specifically, we use 20 labeled nodes per class as the training set, 500 nodes as the validation set, and 1000 nodes as the test set for all models. For fixed split, we follow the experimental setup in \\cite{yang2016revisiting}. We compare Multiplayer Perception (MLP) ,GCN \\cite{kipf2016semi}, GAT \\cite{velivckovic2017graph}, SGC \\cite{wu2019simplifying}, DAGNN \\cite{liu2020towards} and APPNP \\cite{klicpera2018predict} with GCN+. Although DropEdge \\cite{rong2019dropedge}, PairNorm \\cite{zhao2019pairnorm} are proposed to tackle over-smoothing issue recently, our baseline methods don't include them as they do not help to boost the performance on node classification task. \nTable \\ref{Plantoid-semi} compares the average test accuracy of 10 runs for each model on Planetoid dataset. As shown, GCN+ outperforms better than the representative baselines. Note that the shallow model APPNP achieves better performance than GCN and GAT. Recent deeper model named DAGNN shows competitive result and robustness on these datasets and GCN+ performs slightly better than it.\n\\subsubsection{OGB}\nWe adopt the setting of \\cite{hu2020open}, which is more challenging and realistic. We consider the following representative models GCN \\cite{kipf2016semi}, GraphSAGE \\cite{hamilton2017inductive} and GCNII \\cite{chen2020simple} as our baselines. In particular, we use the reported metric of the leaderboards of OGB team, which provide an open benchmark on several tasks and datasets. \n\nTable \\ref{ogb-semi} compares the average test accuracy\/ROC-AUC on OGB datasets. As shown, GCN+ outperforms the GCN and GraphSAGE. It is clear that our proposed GCN+ outperform SOTA in two middle scale datasets.\n\nIn summary, GCN+ achieves superior performance on several benchmarks, which shows that considering the information of high-order neighbors makes sense and we need more reasonable way to deepen GCNs or make use of the high-order neighbors. Note that GCN+ ($\\beta \\ne0$) is slightly better than GCN+ ($\\beta =0$) which is benefit from the third term of Eq. (\\ref{full-opt}). \n\\subsection{Over-smoothing Analysis} \\label{oversmooth-of-gcn+}\nWe employ the two proposed metrics to measure the node embeddings learned by GCN+. The results on \\textit{Cora} are shown in Fig. \\ref{oversmooth}. We can observe that as the number of hops increases, the $M_{\\textit{smooth}}$ values nearly remains a small constant which is lower than vanilla deep GCN. This implies that GCN+ use the information of long-range neighbors and do not suffer from over-smoothing. \n\nFig. \\ref{model_vs} also compares the final output embeddings of GCN+ with multiple hops, which shows different behaviors with GCN. GCN+ relieves the over-smoothing and learns the meaningful embeddings with the increasing hops.\n\n\\subsection{Hyperparameter Analysis}\nIn the previous sections, we use $\\tilde{A}$ to refer the $\\tilde{A}_{\\textit{sym}}$ and $\\tilde{A}_{\\textit{rw}}$. Here we compare the different choices of propagation matrix $\\tilde{A}$. Fig. \\ref{choice} depicts the test accuracy achieved by varying the hops of different propagation matrices. The result illustrates that \n$\\tilde{A}_{\\textit{sym}}$ is slightly better than $\\tilde{A}_{\\textit{rw}}$. \n\nWe consider three hyperparameter of GCN+, that is $\\alpha$, $\\beta$ and number of power iteration steps $k$. Fig. \\ref{alpha and beta} compares the effect of these hyperparameters on \\textit{Cora}. We can see that $k=16,32$ is suitable and more steps does not boost the performance significantly. For \\textit{Cora}, when $\\alpha=9$ (that means the fraction of retained initial node features is 0.1.), GCN+ achieve the best performance. The value of $\\alpha$ varies by different datasets. More results and details listed in the supplementary material.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Related Work}\n\\subsection{Graph Neural Networks}\nGraph neural networks (GNNs) have been extensively studied for the past years. There are different views on designing new architecture, including the spectral-based, spatial-based and other types, such as understand the GNN using dynamic system \\cite{xhonneux2019continuous}. Numerous methods are proposed to model the graph-structure data and apply on a wide range of applications. Besides the GCNs, there are also other types of GNNs, such as attention-based GNN \\cite{velivckovic2017graph} which use multiple attention to aggregate information from neighbors, autoencoder-based GNN \\cite{kipf2016variational}, which use a GCN encoder and decoder to learn meaningful embeddings, and dynamic GNNs \\cite{seo2018structured, hajiramezanali2019variational, yan2020sgrnn} which learn the node embedding over time.\n\\subsection{Deep GCN and Over-smoothing}\nMost GNNs are shallow models as deep architecture suffers from over-smoothing. Several studies explore deep GCNs. \\citet{xu2018representation} introduce Jumping Knowledge Networks, which uses residual connection to combine the output of each layer. \\citet{klicpera2018predict} use Personalized PageRank, which consider the information of root node to replace the graph convolution operator to solve the over-smoothing. DropEdge \\cite{rong2019dropedge} suggests that randomly removing the edge of original graph impede over-smoothing. PairNorm \\cite{zhao2019pairnorm} is another scheme which uses a normalization layer to scale the node features after the convolution layer. \\citet{li2019deepgcns} build on ideas from ResNet to train very deep GCNs. \\citet{li2020deepergcn} further propose MsgNorm, which boosts the performance on several datasets. \\citet{yang2020revisiting} present NodeNorm to scale the node features. \\cite{chen2020simple} propose a deep GCN models which use initial residual connection and identity mapping. \n\nA few work analyzes the cause and behaviors of over-smoothing theoretically. \\citet{oono2019graph} investigate the asymptotic behaviors of GCNs as the layer size tends to infinity and reveals the information loss in deep GCNs. \\citet{cai2020note} further extend analysis of \\cite{oono2019graph} from linear GNNs to the nonlinear architecture.\n\n\n\\section{Conclusion}\nWe summarize the existing different views on the mechanism of GCNs, which help us understand and design the graph convolutional kernel. We further provide a general optimization framework named GCN+. Based on this framework, we derive two forms of GCN+ and propose two metrics to measure the smoothness of output node representations. Extensive empirical studies on several real-world datasets demonstrate that GCN+ compares favorably to state of the art with a small amount of parameters. For future work, we will consider different optimization objectives which encode the graph structure and node features adaptively. As we do not limit the transformation from $X$ to $\\bar{X}$, another reasonable formulas can be further explored.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzdtbv b/data_all_eng_slimpj/shuffled/split2/finalzzdtbv new file mode 100644 index 0000000000000000000000000000000000000000..20b96e78fe959b1f2462075f74bc56463db3b822 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzdtbv @@ -0,0 +1,5 @@ +{"text":"\\section{INTRODUCTION}\nLocalization is an essential part of autonomous driving. For past years, there has been a wide variety of works in different approaches like \\textit{Simultaneous Localization and Mapping} (SLAM) \\cite{cadena2016past}, localization in previously built HD maps \\cite{pauls2020monocular}, and geo-referencing using aerial imagery \\cite{hu2019accurate}. A data association process becomes necessary for all these approaches to find correspondences between landmarks in maps and detections from the onboard sensors. The typical probabilistic models used in localization, such as factor-graphs or Bayesian filters, need these correspondences for inference. Hence, these associations are crucial for pose estimation.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=220pt]{figures\/01_overview.pdf}\n\\caption{An overview of a complete localization system, where the data representation and data association play a key role. Our research focuses on evaluating these modules (inside red dashes) by comparing the presented DA-LMR and the DC-SAC with state-of-the-art approaches.}\n\\label{fig:overview}\n\\end{figure}\n\nThe landmarks and detections used in the process usually depend on the application and the localization approach. In SLAM or localization in HD maps, lane markings, poles, traffic signs, and traffic lights have been used. While these maps usually provide high local accuracy, they also suffer from drift leading to global inconsistencies due to accumulated errors. To avoid this effect, a so-called geo-referencing is performed, where local detections from sensors are aligned with landmarks from aerial imagery. In that context, traffic signs and lights are unobservable, and poles are hard to detect. Here, lane markings are the potentially best observable landmarks for geo-referencing approaches. These lane markings are usually represented in maps as polylines and dashes.\n\nThe question that arises at this point is how to represent polylines and dashes in the data association process. In most works in the literature, there is no evaluation on how lane representations affect the data association, and therefore, the final result of localization. For this reason, in this paper, we focus on evaluating different data association methods using different data representations (yellow and green modules in Fig. \\ref{fig:overview}). For this, we propose the \\textit{Delta-Angle Lane Marking Representation} (DA-LMR), which represents polylines and dashes as points where an additional dimension represents a proportional value of the differential angle concerning the adjacent line segments. In addition, for data association, we propose a heuristic version of RANSAC, called \\textit{Distance-Compatible Sample Consensus} (DC-SAC), that limits possible samples to distance compatible ones.\n\nTo summarize, the main contributions are the following:\n\\begin{itemize}\n \\item DA-LMR, a new way of representing lane markings for data association.\n \\item A formalization of the data association method DC-SAC, used in our previous work \\cite{munoz2020targetless}.\n \\item An evaluation of the possible combinations of data representation and data association approaches, including both state-of-the-art, and those proposed in this paper.\n\\end{itemize}\n\nOne of the possible applications of the data representation and data association proposed in this work is the geo-referencing localization using aerial imagery. For this reason, we present this work assuming a 2D world representation.\n\n\\section{RELATED WORK}\n\\label{sec:related_work}\nIn this section, we briefly review existing data association methods and existing data representations for lane markings.\n\n\\subsection{Data Association}\nWe review the data association methods distinguishing two categories: \\textit{pose estimation} and \\textit{graph-theoretic}. \n\n\\textit{Pose estimation} methods search for the transformation that minimizes the error between both a set of detections and a set of landmarks or maximizes its number of associations inliers. One of the most widely used \\textit{pose estimation} method for data association in localization is Iterative Closest Point (ICP). SLAM approaches such as \\cite{weichen2020hector,hu2019mapping,soatti2018implicit}, rely on the ICP for LiDAR scan matching. Currently, state-of-the-art Lidar Odometry And Mapping (LOAM) approaches also implement ICP for the data association process \\cite{shan2018lego,shan2020lio}. Nevertheless, this is a local method; hence, it requires a accurate initialization to converge in a global minimum. Further, it is more sensitive to suffer from drift in high outlier scenarios. We do not consider ICP a potential solution for data association as in the context of geo-referencing using aerial imagery no sufficiently accurate pose is available. The random Sample Consensus (RANSAC) method \\cite{fischler1981random} is a global data association approach common in the literature. RANSAC follows a strategy that samples hypothesis transformations depending on the data structure. Different authors use RANSAC for localization \\cite{yang2010ransac,jiao2020globally} (include aerial imagery-based \\cite{hu2019accurate,ramalingam2011pose}), and mapping \\cite{cunningham2012fully}.\n\n\\textit{Graph-theoretic} methods are based on searching a maximum number of associations being compatible. One common measure of compatibility is Distance Compatibility (DC). This problem is also known in the literature as the maximum clique (MC) problem \\cite{wu2015review}. In the past decades, this approach has been used in localization \\cite{bailey2000data,mangelson2018pairwise}, including loop closures \\cite{frey2019efficient,tian2021kimera}. This is also a global data association approach, and we consider it potentially suitable for localization in aerial imagery. In past years, there has been research focused on a weighed version of the MC problem \\cite{wu2012multi,benlic2013breakout}. An interesting variant is CLIPPER \\cite{lusk2020clipper}, where the authors implement a weighed MC problem using a continuous relaxation for the optimization process. We can also consider the proposed DC-SAC as a variant of this kind of association.\n\nIt is worth noting that there are other data association methods that we don't include in this work because we consider them strongly coupled with the SLAM problem. That is the case of the Hungarian \\cite{welte2020improved}, Nearest Neighbor (NN) \\cite{castellanos1999spmap}, Joint Compatibility Branch and Bound (JCBB) \\cite{neira2001data,yang2020improved}, probabilistic data association based on Mixture Models \\cite{zhang2019hierarchical,doherty2020probabilistic}, and Multi-Hypothesis based data association \\cite{hsiao2019mh,jiang2021imhs}.\n\n\\subsection{Data Representations for polylines}\n\\label{sec:related_work_dr}\nThe most common approach presented in the literature to represent data in the data association process is 2D points for 3-DOF localization \\cite{heidenreich2015laneslam,vivacqua2017self}. To represent lane markings as 2D points, a previous sampling of polylines becomes necessary. When this process is computed for both landmarks and detections, it is called Point-to-Point (P2P) association. Another strategy is the Point-to-Line association (P2L) \\cite{poggenhans2018precise,welte2019estimating}. In this case, the authors project point detections onto the map polylines in each \\textit{pose estimation} iteration. However, after projection, the distance is measured between points. Then, we can consider the polylines (landmarks) sampled and converted into 2D points and, roughly speaking, the representation becomes the P2P strategy. Hence, we consider both strategies, P2P and P2L, as unique 2D points-based representations. In other works \\cite{hu2019accurate,kummerle2019accurate}, the authors represent the data as lines, also with a previous sampling. In these cases, the authors use a distance metric for line segments, such as Hausdorff distance \\cite{yang2021matching,quehl2017good}.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=130pt]{figures\/02_invariance.pdf}\n\\caption{Depiction of direction invariance of the delta-angle calculation: in \\textit{red}, the $i$-th point, and in \\textit{blue}, the $j$-th point are in inverse direction. In both cases, the delta-angle represents the same value.}\n\\label{fig:delta_angle}\n\\end{figure}\n\n\\section{DELTA ANGLE LANE MARKINGS REPRESENTATION (DA-LMR)}\nAs discussed in the previous section, the most common representations of lane markings in the process of data association are point-based. Nevertheless, transforming lines and polylines into isolated points entails a loss of information. In a polyline, how the angle of each point changes in a curve provides additional information about the geometric structure of that curve. Following this assumption, we present the \\textit{Delta-Angle Lane Marking Representation} (DA-LMR). In the following subsections, we explain how to derive this representation for both polylines and dashes.\n\n\\subsection{DA-LMR for polylines}\nWe can describe a polyline as a set of 2D points $\\mathbf{P}^{2D} = (\\mathbf{p}^{2D}_0, \\mathbf{p}^{2D}_1, ..., \\mathbf{p}^{2D}_N)$, where $\\mathbf{p}^{2D}_i = (x_i, y_i)$, and where each point has a connection with its adjacent ones. Alternatively, given this connection, we can describe the polyline as a set of vectors $\\mathbf{V} = (\\vec{\\mathbf{v}}_{0}, \\vec{\\mathbf{v}}_{1}, ..., \\vec{\\mathbf{v}}_{N-1})$, where each vector is $\\vec{\\mathbf{v}}_{i} = (x_{i+1} - x_{i}, y_{i+1} - y_{i})$. This vectorial representation provides information about the orientation of each $i$-th point in a polyline. However, representing the orientation in a global reference frame can lead to problems due to the Euler angle wrap-around limitations. To obtain a compact and invariant angle representation, we use the differential angle between adjacent vectors $\\vec{\\mathbf{v}}_{i-1}$ and $\\vec{\\mathbf{v}}_{i}$. Then, for each $i$-th point in a polyline, we can obtain the delta-angle as follows:\n\n$$\n\\Delta\\alpha_{i} = \\arccos{\\left( \\frac{\\vec{\\mathbf{v}}_{i-1} \\cdot \\vec{\\mathbf{v}}_{i}}{\\left\\lVert \\vec{\\mathbf{v}}_{i-1}\\right\\rVert \\left\\lVert\\vec{\\mathbf{v}}_{i}\\right\\rVert} \\right)}. \n\\eqno{(1)}\n$$\n\nIt is worth noting, that the points $i = 0$ and $i = N$ does not have adjacent information, and therefore we assign them a default value of $\\Delta\\alpha_{0} = \\Delta\\alpha_{N} = 0$. The delta-angle calculation defined in (1) is a non-oriented angle calculation. This entails that the representation is also invariant to the direction in the polyline, i.e., we can sort the polyline from $0$ to $N$ or from $N$ to $0$, obtaining the same result. In Fig. \\ref{fig:delta_angle}, we show an example of this direction invariance. In practice, this is important as the representation is independent of the direction from which it is observed.\n\nAs the final step of DA-LMR, we use delta-angle information as an extra dimension in the 2D point representation to obtain a compact description suitable for data association methods. Now, the polyline is converted in a set of 3D points $\\mathbf{P}^{3D} = (\\mathbf{p}^{3D}_0, \\mathbf{p}^{3D}_1, ..., \\mathbf{p}^{3D}_N)$, where\n\n$$\n\\mathbf{p}^{3D}_i = (x_i, y_i, \\Delta\\alpha_i w).\n\\eqno{(2)}\n$$\n\nHere $w$ is a weight that tunes how strongly the delta-angle describes the data structuring. Note that when $w \\xrightarrow{} 0$, the representation tends to a traditional 2D representation. In Fig. \\ref{fig:dalmr_example}, we show an example of the DA-LMR in contrast to line segments and 2D points. In the case of DA-LMR, we represent the delta angle as the size of the circles. Given this additional dimension, DA-LMR is more informative than previously mentioned representations.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=220pt]{figures\/03_polylines.pdf}\n\\caption{Comparison of different representations of the same line: in \\textit{black}, line segments representation as polyline, in \\textit{blue}, 2D point representation, and in \\textit{red}, the proposed DA-LMR. We depict the delta angle for DA-LMR by the size of the circles.}\n\\label{fig:dalmr_example}\n\\end{figure}\n\n\\subsection{DA-LMR for dashes}\nFor dashed lines, it is not trivial to determine adjacent lines. Hence, for that case, we move the representation formulation to the distance measurement step in the data association process. This distance could be used for the error measurement in \\textit{pose estimation} data association, or otherwise, for distance compatibility measurement in \\textit{graph-theoretic} data association. \n\nWe can describe dashes as vectors where $\\vec{\\mathbf{v}}_i = (x_{i_e}-x_{i_s}, y_{i_e}-y_{i_s})$, and where subscripts $i_s$ and $i_e$ denotes start point and end point, respectively for $i$-th vector. Then, given a pair of dashes $\\vec{\\mathbf{v}}_i$ and $\\vec{\\mathbf{v}}_j$, we can formulate the distance measurement for DA-LMR as follows:\n\n$$\nd_{ij} = \\sqrt{(x_j-x_i)^2 + (y_j-y_i)^2 + (\\Delta\\alpha_{ij} w)^2}.\n\\eqno{(3)}\n$$\n\nWhere $(x_i, y_i)$ and $(x_j, y_j)$ are the centroid of each dash respectively. And where\n\n$$\n\\Delta\\alpha_{ij} = \\arccos{\\left( \\frac{\\vec{\\mathbf{v}}_{i} \\cdot \\vec{\\mathbf{v}}_{j}}{\\left\\lVert \\vec{\\mathbf{v}}_{i}\\right\\rVert \\left\\lVert\\vec{\\mathbf{v}}_{j}\\right\\rVert} \\right)}. \n\\eqno{(4)}\n$$\n\nIn Fig. \\ref{fig:dalmr_dashes}, we show an example of two dashes intersecting in its centroid but misaligned. In this case, if we use a 2D point representation, we obtain a distance measurement $d^{pt}_{ij} = 0$, which doesn't describe the misalignment. Using DA-LMR, we obtain a distance measurement $d^{dalmr}_{ij} = \\Delta\\alpha_{ij} w$ that uncovers the inconsistency.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=130pt]{figures\/04_dashes.pdf}\n\\caption{Depiction of two dashes intersecting in its centroid. If we compute the distance measurement (3) for a 2D point representation, the result is $d^{pt}_{ij} = 0$. In contrast, if we compute (3) for DA-LMR, the result is $d^{dalmr}_{ij} = \\Delta\\alpha_{ij} w$, showing the misalignment.}\n\\label{fig:dalmr_dashes}\n\\end{figure}\n\n\\section{DC-SAC DATA ASSOCIATION}\n\\textit{Distance-Compatible Sample Consensus} (DC-SAC) could be seen as a heuristic version of the RANSAC method. This is a formalization of a previous work \\cite{munoz2020targetless} for camera-LiDAR calibration. In \\cite{quan2020compatibility}, the authors propose a similar approach but focus on dense point clouds and geometric descriptors.\n\nDA-LMR is a \\textit{pose estimation} method that samples the possible transformations. How to sample the data for a $\\Delta_T \\in SE(2)$ transformation candidate is the key difference between RANSAC and DC-SAC. \n\nGiven a set of landmarks $\\mathcal{L}$ and a set of detections $\\mathcal{D}$, RANSAC chooses a pair of points randomly from each set. Thereafter, it estimates $\\Delta_T \\in SE(2)$ by minimizing \n\n$$\ne(\\Delta_T) = \\left\\lVert\\mathbf{p}^{d}_1 - \\mathbf{p}^{l}_1\\right\\rVert + \\left\\lVert\\mathbf{p}^{d}_2 - \\mathbf{p}^{l}_2\\right\\rVert.\n\\eqno{(5)}\n$$\n\nSuperscripts $l$ and $d$ mean landmarks and detections, respectively. Then, all the points in $\\mathcal{D}$ are transformed using the estimated $\\Delta_T^*$, obtaining a new set $\\mathcal{D}^*$. Thereafter, an error $\\epsilon$ is computed by the sum of the distance measurement between each detection in $\\mathcal{D}^*$ and the nearest neighbor in $\\mathcal{L}$. RANSAC iterates this process obtaining a new $\\epsilon$ in each iteration until the error satisfies the condition $\\epsilon < \\xi$, where $\\xi$ is a configurable threshold. The threshold limits the accuracy of the method. For example, a high value for $\\xi$ can miss potentially optimal hypotheses producing inaccurate results. In contrast, a low-value configuration of $\\xi$ can dramatically increase computation time, possibly making the method unusable.\n\nIn the case of DC-SAC, we sample a pair of points in $\\mathcal{D}$. However, in contrast to RANSAC, we randomly choose a pair of points in $\\mathcal{L}$ that are distance compatible, i.e., that satisfies\n\n$$\n\\gamma > \\left\\lvert \\left\\lVert\\mathbf{p}^{d}_1 - \\mathbf{p}^{d}_2\\right\\rVert - \\left\\lVert\\mathbf{p}^{l}_1 - \\mathbf{p}^{l}_2\\right\\rVert \\right\\rvert.\n\\eqno{(6)}\n$$\n\nWhere $\\gamma$ is a configurable value. The constraint proposed in (6) could be applied in a general way for data association. Additionally, as we present this work in localization, we can propose other constraints in the hypothesis space $\\mathcal{H}$ depending on the uncertainty of the prior. Given the distribution of $SE(2)$ localization, we can get the standard deviation values for each dimension $(\\sigma_x, \\sigma_y, \\sigma_\\theta)$. Then, by considering $\\sigma_x$ and $\\sigma_y$, we can limit the area of sampling points in $\\mathcal{L}$. By using $\\sigma_\\theta$ and given $\\vec{\\mathbf{v}}_l = (\\mathbf{p}^{l}_1 - \\mathbf{p}^{l}_2)$ and $\\vec{\\mathbf{v}}_d = (\\mathbf{p}^{d}_1 - \\mathbf{p}^{d}_2)$, we can define an orientation constraint between pairs of samples as follows:\n\n$$\n\\theta_t > \\arccos{\\left( \\frac{\\vec{\\mathbf{v}}_{l} \\cdot \\vec{\\mathbf{v}}_{d}}{\\left\\lVert \\vec{\\mathbf{v}}_{l}\\right\\rVert \\left\\lVert\\vec{\\mathbf{v}}_{d}\\right\\rVert} \\right)}. \n\\eqno{(7)}\n$$\n\nWhere $\\theta_t$ is a configurable value that depend on $\\sigma_\\theta$. The above-presented heuristic constraints reduce the size of $\\mathcal{H}$ dramatically. In this way, we can search the full space $\\mathcal{H}$ for the minimum value of $\\epsilon$ instead of iterating until $\\epsilon < \\xi$. This is the best scenario for converging to a potentially optimal solution. In the next section, we demonstrate experimentally how DC-SAC produces more accurate results than RANSAC by configuring a value of $\\xi$ where both methods run with similar computation time.\n\n\\begin{figure}[t]\n \\centering\n \\begin{tikzpicture}\n \\begin{axis}[\n name=plot,\n xlabel={$\\sigma$ (m)}, ylabel={\\%},\n xmin=0.1, xmax = 0.5, ymin=70, ymax=100,\n width=230pt,height=200pt,\n grid=both,\n grid style={line width=.1pt, draw=gray!10},\n major grid style={line width=.2pt,draw=gray!50},\n legend pos=south west,\n legend style={nodes={scale=0.5, transform shape}}\n ]\n \n \\addplot[color={rgb:red,4;green,0;yellow,5},mark=pentagon*,very thick] table{.\/data\/05_clipper_lsr_f1.txt};\n \\addlegendentry{LSR (F1 score)}\n \\addplot[color={rgb:red,4;green,0;yellow,5},dotted,very thick] table{.\/data\/05_clipper_lsr_p.txt};\n \\addlegendentry{LSR (Precision)}\n \\addplot[color={rgb:red,4;green,0;yellow,5},dashed,very thick] table{.\/data\/05_clipper_lsr_r.txt};\n \\addlegendentry{LSR (Recall)}\n\n \\addplot[color={rgb:red,1;green,2;blue,5},mark=triangle*,very thick] table{.\/data\/05_clipper_2dpr_f1.txt};\n \\addlegendentry{PR (F1 score)}\n \\addplot[color={rgb:red,1;green,2;blue,5},dotted,very thick] table{.\/data\/05_clipper_2dpr_p.txt};\n \\addlegendentry{PR (Precision)}\n \\addplot[color={rgb:red,1;green,2;blue,5},dashed,very thick] table{.\/data\/05_clipper_2dpr_r.txt};\n \\addlegendentry{PR (Recall)}\n\n \\addplot[color={rgb:red,4;green,0;yellow,1},mark=square*,very thick] table{.\/data\/05_clipper_dalmr_f1.txt};\n \\addlegendentry{DA-LMR (F1 score)}\n \\addplot[color={rgb:red,4;green,0;yellow,1},dotted,very thick] table{.\/data\/05_clipper_dalmr_p.txt};\n \\addlegendentry{DA-LMR (Precision)}\n \\addplot[color={rgb:red,4;green,0;yellow,1},dashed,very thick] table{.\/data\/05_clipper_dalmr_r.txt};\n \\addlegendentry{DA-LMR (Recall)}\n\n \\end{axis}\n \\end{tikzpicture}\n \\caption{Comparison in F1 score (\\textit{solid}), precision (\\textit{dotted}) and recall (\\textit{dashed}) metrics between LSR, PR, and DA-LMR for CLIPPER \\cite{lusk2020clipper} as data association method.}\n \\label{fig:clipper_p}\n\\end{figure}\n\n\\begin{figure}[t]\n \\centering\n \\begin{tikzpicture}\n \\begin{axis}[\n name=plot,\n xlabel={$\\sigma$ (m)}, ylabel={\\%},\n xmin=0.1, xmax = 0.5, ymin=70, ymax=100,\n width=230pt,height=200pt,\n grid=both,\n grid style={line width=.1pt, draw=gray!10},\n major grid style={line width=.2pt,draw=gray!50},\n legend pos=south west,\n legend style={nodes={scale=0.5, transform shape}}\n ]\n \n \\addplot[color={rgb:red,4;green,0;yellow,5},mark=pentagon*,very thick] table{.\/data\/06_mc_lsr_f1.txt};\n \\addlegendentry{LSR (F1 score)}\n \\addplot[color={rgb:red,4;green,0;yellow,5},dotted,very thick] table{.\/data\/06_mc_lsr_p.txt};\n \\addlegendentry{LSR (Precision)}\n \\addplot[color={rgb:red,4;green,0;yellow,5},dashed,very thick] table{.\/data\/06_mc_lsr_r.txt};\n \\addlegendentry{LSR (Recall)}\n\n \\addplot[color={rgb:red,1;green,2;blue,5},mark=triangle*,very thick] table{.\/data\/06_mc_2dpr_f1.txt};\n \\addlegendentry{PR (F1 score)}\n \\addplot[color={rgb:red,1;green,2;blue,5},dotted,very thick] table{.\/data\/06_mc_2dpr_p.txt};\n \\addlegendentry{PR (Precision)}\n \\addplot[color={rgb:red,1;green,2;blue,5},dashed,very thick] table{.\/data\/06_mc_2dpr_r.txt};\n \\addlegendentry{PR (Recall)}\n\n \\addplot[color={rgb:red,4;green,0;yellow,1},mark=square*,very thick] table{.\/data\/06_mc_dalmr_f1.txt};\n \\addlegendentry{DA-LMR (F1 score)}\n \\addplot[color={rgb:red,4;green,0;yellow,1},dotted,very thick] table{.\/data\/06_mc_dalmr_p.txt};\n \\addlegendentry{DA-LMR (Precision)}\n \\addplot[color={rgb:red,4;green,0;yellow,1},dashed,very thick] table{.\/data\/06_mc_dalmr_r.txt};\n \\addlegendentry{DA-LMR (Recall)}\n\n \\end{axis}\n \\end{tikzpicture}\n \\caption{Comparison in F1 score (\\textit{solid}), precision (\\textit{dotted}) and recall (\\textit{dashed}) metrics between LSR, PR, and DA-LMR for Max Clique \\cite{konc2007improved} as data association method.}\n \\label{fig:mc_p}\n\\end{figure}\n\n\\section{EVALUATION}\n\nThe evaluation step aims to compare the presented DA-LMR with other data representations as well as the presented DC-SAC with other data association methods. It is difficult to perform a quantitative comparison using maps and real onboard sensor data since it is hard to obtain ground truth. Hence, in Section \\ref{sec:quantitative}, we generate synthetic detections $\\mathcal{D}^s$ from the original landmarks $\\mathcal{L}$ of a real map by cropping, transforming, and adding noise. In Section \\ref{sec:qualitative}, we show qualitative results using the previously used $\\mathcal{L}$ and real detections $\\mathcal{D}^r$ from stereo cameras in a real autonomous vehicle.\n\n\\subsection{Quantitative results}\n\\label{sec:quantitative}\n\nFor quantitative evaluation, we use a map that contains continuous and dashed lane markings. This map was labeled by hand from aerial imagery from the city of Karlsruhe, Germany. We define the set of landmarks $\\mathcal{L}$ by sampling the map polylines with steps of $\\SI{1}{\\meter}$, and we use this labeled data as ground truth. Then, using a pre-recorded trajectory in the mapped area as a reference, we apply a sliding window to $\\mathcal{L}$. In each iteration, we crop a set of data that forms a new $\\mathcal{D}^s_i$, where the subscript $i$ indicates the iteration of the sliding window, and the superscript $s$ denotes synthetic. Thereafter, we apply a random 3-DOF rigid body transformation $\\mathbf{t} = (t_x, t_y)$ and $\\mathbf{R} = f(r_\\theta)$ to each $\\mathcal{D}^s_i$. Where $t_x$ and $t_y$ are uniformly distributed random variables with range $(\\SI{-5}{\\meter}, \\SI{5}{\\meter})$, and where $r_\\theta$ is also uniformly distributed random variable with range $(\\SI{-5}{\\degree}, \\SI{5}{\\degree})$. Also, for each experiment, we produce additive Gaussian noise with a range of $\\sigma$ between $\\SI{0.1}{\\meter}$ and $\\SI{0.5}{\\meter}$ with steps of $\\SI{0.1}{\\meter}$. Finally, we include outliers, where the number of outliers is $\\SI{10}{\\percent}$ of the samples in $\\mathcal{D}^s_i$. It is worth noting that it is not recommendable to execute the data association process in the complete set $\\mathcal{L}$. Hence, assuming we have a prior localization, we generate a set $\\mathcal{L}_i$ bigger than $\\mathcal{D}^s_i$ in each iteration of the sliding window that covers an area around it.\n\n\\begin{figure}[t]\n \\centering\n \\begin{tikzpicture}\n \\begin{axis}[\n name=plot,\n xlabel={$\\sigma$ (m)}, ylabel={\\%},\n xmin=0.1, xmax = 0.5, ymin=90, ymax=100,\n width=230pt,height=195pt,\n grid=both,\n grid style={line width=.1pt, draw=gray!10},\n major grid style={line width=.2pt,draw=gray!50},\n legend pos=south west,\n legend style={nodes={scale=0.5, transform shape}}\n ]\n \n \\addplot[color={rgb:red,4;green,0;yellow,5},mark=pentagon*,very thick] table{.\/data\/07_ransac_lsr_f1.txt};\n \\addlegendentry{LSR (F1 score)}\n \\addplot[color={rgb:red,4;green,0;yellow,5},dotted,very thick] table{.\/data\/07_ransac_lsr_p.txt};\n \\addlegendentry{LSR (Precision)}\n \\addplot[color={rgb:red,4;green,0;yellow,5},dashed,very thick] table{.\/data\/07_ransac_lsr_r.txt};\n \\addlegendentry{LSR (Recall)}\n\n \\addplot[color={rgb:red,1;green,2;blue,5},mark=triangle*,very thick] table{.\/data\/07_ransac_2dpr_f1.txt};\n \\addlegendentry{PR (F1 score)}\n \\addplot[color={rgb:red,1;green,2;blue,5},dotted,very thick] table{.\/data\/07_ransac_2dpr_p.txt};\n \\addlegendentry{PR (Precision)}\n \\addplot[color={rgb:red,1;green,2;blue,5},dashed,very thick] table{.\/data\/07_ransac_2dpr_r.txt};\n \\addlegendentry{PR (Recall)}\n\n \\addplot[color={rgb:red,4;green,0;yellow,1},mark=square*,very thick] table{.\/data\/07_ransac_dalmr_f1.txt};\n \\addlegendentry{DA-LMR (F1 score)}\n \\addplot[color={rgb:red,4;green,0;yellow,1},dotted,very thick] table{.\/data\/07_ransac_dalmr_p.txt};\n \\addlegendentry{DA-LMR (Precision)}\n \\addplot[color={rgb:red,4;green,0;yellow,1},dashed,very thick] table{.\/data\/07_ransac_dalmr_r.txt};\n \\addlegendentry{DA-LMR (Recall)}\n\n \\end{axis}\n \\end{tikzpicture}\n \\caption{Comparison in F1 score (\\textit{solid}), precision (\\textit{dotted}) and recall (\\textit{dashed}) metrics between LSR, PR, and DA-LMR for RANSAC \\cite{hu2019accurate} as data association method.}\n \\label{fig:ransac_p}\n\\end{figure}\n\n\\begin{figure}[t]\n \\centering\n \\begin{tikzpicture}\n \\begin{axis}[\n name=plot,\n xlabel={$\\sigma$ (m)}, ylabel={\\%},\n xmin=0.1, xmax = 0.5, ymin=90, ymax=100,\n width=230pt,height=195pt,\n grid=both,\n grid style={line width=.1pt, draw=gray!10},\n major grid style={line width=.2pt,draw=gray!50},\n legend pos=south west,\n legend style={nodes={scale=0.5, transform shape}}\n ]\n \n \\addplot[color={rgb:red,4;green,0;yellow,5},mark=pentagon*,very thick] table{.\/data\/08_dcsac_lsr_f1.txt};\n \\addlegendentry{LSR (F1 score)}\n \\addplot[color={rgb:red,4;green,0;yellow,5},dotted,very thick] table{.\/data\/08_dcsac_lsr_p.txt};\n \\addlegendentry{LSR (Precision)}\n \\addplot[color={rgb:red,4;green,0;yellow,5},dashed,very thick] table{.\/data\/08_dcsac_lsr_r.txt};\n \\addlegendentry{LSR (Recall)}\n\n \\addplot[color={rgb:red,1;green,2;blue,5},mark=triangle*,very thick] table{.\/data\/08_dcsac_2dpr_f1.txt};\n \\addlegendentry{PR (F1 score)}\n \\addplot[color={rgb:red,1;green,2;blue,5},dotted,very thick] table{.\/data\/08_dcsac_2dpr_p.txt};\n \\addlegendentry{PR (Precision)}\n \\addplot[color={rgb:red,1;green,2;blue,5},dashed,very thick] table{.\/data\/08_dcsac_2dpr_r.txt};\n \\addlegendentry{PR (Recall)}\n\n \\addplot[color={rgb:red,4;green,0;yellow,1},mark=square*,very thick] table{.\/data\/08_dcsac_dalmr_f1.txt};\n \\addlegendentry{DA-LMR (F1 score)}\n \\addplot[color={rgb:red,4;green,0;yellow,1},dotted,very thick] table{.\/data\/08_dcsac_dalmr_p.txt};\n \\addlegendentry{DA-LMR (Precision)}\n \\addplot[color={rgb:red,4;green,0;yellow,1},dashed,very thick] table{.\/data\/08_dcsac_dalmr_r.txt};\n \\addlegendentry{DA-LMR (Recall)}\n\n \\end{axis}\n \\end{tikzpicture}\n \\caption{Comparison in F1 score (\\textit{solid}), precision (\\textit{dotted}) and recall (\\textit{dashed}) metrics between LSR, PR, and DA-LMR for the proposed DC-SAC as data association method.}\n \\label{fig:dcsac_p}\n\\end{figure}\n\n\\begin{figure*}[t]\n\\centering\n\\includegraphics[width=500pt]{figures\/08_mosaic.pdf}\n\\caption{Examples of data association results. In \\textit{red}, we show the $\\mathcal{L}_i$, while in \\textit{blue}, we show $\\mathcal{D}^r_i$. \\textit{Green} lines indicate the association between landmarks and detections. The size of circles is proportional to the Z-axis value.}\n\\label{fig:mosaic}\n\\end{figure*}\n\nAt this point, given $\\mathcal{L}_i$ and $\\mathcal{D}^s_i$, we run a data association process for each data representation and each $\\sigma$ value of the range mentioned above. We use the representations shown Fig. \\ref{fig:dalmr_example}, i.e., line segments representation (LSR), 2D points representation (PR), and the proposed DA-LMR with $w = \\SI{5}{\\frac{\\meter}{\\radian}}$. For metrics, we use Euclidean distance for PR and DA-LMR and Hausdorff distance for LSR. Finally, we repeat the process for the different data association methods: CLIPPER \\cite{lusk2020clipper}, Max Clique \\cite{konc2007improved}, RANSAC \\cite{hu2019accurate}, and the proposed DC-SAC.\n\nIn Fig. \\ref{fig:clipper_p}, we show a comparison in F1 score, precision and recall metrics between the representation approaches using CLIPPER \\cite{lusk2020clipper} as data association method. The statistics are accumulated for all $i$ values, i.e., for the complete pre-recorded trajectory. For this experiment, we configure the distance compatibility (6) parameter as $\\gamma = 3 \\sigma$\\footnote{It is worth noting that the parameter $\\gamma$ only defined for DC-SAC is also used in graph-theoretic approaches because such methods are based on distance compatibility constraints too.}. As we can see in Fig. \\ref{fig:clipper_p}, DA-LMR improves the results of PR for both precision and recall. LSR shows the best results in precision. However, it generates the worst results in recall. In other words, LSR can associate less data than the other, but with better precision. \n\nIn the case of Max Clique \\cite{konc2007improved}, for this experiment, we also configure the distance compatibility (6) parameter as $\\gamma = 3 \\sigma$. We can see in Fig. \\ref{fig:mc_p} that the behavior of the different representations is similar to the previous. But in this case, LSR seems more sensitive against the noise in recall statistics than PR and DA-LMR.\n\nFor RANSAC \\cite{hu2019accurate}, we configure $\\xi$ with a value that produces a computational time similar to the DC-SAC method. The results for RANSAC are strongly correlated with the threshold $\\xi$. For this reason, the results are stable against the noise. We can see this behavior in Fig. \\ref{fig:ransac_p} for PR and DA-LMR, where DA-LMR improves the results of PR. However, LSR shows different behavior and presents less stability against noise again.\n\nIn Fig. \\ref{fig:dcsac_p}, we compare the data representation approaches using the presented DC-SAC data association method and configure $\\gamma = 3 \\sigma$. In this case, we can see the best results in precision with DA-LMR. While in recall, LSR shows the best results, but the difference is relatively small.\n\nIn general, LSR shows good results in low-noise scenarios, but it is more sensitive to noise disturbances. The PR and DA-LMR show more robustness against noise, and DA-LMR improves the results of PR in all cases.\n\nComparing the data association methods, we found the \\textit{graph-theoretic} ones (CLIPPER and Max Clique) more sensitive against noise. Especially the CLIPPER method. Max Clique shows good results for low-noise, but for high noise, the precision is similar to RANSAC and the recall much worst. In contrast, we found the \\textit{pose estimation} methods more robust against noise. RANSAC shows reasonably good results. In precision it is similar to the worst case of Max Clique, but in recall shows more stability. DC-SAC shows, in general, the best results in precision and recall and, like RANSAC, high robustness against noise.\n\nFinally, in Table \\ref{tab:time_consuming}, we show the evaluation of the computational time for each data association and each data representation depending on the samples number $\\left\\lvert \\mathcal{L}_i \\right\\rvert + \\left\\lvert \\mathcal{D}^s_i \\right\\rvert$. The experiments were performed on an i7-7700HQ CPU with 16 GB of RAM in a C++ compiled code. We can see that RANSAC and DC-SAC show similar results for the configuration described in this section and are reasonable. In the case of Max Clique, it improves the time consumption for low sample scenarios, but for $>200$ samples, it is slower than SAC-based methods. The CLIPPER method is the slowest of all. For 400 samples, the CPU cannot even run a complete process. Comparing data representation, LSR shows slower behavior than PR and DA-LMR for SAC-based methods. However, for \\textit{graph-theoretic} approaches, the behavior is different. In that case, LSR is faster than the others representations. In all cases, DA-LMR is faster than PR.\n\n\\begin{table}[ht]\n\\caption{Computation time evaluation in seconds.}\n\\label{tab:time_consuming}\n\\begin{center}\n\\begin{tabular}{c c c c c c c}\n\\hline\nSamples & DR & \\textbf{DC-SAC} & \\textbf{RANSAC} & \\textbf{MC} & \\textbf{CLIPPER} \\\\\n\\hline\n\\hline\n100 & LSR & 0.09 & 0.06 & 0.02 & 0.17 \\\\\n & PR & 0.06 & 0.06 & 0.03 & 0.69 \\\\\n & DA-LMR & 0.06 & 0.06 & 0.03 & 0.31 \\\\\n\\hline\n200 & LSR & 0.91 & 1.12 & 0.27 & 4.79 \\\\\n & PR & 0.67 & 0.69 & 0.53 & 21.16 \\\\\n & DA-LMR & 0.61 & 0.59 & 0.55 & 10.50 \\\\\n\\hline\n300 & LSR & 4.58 & 5.15 & 2.86 & 41.32 \\\\\n & PR & 3.27 & 2.97 & 4.86 & 151.3 \\\\\n & DA-LMR & 2.91 & 2.83 & 4.35 & 55.62 \\\\\n\\hline\n400 & LSR & 18.55 & 19.72 & 12.77 & - \\\\\n & PR & 12.88 & 13.05 & 18.61 & - \\\\\n & DA-LMR & 11.91 & 12.64 & 18.53 & - \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\end{table}\n\nAs we obtain the highest values on precision and recall, we consider that the combination of the proposed data representation approach DA-LMR, and the presented data association DC-SAC, is the best combination among the evaluated for a localization application and specially for geo-referencing using aerial imagery.\n\n\\subsection{Qualitative results}\n\\label{sec:qualitative}\nIn Section \\ref{sec:quantitative}, we compared different data association methods with different data representations, and we concluded that DA-LMR and DC-SAC are the preferable combination. In this section, we show some qualitative results with the chosen combination. For the evaluation, we use the same map as in the previous section and the same pre-recorded trajectory. We use the same $\\mathcal{L}_i$ as a set of landmarks for each sliding window iteration. However, in this case, we use real data for detections ($\\mathcal{D}^r_i$) obtained while driving the pre-recorded trajectory. Detections were obtained using stereo cameras from our\nexperimental vehicle \\textit{BerthaOne} \\cite{tacs2017making}. Then, given $\\mathcal{L}_i$ and $\\mathcal{D}^r_i$, we iteratively run the process of DA-LMR and DC-SAC for the complete trajectory.\n\nIn Fig. \\ref{fig:mosaic}, we show some examples of data association results. In \\textit{red}, we show the $\\mathcal{L}_i$, while in \\textit{blue}, we show $\\mathcal{D}^r_i$. \\textit{Green} lines indicate the association between landmarks and detections. The size of circles is proportional to the delta angles. The results look very reasonable for its implementation in a complete localization approach.\n\n\\section{CONCLUSIONS}\nWe proposed DA-LMR, a robust data representation for lane markings in data association processes for localization. As we demonstrated experimentally, this representation provides richer information of geometrical data structure than others, such as line segments and 2D points. Furthermore, we proposed DC-SAC, a data association method that can improve the results by eliminating potentially non-optimal hypotheses in the space using distance compatibility constraints. We also demonstrate experimentally how the presented method improves the results of other state-of-the-art ones, such as RANSAC, Max Clique, and CLIPPER.\n\nIn future work, we will apply the combination of DA-LMR and DC-SAC for a geo-referencing localization using aerial imagery. Although we have emphasized this kind of localization in this work, we also plan to apply the presented method to localization in HD maps.\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe approximate Nonnegative Matrix Factorization (NMF) of\nnonnegative matrices is a data analysis technique only recently\nintroduced \\cite{leeseung1999, sullivan2000}. Roughly speaking the\nproblem is to find, for a given nonnegative matrix $V \\in\n\\mathbb{R}_+^{m\\times n}$, and an assigned $k$, a pair of nonnegative\nmatrices $W\\in\\mathbb{R}_+^{m\\times k}$ and $H\\in\\mathbb{R}_+^{k\\times n}$ such\nthat, in an appropriate sense, $V \\approx WH$. In\n\\cite{leeseung1999} EM like algorithms for the construction of a\nfactorization have been proposed. The algorithms have been later\nderived in \\cite{leeseung2001} by using an {\\em ad-hoc} auxiliary\nfunction, a common approach in deriving EM algorithms. In\n\\cite{sullivan2000} the connection with the classic alternating\nminimization of the I-divergence \\cite{ct1984} has been pointed\nout but not fully investigated. In this paper we pose the NMF\nproblem as a minimum I-divergence problem that can be solved by\nalternating minimization and derive, from this point of view, the\nalgorithm proposed in \\cite{leeseung1999}. There are alternative\napproaches to approximate nonnegative matrix factorization. For\ninstance, recently, see~\\cite{laa2004}, results have been obtained\nfor the approximate factorization (w.r.t. the Frobenius norm) of\n{\\em symmetric} nonnegative matrices.\n\nAlthough only recently introduced the NMF has found many\napplications as a data reduction procedure and has been advocated\nas an alternative to Principal Components Analysis (PCA) in cases\nwhere the positivity constraint is relevant (typically image\nanalysis). The title of \\cite{sullivan2000} is a clear indication\nof this point of view, but a complete analysis of the relations\nbetween NMF and PCA is still lacking.\nOur interest in NMF stems from the system theoretic problem of\napproximate realization (or order reduction) of Hidden Markov\nModels. Partial results have already been obtained \\cite{fs2002}.\n\nThis paper is organized as follows. In section~\\ref{sec:problem}\nwe pose the approximate nonnegative matrix factorization problem,\ndefine the I-divergence between matrices and discuss the solution\nproposed in \\cite{leeseung1999, leeseung2001}. In\nsection~\\ref{section:lift} we pave the way for the alternating\nminimization algorithm presenting the properly lifted version of\nthe minimization problem and solving the two partial minimizations\nin the style of Csisz\\'ar and Tusn\\'ady \\cite{ct1984}. In\nsection~\\ref{section:altmin} we construct the alternating\nminimization algorithm and compute the iteration gain. One of the\nadvantages of working with the lifted problem is that it sheds a\nnew light also on the derivation of the algorithm via auxiliary\nfunctions given in \\cite{leeseung2001}. In\nsection~\\ref{section:auxfunc} we will use the results of\nsection~\\ref{section:lift} to construct a very natural auxiliary\nfunction to solve the original problem. A discussion of the\nconvergence properties of the algorithm is given in\nsection~\\ref{section:convprop}. In the concluding\nsection~\\ref{section:otherprob} we establish a connection between\nthe approximate NMF problem and the Archetypal Analysis algorithm\nof Cutler and Breiman \\cite{cb1994}. The present paper is an\nextended version of~\\cite{MTNS2004}.\n\n\\section{Preliminaries and problem statement}\\label{sec:problem}\n\nThe NMF is a long standing problem in linear algebra \\cite{haz,\nphs}. It can be stated as follows. Given $V \\in \\mathbb{R}_+^{m\\times n}$,\nand $1 \\le k \\le \\min\\{m,n\\}$, find a pair of matrices\n$W\\in\\mathbb{R}_+^{m\\times k}$ and $H\\in\\mathbb{R}_+^{k\\times n}$ such that $V =\nWH$. The smallest $k$ for which a factorization exists is called\nthe positive rank of $V$, denoted ${\\rm prank}(V)$. This\ndefinition implies that ${\\rm rank}(V) \\le {\\rm prank}(V) \\le\n\\min\\{m,n\\}$. It is well known that ${\\rm prank}(V)$ can assume\nall intermediate values, depending on $V$. Examples for which\nnonnegative factorizations do not exist, and examples for which\nfactorization is possible only for $k > {\\rm rank}(V)$ have been\nconstructed in the literature \\cite{haz}. The ${\\rm prank}$ has\nbeen characterized only for special classes of matrices \\cite{phs}\nand algorithms for the construction of a NMF of a general positive\nmatrix are not known.\n\nThe \\emph{approximate} NMF has been recently introduced in\n\\cite{leeseung1999} independently from the exact NMF problem. The\nset-up is the same, but instead of exact factorization it is\nrequired that $V \\approx WH$ in an appropriate sense. In\n\\cite{leeseung1999}, and in this paper, the approximation is to be\nunderstood in the sense of minimum I-divergence. For two\nnonnegative numbers $p$ and $q$ the I-divergence is defined as\n\\begin{equation*}\nD(p||q)= p\\log\\frac{p}{q}-p+q,\n\\end{equation*}\nwith the conventions $0\/0=0$, $0\\log 0=0$ and $p\/0=\\infty$ for\n$p>0$. From the inequality $x\\log x\\geq x-1$ it follows that\n$D(p||q)\\geq 0$ with equality iff $p=q$. For two nonnegative\nmatrices $M=(M_{ij})$ and $N=(N_{ij})$, of the same size, the\nI-divergence is defined as\n\\begin{equation*}\nD(M||N)= \\sum_{ij}D(M_{ij}||N_{ij}).\n\\end{equation*}\nAgain it follows that $D(M||N)\\geq 0$ with equality iff $M=N$. For\nnonnegative vectors or tensors of the same size a similar\ndefinition applies.\n\n\\noindent The problem of approximate NMF is to find for given $V$\nand a fixed number $k$ (often referred to as the {\\em inner size}\nof the factorization)\n\\begin{equation}\\label{eq:dmin}\n\\arg \\min_{W,H}D(V||WH).\n\\end{equation}\nThe function $D: (W,H) \\to D(V||WH)$ will sometimes be referred to\nas the {\\em objective function}. The {\\em domain} of $D$ is the\nset of pairs $(W,H)$ with nonnegative entries. The {\\em interior}\nof the domain is the subset of pairs $(W,H)$ with positive ($>0$)\nentries, whereas pairs on the {\\em boundary} have at least one\nentry equal to zero.\n\nAlthough the objective function $(W,H)\\mapsto D(V||WH)$ is easily\nseen to be convex in $W$ and $H$ separately, it is not jointly\nconvex in the two variables. Hence $(W,H)\\mapsto D(V||WH)$ may\nhave several (local) minima and saddle points, that may prevent\nnumerical minimization algorithms to converge to the global\nminimizer. However $D(V||WH)$ cannot have a local maximum in an\ninterior point $(W_0,H_0)$, because then also $W\\mapsto\nD(V||WH_0)$ would have a local maximum in $W_0$, which contradicts\nconvexity. Local maxima at the boundary are not a priori excluded.\n\n\\medskip\nIt is not immediately obvious that the approximate NMF problem\nadmits a solution. The following result is therefore relevant.\n\\begin{prop}\\label{prop:exist}\nThe minimization problem~(\\ref{eq:dmin}) has a solution.\n\\end{prop}\nThe proof of this proposition is deferred to\nsection~\\ref{section:altmin}.\n\n\\medskip \\noindent Notice that, increasing the inner size from $k$ to\n$k+1$, the optimal value of the objective function decreases. This\nfollows from the fact that one can trivially embed the\nfactorization problem with inner size $k$ into the problem with\ninner size $k+1$ simply adding a zero last column to the optimal\n$W$ and an arbitrary last row to the optimal $H$ of the problem\nwith inner size $k$. Unfortunately, unlike the SVD of a matrix,\nthe best approximations with increasing $k$ are not embedded one\ninto another. For increasing $k$ the computations are to be\ncarried out anew.\n\nAlthough, according to proposition~\\ref{prop:exist}, a solution to\nthe minimization problem exists, it will certainly not be unique.\nIn order to rule out too many trivial multiple solutions, we\nimpose the condition that $H$ is row stochastic, so\n$\\sum_jH_{lj}=1$ for all $l$. This is not a restriction. Indeed,\nfirst we exclude without loss of generality the case where $H$ has\none or more zero rows, since we would then in fact try to minimize\nthe I-divergence with inner size smaller than $k$. Let $h$ be the\ndiagonal matrix with elements $h_i=\\sum_j H_{ij}$, then\n$WH=\\tilde{W}\\tilde{H}$ with $\\tilde{W}=Wh$, $\\tilde{H}=h^{-1}H$\nand $\\tilde{H}$ is by construction row stochastic. The convention\nthat $H$ is row stochastic still does not rule out non-uniqueness.\nThink e.g.\\ of post-multiplying $W$ with a permutation matrix\n$\\Pi$ and pre-multiplying $H$ with $\\Pi^{-1}$.\n\n\n\nLet $e_n$ ($e_n^\\top$) be the column (row) vector of size $n$\nwhose elements are all equal to one. Given $k$, the (constrained)\nproblem we will look at from now on is\n\\begin{equation}\\label{maxc}\n\\min_{W,H: He_m=e_k} D(V||WH).\n\\end{equation}\nFor the sake of brevity we will often write $e$ for a vector of\n$1$'s of generic size. The constraint in the previous problem will\nthen read as $He=e$.\n\n\\medskip\nTo carry out the minimization numerically, Lee and\nSeung~\\cite{leeseung1999,leeseung2001} proposed the following\niterative algorithm. Denoting by $W^t$ and $H^t$ the matrices at\nstep $t$, the update equations are\n\\begin{align}\nW^{t+1}_{il} & = W^t_{il} \\sum_j\n\\frac{H^t_{lj}V_{ij}}{(W^tH^t)_{ij}}\\label{eq:wbar2}\\\\\nH^{t+1}_{lj} & = H^t_{lj} \\sum_i\n\\frac{W^t_{il}V_{ij}}{(W^tH^t)_{ij}} \\Big\/ \\sum_{ij}\n\\frac{W^t_{il}H^t_{lj}V_{ij}}{(W^tH^t)_{ij}}.\\label{eq:hbar2}\n\\end{align}\nThe initial condition $(W^0, H^0)$ will always be assumed to be in\nthe interior of the domain. Only a partial justification for this\nalgorithm is given in \\cite{leeseung2001}, although the update\nsteps~(\\ref{eq:wbar2}) and~(\\ref{eq:hbar2}) are like those in the\nEM algorithm, known from statistics, see~\\cite{em}. Likewise the\nconvergence properties of the algorithm are unclear. In the next\nsection the minimization problem will be cast in a different way\nto provide more insight in the specific form of the update\nequations and on the convergence properties of the algorithm.\n\n\\medskip\nWe will now show that the $V$ matrix in the approximate NMF\nproblem can always be taken as a probability matrix $P$ i.e. such\nthat $P_{ij}\\ge 0, \\sum_{ij}P_{ij}=1$. This will pave the way for\nthe probabilistic interpretation of the exact and approximate NMF\nproblems to be given later.\n\nLet $P =\\frac{1}{e^\\top Ve}V$, $Q_- =\\frac{1}{e^\\top We}W$,\n$w=e^\\top We$ and $Q_+ = H$. Notice that $e^\\top Pe=e^\\top Q_-e=1$\nand $Q_+e=e$. Using the definition of divergence and elementary\ncomputations, we obtain the decomposition\n\\[\nD(V||WH)=e^\\top Ve\\, D(P||Q_-Q_+)+ D(e^\\top Ve||w).\n\\]\nHence, since the number $e^\\top V e$ is known, minimizing\n$D(V||WH)$ w.r.t. $(W,H)$ is equivalent to minimizing\n$D(P||Q_-Q_+)$ w.r.t.\\ $(Q_-, Q_+)$ and $D(e^\\top Ve||w)$ w.r.t.\\\n$w$. The minimizers of the three problems satisfy the relations\n$W^*=e^\\top Ve\\, Q_-^*$, $H^* = Q_+^*$, and $w^*=e^\\top Ve$.\nMinimizing $D(V||WH)$ is therefore equivalent to minimizing\n$D(P||Q_-Q_+)$. This enables us to give the problem a\nprobabilistic interpretation. Indeed,\n\\begin{equation}\\label{eq:divnew}\nD(P||Q_-Q_+)=\\sum_{ij} D(P_{ij}||(Q_-Q_+)_{ij}) =\n\\sum_{ij}P_{ij}\\log\\frac{P_{ij}}{(Q_-Q_+)_{ij}},\n\\end{equation}\nwhich is the usual I-divergence (Kullback-Leibler distance)\nbetween (finite) probability measures. This will be exploited in\nlater sections. From now on we will always consider the following\nproblem. Given the probability matrix $P$ and the integer $k$ find\n\\[\n\\min_{Q_-, Q_+ : Q_+e=e} D(P||Q_-Q_+).\n\\]\nFor typographical reasons we often, but not always, denote the\nentries of $P$ by $P(ij)$ instead of $P_{ij}$ and likewise for\nother matrices. \\medskip\\\\\nThe minimization algorithm is easily seen to be {\\em invariant\nunder the previous normalizations}. Let $Q_-^t=\\frac{W^t}{e^\\top\nW^te}$ and $Q_-^t=H^t$. Substitute the definitions of $(P, Q_-^t,\nQ_+^t)$ into~(\\ref{eq:wbar2}) and~(\\ref{eq:hbar2}) and use the\neasily verified fact that $e^\\top W^te=e^\\top Ve$ for $t\\geq 1$ to\nobtain the update equations in the new notations\n\\begin{align}\nQ_-^{t+1}(il) & = Q_-^t(il) \\sum_j\n\\frac{Q_+^t(lj)P(ij)}{(Q_-^tQ_+^t)(ij)}\\label{eq:q-bar2}\\\\\nQ_+^{t+1}(lj) & = Q_+^t(lj) \\sum_i\n\\frac{Q_-^t(il)P(ij)}{(Q_-^tQ_+^t)(ij)} \\Big\/ \\sum_{ij}\n\\frac{Q_-^t(il)Q_+^t(lj)P(ij)}{(Q_-^tQ_+^t)(ij)}.\\label{eq:q+bar2}\n\\end{align}\n\\rm\n\n\\section{Lifted version of the problem}\\label{section:lift}\n\nIn this section we lift the I-divergence minimization problem to\nan equivalent minimization problem where the `matrices' (we should\nspeak of {\\em tensors}) have three indices.\n\n\\subsection{Setup}\nLet be given a probability matrix $P$ (i.e. $P(ij) \\ge 0, \\,\\,\n\\sum_{ij}P(ij)=1$) and an integer $k\\le \\min\\{m,n\\}$. We introduce\nthe following sets\n\\begin{align*}\n\\mbox{{\\boldmath $\\mathcal{P}$}} = & \\left\\{\\mathbf{P}\\in\\mathbb{R}^{m\\times k\\times n}_+ \\,:\n\\,\\, \\sum_l\\mathbf{P}(ilj)=P(ij)\\right\\}, \\\\ \\\\\n\\mbox{{\\boldmath $\\mathcal{Q}$}} = & \\big\\{\\mathbf{Q}\\in\\mathbb{R}^{m\\times k\\times n}_+ \\, :\n\\,\\, \\mathbf{Q}(ilj)=Q_-(il)Q_+(lj), \\big.\\\\ & \\,\\, \\big.\n\\qquad\\qquad\\qquad\\qquad Q_- ,\\,\\, Q_+ \\ge 0,\\,\\,\\, Q_+e=e, \\,\\,\n e^\\top Q_-e=1 \\big\\}, \\\\ \\\\\n\\mathcal{Q} = & \\left\\{ Q\\in \\mathbb{R}^{m\\times n}_+ \\, : \\,\\,\nQ(ij)=\\sum_l\\mathbf{Q}(ilj) \\quad {\\rm for \\,\\, some} \\,\\,\n\\mathbf{Q}\\in \\mbox{{\\boldmath $\\mathcal{Q}$}} \\right\\}.\n\\end{align*}\n\n\\medskip\n\\noindent The interpretation of the sets $\\mbox{{\\boldmath $\\mathcal{P}$}}, \\mbox{{\\boldmath $\\mathcal{Q}$}}, \\mathcal{Q}$\nis given next.\n\n\\medskip\n\\noindent Suppose one is given random variables $(Y_-,X,Y_+)$,\ntaking values in $\\{1,\\dots ,m\\}\\times \\{1,\\dots\n,k\\}\\times\\{1,\\dots ,n\\}$. For convenience we can think of the\nr.v.'s as defined on the canonical measurable space\n$(\\Omega,\\mathcal{F})$, where $\\Omega$ is the set of all triples\n$(i,l,j)$ and $\\mathcal{F}$ is $2^\\Omega$. For $\\omega=(i,l,j)$ we\nhave the identity mapping $(Y_-,X,Y_+)(\\omega)=(i,l,j)$. If\n$\\mathbb{R}$ a given probability measure on this space, then the\ndistribution of the triple $(Y_-,X,Y_+)$ under $\\mathbb{R}$ is\ngiven by the {\\em tensor} $\\mathbf{R}$ defined by\n\\begin{equation}\\label{eq:probtens}\n\\mathbf{R}(ilj)=\\mathbb{R}(Y_-=i,X=l,Y_+=j).\n\\end{equation}\nConversely, a given tensor $\\mathbf{R}$ defines a probability\nmeasure $\\mathbb{R}$ on $(\\Omega,\\mathcal{F})$. We will use the\nnotation $D$ both for I-divergence between tensors and matrices\nand for the Kullback-Leibler divergence between probabilities. If\n$\\mathbf{P}$, $\\mathbf{Q}$ are tensors related to probability\nmeasures $\\mathbb{P}$ and $\\mathbb{Q}$ like in~(\\ref{eq:probtens})\nwe obviously have\n$D(\\mathbf{P}||\\mathbf{Q})=D(\\mathbb{P}||\\mathbb{Q})$.\n\n\\medskip \\noindent The sets $\\mbox{{\\boldmath $\\mathcal{P}$}}, \\mbox{{\\boldmath $\\mathcal{Q}$}}$ correspond to subsets of the\nset of all measures on $(\\Omega,\\mathcal{F})$. In particular $\\mbox{{\\boldmath $\\mathcal{P}$}}$\ncorresponds to the subset of all measures whose $Y=(Y_-, Y_+)$\nmarginal coincides with the given $P$, while $\\mbox{{\\boldmath $\\mathcal{Q}$}}$ corresponds to\nthe subset of measures under which $Y_-$ and $Y_+$ are\nconditionally independent given $X$. The first assertion is\nevident by the definition of $\\mbox{{\\boldmath $\\mathcal{P}$}}$. To prove the second assertion\nnotice that if\n$\\mathbb{Q}(Y_-=i,X=l,Y_+=j)=\\mathbf{Q}(ilj)=Q_-(il)Q_+(lj)$, then\nsumming over $j$ one gets $\\mathbb{Q}(Y_-=i,X=l)=Q_-(il)$ (since\n$Q_+e=e$) and similarly $\\mathbb{Q}(Y_+=j|X=l)= Q_+(lj)$. It\nfollows that $\n\\mathbb{Q}(Y_-=i,X=l,Y_+=j)=\\mathbb{Q}(Y_-=i,X=l)\\mathbb{Q}(Y_+=j|X=l)$\n which is equivalent to\n\\[\n\\mathbb{Q}(Y_-=i,Y_+=j|X=l)=\\mathbb{Q}(Y_-=i|X=l)\\mathbb{Q}(Y_+=j|X=l)\n\\]\ni.e. $Y_-, Y_+$ are conditionally independent given $X$.\n\n\\medskip\n\\noindent Finally the set $\\mathcal{Q}$ is best interpreted\nalgebraically as the set of $m\\times n$ probability matrices that\nadmit exact NMF of size $k$.\n\n\\medskip\nThe following observation (taken from~\\cite{ps}) motivates our\napproach.\n\n\\begin{lem}\\label{lemma:61}\n$P$ admits exact factorization of inner size $k$ iff\n$\\mbox{{\\boldmath $\\mathcal{P}$}}\\cap\\mbox{{\\boldmath $\\mathcal{Q}$}}\\neq\\emptyset$.\n\\end{lem}\n{\\bf Proof.} If $\\mbox{{\\boldmath $\\mathcal{P}$}}\\cap\\mbox{{\\boldmath $\\mathcal{Q}$}}\\neq\\emptyset$ then there exists a\nmatrix $\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}$ which also belongs to $\\mbox{{\\boldmath $\\mathcal{P}$}}$, therefore\n$P = Q_-Q_+$. Conversely, if we have $P=Q_-Q_+$ with inner size\n$k$, then the tensor $\\mathbf{P}$ given by\n$\\mathbf{P}(ilj)=Q_-(il)Q_+(lj)$ clearly belongs to $\\mbox{{\\boldmath $\\mathcal{P}$}}$. As in\nsection~\\ref{sec:problem} we can w.l.o.g.\\ assume that $Q_+e=e$,\nso that $\\mathbf{P}$ belongs to $\\mbox{{\\boldmath $\\mathcal{Q}$}}$ as well.~\\hfill $\\square$\n\n\\medskip\n\\noindent We are now ready to give a natural probabilistic\ninterpretation to the exact NMF problem. The probability matrix\n$P$ admits exact NMF $P=Q_-Q_+$ iff there exists at least one\nmeasure on $(\\Omega,\\mathcal{F})$ whose $Y=(Y_-,Y_+)$ marginal is\n$P$ and at the same time making $Y_-$ and $Y_+$ conditionally\nindependent given $X$.\n\n\\bigskip\n\\noindent Having shown that the exact NMF factorization $P=Q_-Q_+$\nis equivalent to $\\mbox{{\\boldmath $\\mathcal{P}$}}\\cap\\mbox{{\\boldmath $\\mathcal{Q}$}}\\neq\\emptyset$ it is not surprising\nthat the approximate NMF, corresponding to $\\mbox{{\\boldmath $\\mathcal{P}$}}\\cap\\mbox{{\\boldmath $\\mathcal{Q}$}}\n=\\emptyset$, can be viewed as a double minimization over the sets\n$\\mbox{{\\boldmath $\\mathcal{P}$}}$ and $\\mbox{{\\boldmath $\\mathcal{Q}$}}$.\n\\begin{prop}\\label{prop:pqq}\nLet $P$ be given. The function $(\\mathbf{P},\\mathbf{Q})\\mapsto\nD(\\mathbf{P}||\\mathbf{Q})$ attains a minimum on $\\mbox{{\\boldmath $\\mathcal{P}$}}\\times \\mbox{{\\boldmath $\\mathcal{Q}$}}$\nand it holds that\n\\begin{equation*}\n\\min_{Q\\in\\mathcal{Q}}D(P||Q)=\\min_{\\mathbf{P}\\in\\mbox{{\\boldmath $\\mathcal{P}$}},\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}}D(\\mathbf{P}||\\mathbf{Q}).\n\\end{equation*}\n\\end{prop}\n\n\\noindent The proof will be given in\nsubsection~\\ref{subsection:pm}.\n\n\\begin{remark}\\label{remark:lessk}\n{\\em Let $\\mathbf{P}^*$ and $\\mathbf{Q}^*$ be the minimizing\nelements in proposition~\\ref{prop:pqq}. If there is $l_0$ such\nthat $\\sum_{ij}\\mathbf{P}^*(il_0 j)=0$, then all\n$\\mathbf{Q}^*(il_0j)$ are zero as well. Similarly, if there is\n$l_0$ such that $\\sum_{ij}\\mathbf{Q}^*(i l_0 j)=0$, then all\n$\\mathbf{P}^*(il_0j)$ are zero as well. In each (and hence both)\nof these cases the optimal approximate factorization $Q^*_-Q^*_+$\nof $P$ is of inner size less than $k$ (delete the column\ncorresponding to $l_0$ from $Q^*_-$ and the corresponding row of\n$Q^*_+$).}\n\\end{remark}\n\n\\subsection{Two partial minimization\nproblems}\\label{subsection:pm}\n\nIn the next section we will construct the algorithm for the\nsolution of the double minimization problem\n$$\n\\min_{\\mathbf{P}\\in\\mbox{{\\boldmath $\\mathcal{P}$}},\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}}D(\\mathbf{P}||\\mathbf{Q}),\n$$\nof proposition~\\ref{prop:pqq}, as an alternating minimization\nalgorithm over the two sets $\\mbox{{\\boldmath $\\mathcal{P}$}}$ and $\\mbox{{\\boldmath $\\mathcal{Q}$}}$. This motivates us to\nconsider here two partial minimization problems. In the first one,\ngiven $\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}$ we minimize the I-divergence\n$D(\\mathbf{P}||\\mathbf{Q})$ over $\\mathbf{P}\\in\\mbox{{\\boldmath $\\mathcal{P}$}}$. In the second\nproblem, given $\\mathbf{P} \\in\\mbox{{\\boldmath $\\mathcal{P}$}}$ we minimize the I-divergence\n$D(\\mathbf{P}||\\mathbf{Q})$ over $\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}$.\n\n\\medskip\nLet us start with the first problem. The unique solution\n$\\mathbf{P}^*=\\mathbf{P}^*(\\mathbf{Q})$ can easily be computed\nanalytically and is given by\n\\begin{equation}\\label{eq:p*}\n\\mathbf{P}^*(ilj)=\\frac{\\mathbf{Q}(ilj)}{Q(ij)}\\, P(ij),\n\\end{equation}\nwhere $Q(ij)=\\sum_l\\mathbf{Q}(ilj)$. We also adopt the convention\nto put $\\mathbf{P}^*(ilj)=0$ if $Q(ij)=0$, which ensures that,\nviewed as measures, $\\mathbf{P}^*\\ll\\mathbf{Q}$.\n\n\\medskip\nNow we turn to the second partial minimization problem. The unique\nsolution $\\mathbf{Q}^*=\\mathbf{Q}^*(\\mathbf{P})$ to this problem\ncan also be easily computed analytically and is given by\n\\begin{align}\nQ^*_-(il) & = \\sum_j\\mathbf{P}(ilj)\\label{eq:q-} \\\\\nQ^*_+(lj) & = \\frac{\\sum_i\n\\mathbf{P}(ilj)}{\\sum_{ij}\\mathbf{P}(ilj)},\\label{eq:q+}\n\\end{align}\nwhere we assign arbitrary values to the $Q^*_+(lj)$ (complying\nwith the constraint $Q_+e=e$) for those $l$ with\n$\\sum_{ij}\\mathbf{P}(ilj)=0$.\n\n\\bigskip \\noindent The two partial minimization problems and their\nsolutions have a nice probabilistic interpretation.\n\n\\medskip \\noindent In the first minimization problem, one is given a distribution\n$\\mathbf{Q}$, which makes the pair $Y=(Y_-, Y_+)$ conditionally\nindependent given $X$, and finds the best approximation to it in\nthe set $\\mbox{{\\boldmath $\\mathcal{P}$}}$ of distributions with the marginal of $Y$ given by\n$P$. Let $\\mathbf{P}^*$ denote the optimal distribution of\n$(Y_-,X,Y_+)$. Equation~(\\ref{eq:p*}) can then be interpreted, in\nterms of the corresponding measures, as\n$$\n\\mathbb{P}^*(Y_-=i,X=l,Y_+=j) = \\mathbb{Q}(X=l|Y_-=i,Y_+=j)P(ij).\n$$\nNotice that the conditional distributions of $X$ given $Y$ under\n$\\mathbb{P}^*$ and $\\mathbb{Q}$ are the same. We will see below\nthat this is not a coincidence.\n\n\\medskip\n\\noindent In the second minimization problem, one is given a\ndistribution $\\mathbf{P}$, with the marginal of $Y$ given by $P$\nand finds the best approximation to it in the set $\\mbox{{\\boldmath $\\mathcal{Q}$}}$ of\ndistributions which make $Y=(Y_-, Y_+)$ conditionally independent\ngiven $X$. Let $\\mathbf{Q}^*$ denote the optimal distribution of\n$(Y_-,X,Y_+)$. Equations~(\\ref{eq:q-}) and~(\\ref{eq:q+}) can then\nbe interpreted, in terms of the corresponding measures, as\n\\[\n\\mathbb{Q}^*(Y_-=i,X=l)=\\mathbb{P}(Y_-=i,X=l)\n\\]\nand\n\\[\n\\mathbb{Q}^*(Y_+=j|X=l)=\\mathbb{P}(Y_+=j|X=l).\n\\]\nWe see that the optimal solution $\\mathbb{Q}^*$ is such that the\nmarginal distributions of $(X,Y_-)$ under $\\mathbb{P}$ and\n$\\mathbb{Q}^*$ coincide as well as the conditional distributions\nof $Y_+$ given $X$ under $\\mathbb{P}$ and $\\mathbb{Q}^*$. Again,\nthis is not a coincidence, as we will explain below.\n\n\\medskip\n\\begin{remark}\n{\\em As a side remark we notice that the minimization of\n$D(\\mathbf{Q}||\\mathbf{P})$ over $\\mathbf{P}\\in\\mbox{{\\boldmath $\\mathcal{P}$}}$ for a given\n$\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}$ yields the same solution $\\mathbf{P}^*$. A\nsimilar result does not hold for the second minimization problem.\nThis remark is not relevant for what follows.}\n\\end{remark}\n\n\n\\noindent We can now state the so called {\\em Pythagorean rules}\nfor the two partial minimization problems. This terminology was\nintroduced by Csisz\\'ar \\cite{c1975}.\n\\begin{lem}\\label{lemma:pyth}\nFor fixed $\\mathbf{Q}$ and $\\mathbf{P}^*=\\mathbf{P}^*(\\mathbf{Q})$\nit holds that, for any $\\mathbf{P} \\in\\mbox{{\\boldmath $\\mathcal{P}$}}$,\n\\begin{equation}\\label{eq:pythp}\nD(\\mathbf{P}||\\mathbf{Q})=D(\\mathbf{P}||\\mathbf{P}^*)+D(\\mathbf{P}^*||\\mathbf{Q}),\n\\end{equation}\nmoreover\n\\begin{equation}\\label{eq:p0q0}\nD(\\mathbf{P}^*||\\mathbf{Q})=D(P||Q),\n\\end{equation}\nwhere\n\\begin{equation}\\label{eq:qq}\nQ(ij)=\\sum_l\\mathbf{Q}(ilj).\n\\end{equation}\nFor fixed $\\mathbf{P}$ and $\\mathbf{Q}^*=\\mathbf{Q}^*(\\mathbf{P})$\nit holds that, for any $\\mathbf{Q} \\in\\mbox{{\\boldmath $\\mathcal{Q}$}}$,\n\\begin{equation}\\label{eq:pythq}\nD(\\mathbf{P}||\\mathbf{Q})=D(\\mathbf{P}||\\mathbf{Q}^*)+D(\\mathbf{Q}^*||\\mathbf{Q}).\n\\end{equation}\n\\end{lem}\n{\\bf Proof.} To prove the first rule we compute\n\\begin{eqnarray*}\n\\lefteqn{D(\\mathbf{P}||\\mathbf{P}^*)+ D(\\mathbf{P}^*||\\mathbf{Q})}\\\\\n& = &\n\\sum_{ilj}\\mathbf{P}(ilj)\\log\\frac{\\mathbf{P}(ilj)Q(ij)}{\\mathbf{Q}(ilj)P(ij)}\n+\n\\sum_{ilj}\\mathbf{Q}(ilj)\\frac{P(ij)}{Q(ij)}\\log \\frac{P(ij)}{Q(ij)} \\\\\n& = &\n\\sum_{ilj}\\mathbf{P}(ilj)\\log\\frac{\\mathbf{P}(ilj)}{\\mathbf{Q}(ilj)}\n+\n\\sum_{ilj}\\mathbf{P}(ilj)\\log\\frac{Q(ij)}{P(ij)} \\\\\n& & \\mbox{} + \\sum_{ij}Q(ij)\\frac{P(ij)}{Q(ij)}\\log\n\\frac{P(ij)}{Q(ij)} = D(\\mathbf{P}||\\mathbf{Q}).\n\\end{eqnarray*}\nThe first rule follows. To prove the relation~(\\ref{eq:p0q0})\ninsert equation~(\\ref{eq:p*}) into $D(\\mathbf{P}^*||\\mathbf{Q})$\nand sum over $l$ to get\n\\[\nD(\\mathbf{P}^*||\\mathbf{Q})=\\sum_{ilj}P(ij)\\frac{\\mathbf{Q}(ilj)}{Q(ij)}\\log\n\\frac{P(ij)}{Q(ij)} = D(P||Q).\n\\]\n\n\\noindent To prove the second rule we first introduce some\nnotation. Let $\\mathbf{P}(il\\cdot)=\\sum_j \\mathbf{P}(ilj)$,\n$\\mathbf{P}(\\cdot lj)=\\sum_i\\mathbf{P}(ilj)$ and\n$\\mathbf{P}(j|l)=\\mathbf{P}(\\cdot lj)\/\\sum_j\\mathbf{P}(\\cdot lj)$.\nFor $\\mathbf{Q}$ we use similar notation and observe that\n$\\mathbf{Q}(il\\cdot)=Q_-(il)$, and\n$\\mathbf{Q}(j|l)=Q_+(lj)\/\\sum_jQ_+(lj)$, and\n$Q^*_-(il)=\\mathbf{P}(il\\cdot)$ and $Q^*_+(lj)=\\mathbf{P}(j|l)$.\nWe now compute\n\\begin{align*}\nD(\\mathbf{P}||\\mathbf{Q})-D(\\mathbf{P}||\\mathbf{Q}^*)\n&=\\sum_{ilj}\\mathbf{P}(ilj) \\left( \\log\n\\frac{\\mathbf{P}(il\\cdot)}{Q_-(il)}+\\log\\frac{\\mathbf{P}(j|l)}{Q_+(lj)} \\right)\\\\\n& = \\sum_{il}\\mathbf{P}(il\\cdot)\\log\n\\frac{\\mathbf{P}(il\\cdot)}{Q_-(il)} + \\sum_{lj}\\mathbf{P}(\\cdot\nlj)\\log\\frac{\\mathbf{P}(j|l)}{Q_+(lj)} \\\\\n& = D(\\mathbf{Q}^*||\\mathbf{Q}).\n\\end{align*}\nThe second rule follows. \\hfill$\\square$\n\n\\bigskip \\noindent With the aid of the relation~(\\ref{eq:p0q0}) we can now\nprove\nproposition~\\ref{prop:pqq}. \\medskip\\\\\n{\\bf Proof of proposition~\\ref{prop:pqq}.} With\n$\\mathbf{P}^*=\\mathbf{P}^*(\\mathbf{Q})$, the optimal solution of\nthe partial minimization over $\\mbox{{\\boldmath $\\mathcal{P}$}}$, we have\n\\begin{align*}\nD(\\mathbf{P}||\\mathbf{Q})& \\geq D(\\mathbf{P}^*||\\mathbf{Q}) \\\\\n& = D(P||Q) \\\\\n& \\geq \\min_{Q\\in\\mathcal{Q}}D(P||Q).\n\\end{align*}\nIt follows that\n$\\inf_{\\mathbf{P}\\in\\mbox{{\\boldmath $\\mathcal{P}$}},\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}}D(\\mathbf{P}||\\mathbf{Q})\\geq\\min_{Q\\in\\mathcal{Q}}D(P||Q)$.\n\\\\\nConversely, let $\\mathbf{Q}$ in $\\mbox{{\\boldmath $\\mathcal{Q}$}}$ be given and let $Q$ be\ndefined by $Q(ij)=\\sum_l\\mathbf{Q}(ilj)$ . From\n\\begin{align*}\nD(P||Q) & = D(\\mathbf{P}^*(\\mathbf{Q})|| \\mathbf{Q})\\\\\n& \\geq\n\\inf_{\\mathbf{P}\\in\\mbox{{\\boldmath $\\mathcal{P}$}},\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}}D(\\mathbf{P}||\\mathbf{Q}),\n\\end{align*}\nwe obtain\n\\[\n\\min_{Q\\in\\mathcal{Q}}D(P||Q)\\geq\\inf_{\\mathbf{P}\\in\\mbox{{\\boldmath $\\mathcal{P}$}},\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}}D(\\mathbf{P}||\\mathbf{Q}).\n\\]\nFinally we show that we can replace the infima by minima. Let\n$Q^*_-$ and $Q^*_+$ be such that $(Q_-,Q^+)\\mapsto D(P||Q_-Q^+)$\nis minimized (their existence is guaranteed by\nproposition~\\ref{prop:exist}). Let $\\mathbf{Q}^*$ be a\ncorresponding element in $\\mbox{{\\boldmath $\\mathcal{Q}$}}$ and\n$\\mathbf{P}^*=\\mathbf{P}^*(\\mathbf{Q}^*)$. Then\n$D(\\mathbf{P}^*||\\mathbf{Q}^*)=D(P||Q^*_-Q^*_+)$ and the result\nfollows.\n\\hfill$\\square$\n\n\\bigskip\nFor a probabilistic derivation of the solutions of the two partial\nminimization problems and of their corresponding Py\\-tha\\-go\\-rean\nrules, we use a general result (lemma~\\ref{lemma:uv} below) on the\nI-divergence between two joint laws of any random vector $(U,V)$.\nWe denote the law of $(U,V)$ under arbitrary probability measures\n$\\mathbb{P}$ and $\\mathbb{Q}$ by $\\mathbb{P}^{U,V}$ and\n$\\mathbb{Q}^{U,V}$. The conditional distributions of $U$ given $V$\nare summarized by the matrices $\\mathbb{P}^{U|V}$ and\n$\\mathbb{Q}^{U|V}$, with the obvious convention\n$\\mathbb{P}^{U|V}(ij)=\\mathbb{P}(U=j|V=i)$ and likewise for\n$\\mathbb{Q}^{U|V}$.\n\\begin{lem}\\label{lemma:uv}\nIt\nholds that\n\\begin{equation}\\label{eq:duv}\nD(\\mathbb{P}^{U,V}||\\mathbb{Q}^{U,V})=\\mathbb{E}_\\mathbb{P}\nD(\\mathbb{P}^{U|V}||\\mathbb{Q}^{U|V}) +\nD(\\mathbb{P}^V||\\mathbb{Q}^V),\n\\end{equation}\nwhere\n\\[\nD(\\mathbb{P}^{U|V}||\\mathbb{Q}^{U|V}) = \\sum_j\nP(U=j|V)\\log\\frac{P(U=j|V)}{Q(U=j|V)}.\n\\]\nIf moreover $V=(V_1,V_2)$, and $U, V_2$ are conditionally\nindependent given $V_1$ under $\\mathbb{Q}$, then the first term on\nthe RHS of~(\\ref{eq:duv}) can be written as\n\\begin{equation}\\label{eq:duv1} \\mathbb{E}_\\mathbb{P}\nD(\\mathbb{P}^{U|V}||\\mathbb{Q}^{U|V}) =\\mathbb{E}_\\mathbb{P}\nD(\\mathbb{P}^{U|V}||\\mathbb{P}^{U|V_1}) + \\mathbb{E}_\\mathbb{P}\nD(\\mathbb{P}^{U|V_1}||\\mathbb{Q}^{U|V_1}).\n\\end{equation}\n\\end{lem}\n{\\bf Proof.} It follows from elementary manipulations.\n\\hfill $\\square$\\medskip\\\\\nThe first minimization problem can be solved probabilistically as\nfollows. Given $\\mathbf{Q}$ we are to find its best\napproximation within $\\mbox{{\\boldmath $\\mathcal{P}$}}$. Let $\\mathbb{Q}$ correspond to the\ngiven $\\mathbf{Q}$ and $\\mathbb{P}$ correspond to the generic\n$\\mathbf{P} \\in \\mbox{{\\boldmath $\\mathcal{P}$}}$. Choosing $U=X$, $V=Y=(Y_-,Y_+)$ in lemma\n\\ref{lemma:uv}, and remembering that $\\mathbb{P}^Y$ is determined\nby $P$ for all $\\mathbf{P} \\in \\mbox{{\\boldmath $\\mathcal{P}$}}$, equation (\\ref{eq:duv}) now\nreads\n\\begin{equation}\\label{eq:pq1}\nD(\\mathbf{P}||\\mathbf{Q})=\\mathbb{E}_{\\mathbb{P}}\nD(\\mathbb{P}^{X|Y}||\\mathbb{Q}^{X|Y}) + D(P||Q),\n\\end{equation}\nwhere the matrix $Q$ is as in~(\\ref{eq:qq}).\nThe problem is equivalent to the minimization\nof $\\mathbb{E}_{\\mathbb{P}}D(\\mathbb{P}^{X|Y}||\\mathbb{Q}^{X|Y})$\nw.r.t. $\\mathbf{P} \\in \\mbox{{\\boldmath $\\mathcal{P}$}}$, which is attained (with value $0$) at\n$\\mathbb{P}^*$ with $\\mathbb{P}^{*\\, X|Y}=\\mathbb{Q}^{X|Y}$ and\n$\\mathbb{P}^{*Y}= P$. To derive probabilistically the\ncorresponding Pythagorean rule, we apply~(\\ref{eq:duv}) with\n$\\mathbb{P}^*$ instead of $\\mathbb{Q}$. We obtain, using\n$\\mathbb{P}^Y=\\mathbb{P}^{*Y}$,\n\\begin{equation}\\label{eq:pq8}\nD(\\mathbb{P}^{X,Y}||\\mathbb{P}^{*^{X,Y}})=\n\\mathbb{E}_\\mathbb{P}D(\\mathbb{P}^{X|Y}||\\mathbb{P}^{*^{X|Y}}).\n\\end{equation}\nSince also\n\\begin{equation}\\label{eq:pq2}\n\\mathbb{E}_\\mathbb{P}D(\\mathbb{P}^{X|Y}||\\mathbb{Q}^{X|Y})=\\mathbb{E}_\\mathbb{P}\nD(\\mathbb{P}^{X|Y}||\\mathbb{P}^{*^{X|Y}}),\n\\end{equation}\nwe combine equations~(\\ref{eq:pq8}) and~(\\ref{eq:pq2}) and insert\nthe result into~(\\ref{eq:pq1}). Recognizing the fact that\n$D(\\mathbf{P}||\\mathbf{P}^*)=D(\\mathbb{P}^{X,Y}||\\mathbb{P}^{*^{X,Y}})$,\nand using $D(\\mathbf{P}^*||\\mathbf{Q})=D(P||Q)$\naccording to (\\ref{eq:p0q0}), we then identify~(\\ref{eq:pq1}) as\nthe first Pythagorean rule~(\\ref{eq:pythp}).\n\\medskip\\\\\nThe treatment of the second minimization problem follows a similar\npattern. Given $\\mathbf{P}$ we are to find its best\napproximation within $\\mbox{{\\boldmath $\\mathcal{Q}$}}$. Let $\\mathbb{P}$ correspond to the\ngiven $\\mathbf{P}$ and $\\mathbb{Q}$ correspond to the generic\n$\\mathbf{Q} \\in \\mbox{{\\boldmath $\\mathcal{Q}$}}$. Choosing $U=Y_+$, $V_1=X$ and $V_2=Y_-$ in\nlemma \\ref{lemma:uv}, and remembering that under any\n$\\mathbf{Q}\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}$ the r.v. $Y_-, Y_+$ are conditionally\nindependent given $X$, equation (\\ref{eq:duv}) refined with\n(\\ref{eq:duv1}) now reads\n\\begin{align*}\nD(\\mathbf{P}||\\mathbf{Q})= &\n\\mathbb{E}_\\mathbb{P}D(\\mathbb{P}^{Y_+|X,Y_-}||\\mathbb{P}^{Y_+|X})\\\\\n& \\mbox{}\n+\\mathbb{E}_\\mathbb{P}D(\\mathbb{P}^{Y_+|X}||\\mathbb{Q}^{Y_+|X})+D(\\mathbb{P}^{Y_-,X}||\\mathbb{Q}^{Y_-,X}).\n\\end{align*}\nThe problem is equivalent to the minimizations of the second and\nthird I-divergences on the RHS w.r.t. $\\mathbf{Q} \\in \\mbox{{\\boldmath $\\mathcal{Q}$}}$, which\nare attained (both with value $0$) at $\\mathbb{Q}^*$ with\n$\\mathbb{Q}^{*\\, Y_+|X}=\\mathbb{P}^{Y_+|X}$ and\n$\\mathbb{Q}^{*Y_-,X}= \\mathbb{P}^{Y_-,X}$. Note that $X$ has the\nsame distribution under $\\mathbb{P}$ and $\\mathbb{Q}^*$. To derive\nprobabilistically the corresponding Pythagorean rule we notice\nthat\n\\begin{equation}\\label{eq:pq3}\nD(\\mathbf{P}||\\mathbf{Q})-D(\\mathbf{P}||\\mathbf{Q}^*) =\n\\mathbb{E}_{\\mathbb{Q}^*}D(\\mathbb{Q}^*{^{Y_+|X}}||\\mathbb{Q}^{Y_+|X})+D(\\mathbb{Q}^{*^{Y_-,X}}||\\mathbb{Q}^{Y_-,X}).\n\\end{equation}\nIn the right hand side of~(\\ref{eq:pq3}) we can, by conditional\nindependence, replace\n$\\mathbb{E}_{\\mathbb{Q}^*}D(\\mathbb{Q}^*{^{Y_+|X}}||\\mathbb{Q}^{Y_+|X})$\nwith\n$\\mathbb{E}_{\\mathbb{Q}^*}D(\\mathbb{Q}^*{^{Y_+|X,Y_-}}||\\mathbb{Q}^{Y_+|X,Y-})$.\nBy yet another application of~(\\ref{eq:duv}), we thus see that\n$D(\\mathbf{P}||\\mathbf{Q})-D(\\mathbf{P}||\\mathbf{Q}^*)=D(\\mathbf{Q}^*||\\mathbf{Q})$,\nwhich is the second Pythagorean rule~(\\ref{eq:pythq}).\n\n\\section{Alternating minimization algorithm}\\label{section:altmin}\n\nThe results of the previous section are aimed at setting up an\nalternating minimization algorithm for obtaining $\\min_Q D(P||Q)$,\nwhere $P$ is a given nonnegative matrix. In view of\nproposition~\\ref{prop:pqq} we can lift this problem to the\n$\\mbox{{\\boldmath $\\mathcal{P}$}}\\times \\mbox{{\\boldmath $\\mathcal{Q}$}}$ space. Starting with an arbitrary $\\mathbf{Q}^0\n\\in\\mbox{{\\boldmath $\\mathcal{Q}$}}$ with positive elements, we adopt the following alternating\nminimization scheme\n\\begin{equation}\\label{eq:altalgo}\n \\to \\mathbf{Q}^t \\to \\mathbf{P}^t\\to \\mathbf{Q}^{t+1}\\to \\mathbf{P}^{t+1}\n\\to\n\\end{equation}\nwhere $\\mathbf{P}^t=\\mathbf{P}^*(\\mathbf{Q}^t)$,\n$\\mathbf{Q}^{t+1}=\\mathbf{Q}^*(\\mathbf{P}^t)$.\n\\medskip\\\\\nTo relate this algorithm to the one of section~\\ref{sec:problem}\n(formulas (\\ref{eq:q-bar2}) and (\\ref{eq:q+bar2})) we combine two\nsteps of the alternating minimization at a time.\nFrom~(\\ref{eq:altalgo}) we get\n$$\\mathbf{Q}^{t+1}=\\mathbf{Q}^*(\\mathbf{P}^*(\\mathbf{Q}^t)).$$\n\\noindent Computing the optimal solutions according\nto~(\\ref{eq:p*}),~(\\ref{eq:q-}) and (\\ref{eq:q+}) one gets from\nhere the formulas (\\ref{eq:q-bar2}) and (\\ref{eq:q+bar2}) of\nsection~\\ref{sec:problem}.\n\n\\medskip\n\\noindent The Pythagorean rules allow us to easily compute the\nupdate gain $D(P||Q^t)-D(P||Q^{t+1})$ of the algorithm.\n\\begin{prop}\\label{prop:gain}\nThe update gain at each iteration of the\nalgorithm~(\\ref{eq:altalgo}) in terms of the matrices $Q^t$ is\ngiven by\n\\begin{equation}\\label{eq:gain}\nD(P||Q^{t}) - D(P||Q^{t+1}) =\nD(\\mathbf{P}^{t}||\\mathbf{P}^{t+1})+D(\\mathbf{Q}^{t+1}||\\mathbf{Q}^t).\n\\end{equation}\n\\end{prop}\n{\\bf Proof.} The two Pythagorean rules from lemma~\\ref{lemma:pyth}\nnow take the forms\n\\begin{align*}\nD(\\mathbf{P}^t||\\mathbf{Q}^t) & =\nD(\\mathbf{P}^t||\\mathbf{Q}^{t+1})+D(\\mathbf{Q}^{t+1}||\\mathbf{Q}^t),\n\\\\\nD(\\mathbf{P}^{t}||\\mathbf{Q}^{t+1}) & =\nD(\\mathbf{P}^{t}||\\mathbf{P}^{t+1})+D(\\mathbf{P}^{t+1}||\\mathbf{Q}^{t+1}).\n\\end{align*}\nAddition of these two equations\nresults in\n\\begin{align*}\nD(\\mathbf{P}^{t}||\\mathbf{Q}^t) & =\nD(\\mathbf{P}^{t}||\\mathbf{P}^{t+1})+D(\\mathbf{P}^{t+1}||\\mathbf{Q}^{t+1})+D(\\mathbf{Q}^{t+1}||\\mathbf{Q}^t),\n\\end{align*}\nand since $D(\\mathbf{P}^{t}||\\mathbf{Q}^t) = D(P||Q^{t})$ from\n(\\ref{eq:p0q0}), the result follows. \\hfill$\\square$\n\n\n\\begin{remark}\n{\\em If one starts the algorithm with matrices $(Q^0_-, Q^0_+)$ in\nthe interior of the domain, the iterations will remain in the\ninterior. Suppose that, at step $n$, the update gain is zero.\nThen, from~(\\ref{eq:gain}), we get that\n$D(\\mathbf{Q}^{t+1}||\\mathbf{Q}^t)=0$. Hence the tensors\n$\\mathbf{Q}^{t+1}$ and $\\mathbf{Q}^t$ are identical. From this it\nfollows by summation that $Q^{t+1}_-=Q^t_-$. But then we also have\nthe equality $Q^t_-(il)Q^{t+1}_+(lj)=Q^t_-(il)Q^{t}_+(lj)$ for all\n$i,l,j$. Since all $Q^t_-(il)$ are positive, we also have\n$Q^{t+1}_+=Q^t_+$. Hence, the updating formulas strictly decrease\nthe objective function until the algorithm reaches a fixed point.}\n\\end{remark}\n\\rm\nWe close this section with the proof of\nproposition~\\ref{prop:exist} in which we use the result of\nproposition~\\ref{prop:gain}.\n\\medskip\\\\\n{\\bf Proof of proposition~\\ref{prop:exist}.} We first prove that\nthere exists a pair of matrices $(W,H)$ with $He_m=e_k$ and\n$We_k=Ve_n$ for which $D(V||WH)$ is finite. Put\n$W=\\frac{1}{k}Ve_ne_k^\\top$ and $H=\\frac{1}{e_m^\\top Ve_n}e_k\ne_m^\\top V$. Note that indeed $He_m=e_k$ and $We_k=Ve_n$ and that\nall elements of $W$ and $H$, and hence those of $WH$, are\npositive, $D(V||WH)$ is therefore finite.\n\nNext we show that we can restrict ourselves to minimization over a\ncompact set $\\mathcal{K}$ of matrices. Specifically, we will show\nthat for all positive matrices $W$ and $H$, there exist positive\nmatrices $W'$ and $H'$ with $(W',H')\\in \\mathcal{K}$ such that\n$D(V||W'H')\\leq D(V||WH)$. We choose for arbitrary $W^0$ and $H^0$\nthe matrices $W^1$ and $H^1$ according to~(\\ref{eq:wbar2})\nand~(\\ref{eq:hbar2}). It follows from proposition~\\ref{prop:gain}\nthat indeed $D(V||W^1H^1)\\leq D(V||W^0H^0)$. Moreover, it is\nimmediately clear from~(\\ref{eq:wbar2}) and~(\\ref{eq:hbar2}) that\nwe have $W^1e=Ve$ and $H^1e=e$. Hence, it is sufficient to confine\nsearch to the compact set $\\mathcal{L}$ where $He=e$ and $We=Ve$.\n\nFix a pair of indices $i,j$. Since we can compute the divergence\nelementwise we have the trivial estimate\n\\[\nD(V||WH)\\geq V_{ij}\\log \\frac{V_{ij}}{(WH)_{ij}}-V_{ij}+(WH)_{ij}.\n\\]\nSince for $V_{ij}>0$ the function $d_{ij}:x\\to V_{ij}\\log\n\\frac{V_{ij}}{x}-V_{ij}+x$ is decreasing on $(0,V_{ij})$, we have\nfor any sufficiently small $\\varepsilon>0$ (of course $\\varepsilon < V_{ij}$)\nthat $d_{ij}(x)>d_{ij}(\\varepsilon)$ for $x\\leq \\varepsilon$ and of course\n$\\lim_{\\varepsilon\\to 0}d_{ij}(\\varepsilon)=\\infty$. Hence to find the minimum\nof $d_{ij}$, it is sufficient to look at $x\\geq \\varepsilon$. Let $\\varepsilon_0\n>0$ and such that $\\varepsilon_0 <\\min\\{V_{ij}:V_{ij}>0\\}$. Let $\\mathcal{G}$ be\nthe set of $(W,H)$ such that $(WH)_{ij}\\geq \\varepsilon_0$ for all $i,j$\nwith $V_{ij}>0$. Then $\\mathcal{G}$ is closed. Take now\n$\\mathcal{K}=\\mathcal{L}\\cap \\mathcal{G}$, then $\\mathcal{K}$ is\nthe compact set we are after. Let us observe that $\\mathcal{K}$ is\nnon-void for sufficiently small $\\varepsilon_0$. Clearly the map\n$(W,H)\\mapsto D(V||WH)$ is continuous on $\\mathcal{K}$ and thus\nattains its minimum. \\hfill\\ $\\square$\n\n\\section{Auxiliary functions}\\label{section:auxfunc}\n\nAlgorithms for recursive minimization can often be constructed by\nusing {\\em auxiliary functions}. For the problem of minimizing the\ndivergence $D(V||WH)$, some such functions can be found\nin~\\cite{leeseung2001} and they are analogous to functions that\nare used when studying the EM algorithm, see~\\cite{wu}. The choice\nof an auxiliary function is usually based on {\\em ad hoc}\nreasoning, like for instance finding a Lyapunov function for\nstudying the stability of the solutions of a differential\nequation. We show in this section that the lifted version of the\ndivergence minimization problem leads in a natural way to useful\nauxiliary functions. Let us first explain what is meant by an\nauxiliary function.\n\nSuppose one wants to minimize a function $x\\mapsto F(x)$, defined\non some domain. The function $(x,x')\\mapsto G(x,x')$ is an\nauxiliary function for $F$ if\n\\begin{align*}\nG(x,x') & \\geq F(x'), \\,\\,\\, \\forall x,x',\\\\\nG(x,x) & = F(x), \\quad \\forall x.\n\\end{align*}\nIf we define (assuming that the $\\arg\\min$ below exists and is\nunique)\n\\begin{equation}\\label{eq:update}\nx'=x'(x)=\\arg\\min G(x,\\cdot),\n\\end{equation}\nthen we have\n\\[\nF(x')\\leq G(x,x') \\leq G(x,x)=F(x),\n\\]\nand hence the value of $F$ decreases by replacing $x$ with $x'$. A\nrecursive procedure to find the minimum of $F$ can be based on the\nrecipe~(\\ref{eq:update}) by taking $x=x^t$ and $x'=x^{t+1}$. To be\nuseful an auxiliary function $G$ must allow for a simple\ncomputation or characterization of $\\arg\\min G(x,\\cdot)$.\n\n\\medskip\nWe consider now the minimization of $D(P||Q)$ and its lifted\nversion, the minimization of $D(\\mathbf{P}||\\mathbf{Q})$ as in\nsection~\\ref{section:lift}. In particular, with reference to the\nalternating minimization scheme~(\\ref{eq:altalgo}), with the\nnotations of section~\\ref{section:altmin}, we know that\n$\\mathbf{Q}^{t+1}$ is found by minimizing $\\mathbf{Q}'\\mapsto\nD(\\mathbf{P}^*(\\mathbf{Q}^t)||\\mathbf{Q}')$. This strongly\nmotivates the choice of the function\n\\[ (\\mathbf{Q},\\mathbf{Q}') \\mapsto G(\\mathbf{Q},\\mathbf{Q}')=D(\\mathbf{P}^*(\\mathbf{Q})||\\mathbf{Q}')\n\\]\nas an auxiliary function for minimizing $D(P||Q)$ w.r.t. $Q$.\n\n\\medskip\nUsing the decomposition of the divergence in\nequation~(\\ref{eq:duv}) we can rewrite $G$ as\n\\begin{equation}\\label{eq:div29}\nG(\\mathbf{Q},\\mathbf{Q}')=D(\\mathbb{P}^{*^Y}||\\mathbb{Q}'^Y)+\\mathbb{E}_{\\mathbb{P}^*}D(\\mathbb{P}^{*^{X|Y}}||\\mathbb{Q}'^{X|Y}).\n\\end{equation}\nSince $\\mathbb{P}^{*X|Y} = \\mathbb{Q}^{X|Y}$, and $\\mathbb{P}^{*Y}\n= P$ we can rewrite~(\\ref{eq:div29}) as\n\\begin{equation}\\label{eq:div30}\nG(\\mathbf{Q},\\mathbf{Q}')=D(P||\\mathbb{Q}'^Y)+\\mathbb{E}_PD(\\mathbb{Q}^{X|Y}||\\mathbb{Q}'^{X|Y}).\n\\end{equation}\nFrom~(\\ref{eq:div30}) it follows that\n$G(\\mathbf{Q},\\mathbf{Q}')\\geq D(P||Q')$, and that\n$G(\\mathbf{Q},\\mathbf{Q})= D(P||Q)$, precisely the two properties\nthat define an auxiliary function for $D(P||Q)$.\n\n\\medskip\\noindent\nIn~\\cite{leeseung2001} one can find two auxiliary functions for\nthe original minimization problem $D(V||WH)$. One function is for\nminimization over $H$ with fixed $W$, the other for minimization\nover $W$ with fixed $H$. To show the connection with the function\n$G$ defined above, we first make the dependence of $G$ on\n$Q_-,Q_+,Q'_-,Q'_+$ explicit by writing\n$G(\\mathbf{Q},\\mathbf{Q}')$ as $G(Q_-,Q_+,Q'_-,Q'_+)$.\n\n\\medskip \\noindent The auxiliary function for minimization with fixed\n$Q_-$ can then be taken as \\[Q'_+\\mapsto\nG^+_{\\mathbf{Q}}(Q'_+)=G(Q_-,Q_+,Q_-,Q'_+),\\]\n\n\\noindent whereas the auxiliary function for minimization with\nfixed $Q_+$ can be taken as \\[Q'_-\\mapsto\nG^-_{\\mathbf{Q}}(Q'_-)=G(Q_-,Q_+,Q'_-,Q_+)\\]\n\n\\medskip \\noindent The functions $G^+_{\\mathbf{Q}}$ and $G^-_{\\mathbf{Q}}$\ncorrespond to the auxiliary functions in~\\cite{leeseung2001},\nwhere they are given in an explicit form, but where no rationale\nfor them is given.\n\nFor the different auxiliary functions introduced above, we will\nnow compute the update gains and compare these expressions with\n(\\ref{eq:gain}).\n\\begin{lem}\\label{lemma:auxdiff}\nConsider the auxiliary functions $G$ ,$G^-_{\\mathbf{Q}}$ ,\n$G^+_{\\mathbf{Q}}$ above. Denote by $Q'_-$ and $Q'_+$ the\nminimizers of the auxiliary functions in all three cases. The\nfollowing equalities hold\n\\begin{align}\nD(P||Q_-Q_+)-G^-_{\\mathbf{Q}}(Q'_-) & =\nD(\\mathbb{Q}'^{Y_-,X}||\\mathbb{Q}^{Y_-,X}) \\label{eq:auxdiff1}\\\\\nD(P||Q_-Q_+)-G^+_{\\mathbf{Q}}(Q'_+) & =\n\\mathbb{E}_{\\mathbb{P}^*}D(\\mathbb{Q}'^{Y_+|X}||\\mathbb{Q}^{Y_+|X})\\label{eq:auxdiff2}\\\\\nD(P||Q_-Q_+)-G(Q_-,Q_+,Q'_-,Q'_+) & =\nD(\\mathbb{Q}'^{Y_-,X}||\\mathbb{Q}^{Y_-,X}) \\nonumber \\\\\n& \\,\\, \\mbox{~~~}\n+\\mathbb{E}_{\\mathbb{Q}'}D(\\mathbb{Q}'^{Y_+|X}||\\mathbb{Q}^{Y_+|X}).\n\\label{eq:auxdiff3}\n\\end{align}\n\\end{lem}\n{\\bf Proof.} We prove~(\\ref{eq:auxdiff3}) first. The other two\nfollow from this. A simple computation, valid for any $Q_-$a nd $Q_+$, yields\n\\begin{align}\n\\lefteqn{D(P||Q_-Q_+)-G(Q_-,Q_+,Q'_-,Q'_+)} \\\\\n&\n=\\sum_{ij}P(ij)\\sum_l\\frac{\\mathbf{Q}(ilj)}{Q(ij)}\\left(\\log\\frac{Q'_-(il)}{Q_-(il)}\n+\\log\\frac{Q'_+(lj)}{Q_+(lj)}\\right) \\nonumber\\\\\n& =\n\\sum_{il}\\big(\\sum_j\\frac{P(ij)\\mathbf{Q}(ilj)}{Q(ij)}\\big)\\log\\frac{Q'_-(il)}{Q_-(il)}\n +\n\\sum_{lj}\\big(\\sum_i\\frac{P(ij)\\mathbf{Q}(ilj)}{Q(ij)}\\big)\\log\\frac{Q'_+(lj)}{Q_+(lj)}\\label{eq:dg}\n\\end{align}\nNow we exploit the known formulas~(\\ref{eq:q-bar2})\nand~(\\ref{eq:q+bar2}) for the optimizing $Q'_-$ and $Q'_+$. The\nfirst term in~(\\ref{eq:dg}) becomes in view of~(\\ref{eq:q-bar2})\n(or, equivalently, in view of~(\\ref{eq:p*}) and~(\\ref{eq:q-}))\n\\[\n\\sum_{il}Q'_-(il)\\log\\frac{Q'_-(il)}{Q_-(il)},\n\\]\nwhich gives the first term on the RHS of~(\\ref{eq:auxdiff3}). Similarly, the\nsecond term in~(\\ref{eq:dg}) can be written in view\nof~(\\ref{eq:q+bar2}) as\n\\[\n\\sum_{l}\\big(\\sum_{ij}\\mathbf{Q}'(ilj)\\big)\\sum_{j}Q'_+(lj)\\log\\frac{Q'_+(lj)}{Q_+(lj)},\n\\]\nwhich yields the second term on the RHS of\nformula~(\\ref{eq:auxdiff3}). Formulas~(\\ref{eq:auxdiff1})\nand~(\\ref{eq:auxdiff2}) are obtained similarly, noticing that\noptimization of $G^+_{\\mathbf{Q}}$ and $G^-_{\\mathbf{Q}}$\nseparately yield the same $Q'_+$, respectively $Q'_-$, as those\nobtained by minimization of $G$.\n \\hfill$\\square$\n\\begin{remark}\n{\\em Notice that although for instance $G^-_{\\mathbf{Q}}(Q'_-)\\geq\nD(P||Q'_-Q'_+)$ for all $Q'_-$ and $Q'_+$, we have for the optimal\n$Q'_-$ that $G^-_{\\mathbf{Q}}(Q'_-)\\leq D(P||Q_-Q_+)$.\n}\n\\end{remark}\n\\begin{cor}\\label{cor:corgain}\nThe update gain of the\nalgorithm~(\\ref{eq:q-bar2}),~(\\ref{eq:q+bar2}) can be represented\nby\n\\begin{align}\\label{eq:corgain}\nD(P||Q^t)-D(P||Q^{t+1}) =\n& D(\\mathbb{Q}^{{t+1}^{Y_-,X}}||\\mathbb{Q}^{t^{Y_-,X}}) \\nonumber\\\\\n& +\n\\mathbb{E}_{\\mathbb{Q}^{t+1}}D(\\mathbb{Q}^{{t+1}^{Y_+|X}}||\\mathbb{Q}^{t^{Y_+|X}})\n\\nonumber\\\\\n& + \\mathbb{E}_PD(\\mathbb{Q}^{t^{X|Y}}||\\mathbb{Q}^{{t+1}^{X|Y}}).\n\\end{align}\n\\end{cor}\n{\\bf Proof.} Write\n\\begin{align*}\n\\lefteqn{D(P||Q^t)-D(P||Q^{t+1})=} \\\\ & \\mbox{~~~~}\nD(P||Q^t)-G(\\mathbf{Q}^t,\\mathbf{Q}^{t+1})+G(\\mathbf{Q}^t,\\mathbf{Q}^{t+1})-D(P||Q^{t+1})\n\\end{align*}\nand use equations~(\\ref{eq:div29}) and~(\\ref{eq:auxdiff3}).\n\\hfill$\\square$\n\\medskip\\\\\nWe return to the update formula~(\\ref{eq:gain}). A computation\nshows the following equalities.\n\\begin{align}\nD(\\mathbf{P}^t||\\mathbf{P}^{t+1})= &\n\\mathbb{E}_{P}D(\\mathbb{Q}^{t^{X|Y}}||\\mathbb{Q}^{{t+1}^{X|Y}})\\label{eq:gain1}\n\\\\\nD(\\mathbf{Q}^{t+1}||\\mathbf{Q}^t) = &\nD(\\mathbb{Q}^{{t+1}^{Y_-,X}}||\\mathbb{Q}^{{t}^{Y_-,X}}) \\nonumber\\\\\n& +\n\\mathbb{E}_{\\mathbb{Q}^{t+1}}D(\\mathbb{Q}^{{t+1}^{Y_+|X}}||\\mathbb{Q}^{{t}^{Y_+|X}}).\\label{eq:gain2}\n\\end{align}\nIn equation~(\\ref{eq:gain1}) we recognize the second term in the\nauxiliary function, see~(\\ref{eq:div30}).\nEquation~(\\ref{eq:gain2}) corresponds to\nequation~(\\ref{eq:auxdiff3}) of lemma~\\ref{lemma:auxdiff} and\nwe see that formula~(\\ref{eq:gain}) is indeed the same\nas~(\\ref{eq:corgain}) .\n\\medskip\\\\\nThe algorithm~(\\ref{eq:q-bar2}),~(\\ref{eq:q+bar2}) is to be\nunderstood by using these two equations simultaneously. As an\nalternative one could first use~(\\ref{eq:q-bar2}) to obtain\n$Q^{t+1}_-$ and, instead of using $Q^t_-$, feed this result\ninto~(\\ref{eq:q+bar2}) to obtain $Q^{t+1}_+$. If we do this, we\ncan express the update gain of the first partial step, like in the\nproof of corollary~\\ref{cor:corgain}, by adding the result of\nequation~(\\ref{eq:auxdiff1}) to the second summand\nof~(\\ref{eq:div30}), with the understanding that $\\mathbb{Q}'$ is\nnow given by the $Q^{t+1}(ij)Q^t(lj)$. The update gain of the\nsecond partial step is likewise obtained by combining the result\nof~(\\ref{eq:auxdiff2}) and the second summand of~(\\ref{eq:div30}),\nwith the understanding that now $\\mathbb{Q}$ is to be interpreted\nas given by the $Q^{t+1}(ij)Q^t(lj)$. Of course, as another\nalternative, the order of the partial steps can be reversed.\nClearly, the expressions for the update gains for these cases also\nresult from working with the auxiliary functions\n$G^-_{\\mathbf{Q}}$ and $G^+_{\\mathbf{Q}}$, the\nequations~(\\ref{eq:auxdiff1}) and~(\\ref{eq:auxdiff2}) and\nproceeding as in the proof of corollary~\\ref{cor:corgain}.\n\n\\section{Convergence properties}\\label{section:convprop}\n\nIn this section we study the convergence properties of the\ndivergence minimization algorithm (\\ref{eq:q-bar2}),\n(\\ref{eq:q+bar2}).\n\nThe next theorem states that the sequences generated by the\nalgorithm converge for every (admissible) initial value. Of course\nthe limits will in general depend on the initial value.\n\n\n\\begin{thm}\\label{thm:conv}\nLet $Q^t_-(il)$, $Q_+^t(lj)$ be generated by the algorithm\n(\\ref{eq:q-bar2}), (\\ref{eq:q+bar2}) and $\\mathbf{Q}^t$ the\ncorresponding tensors. Then the $Q^t_-(il)$ converge to limits\n$Q_-^\\infty(il)$ and the $\\mathbf{Q}^t$ converges to a limit\n$\\mathbf{Q}^\\infty$ in $\\mbox{{\\boldmath $\\mathcal{Q}$}}$. The $Q_+^t(lj)$ converge to limits\n$Q_+^\\infty(lj)$ for all $l$ with $\\sum_iQ_+^\\infty(il)>0$.\n\\end{thm}\n{\\bf Proof.} We first show that the $Q^t_-$ and $Q^t_+$ form\nconvergent sequences. We start with equation~(\\ref{eq:gain}). By\nsumming over $n$ we obtain\n\\[\nD(P||Q^0)-D(P||Q^t)=\\sum_{k=1}^{t-1}\\Big(D(\\mathbf{P}^{s}||\\mathbf{P}^{s+1})\n+ D(\\mathbf{Q}^{s+1}||\\mathbf{Q}^s)\\Big).\n\\]\nIt follows that\n$\\sum_{k=1}^{\\infty}D(\\mathbf{P}^{s}||\\mathbf{P}^{s+1})$ and\n$\\sum_{k=1}^{\\infty}D(\\mathbf{Q}^{s+1}||\\mathbf{Q}^s)$ are finite.\nNow we use that fact that for any two probability measures, the\nKullback-Leibler divergence $D(\\mathbb{P}||\\mathbb{Q})$ is greater\nthan or equal to their Hellinger distance\n$H(\\mathbb{P},\\mathbb{Q})$, which is the $L^2$ distance between\nthe square roots of corresponding densities w.r.t.~some dominating\nmeasure, see~\\cite[p.~368]{shiryaev}. In our case we have\n$H(\\mathbb{Q}^{s},\\mathbb{Q}^{s+1})=\\sum_{ilj}\n(\\sqrt{\\mathbf{Q}^{s+1}(ilj)}-\\sqrt{\\mathbf{Q}^{s}(ilj)})^2$. So\nwe obtain that\n\\[\n\\sum_{k=1}^{\\infty}H(\\mathbf{Q}^{s+1},\\mathbf{Q}^s)<\\infty.\n\\]\nWe therefore have that, pointwise, the tensors $\\mathbf{Q}^t$ form\na Cauchy sequence and hence have a limit $\\mathbf{Q}^\\infty$. We\nwill show that $\\mathbf{Q}^\\infty$ belongs to $\\mbox{{\\boldmath $\\mathcal{Q}$}}$. Since the\n$\\mathbf{Q}^t(ilj)$ converge to limits $\\mathbf{Q}^\\infty(ilj)$,\nby summation we have that the marginals\n$Q^t_-(il)=\\mathbf{Q}^t(il\\cdot)$ converge to limits\n$\\mathbf{Q}^\\infty(il\\cdot)$ (we use the notation of the proof of\nlemma~\\ref{lemma:pyth}), and likewise we have convergence of the\nmarginals $\\mathbf{Q}^t(\\cdot lj)$ to $\\mathbf{Q}^\\infty(\\cdot\nlj)$ and $\\mathbf{Q}^t(\\cdot l\\cdot)$ to $\\mathbf{Q}^\\infty(\\cdot\nl\\cdot)$. Hence, if $\\mathbf{Q}^\\infty(\\cdot l\\cdot)>0$, then the\n$Q^t_+(lj)$ converge to $Q^\\infty_+(ij):=\\mathbf{Q}^\\infty(\\cdot\nlj)\/\\mathbf{Q}^\\infty(\\cdot l\\cdot)$ and we have\n$\\mathbf{Q}^\\infty(ilj)=\\mathbf{Q}^\\infty(il\\cdot)Q^\\infty_+(ij)$.\nNow we analyze the case where $\\mathbf{Q}^\\infty(\\cdot\nl_0\\cdot)=0$ for some $l_0$. Since in this case both\n$\\mathbf{Q}^\\infty(il_0j)$ and $\\mathbf{Q}^\\infty(il_0\\,\\cdot)$\nare zero, we have still have a factorization\n$\\mathbf{Q}^\\infty(il_0j)=Q^\\infty_-(il_0)Q^\\infty_+(l_0j)$, where\nwe can assign to the $Q^\\infty_+(l_0j)$ arbitrary values. Let $L$\nbe the set of $l$ for which $\\sum_iQ^\\infty_-(il)>0$. Then\n$Q^\\infty(ij)=\\sum_{l\\in L}Q^\\infty_-(il)Q^\\infty_+(lj)$ and the\n$Q^t$ converge to $Q^\\infty$. This proves the theorem.\n\\hfill$\\square$\n\\begin{remark}\\label{remark:strange}\n{\\em Theorem~\\ref{thm:conv} says nothing of the convergence of the\n$Q_+^t(lj)$ for those $l$ where $\\sum_iQ^\\infty_-(il)=0$. But\ntheir behavior is uninteresting from a factorization point of\nview. Indeed, since the $l$-th column of $Q^\\infty_-$ is zero, the\nvalues of the $l$-th row of $Q^\\infty_+$ are not relevant, since\nthey don't appear in the product $Q^\\infty_-Q^\\infty_+$. As a\nmatter of fact, we now deal with an approximate nonnegative\nfactorization with a lower inner size. See also\nremark~\\ref{remark:lessk}. }\n\\end{remark}\n\\noindent In the next theorem we characterize the properties of\nthe fixed points of the algorithm. Recall from\nsection~\\ref{sec:problem} that the objective function has no local\nmaxima in the interior of the domain.\n\n\\begin{thm}\\label{thm:fixed}\nIf $(Q_-,Q_+)$ is a limit point of the\nalgorithm~(\\ref{eq:q-bar2}),~(\\ref{eq:q+bar2}) in the interior of\nthe domain, then it is a stationary point of the objective\nfunction $D$. If $(Q_-,Q_+)$ is a limit point on the boundary of\nthe domain corresponding to an approximate factorization where\nnone of the columns of $Q_-$ is zero ($\\sum_iQ_-(il)>0$ for all\n$l$), then all partial derivatives $\\frac{\\partial D}{\\partial\nQ_-(il)}$ and $\\frac{\\partial D}{\\partial Q_+(lj)}$ are\nnonnegative.\n\\end{thm}\n{\\bf Proof.} By computing the first order partial derivatives of\nthe objective function, using the middle term of\nequation~(\\ref{eq:divnew}), we can rewrite the update\nequations~(\\ref{eq:q-bar2}),~(\\ref{eq:q+bar2}) as\n\\begin{equation}\\label{eq:qn-}\nQ_-^{t+1}(il) = Q_-^t (il) \\left(-\\frac{\\partial D^t}{\\partial\nQ_-(il)} + 1 \\right)\n\\end{equation}\nand\n\\begin{equation}\\label{eq:qn+}\nQ_+^{t+1}(lj) \\left(\\sum_{i}Q_-^{t+1}(il) \\right) = Q_+^t(lj)\n\\left(-\\frac{\\partial D^t}{\\partial Q_+(lj)}+\\sum_i\nQ_-^t(il)\\right).\n\\end{equation}\nwhere $\\frac{\\partial D^t}{\\partial Q_-(il)}$ stands for the\npartial derivative $\\frac{\\partial D}{\\partial Q_-(il)}$ evaluated\nat $(Q_-^t,Q_+^t)$ and likewise for $\\frac{\\partial D^t}{\\partial\nQ_+(lj)}$.\n\n\n\nLet $(Q_-,Q_+)$ be a limit point of the algorithm.\nEquations~(\\ref{eq:qn-}) and~(\\ref{eq:qn+}) become\n\\[\nQ_-(il)=Q_-{il} \\left(-\\frac{\\partial D}{\\partial Q_-(il)} +\n1\\right)\n\\]\n\\[\nQ_+(lj)\\left(\\sum_{i}Q_-(il)\\right)=Q_+(lj)\\left(-\\frac{\\partial\nD}{\\partial Q_+(lj)}+\\sum_i Q_-(il)\\right).\n\\]\nIt follows that we then have the relations\n\\[\nQ_-(il)\\,\\,\\frac{\\partial D}{\\partial Q_-(il)}=0\n\\] and\n\\[ Q_+(lj)\\,\\,\\frac{\\partial D}{\\partial Q_+(lj)}=0. \\]\nWe first consider $Q_-$. Suppose that for some $i$ and $l$ we have\n$Q_-(il)>0$, then necessarily $\\frac{\\partial D}{\\partial\nQ_-(il)}=0$. Suppose now that for some $i,l$ we have $Q_-(il)=0$\nand that $\\frac{\\partial D}{\\partial Q_-(il)}<0$. Of course, by\ncontinuity, this partial derivative will be negative in a\nsufficiently small neighborhood of this limit point. Since we deal\nwith a limit point of the algorithm, we must have infinitely often\nfor the iterates that $Q_-^{t+1}(il)0$. Clearly, this contradicts\nour assumption of a negative partial derivative, since eventually\nthe iterates will be in the small neighborhood of the limit point,\nwhere the partial derivative is positive. Hence, we conclude that\n$\\frac{\\partial D}{\\partial Q_-(il)}\\geq 0$, if $Q_-(il)=0$. The\nproof of the companion statement for the $Q_+(lj)$ is similar. If\n$Q_+(lj)>0$, the corresponding partial derivative is zero. Let $l$\nbe such that\n $Q_+(lj)=0$ and suppose that\nwe have that $\\frac{\\partial D}{\\partial Q_+(lj)}<0$. If we run\nthe algorithm, then $\\frac{\\partial D^t}{\\partial\nQ_+(lj)}\/\\sum_iQ^{t+1}_-(il)$ converges to a negative limit,\n whereas $\\sum_iQ^{t}_-(il)\/\\sum_iQ^{t+1}_-(il)$ converges to one.\n Hence there is $\\eta>0$ such that eventually\n$\\frac{\\partial D^t}{\\partial\nQ_+(lj)}\/\\sum_iQ^{t+1}_-(il)<-2\\eta\/3$ and\n$\\sum_iQ^{t}_-(il)\/\\sum_iQ^{t+1}_-(il)>1- \\eta\/3$. Hence\neventually we would have, see~(\\ref{eq:qn+}),\n\\[\nQ_+^{t+1}(lj) -Q_+^{t}(lj) = Q_+^t(lj) \\left(-\\frac{\\frac{\\partial\nD^t}{\\partial Q_+(lj)}}{\\sum_i Q_-^{t+1}(il)}+\\frac{\\sum_i\nQ_-^t(il)}{\\sum_i Q_-^{t+1}(il)} -1\\right)>\\eta\/3,\n\\]\nwhich contradicts convergence of $Q_+^{t}(lj)$ to zero.\n\\hfill$\\square$\n\n\\begin{remark}\n{\\em If it happens that a limit point $Q_-$ has a zero $l$-th\ncolumn, then it can easily be shown that the partial derivatives\n$\\frac{\\partial D}{\\partial Q_+(lj)}$ of $D$ are zero. Nothing can\nbe said of the values of the partial derivatives $\\frac{\\partial\nD}{\\partial Q_-(il)}$ for such $l$. But, see also\nremark~\\ref{remark:strange}, this case can be reduced to one with\na lower inner size factorization, for which the assertion of\ntheorem~\\ref{thm:fixed} is valid. }\n\\end{remark}\n\\rm\n\\begin{cor}\nThe limit points of the algorithm with $\\sum_iQ_-(il)>0$ for all\n$l$ are all Kuhn-Tucker points for minimization of $D$ under the\ninequality constraints $Q_-\\geq 0$ and $Q_+\\geq 0$.\n\\end{cor}\n{\\bf Proof.} Consider the Lagrange function $L$ defined by\n\\[\nL(Q_-,Q_+)=D(P||Q_-Q_+)-\\lambda\\cdot Q_--\\mu\\cdot Q_+, \\] where\nfor instance the inner product $\\lambda\\cdot Q_-$ is to be read as\n$\\sum_{il}\\lambda_{il}Q_-(il)$ for $\\lambda_{il}\\in\\mathbb{R}$.\nLet us focus on a partial derivative $\\frac{\\partial L}{\\partial\nQ_-(il)}$ in a fixed point of the algorithm. The treatment of the\nother partial derivatives is similar. From the proof of\ntheorem~\\ref{thm:fixed} we know that in a fixed point we have\n$Q_-(il)\\frac{\\partial D}{\\partial Q_-(il)}=0$. Suppose that\n$Q_-(il)>0$, then $\\frac{\\partial D}{\\partial Q_-(il)}=0$ and the\nKuhn-Tucker conditions for this variable are satisfied with\n$\\lambda_{il}=0$. If $Q_-(il)=0$, then we know from\ntheorem~\\ref{thm:fixed} that $\\frac{\\partial D}{\\partial\nQ_-(il)}\\geq 0$. By taking $\\lambda_{il}=\\frac{\\partial\nD}{\\partial Q_-(il)}\\geq 0$, we see that also here the Kuhn-Tucker\nconditions are satisfied. \\hfill$\\square$\n\\begin{remark}\n{\\em Wu~\\cite{wu} has a number of theorems that characterize the\nlimit points of the closely related EM algorithm, or generalized\nEM algorithm. These are all consequence of a general convergence\nresult in Zangwill~\\cite{zangwill}. The difference of our results\nwith his is, that we also {\\em have to} consider possible limit\npoints on the boundary, whereas Wu's results are based on the\nassumption that all limit points lie in the interior of the\ndomain.}\n\\end{remark}\\rm\n\n\\section{Relation with other minimization problems}\\label{section:otherprob}\n\nOther data analysis methods proposed in the literature enforce\nsome form of positivity constraint and it is useful to investigate\nthe connection between NMF and these methods. An interesting\nexample is the so called Archetypal Analysis (AA) technique\n\\cite{cb1994}. Assigned a matrix $X \\in \\mathbb{R}^{m\\times n}$ and an\ninteger $k$, the AA problem is to find, in the convex hull of the\ncolumns of $X$, a set of $k$ vectors whose convex combinations can\noptimally represent $X$. To understand the relation between NMF\nand AA we choose the $L_2$ criterion for both problems. For any\nmatrix $A$ and positive definite matrix $\\Sigma$ define\n$||A||_\\Sigma = (\\rm{tr} (A^T \\Sigma A))^{1\/2} $. Denote $||A||_I\n= ||A||$. The solution of the NMF problem is then\n$$\n(W, H) = \\arg \\min_{W, H} || V - WH ||\n$$\nwhere the minimization is constrained to the proper set of\nmatrices. The solution to the AA problem is given by the pair of\ncolumn stochastic matrices $(A, B)$ of respective sizes $k \\times\nn$ and $m \\times k$ such that $||X - XBA||$ is minimized (the\nconstraint to column stochastic matrices is imposed by the\nconvexity). Since $||X - XBA|| = ||I - BA||_{X^TX}$ the solution\nof the AA problem is\n$$\n(A, B) = \\arg \\min_{A, B} || I - BA ||_{X^TX}.\n$$\nAA and NMF can therefore be viewed as special cases of a more\ngeneral problem which can be stated as follows. Given any matrix\n$P \\in \\mathbb{R}_+^{m\\times n}$, any positive definite matrix $\\Sigma$,\nand any integer $k$, find the best nonnegative factorization $P\n\\approx Q_1Q_2$ (with $Q_1 \\in\\mathbb{R}_+^{m\\times k}, \\,\\, Q_2\n\\in\\mathbb{R}_+^{k\\times n}$) in the $L_2$ sense, {\\it i.e.}\n$$\n(Q_1, Q_2) = \\arg \\min_{Q_1, Q_2} ||P - Q_1Q_2||_\\Sigma.\n$$\n{\\bf Acknowledgement.} An anonymous referee is gratefully\nacknowledged for helping us to improve the quality of the\npresentation and for suggesting to us to investigate the boundary\nbehavior of the algorithm, similar to what has been reported\nin~\\cite{laa2004}. \\rm\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:introduction}\nEvery year, millions of children enter a new public school. \nFrequently, they have to choose between a number of different schools, whose capacity is limited, such that not all wishes can be accommodated perfectly.\nA school choice mechanism is a procedure that collects the students' (or the parents') preferences over schools and determines a matching of students to schools.\nOne such mechanism is the \\emph{Boston mechanism}, which is frequently used in practice, but has also been heavily criticized for its manipulability.\nIn this paper, we consider a setting with no priority structure, in which we study two variants of the Boston mechanism in terms of incentives and efficiency.\n\\subsection{Strategyproofness versus Efficiency}\n\\label{sec:introduction:SPvsEFF}\nBy assuming no priority structure, the school choice problem becomes equivalent to the \\emph{one-sided matching problem}, where indivisible objects must be allocated to self interested agents and monetary transfers are not permitted.\nAs mechanism designers, we are interested in mechanisms that perform well with respect to incentives, efficiency, and fairness.\nUnfortunately, previous research on this problem has revealed that it is impossible to achieve the optimum on all dimensions simultaneously \\citep{Zhou1990}.\n\nStrategyproof mechanisms, such as Random Serial Dictatorship ($\\mathrm{RSD}$), are appealing because they make truthful reporting a dominant strategy for all agents.\nParticipation in the mechanism becomes an easy task as there is no need for deliberation about the best response, thus reducing cognitive costs for the agents and (likely) endowing the mechanism with correct information about agents' preferences.\nWhile strategyproofness is certainly a desirable property, it also imposes severe\nrestrictions.\nIn particular, strategyproofness is incompatible with ordinal efficiency and symmetry \\citep{Bogomolnaia2001}, and it is also incompatible with rank efficiency \\citep{Featherstone2011b}.\n\nThe Boston mechanism is an example of a manipulable mechanism that is frequently used in school choice settings \\citep{Abdulkadiroglu2003SchoolChoice,KojimaUnver2014BostonSchoolChoice}.\nUnder the Boston mechanism, agents submit their preferences in the form of rank ordered lists. \nThe mechanism then lets agents ``apply'' to their reported first choice and the objects are distributed amongst applicants according to some (possibly random) priority ordering. If an agent does not obtain its first choice, it ``applies'' to its second choice in the second round, etc., until all agents have received an object or all objects are exhausted.\nDespite the manipulability of the Boston mechanism, advocates of this mechanism argue that it may lead to ex-ante welfare gains because agents may coordinate on equilibria that they find more desirable under their particular preference intensities \\citep{Abdulkadiroglu2010ExpandingChoiceCADA}.\nOn the other hand, the existence of manipulation opportunities may result in disadvantages for unsophisticated participants \\citep{Miralles2008CaseForTheBostonMechanism} and inferior overall welfare \\citep{ErginSonmez2006}.\n\\citet{CasaGuell2013BCNEvidence} presented evidence from Barcelona, indicating that under a particular variant of the Boston mechanism, parents are so afraid of being matched to an undesirable school that the matching essentially degenerates to the matching determined purely by the neighborhood priority structure.\nIn recent years, some school districts have adopted alternative procedures based on the Deferred Acceptance mechanism (which is strategyproof for students), e.g., Boston and Chicago \\citep{Pathak2013AERComparingMechanismsByVulnerability}.\n\nThis paper remains agnostic with respect to this debate, i.e., we do not argue in favor or against the use of non-strategyproof mechanisms in school choice or other matching domains.\nInstead, we closely examine two variants of the Boston mechanism and compare them to each other and to the strategyproof $\\mathrm{RSD}$\\ mechanism in terms of incentives and efficiency.\nOur analysis provides new insights into the subtle trade-offs between strategyproofness and efficiency that a choice between the two variants of the Boston mechanism and $\\mathrm{RSD}$\\ entails.\n\\subsection{An Adaptive Variant of the Boston Mechanism}\n\\label{sec:introduction:abm}\nUnder the Boston mechanism as outlined above, an agent may apply to an object that has already been exhausted in a previous round.\nIn that case the agent effectively ``wastes'' one round in which it cannot compete with other agents for other objects that are still available.\nThis ``na\\\"{i}ve'' variant of the Boston mechanism ($\\mathrm{NBM}$) is susceptible to a particular type of manipulation: suppose an agent knows that its 2nd choice will be exhausted by other agents in the first round;\nunless it ranks its second choice first the agent has no chance of obtaining that 2nd choice.\nThus, by ranking its $2$nd choice last instead of second, the agent can improve its chances of obtaining a better object either in round $2$ or in any later round without foregoing any chances of the getting its $2$nd choice.\nObviously, $\\mathrm{NBM}$\\ is highly manipulable. \n\nA simple remedy for this particular problem is to let agents skip exhausted objects and instead let them apply to their best \\emph{available choice} in each round.\nThis smalle change creates a slightly different Boston mechanism, which has been largely overlooked in the literature so far. We call this mechanism the \\emph{adaptive Boston mechanisms} ($\\mathrm{ABM}$).\nLike $\\mathrm{NBM}$, $\\mathrm{ABM}$\\ is also used in practice, e.g., for the allocation of students to middle schools in Freiburg, Germany: \nparents apply to their preferred school;\nif the child is rejected, the parents receive a list of schools that still have capacity available;\nwhen the parents then apply to a school from this list, this procedure effectively implements $\\mathrm{ABM}$.\nIn this paper, we formalize and study this mechanism in detail.\n\\subsection{Understanding Trade-offs}\n\\label{sec:introduction:understand_tradeoff}\nThe traditional approach to understanding the trade-off between strategyproofness and efficiency offered by various mechanisms is to verify (or falsify) that one mechanism satisfies a certain property while another mechanism does not.\nSuch ``binary'' properties include strategyproofness, different forms of efficiency, and the existence of equilibria with desirable outcomes.\nHowever, as this paper will illustrate, this approach does not always work, i.e., a comparison of mechanisms via the traditional methods may yield inconclusive results. \n\nFor example, traditional methods do not reveal the efficiency advantages of $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ over $\\mathrm{RSD}$. \nAll three mechanisms are ex-post efficient, but neither ordinally nor rank efficient.\nRegarding incentives, a \\emph{comparison by vulnerability to manipulation} \\citep{Pathak2013AERComparingMechanismsByVulnerability} does not differentiate between $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ in our setting.\nWe therefore apply a set of new methods:\nfirst, regarding incentives, we successfully employ \\emph{partial strategyproofness}, a new, scalable concept to quantify the degree of strategyproofness of non-strategyproof mechanisms \\citep{Mennle2014}.\nSecond, regarding efficiency, we consider existing notions of dominance for allocations and extend these notions towards the comparison of mechanisms.\nIn combination with computational experiments, our methods shed new light on the trade-off between strategyproofness and efficiency when choosing between the two Boston mechanisms and $\\mathrm{RSD}$.\n\\subsection{Overview of Contributions}\n\\label{sec:introduction:contributions}\nIn this paper, we study $\\mathrm{RSD}$, $\\mathrm{NBM}$, and $\\mathrm{ABM}$\\ in a setting with no priority structure.\nWe analyze their incentive and efficiency properties, and we obtain the following results:\n\\begin{enumerate}[(1)]\n\t\\item \\textbf{Price of Strategyproofness:} we show that $\\mathrm{NBM}$\\ imperfectly rank dominates $\\mathrm{RSD}$, i.e., if the outcomes are comparable by rank dominance, then the outcome of $\\mathrm{NBM}$\\ is at least weakly preferable to that of $\\mathrm{RSD}$. This efficiency advantage can be interpreted as the \\emph{cost of strategyproofness} when choosing $\\mathrm{RSD}$\\ over $\\mathrm{NBM}$. \t\n\t\\item \\textbf{Partial Strategyproofness of $\\mathbf{ABM}$:} we formalize the adaptive Boston mechanism and show that it is partially strategyproof, i.e., strategyproof on a domain of uniformly relatively bounded indifference. In contrast, $\\mathrm{NBM}$\\ is not even weakly strategyproof. This clear distinction between $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ in terms of incentives is novel, since the comparison by vulnerability to manipulation \\citep{Pathak2013AERComparingMechanismsByVulnerability} does not differentiate between the two mechanisms in our setting.\n\t\\item{\\textbf{Imperfect Rank Dominance of ABM over RSD in Large Markets:}} we show that $\\mathrm{ABM}$\\ essentially imperfectly rank dominates $\\mathrm{RSD}$, i.e., while $\\mathrm{RSD}$\\ may rank dominate $\\mathrm{ABM}$\\ at certain type profiles, we find that this is almost never the case. We show that the share of type profiles at which $\\mathrm{RSD}$\\ rank dominates $\\mathrm{ABM}$\\ becomes arbitrarily small in large markets. Numerical evidence suggests that the efficiency advantages of $\\mathrm{ABM}$\\ over $\\mathrm{RSD}$\\ are similar in magnitude to those of $\\mathrm{NBM}$\\ over $\\mathrm{RSD}$. \t\n\t\\item{\\textbf{Price of Partial Strategyproofness:}} we show that a formal imperfect rank dominance comparison of $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ is inconclusive, i.e., $\\mathrm{NBM}$\\ is not clearly preferable to $\\mathrm{ABM}$. However, numerical evidence suggests that, conditional on comparability, $\\mathrm{NBM}$\\ is ``usually'' more efficient than $\\mathrm{ABM}$, which can be interpreted as the \\emph{cost of partial strategyproofness} when choosing $\\mathrm{ABM}$\\ over $\\mathrm{NBM}$.\n\\end{enumerate}\nIn summary, we find that choosing between $\\mathrm{NBM}$, $\\mathrm{ABM}$, and $\\mathrm{RSD}$\\ remains a question of trade-offs between efficiency and incentives.\n$\\mathrm{ABM}$\\ presents a viable alternative to $\\mathrm{NBM}$\\ when partial strategyproofness is desirable and full strategyproofness is not a hard requirement.\n\\section{Related Work}\n\\label{sec:related_work}\nThe Boston mechanism has received significant attention, because it is frequently used for the allocation of students to public schools in many school districts around the world.\nThe mechanism has been heavily criticized for its manipulability and the resulting loss in welfare and fairness:\nfor the case of strict priorities,\n\\citet{Abdulkadiroglu2003SchoolChoice} found that the Boston mechanism is neither strategyproof nor stable, and they suggested the Student Proposing Deferred Acceptance mechanism \\citep{GaleShapley1962CollegeAdmissionStability} as an alternative that is strategyproof for students.\n\\citet{ErginSonmez2006} showed that with full information, the Boston mechanism has undesirable equilibrium outcomes.\nExperimental studies by \\citet{Chen2006SchoolChoiceExperimentalStudy} and \\citet{PaisPinter2008SCandInforamtionExp} revealed that the Boston mechanism is indeed manipulated more frequently than alternative, strategyproof mechanisms.\n\n\\citet{KojimaUnver2014BostonSchoolChoice} provided an axiomatic characterization of this mechanism for the case of strict priorities.\nHowever, they also pointed out that the assumption of \\emph{strict} priorities is usually violated in school choice problems.\nSome recent work has considered coarse priorities, uncovering a number of surprising properties of the Boston mechanism:\n\\citet{Abdulkadiroglu2010ExpandingChoiceCADA} demonstrated that in a setting with \\emph{coarse} priorities and perfectly correlated preferences, the Boston mechanism can lead to higher ex-ante welfare than $\\mathrm{RSD}$.\nSimilarly, simulations conducted by \\citet{Miralles2008CaseForTheBostonMechanism} illustrate that for a setting without priorities, equilibria of the Boston mechanism can have desirable ex-ante welfare properties.\nIn this paper, we consider a setting with no priority structure and give a systematic description of the efficiency advantages of the Boston mechanism over the strategyproof alternative $\\mathrm{RSD}$\\ (which is equivalent to Student Proposing Deferred Acceptance if there is only one priority class and ties are broken using single uniform tie breaking).\n\n\\citet{Miralles2008CaseForTheBostonMechanism} also found that the manipulability of the Boston mechanism can lead to lower welfare for unsophisticated participants, and he noted that an adaptive adjustment could mitigate this effect.\nFor the case of strict priorities, \\citet{Dur2013} characterized the adaptive Boston mechanism and showed that it is less vulnerable to manipulation than the traditional Boston mechanism.\nIn this paper, we formalize the adaptive Boston mechanism in settings without priorities and study its properties.\nFor coarse priorities, a characterization of either the traditional or the adaptive Boston mechanism remains an open research question.\n\nIn the absence of priorities, $\\mathrm{RSD}$\\ is known to be strategyproof, ex-post efficient, and anonymous.\n\\citet{AbdulSonmez1998RSDandTheCore} showed that it is equivalent to the Core from Random Endowments mechanism for house allocation, and \\citet{Bade2013RSDOneAndOnly} recently extended their result: she showed that any mechanism that randomly assigns agents to roles in some strategyproof, ex-post efficient, deterministic mechanism is equivalent to $\\mathrm{RSD}$.\nHowever, it remains an open conjecture whether $\\mathrm{RSD}$\\ is the \\emph{unique} mechanism that is ex-post efficient, strategyproof, and anonymous.\n\n\\citet{Bogomolnaia2001} introduced the Probabilistic Serial mechanism and showed that it satisfies ordinal efficiency, a strict refinement of ex-post efficiency.\nHowever, they also showed that no mechanism can be ordinally efficient, strategyproof, and symmetric.\nRank efficiency, introduced by \\citet{Featherstone2011b}, is an even stronger efficiency requirement, but it is incompatible with strategyproofness, independent of any fairness requirements.\nIn this paper, we find that both variants of the Boston mechanism are ex-post efficient, but neither ordinally nor rank efficient.\nHowever, we show that both variants of the Boston mechanism provide significant efficiency gains over $\\mathrm{RSD}$.\n\nRecently, \\citet{Pathak2013AERComparingMechanismsByVulnerability} introduced a general framework to compare different mechanisms by their vulnerability to manipulation.\nThis framework is applicable for strict priority structures, but must be slightly extended for the study of mechanisms with coarse priorities in this paper.\nUnfortunately, even the weakest conceivable extension of the comparison concept does not differentiate between $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ in terms of manipulability.\nIn \\citep{Mennle2014} we have recently introduced \\emph{partial strategyproofness}, which yields a parametric measure for the degree of strategyproofness of non-strategyproof mechanisms.\nIn this paper, we use this new concept to compare the incentives of $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$.\nWe find that $\\mathrm{ABM}$\\ is partially strategyproof while $\\mathrm{NBM}$\\ is not.\n\\section{Model}\n\\label{sec:model}\n\\subsection{Preferences}\n\\label{sec:model:settings_preferences}\nA \\emph{setting} $(N,M,\\mathbf{q})$ consists of a set $N$ of $n$ \\emph{agents}, a set $M$ of $m$ \\emph{objects}, and a vector $\\mathbf{q} = (q_1, \\ldots, q_m)$ of \\emph{capacities}, i.e., there are $q_j$ units of object $j$ available. We assume that supply satisfies demand, i.e., $n \\leq \\sum_{j \\in M} q_j$, since we can always add dummy objects.\nAgents are endowed with \\emph{von Neumann-Morgenstern (vNM) utilities} $u_i, i \\in N,$ over the objects. If $u_i(a) > u_i(b)$, we say that agent $i$ \\emph{prefers object $a$ over object $b$}, which we denote by $a \\succ_i b$. We exclude indifferences, i.e., $u_i(a) = u_i(b)$ implies $a=b$. A utility function $u_i$ is \\emph{consistent} with \\emph{preference ordering} $\\succ_i$ if $a \\succ_i b$ whenever $u_i(a) > u_i(b)$. Given a preference ordering $\\succ_i$, the corresponding \\emph{type} $t_{i}$ is the set of all utilities that are consistent with $\\succ_i$, and $T$ is the set of all types, called the \\emph{type space}. We use types and preference orderings synonymously.\n\\subsection{Allocations}\n\\label{sec:model:allocations}\nAn \\emph{allocation} is a (possibly probabilistic) assignment of objects to agents. It is represented by an $n\\times m$-matrix $x = (x_{i,j})_{i \\in N, j \\in M}$ satisfying the \\emph{fulfillment constraint} $\\sum_{j\\in M} x_{i,j} = 1$, the \\emph{capacity constraint} $\\sum_{i\\in N} x_{i,j} \\leq q_j$, and $x_{i,j} \\in [0,1]$ for all $i\\in N,j \\in M$. The entries of the matrix are interpreted as probabilities, where agent $i$ gets object $j$ with probability $x_{i,j}$. An allocation is \\emph{deterministic} if $x_{i,j} \\in \\{0,1\\}$ for all $i \\in N, j \\in M$. The Birkhoff-von Neumann Theorem and its extensions \\citep{Budishetal2013DesignRandomAllocMechsTheoryAndApp} ensure that, given any allocation, we can always find a lottery over deterministic assignments that implements these marginal probabilities. Finally, let $\\mathcal{X}$ denote the space of all allocations.\n\\subsection{Mechanisms}\n\\label{sec:model:mechanisms}\nA \\emph{mechanism} is a mapping $ f: T^n \\rightarrow \\mathcal{X}$ that chooses an allocation based on a profile of reported types. We let $f_i(t_i,t_{-i})$ denote the allocation that agent $i$ receives if it reports type $t_i$ and the other agents report $t_{-i} = (t_1,\\ldots, t_{i-1},t_{i+1},\\ldots,t_n) \\in T^{n-1}$.\nNote that mechanisms only receive type profiles as input.\nThus, we consider \\emph{ordinal} mechanisms, where the allocation is independent of the actual vNM utilities.\nIf $i$ and $t_{-i}$ are clear from the context, we may abbreviate $f_i(t_i,t_{-i})$ by $f(t_i)$.\nThe expected utility for $i$ is given by the dot product $\\left\\langle u_i,f(t_i)\\right\\rangle$, i.e.,\n\\begin{equation}\n\t\\mathbb{E}_{f_i(t_i,t_{-i})}(u_i) = \\sum_{j \\in M} u_i(j) \\cdot f_i(t_i,t_{-i})(j) =\\left\\langle u_i,f(t_i)\\right\\rangle.\n\\end{equation}\n\\subsection{A Note on Priorities}\n\\label{sec:model:priorities}\nThe classical school choice problem can be considered a hybrid between the \\emph{two-sided matching} (or \\emph{marriage}) problem and the \\emph{one-sided matching} (or \\emph{allocation}) problem. In two-sided matching, agents on both sides of the market have preferences over the agents on the other side, while in one-sided matching, objects are completely indifferent to whom they are allocated.\nIn school choice, \\emph{priorities} take the role of the schools' preferences, and they are usually not strict, i.e., many students may have the same priority, so that ties must be broken randomly.\n\nIn this paper, we do not model priorities explicitly.\nInstead, we can incorporate priorities implicitly in the formulation of the mechanism.\nThe variants of the Boston mechanisms defined in Sections \\ref{sec:nbm} and \\ref{sec:abm} choose a single priority ordering uniformly at random, i.e., all students are part of a single, large priority class, and ties are broken in the same way at all schools. This is consistent with \\citep{Miralles2008CaseForTheBostonMechanism} and \\citep{Abdulkadiroglu2010ExpandingChoiceCADA}, and ``orthogonal'' to the case of strict priorities.\nNonetheless, one of our main results (Theorem \\ref{thm:rsd_not_rd_nbm}) generalizes to settings with some priority requirements.\nThe definition of the Boston mechanisms for diverse priorities would not differ significantly from ours, but we leave the study of their properties in the more general environment to future research.\n\\section{Preliminaries}\n\\label{sec:preliminaries}\nIn this section, we review common notions of strategyproofness, efficiency, and fairness, and we define a number of new concepts that we need for the comparison of mechanisms.\n\\subsection{Strategyproofness Concepts}\n\\label{sec:preliminaries:sp}\nWe begin with a revision of the standard strategyproofness requirement.\n\\begin{definition}[Strategyproofness] \\label{def:SP} A mechanism is \\emph{strategyproof} if for any agent $i\\in N$, any type profile $\\mathbf{t}=(t_i,t_{-i})\\in T^n$, any misreport $t_{i}' \\in T$, and any utility $u_i \\in t_i$ we have\n\\begin{equation}\n\t\\left\\langle u_i , f(t_i) - f(t'_i)\\right\\rangle \\geq 0.\n\\label{eq:incentive_constraint_SP}\n\\end{equation}\n\\end{definition}\nStrategyproofness is particularly attractive because it makes reporting truthfully a dominant strategy for all agents. This removes the need for agents to deliberate about potentially beneficial manipulations, \nand agents do not need to exert effort to coordinate on certain equilibria.\n\nIf agents are only interested in manipulations that improve the outcome (for them) in a first order-stochastic dominance sense (e.g., if they are not aware of their own preference intensities), then weak strategyproofness becomes useful.\nWeak strategyproofness was first used by \\citet{Bogomolnaia2001} to describe the incentive properties of the Probabilistic Serial mechanism.\n\\begin{definition}[Weak Strategyproofness] \\label{def:wsp} A mechanism is \\emph{weakly strategyproof} if for any type profile $\\mathbf{t}=(t_i,t_{-i}) \\in T^n$, the outcome from truthful reporting is never strictly first order-stochastically dominated by the outcome from any misreport for agent $i$.\n\\end{definition}\nWeak strategyproofness is a very weak requirement for multiple reasons. \nIn particular, it does not even guarantee that there exists a single utility profile for which truthful reporting is a best response (in terms of expected utility) \\citep{Mennle2014}.\n\nIn \\citep{Mennle2014} we have introduced \\emph{partial strategyproofness}, a scalable requirement that bridges the gap between strategyproofness and weak strategyproofness.\nIntuitively, partially strategyproof mechanisms are strategyproof, but only on a particular domain restriction, i.e., the agents can still have any type, but their underlying utility functions are constrained. First we define this domain restriction.\n\\begin{definition}[Uniformly Relatively Bounded Indifference] \\label{def:URBI} A utility function $u$ satisfies \\emph{uniformly relatively bounded indifference with respect to bound $r \\in [0,1]$ ($\\mathrm{URBI}(r)$)} if for any objects $a,b$ with $u(a) > u(b)$ we have\n\\begin{equation}\nr\\cdot (u(a)-\\min(u)) \\geq u(b)-\\min(u).\n\\label{eq:urbi_constraint}\n\\end{equation}\nDenote by $\\mathrm{URBI}(r)$\\ the set of all utility functions that satisfy uniformly relatively bounded indifference with respect to bound $r$.\n\\end{definition}\nA mechanism is partially strategyproof if it is strategyproofness in the domain restricted by the $\\mathrm{URBI}(r)$\\ constraint. Formally:\n\\begin{definition}[$r$-partial Strategyproofness] \\label{def:PSP} Given a setting $(N,M,\\mathbf{q})$ and a bound $r \\in [0,1]$, mechanism $f$ is \\emph{$r$-partially strategyproof in the setting $(N,M,\\mathbf{q})$} if for any agent $i\\in N$, any type profile $\\mathbf{t} = (t_i,t_{-i}) \\in T^n$, any misreport $t_i' \\in T$, and any utility $u_i \\in \\mathrm{URBI}(r) \\cap t_i$, we have\n\\begin{equation}\n\t\\left\\langle u_i , f(t_i) - f(t'_i)\\right\\rangle \\geq 0.\n\t\\label{eq:incentive_constraint_urbi_psp}\n\\end{equation}\n\\end{definition}\nSometimes, we simply want to state that a mechanism is $r$-partially strategyproof for some non-trivial $r > 0$ without explicitly naming the parameter $r$. In this case, we simply say that the mechanism is \\emph{partially strategyproof}.\n\nThe partial strategyproofness concept has an axiomatic characterization.\nFor details, please see Appendix \\ref{app:review_psp} or \\citep{Mennle2014}. In the following we only briefly review the two axioms we need throughout this paper. \n\nEach axiom restricts the way in which a misreport from the neighborhood of the agent's true type, i.e., a misreport involving only a single swap, can affect the allocation of the reporting agent.\n\\begin{axiom}[Swap Monotonic] \\label{ax:SM} A mechanism $f$ is \\emph{swap monotonic} if for any agent $i \\in N$, any type profile $\\mathbf{t} = (t_i,t_{-i}) \\in T^n$, and any type $t_i' \\in N_{t_i}$ (i.e., in the neighborhood of $t_i$) with $a_k \\succ a_{k+1}$ under $t_i$ and $a_{k+1} \\succ a_k$ under $t_i'$, one of the following holds:\n\\begin{enumerate}[1)]\n\t\\item $i$'s allocation is unaffected by the swap, i.e.,\n\t\t$f(t_i) = f(t_i')$, or\n\t\\item $i$'s allocation for $a_k$ strictly decreases and its allocation for $a_{k+1}$ strictly increases, i.e.,\n\t\\begin{equation} f(t_i)(a_k) > f(t_i')(a_k) \\;\\;and\\;\\; f(t_i)(a_{k+1}) < f(t_i')(a_{k+1}). \\end{equation}\n\\end{enumerate}\n\\end{axiom}\n\\begin{axiom}[Upper Invariance] \\label{ax:UI} A mechanism $f$ is \\emph{upper invariant} if for any agent $i \\in N$, any type profile $\\mathbf{t} = (t_i,t_{-i}) \\in T^n$, and any type $t_i' \\in N_{t_i}$ with $a_k \\succ a_{k+1}$ under $t_i$ and $a_{k+1} \\succ a_{k}$ under $t_i'$, $i$'s allocation for the upper contour set $U(a_k,t_i)$ is unaffected by the swap, i.e., $ f(t_i)(j) = f(t_i')(j) $ for all $j \\in U(a_k,t_i)$.\n\\end{axiom}\n\\emph{Lower invariance} is defined analogously by replacing the upper contour set by the lower contour set.\n\nIn \\citep{Mennle2014}, we have shown that a mechanism is strategyproof if and only if it is swap monotonic, upper invariant, and lower invariant. Furthermore, by dropping lower invariance, the class of partially strategyproof mechanisms arises, i.e., a mechanism is partially strategyproof if and only if it is swap monotonic and upper invariant. \n\nFor any partially strategyproof mechanism, we can consider the largest admissible indifference bound $r$ as a single-parameter measure for ``how strategyproof'' the mechanism is.\n\\begin{definition} \\label{def:dosp} For a setting $(N,M,\\mathbf{q})$, the \\emph{degree of strategyproofness (DOSP)} of a mechanism $f$ is given by\n\\begin{equation}\n\t\\rho_{(N,M,\\mathbf{q})}(f) = \\max\\{r \\in [0,1] : f\\text{ is }r\\text{-partially strategyproof in }(N,M,\\mathbf{q})\\}.\n\\end{equation}\n\\end{definition}\nIn \\citep{Mennle2014} we have also shown that the DOSP is computable and consistent with the vulnerability to manipulation concept \\citep{Pathak2013AERComparingMechanismsByVulnerability}.\n\\subsection{Dominance and Efficiency}\n\\label{sec:preliminaries:dominance_efficiency}\nIn matching markets, welfare usually cannot be expressed as the sum of the agents' cardinal utilities, since the welfare of individual agents may not be comparable. \nInstead, we consider whether or not an allocation can be improved upon, i.e., whether it is \\emph{efficient}.\n\nFirst, we review ex-post (Pareto) dominance and efficiency.\n\\begin{definition}[Ex-post Dominance] \\label{def:expost_dom} Given a type profile $\\mathbf{t} =(t_i,t_{-i}) \\in T^n$, a deterministic allocation $x$ \\emph{ex-post dominates} another deterministic allocation $y$ \\emph{at} $\\mathbf{t}$ if all agents weakly prefer their allocation under $x$ to their allocation under $y$. The dominance is \\emph{strict} if at least one agent strictly prefers its allocation under $x$.\n\\end{definition}\n\\begin{definition}[Ex-post Efficiency] \\label{def:expost_eff} Given a type profile $\\mathbf{t} =(t_i,t_{-i}) \\in T^n$, a deterministic allocation $x$ is \\emph{ex-post efficient at} $\\mathbf{t}$ if it is not strictly ex-post dominated by any other deterministic allocation at $\\mathbf{t}$. Finally, a probabilistic allocation is \\emph{ex-post efficient} if it has a lottery decomposition consisting only of ex-post efficient deterministic allocations.\n\\end{definition}\nEx-post efficiency is ubiquitous in the matching literature and can be considered a baseline requirement for mechanism design.\nHowever, even when considering ex-post efficient probabilistic allocations, some unambiguous welfare gains may be possible: agents may wish to trade probability shares at some objects in such a way that all agents are weakly better off and some strictly (see \\citep{Bogomolnaia2001} for an example).\nThis kind of \\emph{ex-interim improvement}, i.e., before the lottery is implemented, is formalized by the following ordinal dominance and efficiency concepts.\n\\begin{definition}[Ordinal Dominance] \\label{def:ordinal_dominance} For a type $t : a_1 \\succ \\ldots \\succ a_m$ and two allocation vectors $v = v_{j \\in M}$ and $ w = w_{j \\in M}$, the allocation vector $v$ \\emph{first order-stochastically dominates} $w$ if for all ranks $k \\in \\{1,\\ldots,m\\}$\n\\begin{equation}\n\\sum_{j \\in M: j \\succ a_k} v_{j} \\geq \\sum_{j \\in M: j \\succ a_k} w_{j}.\n\\label{eq:fosd}\n\\end{equation}\nFor a type profile $\\mathbf{t}$, an allocation $x$ \\emph{ordinally dominates} another allocation $y$ \\emph{at} $\\mathbf{t}$ if for all agents $i \\in N$ their respective allocation vector $x_i$ first order-stochastically dominates $y_i$ (for type $t_i$). $x$ \\emph{strictly} ordinally dominates $y$ if in addition, inequality (\\ref{eq:fosd}) is strict for some agent $i \\in N$ and some rank $k \\in \\{1,\\ldots,m\\}$.\nWe let $\\succeq_O$ and $\\succ_O$ denote weak and strict ordinal dominance, respectively.\n\\end{definition}\n\\begin{definition}[Ordinal Efficiency] \\label{def:ordinal_eff}\nFor a type profile $\\mathbf{t}$, an allocation $x$ is \\emph{ordinally efficient at} $\\mathbf{t}$ if it is not strictly ordinally dominated by any other allocation at $\\mathbf{t}$.\n\\end{definition}\nA refinement of ordinal efficiency is rank efficiency. Rank efficiency \\citep{Featherstone2011b} captures the intuition that allocating two first choices and one second choice is preferable to allocating one first and two second choices.\n\\begin{definition}[Rank Dominance] \\label{def:rank_dom} For a type\n$t \\in T$ and rank $k \\in \\{1,\\ldots,m\\}$ let $\\mathrm{ch}(k,t)$ denote the $k$th choice object of an agent of type $t$.\nThe \\emph{rank distribution} of an allocation $x$ at type profile $\\mathbf{t}$ is the vector $\\mathbf{d}^x = (d_1^x,\\ldots,d_m^x)$ with\n\\begin{equation}\nd_k^x = \\sum_{i \\in N} x_{i,\\mathrm{ch}(k,t_i)} \\mathrm{\\;for\\;}k\\in \\{1,\\ldots,m\\},\n\\label{eq:def_rank_dist}\n\\end{equation}\ni.e., $d_k^x$ is the expected number of $k$th choices allocated under $x$ with respect to type profile $\\mathbf{t}$.\n\nAn allocation $x$ \\emph{rank dominates} another allocation $y$ at $\\mathbf{t}$ if the rank distribution $\\mathbf{d}^x$ first order-stochastically dominates $\\mathbf{d}^y$. $x$ \\emph{strictly rank dominates} $y$ if in addition, inequality (\\ref{eq:def_rank_dist}) is strict for some rank $r\\in\\{1,\\ldots,m\\}$.\nLet $\\succeq_R$ and $\\succ_R$ denote weak and strict rank dominance, respectively.\n\\end{definition}\n\\begin{definition}[Rank Efficiency] \\label{def:rank_eff}\nAn allocation $x$ is \\emph{rank efficient} at $\\mathbf{t}$ if it is not strictly rank dominated by any other allocation at $\\mathbf{t}$.\n\\end{definition}\nRank efficient allocations can be interpreted as maximizing some form of social value: suppose there is a value $v_k$ associated with giving any agent its $k$th choice object.\nA vector $\\mathbf{v}=(v_1,\\ldots, v_m)$ with $v_1 > v_2 > \\ldots > v_m$ is called \\emph{rank valuation}.\nAn allocation $x$ is rank efficient if and only if there exists a rank valuation $v$ such that\n\\begin{equation}\nx \\in \\arg\\max_{y \\in \\mathcal{X}} \\sum_{k = 1}^m \\sum_{i \\in N} v_k y_{i,\\mathrm{ch}(k,t_i)}.\n\\end{equation}\n\nSo far, we have only discussed dominance concepts for allocations. Dominance can easily be extended to mechanisms by requiring the allocations resulting from one mechanism to always dominate those from the other mechanism.\n\\begin{definition}[Dominance for Mechanisms] \\label{def:perf_dom} Consider two mechanisms $f$ and $g$. \nMechanism $g$ \\emph{weakly ordinally dominates} $f$ if $g(\\mathbf{t}) \\succeq_O f(\\mathbf{t})$ at $\\mathbf{t}$ for all type profiles $\\mathbf{t} \\in T^n$.\n$g$ \\emph{strictly ordinally dominates} $f$ if in addition $g(\\mathbf{t}) \\succ_O f(\\mathbf{t})$ at $\\mathbf{t}$ for some type profiles $\\mathbf{t} \\in T^n$. \n\\emph{Weak} and \\emph{strict rank dominance} are defined analogously.\nWe denote weak and strict ordinal dominance for mechanisms by $\\succeq_{O}$ and $\\succ_{O}$, and weak and strict rank dominance for mechanisms by $\\succeq_{R}$ and $\\succ_{R}$, respectively, simply extending the notation from allocations.\n\\end{definition}\nTo determine whether a mechanism can be improved upon with respect to efficiency, one must check whether there exists another mechanism that is unambiguously more efficient, but otherwise has the same desirable properties. This is captured by the \\emph{efficient frontier}.\n\\begin{definition}[Efficient Frontier] \\label{def:frontier} For any set of properties $\\mathcal{E}$ and a dominance concept $\\succ \\in \\{\\succ_O,\\succ_R\\}$ (in the sense of Definition \\ref{def:perf_dom}), a mechanism is on the \\emph{efficient frontier (with respect to $\\succ$, subject to $\\mathcal{E}$)}, if it is not $\\succ$-dominated by any mechanism that also satisfies $\\mathcal{E}$.\n\\end{definition}\n\nUnfortunately, the dominance concept from Definition \\ref{def:perf_dom} may be overly restrictive for the comparison of many popular mechanisms, i.e., essentially all common mechanisms are not comparable in this way (e.g., Random Serial Dictatorship, Probabilistic Serial, variants of the Boston mechanism). Therefore, we introduce \\emph{imperfect dominance} for mechanisms, which only requires that one mechanism should produce dominant allocations \\emph{whenever the resulting allocations are comparable}.\n\\begin{definition}[Imperfect Dominance for Mechanisms] \\label{def:imp_dom} Consider two mechanisms $f$ and $g$. $g$ \\emph{weakly imperfectly ordinally dominates} $f$ if\n$g(\\mathbf{t}) \\succeq_O f(\\mathbf{t})$ at $\\mathbf{t}$ for all type profiles $\\mathbf{t} \\in T^n$ where $g(\\mathbf{t})$ and $f(\\mathbf{t})$ are comparable by the dominance relation.\n$g$ \\emph{strictly imperfectly ordinally dominates} $f$ if in addition $g(\\mathbf{t}) \\succ_O f(\\mathbf{t})$ at $\\mathbf{t}$ for some type profiles $\\mathbf{t} \\in T^n$. \\emph{Weak} and \\emph{strict imperfect rank dominance} are defined analogously.\nWe denote weak and strict imperfect ordinal dominance by $\\succeq_{IO}$ and $\\succ_{IO}$, and weak and strict imperfect rank dominance by $\\succeq_{IR}$ and $\\succ_{IR}$, respectively.\n\\end{definition}\n\\subsection{Fairness}\n\\label{sec:preliminaries:fairness}\nIn the absence of strict priorities, \\emph{anonymity} is a common fairness requirement, which all mechanisms we study in this paper satisfy.\n\\begin{definition}[Anonymity] A mechanism is \\emph{anonymous} if the allocation only depends on the reports of the agents, but not their names, i.e., for any bijection $\\phi:N\\rightarrow N$ and all $i \\in N$\n\\begin{equation}\n\tf_{\\phi(i)}(t_{\\phi(1)},\\ldots,t_{\\phi(n)}) = f_{i}(t_1,\\ldots,t_n).\n\\end{equation}\n\\end{definition}\nAnonymity implies \\emph{symmetry} (also called \\emph{equal treatment of equals}), which requires that agents with the same report also receive the same allocation. However, the inverse is not true.\n\nAnother important fairness requirement in school choice is the \\emph{absence of justified envy}: after the implementation of the lottery, agent $i$ \\emph{envies} another agent $i'$ if $i$ prefers the object that $i'$ received to its own object.\nThe envy is \\emph{justified} if $i$ has a higher priority at that object than $i'$.\nSince in this paper, we consider mechanisms in the absence of strict priorities, an agent cannot have higher priority at any object ex-ante, and thus, justified envy is no concern.\n\nNonetheless, we can consider the absence of justified envy \\emph{ex-post}, i.e., after the implementation of the lottery and with respect to the particular tie-breaker.\nIn this ex-post sense, Random Serial Dictatorship eliminates justified envy, while the Boston mechanisms may not. However, the Boston mechanisms do eliminate \\emph{observable justified envy}: \nif agents are not informed about the (randomly chosen) priority ordering, they only learn their relative priority with respect to other agents against whom they have competed (and won or lost) at some objects. \nFrom the perspective of an agent $i$, if any agent $i'$ received an object that $i$ prefers to its own allocation, then $i'$ either applied to that object in an earlier round, in which case the relative priority of $i$ and $i'$ is unobservable, \nor $i'$ applied to the object in the same round as $i$ and $i'$ had the higher priority. Thus, for any agent that $i$ envies, that agent either has a higher priority or the priority is unobservable to $i$.\n\\subsection{The Random Serial Dictatorship Mechanism}\n\\label{sec:preliminaries:rsd}\nWe now define the Random Serial Dictatorship mechanism.\n\\begin{definition} \\label{def:rsd} The \\emph{Random Serial Dictatorship ($\\mathrm{RSD}$) mechanism} works as follows:\n\\begin{enumerate}\n\t\\item Collect all type reports $\\hat{\\mathbf{t}}=(\\hat{t}_1,\\ldots,\\hat{t}_n) \\in T^n $ from the agents.\n\t\\item Draw an ordering $\\pi$ of all agents uniformly at random from the space of all possible bijections $\\pi: N \\rightarrow \\{1,\\ldots,n\\}$. $\\pi(i) < \\pi(i')$ means that $i$ has priority over $i'$.\n\t\\item Step 1: the agent with the highest priority gets a copy of the object it likes best and is removed together with that copy of the object.\n\t\\item Step $k$: in each subsequent step, the agent with the highest priority (amongst the remaining agents) gets the object it likes best out of all objects that still have capacity left.\n\t\\item The process ends when all agents have been allocated an object.\n\\end{enumerate}\n\\end{definition}\nLet $\\mathrm{SD}_{\\pi}(\\hat{\\mathbf{t}})$ denote the allocation if a Serial Dictatorship ($\\mathrm{SD}$) is applied to reports $\\hat{\\mathbf{t}}$ with fixed priority ordering $\\pi$.\n$\\mathrm{RSD}$\\ can be viewed as producing a probabilistic allocation, because the priority ordering over the agents is unknown when the agents report their types. The probabilistic allocation is given by the convex combination over all possible deterministic allocations\n\\begin{equation}\n\\mathrm{RSD}(\\hat{\\mathbf{t}}) = \\sum_{\\pi:N\\rightarrow\\{1,\\ldots,n\\}\\mathrm{\\ bijection}} \\frac{1}{n!} \\mathrm{SD}_{\\pi}(\\hat{\\mathbf{t}}).\n\\label{eq:prob_alloc_rsd}\n\\end{equation}\n\\begin{remark} \\label{rem:SD_supports_EE} Any ex-post efficient deterministic allocation is supported by $\\mathrm{SD}$\\ with respect to a particular priority ordering $\\pi$ of the agents \\citep{Featherstone2011b}.\nThis implies that $\\mathrm{RSD}$\\ performs a lottery over \\emph{all} ex-post efficient deterministic allocations, where each allocation is weighted by the number of different priority orderings $\\pi$ for which $\\mathrm{SD}$\\ produces that allocation.\n\\end{remark}\n\\begin{remark} \\label{rem:RSD_equiv_DA}\nWith a single, uniform tie-breaker, the Student Proposing Deferred Acceptance mechanism \\citep{GaleShapley1962CollegeAdmissionStability} is equivalent to $\\mathrm{RSD}$.\n\\end{remark}\n\\section{The Na\\\"{i}ve Boston Mechanism}\n\\label{sec:nbm}\nIn this section, we define and study the traditional (na\\\"{i}ve) Boston mechanism ($\\mathrm{NBM}$) in the absence of priorities and with single uniform tie-breaking.\nIt is well known that this mechanism is ex-post efficient, but not even weakly strategyproof.\nWe show that it is neither ordinally nor rank efficient, nor is it on the efficient frontier.\nDespite these negative findings, our first main result (Theorem \\ref{thm:rsd_not_rd_nbm}) establishes that $\\mathrm{NBM}$\\ does have efficiency advantages over $\\mathrm{RSD}$.\n\\subsection{Formalization of NBM}\n\\label{sec:nbm:formalization}\n\\begin{definition} \\label{def:nbm} The \\emph{na\\\"{i}ve Boston mechanism ($\\mathrm{NBM}$)}\\footnote{The mechanism is called the \\emph{Boston mechanism}, despite the fact that it is no longer used in Boston. Some researchers argue that calling it the \\emph{Immediate Acceptance mechanism} would be more consistent with the naming of the \\emph{Deferred Acceptance mechanism}.} works as follows:\n\\begin{enumerate}\n\t\\item Collect all type reports $\\hat{\\mathbf{t}}=(\\hat{t}_1,\\ldots,\\hat{t}_n) \\in T^n $ from the agents.\n\t\\item Draw an ordering $\\pi$ of all agents uniformly at random from the space of all possible bijections $\\pi: N \\rightarrow \\{1,\\ldots,n\\}$. $\\pi(i) < \\pi(i')$ means that $i$ has priority over $i'$.\n\t\\item Round 1: all agents apply to their reported first choice object. At every object, if the object's capacity is sufficient to satisfy all applications to this object, all the applicants get a copy of the object. Otherwise, the agents with the highest priority get copies of the object until it is exhausted. All remaining agents do not receive a copy and enter the next round.\n\t\\item Round $k$: an agent who did not receive an object in any of the previous $k-1$ rounds applies to its reported $k$th choice object. Analogous to round 1, the agents with the highest priorities are allocated copies of the objects for which they applied. If an object has more applicants than remaining capacity, the remaining agents enter the next round.\n\t\\item The process ends when all agents have been allocated an object.\n\\end{enumerate}\n\\end{definition}\nLet $\\mathrm{NBM}_{\\pi}(\\hat{\\mathbf{t}})$ denote the allocation if $\\mathrm{NBM}$\\ is applied to reports $\\hat{\\mathbf{t}}$ with priority ordering $\\pi$.\nLike $\\mathrm{RSD}$, $\\mathrm{NBM}$\\ produces probabilistic allocations, because it randomizes over priority orderings. The probabilistic allocation is given by the convex combination\n\\begin{equation}\n\\mathrm{NBM}(\\hat{\\mathbf{t}}) = \\sum_{\\pi:N\\rightarrow\\{1,\\ldots,n\\}\\mathrm{\\ bijection}} \\frac{1}{n!} \\mathrm{NBM}_{\\pi}(\\hat{\\mathbf{t}}).\n\\label{eq:prob_alloc_nbm}\n\\end{equation}\n\n\\subsection{Manipulability and Upper Invariance of NBM}\n\\label{sec:nbm:incentives}\n$\\mathrm{NBM}$\\ is known to be manipulable in a first order-stochastic dominance sense.\n\\begin{proposition}\\label{prop:nbm_not_wsp} $\\mathrm{NBM}$\\ is not weakly strategyproof.\n\\end{proposition}\n\\begin{proof} Consider a setting $(N,M,\\mathbf{q})$ with $N = \\{1,\\ldots,4\\}$, $M = \\{a,b,c,d\\}$, objects have unit capacities, and the type profile is\n\\begin{eqnarray*}\n\tt_1 & : & a \\succ b \\succ c \\succ d, \\\\\n\tt_2,t_3 & : & a \\succ c \\succ d \\succ b, \\\\\n\tt_4 & : & b \\succ \\ldots.\n\\end{eqnarray*}\nAgent 1's allocation is $(1\/3,0,0,2\/3)$ for the objects $a$ through $d$, respectively. If agent 1 swaps $b$ and $c$ in its report, its allocation will be $(1\/3,0,1\/3,1\/3)$, which first order-stochastically dominates the allocation under truthful reporting.\n\\end{proof}\n\nDespite this obvious manipulability, $\\mathrm{NBM}$\\ does have one attractive incentive property: it satisfies upper invariance, i.e., changing the order of two adjacent objects in an agent's type report ($t: a \\succ b$ to $t':b \\succ a$, say) will not influence the allocation of any object that this agent strictly prefers to $a$.\n\\begin{proposition} \\label{prop:nbm_ui} $\\mathrm{NBM}$\\ is upper invariant.\n\\end{proposition}\n\\begin{proof}[Proof outline (formal proof in Appendix \\ref{app:proofs:nbm:nbm_ui_and_abm_ui})] Consider each priority ordering $\\pi$ separately. \nWhen swapping two objects $a$ and $b$, observe that if an object in the upper contour set is allocated, the swap has no effect, and if an object from the lower contour set is allocated, the swap will not lead to an allocation from the upper contour set. Therefore, the probabilities for the objects in the upper contour set remain unchanged.\n\\end{proof}\n\\begin{proposition} \\label{prop:nbm_not_sm_li} $\\mathrm{NBM}$\\ is neither swap monotonic nor lower invariant.\n\\end{proposition}\n\\begin{proof} The example in the proof of Proposition \\ref{prop:nbm_not_wsp} shows that $\\mathrm{NBM}$\\ has neither property.\n\\end{proof}\n\n\\subsection{Efficiency of NBM}\n\\label{sec:nbm:efficiency_failure}\nIn this section, we check whether $\\mathrm{NBM}$\\ satisfies any existing efficiency concepts or lies on the efficient frontier, subject to upper invariance.\nFirst, we show that like $\\mathrm{RSD}$, $\\mathrm{NBM}$\\ is ex-post efficient. \nHowever, also like $\\mathrm{RSD}$, $\\mathrm{NBM}$\\ is neither ordinally nor rank efficient, nor does it lie on the efficient frontier. This motivates a direct comparison of $\\mathrm{NBM}$\\ to $\\mathrm{RSD}$.\n\\subsubsection{Ex-post Efficiency of NBM}\n\\label{sec:nbm:efficiency_failure:ex_post}\nFirst, we state the well-known fact that $\\mathrm{NBM}$\\ is ex-post efficient.\n\\begin{proposition} \\label{prop:nbm_ex_post} $\\mathrm{NBM}$\\ is ex-post efficient at all type profiles.\n\\end{proposition}\n\\begin{proof}[Proof outline (formal proof in Appendix \\ref{app:proofs:nbm:nbm_ex_post_and_abm_ex_post})] For each type profile $\\mathbf{t}$ and each priority ordering $\\pi$, we construct a priority order $\\pi'$ such that $\\mathrm{NBM}_{\\pi}(\\mathbf{t}) = \\mathrm{SD}_{\\pi'}(\\mathbf{t})$. \nThe result follows, because $\\mathrm{SD}$ is ex-post efficient for all priority orders $\\pi$. \n\\end{proof}\n\\subsubsection{Failure of Ordinal and Rank Efficiency}\n\\label{sec:nbm:efficiency_failure:ordinal_and_rank}\nSince rank efficiency implies ordinal efficiency, ordinal inefficiency implies rank inefficiency. Thus, the following Proposition \\ref{prop:nbm_not_OE} shows ordinal inefficiency and rank inefficiency of $\\mathrm{NBM}$\\ at the same time.\n\\begin{proposition} \\label{prop:nbm_not_OE} $\\mathrm{NBM}$\\ and $\\mathrm{RSD}$\\ are neither ordinally efficient nor rank efficient.\n\\end{proposition}\n\\begin{proof}\nConsider the setting with $N = \\{1,\\ldots,4\\}$, $M = \\{a,b,c,d\\}$, unit capacities, and the type profile\n\\begin{eqnarray*}\n\tt_1 & : & a \\succ b \\succ c \\succ d, \\\\\n\tt_2 & : & a \\succ b \\succ d \\succ c, \\\\\n\tt_3\t& : & b \\succ a \\succ c \\succ d, \\\\\n\tt_4 & : & b \\succ a \\succ d \\succ c.\n\\end{eqnarray*}\nThe allocations are\n\\begin{equation}\n\\mathrm{RSD}(\\mathbf{t}) = \\left(\\begin{array}{cccc}\n\\frac{5}{12} & \\frac{1}{12} & \\frac{5}{12} & \\frac{1}{12} \\\\\n\\frac{5}{12} & \\frac{1}{12} & \\frac{1}{12} & \\frac{5}{12} \\\\\n\\frac{1}{12} & \\frac{5}{12} & \\frac{5}{12} & \\frac{1}{12} \\\\\n\\frac{1}{12} & \\frac{5}{12} & \\frac{1}{12} & \\frac{5}{12} \t\n\\end{array}\\right) \\text{ and }\n\\mathrm{NBM}(\\mathbf{t}) = \\left(\\begin{array}{cccc}\n\\frac{1}{2} & 0 & \\frac{3}{8} & \\frac{1}{8} \\\\\n\\frac{1}{2} & 0 & \\frac{1}{8} & \\frac{3}{8} \\\\\n0 & \\frac{1}{2} & \\frac{3}{8} & \\frac{1}{8} \\\\\n0 & \\frac{1}{2} & \\frac{1}{8} & \\frac{3}{8} \t\n\\end{array}\\right),\n\\end{equation}\nwhich are both ordinally dominated by\n\\begin{equation}\n\\mathrm{PS}(\\mathbf{t}) = \\left(\\begin{array}{cccc}\n\\frac{1}{2} & 0 & \\frac{1}{2} & 0 \\\\\n\\frac{1}{2} & 0 & 0 & \\frac{1}{2} \\\\\n0 & \\frac{1}{2} & \\frac{1}{2} & 0 \\\\\n0 & \\frac{1}{2} & 0 & \\frac{1}{2} \t\n\\end{array}\\right).\n\\end{equation}\nSince ordinal dominance implies rank dominance, failure of ordinal efficiency implies failure of rank efficiency.\n\\end{proof}\n\\subsubsection{Failure of Efficient Frontier}\n\\label{sec:nbm:efficiency_failure:frontier}\nIn the proof of Proposition \\ref{prop:nbm_not_OE}, the Probabilistic Serial mechanism ordinally dominates $\\mathrm{NBM}$\\ at a particular type profiles, i.e., it is more efficient. \nSince $\\mathrm{PS}$\\ is ordinally efficient, it is a particularly interesting candidate mechanism which might ordinally dominate $\\mathrm{NBM}$\\ \\emph{at all type profiles}.\nHowever, this is not the case, i.e., at some type profile, $\\mathrm{PS}$\\ does not ordinally dominate $\\mathrm{NBM}$.\nInstead, $\\mathrm{NBM}$\\ may even \\emph{rank dominate} $\\mathrm{PS}$\\ at some type profile, as the following Proposition \\ref{prop:nbm_rank_dom_ps} shows.\n\\begin{proposition} \\label{prop:nbm_rank_dom_ps} $\\mathrm{PS}$\\ does not ordinally dominate $\\mathrm{NBM}$\\ at all type profiles, and $\\mathrm{NBM}$\\ rank dominates $\\mathrm{PS}$\\ at some type profile.\n\\end{proposition}\n\\begin{proof}\nConsider the setting with $N = \\{1,2,3\\}$, $M = \\{a,b,c\\}$, unit capacities, and the type profile\n\\begin{eqnarray*}\n\tt_1 & : & a \\succ b \\succ c, \\\\\n\tt_2 & : & a \\succ b \\succ c, \\\\\n\tt_3\t& : & b \\succ a \\succ c.\n\\end{eqnarray*}\nThe allocations for $\\mathrm{NBM}$\\ is\n\\begin{equation}\n\\mathrm{NBM}(\\mathbf{t}) = \\left(\\begin{array}{ccc}\n\\frac{1}{2} & 0 & \\frac{1}{2} \\\\\n\\frac{1}{2} & 0 & \\frac{1}{2} \\\\\n0 & 1 & 0\n\\end{array}\\right),\n\\end{equation}\nwhich has rank distribution $\\mathbf{d} = (2,0,1)$. Probabilistic Serial on the other hand yields\n\\begin{equation}\n\\mathrm{PS}(\\mathbf{t}) = \\mathrm{RSD}(\\mathbf{t}) = \\left(\\begin{array}{ccc}\n\\frac{1}{2} & \\frac{1}{6} & \\frac{1}{3} \\\\\n\\frac{1}{2} & \\frac{1}{6} & \\frac{1}{3} \\\\\n0 & \\frac{2}{3} & \\frac{1}{3}\n\\end{array}\\right),\n\\end{equation}\nwhich has a strictly worse rank distribution $\\mathbf{d} = (5\/3,1\/3,1)$. Thus, $\\mathrm{NBM}$ strictly rank dominates $\\mathrm{PS}$ at $\\mathbf{t}$, and therefore $\\mathrm{PS}$ does not ordinally dominate $\\mathrm{NBM}$ even weakly at $\\mathbf{t}$.\n\\end{proof}\nSince $\\mathrm{NBM}$\\ is not ordinally dominated by the particularly interesting candidate $\\mathrm{PS}$, it could lie on the efficient frontier.\nHowever, we find that this is not the case, i.e., it is possible to construct a mechanism that is upper invariant and ordinally dominates $\\mathrm{NBM}$.\n\\begin{proposition} \\label{prop:nbm_not_frontier} $\\mathrm{NBM}$\\ is not on the efficient frontier with respect to ordinal dominance, subject to upper invariance.\n\\end{proposition}\n\\begin{proof}[Proof outline (formal proof in Appendix \\ref{app:proofs:nbm:nbm_not_frontier_and_abm_not_frontier})]\nWe construct a mechanism $\\mathrm{NBM}^+$\\ that is essentially equal to $\\mathrm{NBM}$, except for a certain set of type profiles. For these type profiles, $\\mathrm{NBM}^+$\\ chooses the allocation selected by $\\mathrm{PS}$. \nIt is easy to show that $\\mathrm{NBM}^+$\\ ordinally dominates $\\mathrm{NBM}$, and the dominance may be strict.\nTo show that $\\mathrm{NBM}^+$\\ satisfies upper invariance, we argue that under any swap, the mechanism's changes to the allocation are consistent with the axiom. In particular, this is true for transitions between the special type profiles where $\\mathrm{NBM}^+$\\ and $\\mathrm{NBM}$\\ choose different allocations and those type profiles where the allocations of both mechanisms are equal.\n\\end{proof}\n\\subsection{NBM versus RSD - The Price of Strategyproofness}\n\\label{sec:nbm:nbm_ird_RSD}\nSo far we have not identified any efficiency advantage of $\\mathrm{NBM}$\\ over $\\mathrm{RSD}$. \nIn this section, we present our first main result, which shows in what sense $\\mathrm{NBM}$\\ is in fact more efficient than $\\mathrm{RSD}$.\n\\subsubsection{Imperfect Rank Dominance of NBM over RSD}\n\\label{sec:nbm:nbm_ird_RSD:nbm_ird_rsd}\n\\begin{theorem} \\label{thm:rsd_not_rd_nbm} $\\mathrm{NBM}$\\ strictly imperfectly rank dominates $\\mathrm{RSD}$, i.e., $\\mathrm{NBM}$\\ strictly rank dominates $\\mathrm{RSD}$\\ at some type profiles, but is never strictly rank dominated by $\\mathrm{RSD}$\\ at any type profile.\n\\end{theorem}\n\\begin{proof}[Proof outline (formal proof in Appendix \\ref{app:proofs:nbm:rsd_not_rd_nbm})]\nProposition \\ref{prop:nbm_rank_dom_ps} has already established that $\\mathrm{NBM}$\\ may strictly rank dominate $\\mathrm{RSD}$.\nFor any fixed priority order $\\pi$ we show that if $\\mathrm{SD}_{\\pi}$ and $\\mathrm{NBM}_{\\pi}$ allocate the same number of first choices, they will in fact allocate these first choices to the same agents. We can remove these agents and the corresponding objects and proceed by induction, carefully handling the case when some object has capacity zero. Averaging across all priority orderings, we find that if $\\mathrm{RSD}$\\ rank dominates $\\mathrm{NBM}$, the allocation from both mechanism must be the same, and therefore $\\mathrm{RSD}$\\ never strictly rank dominates $\\mathrm{NBM}$.\n\\end{proof}\nImperfect rank dominance of $\\mathrm{NBM}$\\ over $\\mathrm{RSD}$\\ by Theorem \\ref{thm:rsd_not_rd_nbm} can be viewed as a lower bound on the price of strategyproofness, i.e., by choosing $\\mathrm{RSD}$\\, we forego some unambiguous improvements of the rank distribution that could be attained by using $\\mathrm{NBM}$\\ instead.\n\\begin{remark} \\label{rem:extension_priorities} Theorem \\ref{thm:rsd_not_rd_nbm} remains valid even if some priorities are imposed:\nlet $\\Pi$ be the set of all possible priority orderings, i.e., bijections $\\pi: N \\rightarrow \\{1,\\ldots,n\\}$, and consider any probability distribution $\\mathds{P}$ on $\\Pi$.\nMechanisms $\\mathrm{RSD}_{\\mathds{Pi}}$ and $\\mathrm{NBM}_{\\mathds{Pi}}$ can be defined analogously to Definitions \\ref{def:rsd} and \\ref{def:nbm}, but each mechanism draws the priority ordering $\\pi$ from $\\mathds{P}$ instead of the uniform distribution.\nThe proof of Theorem \\ref{thm:rsd_not_rd_nbm} considers every priority ordering separately, and thus, $\\mathrm{RSD}_{\\mathds{Pi}}$ never strictly ordinally dominates $\\mathrm{NBM}_{\\mathds{Pi}}$.\n\nIn this way it is possible to give priority to some agent $i$ over another agent $i'$, e.g., by setting $\\mathds{P}(\\pi) = 0$ for all $\\pi$ with $\\pi(i) > \\pi(i')$. Thus, Theorem \\ref{thm:rsd_not_rd_nbm} remains valid if some groups of agents (e.g., minorities) are given preference \\emph{at all objects}. \nMore complex priority structures where priorities can vary across different objects (e.g., walk zone priorities) lie outside the scope of this result.\n\\end{remark}\nWe conclude that $\\mathrm{NBM}$\\ is more efficient than the strategyproof baseline mechanism $\\mathrm{RSD}$.\nThis result differs from prior attempts to compare Boston mechanisms to other mechanism, which relied on stylized assumptions about agents' preference.\nOur results highlight that choosing between $\\mathrm{NBM}$\\ and $\\mathrm{RSD}$\\ involves a non-trivial decision about the trade-off between strategyproofness and efficiency.\n\\subsubsection{Simulation Results}\n\\label{sec:nbm:nbm_ird_RSD:simulation}\nTo determine how strongly $\\mathrm{NBM}$\\ imperfectly rank dominates $\\mathrm{RSD}$, we conducted simulations: for settings with $n=m \\in \\{3, \\ldots, 10\\}$ and $q_j = 1 $ for all $j \\in M$, we sampled 100'000 type profiles uniformly at random for each value of $n$. We then determined the rank dominance relation between the resulting allocations under $\\mathrm{NBM}$\\ and $\\mathrm{RSD}$\\ at the sampled type profiles.\nFigure \\ref{fig:nbm_rsd} shows how often $\\mathrm{NBM}$\\ strictly rank dominates (red bars) or has the same rank distribution as $\\mathrm{RSD}$\\ (green bars), and how often the two mechanisms are incomparable (blue bars). We see that the share where $\\mathrm{NBM}$\\ strictly rank dominates $\\mathrm{RSD}$\\ shrinks for an increasing number of objects, but only slowly.\n\\begin{figure}%\n\\includegraphics[width=\\columnwidth]{NBM_RSD_1.pdf}%\n\\caption{Share of type profiles by rank dominance relation between $\\mathrm{NBM}$ and $\\mathrm{RSD}$, for settings with unit capacity, $n=m$ agents, and 100'000 sample type profiles for each $n$.}%\n\\label{fig:nbm_rsd}%\n\\end{figure}\n\\section{The Adaptive Boston Mechanism}\n\\label{sec:abm}\nThis section contains our second main contribution: we define the adaptive Boston mechanism ($\\mathrm{ABM}$) and study its properties.\nThe main take-away is that $\\mathrm{ABM}$\\ has significantly better incentive properties than $\\mathrm{NBM}$, but has almost the same efficiency advantages over $\\mathrm{RSD}$.\n\nLike $\\mathrm{NBM}$, $\\mathrm{ABM}$\\ is manipulable. However, we show that, in contrast to $\\mathrm{NBM}$, it is partially strategyproof, i.e., strategyproof on the domain of uniformly bounded indifference.\nLike $\\mathrm{NBM}$, $\\mathrm{ABM}$\\ is ex-post efficient, but neither ordinally nor rank efficient.\nWe also show that it is not on the efficient frontier, subject to partial strategyproofness.\nThus, we are in the same situation as with $\\mathrm{NBM}$, where traditional efficiency measures cannot be used to distinguish between $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$.\nTherefore, we compare $\\mathrm{ABM}$\\ to $\\mathrm{RSD}$\\ in terms of rank dominance.\nRather surprisingly, we find that $\\mathrm{RSD}$\\ may rank dominate $\\mathrm{ABM}$\\ at certain type profiles. However, we also show that these cases are extremely rare, i.e., $\\mathrm{ABM}$\\ imperfectly rank dominates $\\mathrm{RSD}$\\ in the limit as markets become large. Numerically, we find evidence that the efficiency advantages of $\\mathrm{ABM}$\\ over $\\mathrm{RSD}$\\ are almost the same as those of $\\mathrm{NBM}$\\ over $\\mathrm{RSD}$\\ and that the theoretical possibility that $\\mathrm{RSD}$\\ may rank dominate $\\mathrm{ABM}$\\ is negligible.\n\\subsection{Formalization of ABM}\n\\label{sec:abm:formalization}\nThe adaptive Boston mechanism is designed to eliminate the obvious manipulations described in the introduction. Instead of applying to their $k$th choice object in the $k$th round, in each round agents apply to the object that they prefer most out of the objects that are still available. Formally:\n\\begin{definition} \\label{def:abm} The \\emph{adaptive Boston mechanism ($\\mathrm{ABM}$)} works as follows:\n\\begin{enumerate}\n\t\\item Collect all type reports $\\hat{\\mathbf{t}}=(\\hat{t}_1,\\ldots,\\hat{t}_n) \\in T^n $ from the agents.\n\t\\item Draw an ordering $\\pi$ of all agents uniformly at random from the space of all possible bijections $\\pi: N \\rightarrow \\{1,\\ldots,n\\}$. $\\pi(i) < \\pi(i')$ means that $i$ has priority over $i'$.\n\t\\item Round 1: all agents apply to their reported first choice object. At every object, if the object's capacity is sufficient to satisfy all applications to this object, all the applicants get a copy of the object. Otherwise, the agents with the highest priority get copies of the object until it is exhausted. All remaining agents do not receive a copy and enter the next round.\n\t\\item Round $k$: an agent who did not receive an object in any of the previous $k-1$ rounds applies to the object which it reported to prefer most out of the objects with non-zero remaining capacity. Analogous to round 1, the agents with the highest priorities are allocated copies of the objects for which they applied. If an object has more applicants than capacity, the remaining agents enter the next round.\n\t\\item The process ends when all agents have been allocated an object.\n\\end{enumerate}\n\\end{definition}\nLet $\\mathrm{ABM}_{\\pi}$ denote the adaptive Boston mechanism when the mechanism is used for a single, fixed priority order $\\pi$, in which case the resulting allocation is deterministic. We have\n\\begin{equation}\n\\mathrm{ABM}(\\hat{\\mathbf{t}}) = \\sum_{\\pi:N\\rightarrow\\{1,\\ldots,n\\}\\mathrm{\\ bijection}} \\frac{1}{n!} \\mathrm{ABM}_{\\pi}(\\hat{\\mathbf{t}}).\n\\label{eq:prob_alloc_abm}\n\\end{equation}\n\\subsection{Partial Strategyproofness of ABM}\n\\label{sec:abm:psp}\nIn this section, we show that $\\mathrm{ABM}$\\ is partially strategyproof and we asses its degree of strategyproofness.\n\\subsubsection{Partial Strategyproofness Result for ABM}\n\\label{sec:abm:psp:result}\nLike $\\mathrm{NBM}$, $\\mathrm{ABM}$\\ it is upper invariant, but not strategyproof. However, we also show that $\\mathrm{ABM}$\\ is swap monotonic. Thus, $\\mathrm{ABM}$\\ is partially strategyproof, while $\\mathrm{NBM}$\\ is not even weakly strategyproof.\n\\begin{proposition} \\label{prop:abm_ui} $\\mathrm{ABM}$\\ is upper invariant.\n\\end{proposition}\nThe proof of Proposition \\ref{prop:abm_ui} is essentially the same as for Proposition \\ref{prop:nbm_ui}.\n$\\mathrm{ABM}$\\ is also swap monotonic, as Theorem \\ref{thm:abm_sm} shows.\n\\begin{theorem} \\label{thm:abm_sm} $\\mathrm{ABM}$\\ is swap monotonic.\n\\end{theorem}\n\\begin{proof}[Proof outline (formal proof in Appendix \\ref{app:proofs:abm:abm_sm})] We consider each priority ordering $\\pi$ separately. \nFor each $\\pi$, we consider the reaction of the mechanism to a swap from $t_i:a \\succ b$ to $t_i':b \\succ a$ by some agent $i$.\nWe show that\n\\begin{itemize}\n\t\\item if $\\pi$ leads to an allocation of $b$ to $i$ under $t$, it will still lead to an allocation of $b$ under $t'$, i.e., the probability that $b$ is allocated to $i$ weakly increases,\n\t\\item if $\\pi$ does not lead to an allocation of $a$ to $i$ under $t$, it will not lead to an allocation of $a$ to $i$ under $t'$ either, i.e., the probability that $a$ is allocated to $i$ weakly decreases,\n\t\\item if the allocation changes at all under some $\\pi$, we can construct another ordering $\\pi'$, such that under $\\pi'$ a change of report from $t$ to $t'$ leads to a change in allocation from $a$ to $b$.\n\\end{itemize}\nThus, the probabilities for $a$ and $b$ change strictly if the allocation changes at all, which corresponds to the requirement of swap monotonicity.\n\\end{proof}\n\\begin{corollary} \\label{cor:abm_psp} $\\mathrm{ABM}$\\ is partially strategyproof.\n\\end{corollary}\nCorollary \\ref{cor:abm_psp} follows immediately if we consider that $\\mathrm{ABM}$\\ is swap monotonic (Theorem \\ref{thm:abm_sm}) and upper invariant (Proposition \\ref{prop:abm_ui}) and hence partially strategyproof (Fact \\ref{app_fact:char_psp} in Appendix \\ref{app:review_psp}).\n\\subsubsection{Degree of Strategyproofness of ABM}\n\\label{sec:abm:psp:dosp}\nSince $\\mathrm{ABM}$\\ is partially strategyproof, the degree of strategyproofness measure\n\\begin{equation}\n\t\\rho_{(N,M,\\mathbf{q})}(\\mathrm{ABM}) = \\max\\{r \\in [0,1] : \\mathrm{ABM}\\text{ is }r\\text{-partially strategyproof in }(N,M,\\mathbf{q})\\}\n\\end{equation}\nis positive for any setting.\nWe are interested in learning ``how strategyproof'' $\\mathrm{ABM}$\\ is in different settings.\nUnfortunately, $\\rho_{(N,M,\\mathbf{q})}(\\mathrm{ABM})$ becomes small when the number of objects increases (assuming unit capacity and an equal number of objects and agents), which means intuitively that $\\mathrm{ABM}$\\ becomes ``less strategyproof.''\n\\begin{proposition} \\label{prop:dosp_abm_to_zero} For settings where $n = m, q_j=1$ for all $j \\in M$\n\\begin{equation}\n\\lim_{n\\rightarrow \\infty}\\rho_{(N,M,\\mathbf{q})}(\\mathrm{ABM}) = 0.\n\\end{equation}\n\\end{proposition}\n\\begin{proof}[Proof outline (formal proof in Appendix \\ref{app:proofs:abm:dosp_abm_to_zero})] We construct a particular sequence of type profiles.\nIn each step, we consider a certain agent and show that this agent can exchange very little probability at its first choice for substantial probability at its second choice.\nThis trade-off is beneficial for the agent, unless the relative difference in valuation between the first and second choice is very high.\n\\end{proof}\nAlthough the degree of strategyproofness becomes small for large numbers of objects and agents, numerical evidence suggests that for a fixed number of objects $m$ and evenly distributed capacity ($q_j = q$ for all $j \\in M$), the degree of strategyproofness does not decrease as more agents (and more capacity) are added to the setting, as long as the capacity is evenly distributed. This can be seen in Table \\ref{tbl:rho_abm} and is formalized by the following conjecture.\n\\begin{table}%\n\\begin{center}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\t\t\t\t\t\t\t\t\t\t\t\t& \\multicolumn{3}{c|}{Capacity $q$} \\\\\n\\hline\n\tNumber of objects $m$ & $1$ & $2$ & $3$ \\\\\n\\hline\n\\hline\n\t$3$\t\t\t\t\t\t\t\t\t\t& $0.5$ & $0.5$ & $0.5$ \\\\\n\\hline\n\t$4$ \t\t\t\t\t\t\t\t\t& $0.33$ & $0.33$ & ? \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\caption{Degree of strategyproofness of ABM with balanced capacities and $n =m q$ agents.}\n\\label{tbl:rho_abm}\n\\end{table}\n\\begin{conjecture} \\label{conj:rho_abm_const} For $q \\neq q'$ and two settings $(N,M,\\mathbf{q})$ and $(N',M',\\mathbf{q}')$ with\n\\begin{itemize}\n\t\\item $M = M'$,\n\t\\item $\\mathbf{q} = (q,\\ldots,q)$ and $\\mathbf{q}' = (q',\\ldots,q')$,\n\t\\item $n = \\# N = q m$ and $n' = \\#N' = q' m$,\n\\end{itemize}\nwe have\n\\begin{equation}\n\t\\rho_{(N,M,\\mathbf{q})}(\\mathrm{ABM}) = \t\\rho_{(N',M',\\mathbf{q}')}(\\mathrm{ABM}).\n\\end{equation}\n\\end{conjecture}\n\\subsection{Efficiency of ABM}\n\\label{sec:abm:efficiency_failure}\nAnalogous to our findings for $\\mathrm{NBM}$, the traditional methods fail to differentiate between $\\mathrm{ABM}$\\ and $\\mathrm{RSD}$\\ in terms of efficiency. \n$\\mathrm{ABM}$\\ is ex-post efficient, but neither ordinally nor rank efficient, nor on the efficient frontier, even if we consider only partially strategyproof mechanisms.\n\\begin{proposition} \\label{prop:abm_ex_post} $\\mathrm{ABM}$\\ is ex-post efficient at all type profiles.\n\\end{proposition}\nThe proof of Proposition \\ref{prop:abm_ex_post} is analogous to the proof of Proposition \\ref{prop:nbm_ex_post}.\n\\begin{proposition} \\label{prop:abm_not_OE} $\\mathrm{ABM}$\\ is neither ordinally efficient nor rank efficient.\n\\end{proposition}\nProposition \\ref{prop:abm_not_OE} follows from the same example as Proposition \\ref{prop:nbm_not_OE}, because in the specific setting and type profile the results of $\\mathrm{ABM}$\\ and $\\mathrm{NBM}$\\ coincide.\n\\begin{proposition} \\label{prop:abm_rank_dom_ps}\n$\\mathrm{PS}$\\ does not ordinally dominate $\\mathrm{ABM}$\\ at all type profiles, and $\\mathrm{ABM}$\\ rank dominates $\\mathrm{PS}$\\ at some type profile.\n\\end{proposition}\nProposition \\ref{prop:abm_rank_dom_ps} follows from the same example as Proposition \\ref{prop:nbm_rank_dom_ps}.\n\\begin{proposition} \\label{prop:abm_not_frontier}\n$\\mathrm{ABM}$\\ is not on the efficient frontier with respect to ordinal dominance, subject\nto partial strategyproofness\n\\end{proposition}\nThe proof is analogous to the proof of Proposition \\ref{prop:nbm_not_frontier}.\n\\begin{remark} The mechanism $\\mathrm{ABM}^+$\\ constructed in the proof of Propositions \\ref{prop:nbm_not_frontier} \/ \\ref{prop:abm_not_frontier} even satisfies partial strategyproofness.\nIt remains an open question for future research whether compared to $\\mathrm{ABM}$, $\\mathrm{ABM}^+$\\ has the same, higher, or lower degree of strategyproofness.\n\\end{remark}\n\\subsection{ABM versus RSD - Imperfect Rank Dominance in Large Markets}\n\\label{sec:abm:abm_ird_rsd}\nIn the previous section, we have shown that the efficiency of $\\mathrm{ABM}$\\ is not differentiable from the efficiency of $\\mathrm{RSD}$\\ by traditional methods.\nThus, we again consider a direct comparison of $\\mathrm{ABM}$\\ and $\\mathrm{RSD}$\\ in terms of imperfect rank dominance.\nSurprisingly, $\\mathrm{ABM}$\\ does not imperfectly rank dominate $\\mathrm{RSD}$, i.e., there exists at least one type profile at which $\\mathrm{RSD}$\\ actually produces a rank dominant allocation.\nHowever, we recover imperfect rank dominance of $\\mathrm{ABM}$\\ over $\\mathrm{RSD}$\\ for large markets, and numerical evidence suggests that the violation of imperfect dominance is insignificant, even in finite markets.\n\\subsubsection{Failure of Imperfect Rank Dominance}\n\\label{sec:abm:abm_ird_rsd:failure_ird}\nThe following Proposition yields the surprising result that $\\mathrm{RSD}$\\ may rank dominate $\\mathrm{ABM}$.\n\\begin{proposition} \\label{prop:abm_not_ird_rsd} $\\mathrm{ABM}$\\ does not imperfectly rank dominate $\\mathrm{RSD}$.\n\\end{proposition}\n\\begin{proof} We present a counter example, i.e., a setting and type profile for which $\\mathrm{RSD}$\\ strictly rank dominates $\\mathrm{ABM}$.\nConsider a setting with 6 agents, 6 objects in unit capacity, and the type profile\n\\begin{eqnarray*}\nt_1, \\ldots ,t_4 & : & a \\succ b \\succ c \\succ d \\succ e \\succ f, \\\\\nt_5, t_6 & : & e \\succ b \\succ a \\succ d \\succ f \\succ c.\n\\end{eqnarray*}\nConsider the ordering $\\pi = \\mathrm{id}$ of the agents. With this ordering, $\\mathrm{RSD}$\\ will allocate objects $a$ through $d$ to agents $1$ through $4$. Agents $5$ and $6$ get objects $e$ and $f$. Thus, $\\mathrm{RSD}$\\ allocates 2 first, 1 second, 1 third, 1 fourth, and 1 fifth choice. For the same ordering, $\\mathrm{ABM}$\\ will allocate $a,b,c$ to agents $1, 2, 3$, respectively. Agents $5$ and $6$ will get $e$ and $d$, which leaves agent $4$ with $f$. Observe that with this ordering, $\\mathrm{ABM}$\\ allocates 2 first, 1 second, 1 third, 1 fourth, no fifth, and 1 sixth choice. This is strictly rank dominated by the rank efficient allocation chosen by $\\mathrm{RSD}$.\n\nThe allocation under $\\mathrm{ABM}$\\ is\n\\begin{equation}\n\\mathrm{ABM}(\\mathbf{t}) = \\frac{1}{60}\\left(\\begin{array}{cccccc}\n15 & 12 & 15 & 3 & 0 & 15 \\\\\n15 & 12 & 15 & 3 & 0 & 15 \\\\\n15 & 12 & 15 & 3 & 0 & 15 \\\\\n15 & 12 & 15 & 3 & 0 & 15 \\\\\n0 & 6 & 0 & 24 & 30 & 0 \\\\\n0 & 6 & 0 & 24 & 30 & 0\n\\end{array}\\right),\n\\end{equation}\nand the allocation under $\\mathrm{RSD}$\\ is\n\\begin{equation}\n\\mathrm{RSD}(\\mathbf{t}) = \\frac{1}{60}\\left(\\begin{array}{cccccc}\n15 & 12 & 15 & 8 & 0 & 10 \\\\\n15 & 12 & 15 & 8 & 0 & 10 \\\\\n15 & 12 & 15 & 8 & 0 & 10 \\\\\n15 & 12 & 15 & 8 & 0 & 10 \\\\\n0 & 6 & 0 & 14 & 30 & 10 \\\\\n0 & 6 & 0 & 14 & 30 & 10\n\\end{array}\\right).\n\\end{equation}\nThe fact that all allocations chosen by $\\mathrm{ABM}$\\ are rank inefficient, but the allocations chosen by $\\mathrm{RSD}$\\ are sometimes rank efficient implies that $\\mathrm{RSD}$\\ strictly rank dominates $\\mathrm{ABM}$\\ for this type profile.\n\\end{proof}\nThe example in the proof of Proposition \\ref{prop:abm_not_ird_rsd} raises the question how frequently $\\mathrm{RSD}$\\ rank dominates $\\mathrm{ABM}$.\nInterestingly, complete enumeration (using a computer) has revealed that $\\mathrm{RSD}$\\ does not rank dominate $\\mathrm{ABM}$\\ for any setting with less than 6 objects and unit capacities.\nIn Section \\ref{sec:abm:abm_ird_rsd:house_allocation}, we will show via simulation that even for more objects, such examples are pathological, i.e., out of 500'000 sampled type profiles (100'000 for each $n \\in \\{6,\\ldots,10\\}$), there was not a single occurrence.\n\nWith this intuition in mind, we ask what the rank dominance relation looks like in large markets.\nWe consider two independent notions of how settings ``get large.'' In Section \\ref{sec:abm:abm_ird_rsd:school_choice} we employ the first notion, which is adopted from \\citep{Kojima2010IncentivesInTheProbabilisticSerial}, where the capacities of a fixed number of objects increase. This approximates school choice settings, where the capacity of public schools frequently exceeds 100 seats.\nFor the second notion, which we use in Section \\ref{sec:abm:abm_ird_rsd:house_allocation}, we let the number of objects increase and they all have unit capacity. This is more adequate for house allocation problems, where every ``house'' is different and can be given to only one agent.\n\\subsubsection{Limit Result for Large School Choice Markets}\n\\label{sec:abm:abm_ird_rsd:school_choice}\nIn this section, we present our first limit result, showing that as the capacity of the objects increases the share of type profiles where $\\mathrm{RSD}$\\ rank dominates $\\mathrm{ABM}$\\ becomes arbitrarily small.\n\nBefore we formulate our result, we introduce first-choice-maximizing allocations and provide an intuition of why the convergence holds.\nIn words, an allocation $x$ is \\emph{first-choice-maximizing} at type profile $\\mathbf{t}$ if it can be represented as a lottery over deterministic allocations that give the maximum number of first choices, i.e., where\n\\begin{equation}\n\td^x_1 = \\sum_{i \\in N} x_{i,\\mathrm{ch}(1,t_i)} = \\max_{y \\in \\mathcal{X}} d^y_1.\n\\end{equation}\n\n\\begin{remark} \\label{rem:rsd_fcm} We already observed that $\\mathrm{RSD}$\\ puts positive probability on \\emph{all} ex-post efficient, deterministic allocations, while $\\mathrm{ABM}$\\ assigns different probabilities, which may be zero.\nOne key observation is that $\\mathrm{ABM}$\\ maximizes the expected number of allocated first choices.\nThus, $\\mathrm{ABM}$\\ gives no weight to any allocation that does not yield the maximum possible number of first choices.\nIn contrast, if at type profile $\\mathbf{t}$ there exists any ex-post efficient deterministic allocation that is not first-choice-maximizing, then $\\mathrm{RSD}$\\ does not allocate the maximum expected number of first choices, i.e., it is not first-choice-maximizing.\n\\end{remark}\nUsing the observation from Remark \\ref{rem:rsd_fcm}, we can now prove the following Proposition \\ref{prop:RSD_not_first_choice_max_school_choice}, which in turn yields the limit result, Theorem \\ref{thm:rsd_not_rd_abm_in_the_limit_school_choice}.\n\\begin{proposition}\\label{prop:RSD_not_first_choice_max_school_choice} For any fixed number of objects $m \\geq 3$ and any $\\epsilon > 0$, there exists a $q_{\\min} \\in \\mathds{N}$, such that for any capacity vector $\\mathbf{q} = (q_1, \\ldots, q_m)$ with $q_j \\geq q_{\\min}$ for all $j \\in M$ and $n = \\sum_{j \\in M} q_j$ agents, and for $\\mathbf{t}$ chosen uniformly at random, the probability that $\\mathrm{RSD}(\\mathbf{t})$ is first-choice-maximizing is smaller than $\\epsilon$.\n\\end{proposition}\n\\begin{proof}[Proof outline (formal proof in Appendix \\ref{app:proofs:abm:prop:RSD_not_first_choice_max_school_choice})]\nConsider a fixed type profile $\\mathbf{t}$.\nAs discussed previously, $\\mathrm{RSD}$\\ is first-choice-maximizing only if all ex-post efficient deterministic allocations with respect to $\\mathbf{t}$ are first-choice-maximizing.\nTo prove the proposition, we show that an ex-post efficient allocation that is not first-choice-maximizing can almost always be found (for sufficiently high capacities).\nIntuitively, we exploit the following: if objects $j,j' \\in M$ are the first choice of more than $q_j$ and $q_{j'}$ agents, respectively, then we can construct a priority ordering $\\pi$ that ranks the agents with first choice $j$ first. Then under $\\mathrm{SD}_{\\pi}$ some $q_j$ agents get their first choice $j$, but it turns out to be ``unlikely'' that none of the remaining agents with first choice $j$ have $j'$ as their second choice.\n\\end{proof}\nThus, $\\mathrm{RSD}$\\ is almost never first-choice-maximizing as the capacities of the objects grow.\nSince $\\mathrm{ABM}$\\ is first-choice-maximizing, it will allocate strictly more first choices than $\\mathrm{RSD}$\\ at almost all type profiles.\nThus, even weak rank dominance of $\\mathrm{RSD}$\\ over $\\mathrm{ABM}$\\ almost always breaks already at the first choice.\n\\begin{theorem} \\label{thm:rsd_not_rd_abm_in_the_limit_school_choice} For any $m$ and any sequence of settings $(N^k,M^k,\\mathbf{q^k})_{k \\geq 0}$ with\n\\begin{itemize}\n\t\\item $\\# M^k = m$ (i.e., constant number of objects),\n\t\\item $ \\min_{j \\in M^k} q_j^k \\rightarrow \\infty$ as $k \\rightarrow \\infty$ (i.e., growing capacities),\n\t\\item $n_k = \\# N^k = \\sum_{j \\in M^k} q_j$ (i.e., total supply matches total demand),\n\\end{itemize}\nwe have\n\\begin{equation}\n\t\\frac{\\#\\{\\mathbf{t} \\in T^{n_k} | \\mathrm{RSD}(\\mathbf{t}) \\succeq_{R} \\mathrm{ABM}(\\mathbf{t}) \\text{ in the setting }(N^k,M^k,\\mathbf{q}^k) \\}}{\\#\\{\\mathbf{t}\\in T^{n_k}\\}} \\rightarrow 0 \\;\\;\\;(k \\rightarrow \\infty).\n\\end{equation}\n\\end{theorem}\n\n\n\\subsubsection{Limit Result for Large House Allocation Markets}\n\\label{sec:abm:abm_ird_rsd:house_allocation}\nIn this section, we present our second limit result for large house allocation markets: we show that as the number of objects with unit capacity and agents increases, the share of type profiles where RSD rank dominates ABM becomes arbitrarily small.\nAs in the previous section, we use the fact that $\\mathrm{RSD}$\\ is almost never first-choice-maximizing to prove the convergence result.\n\\begin{proposition} \\label{prop:rsd_not_first_choice_max_in_limit_house_allocation} For any $\\epsilon > 0$, there exists $n \\in \\mathds{N}$, such that for any setting $(N,M,\\mathbf{q})$ with $\\#M = \\#N \\geq n $ and $q_j =1 $ for all $j \\in M$, and for $\\mathbf{t}$ chosen uniformly at random, the probability that $\\mathrm{RSD}(\\mathbf{t})$ is first-choice-maximizing is smaller than $\\epsilon$.\n\\end{proposition}\n\\begin{proof}[Proof outline (formal proof in Appendix \\ref{app:proofs:abm:prop:rsd_not_first_choice_max_in_limit_house_allocation})] The proof idea is similar to the proof of Proposition \\ref{prop:RSD_not_first_choice_max_school_choice}, but the formal analysis requires advanced combinatorics. \nWe derive an upper bound for the probability that all agents with over-demanded first choices have second choices such that they do not exhaust some other agent's first choice. We then show that this upper bound converges to zero.\n\\end{proof}\nTheorem \\ref{thm:rsd_not_rd_abm_in_the_limit_house_allocation} follows from Proposition \\ref{prop:rsd_not_first_choice_max_in_limit_house_allocation} in the same way in which Theorem \\ref{thm:rsd_not_rd_abm_in_the_limit_school_choice} follows from Proposition \\ref{prop:RSD_not_first_choice_max_school_choice}.\n\\begin{theorem} \\label{thm:rsd_not_rd_abm_in_the_limit_house_allocation}\nFor settings with $n = m$ and $q_j = 1$, $\\mathrm{ABM}$\\ is not rank-dominated by $\\mathrm{RSD}$\\ in the limit, i.e.,\n\\begin{equation}\n\t\\frac{\\#\\{\\mathbf{t} \\in T^n | \\mathrm{RSD}(\\mathbf{t}) \\succeq_{R} \\mathrm{ABM}(\\mathbf{t}) \\}}{\\#\\{\\mathbf{t}\\in T^n\\}} \\rightarrow 0 \\;\\;\\;(n \\rightarrow \\infty).\n\\end{equation}\n\\end{theorem}\n\\subsubsection{Simulation Results}\n\\label{sec:abm:abm_ird_rsd:simulation}\nWe conducted simluatons similar to those in Section \\ref{sec:nbm:nbm_ird_RSD:simulation} to compare $\\mathrm{ABM}$\\ and $\\mathrm{RSD}$: \nfor settings with $n=m \\in \\{3, \\ldots, 10\\}$ and $q_j = 1 $ for all $j \\in M$, we sampled 100'000 type profiles uniformly at random for each value of $n$. We then determined the rank dominance relation between the resulting allocations under $\\mathrm{ABM}$\\ and $\\mathrm{RSD}$\\ at the sampled type profiles.\nFigure \\ref{fig:abm_rsd} shows how often $\\mathrm{ABM}$\\ strictly rank dominates (red bars) or has the same rank distribution (green bars) as $\\mathrm{RSD}$, and how often the two mechanisms are incomparable (blue bars).\nNote that the results are very similar to the findings for $\\mathrm{NBM}$\\ and $\\mathrm{RSD}$\\ (Figure \\ref{fig:nbm_rsd}), i.e., the share of profiles with comparability shrinks, but the share of those profiles where $\\mathrm{ABM}$\\ strictly rank dominates $\\mathrm{RSD}$\\ shrinks very slowly.\nFurthermore, the case where $\\mathrm{RSD}$\\ rank dominates $\\mathrm{ABM}$\\ never occurred in the entire sample. This suggests that such cases are extremely rare and should not matter for an evaluation of $\\mathrm{ABM}$\\ against $\\mathrm{RSD}$.\nWe conclude that $\\mathrm{ABM}$\\ is ``more efficient'' than $\\mathrm{RSD}$. \n\\section{NBM versus ABM - The Price of Partial Strategyproofness}\n\\label{sec:price_of_psp}\nIn Section \\ref{sec:nbm}, we have established the \\emph{price of strategyproofness} in terms of efficiency:\n$\\mathrm{RSD}$\\ is less efficient than $\\mathrm{NBM}$, a price we have to pay if we want to achieve full strategyproofness using $\\mathrm{RSD}$. \nWe will now assess the \\emph{price of partial strategyproofness} by comparing $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ in terms of rank dominance.\nContrary to intuition, this comparison is not unambiguous in favor of $\\mathrm{NBM}$: while $\\mathrm{NBM}$\\ strictly rank dominates $\\mathrm{ABM}$\\ at some type profiles, there also exist type profiles at which the opposite holds.\nFurthermore, we also present numerical evidence that the two mechanisms are usually not comparable by rank dominance, but if they are comparable, then dominance of $\\mathrm{NBM}$\\ over $\\mathrm{ABM}$\\ occurs more frequently than the opposite case.\nTherefore, when choosing $\\mathrm{ABM}$\\ in favor of $\\mathrm{NBM}$, the fact that $\\mathrm{NBM}$\\ rank dominates $\\mathrm{ABM}$\\ more often than vice versa can be considered a price that we pay for partial strategyproofness. Of course, since neither mechanism is on the efficient frontier, there may be other, more desirable trade-offs that are not accessible via $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$.\n\\begin{figure}%\n\\includegraphics[width=\\columnwidth]{ABM_RSD_1.pdf}%\n\\caption{Share of type profiles by rank dominance relation between $\\mathrm{ABM}$ and $\\mathrm{RSD}$, for settings with unit capacity, $m=n$ agents, and 100'000 sampled type profiles for each $n$.}%\n\\label{fig:abm_rsd}%\n\\end{figure}\n\\subsection{Failure of Imperfect Rank Dominance}\n\\label{sec:price_of_psp:failure_ird}\nWe first attempt a direct comparison between $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ via imperfect rank dominance. Interestingly, $\\mathrm{NBM}$\\ is not always the more efficient mechanism, i.e., while there exists type profiles at which $\\mathrm{NBM}$\\ strictly rank dominates $\\mathrm{ABM}$\\ (Proposition \\ref{prop:nbm_rank_dom_abm}), there also exist type profiles where the opposite holds (Proposition \\ref{prop:abm_rank_dom_nbm}).\n\\begin{proposition} \\label{prop:nbm_rank_dom_abm} $\\mathrm{NBM}$\\ strictly rank dominates $\\mathrm{ABM}$\\ at some type profiles.\n\\end{proposition}\n\\begin{proof} Consider the setting with $N = \\{1,\\ldots,4\\}$, $M = \\{a,b,c,d\\}$, unit capacities, and the type profile\n\\begin{eqnarray*}\n\tt_1 & : & a \\succ b \\succ c \\succ d, \\\\\n\tt_2,t_3 & : & a \\succ c \\succ d \\succ b, \\\\\n\tt_4 & : & b \\succ \\ldots,\n\\end{eqnarray*}\nwhich is the same as Example \\ref{ex:nbm_more_manip}.\nThe allocations from $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ are\n\\begin{equation}\n\\mathrm{NBM}(\\mathbf{t}) = \\left(\\begin{array}{cccc}\n\\frac{1}{3} & 0 & 0 & \\frac{2}{3} \\\\\n\\frac{1}{3} & 0 & \\frac{1}{2} & \\frac{1}{6} \\\\\n\\frac{1}{3} & 0 & \\frac{1}{2} & \\frac{1}{6} \\\\\n0 & 1 & 0 & 0 \t\n\\end{array}\\right)\n\\text{ and }\n\\mathrm{ABM}(\\mathbf{t}) = \\left(\\begin{array}{cccc}\n\\frac{1}{3} & 0 & \\frac{1}{3} & \\frac{1}{3} \\\\\n\\frac{1}{3} & 0 & \\frac{1}{3} & \\frac{1}{3} \\\\\n\\frac{1}{3} & 0 & \\frac{1}{3} & \\frac{1}{3} \\\\\n0 & 1 & 0 & 0 \t\n\\end{array}\\right).\n\\end{equation}\nThe rank distribution under $\\mathrm{NBM}$\\ is $\\mathbf{d} =(2,1,0,1)$, which dominates $\\mathbf{d}' = (2,2\/3,1\/3,1)$, the rank distribution under $\\mathrm{ABM}$.\n\\end{proof}\nThe next result is more surprising: it shows that $\\mathrm{ABM}$\\ may strictly rank dominate $\\mathrm{NBM}$.\nNote that the result holds for strict priorities as well.\n\\begin{proposition} \\label{prop:abm_rank_dom_nbm} $\\mathrm{ABM}$\\ strictly rank dominates $\\mathrm{NBM}$\\ at some type profiles.\n\\end{proposition}\n\\begin{proof} Consider the setting with $N = \\{1,\\ldots,5\\}$, $M = \\{a,b,c,d,e\\}$, unit capacities, and the type profile\n\\begin{eqnarray*}\n\tt_1,t_2 & : & a \\succ b \\succ c \\succ d \\succ e, \\\\\n\tt_3,t_4 & : & a \\succ d \\succ c \\succ e \\succ b, \\\\\n\tt_5 & : & b \\succ \\ldots.\n\\end{eqnarray*}\nThe allocations from $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ are\n\\begin{equation}\n\\mathrm{NBM}(\\mathbf{t}) = \\frac{1}{60}\\left(\\begin{array}{ccccc}\n15 & 0 & 25 & 0 & 20 \\\\\n15 & 0 & 25 & 0 & 20 \\\\\n15 & 0 & 5 & 30 & 10 \\\\\n15 & 0 & 5 & 30 & 10 \\\\\n0 & 60 & 0 & 0 & 0\n\\end{array}\\right)\n\\text{ and }\n\\mathrm{ABM}(\\mathbf{t}) = \\frac{1}{60}\\left(\\begin{array}{ccccc}\n15 & 0 & 30 & 0 & 15 \\\\\n15 & 0 & 30 & 0 & 15 \\\\\n15 & 0 & 0 & 30 & 15 \\\\\n15 & 0 & 0 & 30 & 15 \\\\\n0 & 60 & 0 & 0 & 0\n\\end{array}\\right).\n\\end{equation}\nThe rank distribution under $\\mathrm{NBM}$\\ is $\\mathbf{d} =(2,1,1,1\/3,2\/3)$, which is dominated by $\\mathbf{d}' = (2,1,1,1\/2,1\/2)$, the rank distribution under $\\mathrm{ABM}$.\n\\end{proof}\n\\begin{remark} \\label{rem:strengthen_dur}\nRecall that a \\emph{rank valuation} is a vector that specifies the value of allocating first, second, third, etc. choices to agents.\nIf an allocation $x$ rank dominates another allocation $y$, then the aggregate rank value is higher under $x$ than under $y$ for any rank valuation $v$, i.e.,\n\\begin{equation}\n\t\\sum_{i \\in M}\\sum_{k \\in \\{1,\\ldots,m\\}} v_k \\cdot x_{i,\\mathrm{ch}(k,t_i)} \\geq \\sum_{i \\in M}\\sum_{k \\in \\{1,\\ldots,m\\}} v_k \\cdot y_{i,\\mathrm{ch}(k,t_i)}.\n\\end{equation}\n\\citet{Dur2013} showed that for the case of strict priorities and for \\emph{a particular rank valuation}, $\\mathrm{ABM}$\\ can lead to a higher aggregate rank value than $\\mathrm{NBM}$.\nProposition \\ref{prop:abm_rank_dom_nbm} strengthens this result, since rank dominance of $\\mathrm{ABM}$\\ over $\\mathrm{NBM}$\\ implies that $\\mathrm{ABM}$\\ has higher aggregate rank value for any rank valuation.\n\\end{remark}\n\\subsection{Simulation Results}\n\\label{sec:price_of_psp:simulation}\nSo far, we have found that $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ are incomparable by imperfect rank dominance, because $\\mathrm{NBM} \\succ_{R} \\mathrm{ABM}$ for some type profiles, but $\\mathrm{ABM} \\succ_{R} \\mathrm{NBM}$ for others.\nThis raises the question whether one of the cases is more common.\nNumerically we find that $\\mathrm{NBM}$\\ is ``usually'' more efficient. \n\nAt each type profile $\\mathbf{t}$, one of the following cases can occur:\n\\begin{enumerate}\n\t\\item $\\mathrm{NBM}(\\mathbf{t})$ strictly rank dominates $\\mathrm{ABM}(\\mathbf{t})$,\n\t\\item $\\mathrm{NBM}(\\mathbf{t})$ and $\\mathrm{ABM}(\\mathbf{t})$ have the same rank distribution,\n\t\\item $\\mathrm{ABM}(\\mathbf{t})$ strictly rank dominates $\\mathrm{NBM}(\\mathbf{t})$,\n\t\\item $\\mathrm{NBM}(\\mathbf{t})$ and $\\mathrm{ABM}(\\mathbf{t})$ are incomarable by rank dominance.\n\\end{enumerate}\nFigure \\ref{fig:nbm_abm} shows how often each of these cases occurs when sampling type profiles uniformly at random in settings with unit capacities ($q_j = 1$ for all $j \\in M$) and various $n(=m)$.\nThe results suggest that the two mechanisms are usually not comparable (blue bars).\nHowever, conditioned on comparability, $\\mathrm{NBM}$\\ rank dominates $\\mathrm{ABM}$\\ more often than vice versa (red vs. yellow bars), and the share of profiles where the rank distributions coincide shrinks.\n\n\\begin{remark}\nEven though $\\mathrm{NBM}$\\ outperforms $\\mathrm{ABM}$\\ in this comparison, the share of type profiles for which $\\mathrm{ABM}$\\ dominates $\\mathrm{NBM}$\\ is substantially larger than the share of profiles where $\\mathrm{RSD}$\\ dominates $\\mathrm{ABM}$: conditional on comparability, $\\mathrm{ABM}$\\ dominates $\\mathrm{NBM}$\\ (yellow bars) in about $0.8\\%$ of the cases for $n \\in \\{7,8,9,10\\}$. Thus, while dominance of $\\mathrm{RSD}$\\ over $\\mathrm{ABM}$\\ is negligible, the out-performance is less pronounced for $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$.\n\\end{remark}\n\\begin{figure}%\n\\includegraphics[width=\\columnwidth]{NBM_ABM_1.pdf}%\n\\caption{Share of type profiles by rank dominance relation between $\\mathrm{NBM}$ and $\\mathrm{ABM}$, for settings with unit capacity, $m=n$ agents, and 100'000 sampled type profiles for each $n$.}%\n\\label{fig:nbm_abm}%\n\\end{figure}\n\\section{Conclusion}\n\\label{sec:conclusion}\nIn this paper, we have studied the traditional (na\\\"{i}ve) and a new (adaptive) variant of the Boston mechanism in the absence of priorities.\nOur findings demonstrate the trade-off between efficiency and strategyproofness that is achievable by these mechanisms.\n\nFirst, we have shown that the na\\\"{i}ve Boston mechanism ($\\mathrm{NBM}$) strictly imperfectly rank dominates $\\mathrm{RSD}$, and we have presented numerical evidence suggesting that strict dominance occurs for a considerable share of type profiles.\nOn the other hand, $\\mathrm{RSD}$\\ is strategyproof, while $\\mathrm{NBM}$\\ is not even weakly strategyproof.\nThus, the efficiency advantages of $\\mathrm{NBM}$\\ over $\\mathrm{RSD}$\\ can be interpreted as a lower bound for the \\emph{price of strategyproofness} in terms of efficiency.\n\nSecond, we have introduced the adaptive Boston mechanism ($\\mathrm{ABM}$).\nWe have shown that it satisfies partial strategyproofness, a relaxed notion of strategyproofness, which $\\mathrm{NBM}$\\ fails to satisfy. Furthermore, $\\mathrm{ABM}$\\ has almost the same efficiency advantages over $\\mathrm{RSD}$\\ as $\\mathrm{NBM}$: \nthe imperfect rank dominance of $\\mathrm{ABM}$\\ over $\\mathrm{RSD}$\\ has similar magnitude, and the share of type profiles where $\\mathrm{RSD}$\\ rank dominants $\\mathrm{ABM}$\\ converges to 0 as the markets get large.\n\nThird, $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ are incomparable by imperfect rank dominance. \nVia simulations we have shown that - conditioned on comparability - $\\mathrm{NBM}$\\ frequently rank dominates $\\mathrm{ABM}$\\ (but not always). \nThese efficiency advantages of $\\mathrm{NBM}$\\ over $\\mathrm{ABM}$\\ can be viewed as the \\emph{price of partial strategyproofness} one pays when choosing $\\mathrm{ABM}$\\ over $\\mathrm{NBM}$.\n\nThroughout the paper, it has become apparent that traditional analysis methods frequently fail to differentiate between two matching mechanisms.\nWhile a comparison by \\emph{vulnerability to manipulation} is inconclusive for $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$, except in the most basic case (see Appendix \\ref{app:manipulability_comparison}), \\emph{partial strategyproofness} clearly captures the better incentive properties of $\\mathrm{ABM}$. Similarly, all mechanisms we considered are ex-post efficient, but neither ordinally nor rank efficient. Furthermore, $\\mathrm{NBM}$\\ and $\\mathrm{ABM}$\\ are not even on the efficient frontier. Nonetheless, a comparison by \\emph{imperfect rank dominance}, \\emph{limit arguments}, and \\emph{numerical analysis} revealed a hierarchy between $\\mathrm{NBM}$, $\\mathrm{ABM}$, and $\\mathrm{RSD}$: $\\mathrm{NBM}$\\ has the most appeal in terms of rank dominance, but $\\mathrm{ABM}$\\ is still more appealing than $\\mathrm{RSD}$;\non the other hand, $\\mathrm{NBM}$\\ exhibits the worst incentive properties, while $\\mathrm{ABM}$\\ is at least partially strategyproof, and $\\mathrm{RSD}$\\ is fully strategyproof.\n\nIn summary, we have found that a decision between $\\mathrm{NBM}$, $\\mathrm{ABM}$, and $\\mathrm{RSD}$\\ requires a non-trivial trade-off between strategyproofness and efficiency.\nOur new methods facilitate the comparison in situations where traditional methods fail to differentiate, but where a decision is nonetheless essential.\nFurthermore, $\\mathrm{ABM}$\\ constitutes an attractive design alternative to $\\mathrm{NBM}$\\ when incentive properties are a concern: $\\mathrm{ABM}$\\ has almost the same efficiency advantages over the strategyproof alternative $\\mathrm{RSD}$, but it has significantly better incentive properties than $\\mathrm{NBM}$.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nSince their discovery, single-walled carbon nanotubes (CNTs) have attracted major research interest due to their extraordinary mechanical, chemical and electronic properties \\cite{saito98}.\nThey are metallic or semiconducting depending on their chirality and as-synthesized material is normally a mixture of both types. \nFor many applications, however, purified samples of only a certain type are in high demand. Purified semiconducting tubes are required, for instance, to achieve a large on\/off ratio and high carrier mobility in thin-film field effect transistors \\cite{chen05,lee11,bisri12,sangwan12,ding14,brady14} and\nhigh power conversion efficiency for photovoltaics \\cite{bindl10,holt10}. Moreover, for optoelectronic applications working in a specific wavelength range, the sorting of semiconducting CNTs according to diameter is of great importance.\n\nIn view of such demands, methods for the selective synthesis of CNTs of a certain electronic type or chiralites have been developed \\cite{ding09,sanchez14}. A low-cost mass production of selected CNTs is yet to be achieved, however, and post-synthesis methods are often relied on \\cite{hersam08}.\nA promising post-synthesis selection method discovered recently is based on the\nphysisorption of polymers on the surface of CNTs, which has the advantage of leaving the electronic properties of CNT nearly unperturbed \\cite{hersam08}. There is a relatively long history of using polymers to disperse CNTs in aqueous or organic solutions \\cite{star01,oconnell01}. A recent finding is that, by using suitable \npolymers, CNTs can be selectively dispersed either for a specific diameter\nrange or for certain chiral angles \\cite{hersam08}. Among those tested, the $\\pi$-conjugated polymer\ngroup of polyfluorene derivatives shows the ability to selectively disperse semi-conducting\nCNTs \\cite{nish07,chen07,gao11,ozawa11,tange12,gomulya13,mistry13,berton14,fukumaru14}. \nIn particular, the di-octyl substituted polyfluorene (PFO) used with\ntoluene as solvent prefers to disperse small-diameter semi-conducting nanotubes with\nchiral angles bigger than about 20 degrees \\cite{nish07}. With longer side-chains, larger-diameter tubes can be dispersed but the chiral angle preference is gradually lost \\cite{gomulya13,ding14}. More recently, copolymers of polyfluorene with anthracene or pyridine groups were found to selectively disperse large-diameter semiconducting nanotubes with high purity and high yields \\cite{mistry13,berton14}. This fits well with the need to fabricate photoelectronic devices working in the infrared wavelength range \\cite{avouris08}. \nLarge-diameter nanotubes also benefit from a diminishing contact resistance and higher carrier mobilities.\nPurified semiconducting CNTs have been used to fabricate high-performance field-effect transistors with high carrier mobilities and large on-off ratios \\cite{lee11,bisri12,sangwan12,ding14,brady14}. \n\nIntensive experimental and numerical works have been undertaken to study the conformation of polymers adsorbed on the CNT surface. For DNAs and some bio-macromolecules \\cite{zheng03}, which prefer to take a helical conformation even in the free state, a helically wrapped configuration on CNT was naturally expected. Studies on the adsorption conformation of linear conjugated polymers are less conclusive. For instance, poly(aryleneethynylene)s (PAE) were found to align linearly along the CNT when dispersed with toluene \\cite{chen02}. The similar poly(p-phenyleneethynylene) polymer, poly[p-{2,5-bis(3-propoxysulfonicacidsodiumsalt)}phenylene]ethynylene, was found to form helically wrapped structures in aqueous dispersion \\cite{kang09}. Imaged {\\it via} SEM, it was shown that when dispersed in chloroform, poly(3-hexyl-thiophene) (P3HT) forms a helically wrapped structure on the surface of multi-walled CNTs \\cite{giulianini09}. Recently, regioregular poly(3-alkylthiophene)s (rr-P3ATs) were used with toluene as solvent to \nenrich semi-conducting CNTs, and MD simulations showed that P3ATs take quasi-linear conformations, as adsorbed on CoMocat CNTs \\cite{wang14}. In 2014 Shea \\textit{et al.} reported the first experimental study on the adsorption configuration of PFOs on CNTs by using photoluminescence energy transfer and anisotropy measurements \\cite{shea14}. Their data, however, are open to interpretation \\cite{supp}. \n\n\nIn the past, efforts were also made to theoretically explain the selection mechanism. For DNAs, the intrinsic helical nature was believed to play a crucial role in their selective adsorption on CNTs \\cite{zheng03}.\nFor aromatic polymers, Nish {\\it et al.} \\cite{nish07} found that PFOs on CNT surfaces form n-fold symmetric structures with their backbones aligned along the tube axis.\nThe magnitude of the binding energy between CNTs and polymers was shown to increase with the tube diameter, a trend that was later confirmed by several authors \n\\cite{ozawa11,gomulya13,fukumaru14}. \nIf the stability of adsorption complexes, as indicated by the binding energy, would determine the dispersibility of CNTs, the above results \\cite{nish07,ozawa11,gomulya13,fukumaru14} would imply that large-diameter CNTs are more easily dispersed than small-diameter ones.\nThis, however, is in contradiction to experimental observations that PFO prefers to disperse small-diameter CNTs \\cite{nish07,ozawa11,fukumaru14}. \nFurthermore, helically wrapped PFO structures on CNTs were used to explain the chirality preference of PFO \\cite{gao11}. We will show below, however, that such helical structures are not dynamically stable.\nRecently, a coarse-grained model was developed and used together with statistical mechanic arguments to explain the diameter preference of several pyridine-containing copolymers \\cite{berton14}. But it is unclear how well the method can be transferred to other systems. \nDespite these advances, it is fair to say that a thorough understanding of the diameter and chirality selectivity of the polymer adsorption method is still lacking.\n\n\\section{Results and Discussion}\nThis article focuses\non understanding the diameter selectivity of the polymer adsorption method since the band gap and related electronic\/optical properties of semiconducting CNTs are mainly determined by the diameter \\cite{white93}. \nIn particular, we propose that diameter selectivity results from a competition between the adsorption of polymers on the CNT surface and the bundling of individual CNTs (see Fig. \\ref{fgr:fig3}). \nOur results on four relevant polymers are in excellent agreement with \nexperimentally observed diameter preferences \\cite{nish07,mistry13,berton14} and, thus, resolve a controversy on the nature of the mechanism that underlies the diameter selection process.\nDespite the complexity of the competitive dispersion of CNTs, the success of our simple energetic model regarding diameter selectivity relies on its correct representation of some key factors including steric effects\/coverage.\n\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[scale=1.0]{scheme-competition2-b-resize.pdf}\n\\caption{The proposed mechanism of diameter-selective dispersion: a competition between bundling of carbon nanotubes (CNTs) and adsorption of polymers on the surface of CNTs. The initial state of the process, given by individual CNTs and individual polymers, is a transition state (on the top of a potential energy hill) created by sonication. \n}\n\\label{fgr:fig3}\n\\end{figure}\n\n{\\bf Simulations:}\nTo study the CNT-dispersion process, we performed classical molecular dynamics (MD) simulations using force fields.\nTip sonication treatment is known to generate high, local energy densities that break bundles into individual CNTs \\cite{mason,huang12}. \nFor the dilute polymer concentrations used in typical dispersion processes, the polymers exist as individual molecules \\cite{justino11}. \nTherefore, isolated, individual CNTs and polymers were assumed as the initial configuration. Solvent molecules of toluene were usually not explicitly included here. \nWe tested that their inclusion \ndid not significantly change the results but mostly slowed down the adsorption dynamics.\nFour representative types of polymers were considered in this study: the homopolymer of polyfluorene with side-chain length C8 (PFO) or C6 (PFH), and copolymers with anthracene group poly[(9,9-dihexylfluorenyl-2,7-diyl)-co-(9,10-anthracene)] (PFH-A),\nor pyridine groups poly[9,9-didodecylfluorene-2,7-diyl-alt-pyridine-2,6-diyl] (PFD-Py). The chemical structures of these four polymers are presented in the Supporting Information.\nFurthermore, 13 CNTs with diameters in the range from 0.8 to 1.4 nm\nwere considered. Such diameters are typically obtained in high-pressure CO conversion (HiPco) or pulsed laser vaporization (PLV) synthesis.\nFurther information on our simulation methods can be found in the Method section.\n\n\n{\\bf Adsorption complexes:}\nThe geometries of adsorption complexes were obtained by MD simulations using many different initial configurations, temperatures, and CNT diameters. \nThe simulations always lead to an almost linear alignment of PFO chains on the CNT surface, even after using initial conditions that promote the formation of helically wrapped structures.\nThrough geometry optimizations, we found that a multitude of such helically wrapped structures \\cite{gao11}, with different pitches and surface coverages, are local minima on the potential energy landscape (see Fig.~\\ref{fgr:fig1} (a) and S2) \\cite{supp}. \nHowever, if they were subject to MD simulations\nunwrapping proceeds gradually and after a sufficiently long run, a linearly aligned structure, as shown in Fig. \\ref{fgr:fig1} (b), was always obtained. We conclude that helically wrapped adsorption complexes are metastable \\cite{supp}. \n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[scale=1.0]{fig1-nv2-e.pdf}\n\\caption{The geometry of adsorption complexes: (a) A helically wrapped configuration is metastable, {\\it i.e.}, a local minimum on the potential energy landscape. \n(b) A snapshot of the much more stable, linearly aligned configuration of PFO on a (8,6) CNT.\n}\n\\label{fgr:fig1}\n\\end{figure}\n\n\n{\\bf Binding energy and stability of adsorption complexes:}\nA standard measure used to characterize the stability of the adsorption complex is the binding energy. It is defined as the difference between the potential energy of an adsorption complex and the sum of its constituent molecules. For the adsorption of polymers on CNT, it reads\n\\begin{equation}\n E^{binding}_{CNT\\mhyphen Polymer}=E_{CNT\\mhyphen Polymer}-E_{CNT}-E_{Polymer}.\\label{eqn:ebin}\n\\end{equation}\nThe binding energy for the adsorption of a single polymer chain on a CNT is shown in Fig.~\\ref{fgr:fig2} (a). Note that the magnitude of the binding energy increases with the tube diameter in agreement with previous results \\cite{nish07,ozawa11,gomulya13,fukumaru14}. \nThis is caused by a better contact between polymers and CNT owing to the increasingly flatter surface of large-diameter CNTs \\cite{supp}.\nThe side chain contributes a large part, about two-thirds, to the total binding energy of PFO. Consistently, the binding energy for PFH is smaller due to a shorter side-chain length \\cite{lee11,gomulya13,wang14}.\nThe magnitude of the binding energy of \nPFH-A is smallest, which means that, for all the tested polymers with similar length, it is the easiest to remove from a CNT surface. This is in qualitative agreement with our recent experimental observation that PFH-A can be washed away from thin films deposited using dispersed CNTs (unpublished results). In contrast, PFO cannot be washed away in the same manner. \n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[scale=1.0]{fig3-H-150227.pdf}\n\\caption{The binding energy $E^{binding}_{CNT\\mhyphen Polymer}$ (Eq.~\\ref{eqn:ebin}) of adsorption complexes for \n(a) a single polymer chain and (b) the maximal coverage of the CNT surface. The magnitude of the binding energy increases with the nanotube diameter.\nTherefore, the binding energy $E^{binding}_{CNT\\mhyphen Polymer}$ alone cannot explain why polymers selectively disperse CNTs with specific diameters. \nNote the discontinuities of $E^{binding}_{CNT\\mhyphen Polymer}$ in (b) that are due to abrupt changes in the surface coverage of the CNTs.\n}\n\\label{fgr:fig2}\n\\end{figure}\n\n{\\bf Surface coverage of CNTs by polymers and binding energy of CNT-polymer complexes:}\nTo avoid the rebundling of CNTs after sonication, it is necessary to sufficiently cover the CNT surface with polymers.\n\nWe concentrate here on the situations where there is an excess of polymers and maximal coverage of the CNT surface is expected.\nBinding energies for the maximal coverage of CNTs by polymers are shown in Fig.~\\ref{fgr:fig2} (b). \nNote the discontinuities in the binding energy \nthat are due to a sudden change in the number of polymers needed for maximal surface coverage \\cite{nish07}. The positions of the discontinuities are different from those reported previously in the literature \\cite{nish07,ozawa11,gomulya13,fukumaru14} because our MD simulations lead to different surface coverages \nthan the geometry optimizations performed in those works \\cite{supp}. \nThese discontinuities have a direct relation to the diameter preference of polymers, as will be discussed below. \n\n{\\bf Polymer-assisted dispersion as a competition between adsorption and bundling:}\nThe binding energy $E^{binding}_{CNT\\mhyphen Polymer}$ alone cannot\nexplain the selectivity of the polymer adsorption method, because \nits magnitude simply increases with the diameter (see Fig. \\ref{fgr:fig2}). \nThis would imply that large-diameter CNTs are more easily dispersed than small-diameter ones. \nHowever, this is in clear contrast to the experimental observations discussed above \\cite{nish07,mistry13}. The binding energy between polymer-wrapped CNTs could explain well the polymer-assisted dispersion of CNTs in certain solvents but not the selectivity on CNTs.\nThe key factor for understanding the selection mechanism\nis competition between the bundling of CNTs on the one hand and the adsorption of polymers on the CNT surface on the other (see Fig. \\ref{fgr:fig3}). \nThis reasoning is based on the observation that CNT dispersions in toluene without polymers are not stable and the CNTs eventually rebundle. \nFor this competition to take place, the initial state to be considered is a transition state consisting of individual polymers and individual CNTs. This transition state is experimentally realized by sonication, an integral work step of all selection methods.\nTherefore, the selectivity of CNTs is determined by the \\textit{difference} between the binding energy for CNT bundling and the binding energy for polymer adsorption, which reads \n\\begin{equation}\n\\Delta E^{binding}= E^{binding}_{CNT\\mhyphen Polymer} - E^{binding}_{CNT\\mhyphen CNT}. \\label{eqn:ediff}\n\\end{equation}\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[scale=1.0]{fig4-H-150227.pdf}\n\\caption{The energetics of CNT bundling: (a) Weighted average $\\bar E_i$ (Eq.~\\ref{eqn:ecntpair}) of the pair binding energy of the interaction between a CNT of a given diameter and another CNT, arbitrarily selected from the sample. (b) Average number of neighbors $N_{avg}$ of a CNT with a given diameter in a bundle with mixed diameters (fractional numbers are a result of the non-uniform diameter distribution). \n(c) Binding energy $E^{binding}_{CNT\\mhyphen CNT} = \\bar E_i N_{avg}$ of CNT bundling. The variation of $E^{binding}_{CNT\\mhyphen CNT}$ with the diameter follows the same trend as $E^{binding}_{CNT\\mhyphen Polymer}$ in Fig.~\\ref{fgr:fig2}, and only the competition between adsorption and bundling leads to selectivity. \n}\n\\label{fgr:fig5}\n\\end{figure}\n\n{\\bf Binding energy of CNT bundles:}\nAs-produced CNTs are normally a mixture of different diameters. This polydisperse nature makes a direct simulation of bundling computationally very expensive.\nTo overcome the difficulties, we first calculated the average pair binding energy $\\bar E_i$ of the interaction between a CNT of a given diameter and another CNT, arbitrarily selected from a sample of mixed CNTs. \nIt reads \n\\begin{equation}\n\\bar E_i= \\sum_j w_j E_{ij}, \\label{eqn:ecntpair}\n\\end{equation}\nwhere $E_{ij}$ is the pair binding energy between CNT species $i$ and $j$, and $w_j$ is the population weight (abundance) of CNT species $j$ in the sample.\nAs shown in Fig. \\ref{fgr:fig5} (a), the magnitude of $\\bar E_i$ increases with the CNT diameter, due to the increase in the contact area between CNTs. \nNext, we estimate the average number of neighbors $N_{avg}$ of a CNT in bundles. A simple approach would be to ignore the polydispersity and assume that all CNTs have just six neighbors. But our method is to\nconsider the surface of a CNT of a given diameter to be covered by CNTs having the average diameter of the considered sample, {\\it i.e.} HiPco or PLV.\nTherefore, the average number of neighbors can be a non-integer. The estimates of $N_{avg}$ for two CNT samples are presented in Fig.~\\ref{fgr:fig5} (b).\nFinally, the binding energy for CNT bundling $E^{binding}_{CNT\\mhyphen CNT}$ can be calculated as \n\\begin{equation}\nE^{binding}_{CNT\\mhyphen CNT} = \\bar E_i N_{avg}.\n\\label{eqn:ecntbundlg}\n\\end{equation}\nAs shown in Fig. \\ref{fgr:fig5} (c) for both samples, the magnitude of the binding energy of CNT bundles increases with the CNT diameter.\n\n{\\bf Binding energy difference and diameter selectivity:}\nAs discussed already, for both CNT bundles and CNT-polymer complexes, the magnitude of the binding energy increases with the tube diameter. \nIn the binding energy difference $\\Delta E^{binding}$, the two trends \nnearly compensate for each other and only their competition leads to the preference for certain diameters, \nwhich are reflected in the location of the minima of $\\Delta E^{binding}$ in Fig.~\\ref{fgr:fig6}. \n\nConsider first the adsorption of PFO on HiPco CNTs in Fig \\ref{fgr:fig6}. Except for the CNT with the smallest diameter, $\\Delta E^{binding}$ increases with tube diameter. \nSince the number of polymers needed for the maximal surface coverage of the CNTs changes from three to four, $E^{binding}_{CNT\\mhyphen Polymer}$ abruptly changes at 0.83 nm (see inset of Fig.~\\ref{fgr:fig2} (b)), causing $\\Delta E^{binding}$ to have a minimum at about the same diameter. \nThe behavior of $\\Delta E^{binding}$ explains \n(i) the preference of PFO to disperse HiPco CNTs in the diameter range 0.8--0.95 nm, a fact that has been repeatedly reported by different groups \\cite{nish07,ozawa11,fukumaru14}, and\n(ii) why CNTs with diameter smaller than 0.8 nm are not well-dispersed by PFO. These two insights explain the dominance of (8,6) CNTs (d = 0.95 nm) in HiPco CNT dispersions and the elimination of (6,5) CNTs (d = 0.75 nm) in CoMoCAT CNT dispersions \\cite{nish07}. \n\nFor PFH, with side-chains two carbon atoms shorter than PFO, more polymer chains are needed to cover the surface of a CNT. Therefore, the discontinuity in $\\Delta E^{binding}$\nis upshifted to 1.03 nm. This explains why,\nfor HiPco CNTs using PFH instead of PFO, the dominant CNTs in the dispersion become (8,7) (d = 1.02 nm) and (9,7) (d = 1.09 nm) (see Fig.1b of Nish {\\it et al.} \\cite{nish07}).\n\nFor the copolymer PFD-Py, the \nminimum of \n$\\Delta E^{binding}$ is at about 1.25 nm. This agrees with recent experimental findings that, for HiPco CNTs, PFD-Py prefers to disperse CNTs with diameters of about 1.23 nm (see Fig.1n of Berton {\\it et al.} \\cite{berton14}).\n \nThe $\\Delta E^{binding}$ of PFH-A increases continuously \nin the considered diameter range (0.8--1.4 nm)\nand no minimum is discernible.\nMistry {\\it et al.} performed a systematic study on the selectivity of PFH-A on CNTs synthesized {\\it via} laser vaporization of graphite at different temperatures and found that\nit always prefers to disperse CNTs with the smallest diameters in the sample \\cite{mistry13}. The absence of a minimum in that range is again consistent with the experiments even though the simulations are based on HiPco CNTs. Further discussions on the selectivity of PFH-A can be found in the Supporting Information.\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[scale=1.0]{fig5-150305d.pdf}\n\\caption{Diameter selectivity as competition between CNT bundling and polymer adsorption. \nThe diameter preferences of specific polymers for HiPco CNTs in our simulations are defined by the minima of the corresponding binding energy difference $\\Delta E^{binding}$ (Eq.~\\ref{eqn:ediff}). \nThey are in excellent agreement with experimental results indicated by the shadowed regions\n\\cite{nish07,berton14,mistry13}. \n}\n\\label{fgr:fig6}\n\\end{figure}\n\nTo summarize, for \nthe polymers PFO, PFH and PFD-Py, the minima of the \nbinding energy difference $\\Delta E^{binding}$ \nmatch perfectly the experimentally reported diameters that are dominantly dispersed by those polymers.\nThis excellent agreement strongly suggests \nthat the mechanism of diameter selectivity is a competition between CNT bundling and polymer adsorption.\n\nIt is interesting to note that the sign of $\\Delta E^{binding}$ is negative for PFO, PFH and PFD-Py. This indicates a preference for the formation of CNT-polymer adsorption complexes over CNT-CNT bundling.\nTherefore, for long sonication times, SWNTs of all diameters can be dispersed in principle. \nWith increasing sonication time, the amount of dispersed CNTs will increase and the selectivity will gradually diminish.\nTherefore, an optimal sonication time should be experimentally determined, providing a compromise between yield and purity.\nPositive values of $\\Delta E^{binding}$ for PFH-A mean that \nthe rebundling should happen more frequently than adsorption,\nwhich implies a potential lower yield of the dispersion process using PFH-A.\n\nNote also that, due to the possibility of partial adsorption and other \"imperfect\" packing configurations the transition in Fig. 3(b) may turn out to be not so abrupt, see also the radial distribution functions shown in Fig.S6 and the corresponding discussions in the Supporting Information. Therefore, in experiments a range of diameters is often selected by a certain polymer.\n\nOne can also view the sonication-assisted dispersion process as a reversible reaction,\n\\begin{equation}\nCNT@CNT + PFO \\rightleftharpoons PFO@CNT . \\label{eqn:ereac}\n\\end{equation} \nIn this language, the initial configuration of isolated CNTs and polymers corresponds to a transition state, which is achieved with the aid of ultrasonication treatment \\cite{mason,huang12}. \nThe adsorption of polymers on the CNT surface and the bundling of CNTs are the \nrate-determining steps for forward and backward reactions, respectively. The two binding energies are the (negative) activation energies for reactions in the two directions. \nFor this reversible reaction, the binding energy difference only estimates energetic contributions to\nthe reaction rates, neglecting \nentropic contributions, reaction orders and the concentrations of the reactants.\n\nThe focus of the current study is on the diameter selectivity of CNTs by aromatic polymers. For the chirality selection, the match\/mismatch between the atomic structures of polymers and CNTs will be quite crucial. The popular implementations of van der Waals' interaction, as used here, were found unsuitable for the purpose and the anisotropic intermolecular potentials turn out to be a better alternative \\cite{kc05}. For the sorting of CNTs with respect to electronic properties, {\\it ab initio} quantum simulations with the electronic interactions being included would be more appropriate. All these issues deserve their own separate publications. The mentioned success of our simple energetic model implies that the key factors determining the diameter selectivity of (semi-conducting) CNTs were properly represented. The model can certainly be further improved by including: (i) entropy factors for a proper estimate of Gibbs free energy, which is of direct relevance to \nthe reaction kinetics, and (ii) the effect of explicit solvent.\nCalculations with explicit solvents and estimates of entropic contributions are provided in the Supporting Information.\n\n\nIn conclusion, we explain the diameter selectivity of polymer adsorption methods to be the result of a competition between the bundling of CNTs and the adsorption of polymers on the CNT surface. \nThe preference of certain diameters corresponds to local minima of the binding energy difference between these two processes. Such minima occur due to abrupt changes in the CNT's coverage with polymers at certain diameters.\nFor all tested polymers including two homopolymers of polyfluorene with different side-chain lengths and two copolymers with anthracene or pyridine groups, our simulation results are in excellent agreement with the experimental findings regrading the diameter selectivity. Interestingly, even the influence of a fine-tuning of side-chain length on the selectivity was correctly captured in our method. Our insights resolve a long-standing controversy regarding the understanding of CNT selection schemes and \nare important for the further development of dispersion\/extraction methods, {\\it i.e.}, they enable MD simulations to be used for the screening of polymer candidates, tailoring of polymer structures, and obtaining further scientific insights. The proposed mechanism is general enough to be valid for other (sonication-aided) dispersion processes, for instance, the exfoliation of layered materials \\cite{nicolosi13}, and the dispersion of CNTs by DNAs and mononucleotides \\cite{zheng03,zheng09,ju08,johnson08}.\n\n\n\\section{Methods}\n\n\nThe adsorption of polymers on single-walled CNTs and the bundling of CNTs were studied with classical molecular dynamics (MD) simulations by using the CP2K \\cite{cp2k} and Gromacs packages \\cite{gromacs}. MD simulations were performed in NVT ensemble at $T=300K$ using the Nose-Hoover or Langevin thermostats.\nThe standard CHARMM force field parameters for the intra-molecular interaction \\cite{charmm} were benchmarked against the density functional method (DFT) MD simulations with Grimme dispersion corrections DFT-D3 \\cite{grimme10} and the BLYP exchange-correlation functional \\cite{blyp} in CP2K and classical MD simulations with the MM3 force field \\cite{mm3} using the Tinker package \\cite{tinker}. The torsion angle parameter, describing amongst others the twist of the backbones of polymers, was modified to match the results of DFT first principles MD simulation. \nThe inter-molecular interactions include an electrostatic part due to partial charges on atoms and a dispersion force part modeled by a Lennard-Jones potential as usual in standard CHARMM force field implementations \\cite{charmm}. \n\nFor the adsorption of polymers on the CNT surface, the polymer backbones were initially aligned parallel to the CNT axis. Multiple chains of polymers were arranged in an n-fold symmetric structure surrounding the tube. The initial distance between the backbone and the CNT surface was set to 1 to 1.5 nm depending on the CNT diameter and the number of polymers. For the binding energy calculation of CNT pairs, the two CNTs were placed in parallel with the initial distance between the surfaces of 0.6 nm. The time step for the integration of Newton's equation of motion was 1 fs. The duration of MD simulations ranged from 1 ns to 20 ns. For the calculation of thermodynamic averages, the equilibration time, ranging between 0.2 and 2 ns, was not considered.\nTo check the stability of self-constructed helical adsorption structures, geometry optimizations were performed using the CP2K package. The criteria of convergence are $3\\times 10^{-3}$ $Bohr$ for the geometry change and $4.5\\times 10^{-4}$ $Hartree\/Bohr$ for the change in the force. \n\nIt is known that the solvent plays an important role in the selective dispersion of CNTs by polymers \\cite{huang08}. However, here we are interested in studying the effect of different polymers in combination with the same weakly polarized solvent, toluene.\nMoreover, our tests showed that the explicit inclusion of solvent molecules of toluene in MD simulations did not change the structures of adsorption but significantly slowed down the dynamics of the adsorption process. Therefore, to enable MD simulations within reasonable times, solvent molecules were not explicitly included in our production runs. Our additional MD simulations with explicit solvents showed that, the adsorption configurations of the polymer backbones and sidechains, which are in contact with CNT surface, hardly change with the inclusion of solvents. Only the sidechains, which are not in contact with CNT surface, tend to point outwards to the solvent instead of folding back and aligning along CNT surface as in vacuum. The second-layer of polymers moves a bit away from the CNT surface. Therefore, with the inclusion of solvents the values of binding energies may change by some degree but the surface coverage of CNTs and the related positions of the abrupt changes in Fig.3(b) will be unaffected. Further details on the effect of explicit solvent can be found in the Supporting Information. \nStudies showed that a PFO octamer already has the same selectivity as a PFO polymer. Furthermore, the stability of the oligomer-CNT complex increases strongly with the chain length of the oligomer \\cite{berton12}. \nTo meet the capacity of the available computing resources in our simulation, we used 30-nm-long polymer \nchains, which consist of 32, 22 and 30 monomers of PFO\/PFH, PFD-Py and PFH-A, respectively. \nCNT segments of length between 30 and 36 nm were used, their length varying with the chirality. \n\nTo obtain the binding energy of a polymer-CNT complex, three MD simulations were\nperformed for the adsorption complex, the isolated CNT and the polymer, respectively.\nThe mean value of the potential energy was determined from the corresponding trajectories\nand the binding energy was then calculated. This procedure is different from most cases in the literature where the binding energy was calculated from ($T=0$ K) single-point calculations of the optimized structures. \n\nIt is worth pointing out that, for the adsorption of polymers on a CNT surface, the binding energy can be measured per unit length of a polymer chain, or per unit length of CNT covered completely by polymers. The former describes how hard it is to remove a polymer chain from the CNT surface while the latter is suitable for characterizing the competition for the adsorption on a CNT surface. \n\n\n\n\\begin{acknowledgement}\nWe acknowledge fruitful discussions with Gotthard Seifert. HLY thanks Jia Gao and Elton Carvalho for communications on the preparation of helically wrapped complexes. This work was partially funded by the European Union (ERDF) {\\it via} the FP7 project CARbon nanoTube phOtONic devices on silicon (CARTOON) and is supported by Dresden Center for Computational Materials Science (DCCMS). We also acknowledge the support by the\nGerman Research Foundation (DFG) within the Cluster of\nExcellence ``Center for Advancing Electronics Dresden''\n(cfAED).\nWe acknowledge the Center for Information Services and High Performance Computing (ZIH) at TU Dresden for computational resources.\n\\end{acknowledgement}\n\n\n\\begin{suppinfo}\nCalibration of CHARMM force field parameters, stability of the helically wrapped configurations for PFO on CNTs, the determination of the surface coverage of CNT by PFO, variation of the binding energy of PFO-CNT complexes with CNT diameter, the population distributions of HiPco \\cite{chen07} and PLV \\cite{tange12} CNTs with respect to the diameter, the effect of solvent on the adsorption configurations, and the influence of entropic effect on diameter selectivity.\n\\end{suppinfo}\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{S1}\nLet $\\mathbb{F}_q^n$ be the $n$-dimensional vector space over $\\mathbb{F}_q$, the unique finite field with $q$ elements; $q$ is necessarily a prime power. The set of all subspaces of $\\mathbb{F}_q^n$ is the \\emph{projective space}\\footnote{This terminology is not standard. In other branches of mathematics the term projective space defines the collection of all lines passing through the origin of a vector space.} $\\mathbb{P}_q(n)$ which can be formally defined as\n\\begin{equation*}\n\t\\mathbb{P}_q(n) := \\{V : V \\le \\mathbb{F}_q^n\\},\n\\end{equation*}\nwhere $\\le$ signifies the usual vector space inclusion. The collection of all subspaces in $\\mathbb{P}_q(n)$ with a fixed dimension $k$ is called the \\emph{Grassmannian} of dimension $k$ for all $0 \\le k \\le n$, and is denoted as $\\mathbb{G}_q(n, k)$. In terms of notation, $\\mathbb{G}_q(n, k) := \\{V: V \\le \\mathbb{F}_q^n, \\dim V = k\\}$. Clearly, $\\mathbb{P}_q(n) = \\bigcup\\limits_{k=0}^{n} \\mathbb{G}_q(n, k)$. The \\emph{subspace distance} between two subspaces $X$ and $Y$ in $\\mathbb{P}_q(n)$ is defined as\n\\begin{equation*}\n\td_S(X, Y) := \\dim (X+Y) - \\dim (X \\cap Y),\n\\end{equation*}\nwhere $X+Y$ denotes the smallest subspace containing both $X$ and $Y$. It was proved in \\cite{KK, AAK} that the projective space $\\mathbb{P}_q(n)$ is a metric space under the action of the subspace distance metric. A \\emph{code} in the projective space $\\mathbb{P}_q(n)$ is a subset of $\\mathbb{P}_q(n)$.\n\nCodes in projective spaces have recently gained attention since they were proved to be useful for error and erasure-correction in \\emph{random network coding} \\cite{KK}. An $(n, M, d)$ code in $\\mathbb{P}_q(n)$ is a collection of $M$ number of subspaces of $\\mathbb{F}_q^n$ such that the minimum subspace distance between any two of them is $d$. K\\\"{o}tter and Kschischang showed that an $(n, M, d)$ code can correct any combination of $t$ errors and $\\rho$ erasures during the communication of packets through a volatile network as long as $2(t+\\rho) < d$ \\cite{KK}. Subsequently codes in $\\mathbb{P}_q(n)$ were studied extensively \\cite{EV, HKK, BP, GR}. Such codes are also referred to as \\emph{subspace codes}.\n\nHowever, designing and studying code structures in $\\mathbb{P}_q(n)$ is considered relatively trickier than study of classical error-correction in the \\emph{Hamming space} $\\mathbb{F}_q^n$. This is because unlike in $\\mathbb{F}_q^n$, the volume of a \\emph{sphere} is not independent of the choice of its \\emph{center} in the projective space $\\mathbb{P}_q(n)$. Thus, standard geometric intuitions often do not hold in $\\mathbb{P}_q(n)$. In other words, $\\mathbb{F}_q^n$ is \\emph{distance-regular} while $\\mathbb{P}_q(n)$ is not. This implies that a different framework is required to study codes in projective spaces than the approach taken for block codes in classical error-correction, e.g. in \\cite{MS}. The problem of lack of distance-regularity in $\\mathbb{P}_q(n)$ is, however, tackled to some extent by considering codewords of a fixed dimension. Such class of subspace codes are commonly known as \\emph{constant dimension codes}. A constant dimension code in $\\mathbb{P}_q(n)$ is a subset of $\\mathbb{G}_q(n, k)$ for some $0 \\le k \\le n$. The fact that $\\mathbb{G}_q(n, k)$ is distance-regular is exploited to construct various classes of constant dimension codes \\cite{SE, SE2, GY, XF, TMBR, KoK, ER}.\n\nA lattice framework for studying both binary block codes and subspace codes was discussed in \\cite{MB}. The authors of \\cite{BEV} established that codes in projective spaces are $q$-analogs of binary block codes defined within Hamming spaces using a framework of lattices. A \\emph{lattice} is a partially ordered set wherein both least upper bound and greatest lower bound of any pair of elements exist within the set and are unique. We denote the set of all subsets of the canonical $n$-set $[n] := \\{1, \\ldots, n\\}$ as $\\mathcal{P}(n)$, also known as the \\emph{power set} of $[n]$. The lattices corresponding to the block codes in $\\mathbb{F}_2^n$ and the subspace codes in $\\mathbb{P}_q(n)$ are the \\emph{power set lattice} $(\\mathcal{P}(n), \\cup, \\cap, \\subseteq)$ and the \\emph{linear lattice} $(\\mathbb{P}_q(n), +, \\cap, \\le)$, respectively. Here the notation $\\subseteq$ represents set inclusion.\n\nA lattice is called \\emph{modular} if the modularity condition holds for all elements in it (see Def.~\\ref{LD5}). Many of the well-known coding spaces including both $\\mathbb{F}_q^n$ and $\\mathbb{P}_q(n)$ are examples of a modular lattice. This motivated the work of Kendziorra and Schmidt where they generalized the model of subspace codes introduced in \\cite{KK} to codes in modular lattices \\cite{KS}. There are two significant types of semimodular lattices, viz. \\emph{geometric} lattices and \\emph{distributive} lattices that have inspired a rich variety of literature, e.g. \\cite{B, RS}. While $\\mathbb{F}_2^n$ is a geometric distributive lattice, $\\mathbb{P}_q(n)$ is an example of a geometric lattice which is modular but non-distributive. There have been quite a few attempts to characterize distributive lattices, such as in \\cite{LS, S}. However, no known characterization of geometric distributive lattices exists to the best of our knowledge.\n\nThe notion of ``linearity\" and ``complements\" in $\\mathbb{P}_q(n)$ are not as straightforward as they are in the Hamming space $\\mathbb{F}_2^n$. This is owed to the fact that $\\mathbb{F}_2^n$ is a vector space with respect to the bitwise XOR-operation whereas $\\mathbb{P}_q(n)$ or $\\mathbb{G}_q(n, k)$ are not vector spaces with respect to the usual vector space addition. Therefore, the subspace distance metric is not \\emph{translation invariant} over $\\mathbb{P}_q(n)$ or $\\mathbb{G}_q(n, k)$. Braun et al. addressed this problem in \\cite{BEV} and defined linearity and complements in subsets of $\\mathbb{P}_q(n)$ by elucidating key features from the equivalent notion in $\\mathbb{F}_2^n$.\n\nThe maximum size of a linear code in $\\mathbb{P}_2(n)$ was conjectured to be $2^n$ by Braun et al. \\cite{BEV}. A particular case of this problem was proved by Pai and Rajan where the ambient space $\\mathbb{F}_2^n$ is included as a codeword \\cite{PS}. The maximal code achieving the upper bound was identified as a \\emph{code derived from a fixed basis}. The authors of \\cite{PS} observed that such a code is basically embedding of a distributive lattice into the linear lattice, which is geometric. This motivated them to conjecture a generalized statement which can already be found in literature, e.g. in \\cite[Ch.~IX, Sec.~4, Ex.~1]{B}.\n\\begin{problem}\n\t\\label{Pr1}\n\tThe size of the largest distributive sublattice of a gemetric lattice of height $n$ must be $2^n$.\n\\end{problem}\nLattice-theoretic connection of other classes of linear codes in $\\mathbb{P}_q(n)$ was investigated thoroughly in \\cite{BK}. The findings of \\cite{BK} include the discovery that the only class of linear subspace codes that have a sublattice structure of the corresponding linear lattice must be geometric distributive. Thus it is an interesting problem to find out a unique characterization of geometric distributive lattices should it exist.\n\nIn this paper, we determine the unique criterion for a geometric lattice to be distributive. We in fact prove a more generalized version of this statement. This helps us to bring out the unique characterization of class of geometric distributive lattices. We then use this characterization to solve a few problems involving linear codes and complements in $\\mathbb{P}_q(n)$. Problem~\\ref{Pr1} is also solved by applying the said characterization.\n\nThe rest of the paper is organized as follows. In Section~\\ref{S2} we give a few requisite definitions concerning lattices and formally define linear codes and complements in the projective space $\\mathbb{P}_q(n)$. Section~\\ref{UAL} concerns with the study of uniquely atomistic lattices; in particular we show that any such finite lattice is modular. The \\emph{unique-decomposition theorem} that gives the unique characterization of finite geometric distributive lattices is derived in Section~\\ref{S3} after proving a sequence of results regarding modular lattices and distributive lattices. Section~\\ref{S4} is attributed to various applications of the unique-decomposition theorem in lattice theory that include determining the maximum size of a distributive sublattice of a finite geometric lattice and counting the \\emph{Whitney numbers} of a geometric distributive lattice. In particular, we consider a few problems about linearity and complements in the linear lattice. An important finding is that any distributive sublattice of $\\mathbb{P}_q(n)$ can be used to construct a linear code closed under intersection. Concluding remarks and interesting open problems are listed in Section~\\ref{S5}.\n\\paragraph{Notation.}\n$\\mathbb{F}_q^n$ represents the unique vector space of dimension $n$ over $\\mathbb{F}_q$. The set of all subspaces of $\\mathbb{F}_q^n$ is denoted as $\\mathbb{P}_q(n)$. The usual vector space sum of two disjoint subspaces $X$ and $Y$, called the \\emph{direct sum} of $X$ and $Y$, is written as $X \\oplus Y$. For any subset $\\mathcal{U} \\subseteq \\mathbb{P}_q(n)$, the collection of all $i$-dimensional members of $\\mathcal{U}$ will be denoted as $\\mathcal{U}_i$; $\\mathcal{U}_i := \\{X: X \\in \\mathcal{U}, \\dim X = i\\}$. The notation $\\langle \\mathcal{S}\\rangle$ for any subset $\\mathcal{S}$ of vectors in $\\mathbb{F}_q^n$ will denote the linear span of all the vectors in $\\mathcal{S}$. $\\triangle$ denotes the \\emph{symmetric difference} operator, which can be defined for two sets $S$ and $T$ as\n\\begin{equation*}\n\tS \\triangle T := (S \\cup T) \\backslash (S \\cap T).\n\\end{equation*}\n\\section{Preliminaries}\n\\label{S2}\n\\subsection{An Overview of Lattices}\nWe will go through some standard definitions and results concerning lattices that can be found in the existing literature, e.g. in \\cite{B}.\n\\begin{definition}\n\t\\label{LD1}\n\tFor a set $P$, the pair $(P, \\preceq)$ is called a \\emph{poset} if there exists a binary relation $\\preceq$ on $P$, called the \\emph{order relation}, that satisfies the following for all $x, y, z \\in P$:\n\t\\begin{itemize}\n\t\t\\item[(i)] (Reflexivity) $x \\preceq x$;\n\t\t\\item[(ii)] (Antisymmetry) If $x \\preceq y$ and $y \\preceq x$, then $x = y$; and\n\t\t\\item[(iii)] (Transitivity) If $x \\preceq y$ and $y \\preceq z$, then $x \\preceq z$.\n\t\\end{itemize}\n\tThe \\emph{dual} of a poset $P$ is the poset $P^{*}$ defined on the same set as $P$ such that $y \\preceq x$ in $P^{*}$ if and only if $x \\preceq y$ in $P$.\n\\end{definition}\nThe notation $x \\preceq y$ is read as ``$x$ is less than $y$'' or ``$x$ is contained in $y$''. If $x \\preceq y$ such that $x \\ne y$, then we write $x \\prec y$. In the sequel a poset $(P, \\preceq)$ will be denoted as $P$ when the order relation $\\prec$ is obvious from the context.\n\\begin{definition}\n\t\\label{LD2}\n\tAn upper bound (lower bound) of a subset $S$ of a poset $P$ is an element $p \\in P$ containing (contained in) every $s \\in S$. A least upper bound (greatest lower bound) of $S \\subseteq P$ is an element of $P$ contained in (containing) every upper bound (lower bound) of $S$.\n\\end{definition}\nA least upper bound or a greatest lower bound of a poset, should it exist, is unique according to the antisymmetry property of the order relation $\\preceq$. The least upper bound and the greatest lower bound of a poset $P$ are denoted as $\\sup P$ and $\\inf P$, respectively.\n\\begin{definition}\n\t\\label{LD3}\n\tA \\emph{lattice} $(L, \\vee, \\wedge)$ is a poset $L$ such that $\\sup \\{x, y\\}$ and $\\inf \\{x, y\\}$ exist for all $x, y \\in L$. The notation for the $\\sup \\{x, y\\}$ and the $\\inf \\{x, y\\}$ are $x \\vee y$ (``$x$ \\emph{join} $y$'') and $x \\wedge y$ (``$x$ \\emph{meet} $y$''), respectively.\n\\end{definition}\nOnce again, a lattice $(L, \\vee, \\wedge)$ will be denoted as $L$ whenever the join $\\vee$ and meet $\\wedge$ operations are obvious from the context. In this work we will consider only finite lattices, i.e. when the underlying poset is finite. The unique greatest element and the unique least element of a lattice will be denoted as $I$ and $O$, respectively, unless specified otherwise.\n\\begin{definition}\n\t\\label{LD4}\n\tA \\emph{sublattice} of a lattice $L$ is a subset $S \\subseteq L$ such that $x \\vee y, x \\wedge y \\in S$ for all $x, y \\in S$.\n\\end{definition}\nThe \\emph{Hasse diagram} of a finite poset completely describes the order relations of that poset. If $x \\prec y$ in the poset $P$ such that there exists no $z \\in P$ satisfying $x \\prec z \\prec y$, then $y$ is said to \\emph{cover} $x$; we denote this as $x \\lessdot y$. In the Hasse diagram of a lattice, two elements are joined if and only if one of them covers the other; $y$ is written above $x$ if $y$ covers $x$. Hence, $x \\prec y$ if and only if there exists a path from $x$ moving up to $y$.\n\nThe power set $\\mathcal{P}(m)$ of a finite set $[m]$ and the projective space $\\mathbb{P}_q(n)$ are examples of lattices. The Hasse diagram associated with the lattice of $(\\mathbb{P}_2(2), +, \\cap)$ is shown here (Fig.~\\ref{F1}). This particular lattice is known as $M_3$.\n\\begin{figure}[t]\n\t\\centering\n\t\\begin{tikzpicture}[scale=0.6]\n\t\t\\node (A1) at (0,3) {$\\mathbb{F}_2^2$};\n\t\t\\node (A2) at (-3,0) {$\\langle \\{(0, 1)\\}\\rangle$};\n\t\t\\node (A3) at (0,0) {$\\langle \\{(1, 0)\\}\\rangle$};\n\t\t\\node (A4) at (3,0) {$\\langle \\{(1, 1)\\}\\rangle$};\n\t\t\\node (A5) at (0,-3) {$\\{0\\}$};\n\t\n\t\n\t\t\\draw (A5) -- (A2) -- (A1) -- (A3) -- (A5) -- (A4) -- (A1);\n\t\\end{tikzpicture}\n\t\\caption{$M_3$ lattice representing $(\\mathbb{P}_2(2), +, \\cap)$}\n\t\\label{F1}\n\\end{figure}\n\\begin{definition}\n\t\\label{LD5}\n\tA finite lattice $(L, \\vee, \\wedge)$ is \\emph{semimodular} if the following holds for all $x, y \\in L$:\n\t\\begin{equation*}\n\t\tx \\wedge y \\lessdot x, y \\quad \\Rightarrow \\quad x, y \\lessdot x \\vee y.\n\t\\end{equation*}\n\tA lattice is \\emph{modular} if both the lattice and its dual are semimodular. It can be proved that a finite lattice $L$ is modular if for any $x, y, z \\in L$ the following holds:\n\t\\begin{equation*}\n\t\tx \\preceq z \\Rightarrow x \\vee (y \\wedge z) = (x \\vee y) \\wedge z.\n\t\\end{equation*}\n\\end{definition}\nThe smallest finite lattice that is non-modular is called $N_5$ (Fig.~\\ref{F0}). $N_5$ plays a crucial role in characterizing modular lattices as we will see later.\n\nThe elements of a lattice which cover the least element of the lattice are known as \\emph{atoms}. A lattice with a least element is \\emph{atomic} if for every non-zero element $a$ there exists an atom $p$ such that $p \\preceq a$. An atomic lattice is called \\emph{atomistic} if any element is a join of atoms. A lattice that is \\emph{uniquely} atomistic is defined in the following way.\n\\begin{definition}\n\t\\label{LDA}\n\tA lattice is \\emph{uniquely atomistic} if each element therein is uniquely expressible as join of its atoms. If $L$ is a uniquely atomistic lattice with $\\{x_1, \\ldots, x_m\\}$ as the set of all atoms in $L$ then for any $x \\in L$ there exists a unique subset $S_x \\subseteq [m]$ such that $x = \\bigvee\\limits_{i \\in S_x} x_i$. We denote this relation as $x = \\sup S_x$ when the choice of $m$ is clear from the context.\n\\end{definition}\nAtoms play an important role in defining geometric lattices.\n\\begin{definition}\n\t\\label{LD6}\n\tA finite lattice that is both semimodular and atomistic is called \\emph{geometric}.\n\\end{definition}\nBoth the linear lattice $(\\mathbb{P}_q(n), +, \\cap)$ and the power set lattice $(\\mathcal{P}(m), \\cup, \\cap)$ are examples of a geometric lattice. From Section~\\ref{S4} onwards all lattices considered will be geometric. The variety of modular lattices that will play a key role in this work are the distributive lattices which are defined next.\n\\begin{definition}\n\t\\label{LD7}\n\tA lattice $L$ is \\emph{distributive} if the following two equivalent conditions hold for any $x, y, z \\in L$:\n\t\\begin{eqnarray}\n\t\tx \\vee (y \\wedge z) &=& (x \\vee y) \\wedge (x \\vee z); \\nonumber \\\\\n\t\tx \\wedge (y \\vee z) &=& (x \\wedge y) \\vee (x \\wedge z). \\nonumber\n\t\\end{eqnarray}\n\\end{definition}\nThe power set lattice $\\mathcal{P}(m)$ is an example of a distributive lattice. However, the linear lattice $\\mathbb{P}_q(n)$ is modular but not distributive. In general, any distributive lattice is modular, and the $M_3$ lattice is pivotal in characterizing modular non-distributive lattices. Similarly a modular lattice can be defined by non-inclusion of the lattice $N_5$. The following theorem is due to Dedekind and Birkhoff.\n\\begin{theorem}(\\cite{G}, Page~59)\n\t\\label{TL1}\n\tA lattice is modular if and only if it does not contain a sublattice isomorphic to $N_5$. A modular lattice is non-distributive if and only if it contains a sublattice isomorphic to $M_3$.\n\\end{theorem}\n\\begin{definition}\n\t\\label{LD8}\n\tA real valued function $v: L \\rightarrow \\mathbb{R}$ on a lattice $L$ is called a \\emph{positive isotone valuation} if the following conditions hold for all $x, y \\in L$:\n\t\\begin{itemize}\n\t\t\\item[(i)] (Valuation) $v(x \\vee y) + v(x \\wedge y) = v(x) + v(y)$; \n\t\t\\item[(ii)] (Isotone) $x \\preceq y \\Rightarrow v(x) \\le v(y)$;\n\t\t\\item[(iii)] (Positive) $x \\prec y \\Rightarrow v(x) < v(y)$.\n\t\\end{itemize}\n\\end{definition}\nThe distance function induced by an isotone valuation $v$ is defined as $d_v(x, y) := v(x \\vee y) - v(x \\wedge y)$.\n\\begin{theorem}(\\cite{B})\n\t\\label{TL2}\n\tFor an isotone valuation $v$ defined on a lattice $L$, the function $d_v(x, y) := v(x \\vee y) - v(x \\wedge y)$ is a metric if and only if $v$ is positive.\n\\end{theorem}\nA totally ordered subset of a lattice is called a \\emph{chain}. Given two elements $x$ and $y$ in a lattice $L$, a chain of $L$ between $x$ and $y$ is a chain $\\{x_1, \\ldots, x_l\\}$ such that $x = x_0 \\prec x_1 \\prec \\cdots \\prec x_l = y$. The \\emph{length} of this chain is $l$. The \\emph{height} of an element $x \\in L$ is the maximum length of all chains between $O$ and $x$, and is denoted by $h_L(x)$. We often use the notation $h(x)$ when $L$ is obvious from the context. The \\emph{height of the lattice} $L$ is the number $h_L(I)$ where $I$ is the greatest element in $L$.\n\nFor modular lattices, the following is a consequence of Theorem~\\ref{TL2}.\n\\begin{theorem}[Page~41, Theorem~16, \\cite{B}]\n\t\\label{TL3}\n\tIf $h$ is the height function defined on a finite modular lattice $L$ then $h$ is a positive isotone valuation and $d_h$ is a metric on $L$.\n\\end{theorem}\nBy definition, $h_L(x) = 0$ if and only if $x$ is the least element in $L$; similarly, $h_L(x) = 1$ if and only if $x$ is an atom in $L$.\n\\begin{definition}\n\t\\label{LD9}\n\tThe total number of elements with a given height $k$ of a lattice $L$ with a height function $h$ defined on $L$ is called the \\emph{Whitney number}, denoted as $W_k(L)$. In terms of notation, $W_k(L) := |\\{x \\in L: h_L(x) = k\\}|$.\n\\end{definition}\n\\subsection{Complements and Linearity in Projective Spaces}\nThe notions of complements and linearity in the projective space $\\mathbb{P}_q(n)$ are not straightforward as they are in the Hamming space $\\mathbb{F}_2^n$. Braun et al. introduced the definition of both in \\cite{BEV} by extracting key properties of the same in $\\mathbb{F}_2^n$. We begin with a formal definition of the complement mapping in $\\mathbb{P}_q(n)$.\n\\begin{definition}\n\t\\label{D1}\n\tFor any subset $\\mathcal{U} \\subseteq \\mathbb{P}_q(n)$, a function $f: \\mathcal{U} \\rightarrow \\mathcal{U}$ is a \\emph{complement} on $\\mathcal{U}$ if $f$ satisfies the following conditions:\n\t\\begin{itemize}\n\t\t\\item[(i)] $X \\cap f(X) = \\{0\\}$ and $X \\oplus f(X) = \\mathbb{F}_q^n$ for all $X \\in \\mathcal{U}$;\n\t\t\\item[(ii)] There exists a unique $f(X) \\in \\mathcal{U}_{n-k}$ for each $X \\in \\mathcal{U}_k$ for all $0 \\le k \\le n$;\n\t\t\\item[(iii)] $f(f(X)) = X$ for all $X \\in \\mathcal{U}$; and\n\t\t\\item[(iv)] $d_S(X, Y) = d_S (f(X), f(Y))$ for all $X, Y \\in \\mathcal{U}$.\n\t\\end{itemize}\n\\end{definition}\nIt was proved before that a complement function does not exist in the entirety of $\\mathbb{P}_q(n)$ \\cite[Theorem~10]{BEV}. The largest size of a subset of $\\mathbb{P}_q(n)$ wherein a complement can be defined still remains an open problem. However, we will tackle that question in Section~\\ref{S4} with the additional constraint that a subset has distributive sublattice structure. The following is an upper bound on the number of one-dimensional subspaces in a subset with a complement defined on it.\n\\begin{proposition}({\\cite{BEV}, Proposition~1})\n\t\\label{CP1}\n\tSuppose there exists a complement on the subset $\\mathcal{U} \\subseteq \\mathbb{P}_2(n)$. Then $|\\mathcal{U}_1| \\le 2^{n-1}$.\n\\end{proposition}\nWe will later investigate the same for distributive sublattices of $\\mathbb{P}_q(n)$ for all prime powers $q$.\n\nBraun et al. defined linearity in $\\mathbb{P}_2(n)$ by identifying a subset that is a vector space over $\\mathbb{F}_2$ with respect to some randomly chosen linear operation such that the corresponding subspace distance metric is translation invariant within the chosen subset. Later this definition was generalized for all prime powers \\cite{PS, BK2}.\n\\begin{definition}\n\t\\label{D2}\n\tA subset $\\mathcal{U} \\subseteq \\mathbb{P}_q(n)$ is called a \\emph{linear code} if $\\{0\\} \\in \\mathcal{U}$ and there exists a function $\\boxplus: \\mathcal{U} \\times \\mathcal{U} \\rightarrow \\mathcal{U}$ such that\n\t\\begin{itemize}\n\t\t\\item[(i)] $(\\mathcal{U}, \\boxplus)$ is an abelian group;\n\t\t\\item[(ii)] $X \\boxplus \\{0\\} = X$ for all $X \\in \\mathcal{U}$;\n\t\t\\item[(iii)] $X \\boxplus X = \\{0\\}$ for all $X \\in \\mathcal{U}$; and\n\t\t\\item[(iv)] $d_S(X, Y) = d_S(X \\boxplus W, Y \\boxplus W)$ for all $X, Y, W \\in \\mathcal{U}$.\n\t\\end{itemize}\n\\end{definition}\nThe first three conditions stated in the above definition makes any linear code in $\\mathbb{P}_q(n)$ a vector space over $\\mathbb{F}_2$. It was conjectured in \\cite{BEV} that a linear code in $\\mathbb{P}_2(n)$ can be as large as $2^n$ at most.\n\nThe linear addition of two disjoint codewords in a linear code yields their usual vector space sum.\n\\begin{lemma}(\\cite{BEV}, Lemma~8)\n\t\\label{LL1}\n\tFor two codewords $X$ and $Y$ of a linear code $\\mathcal{U} \\subseteq \\mathbb{P}_q(n)$, $X \\boxplus Y = X + Y$ if $X \\cap Y = \\{0\\}$.\n\\end{lemma}\nA linear code is said to be \\emph{closed under intersection} if it is closed with respect to vector space intersection: The subspace $X \\cap Y$ is a codeword of $\\mathcal{U}$ for any two codewords $X$ and $Y$ if $\\mathcal{U}$ is a linear code closed under intersection. The following is a method to construct such class of linear codes.\n\\begin{theorem}(\\cite{BK2}, Theorem~7)\n\t\\label{LT1}\n\tSuppose there exists a linearly independent subset $\\mathcal{E} = \\{e_1, \\ldots, e_r\\}$ of $\\mathbb{F}_q^n$ over $\\mathbb{F}_q$. Let $\\{\\mathcal{E}_1, \\ldots, \\mathcal{E}_m\\}$ be a partition of $\\mathcal{E}$. Define $\\mathcal{E}_{\\mathcal{I}} := \\bigcup\\limits_{i \\in \\mathcal{I}} \\mathcal{E}_i$ for any nonempty subset $\\mathcal{I} \\subseteq [m]$ and $\\mathcal{E}_{\\phi} := \\phi$. The code $\\mathcal{U} = \\{\\langle \\mathcal{E}_{\\mathcal{I}}\\rangle: \\mathcal{I} \\subseteq [m]\\}$ is linear and closed under intersection.\n\\end{theorem}\nA linear code thus constructed is also referred to as a \\emph{code derived from a partition of a linearly independent set}. The particular case when $r = m = n$ in Theorem~\\ref{LT1} is referred to as a \\emph{code derived from a fixed basis}, and was first introduced in \\cite{PS}. It is known that any linear code closed under intersection can only be constructed in the way described in Theorem~\\ref{LT1} \\cite[Theorem~8]{BK2}. The lattice structure of such class of linear codes was studied in \\cite{BK}.\n\\begin{theorem}(\\cite{BK}, Theorem~18)\n\t\\label{LT2}\n\tA linear code in $\\mathbb{P}_q(n)$ that is closed under intersection forms a distributive sublattice of the linear lattice $\\mathbb{P}_q(n)$.\n\\end{theorem}\nThe maximum size of a linear code closed under intersection was investigated in \\cite{BK2} and it revealed that the maximal case is unique.\n\\begin{theorem}(\\cite{BK2})\n\t\\label{LT3}\n\tThe maximum size of a linear code closed under intersection in $\\mathbb{P}_q(n)$ is $2^n$. The bound is reached if and only if the code is derived from a fixed basis.\n\\end{theorem}\nWe will exploit the lattice theoretic connection of linear codes further in Section~\\ref{S4} using the unique decomposition of geometric distributive lattices.\n\\section{Uniquely Atomistic Lattices}\n\\label{UAL}\nWe will prove modularity of uniquely atomistic lattices in this section via\na series of results. First a few elementary lemmas follow from definition.\n\\begin{lemma}\n\t\\label{UAL1}\n\tIf $x = \\sup S$ and $y = \\sup T$ are two elements of a uniquely atomistic lattice $L$, then $x \\vee y = \\sup (S \\cup T)$.\n\\end{lemma}\n\\begin{proof}\n\tBy definition, if the set of all atoms in $L$ is $\\{x_1, \\ldots, x_m\\}$ then $x = \\bigvee\\limits_{i \\in S} x_i$ and $y = \\bigvee\\limits_{j \\in T} x_j$. Since the join-operation $\\vee$ is associative, it follows that $x \\vee y = \\bigvee\\limits_{k \\in S \\cup T} x_k = \\sup (S \\cup T)$.\n\\end{proof}\n\\begin{lemma}\n\t\\label{UAL2}\n\tSuppose $L$ is an uniquely atomistic lattice. For any distinct $x, y \\in L$ we must have\n\t\\begin{equation*}\n\t\tx \\wedge y = \\sup (S_1 \\cap S_2),\n\t\\end{equation*}\n\twhere $x = \\sup S_1$ and $y = \\sup S_2$.\n\\end{lemma}\n\\begin{proof}\n\tBy definition of a lattice, $x \\vee (x \\wedge y) = x$. We can write $x \\wedge y = \\sup S_3$ for some finite set $S_3$ as $L$ is uniquely atomistic. That $x = \\sup S_1$ and $y = \\sup S_2$ implies that $\\bigvee\\limits_{i \\in S_1 \\cup S_3} x_i = \\bigvee\\limits_{j \\in S_1} x_j$ by Lemma~\\ref{UAL1}. By unique atomisticity, we get $S_3 \\subset S_1$ since $x \\ne x \\wedge y$. Similarly, $S_3 \\subset S_2$. Thus $S_3 \\subseteq S_1 \\cap S_2$.\n\t\n\tAssume that $S_3 \\ne S_1 \\cap S_2$, i.e. $S_3 \\subset S_1 \\cap S_2$. But that means $\\sup (S_1 \\cap S_2)$ is a lower bound of $x$ and $y$ and $x \\wedge y \\prec \\sup (S_1 \\cap S_2)$, a contradiction. Thus the statement follows.\n\\end{proof}\n\\begin{lemma}\n\t\\label{UAL3}\n\tSuppose $x = \\sup S$ and $y = \\sup T$ are elements of a uniquely atomistic lattice $L$. If $x \\prec y$ then $S \\subset T$.\n\\end{lemma}\n\\begin{proof}\n\tAs $x \\prec y$, we have $x \\wedge y = x$. From unique atomisticity of $L$ and Lemma~\\ref{UAL2} it can be observed that $S \\cap T = S$. The rest follows because $S \\ne T$.\n\\end{proof}\nWe will now establish that modularity is inherent in any finite uniquely atomistic lattice.\n\\begin{theorem}\n\t\\label{UAT}\n\tA finite lattice $L$ is modular if $L$ is uniquely atomistic.\n\\end{theorem}\n\\begin{proof}\n\tAccording to Theorem~\\ref{TL1} it is enough to show that there exists no sublattice of $L$ which is isomorphic to $N_5$. Let the set of all atoms in $L$ be $\\{x_1, \\ldots, x_m\\}$. We proceed by contradiction.\n\t\\begin{figure}[t]\n\t\t\\centering\n\t\t\\begin{tikzpicture}[scale=0.6]\n\t\t\t\\node (A1) at (0,2) {$\\mathcal{M}$};\n\t\t\t\\node (A2) at (-2,1) {$a_1$};\n\t\t\t\\node (A3) at (-2,-1) {$a_2$};\n\t\t\t\\node (A4) at (2,0) {$b$};\n\t\t\t\\node (A5) at (0,-2) {$\\mu$};\n\t\t\n\t\t\n\t\t\t\\draw (A5) -- (A3) -- (A2) -- (A1) -- (A4) -- (A5);\n\t\t\\end{tikzpicture}\n\t\t\\caption{$N_5$ lattice}\n\t\t\\label{F0}\n\t\\end{figure}\t\t\n\t\n\tAssume that there exists a sublattice of $L$ isomorphic to $N_5$ as shown in Fig.~\\ref{F0}. By unique atomisticity of $L$, we can write the following for some fixed subsets $\\mathcal{I}_1, \\mathcal{I}_2, \\mathcal{K} \\subseteq [m]$:\n\t\\begin{equation*}\n\t\ta_1 = \\bigvee\\limits_{i \\in \\mathcal{I}_1} x_i = \\sup \\mathcal{I}_1; \\quad a_2 = \\bigvee\\limits_{j \\in \\mathcal{I}_2} x_j = \\sup \\mathcal{I}_2; \\quad b = \\bigvee\\limits_{k \\in \\mathcal{K}} x_k = \\sup \\mathcal{K}.\n\t\\end{equation*}\n\tSince $\\mathcal{M} = a_1 \\vee b$ and $\\mu = a_2 \\wedge b$, we obtain from Lemmas~\\ref{UAL1} and \\ref{UAL2} that $\\mathcal{M} = \\sup (\\mathcal{I}_1 \\cup \\mathcal{K})$ and $\\mu = \\sup (\\mathcal{I}_2 \\cap \\mathcal{K})$. Similarly, from $\\mathcal{M} = a_2 \\vee b$ and $\\mu = a_1 \\wedge b$ we yield $\\mathcal{M} = \\sup (\\mathcal{I}_2 \\cup \\mathcal{K}), \\mu = \\sup (\\mathcal{I}_1 \\cap \\mathcal{K})$. Combining both, we have the following:\n\t\\begin{eqnarray}\n\t\t\\mathcal{I}_1 \\cup \\mathcal{K} &=& \\mathcal{I}_2 \\cup \\mathcal{K}; \\label{UAE1} \\\\\n\t\t\\mathcal{I}_1 \\cap \\mathcal{K} &=& \\mathcal{I}_2 \\cap \\mathcal{K}. \\label{UAE2}\n\t\\end{eqnarray}\n\tFrom \\eqref{UAE2} it follows that $(\\mathcal{I}_1 \\backslash \\mathcal{I}_2) \\cap \\mathcal{K} = \\phi$. Since $a_2 \\prec a_1$, thus $\\mathcal{I}_2 \\subset \\mathcal{I}_1$ by Lemma~\\ref{UAL3}; i.e. $\\mathcal{I}_1 \\backslash \\mathcal{I}_2$ is nonempty. For any $l \\in \\mathcal{I}_1 \\backslash \\mathcal{I}_2$ we must have $l \\in \\mathcal{I}_2 \\cup \\mathcal{K}$ from \\eqref{UAE1}, i.e. $l \\in \\mathcal{K}$. This implies that $\\mathcal{I}_1 \\backslash \\mathcal{I}_2 \\subseteq \\mathcal{K}$, or in other words $\\mathcal{I}_1 \\backslash \\mathcal{I}_2 = (\\mathcal{I}_1 \\backslash \\mathcal{I}_2) \\cap \\mathcal{K} = \\phi$. This is a contradiction to $\\mathcal{I}_2 \\subset \\mathcal{I}_1$, hence proved.\n\\end{proof}\n\\begin{corollary}\n\t\\label{UAC}\n\tAny finite uniquely atomistic lattice is geometric.\n\\end{corollary}\n\\begin{proof}\n\tFollows directly from Definition~\\ref{LD6} and Theorem~\\ref{UAT}.\n\\end{proof}\n\\begin{remark}\n\tThe converse of the statement of Theorem~\\ref{UAT} is not true, i.e. a modular lattice need not always be uniquely atomistic. E.g. the $M_3$ lattice is not uniquely atomistic. From Fig.~\\ref{F1} one can see that $\\mathbb{F}_2^2 = \\langle \\{0, 1\\}\\rangle + \\langle \\{1, 0\\}\\rangle = \\langle \\{0, 1\\}\\rangle + \\langle \\{1, 1\\}\\rangle$, where $+$ denotes the usual vector space addition.\n\\end{remark}\nTheorem~\\ref{UAT} brings us to a position where we can prove the unique-decomposition theorem in the next section.\n\n\\section{The Unique-decomposition Theorem}\n\\label{S3}\nIn this section we will establish the unique criterion needed for an atomistic lattice to be distributive and use that to characterize finite geometric distributive lattices. Atoms of geometric distributive lattices play an important part in the unique characterization. The first step towards that is observing that the greatest lower bound of two atoms in any lattice is the least element of that lattice.\n\\begin{lemma}\n\t\\label{L1}\n\tFor any two distinct atoms $x_1, x_2$ in a lattice $(L, \\vee, \\wedge)$, we have $x_1 \\wedge x_2 = O$, where $O$ is the least element of $L$.\n\\end{lemma}\n\\begin{proof}\n\tSuppose $x_1 \\wedge x_2 \\neq O$. By definition, $x_1 \\wedge x_2 \\preceq x_1$. As $x_1$ is an atom in $L$, hence $O \\lessdot x_1$, which means $x_1 \\wedge x_2 = x_1$ by our supposition. Similarly we obtain $x_1 \\wedge x_2 = x_2$. Since $x_1, x_2$ are distinct, this is a contradiction and the result follows.\n\\end{proof}\n\nNext we will prove the generalization of the above lemma for any finite number of atoms in a distributive lattice.\n\\begin{lemma}\n\t\\label{L2}\n\tLet $\\{x_1, \\ldots, x_m\\}$ be a set of atoms in a distributive lattice $M$. Then for all $i \\in [m]$,\n\t\\begin{equation*}\n\t\tx_i \\wedge (\\bigvee\\limits_{j \\in [m] \\backslash \\{i\\}} x_j) = O.\n\t\\end{equation*}\n\\end{lemma}\n\\begin{proof}\n\tBy distributivity in $M$ we can write,\n\t\\begin{equation*}\n\t\tx_i \\wedge (\\bigvee\\limits_{j \\in [m] \\backslash \\{i\\}} x_j) = \\bigvee\\limits_{j \\in [m] \\backslash \\{i\\}} (x_i \\wedge x_j).\n\t\\end{equation*}\n\tAccording to Lemma~\\ref{L1}, $x_i \\wedge x_j = O$ for $i \\ne j$, and the statement is proved.\n\\end{proof}\n\nThe height of join of two atoms in a modular lattice (if the height function is defined) is the sum of heights of the individual atoms, as illustrated in the following lemma.\n\\begin{lemma}\n\t\\label{L3}\n\tSuppose $L$ is a modular lattice and $h$ is the height function defined on $L$. For any two atoms $x$ and $y$ in $L$ the following holds true:\n\t\\begin{equation*}\n\t\th(x \\vee y) = h(x) + h(y).\n\t\\end{equation*}\n\\end{lemma}\n\\begin{proof}\n\tThe height function $h$ is a valuation which according to Definition~\\ref{LD8} implies that $h(x \\vee y) = h(x) + h(y) - h(x \\wedge y)$. As $x$ and $y$ are atoms in $L$, Lemma~\\ref{L1} dictates that $x \\wedge y = O$. That $h(O) = h(x \\wedge y) = 0$ which proves the rest.\n\\end{proof}\n\nWe are now going to generalize Lemma~\\ref{L3} for any $m \\ge 2$ number of atoms if the lattice is also distributive.\n\\begin{lemma}\n\t\\label{L3a}\n\tConsider a set $\\{x_1, \\ldots, x_m\\}$ of $m \\ge 2$ atoms in a distributive lattice $L$. If $h$ is the height function defined on $L$ then\n\t\\begin{equation*}\n\t\th(\\bigvee\\limits_{i \\in [m]} x_i) = \\sum\\limits_{i \\in [m]}h(x_i).\n\t\\end{equation*}\n\\end{lemma}\n\\begin{proof}\n\tThe proof is by induction. The base case for $m = 2$ is covered by Lemma~\\ref{L3}. Suppose the statement holds true for any $(m-1)$ atoms in $L$, i.e., $h(\\bigvee\\limits_{i \\in [m-1]} x_i) = \\sum\\limits_{i \\in [m-1]}h(x_i)$. Since the join operation $\\vee$ is associative over the elements of $L$, we can write $\\bigvee\\limits_{i \\in [m]} x_i = x_m \\vee (\\bigvee\\limits_{j \\in [m-1]} x_j)$. The height of $\\bigvee\\limits_{i \\in [m]} x_i$ can therefore be expressed as:\n\t\\begin{equation*}\n\t\th(\\bigvee\\limits_{i \\in [m]} x_i) = h(x_m) + h(\\bigvee\\limits_{i \\in [m-1]} x_i) - h(x_m \\wedge (\\bigvee\\limits_{i \\in [m-1]} x_i)).\n\t\\end{equation*}\n\tAs $h(x_m \\wedge (\\bigvee\\limits_{i \\in [m-1]} x_i)) = 0$ according to Lemma~\\ref{L2}, the rest follows from the induction hypothesis.\n\\end{proof}\n\nConsequence of Lemma~\\ref{L3a} is that the number of atoms in a distributive sublattice of a finite modular lattice of height $n$ cannot exceed $n$. We formally state the result.\n\\begin{proposition}\n\t\\label{P2}\n\tIf $L$ is a finite modular lattice of height $n$ then the number of atoms in a distributive sublattice $M$ of $L$ can be at most $n$. The maximum number of atoms is reached if and only if all atoms of $M$ are also atoms in $L$ and the greatest element of $L$ is join of the atoms in $M$.\n\\end{proposition}\n\\begin{proof}\n\tSuppose $\\{x_1, \\ldots, x_m\\}$ be the set of atoms in $M$. If $I$ is the greatest element in $L$ then certainly $\\bigvee\\limits_{i \\in [m]} x_i \\preceq I$. As $h$ is an isotone valuation, Definition~\\ref{LD8}(ii) implies that $h_L(\\bigvee\\limits_{i \\in [m]} x_i) \\le h_L(I)$. Observe that $h_L(y) \\ge h_M(y)$ for all $y \\in M$. Since $h_L(I) = n$, by Lemma~\\ref{L3a} we have the following inequality that proves the claim:\n\t\\begin{equation}\n\t\t\\label{E0}\n\t\tm = \\sum\\limits_{i \\in [m]} h_M(x_i) = h_M(\\bigvee\\limits_{i \\in [m]} x_i) \\le h_L(\\bigvee\\limits_{i \\in [m]} x_i) \\le h_L(I) = n.\n\t\\end{equation}\n\tSince $h$ is positive isotone, it is evident from \\eqref{E0} that $m = n$ if and only if $I = \\bigvee\\limits_{i \\in [m]} x_i$ and $h_M(\\bigvee\\limits_{i \\in [m]} x_i) = h_L(\\bigvee\\limits_{i \\in [m]} x_i)$. That $x_i$'s are atoms in $L$ for all $i \\in [m]$ if and only if $x_1 \\prec x_1 \\vee x_2 \\prec \\cdots \\prec \\bigvee\\limits_{j \\in [m-1]} x_j \\prec \\bigvee\\limits_{i \\in [m]} x_i$ is a maximal chain in $L$ proves the rest.\n\\end{proof}\n\nAny element in a geometric lattice can be expressed as a join of atoms (Definition~\\ref{LD6}). However, such representation is not unique. To elaborate, if $\\{x_1, \\ldots, x_m\\}$ is the set of atoms in a geometric lattice $L$ then there may exist $y \\in L$ such that $y = \\bigvee\\limits_{i \\in \\mathcal{I} \\subseteq [m]} x_i = \\bigvee\\limits_{j \\in \\mathcal{J} \\subseteq [m]} x_j$ for two different subsets $\\mathcal{I}, \\mathcal{J} \\subseteq [m]$. We will now show that representation of elements as join of atoms is unique if and only if the lattice is also distributive. In fact we will prove the following which is a more generalized statement.\n\\begin{theorem}[Unique-decomposition Theorem]\n\t\\label{T1}\n\tA finite atomistic lattice is distributive if and only if it is uniquely atomistic.\n\n\\end{theorem}\n\\begin{proof}\n\tLet $M$ be an atomistic lattice with $\\{x_1, \\ldots, x_m\\}$ as its set of all atoms. First we prove that $M$ is uniquely atomistic, i.e. $\\bigvee\\limits_{i \\in \\mathcal{I} \\subseteq [m]} x_i \\in M$ is uniquely determined by $\\mathcal{I} \\subseteq [m]$ when $M$ is distributive.\n\t\n\tThe proof is by contradiction. Suppose the claim is false, i.e. there exist subsets $\\mathcal{I}, \\mathcal{J} \\subseteq [m]$ such that $\\mathcal{I} \\ne \\mathcal{J}$ and $\\bigvee\\limits_{i \\in \\mathcal{I}} x_i = \\bigvee\\limits_{j \\in \\mathcal{J}} x_j$. Since $\\mathcal{I}$ and $\\mathcal{J}$ are distinct, at least one of them is not contained within the other. Without loss of generality, suppose $\\mathcal{I} \\nsubseteq \\mathcal{J}$. Then the difference set $\\mathcal{I} \\backslash \\mathcal{J}$ is nonempty, i.e. there exists an integer $l \\in \\mathcal{I} \\backslash \\mathcal{J}$. Thus we can write as per our supposition:\n\t\\begin{equation}\n\t\t\\label{E1}\n\t\tx_l \\wedge (\\bigvee\\limits_{i \\in \\mathcal{I}} x_i) = x_l \\wedge (\\bigvee\\limits_{j \\in \\mathcal{J}} x_j).\n\t\\end{equation}\n\t\n\tThe individual terms can be decomposed further. Since $l \\in \\mathcal{I}$, by distributivity in $M$ we obtain $x_l \\wedge (\\bigvee\\limits_{i \\in \\mathcal{I}} x_i) = (x_l \\wedge (\\bigvee\\limits_{i \\in \\mathcal{I} \\backslash \\{l\\}} x_i)) \\bigvee (x_l \\wedge x_l) = O \\vee x_l = x_l$. The penultimate step follows from Lemma~\\ref{L2}. Similar technique yields $x_l \\wedge (\\bigvee\\limits_{j \\in \\mathcal{J}} x_j) = O$ as $l \\notin \\mathcal{J}$. Hence \\eqref{E1} suggests $x_l = O$. However, this is a contradiction since $l \\in \\mathcal{I} \\subseteq [m]$, i.e. $x_l$ is an atom. We conclude that our initial assumption was wrong and the representation $\\bigvee\\limits_{i \\in \\mathcal{I}} x_i$ is uniquely determined by $\\mathcal{I}$.\n\t\n\tNow it remains to prove that $M$ is distributive if it is uniquely atomistic. Once again we proceed by contradiction. That $M$ is modular follows at once from Theorem~\\ref{UAT}. Suppose $M$ is modular non-distributive. By Theorem~\\ref{TL1} $M$ must contain a sublattice isomorphic to $M_3$. In other words there exist $y_1, y_2, y_3 \\in M$ such that $y_1 \\vee y_2 = y_2 \\vee y_3 = y_3 \\vee y_1$ and $y_1 \\wedge y_2 = y_2 \\wedge y_3 = y_3 \\wedge y_1$, where $y_i \\npreceq y_j$ for $i \\neq j$ (See Fig.~\\ref{F2}). Suppose $\\mathcal{M} = y_1 \\vee y_2$ and $\\mathfrak{m} = y_1 \\wedge y_2$. By imposition of unique atmosticity, $y_j = \\bigvee\\limits_{i \\in \\mathcal{J}_j} x_i$ for $j = 1, 2, 3$, where $\\mathcal{J}_j \\subseteq [m]$ uniquely determines $y_j$. This implies the following:\n\t\\begin{equation}\n\t\t\\label{E1a}\n\t\t\\mathcal{M} = \\bigvee\\limits_{i \\in \\mathcal{J}_1 \\cup \\mathcal{J}_2} x_i = \\bigvee\\limits_{j \\in \\mathcal{J}_2 \\cup \\mathcal{J}_3} x_j = \\bigvee\\limits_{k \\in \\mathcal{J}_3 \\cup \\mathcal{J}_1} x_k.\n\t\\end{equation}\n\n\n\n\n\tSince $M$ is uniquely atomistic, applying Lemma~\\ref{UAL1} to \\eqref{E1a} implies that $\\mathcal{J}_1 \\cup \\mathcal{J}_2 = \\mathcal{J}_2 \\cup \\mathcal{J}_3 = \\mathcal{J}_3 \\cup \\mathcal{J}_1$.\n\t\n\t\\begin{figure}\n\t\t\\centering\n\t\t\\begin{tikzpicture}[scale=0.7]\n\t\t\t\\node (A1) at (0,2) {$\\mathcal{M}$};\n\t\t\t\\node (A2) at (-2,0) {$y_1$};\n\t\t\t\\node (A3) at (0,0) {$y_2$};\n\t\t\t\\node (A4) at (2,0) {$y_3$};\n\t\t\t\\node (A5) at (0,-2) {$\\mathfrak{m}$};\n\t\t\t\\node[right=0pt of A1,inner xsep=0pt] {$= y_1 \\vee y_2 = y_2 \\vee y_3 = y_3 \\vee y_1$};\n\t\t\t\\node[right=0pt of A5,inner xsep=0pt] {$= y_1 \\wedge y_2 = y_2 \\wedge y_3 = y_3 \\wedge y_1$};\n\t\t\t\\draw (A5) -- (A2) -- (A1) -- (A3) -- (A5) -- (A4) -- (A1);\n\t\t\\end{tikzpicture}\n\t\t\\caption{$M_3$-sublattice in $M$}\n\t\t\\label{F2}\n\t\\end{figure}\n\t\n\tOn the other hand $\\mathfrak{m} = y_1 \\wedge y_2$, where $y_1 = \\sup \\mathcal{J}_1$ and $y_2 = \\sup \\mathcal{J}_2$. If $\\mathcal{L} \\subseteq [m]$ uniquely determines $\\mathfrak{m}$, i.e. $\\mathfrak{m} = \\sup \\mathcal{L}$, then Lemma~\\ref{UAL2} implies that $\\mathcal{L} = \\mathcal{J}_1 \\cap \\mathcal{J}_2$. Similarly we can deduce for $y_2 \\wedge y_3$ and $y_3 \\wedge y_1$ which indicates that $\\mathcal{J}_1 \\cap \\mathcal{J}_2 = \\mathcal{J}_2 \\cap \\mathcal{J}_3 = \\mathcal{J}_3 \\cap \\mathcal{J}_1$.\n\t\n\tWe can now express $\\mathcal{J}_1$ as $\\mathcal{J}_1 = \\mathcal{J}_1 \\cap (\\mathcal{J}_1 \\cup \\mathcal{J}_2) = \\mathcal{J}_1 \\cap (\\mathcal{J}_2 \\cup \\mathcal{J}_3) = (\\mathcal{J}_1 \\cap \\mathcal{J}_2) \\cup (\\mathcal{J}_1 \\cap \\mathcal{J}_3) = \\mathcal{J}_1 \\cap \\mathcal{J}_2$, i.e. $\\mathcal{J}_1 \\subseteq \\mathcal{J}_2$. This means $y_1 \\preceq y_2$, which contradicts our initial assumption. Hence, $M$ is distributive.\n\\end{proof}\n\n\\begin{corollary}\n\t\\label{UDT}\n\tA geometric lattice is distributive if and only if it is uniquely atomistic.\n\\end{corollary}\n\\begin{proof}\n\tA geometric lattice is finite atomistic by definition, which concludes the proof.\n\\end{proof}\n\\begin{remark}\n\tThe semimodularity of a geometric lattice is not required for it to be distributive.\n\\end{remark}\nThe consequence of Corollary~\\ref{UDT} is that any element of a geometric distributive lattice can be uniquely decomposed as join of its atoms. The next statement also follows from Theorem~\\ref{T1}:\n\\begin{corollary}\n\t\\label{P3}\n\tThe greatest element in a geometric distributive lattice is the join of all of its atoms.\n\\end{corollary}\n\\begin{proof}\n\tLet $\\{x_1, \\ldots, x_m\\}$ be the set of all atoms in a geometric distributive lattice $M$. We aim to show that the greatest element in $M$ is $g := \\bigvee\\limits_{i \\in [m]} x_i$. To that end, say $y \\in M$ is the greatest element in $M$. By Theorem~\\ref{T1} we can write $y = \\bigvee\\limits_{i \\in \\mathcal{I}} x_i$ for some $\\mathcal{I} \\subseteq [m]$. However, by associativity of the join operation $\\vee$ in $M$, $g$ can also be decomposed as $g = (\\bigvee\\limits_{j \\in [m] \\backslash \\mathcal{I}} x_j) \\vee (\\bigvee\\limits_{i \\in \\mathcal{I}} x_i) = (\\bigvee\\limits_{j \\in [m] \\backslash \\mathcal{I}} x_j) \\vee y$, which implies $y \\preceq g$; thus the only possibility is $y = g$, which concludes the proof.\n\\end{proof}\nIn the following section we will see a few applications of the Unique-decomposition theorem, mainly in the context of linearity and complements in $\\mathbb{P}_q(n)$.\n\n\\section{Applications of the Unique-decomposition Theorem}\n\\label{S4}\nThe unique-decomposition theorem, akin to unique decomposition in context of linear subspace codes \\cite[Proposition~12]{BK2}, lays the path for determining the maximum size of a distributive sublattice of a finite geometric lattice. We also characterize the extremal case.\n\\begin{theorem}\n\t\\label{T2}\n\tThe size of any distributive sublattice of a finite geometric lattice of height $n$ can be at most $2^n$. The bound is reached if and only if each of the atoms in the sublattice is also an atom in the geometric lattice and the greatest element of the geometric lattice belongs to the distributive sublattice.\n\\end{theorem}\n\\begin{proof}\n\tSuppose $M$ is a distributive sublattice of a finite geometric lattice $L$ with the height function $h$ defined on $L$ such that $h(I) = n$, where $I$ is the greatest element of $L$. We require to show that $|M| \\le 2^n$.\n\t\n\tBy supposition $M$ is geometric. Let $\\{x_1, \\ldots, x_m\\}$ be the set of all atoms in $M$. We define a mapping $\\Phi$ from $\\mathcal{P}(m)$ to $M$ as below:\n\t\\begin{eqnarray*}\n\t\t\\Phi : \\mathcal{P}(m) &\\longrightarrow& M \\nonumber \\\\\n\t\t\\mathcal{I} &\\mapsto& \\bigvee\\limits_{i \\in \\mathcal{I}} x_i.\n\t\\end{eqnarray*}\n\tBy definition the map $\\Phi$ is well-defined. Suppose there exist $\\mathcal{I}, \\mathcal{J} \\in \\mathcal{P}(m)$ such that $\\Phi(\\mathcal{I}) = \\Phi(\\mathcal{J})$, i.e., $\\bigvee\\limits_{i \\in \\mathcal{I}} x_i = \\bigvee\\limits_{j \\in \\mathcal{J}} x_j$. As $M$ is geometric distributive, Corollary~\\ref{UDT} dictates that $\\mathcal{I} = \\mathcal{J}$; thus $\\Phi$ is injective. To check that $\\Phi$ is also surjective, any $y \\in M$ can be expressed as $y = \\bigvee\\limits_{i \\in \\mathcal{S} \\subseteq [m]} x_i$ for some $\\mathcal{S} \\subseteq [m]$ as $M$ is geometric; the choice of $\\mathcal{S}$ is unique according to Corollary~\\ref{UDT}, hence $y = \\Phi(\\mathcal{S})$. Therefore $\\Phi$ is a bijective map, which implies that $|M| = |\\mathcal{P}(m)| = 2^m$. Combining this with Proposition~\\ref{P2} yields $|M| \\le 2^n$.\n\n\n\n\n\t\n\tFor the extremal case we must have $m = n$. The rest then follows from Proposition~\\ref{P2}.\n\\end{proof}\n\\begin{remark}\n\tAn atom in a sublattice $M$ of a geometric lattice $L$ may not be an atom in $L$. E.g. consider the lattice $\\mathcal{P}(4)$, the set of all subsets of $\\{1, 2, 3, 4\\}$. It is a geometric lattice with atoms $\\{1\\}, \\{2\\}, \\{3\\}$ and $\\{4\\}$. The sublattice $M = \\{\\phi, \\{1, 2\\}, \\{3, 4\\}, \\{1, 2, 3, 4\\}\\}$ of $\\mathcal{P}(4)$ has atoms $\\{1, 2\\}$ and $\\{3, 4\\}$, none of which is an atom in $\\mathcal{P}(4)$. On the other hand, a proper sublattice of a geometric lattice $L$ does not contain all atoms of $L$.\n\\end{remark}\n\\begin{remark}\n\tIrrespective of the choice of the lattice $M$, the mapping $\\Phi$ in Theorem~\\ref{T2} always maps the ground set $[m]$ to $I$ and the empty subset $\\phi$ to $O$.\n\\end{remark}\nClass of distributive sublattices of maximum size in a finite geometric lattice can be characterized in an alternative way.\n\\begin{corollary}\n\t\\label{C1}\n\tThe size of a distributive sublattice $M$ of a finite geometric lattice $L$ of height $n$ is $2^n$ if and only if $M$ contains $n$ atoms of $L$.\n\\end{corollary}\n\\begin{proof}\n\tBy Proposition~\\ref{P2} $M$ can have at most $n$ atoms, and for the extremal case all of them belong to $L$. Since $M$ is geometric distributive, proof technique of Theorem~\\ref{T2} suggests that $|M| = |\\mathcal{P}(n)| = 2^n$.\n\t\n\tConversely, if $|M| = 2^n$ then according to Theorem~\\ref{T2} each atom of $M$ is also an atom in $L$ and $I \\in M$. Suppose $\\{x_1, \\ldots, x_m\\}$ is the set of all atoms in $M$; thus $h_L(x_i) = 1$ for all $i \\in [m]$. By Corollary~\\ref{P3}, $\\bigvee\\limits_{i \\in [m]} x_i$ is the greatest element in $M$, which implies that $I = \\bigvee\\limits_{i \\in [m]} x_i$. By Lemma~\\ref{L3a}, $m = \\sum\\limits_{i \\in [m]}h_L(x_i) = h_L(\\bigvee\\limits_{i \\in [m]} x_i) = n$, which settles the proof.\n\n\n\n\\end{proof}\n\\begin{proof}[Proof of Theorem~\\ref{LT3}]\n\tThe upper bound follows at once from Theorem~\\ref{LT2} and Theorem~\\ref{T2}. Corollary~\\ref{C1} implies that the maximal case occurs if and only if the number of one-dimensional codewords is $n$, i.e. the code is derived from a fixed basis.\n\\end{proof}\nWe now consider the Whitney numbers of a geometric distributive lattice.\n\\begin{corollary}\n\t\\label{C2}\n\tThe Whitney numbers of a distributive sublattice $L$ of a finite geometric lattice of height $n$ are bounded by $W_k(L) \\le \\binom{n}{k}$ for all $k \\in \\{0, 1, \\ldots, n\\}$. Equality occurs if and only if $L$ contains $n$ atoms.\n\\end{corollary}\n\\begin{proof}\n\tLet the set of atoms in $L$ be $\\{x_1, \\ldots, x_m\\}$. The case of $k = 0$ is obvious since $h(O) = 0$. As shown in proof of Theorem~\\ref{T2}, there exists a bijection from $L$ to $\\mathcal{P}(m)$ that sends $\\bigvee_{i \\in \\mathcal{I}} x_i$ to $\\mathcal{I} \\subseteq [m]$. From Lemma~\\ref{L3a} it follows that $W_k(L) = \\binom{m}{k}$ for all $k \\in [m]$. As $m \\le n$ by Proposition~\\ref{P2}, the result follows. The bound is achieved if and only if $m = n$.\n\\end{proof}\n\\begin{remark}\n\tThe statement in Corollary~\\ref{C2} is similar in nature to the main result in Pai and Rajan's paper \\cite[Theorem~2]{PS}.\n\\end{remark}\nIn the sequel we will consider the linear lattice $\\mathbb{P}_q(n)$ instead of a geometric lattice in general. $\\mathbb{P}_q(n)$ is non-distributive geometric. There remain a few unanswered questions regarding linearity and complements in $\\mathbb{P}_q(n)$ that can be resolved by applying the unique-decomposition theorem. It was shown before that a linaer code in $\\mathbb{P}_q(n)$ that is closed under intersection necessarily is a distributive sublattice of the geometric lattice $\\mathbb{P}_q(n)$ \\cite{BK}. Using the unique-decomposition theorem we now prove the converse.\n\\begin{theorem}\n\t\\label{T3}\n\tA subset $\\mathcal{U} \\subseteq \\mathbb{P}_q(n)$ is a distributive sublattice of the corresponding linear lattice $\\mathbb{P}_q(n)$ if and only if $\\mathcal{U}$ is a linear code closed under intersection.\n\\end{theorem}\n\\begin{proof}\n\tSuppose $\\mathcal{U}$ is a distributive sublattice of $\\mathbb{P}_q(n)$ and $\\{X_1, \\ldots, X_m\\}$ is the set of all atoms in $\\mathcal{U}$. Obviously $\\mathcal{U}$ is geometric distributive, which according to Lemma~\\ref{L2} implies that\n\t\\begin{equation}\n\t\t\\label{E3}\n\t\tX_i \\cap (\\sum_{j \\in [m] \\backslash \\{i\\}} X_j) = \\{0\\}, \\qquad \\forall i \\in [m].\n\t\\end{equation}\n\tApplying the unique-decomposition theorem it is easy to see that any $Y \\in \\mathcal{U}$ can be uniquely expressed as $Y = \\sum\\limits_{i \\in \\mathcal{I}} X_i$ for some fixed $\\mathcal{I} \\subseteq [m]$. If we choose arbitrary bases $B_i$ that span $X_i$ over $\\mathbb{F}_q$ for all $i \\in [m]$ then \\eqref{E3} implies that the set $\\{B_1, \\ldots, B_m\\}$ is a partition of $B := \\bigcup_{i=1}^{m} B_i$, a linearly independent subset of $\\mathbb{F}_q^n$ over $\\mathbb{F}_q$. Expressing any $Y = \\sum\\limits_{i \\in \\mathcal{I}} X_i$ as $Y = \\langle B_{\\mathcal{I}}\\rangle$ where $B_{\\mathcal{I}} := \\cup_{i \\in \\mathcal{I}} B_i$, we can say by Theorem~\\ref{LT1} that $\\mathcal{U} = \\{\\langle B_{\\mathcal{I}}\\rangle: \\mathcal{I} \\subseteq [m]\\}$ is a linear code closed under intersection with linear addition $\\boxplus$ of two codewords $Y_1 = \\sum\\limits_{j \\in \\mathcal{I}_1} X_j$ and $Y_2 = \\sum\\limits_{l \\in \\mathcal{I}_2} X_l$ defined as:\n\t\\begin{equation*}\n\t\tY_1 \\boxplus Y_2 := \\langle B_{\\mathcal{I}_1 \\triangle \\mathcal{I}_2}\\rangle = \\sum\\limits_{j \\in \\mathcal{I}_1 \\triangle \\mathcal{I}_2} X_j.\n\t\\end{equation*}\n\t\n\tThe converse is basically the statement of Theorem~\\ref{LT2}.\n\\end{proof}\nThe maximum size of a subset of $\\mathbb{P}_q(n)$ wherein a complement function can be defined is hitherto unknown. We next investigate any such subset of $\\mathbb{P}_q(n)$ that has a distributive sublattice structure.\n\\begin{theorem}\n\t\\label{T4}\n\tA subset $\\mathcal{U} \\subseteq \\mathbb{P}_q(n)$ is a distributive sublattice of the linear lattice $\\mathbb{P}_q(n)$ with a complement function defined on $\\mathcal{U}$ if and only if $\\mathcal{U}$ is a linear code closed under intersection with $\\mathbb{F}_q^n \\in \\mathcal{U}$.\n\\end{theorem}\n\\begin{proof}\n\tSuppose $\\mathcal{U}$ is a distributive sublattice of $\\mathbb{P}_q(n)$ and $f : \\mathcal{U} \\rightarrow \\mathcal{U}$ is a complement on $\\mathcal{U}$. It follows directly from Theorem~\\ref{T3} that $\\mathcal{U}$ is a linear code closed under intersection. For any $X \\in \\mathcal{U}$, the direct sum of $X$ and $f(X)$ is $X \\oplus f(X) = \\mathbb{F}_q^n$. Since $X \\cap f(X) = \\{0\\}$ by definition of $f$, it follows from Lemma~\\ref{LL1} that $X \\boxplus f(X) = X \\oplus f(X) = \\mathbb{F}_q^n \\in \\mathcal{U}$.\n\t\n\tConversely, if $\\mathcal{U}$ is a linear code closed under intersection with $\\mathbb{F}_q^n \\in \\mathcal{U}$ then the function $f$ defined as $f(X) := X \\boxplus \\mathbb{F}_q^n$ for all $X \\in \\mathcal{U}$ serves as a complement function on $\\mathcal{U}$. $\\mathcal{U}$ is a distributive sublattice of $\\mathbb{P}_q(n)$ according to Theorem~\\ref{LT2}.\n\\end{proof}\n\\begin{corollary}\n\t\\label{C3}\n\tThe largest distributive sublattice of $\\mathbb{P}_q(n)$ on which a complement function can be defined is a code derived from a fixed basis.\n\\end{corollary}\n\\begin{proof}\n\tBy Theorem~\\ref{T4} a distributive sublattice of $\\mathbb{P}_q(n)$ on which a complement function can be defined is a linear code closed under intersection containing $\\mathbb{F}_q^n$. Theorem~\\ref{LT3} indicates that the code has a maximum size if and only if it is derived from a fixed basis.\n\\end{proof}\nThe maximum size of a sublattice of $\\mathbb{P}_q(n)$ wherein a complement function can be defined is, however, unknown. It needs to be investigated first whether a complement function can exist in a non-distributive sublattice of $\\mathbb{P}_q(n)$.\n\\section{Conclusion}\n\\label{S5}\nIn this paper we have derived the unique criterion required for an atomistic lattice to be distributive and used that to characterize all finite geometric distributive lattices. Using the unique characterization we were able to prove that the size of a distributive sublattice of a finite geometric lattice of height $n$ is always upper bounded by $2^n$, which was conjectured in \\cite{PS}. We also applied the characterization to the linear lattice $\\mathbb{P}_q(n)$, which is geometric, to obtain certain results regarding linearity and complements in $\\mathbb{P}_q(n)$.\n\nTheorem~\\ref{T3} states that any distributive sublattice of $\\mathbb{P}_q(n)$ can be used to construct a linear code in $\\mathbb{P}_q(n)$ that is closed under intersection. This result is similar to the fact that certain $d$-intersecting families in $\\mathbb{G}_q(n, 2d)$ can always be used to construct \\emph{equidistant} linear codes with constant distance $2d$ \\cite{PB}. It might be interesting to find a more generalized statement that encapsulates the essence of both these results.\n\n\\section*{Acknowledgement}\n\nThe author would like to thank Prof. Navin Kashyap and Dr. Arijit Ghosh for their insightful comments.\n\n\\bibliographystyle{ieeetr}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzevys b/data_all_eng_slimpj/shuffled/split2/finalzzevys new file mode 100644 index 0000000000000000000000000000000000000000..a213a5ff1a6bba9b42f4fd646fabeb62dc5e4867 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzevys @@ -0,0 +1,5 @@ +{"text":"\\section{Catalogue construction}\n\nWhilst semi-analytic models have proven useful in modelling and predicting numerous properties of the galaxy population (Kauffmann, White \\& Guiderdoni \\cite{kwg}; Cole et al \\cite{coleetal94}; Somerville \\& Primack \\cite{somervilleprimack} they provide only limited information on the spatial distribution of galaxies, and so it has been difficult to study in detail the clustering properties of galaxies using these models. Recently, semi-analytic models have been combined with N-body simulations to provide the required spatial information (Kauffmann, Nusser \\& Steinmetz \\cite{kns}; Governato et al \\cite{fabio}; Kauffmann et al \\cite{gketal}). We have used a similar technique to study the clustering of galaxies in CDM universes within well constrained semi-analytic models. The main difference between our technique and that of Kauffmann et al \\cite{gketal} is that whilst they extract the merging history of each dark matter halo directly from the simulation, we construct this history using the extended Press-Schechter theory. We find that this gives the same statistical results as the Kauffmann et al method and allows us to resolve merger trees to much smaller masses.\n\nTo construct a catalogue of galaxies containing spatial information we use the following procedure: (i) take the output from a dissipationless N-body simulation of dark matter and use a group finding algorithm (here we use the friends-of-friends algorithm with the standard linking length of $b=0.2$) to locate bound, virialised haloes of dark matter of 10 or more particles (such groups have been shown to be stable by Kauffmann et al \\cite{gketal}); (ii) determine the mass of each group, the position and velocity of its centre of mass and the positions and velocities of randomly selected particles within the group; (iii) for each group use a semi-analytic model of galaxy formation constrained to match the local B and K-band luminosity functions to determine the population of galaxies living within the dark matter halo; (iv) attach the central galaxy of the halo to the centre of mass of the group and attach any satellite galaxies to randomly selected particles within the halo so that galaxies trace mass within a given dark matter halo (which may not be exactly true in reality because of processes such as dynamical friction).\n\nThis results in a galaxy catalogue which can be analysed to determine the clustering properties of galaxies of any given luminosity, morphology, colour and so on. In fact by using this technique it is possible to produce catalogues of galaxies complete with spatial information (or alternatively redshifts, and angular coordinates) with any observationally motivated selection criteria. Furthermore, by identifying dark matter halos on the past lightcone of an observer a full kock galaxy redshift survey can be constructed. The luminosity functions determined from the galaxy catalogue are shown in Figure \\ref{fig1}. Although they become incomplete at the faint end because of the limited resolution of the N-body simulation there is good agreement between the bright ends and the observed luminosity functions. Since we only consider the clustering of galaxies for which our catalogue is complete the resolution limit is not important. We find however that an accurate match to the bright end of the luminosity function is important as it strongly constrains the resulting two-point correlation function. An example of the information produced by our model is given in Figure \\ref{fig2}, which shows a slice through a $\\Lambda$CDM N-body simulation upon which the positions of galaxies brighter than $M_{\\mathrm B} - 5 \\log h = -19.5$ (we define the Hubble constant to be ${\\mathrm H}_0 = 100 h$ km s$^{-1}$ Mpc$^{-1}$) have been overlaid as circles. It can be seen quite clearly that the galaxies trace the mass to some extent. To determine exactly how well galaxies trace the underlying dark matter we estimate the two-point correlation function of these galaxies.\n\n\\begin{figure}\n\\centering\n\\begin{tabular}{cc}\n\\psfig{file=benson-fig1a.ps,width=80mm} & \\psfig{file=benson-fig1b.ps,width=80mm}\n\\end{tabular}\n\\caption{The local B and K-band luminosity functions from our model compared to various observational determinations. Note the good agreement between model (solid line) and observations (symbols) at the bright end. Our galaxy catalogues become incomplete at the faint end of the luminosity functions (faintwards of $M_{\\mathrm B} - 5 \\log h \\approx -17.5$ in the B-band and $M_{\\mathrm K} - 5 \\log h \\approx -20.5$ in the K-band) due to the limited resolution of the N-body simulation. Our catalogues are constructed using only galaxies for which a complete sample is available within the model.}\n\\label{fig1}\n\\end{figure}\n\n\\begin{figure}\n\\centering\n\\hspace{0.in}\\psfig{file=benson-fig2.ps,width=100mm}\n\\caption{A slice through a $\\Lambda$CDM N-body simulation. The volume shown is 141 $\\times$ 141 $\\times$ 8 $h^{-1}$ Mpc. Dark matter is shown by the greyscale, with the darker areas being the most dense. Overlaid are the positions of all galaxies brighter than $M_{\\mathrm B} - 5 \\log h = -19.5$ indicated by circles.}\n\\label{fig2}\n\\end{figure}\n\n\\section{The two-point correlation function}\n\n\\begin{figure}[t]\n\\centering\n\\hspace{0.in}\\psfig{file=benson-fig3.ps,width=100mm}\n\\caption{The correlation function of galaxies brighter than $M_{\\mathrm B} - 5 \\log h = -19.5$ in a $\\Lambda$CDM cosmology. Points with errorbars show the APM galaxy correlation function of Baugh \\cite{APM}. The dotted line shows the dark matter correlation function whilst the solid line shows the correlation function of galaxies in our model (the dashed lines to either side indicate the Poisson errors on this correlation function).}\n\\label{fig3}\n\\end{figure}\n\nShown in Figure \\ref{fig3} is the two-point correlation function of galaxies brighter than $M_{\\mathrm B} - 5 \\log h = -19.5$ in our $\\Lambda$CDM model (which has $\\Omega _0 = 0.3$, $\\Lambda = 0.7$, $h = 0.7$ and $\\sigma _8 = 0.9$). This is compared to the correlation function of the underlying dark matter and to the observationally determined correlation function of galaxies in the APM survey from Baugh \\cite{APM}. Note that all of the curves shown here are real space correlation functions. The galaxy correlation function shows many interesting properties. Firstly it is biased with respect to the mass correlation function, and furthermore this bias is scale dependent. On small scales there is in fact an antibias, which is exactly what is needed to reconcile the theory with the observed galaxy correlation function. The observed and model galaxy correlation functions agree over a wide range of scales, both showing approximate power law behaviour. The scale-dependant bias seen in these models arises from a complex interplay of effects. On large scales the bias is due to the intrinsic bias of dark matter halos in a CDM universe as described by Mo \\& White \\cite{mowhite}. On smaller scales the bias is controlled by the way halos are populated with galaxies, specifically the variations in number of galaxies per halo for halos of a given mass. The Lagrangian radius exclusion of halos also affects the small scale bias. These issues are explored in greater detail by Benson et al \\cite{meetal}, who also study $\\Omega _0 = 1$ models and find that such models fail to reproduce the observed clustering of galaxies.\n\n\\acknowledgements{}\n\nAJB's attendance at the X$^{\\mathrm th}$ Rencontres de Blois was funded in part by a grant from the European Commision.\n\n\n\\begin{bloisbib}\n\\bibitem{APM} Baugh C. M., 1996, \\mnras {280} {267}\n\\bibitem{meetal} Benson A. J., Cole S., Frenk C. S., Baugh C. M., Lacey C. G., 1998, {\\it in preparation}\n\\bibitem{coleetal94} Cole S., Lacey C. G., Baugh C. M., Frenk C. S., 1994, \\mnras {271} {781}\n\\bibitem{coleetal98} Cole S., Lacey C. G., Baugh C. M., Frenk C. S., 1998, {\\it in preparation}\n\\bibitem{colless} Colless M., Boyle B., 1997, {\\it astro-ph\/9710268}\n\\bibitem{gardner} Gardner J. P., Sharples R. M., Frenk C. S., Carrasco B. E., 1997, \\apj {480} {L99}\n\\bibitem{glazebrook} Glazebrook K. et al, 1995, {\\it astro-ph\/9503116}\n\\bibitem{fabio} Governato F. et al., 1998, \\nat {392} {359}\n\\bibitem{kwg} Kauffmann G., White S. D. M., Guiderdoni B., 1993, \\mnras {264} {201}\n\\bibitem{kns} Kauffmann G., Nusser A., Steinmetz M., 1997, \\mnras {286} {795}\n\\bibitem{gketal} Kauffmann G., Colberg J. M., Diaferio A., White S. D. M., 1998, {\\it astro-ph\/9805283}\n\\bibitem{loveday} Loveday J., Peterson B. A., Efstathiou G., Maddox S. J., 1992, \\apj {390} {338}\n\\bibitem{mowhite} Mo H. J., White S. D. M., 1996, \\mnras {282} {347}\n\\bibitem{arat} Ratcliffe A., Shanks T., Parker Q. A., Fong R., 1998, \\mnras {296} {173}\n\\bibitem{somervilleprimack} Somerville R., Primack J., 1998, {\\it astro-ph\/9802268}\n\\bibitem{szokoly} Szokoly G.P., Subbarao M.U., Conolly A.J., Mobasher B., 1998, {\\it astro-ph\/9801132}\n\\bibitem{zucca} Zucca E. et al, 1997, \\aa {326} {477}\n\\end{bloisbib}\n\\vfill\n\\end{document}\n\n\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:introduction}\n\n\nThe (original) Caldero-Chapoton map $X$ is an important object in\ncluster theory. The arguments of $X$ are certain objects of a cluster category, and the values are the corresponding elements of a cluster algebra. The map $X$ expresses\nthat the cluster category is a categorification of the\ncluster algebra, see \\cite{CC}, \\cite{CK}, \\cite{CK2}, \\cite{FK},\n\\cite{Palu}. For example, Figure \\ref{fig:AR_quiver} shows the\nAuslander-Reiten (AR) quiver of $\\mathsf{C}( A_5 )$, the cluster category of\nDynkin type $A_5$, with a useful ``coordinate system''. Figure\n\\ref{fig:frieze} shows the AR quiver again, with the values of $X$ on\nthe indecomposable objects of $\\mathsf{C}( A_5 )$. The values are Laurent\npolynomials over $\\mathbb{Z}$; indeed, the cluster algebra consists of such\nLaurent polynomials.\n\\begin{figure}\n\\[\n \\xymatrix @+1.8pc @!0 {\n *+[blue]{\\{\\, 5,7 \\,\\}} \\ar[dr] && \\{\\, 6,8 \\,\\} \\ar[dr] && *+[blue]{\\{\\, 1,7 \\,\\}} \\ar[dr] && \\{\\, 2,8 \\,\\} \\ar[dr] && \\{\\, 1,3 \\,\\} \\ar@{.}[dd] \\\\\n & \\{\\, 5,8 \\,\\} \\ar[dr] \\ar[ur] && \\{\\, 1,6 \\,\\} \\ar[dr] \\ar[ur] && *+[red]{\\{\\, 2,7 \\,\\}} \\ar[dr] \\ar[ur] && \\{\\, 3,8 \\,\\} \\ar[dr] \\ar[ur] \\\\\n \\{\\, 4,8 \\,\\} \\ar[dr] \\ar[ur] \\ar@{.}[uu] \\ar@{.}[dd] && \\{\\, 1,5 \\,\\} \\ar[dr] \\ar[ur] && \\{\\, 2,6 \\,\\} \\ar[dr] \\ar[ur] && \\{\\, 3,7 \\,\\} \\ar[dr] \\ar[ur] && \\{\\, 4,8 \\,\\} \\ar@{.}[uu] \\ar@{.}[dd] \\\\\n & \\{\\, 1,4 \\,\\} \\ar[dr] \\ar[ur] && *+[red]{\\{\\, 2,5 \\,\\}} \\ar[dr] \\ar[ur] && \\{\\, 3,6 \\,\\} \\ar[dr] \\ar[ur] && \\{\\, 4,7 \\,\\} \\ar[dr] \\ar[ur] \\\\\n \\{\\, 1,3 \\,\\} \\ar[ur] && *+[blue]{\\{\\, 2,4 \\,\\}} \\ar[ur] && \\{\\, 3,5 \\,\\} \\ar[ur] && \\{\\, 4,6 \\,\\} \\ar[ur] && *+[blue]{\\{\\, 5,7 \\,\\}} \\\\\n }\n\\]\n\\caption{The Auslander-Reiten quiver of the cluster category $\\mathsf{C}( A_5\n )$. The dotted lines should be identified with opposite\n orientations. The red vertices show the direct summands of a rigid\n object $R$, and the red and blue vertices show the direct summands\n of a cluster tilting object $T$.} \n\\label{fig:AR_quiver}\n\\end{figure}\n\\begin{figure}\n\\[\n \\xymatrix @+1.8pc @!0 {\n z \\ar[dr]&& \\frac{ ux + uy + yz + z }{ uyz } \\ar[dr]&& u \\ar[dr]&& \\frac{ y + 1 }{ u } \\ar[dr]&& \\frac{ uvx + vz + xy + y }{ vxy } \\\\\n & \\frac{ ux + yz + z }{ uy } \\ar[dr] \\ar[ur]&& \\frac{ ux + uy + z }{ yz } \\ar[dr] \\ar[ur]&& y \\ar[dr] \\ar[ur]&& \\frac{ uvx + vyz + vz + xy + xy^2 + y+y^2 }{ uvxy } \\ar[dr] \\ar[ur]\\\\\n \\frac{ uvx + vyz + vz + y + y^2 }{ uxy } \\ar[dr] \\ar[ur]\\ar@{.}[uu] \\ar@{.}[dd] && \\frac{ ux + z }{ y } \\ar[dr] \\ar[ur]&& \\frac{ x + y }{ z } \\ar[dr] \\ar[ur]&& \\frac{ vz + xy + y }{ vx } \\ar[dr] \\ar[ur]&& \\frac{ uvx + vyz + vz + y + y^2 }{ uxy } \\ar@{.}[uu] \\ar@{.}[dd] \\\\\n & \\frac{ uvx + vz + y }{ xy } \\ar[dr] \\ar[ur]&& x \\ar[dr] \\ar[ur]&& \\frac{ vz + x + x^2 + xy + y }{ vxz } \\ar[dr] \\ar[ur]&& \\frac{ vz + y }{ x } \\ar[dr] \\ar[ur]\\\\\n \\frac{ uvx + vz + xy + y }{ vxy }\\ar[ur] && v \\ar[ur]&& \\frac{ x + 1 }{ v } \\ar[ur]&& \\frac{ vz + x + y }{ xz } \\ar[ur]&& z \\\\\n }\n\\]\n\\caption{The Auslander-Reiten quiver of $\\mathsf{C}( A_5 )$ with values of\n the original Caldero-Chapoton map $X$. The map depends on the\n cluster tilting object $T$ shown by red and blue vertices in Figure\n \\ref{fig:AR_quiver}.}\n\\label{fig:frieze}\n\\end{figure}\n\nIt is a salient property of $X$ that it is a {\\em frieze} in\nthe sense of \\cite{AD}, that is, if $\\tau c \\rightarrow b \\rightarrow\nc$ is an AR triangle then\n\\[\n X( \\tau c )X( c ) - X( b ) = 1,\n\\]\nsee \\cite[theorem]{DG} and \\cite[prop.\\ 3.10]{CC}. In the case of\n$\\mathsf{C}( A_5 )$, this means that for each ``diamond'' in the AR quiver,\nof the form\n\\begin{equation}\n\\label{equ:diamond}\n\\vcenter{\n \\xymatrix @-1.2pc {\n & b_1 \\ar[dr] & \\\\ \\tau c \\ar[ur] \\ar[dr] & & c \\lefteqn{,} \\\\ & b_2 \\ar[ur] &\n }\n }\n\\end{equation}\nwe have\n\\[\n X( \\tau c )X( c ) - X( b_1 )X( b_2 ) = 1.\n\\]\n\nThe definition of $X$ depends on a cluster tilting object $T$. For\ninstance, the $X$ shown in Figure \\ref{fig:frieze} depends on the $T$\nwhich has the indecomposable summands shown by red and blue vertices in\nFigure \\ref{fig:AR_quiver}.\n\nThis paper is about a modified Caldero-Chapoton map $\\rho$ which is\nmore general than $X$ in two respects: it depends on a rigid object\n$R$ and has values in a general commutative ring $A$. An object $R$\nis rigid if $\\operatorname{Hom}( R,\\Sigma R ) = 0$. This is much weaker than being\ncluster tilting: recall that $T$ is cluster tilting if $\\operatorname{Hom}( T ,\n\\Sigma t ) = 0 \\Leftrightarrow t \\in \\operatorname{add} T \\Leftrightarrow \\operatorname{Hom}( t ,\n\\Sigma T ) = 0$. Our first main result gives conditions under which\n$\\rho$ is a {\\em generalised frieze}, in the sense that if $\\tau c\n\\rightarrow b \\rightarrow c$ is an AR triangle then\n\\[\n \\rho( \\tau c )\\rho( c ) - \\rho( b ) \\in \\{\\, 0,1 \\,\\}.\n\\]\nOur second main result is that the conditions can be satisfied if\n$A$ is chosen to be a Laurent polynomial ring over the integers.\n\nGeneralised friezes with values in the integers were introduced by\ncombinatorial means in \\cite{BHJ}, and it was shown in \\cite{HJ} that\nthey can be recovered from a modified Caldero-Chapoton map. The\ntheory of \\cite{HJ} and the original Caldero-Chapoton map are both special cases of the theory developed here.\n\nFor example, consider $\\mathsf{C}( A_5 )$ again and let $R$ be the rigid\nobject which has the indecomposable summands shown by red vertices in\nFigure \\ref{fig:AR_quiver}. Our results imply that we can choose $A =\n\\mathbb{Z}[ u^{ \\pm 1 } , v^{ \\pm 1 } , z^{ \\pm 1 } ]$, and Figure\n\\ref{fig:generalised_frieze} shows the AR quiver of $\\mathsf{C}( A_5 )$ with\nthe values of $\\rho$ on the indecomposable objects.\n\\begin{figure}\n\\[\n \\xymatrix @+1.8pc @!0 {\n z \\ar[dr]\\ar@{.}[dd] && \\frac{ u+z }{ uz } \\ar[dr] && u \\ar[dr]&& \\frac{ 1 }{ u } \\ar[dr]&& \\frac{ 1+uv+vz }{ v } \\ar@{.}[dd] \\\\\n \\begin{tikzpicture}[xscale=0.5,yscale=0.5,baseline=-0.5ex] \\fill[gray!25] (-1,0) -- (0,1) -- (1,0) -- (0,-1) -- cycle; \\end{tikzpicture}& \\frac{ u+z }{ u } \\ar[dr] \\ar[ur]&& \\frac{ u+z }{ z } \\ar[dr] \\ar[ur]&\\begin{tikzpicture}[xscale=0.5,yscale=0.5,baseline=-0.5ex] \\fill[gray!25] (-1,0) -- (0,1) -- (1,0) -- (0,-1) -- cycle; \\end{tikzpicture}& 1 \\ar[dr] \\ar[ur]&\\begin{tikzpicture}[xscale=0.5,yscale=0.5,baseline=-0.5ex] \\fill[gray!25] (-1,0) -- (0,1) -- (1,0) -- (0,-1) -- cycle; \\end{tikzpicture}& \\frac{ 1+uv+vz }{ uv } \\ar[dr] \\ar[ur]\\\\\n \\frac{ 1+uv+vz }{ u } \\ar[dr] \\ar[ur]\\ar@{.}[dd] && u+z \\ar[dr] \\ar[ur]&& \\frac{ 1 }{ z } \\ar[dr] \\ar[ur] && \\frac{ 1+vz }{ v } \\ar[dr] \\ar[ur]&& \\frac{ 1+uv+vz }{ u } \\ar@{.}[dd] \\\\\n & 1+uv+vz \\ar[dr] \\ar[ur]&\\begin{tikzpicture}[xscale=0.5,yscale=0.5,baseline=-0.5ex] \\fill[gray!25] (-1,0) -- (0,1) -- (1,0) -- (0,-1) -- cycle; \\end{tikzpicture}& 1 \\ar[dr] \\ar[ur]&\\begin{tikzpicture}[xscale=0.5,yscale=0.5,baseline=-0.5ex] \\fill[gray!25] (-1,0) -- (0,1) -- (1,0) -- (0,-1) -- cycle; \\end{tikzpicture}& \\frac{ 1+vz }{ vz } \\ar[dr] \\ar[ur]&& 1+vz \\ar[dr] \\ar[ur]&\\begin{tikzpicture}[xscale=0.5,yscale=0.5,baseline=-0.5ex] \\fill[gray!25] (-1,0) -- (0,1) -- (1,0) -- (0,-1) -- cycle; \\end{tikzpicture} \\\\\n \\frac{ 1+uv+vz }{ v } \\ar[ur]&& v \\ar[ur]&& \\frac{ 1 }{ v } \\ar[ur]&& \\frac{ 1+vz }{ z } \\ar[ur] && z \\\\\n }\n\\]\n\\caption{The Auslander-Reiten quiver of $\\mathsf{C}( A_5 )$ with values of\n the modified Caldero-Chapoton map $\\rho$. The map depends on the\n rigid object $R$ shown by red vertices in Figure\n \\ref{fig:AR_quiver}. The values form a generalised frieze, the grey\n diamonds indicating where $\\rho( \\tau c )\\rho( c ) - \\rho( b_1\n )\\rho( b_2 ) = 1$.}\n\\label{fig:generalised_frieze}\n\\end{figure}\nIn this case, the generalised frieze property means that for\neach ``diamond'' in the AR quiver, of the form \\eqref{equ:diamond}, we\nhave\n\\[\n \\rho( \\tau c )\\rho( c ) - \\rho( b_1 )\\rho( b_2 )\n \\in \\{\\, 0,1 \\,\\}.\n\\]\nThe solid grey diamonds in Figure \\ref{fig:generalised_frieze} indicate where the displayed expression is equal to $1$.\n\nLet us explain how $\\rho$ is defined. Let $k$ be an algebraically\nclosed field, $\\mathsf{C}$ an essentially small $\\operatorname{Hom}$-finite $k$-linear\ntriangulated category. Assume that $\\mathsf{C}$ has split idempotents and\nhas a Serre functor. Note that these are the only assumptions on\n$\\mathsf{C}$ which is hence permitted to be a good deal more general than a\ncluster category. Let $\\Sigma$ denote the suspension functor of $\\mathsf{C}$\nand write $\\mathsf{C}( -,- )$ instead of $\\operatorname{Hom}_{ \\mathsf{C} }( -,- )$. Let $R$ be a\nrigid object of $\\mathsf{C}$, assumed to be basic for reasons of simplicity,\nwith endomorphism algebra $E = \\operatorname{End}_{ \\mathsf{C} }( R )$. There is a functor\n\\[\n \\begin{array}{rcl}\n \\mathsf{C} & \\stackrel{G}{\\longrightarrow} & \\mathsf{mod}\\,E, \\\\[2mm]\n c & \\longmapsto & \\mathsf{C}( R,\\Sigma c ).\n \\end{array}\n\\]\nLet $A$ be a commutative ring and let\n\\[\n \\alpha : \\operatorname{obj}\\,\\mathsf{C} \\rightarrow A\n\\;\\;,\\;\\;\n \\beta : \\operatorname{K}_0( \\mathsf{mod}\\,E ) \\rightarrow A\n\\]\nbe two maps, where $\\operatorname{obj}\\,\\mathsf{C}$ is the set of objects of $\\mathsf{C}$ and\n$\\operatorname{K}_0$ denotes the Grothendieck group of an abelian category.\n\nThe modified Caldero-Chapoton map is the map $\\rho : \\operatorname{obj}\\,\\mathsf{C}\n\\rightarrow A$ defined as follows.\n\\[\n \\rho( c )\n = \\alpha( c )\n \\sum_e \\chi \\big( \\operatorname{Gr}_e ( Gc ) \\big) \\beta( e )\n\\]\nHere $c \\in \\mathsf{C}$ is an object, the sum is over $e \\in \\operatorname{K}_0( \\mathsf{mod}\\,E\n)$, and $\\chi$ denotes the Euler characteristic defined by \\'{e}tale\ncohomology with proper support. By $\\operatorname{Gr}_e$ is denoted the\nGrassmannian of submodules $M \\subseteq Gc$ with $\\operatorname{K}_0$-class $[ M ] =\ne$. \n\nThe original Caldero-Chapoton map is the special case where $R$ is a\ncluster tilting object and $\\alpha$ and $\\beta$ are two particular\nmaps; see Remark \\ref{rmk:original_CC}. The modified Caldero-Chapoton\nmap of \\cite{HJ} is the special case where $A = \\mathbb{Z}$ and $\\alpha$ and\n$\\beta$ are identically equal to $1$. In general, $\\rho$ is only\nlikely to be interesting if the maps $\\alpha$ and $\\beta$ are chosen\ncarefully, and we formalise this by saying that $\\alpha$ and $\\beta$\nare {\\em frieze-like for the AR triangle} $\\Delta = \\tau c \\rightarrow\nb \\rightarrow c$ if they satisfy the technical conditions in\nDefinition \\ref{def:good}. Observe that the conditions are trivially\nsatisfied if $\\alpha$ and $\\beta$ are identically equal to $1$. Our\nfirst main result is the following.\n\n\n\\bigskip\n{\\bf Theorem A. }\n{\\em \nIf $\\alpha$ and $\\beta$ are frieze-like for each AR triangle in $\\mathsf{C}$,\nthen the modified Caldero-Chapoton map $\\rho : \\operatorname{obj}\\,\\mathsf{C} \\rightarrow\nA$ is a generalised frieze in the sense of \\cite[def.\\ 3.4]{HJ}. That\nis,\n\\begin{enumerate}\n\n \\item $\\rho( c_1 \\oplus c_2 ) = \\rho( c_1 )\\rho( c_2 )$,\n\n\\medskip\n\n \\item if $\\Delta = \\tau c \\rightarrow b \\rightarrow c$ is an AR\n triangle then $\\rho( \\tau c )\\rho( c ) - \\rho( b ) \\in \\{\\, 0,1\n \\,\\}$.\n\n\\end{enumerate}\n}\n\\bigskip\n\nThis follows from Proposition \\ref{pro:exponential} and Theorem\n\\ref{thm:A} which even show\n\\[\n \\rho( \\tau c )\\rho( c ) - \\rho( b )\n = \\left\\{\n \\begin{array}{cl}\n 0 & \\mbox{ if $G( \\Delta )$ is a split short exact sequence, } \\\\[2mm]\n 1 & \\mbox{ if $G( \\Delta )$ is not a split short exact sequence. }\n \\end{array}\n \\right.\n\\]\nNote that $G( \\Delta )$ is never split exact when $R$ is a\ncluster tilting object, that is, in the case of the original\nCaldero-Chapoton map.\n\nOur second main result is that one can find frieze-like maps $\\alpha$\nand $\\beta$, and hence generalised friezes, with values in Laurent\npolynomials.\n\n\n\\bigskip\n{\\bf Theorem B. }\n{\\em\nAssume that $\\mathsf{C}$ is $2$-Calabi-Yau and that the basic rigid object\n$R$ has $r$ indecomposable summands and is a direct summand of a\ncluster tilting object.\n\nThen there are maps $\\alpha$ and $\\beta$ with values in $\\mathbb{Z}[ x_1^{\n \\pm 1 }, \\ldots, x_r^{ \\pm 1 } ]$, using all the variables $x_1$,\n$\\ldots$, $x_r$, which are frieze-like for each AR triangle in $\\mathsf{C}$.\n\nHence there is a modified Caldero-Chapoton map $\\rho : \\operatorname{obj} \\mathsf{C}\n\\rightarrow \\mathbb{Z}[ x_1^{ \\pm 1 }, \\ldots, x_r^{ \\pm 1 } ]$, using\nall the variables $x_1$, $\\ldots$, $x_r$, which is a generalised\nfrieze. \n}\n\\bigskip\n\nThis is established in Definition \\ref{def:alpha_and_beta}, Theorem\n\\ref{thm:B}, and Remark \\ref{rmk:app}. We leave it vague for now what\nit means to ``use all the variables $x_1, \\ldots, x_r$'', but see\nRemark \\ref{rmk:app}. In fact, it is sometimes possible to get values\nin Laurent polynomials in more than $r$ variables. For example, the\nbasic rigid object $R$ defined by the red vertices in Figure\n\\ref{fig:AR_quiver} has $r = 2$ indecomposable summands, but the\ncorresponding $\\rho$ has values in $\\mathbb{Z}[ u^{ \\pm 1 }, v^{ \\pm 1 },\nz^{ \\pm 1 } ]$ as shown in Figure \\ref{fig:generalised_frieze}.\n\nIt is natural to ask if the $\\mathbb{Z}$-algebra generated by the values\nof $\\rho$ is an interesting object. In particular, one can ask how it\nis related to cluster algebras and how it is affected by mutation of\nrigid objects as defined in \\cite[sec.\\ 2]{MP}; see the questions in\nSection \\ref{sec:questions}.\n\nThe paper is organised as follows: Section \\ref{sec:conditions}\ndefines what it means for $\\alpha$ and $\\beta$ to be frieze-like and\nproves Theorem A. Section \\ref{sec:construction} proves Theorem B.\nSection \\ref{sec:example} shows how to obtain the example in Figure\n\\ref{fig:generalised_frieze}. Section \\ref{sec:questions} poses some\nquestions. \n\n\n\n\n\\section{The frieze-like condition on the maps $\\alpha$ and $\\beta$\nimplies that $\\rho$ is a generalised frieze}\n\\label{sec:conditions}\n\n\nThis section proves Theorem A in the introduction. It is a consequence of Proposition\n\\ref{pro:exponential} and Theorem \\ref{thm:A}.\n\nWe start by setting up items to be used in the rest of the paper.\nInstead of a basic rigid object $R$ we will work with a rigid\nsubcategory $\\mathsf{R}$. This is more general since we can set $\\mathsf{R} =\n\\operatorname{add}\\,R$ when $R$ is given, but not every $\\mathsf{R}$ has this form, see\n\\cite[sec.\\ 6]{JP}. The higher generality means that the definitions\nof $G$ and $\\beta$ are different from those in the introduction.\n\n\n\\begin{Setup}\n\\label{set:blanket}\nLet $k$ be an algebraically closed field and let $\\mathsf{C}$ be an\nessentially small $k$-linear $\\operatorname{Hom}$-finite triangulated category with\nsplit idempotents. Hence $\\mathsf{C}$ is a Krull-Schmidt category.\n\nAssume that $\\mathsf{C}$ has a Serre functor. Hence it has AR triangles by\n\\cite[thm.\\ A]{RVdB}, and the Serre functor is $\\Sigma \\circ \\tau$\nwhere $\\Sigma$ is the suspension functor and $\\tau$ is the AR\ntranslation.\n\nLet $\\mathsf{R}$ be a full subcategory of $\\mathsf{C}$ which is closed under direct\nsums and summands, is functorially finite, and rigid, that is, $\\mathsf{C}(\n\\mathsf{R},\\Sigma \\mathsf{R} ) = 0$.\n\nThe category of $k$-vector spaces is denoted $\\mathsf{Mod}\\,k$ and the\ncategory of $k$-linear contravariant functors $\\mathsf{R} \\rightarrow\n\\mathsf{Mod}\\,k$ is denoted $\\mathsf{Mod}\\,\\mathsf{R}$. It is a $k$-linear abelian category\nand its full subcategory of objects of finite length is denoted\n$\\mathsf{fl}\\,\\mathsf{R}$.\n\nLet $A$ be a commutative ring and let\n\\[\n \\alpha : \\operatorname{obj}\\,\\mathsf{C} \\rightarrow A\n\\;\\;,\\;\\;\n \\beta : \\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{R} ) \\rightarrow A\n\\]\nbe maps which are ``exponential'' in the sense that\n\\begin{align*}\n \\alpha( 0 ) = 1 \\;\\; , \\;\\;\n & \\alpha( c \\oplus d ) = \\alpha( c )\\alpha( d ), \\\\[2mm]\n \\beta( 0 ) = 1 \\;\\; , \\;\\;\n & \\beta( e + f ) = \\beta( e )\\beta( f ).\n\\end{align*}\n\\end{Setup}\n\n\n\\begin{bfhpg}\n[The modified Caldero-Chapoton map]\n\\label{bfhpg:CC}\nThere is a functor\n\\[\n \\begin{array}{rcl}\n \\mathsf{C} & \\stackrel{G}{\\longrightarrow} & \\mathsf{Mod}\\,\\mathsf{R}, \\\\[2mm]\n c & \\longmapsto & \\mathsf{C}( -,\\Sigma c ) |_{ \\mathsf{R} }.\n \\end{array}\n\\]\nThe modified Caldero-Chapoton map is defined by the following\nformula.\n\\begin{equation}\n\\label{equ:CC}\n \\rho( c )\n = \\alpha( c )\n \\sum_e \\chi \\big( \\operatorname{Gr}_e ( Gc ) \\big) \\beta( e )\n\\end{equation}\nThe sum is over $e \\in \\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{R} )$, and $\\operatorname{Gr}_e( Gc )$ is the\nGrassmannian of submodules $M \\subseteq Gc$ where $M$ has finite\nlength in $\\mathsf{Mod}\\,\\mathsf{R}$ and class $[ M ] = e$ in $\\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{R} )$.\nThe notation is otherwise as explained in the introduction.\n\n\nThe formula may not make sense for each $c \\in \\mathsf{C}$, but it does make\nsense if $Gc$ has finite length in $\\mathsf{Mod}\\, \\mathsf{R}$ since then $\\operatorname{Gr}_e( Gc\n)$ is finite-dimensional and non-empty for only finitely many values\nof $e$; see \\cite[1.6 and 1.8]{JP}. When the formula makes\nsense, it defines an element $\\rho( c ) \\in A$. Note that\n\\[\n \\rho( 0 ) = 1.\n\\]\n\n\\end{bfhpg}\n\n\n\\begin{Proposition}\n\\label{pro:exponential}\nLet $a,c \\in \\mathsf{C}$ be objects such that $Ga,Gc$ have finite length in\n$\\mathsf{Mod}\\, \\mathsf{R}$. Then $G( a \\oplus c )$ has finite length in $\\mathsf{Mod}\\,\n\\mathsf{R}$ and\n\\[\n \\rho( a \\oplus c ) = \\rho( a )\\rho( c ).\n\\]\n\\end{Proposition}\n\n\\begin{proof}\nThe statement about the length of $G( a \\oplus c )$ is clear.\n\nIt is immediate that\n\\begin{equation}\n\\label{equ:product}\n \\rho( a )\\rho( c )\n = \\alpha( a \\oplus c )\n \\sum_g \n \\Big( \n \\sum_{ e+f=g } \\chi \\big( \\operatorname{Gr}_e ( Ga ) \\times \\operatorname{Gr}_f ( Gc ) \\big)\n \\Big) \\beta( g )\n = ( {\\textstyle *} ).\n\\end{equation}\nIn \\cite[sec.\\ 2]{HJ} we considered a pair of morphisms $a \\rightarrow\nb \\rightarrow c$ in $\\mathsf{C}$ and introduced auxiliary spaces $X_{ e,f }$.\nIf we set $a \\rightarrow b \\rightarrow c$ equal to the canonical\nmorphisms $a \\rightarrow a \\oplus c \\rightarrow c$, then \\cite[lem.\\\n2.4(i+v)]{HJ} and \\cite[rmk.\\ 2.5]{HJ} mean that we can compute as\nfollows.\n\\[\n ( {\\textstyle *} )\n = \\alpha( a \\oplus c )\n \\sum_g \\Big( \\sum_{ e+f = g } \\chi( X_{ e,f } ) \\Big) \\beta( g )\n = \\alpha( a \\oplus c )\n \\sum_g \\chi \\Big( \\operatorname{Gr}_g \\big( G( a \\oplus c ) \\big) \\Big)\\beta( g )\n = \\rho( a \\oplus c )\n\\]\n\\end{proof}\n\n\n\\begin{Definition}\n[Frieze-like $\\alpha$ and $\\beta$]\n\\label{def:good}\nLet \n\\[\n \\Delta = \\tau c \\rightarrow b \\rightarrow c\n\\]\nbe an AR triangle in $\\mathsf{C}$ and assume that $Gc$ and $G( \\tau c )$ have \nfinite length in $\\mathsf{Mod}\\, \\mathsf{R}$. We say that {\\em $\\alpha$ and $\\beta$\nare frieze-like for $\\Delta$} if the following hold.\n\\begin{enumerate}\n\n \\item If $c \\not\\in \\mathsf{R} \\cup \\Sigma^{ -1 }\\mathsf{R}$ and $G( \\Delta )$\n is a split short exact sequence, then\n\\[\n \\alpha( b ) = \\alpha( c \\oplus \\tau c ).\n\\]\n\n\\medskip\n\n \\item If $c \\not\\in \\mathsf{R} \\cup \\Sigma^{ -1 }\\mathsf{R}$ and $G( \\Delta )$\n is a non-split short exact sequence, or if $c = \\Sigma^{-1 }r\n \\in \\Sigma^{ -1 }\\mathsf{R}$, then\n\\[\n \\alpha( b ) = \\alpha( c \\oplus \\tau c ) \n\\;\\;,\\;\\;\n \\alpha( c \\oplus \\tau c )\\beta \\big( [ Gc ] \\big) = 1.\n\\]\n\n\\medskip\n\n \\item If $c = r \\in \\mathsf{R}$, then\n\\[\n \\alpha( c \\oplus \\tau c ) = 1\n\\;\\;,\\;\\;\n \\alpha( b ) = \\beta \\big( [S_r] \\big) \n\\]\nwhere $S_r \\in \\mathsf{Mod}\\,\\mathsf{R}$ is the simple object supported at $r$, see\n\\cite[prop.\\ 2.2]{AusRepII}.\n\n\\end{enumerate}\n\\end{Definition}\n\n\n\\begin{Remark}\nNote that if $c \\not\\in \\mathsf{R} \\cup \\Sigma^{ -1 }\\mathsf{R}$ then $G( \\Delta )$\nis a (split or non-split) short exact sequence, see \\cite[lem.\\\n1.12(iii)]{HJ}. \n\\end{Remark}\n\n\n\\begin{Theorem}\n\\label{thm:A}\nLet \n\\[\n \\Delta = \\tau c \\rightarrow b \\rightarrow c\n\\]\nbe an AR triangle in $\\mathsf{C}$. Assume that $Gc$ and $G( \\tau c )$ have \nfinite length in $\\mathsf{Mod}\\, \\mathsf{R}$ and that $\\alpha$ and $\\beta$ are\nfrieze-like for $\\Delta$. Then $Gb$ has finite length in $\\mathsf{Mod}\\, \\mathsf{R}$\nand\n\\[\n \\rho( \\tau c )\\rho( c ) - \\rho( b )\n =\n \\left\\{\n \\begin{array}{cl}\n 0 & \\mbox{ if $G( \\Delta )$ is a split short exact sequence, } \\\\[2mm]\n 1 & \\mbox{ if $G( \\Delta )$ is not a split short exact sequence. }\n \\end{array}\n \\right.\n\\]\n\\end{Theorem}\n\n\\begin{proof}\nIt is clear from the definition of $G$ that it is a homological\nfunctor, so $G( \\Delta )$ is an exact sequence (albeit not necessarily\nshort exact). The statement about the length of $Gb$ follows.\n\nWe split into cases. First some preparation:\nsetting $a = \\tau c$ in Equation \\eqref{equ:product} gives\n\\begin{equation}\n\\label{equ:product2}\n \\rho( \\tau c )\\rho( c )\n = \\alpha( c \\oplus \\tau c )\n \\sum_g \n \\Big( \n \\sum_{ e+f=g } \\chi \\big( \\operatorname{Gr}_e ( G( \\tau c ) ) \\times \\operatorname{Gr}_f ( Gc ) \\big)\n \\Big) \\beta( g )\n = ( {\\textstyle *} ).\n\\end{equation}\nMoreover, we use the morphisms $a \\rightarrow b \\rightarrow c$ and\nauxiliary spaces $X_{ e,f }$ from \\cite[sec.\\ 2]{HJ} again, this time\nsetting $a \\rightarrow b \\rightarrow c$ equal to the AR triangle\n$\\tau c \\rightarrow b \\rightarrow c$. We can then use the results of\n\\cite[sec.\\ 2]{HJ}.\n\nCase (i): $c \\not\\in \\mathsf{R} \\cup \\Sigma^{ -1 }\\mathsf{R}$ and $G( \\Delta )$ is\na split short exact sequence. \n\nWe start from Equation \\eqref{equ:product2} and\ncompute as follows:\n\\[\n ( {\\textstyle *} )\n \\stackrel{\\rm (a)}{=}\n \\alpha( b )\n \\sum_g \n \\Big( \n \\sum_{ e+f=g } \\chi ( X_{ e,f } )\n \\Big) \\beta( g ) \\\\\n \\stackrel{\\rm (b)}{=}\n \\alpha( b )\n \\sum_g \\chi \\big( \\operatorname{Gr}_g( Gb ) \\big) \\beta( g ) \\\\\n = \\rho(b),\n\\]\nwhere (a) is by Definition \\ref{def:good}(i) and \\cite[lem.\\\n2.4(i+v)]{HJ}, and (b) is by \\cite[rmk.\\ 2.5]{HJ}.\n\nCase (ii): $c \\not\\in \\mathsf{R} \\cup \\Sigma^{ -1 }\\mathsf{R}$ and $G( \\Delta )$ is a\nnon-split short exact sequence.\n\nWe start from Equation \\eqref{equ:product2} and compute as follows:\n\\begin{align*}\n ( {\\textstyle *} )\n & =\n \\alpha( c \\oplus \\tau c )\n \\Big\\{\n \\chi \\big( \\operatorname{Gr}_0 ( G(\\tau c) ) \\times \\operatorname{Gr}_{[Gc]} ( Gc ) \\big)\n \\beta \\big( [Gc] \\big) \\\\\n & \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\n + \\sum_g \\Big(\n \\sum_{ \\substack{ e+f=g \\\\[1mm] (e,f) \\neq (0,[Gc]) }}\n \\chi \\big(\n \\operatorname{Gr}_e ( G(\\tau c) )\n \\times \\operatorname{Gr}_f ( Gc )\n \\big)\n \\Big) \\beta( g ) \n \\Big\\} \\\\\n & \\stackrel{\\rm (c)}{=}\n \\alpha( c \\oplus \\tau c ) \n \\Big\\{ \n \\beta \\big( [Gc] \\big) \n + \\sum_g \\Big(\n \\sum_{ \\substack{ e+f=g \\\\[1mm] (e,f) \\neq (0,[Gc]) }}\n \\chi \\big( X_{ e,f } \\big)\n \\Big) \\beta( g ) \n \\Big\\} \\\\\n & \\stackrel{\\rm (d)}{=}\n 1 + \\alpha(b) \\sum_g \\Big(\n \\sum_{ \\substack{ e+f=g \\\\[1mm] (e,f) \\neq (0,[Gc]) }}\n \\chi \\big( X_{ e,f } \\big)\n \\Big) \\beta( g ) \\\\\n & \\stackrel{\\rm (e)}{=}\n 1 + \\alpha(b) \\sum_g \\Big(\n \\sum_{ e+f=g }\n \\chi \\big( X_{ e,f } \\big)\n \\Big) \\beta( g ) \\\\\n & \\stackrel{\\rm (f)}{=}\n 1 + \\alpha(b) \\sum_g \\chi \\big( \\operatorname{Gr}_g( Gb ) \\big) \\beta( g ) \\\\\n & = 1 + \\rho( b ),\n\\end{align*}\nwhere (c) follows from \\cite[lem.\\ 2.4(ii)+(iv)+(v)]{HJ}, (d) is by\nDefinition \\ref{def:good}(ii), (e) is by \\cite[lem.\\ 2.4(iii)]{HJ},\nand (f) is by \\cite[rmk.\\ 2.5]{HJ}.\n\nCase (iii): $c = \\Sigma^{ -1 }r \\in \\Sigma^{ -1 }\\mathsf{R}$. Then\n$G( \\Delta )$ is not a split short exact sequence, but\n\\begin{equation}\n\\label{equ:Pr}\n G( \\Delta )\n = G( \\tau c \\rightarrow b \\rightarrow c )\n = 0 \\rightarrow \\operatorname{rad}\\,P_r \\rightarrow P_r\n\\end{equation}\nby \\cite[lem.\\ 1.12(i)]{HJ}, where $P_r = \\mathsf{C}( -,r ) \\,|_{ \\mathsf{R} }$ is\nthe indecomposable projective object of $\\mathsf{Mod}\\,\\mathsf{R}$ associated with\n$r$, see \\cite[1.5]{HJ}. In particular, we have $G( \\tau c ) = 0$ whence\n$\\rho( \\tau c ) = \\alpha( \\tau c )$, and this gives the first equality\nin the following computation.\n\\begin{align*}\n \\rho( \\tau c )\\rho( c )\n & = \\alpha( \\tau c )\\alpha( c )\n \\sum_f \\chi \\big( \\operatorname{Gr}_f( Gc ) \\big) \\beta( f ) \\\\\n & = \\alpha( c \\oplus \\tau c )\n \\sum_f \\chi \\big( \\operatorname{Gr}_f( Gc ) \\big) \\beta( f ) \\\\\n & = \\alpha( c \\oplus \\tau c )\n \\Big\\{\n \\chi \\big( \\operatorname{Gr}_{ [Gc] }( Gc ) \\big) \\beta \\big( [Gc] \\big)\n + \\sum_{ f \\neq [Gc] } \\chi \\big( \\operatorname{Gr}_f( Gc ) \\big) \\beta( f )\n \\Big\\} \\\\\n & \\stackrel{\\rm (g)}{=}\n \\alpha( c \\oplus \\tau c )\n \\Big\\{\n \\beta \\big( [Gc] \\big)\n + \\sum_{ f \\neq [Gc] } \\chi \\big( \\operatorname{Gr}_f( Gc ) \\big) \\beta( f )\n \\Big\\} \\\\\n & \\stackrel{\\rm (h)}{=}\n \\alpha( c \\oplus \\tau c )\n \\Big\\{\n \\beta \\big( [Gc] \\big)\n + \\sum_{ f } \\chi \\big( \\operatorname{Gr}_f( Gb ) \\big) \\beta( f )\n \\Big\\} \\\\\n & \\stackrel{\\rm (j)}{=}\n 1 + \\alpha(b) \\sum_{ f } \\chi \\big( \\operatorname{Gr}_f( Gb ) \\big) \\beta( f ) \\\\\n & = 1 + \\rho( b )\n\\end{align*}\nTo see (g), note that for $M' \\subseteq Gc$ we have\n\\begin{equation}\n\\label{equ:1.2}\n [ M' ] = [ Gc ] \\Leftrightarrow M' = Gc\n\\end{equation}\nby \\cite[eq.\\ (1.2)]{HJ}. Hence $\\operatorname{Gr}_{ [Gc] }( Gc )$ has only a\nsingle point whence $\\chi \\big( \\operatorname{Gr}_{ [Gc] }( Gc ) \\big) = 1$.\nTo see (h), note that Equation \\eqref{equ:Pr} says that $Gc$ is an\nindecomposable projective object with radical $Gb$. So the proper\nsubmodules of $Gc$ are precisely all the submodules of $Gb$, whence\nEquation \\eqref{equ:1.2} implies\n\\[\n \\operatorname{Gr}_f( Gb )\n = \\left\\{\n \\begin{array}{cl}\n \\operatorname{Gr}_f( Gc ) & \\mbox{ for $f \\neq [ Gc ]$, } \\\\[2mm]\n \\emptyset & \\mbox{ for $f = [ Gc ]$ }\n \\end{array}\n \\right.\n\\]\nand (h) follows. Finally, (j) holds by Definition\n\\ref{def:good}(ii). \n\nCase (iv): $c = r \\in \\mathsf{R}$. Then $G( \\Delta )$ is not a split short\nexact sequence, but we have\n\\[\n G( \\Delta )\n = G( \\tau c \\rightarrow b \\rightarrow c )\n = I_r \\rightarrow \\operatorname{corad}\\,I_r \\rightarrow 0\n\\]\nby \\cite[lem.\\ 1.12(ii)]{HJ}, where $I_r = \\mathsf{C}( -,\\Sigma\\tau r ) \\,|_{\n \\mathsf{R} }$ is the indecomposable injective object of $\\mathsf{Mod}\\,\\mathsf{R}$\nassociated with $r$, see \\cite[1.10]{HJ}, and $\\operatorname{corad}$ denotes the\nquotient by the socle. Now proceed dually to Case (iii), replacing\nDefinition \\ref{def:good}(ii) by Definition \\ref{def:good}(iii).\n\\end{proof}\n\n\n\n\n\\section{A construction of frieze-like maps $\\alpha$ and $\\beta$ with\nvalues in Laurent polynomials}\n\\label{sec:construction}\n\n\nThis section proves Theorem B in the introduction. It is a consequence of Definition\n\\ref{def:alpha_and_beta}, Theorem \\ref{thm:B}, and Remark\n\\ref{rmk:app}. \n\n\n\\begin{Setup}\n\\label{set:2}\nWe continue to work under Setup \\ref{set:blanket} and \nhenceforth add the assumption that $\\mathsf{C}$ is a $2$-Calabi-Yau category with a\ncluster tilting subcategory $\\mathsf{T}$ which belongs to a cluster\nstructure in the sense of \\cite[sec.\\ II.1]{BIRS}, and which satisfies\n$\\mathsf{R} \\subseteq \\mathsf{T}$.\n\n\nNote that the AR translation of $\\mathsf{C}$ is\n\\[\n \\tau = \\Sigma\n\\] \nand that the Serre functor is $\\Sigma^2$.\n\\end{Setup}\n\n\n\\begin{Remark}\nWhen $\\mathsf{R}$ is a rigid subcategory of $\\mathsf{C}$, it is often possible to\nfind a cluster tilting subcategory $\\mathsf{T}$ with $\\mathsf{R} \\subseteq \\mathsf{T}$.\nNot always, however: if $\\mathsf{C}$ is a cluster tube, then such a $\\mathsf{T}$\ncannot be found since cluster tubes have no cluster tilting\nsubcategories, see \\cite[cor.\\ 2.7]{BMV}.\n\\end{Remark}\n\n\n\\begin{bfhpg}\n[Mutation and exchange triangles]\nLet ${\\mathsf{ind}}\\, \\mathsf{T}$ denote the set of (isomorphism classes of)\nindecomposable objects of $\\mathsf{T}$. Each $t \\in {\\mathsf{ind}}\\, \\mathsf{T}$ has a\nmutation $t^{ {\\textstyle *} }$ which is the unique indecomposable object in\n$\\mathsf{C}$ such that $\\mathsf{T}$ remains a cluster tilting subcategory if $t$ is\nreplaced by $t^{ {\\textstyle *} }$. There are distinguished triangles\n\\begin{equation}\n\\label{equ:exchange_triangles1}\n t^{ {\\textstyle *} } \\rightarrow a \\rightarrow t\n\\;\\;,\\;\\;\n t \\rightarrow a' \\rightarrow t^{ {\\textstyle *} }\n\\end{equation}\nwith $a, a' \\in \\operatorname{add}\\big( ( {\\mathsf{ind}}\\, \\mathsf{T} ) \\setminus t \\big)$, known\nas exchange triangles, see \\cite[sec.\\ II.1]{BIRS}. \n\\end{bfhpg}\n\n\n\\begin{Definition} \n[The subgroup $N$]\n\\label{def:N}\nThe split Grothendieck group of an additive category is denoted by\n$\\operatorname{K}_0^{ \\operatorname{split} }$. It has a relation $[ a \\oplus b ] = [ a ] + [ b ]$\nfor each pair of objects $a,b$, where $[ a ]$ is the $\\operatorname{K}_0^{ \\operatorname{split}\n}$-class of $a$.\n\nDefine a subgroup of $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} )$ as follows.\n\\begin{equation}\n\\label{equ:N}\n N = \\bigg\\langle\\, [a] - [a'] \n \\,\\bigg|\n \\begin{array}{l}\n \\mbox{ $s^{ {\\textstyle *} } \\rightarrow a \\rightarrow s$ \\;,\\;\n $s \\rightarrow a' \\rightarrow s^{ {\\textstyle *} }$\n are exchange } \\\\[2mm]\n \\mbox{ triangles with \n $s \\in {\\mathsf{ind}}\\, \\mathsf{T} \\setminus {\\mathsf{ind}}\\, \\mathsf{R}$ }\n \\end{array}\n \\,\\bigg\\rangle\n\\end{equation}\nLet\n\\[\n Q : \\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \\rightarrow \\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N\n\\]\ndenote the canonical surjection.\n\\end{Definition}\n\n\n\\begin{bfhpg}\n[Simple objects, $\\operatorname{K}$-theory, and the homomorphism $\\overline{ \\theta }$]\n\\label{bfhpg:simples_and_K}\nThe inclusion functor $i : \\mathsf{R} \\rightarrow \\mathsf{T}$ induces an exact functor\n\\[\n i^{ {\\textstyle *} } : \\mathsf{Mod}\\,\\mathsf{T} \\longrightarrow \\mathsf{Mod}\\,\\mathsf{R}\n\\;\\;,\\;\\;\n i^{ {\\textstyle *} }( M ) = M \\circ i.\n\\]\nEach indecomposable object $t \\in {\\mathsf{ind}}\\, \\mathsf{T}$ gives rise to a simple\nobject $\\overline{S}_t \\in \\mathsf{Mod}\\,\\mathsf{T}$, and each $r \\in {\\mathsf{ind}}\\, \\mathsf{R}$\ngives rise to a simple object $S_r \\in \\mathsf{Mod}\\,\\mathsf{R}$, see \\cite[prop.\\\n2.3(b)]{AusRepII}. It is not hard to show\n\\[\n i^{ {\\textstyle *} }\\overline{S}_t \n = \\left\\{\n \\begin{array}{cl}\n S_t & \\mbox{ if $t \\in {\\mathsf{ind}}\\, \\mathsf{R}$, } \\\\[2mm]\n 0 & \\mbox{ if $t \\in {\\mathsf{ind}}\\, \\mathsf{T} \\setminus {\\mathsf{ind}}\\, \\mathsf{R}$. }\n \\end{array}\n \\right.\n\\]\nSince $i^{ {\\textstyle *} }$ is exact and sends simple objects to simple objects\nor $0$, is preserves finite length so restricts to an exact functor\n\\[\n i^{ {\\textstyle *} } : \\mathsf{fl}\\,\\mathsf{T} \\rightarrow \\mathsf{fl}\\,\\mathsf{R}.\n\\]\nLet\n\\[\n \\kappa : \\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{T} ) \\rightarrow \\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{R} )\n\\]\nbe the induced homomorphism. The source is a free group on the\nclasses $[ \\overline{S}_t ]$ for $t \\in {\\mathsf{ind}}\\, \\mathsf{T}$ and the target\nis a free group on the classes $[ S_r ]$ for $r \\in {\\mathsf{ind}}\\, \\mathsf{R}$.\nThe homomorphism $\\kappa$ is surjective and given by\n\\begin{equation}\n\\label{equ:kappa}\n \\kappa\\big( [ \\overline{S}_t ] \\big)\n = \\left\\{\n \\begin{array}{cl}\n [ S_t ] & \\mbox{ if $t \\in {\\mathsf{ind}}\\, \\mathsf{R}$, } \\\\[2mm]\n 0 & \\mbox{ if $t \\in {\\mathsf{ind}}\\, \\mathsf{T} \\setminus {\\mathsf{ind}}\\, \\mathsf{R}$. }\n \\end{array}\n \\right.\n\\end{equation}\n\nThere is a functor\n\\[\n \\begin{array}{rcl}\n \\mathsf{C} & \\stackrel{\\overline{G}}{\\longrightarrow} & \\mathsf{Mod}\\,\\mathsf{T}, \\\\[2mm]\n c & \\longmapsto & \\mathsf{C}( -,\\Sigma c ) |_{ \\mathsf{T} },\n \\end{array}\n\\]\nand $i^{ {\\textstyle *} } \\overline{G} = G$ where $G$ is the functor from\nSubsection \\ref{bfhpg:CC}.\n\nWe define a homomorphism as follows,\n\\begin{equation}\n\\label{equ:overlinetheta}\n \\overline{\\theta} : \\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{T} ) \n \\rightarrow \\operatorname{K}_0^{\\operatorname{split}}( \\mathsf{T} )\n \\;\\;,\\;\\; \\overline{ \\theta }\n \\big( [ \\overline{S}_t ] \\big) = [ a ] - [ a' ],\n\\end{equation}\nwhere $a, a'$ come from the exchange triangles\n\\eqref{equ:exchange_triangles1}, see \\cite[1.5(ii)]{JP}. \n\\end{bfhpg}\n\n\n\\begin{bfhpg}\n[The homomorphism $\\theta$]\nIt is clear from Equations \\eqref{equ:N}, \\eqref{equ:kappa}, and \\eqref{equ:overlinetheta}\nthat there is a unique homomorphism $\\theta$ which makes the following\nsquare commutative.\n\\begin{equation}\n\\label{equ:square}\n\\vcenter{\n \\xymatrix {\n \\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{T} ) \\ar@{->>}_{\\kappa}[d] \\ar^-{ \\overline{ \\theta }}[r] & \\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \\ar@{->>}^{Q}[d]\\\\\n \\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{R} ) \\ar_-{ \\theta }[r] & \\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N \\\\\n }\n }\n\\end{equation}\n\\end{bfhpg}\n\n\n\\begin{bfhpg}\n[Index and coindex]\n\\label{bfhpg:ind}\nFor $c \\in \\mathsf{C}$ there is a distinguished triangle $t_1 \\rightarrow t_0\n\\rightarrow c$ with $t_0, t_1 \\in \\mathsf{T}$ by \\cite[sec.\\ 1]{DK}, and the\nindex $\\operatorname{ind} c = [t_0] - [t_1]$ is a well-defined element of $\\operatorname{K}_0^{\n\\operatorname{split} }( \\mathsf{T} )$. Similarly there is a distinguished triangle $c\n\\rightarrow \\Sigma^2 t^0 \\rightarrow \\Sigma^2 t^1$ with $t^0, t^1 \\in\n\\mathsf{T}$, and the coindex $\\operatorname{coind} c = [t^0] - [t^1]$ is a well-defined\nelement of $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} )$. \n\\end{bfhpg}\n\n\n\\begin{Definition}\n[The maps $\\alpha$ and $\\beta$]\n\\label{def:alpha_and_beta}\nRecall that $A$ is a commutative ring. Let\n\\begin{equation}\n\\label{equ:epsilon_exponential}\n \\varepsilon : \\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N \\rightarrow A\n\\end{equation}\nbe a map which is ``exponential'' in the sense that\n\\[\n \\varepsilon( 0 ) = 1\n\\;\\;,\\;\\;\n \\varepsilon( e+f ) = \\varepsilon( e )\\varepsilon( f ).\n\\]\nDefine\n\\[\n \\alpha : \\operatorname{obj}\\,\\mathsf{C} \\rightarrow A\n\\;\\;,\\;\\;\n \\beta : \\operatorname{K}_0( \\mathsf{fl}\\,\\mathsf{R} ) \\rightarrow A\n\\]\nby\n\\begin{equation}\n\\label{equ:alpha_beta}\n \\alpha( c ) = \\varepsilon Q( \\operatorname{ind} c )\n\\;\\;,\\;\\;\n \\beta( e ) = \\varepsilon \\theta( e ).\n\\end{equation}\nIt is easy to see that $\\alpha$ and $\\beta$ satisfy the conditions in\nSetup \\ref{set:blanket}, and Equation \\eqref{equ:CC} now defines a\nmodified Caldero-Chapoton map $\\rho$ with values in $A$. \n\nRemark \\ref{rmk:app} has further comments to the choice of $\\varepsilon$ which is crucial to the properties of $\\rho$. \n\\end{Definition}\n\n\n\\begin{Remark}\n\\label{rmk:original_CC}\nThe definition of $\\alpha$ and $\\beta$ is motivated by the original\nCaldero-Chapoton map which is recovered as follows when $\\mathsf{R} = \\mathsf{T}$:\nin this case, $N = 0$ and $Q$ is the identity while $\\theta =\n\\overline{ \\theta }$, so Equations \\eqref{equ:alpha_beta} read\n\\[\n \\alpha( c ) = \\varepsilon ( \\operatorname{ind} c )\n\\;\\;,\\;\\;\n \\beta( e ) = \\varepsilon \\overline{ \\theta }( e ).\n\\]\nThe group $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} )$ is free on the classes $[ t ]$ for\n$t \\in \\operatorname{ind}\\,\\mathsf{T}$. Let $A$ be the Laurent polynomial ring on\ngenerators $x_t$ for $t \\in \\operatorname{ind}\\,\\mathsf{T}$ and set $\\varepsilon \\big( [ t\n] \\big) = x_t$ for $t \\in \\operatorname{ind}\\,\\mathsf{T}$. Then the map $\\rho$ from\nEquation \\eqref{equ:CC} is the original Caldero-Chapoton map, see\n\\cite[1.8]{JP}. \n\\end{Remark}\n\n\n\\begin{Lemma}\n\\label{lem:coind}\nThe map $\\overline{\\theta}$ from Equation \\eqref{equ:overlinetheta}\nsatisfies the following. \n\\begin{enumerate}\n\n \\item If $\\overline{G}c$ has finite length in $\\mathsf{Mod}\\, \\mathsf{T}$ then\n\\[\n \\overline{\\theta} \\big( [ \\overline{G}c ] \\big)\n = - ( \\operatorname{ind} c + \\operatorname{ind} \\Sigma c).\n\\]\n\n\\smallskip\n\n \\item Let $\\Sigma c \\stackrel{ \\varphi }{ \\longrightarrow } b\n \\longrightarrow c$ be an AR triangle in $\\mathsf{C}$. If $\\overline{ G }(\n \\Sigma c )$ and $\\overline{ G }c$ have finite length in $\\mathsf{Mod}\\, \\mathsf{T}$\n then\n\\[\n \\operatorname{ind} b\n = \\left\\{\n \\begin{array}{cl}\n - \\overline{ \\theta } \\big( [ \\overline{ G }c ] \\big)\n & \\mbox{ if $c \\not\\in \\mathsf{T}$, } \\\\[2mm]\n \\overline{ \\theta } \\big( [ \\overline{S}_t ] \\big)\n & \\mbox{ if $c = t \\in \\mathsf{T}$. }\n \\end{array}\n \\right.\n\\]\n\\end{enumerate}\n\\end{Lemma}\n\n\\begin{proof}\nFirst note that \\cite[lem.\\ 2.1(2) and prop.\\ 2.2]{Palu} apply to the present setup by \\cite[1.3]{JP}.\n\n(i) Combine \\cite[1.5]{JP} with \\cite[lem.\\ 2.1(2)]{Palu}.\n\n(ii) Observe that\n\\begin{equation}\n\\label{equ:Sigmab1}\n \\operatorname{ind} b\n = \\overline{ \\theta } \\big( [ \\operatorname{Ker} \\overline{ G }\\varphi ] \n - [ \\overline{ G }c ] \\big).\n\\end{equation}\nThis can be seen by combining part (i) of the lemma with \n\\cite[prop.\\ 2.2]{Palu}.\n\n\nThe case $c \\not\\in \\mathsf{T}$: then $\\mathsf{C}( t,b ) \\rightarrow \\mathsf{C}( t,c\n)$ is surjective because $\\Sigma c \\stackrel{ \\varphi }{\n\\longrightarrow } b \\longrightarrow c$ is an AR triangle. The long\nexact sequence \n$\\mathsf{C}( t,b )\n \\longrightarrow \\mathsf{C}( t,c )\n \\longrightarrow \\mathsf{C}( t,\\Sigma^2 c )\n \\stackrel{ ( \\Sigma \\varphi )_* }{ \\longrightarrow }\n \\mathsf{C}( t,\\Sigma b )$\nshows that $( \\Sigma \\varphi )_{ {\\textstyle *} } : \\mathsf{C}( t,\\Sigma^2 c )\n\\rightarrow \\mathsf{C}( t,\\Sigma b )$ is injective. This implies that\n$\\overline{G}\\varphi$ is injective whence Equation \\eqref{equ:Sigmab1}\ngives $\\operatorname{ind} b = - \\overline{ \\theta } \\big( [ \\overline{ G }c ]\n\\big)$ as desired.\n\nThe case $c = t \\in \\mathsf{T}$: then \n\\[\n \\overline{ G }( \\Sigma c\n \\stackrel{ \\varphi }{ \\longrightarrow } b\n \\longrightarrow c )\n = \\overline{ I }_t\n \\stackrel{ \\overline{ G }\\varphi }{ \\longrightarrow }\n \\operatorname{corad} \\overline{ I }_t\n \\rightarrow\n 0\n\\]\nby \\cite[lem.\\ 1.12(ii)]{HJ}. Here $\\overline{ I }_t = \\mathsf{C}(\n-,\\Sigma^2 t )|_{ \\mathsf{T} }$ is the indecomposable injective object of\n$\\mathsf{Mod}\\,\\mathsf{T}$ associated with $t$, and $\\operatorname{corad}$ denotes the quotient by\nthe socle. Hence $\\operatorname{Ker} \\overline{ G }\\varphi = \\overline{ S }_t$ and\n$\\overline{ G }c = 0$, whence Equation \\eqref{equ:Sigmab1} reads $\\operatorname{ind}\nb = \\overline{ \\theta } \\big( [ \\overline{ S }_t ] \\big)$ as desired.\n\\end{proof}\n\n\n\\begin{Theorem}\n\\label{thm:B}\nLet \n\\[\n \\Delta \n = \\Sigma c \\rightarrow b \\rightarrow c\n\\]\nbe an AR triangle in $\\mathsf{C}$ such that $\\overline{ G }c$ and\n$\\overline{ G }( \\Sigma c )$ have finite length in $\\mathsf{Mod}\\, \\mathsf{T}$.\n\nThen $Gc$ and $G( \\Sigma c )$ have finite length in $\\mathsf{Mod}\\, \\mathsf{R}$, and\nthe maps $\\alpha$ and $\\beta$ from Definition \\ref{def:alpha_and_beta}\nare frieze-like for $\\Delta$.\n\\end{Theorem}\n\n\\begin{proof}\nThe statement on lengths holds because $G = i^{ {\\textstyle *} }\\overline{ G }$\nand $i^{ {\\textstyle *} }$ preserves finite length, see Subsection\n\\ref{bfhpg:simples_and_K}. We must now check the conditions of\nDefinition \\ref{def:good}.\n\nFirst, we show that\n\\begin{equation}\n\\label{equ:alpha2}\n \\alpha( c \\oplus \\Sigma c )\\beta \\big( [ Gc ] \\big) = 1,\n\\end{equation}\nin particular establishing the second equation in Definition\n\\ref{def:good}(ii). Equation \\eqref{equ:alpha_beta} gives\n\\begin{equation}\n\\label{equ:alpha10}\n \\alpha( c \\oplus \\Sigma c )\n = \\varepsilon Q \\big( \\operatorname{ind} ( c \\oplus \\Sigma c ) \\big)\n = \\varepsilon Q( \\operatorname{ind} c + \\operatorname{ind} \\Sigma c ).\n\\end{equation}\nOn the other hand, combining Equation \\eqref{equ:alpha_beta}, the fact\nthat $[ Gc ] = [ i^{ {\\textstyle *} }\\overline{ G }c ] = \\kappa[ \\overline{ G }c\n]$, the commutative square \\eqref{equ:square}, and Lemma\n\\ref{lem:coind}(i) gives\n\\[\n \\beta \\big( [ Gc ] \\big)\n = \\varepsilon \\theta \\big( [ Gc ] \\big)\n = \\varepsilon \\theta \\kappa \\big( [ \\overline{ G }c ] \\big)\n = \\varepsilon Q \\overline{ \\theta } \\big( [ \\overline{ G }c ] \\big)\n = \\varepsilon Q \\big( - ( \\operatorname{ind} c + \\operatorname{ind} \\Sigma c ) \\big).\n\\]\nMultiplying the last two equations proves Equation \\eqref{equ:alpha2}. \n\nSecondly, if $c = t \\in \\mathsf{T}$ then it is direct from the\ndefinition of index and coindex that\n\\[\n \\operatorname{ind} c = [ t ]\n\\;\\;,\\;\\;\n \\operatorname{ind} \\Sigma c = - [ t ].\n\\]\nInserting into Equation \\eqref{equ:alpha10} gives\n\\begin{equation}\n\\label{equ:alpha3}\n c \\in \\mathsf{T}\n \\; \\Rightarrow \\;\n \\alpha( c \\oplus \\Sigma c ) = 1,\n\\end{equation}\nin particular establishing the first equation in Definition\n\\ref{def:good}(iii).\n\nThirdly, suppose $c \\not\\in \\mathsf{R}$. We will show\n\\begin{equation}\n\\label{equ:alpha}\n \\alpha( b ) = \\alpha( c \\oplus \\Sigma c ),\n\\end{equation}\nestablishing Definition \\ref{def:good}(i) as well as the first\nequation in Definition \\ref{def:good}(ii).\n\nThe case $c = t \\in \\mathsf{T}$: Note that $c \\not\\in \\mathsf{R}$ implies\n$\\overline{ \\theta } \\big( [ \\overline{ S }_t ] \\big) \\in N$ by\nEquations \\eqref{equ:overlinetheta} and \\eqref{equ:N}. Using\nEquation \\eqref{equ:alpha_beta} and Lemma \\ref{lem:coind}(ii)\ntherefore gives \n\\[\n \\alpha( b )\n = \\varepsilon Q ( \\operatorname{ind} b )\n = \\varepsilon Q \\overline{ \\theta }\\big( [ \\overline{ S }_t ] \\big)\n = \\varepsilon( 0 )\n = 1.\n\\]\nCombining with Equation \\eqref{equ:alpha3} shows Equation\n\\eqref{equ:alpha}. \n\nThe case $c \\not\\in \\mathsf{T}$: combining the two parts of Lemma\n\\ref{lem:coind} shows $\\operatorname{ind} b = \\operatorname{ind} c + \\operatorname{ind} \\Sigma c$. Applying\n$\\varepsilon Q$ shows $\\alpha( b ) = \\alpha( c )\\alpha( \\Sigma c )$\nwhich is equivalent to Equation \\eqref{equ:alpha}.\n\nFinally, suppose $c = r \\in \\mathsf{R}$. We show that\n\\[\n \\alpha( b ) = \\beta \\big( [ S_r ] \\big),\n\\]\nestablishing the second equation in Definition \\ref{def:good}(iii).\nLemma \\ref{lem:coind}(ii) says \n\\[\n \\operatorname{ind} b = \\overline{ \\theta } \\big( [ \\overline{ S }_r ] \\big).\n\\]\nApplying $\\varepsilon Q$ gives the first of the following equalities. \n\\[\n \\alpha( b )\n = \\varepsilon Q \\overline{ \\theta } \\big( [ \\overline{ S }_r ] \\big)\n = \\varepsilon \\theta \\kappa \\big( [ \\overline{ S }_r ] \\big)\n = \\varepsilon \\theta \\big( [ S_r ] \\big)\n = \\beta \\big( [ S_r ] \\big)\n\\]\nThe other equalities are by the commutative diagram \\eqref{equ:square}\nand Equations \\eqref{equ:kappa} and \\eqref{equ:alpha_beta}.\n\\end{proof}\n\n\n\\begin{Remark}\n\\label{rmk:app}\nThe maps $\\alpha$ and $\\beta$ from Definition\n\\ref{def:alpha_and_beta}, and hence the modified Caldero-Chapoton map\n$\\rho$ from Equation \\eqref{equ:CC}, depend on the map $\\varepsilon :\n\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N \\rightarrow A$. The possible choices of\n$\\varepsilon$ are determined by the structure of $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T}\n) \/ N$ which we do not know in general.\n\nHowever, let us suppose that ${\\mathsf{ind}}\\, \\mathsf{R}$ and ${\\mathsf{ind}}\\, \\mathsf{T}$ are\nfinite, with $r$, respectively $r+s$, objects. This is the situation from\nTheorem B in the introduction if we set $R$, respectively $T$, equal\nto the direct sum of the indecomposable objects in $\\operatorname{ind}\\,\\mathsf{R}$,\nrespectively $\\operatorname{ind}\\,\\mathsf{T}$.\nThen we can set $A = \\mathbb{Z}[ x_1^{ \\pm 1 }, \\ldots, x_r^{ \\pm 1\n} ]$ and use all the variables $x_1, \\ldots, x_r$, thereby proving\nTheorem B.\n\nNamely, $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} )$ is a free abelian group\non $r+s$ generators, one per object in ${\\mathsf{ind}}\\, \\mathsf{T}$, and the\nsubgroup $N$ has $s$ generators, one per object in ${\\mathsf{ind}}\\, \\mathsf{T}\n\\setminus {\\mathsf{ind}}\\, \\mathsf{R}$. So $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N$ has a\nquotient group $F$ which is free abelian of rank $( r+s ) - s = r$.\nThe desired map\n$\\varepsilon : \\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N \\rightarrow \\mathbb{Z}[ x_1^{ \\pm 1\n}, \\ldots, x_r^{ \\pm 1 } ]$ can be obtained by sending each generator\nof $F$ to a generator of $\\mathbb{Z}[ x_1^{ \\pm 1 }, \\ldots, x_r^{ \\pm 1 }\n]$.\n\nNote that $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} )$ may have a quotient group which is\nfree abelian of rank $n > r$, see the example in Section\n\\ref{sec:example}. In this case, the above method means that\nwe can even set $A = \\mathbb{Z}[ x_1^{ \\pm 1 }, \\ldots, x_n^{ \\pm 1 } ]$ and\nuse all the variables $x_1, \\ldots, x_n$.\n\\end{Remark}\n\n\n\n\n\\section{Example: a modified Caldero-Chapoton map on the cluster\ncategory of Dynkin type $A_5$}\n\\label{sec:example}\n\n\nThis section shows how to obtain the example in Figure\n\\ref{fig:generalised_frieze} in the introduction.\n\n\\begin{Setup}\nLet the category $\\mathsf{C}$ of Setups \\ref{set:blanket} and \\ref{set:2} be\n$\\mathsf{C}( A_5 )$, the cluster category of Dynkin type $A_5$. There is a\nbijection between ${\\mathsf{ind}}\\, \\mathsf{C}$ and the diagonals of a $8$-gon, see\n\\cite[secs.\\ 2 and 5]{CCS}. We let ${\\mathsf{ind}}\\, \\mathsf{R}$, respectively\n${\\mathsf{ind}}\\, \\mathsf{T}$, be given by the red diagonals, respectively all the\nred and blue diagonals, in Figure \\ref{fig:8gon}. These data satisfy\nour assumptions, see \\cite[sec.\\ 1]{BMRRT}.\n\\begin{figure}\n\\[\n \\begin{tikzpicture}[auto]\n \\node[name=s, shape=regular polygon, regular polygon sides=8,\n minimum size=5cm, draw] at (0,0) {}; \n \\node[name=t, shape=regular polygon, regular polygon sides=8, minimum size=5.8cm] at (0,0) {}; \n \\draw[shift=(t.corner 1)] node {$1$};\n \\draw[shift=(t.corner 2)] node {$2$};\n \\draw[shift=(t.corner 3)] node {$3$};\n \\draw[shift=(t.corner 4)] node {$4$};\n \\draw[shift=(t.corner 5)] node {$5$};\n \\draw[shift=(t.corner 6)] node {$6$};\n \\draw[shift=(t.corner 7)] node {$7$};\n \\draw[shift=(t.corner 8)] node {$8$};\n \\draw[very thick, blue] (s.corner 1) to (s.corner 7);\n \\draw[very thick, blue] (s.corner 2) to (s.corner 4);\n \\draw[very thick, red] (s.corner 2) to (s.corner 5);\n \\draw[very thick, red] (s.corner 2) to (s.corner 7);\n \\draw[very thick, blue] (s.corner 5) to (s.corner 7);\n \\end{tikzpicture} \n\\]\n\\caption{The diagonals of the $8$-gon correspond to the indecomposable\nobjects of $\\mathsf{C}( A_5 )$. The red diagonals define ${\\mathsf{ind}}\\, \\mathsf{R}$ and\nall the red and blue diagonals define ${\\mathsf{ind}}\\, \\mathsf{T}$.} \n\\label{fig:8gon}\n\\end{figure}\n\\end{Setup}\n\n\n\\begin{bfhpg}\n[Some properties of $\\mathsf{C}$]\nWe denote diagonals and their corresponding indecomposable objects by\npairs of vertices, so $\\{\\, 2,7 \\,\\}$ is both a red diagonal in Figure\n\\ref{fig:8gon} and an object of ${\\mathsf{ind}}\\, \\mathsf{R}$. The AR quiver of\n$\\mathsf{C}$ and the objects of $\\operatorname{ind}\\,\\mathsf{R}$, respectively $\\operatorname{ind}\\,\\mathsf{T}$, are\nshown in Figure \\ref{fig:AR_quiver} in the introduction. \n\nAt the level of objects, the suspension functor $\\Sigma$ is given by\n$\\Sigma \\{\\, i,j \\,\\} = \\{\\, i-1,j-1 \\,\\}$. Note that vertex numbers\nare taken modulo $8$.\n\nIf $x, y \\in {\\mathsf{ind}}\\, \\mathsf{C}$ then\n\\begin{equation}\n\\label{equ:C7_Homs}\n \\mathsf{C}( x,\\Sigma y )\n =\n \\left\\{\n \\begin{array}{cl}\n k & \\mbox{ if the diagonals corresponding to $x$ and $y$ cross, } \\\\[2mm]\n 0 & \\mbox{ if not. }\n \\end{array}\n \\right.\n\\end{equation}\nIf $i,k,j,\\ell$ are four vertices in anticlockwise\norder on the polygon, then $\\{\\, i,j \\,\\}$ and $\\{\\, k,\\ell \\,\\}$ are\ncrossing diagonals, and there are the following non-split distinguished\ntriangles,\n\\begin{equation}\n\\label{equ:exchange_triangles}\n \\{\\, i,j \\,\\}\n \\rightarrow \\{\\, i,\\ell \\,\\} \\oplus \\{\\, j,k \\,\\}\n \\rightarrow \\{\\, k,\\ell \\,\\}\n \\;\\; , \\;\\;\n \\{\\, k,\\ell \\,\\}\n \\rightarrow \\{\\, i,k \\,\\} \\oplus \\{\\, j,\\ell \\,\\}\n \\rightarrow \\{\\, i,j \\,\\},\n\\end{equation}\nwhere a pair of neighbouring vertices must be interpreted as $0$.\n\\end{bfhpg}\n\n\n\\begin{bfhpg}\n[$\\operatorname{K}$-theory]\nThe category $\\mathsf{T}$ has the following indecomposable objects.\n\\[\n \\{\\, 1,7 \\,\\}\n \\;\\;,\\;\\; \\{\\, 2,4 \\,\\}\n \\;\\;,\\;\\; \\{\\, 2,5 \\,\\}\n \\;\\;,\\;\\; \\{\\, 2,7 \\,\\}\n \\;\\;,\\;\\; \\{\\, 5,7 \\,\\}\n\\]\nTheir $\\operatorname{K}_0^{ \\operatorname{split} }$-classes are free generators of $\\operatorname{K}_0^{ \\operatorname{split}\n}( \\mathsf{T} )$. To save parentheses, the classes are denoted $[ 1,7 ]$\netc. The objects in ${\\mathsf{ind}}\\, \\mathsf{T} \\setminus {\\mathsf{ind}}\\, \\mathsf{R}$ are\n\\[ \n \\{\\, 1,7 \\,\\} \\;\\;,\\;\\; \\{\\, 2,4 \\,\\} \\;\\;,\\;\\; \\{\\, 5,7 \\,\\},\n\\]\nand Equation \\eqref{equ:exchange_triangles} means that they sit in the\nfollowing exchange triangles.\n\\[\n \\xymatrix @R=1ex {\n \\{\\, 2,8 \\,\\} \\ar[r] & 0 \\ar[r] & \\{\\, 1,7 \\,\\}\n & \\{\\, 1,7 \\,\\} \\ar[r] & \\{\\, 2,7 \\,\\} \\ar[r] & \\{\\, 2,8 \\,\\} \\\\\n \\{\\, 3,5 \\,\\} \\ar[r] & 0 \\ar[r] & \\{\\, 2,4 \\,\\}\n & \\{\\, 2,4 \\,\\} \\ar[r] & \\{\\, 2,5 \\,\\} \\ar[r] & \\{\\, 3,5 \\,\\} \\\\\n \\{\\, 2,6 \\,\\} \\ar[r] & \\{\\, 2,7 \\,\\} \\ar[r] & \\{\\, 5,7 \\,\\}\n & \\{\\, 5,7 \\,\\} \\ar[r] & \\{\\, 2,5 \\,\\} \\ar[r] & \\{\\, 2,6 \\,\\}\n }\n\\]\nAccordingly, the subgroup $N$ of Definition \\ref{def:N} is\n\\[\n N = \\big\\langle\\,\n - [ 2,7 ] \\;,\\; - [ 2,5 ] \\;,\\; [ 2,7 ] - [ 2,5 ]\n \\,\\big\\rangle \\\\[2mm]\n = \\big\\langle\\,\n [ 2,5 ] \\;,\\; [ 2,7 ]\n \\,\\big\\rangle,\n\\]\nand $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N$ is the free abelian group generated\nby\n\\[\n [ 1,7 ] + N\n \\;\\;,\\;\\; [ 2,4 ] + N\n \\;\\;,\\;\\; [ 5,7 ] + N.\n\\]\n\nThe category $\\mathsf{fl}\\, \\mathsf{R}$ has the simple objects\n\\[\n S_{\\{\\, 2,5 \\,\\}} \\;\\;,\\;\\; S_{\\{\\, 2,7 \\,\\}}\n\\]\nwhose $\\operatorname{K}_0$-classes are free generators of $\\operatorname{K}_0( \\mathsf{fl}\\, \\mathsf{R} )$, and\n$\\mathsf{fl}\\, \\mathsf{T}$ has the simple objects\n\\[\n \\overline{ S }_{\\{\\, 1,7 \\,\\}}\n \\;\\;,\\;\\; \\overline{ S }_{\\{\\, 2,4 \\,\\}}\n \\;\\;,\\;\\; \\overline{ S }_{\\{\\, 2,5 \\,\\}}\n \\;\\;,\\;\\; \\overline{ S }_{\\{\\, 2,7 \\,\\}}\n \\;\\;,\\;\\; \\overline{ S }_{\\{\\, 5,7 \\,\\}}\n\\]\nwhose $\\operatorname{K}_0$-classes are free generators of $\\operatorname{K}_0( \\mathsf{fl}\\, \\mathsf{T} )$. To\nsave parentheses, the simple objects will be denoted $S_{ 2,5 }$,\nrespectively $\\overline{ S }_{ 1,7 }$, etc.\n\\end{bfhpg}\n\n\n\\begin{Definition}\nLet the map\n\\[\n \\varepsilon : \\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N\n \\rightarrow\n \\mathbb{Z}[ u^{ \\pm 1 } , v^{ \\pm 1 } , z^{ \\pm 1 } ]\n\\]\nbe given by\n\\begin{equation}\n\\label{equ:epsilon}\n \\varepsilon \\big( [ 1,7 ] + N \\big) = u \n\\;\\;,\\;\\;\n \\varepsilon \\big( [ 2,4 ] + N \\big) = v \n\\;\\;,\\;\\;\n \\varepsilon \\big( [ 5,7 ] + N \\big) = z. \n\\end{equation}\nEquation \\eqref{equ:alpha_beta} defines the maps $\\alpha$ and $\\beta$, \nand the modified Caldero-Chapton map $\\rho$ is defined by Equation\n\\eqref{equ:CC}.\n\\end{Definition}\n\n\n\\begin{Example}\nLet us compute $\\rho \\big( \\{\\, 4,6 \\,\\} \\big)$.\n\nEquation \\eqref{equ:alpha_beta} gives\n\\[\n \\alpha \\big( \\{\\, 4,6 \\,\\} \\big)\n = \\varepsilon Q( \\operatorname{ind} \\{\\, 4,6 \\,\\} )\n = ( {\\textstyle *} ).\n\\]\nNow $\\{\\, 4,6 \\,\\} = \\Sigma \\{\\, 5,7 \\,\\}$ so $\\operatorname{ind} \\{\\, 4,6 \\,\\} =\n\\operatorname{ind} \\Sigma \\{\\, 5,7 \\,\\} = - [ 5,7 ]$, where the last equality is\ndirect from the definition of index because $\\{\\, 5,7 \\,\\} \\in \\mathsf{T}$,\nsee Subsection \\ref{bfhpg:ind}. Hence Equation \\eqref{equ:epsilon}\ngives\n\\[\n ( {\\textstyle *} )\n = \\varepsilon Q \\big( -[ 5,7 ] \\big)\n = \\varepsilon \\big( -[ 5,7 ] + N \\big)\n = z^{ -1 }.\n\\]\n\n\nWe have $G \\big( \\{\\, 4,6 \\,\\} \\big) = \\mathsf{C}( -,\\Sigma \\{\\, 4,6 \\,\\}\n)|_{ \\mathsf{R} }$. Moreover, $\\mathsf{R} = \\operatorname{add} \\big\\{\\, \\{\\, 2,5 \\,\\} , \\{\\, 2,7\n\\,\\} \\,\\big\\}$, and it is direct from Equation\n\\eqref{equ:C7_Homs} that $G \\big( \\{\\, 4,6 \\,\\} \\big)$ is supported\nonly at $\\{\\, 2,5 \\,\\}$ where it has the value $k$. That is,\n\\[\n G \\big( \\{\\, 4,6 \\,\\} \\big) = S_{ 2,5 }.\n\\]\nIt follows that the only non-empty Grassmannians appearing in Equation\n\\eqref{equ:CC} when computing $\\rho \\big( \\{\\, 4,6 \\,\\} \\big)$ are\n$\\operatorname{Gr}_0 \\big( G\\{\\, 4,6 \\,\\} \\big)$ and $\\operatorname{Gr}_{ [ S_{ 2,5 } ] } \\big(\nG\\{\\, 4,6 \\,\\} \\big)$, and it is clear that each is a point so has\nEuler characteristic $1$.\n\nFinally, Equations \\eqref{equ:kappa} and \\eqref{equ:alpha_beta} and\ndiagram \\eqref{equ:square} give \n\\[\n \\beta \\big( [ S_{ 2,5 } ] \\big)\n = \\varepsilon \\theta \\big( [ S_{ 2,5 } ] \\big)\n = \\varepsilon \\theta \\kappa \n \\big( [ \\overline{ S }_{ 2,5 } ] \\big)\n = \\varepsilon Q \\overline{ \\theta }\n \\big( [ \\overline{ S }_{ 2,5 } ] \\big)\n = ( {\\textstyle *} {\\textstyle *} ).\n\\]\nEquation \\eqref{equ:exchange_triangles} gives exchange triangles\n\\[\n \\xymatrix @R=1ex {\n \\{\\, 4,7 \\,\\} \\ar[r] & \\{\\, 2,4 \\,\\} \\oplus \\{\\, 5,7 \\,\\} \\ar[r] & \\{\\, 2,5 \\,\\}\n & \\{\\, 2,5 \\,\\} \\ar[r] & \\{\\, 2,7 \\,\\} \\ar[r] & \\{\\, 4,7 \\,\\}\n }\n\\]\nand Equation \\eqref{equ:overlinetheta} gives $\\overline{ \\theta }\n\\big( [ \\overline{ S }_{ 2,5 } ] \\big) = [ 2,4 ] + [ 5,7 ] -\n[ 2,7 ]$ whence Equation \\eqref{equ:epsilon} gives\n\\[\n ( {\\textstyle *} {\\textstyle *} )\n = \\varepsilon Q\n \\big( [ 2,4 ] + [ 5,7 ] - [ 2,7 ] \\big)\n = vz.\n\\]\n\nHence Equation \\eqref{equ:CC} says\n\\begin{align*}\n \\rho \\big( \\{\\, 4,6 \\,\\} \\big)\n & = \\alpha \\big( \\{\\, 4,6 \\,\\} \\big)\n \\sum_e \\chi \\big( \\operatorname{Gr}_e( G\\{\\, 4,6 \\,\\} ) \\big) \\beta( e ) \\\\\n & = z^{ -1 } \\cdot \\Big(\n \\chi \\big( \\operatorname{Gr}_0( S_{ 2,5 } ) \\big) \n \\beta( 0 )\n + \\chi \\big( \\operatorname{Gr}_{[ S_{ 2,5 } ]}( S_{ 2,5 } ) \\big) \n \\beta \\big( [S_{ 2,5 }] \\big)\n \\Big) \\\\\n & = z^{ -1 } \\cdot ( 1 + vz ) \\\\\n & = \\frac{ 1+vz }{ z }.\n\\end{align*}\n\nSimilar computations for the other indecomposable objects finally\nproduce the generalised frieze in Figure \\ref{fig:generalised_frieze}\nin the introduction.\n\\end{Example}\n\n\n\n\n\\section{Questions}\n\\label{sec:questions}\n\n\nWe end the paper with some questions.\n\n\\begin{enumerate}\n\n\\item The group $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} )$ is free abelian on ${\\mathsf{ind}}\\,\n \\mathsf{T}$, and the subgroup $N$ of Definition \\ref{def:N} is generated by\n all expressions $[a] - [a']$ where $s^{ {\\textstyle *} } \\rightarrow a\n \\rightarrow s$ and $s \\rightarrow a' \\rightarrow s^{ {\\textstyle *} }$ are\n exchange triangles with $s \\in {\\mathsf{ind}}\\, \\mathsf{T} \\setminus {\\mathsf{ind}}\\, \\mathsf{R}$.\n\n\\medskip\n\\noindent\nWhat is the rank $n$ of the quotient $\\operatorname{K}_0^{ \\operatorname{split} }( \\mathsf{T} ) \/ N$?\nNote that when $n$ is finite, it is the largest integer such that the\nmethod of Remark \\ref{rmk:app} results in a modified Caldero-Chapoton\nmap $\\rho : \\operatorname{obj} \\mathsf{C} \\rightarrow \\mathbb{Z}[ x_1^{ \\pm 1 }, \\ldots, x_n^{ \\pm\n 1 } ]$ using all the variables $x_1, \\ldots, x_n$.\n\n\\medskip\n\n\\item Consider the $\\mathbb{Z}$-subalgebra of $\\mathbb{Z}[ x_1^{ \\pm 1 }, \\ldots, x_n^{\n \\pm 1 } ]$ generated by the values of the modified Caldero-Chapoton map $\\rho$. \n\n\\medskip\n\\noindent\n What is its relation to the cluster algebra?\n\n\\medskip\n\n\\item Let $T$ be a cluster tilting object and use it to define a\n Caldero-Chapoton map $X$. If $T$ is subjected to cluster mutation,\n then the values of $X$ change in a well-understood way, see\n \\cite[proof of cor.\\ 5.4]{Palu}.\n\n\\medskip\n\\noindent\n There is a notion of mutation of rigid objects due to\n \\cite[sec.\\ 2]{MP}. What happens to the values of the modified\n Caldero-Chapoton map under such mutation?\n\n\\end{enumerate}\n\n\n\n\n\\medskip\n\\noindent\n{\\bf Acknowledgement.}\nThis paper is a direct continuation of \\cite{HJ}. Both papers grew\nout of \\cite{BHJ} with Christine Bessenrodt, and we are grateful to\nher for the fruitful collaboration.\n\nWe thank the referee for several interesting comments and Robert Marsh\nfor answering a question about rigid subcategories.\n\nPart of this work was done while Peter J\\o rgensen was visiting the\nLeibniz Universit\\\"{a}t Hannover. He thanks Christine Bessenrodt,\nThorsten Holm, and the Institut f\\\"{u}r Algebra, Zahlentheorie und\nDiskrete Mathematik for their hospitality. He gratefully acknowledges\nsupport from Thorsten Holm's grant HO 1880\/5-1, which falls under the\nresearch priority programme SPP 1388 {\\em Darstellungstheorie} of the\nDeutsche Forschungsgemeinschaft (DFG).\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:intro}\n\nOntologies are witnessing an increasing popularity outside specialized AI\ncommunities. While this is mostly due to Semantic Web\napplications~\\cite{ber01}, we must also credit their ability to\ncope with taxonomies and part-whole relationships, to\nhandle heterogeneous attributes, and their provision for various\nautomated reasoning services --- see, e.g.,~\\cite{staab2013handbook}. \nThese features have been recognized since long time in system engineering,\nthe community encompassing all areas of research devoted to design,\nimplementation, monitoring and diagnosis of technical processes.\nFor instance, in the operations and maintenance sub-community, the use\nof ontologies is explicitly advocated\\footnote{See, e.g., the MIMOSA\nopen standard architecture at \\url{www.mimosa.org}.}. \nAlso standards like ISO 13374\n(\\textit{Condition monitoring and diagnostics of \nmachines -- Data processing, communication and presentation}) suggest\nthe use of ontologies for several tasks, mostly related to data\nconceptualization. However, the adoption of ontologies\nfaces some challenges, mostly due to speed and reliability constraints\nimposed by industrial settings. \n\nHere we investigate this issue by considering four contributions of\nours to application domains wherein ontologies provide key\ncapabilities in system engineering. The first case study is\nabout an on-board rolling-stock condition analyzer, i.e., a\nsystem to perform fault detection and\nclassification~\\cite{AmbrosiGT09}. The second one is about monitoring \nan intermodal logistic system~\\cite{CasuCT13}. The third one is about an\nontology-based framework to generate diagnostic-decision support\nsystems~\\cite{CicalaLOT16}. Finally, a fourth case study is an \napplication to computer-automated design of elevator\nsystems.\nIn the following, we briefly introduce each case study, giving details\nabout its context, underlying motivation and intended objectives. The \nultimate goal of the paper is to discuss and compare the results\nobtained to assess the effectiveness of ontologies in such\napplication domains. \n\n\\noindent\n\\textit{Ontologies for condition analysis.}\nWe introduced an ontology-based condition analyzer\n(CA)~\\cite{AmbrosiGT09} in the context of the EU project \nIntegrail\\footnote{More details about Integrail\nat \\url{http:\/\/www.integrail.eu\/}.}. \nOur CA collects signals from control logic installed on locomotives,\nand it leverages an ontology to correlate\nobserved data, symptoms and faults. \nThe CA must mate two competing\nneeds: $(i)$ railway regulations\nrequire hardware which is highly reliable, and whose performances are\nthus far even from desktop workstations; $(ii)$\nontology-related tools, e.g., description logic reasoners,\nhave relatively large memory, processor and storage footprints.\nIn this experience, the main \ngoal was thus to check whether reasoning with ontologies can provide\nuseful diagnostic feedback in a resource-restricted scenario. \n\n\\noindent\n\\textit{Ontologies for system monitoring.}\nIn~\\cite{CasuCT13} we provided strong evidence of practical uses\nfor ontologies in complex systems engineering by implementing\na monitor for \\emph{Intermodal Logistics Systems} (ILSs), i.e.,\nsystems supporting the movement of containerized goods. In\nparticular, we considered combination of rail and road transport,\nwhere rail transport is provided by short-distance shuttle trains,\nand network coverage is\nachieved through connections at specialized terminals. In this\nexperience, the main goal was to gather data about terminal operations\nand compute global performances indicators, where access to data is\nmediated by an ontology --- ontology-based data access\n(OBDA)~\\cite{cal05}. Here, unlike the CA case study, \nthe ability to handle large amount of data is crucial, but reasoning\nis limited to SPARQL query answering.\n\n\\noindent\n\\textit{Ontologies for diagnostic support system generation.}\nDiagnostic Decision Support Systems (DDSSs) help humans to \ninfer the health status of physical systems. In~\\cite{CicalaLOT16} we\nintroduced DiSeGnO --- for ``Diagnostic Server Generation\nthrough Ontology'' --- to generate customized DDSSs.\nAs in the ILS monitoring case study, since it is\nexpected that large quantities of data should be handled, \nthe ontology language is\nrestricted to those designed for tractable reasoning --- see,\ne.g.,~\\cite{cal05}. In this case, ontology-based \nreasoning is not leveraged, as DiSeGnO generates relational databases\nfrom the domain ontology and then computes diagnostic rules\nwith \\textsc{Ptolemy II}{}~\\cite{Ptolemy}, an open-source software simulating\nactor-based models. \n\n\\noindent\n\\textit{Ontologies for computer-automated design.}\nAs mentioned in~\\cite{ByeOPHS16}, the first scientific report of\nintelligent computer-automated design (CautoD) is the paper by\nKamentsky and Liu~\\cite{KamentskyL63}, who created a computer program\nfor designing character-recognition logic circuits satisfying given\nhardware constraints. In mechanical design --- see,\ne.g.,~\\cite{rao2012mechanical} --- the term usually refers to\ntechniques that mitigate the effort in exploring alternative\nsolutions for structural implements. In our \\textsc{LiftCreate}{}\nCautoD program for elevator systems\\footnote{Part of the \\textsc{AiLift}{}\nsoftware suite \\url{www.ailift.it}.}, ontologies \nsupport intelligent design creation and optimization by managing\ndetailed part-whole taxonomies, wherein different relations among\ncomponents can be expressed. This case study provides thus yet another\napplication of ontologies, mostly oriented to intelligent computation\nand data persistency.\n\nOverall, the case studies considered witness the great flexibility\nthat ontologies provide in handling diverse application scenarios,\nfrom condition analysis of locomotives, to automated design of\nelevators, considering both cases wherein they provide the basis for\nlogic reasoning services, or just advanced data-modeling capabilities. \nThe rest of the paper is structured as follows. In\nSections~\\ref{sec:condition}, \\ref{sec:ilog}, \\ref{sec:ondaBrief}\nand \\ref{sec:elevator} we sketch the design, the implementation and\nthe results obtained in the case studies described above.\nSection~\\ref{sec:concl} concludes the paper by summarizing the results\nand providing some discussion thereof. \n\n\n\n\\section{Rolling stock condition analysis}\n\\label{sec:condition}\n\\begin{figure}[t!]\n\\begin{center}\n \\includegraphics[scale=0.27]{OntologyTraction.png}\n \\caption{\\label{fig:e414ont}A portion of the E414 ontology regarding\n traction faults. Concepts are nodes and\n object properties are edges: white nodes are SP3A\n concepts, grey ones are E414-specific concepts.}\n\\end{center}\n\\end{figure}\n\nThe CA prototype described in~\\cite{AmbrosiGT09} focuses on \nfault detection on Trenitalia E414 locomotive.\nThe main task \nof the CA is to perform fault classification according to \npriority for maintenance, and impact on mission-related and\nsafety-related aspects.\nHere, we focus on traction groups as an example of\nsubsystem that can generate a faulty condition.\nOur ontology for the E414 locomotive is written in OWL 2 language\nand it builds on the SP3A core ontology \n--- see~\\cite{AmbrosiGT09} for details. \nIn particular, the E414 ontology leverages the SP3A concepts of {\\sc\n ObservationData}, i.e., process variables, and {\\sc Observation},\ni.e., sequences of observation data from which individuals of class\n{\\sc Symptom} and {\\sc Fault} arise. {\\sc Symptom} individuals\nare related to {\\sc Observation} individuals via the {\\sc\n refersToObservation} property and to {\\sc Fault} individuals via the\n{\\sc refersToFault} property. {\\sc Fault} is a concept whose\nindividuals are defined in terms of the necessary {\\sc hasSymptom}\nrelationship with {\\sc Symptom} individuals.\nTwo subclasses of {\\sc Fault} are defined: {\\sc\n PriorityFault} and {\\sc NonPriorityFault}, with obvious\nmeaning.\nIn Figure~\\ref{fig:e414ont} we show a portion of the E414 ontology\nrelated to traction faults, where concepts have been specialized \nin subclasses whose individuals correspond to actual signals and\nsubsystems. Fault and symptom classification is obtained by\na Description Logic (DL) reasoner considering the patterns \nobserved. For instance, in the case of {\\sc\n TractionHighTemperatureObservation}, three ranges of temperatures\nare defined that correspond to ``interesting'' patterns: from 70 to 80\ndegrees, from 80 to 130 degrees, and over 130 degrees. It is \npostulated that observations falling in the second and in the\nthird ranges are to be considered mission critical, while the ones in\nthe first category are only maintenance critical.\n\n\n\n\nA detailed description of the CA architecture can be found\nin~\\cite{AmbrosiGT09}. Here we provide some intuition on how the\nanalyzer works considering high temperatures in the traction groups. \nWhen the temperature of a group is higher than 70 \ndegrees for at least 3 consecutive samples read from the field bus,\nthe CA starts tracking a potential anomalous pattern.\nOnce such a pattern is detected, the corresponding\nindividuals in the classes \\textsc{TractionObservationData} \nand \\textsc{TractionHighTemperatureObservation}\nare recorded. {\\sc Symptom} individuals\nare built along with all the properties required by the ontology\nspecification. For example, if an observation of the class\n\\textsc{TractionHighTemperatureObservation} has been created, \na specific individual \n\\textsc{TractionHighTemperatureObservation} is related to a new\n\\textsc{Symptom} individual by the\n\\textsc{refersToObservation} property.\n{\\sc Fault} individuals for each {\\sc Symptom} individual are created \ntogether with the {\\sc causedBySymptom} property.\n{\\sc Fault} as well as {\\sc Symptom} individuals are built of generic\ntype, leaving their classification to the DL reasoner. Once\nthe classification is done, the CA publishes the results, transmitting\nthem to external agents. As an example, let us assume that $i$ is an\nindividual of the class \\textsc{TractionHighTemperatureObservation}\nwhose property \\textsc{isAt} is set to the constant\n\\textsc{\\_130degrees}, $s$ is the \\textsc{Symptom} individual related\nto $i$, and $f$ is the \\textsc{Fault} individual related to $s$. \nThe E414 ontology postulates that all symptoms such that the\ncorresponding observation is an instance of\n\\textsc{TractionHighTemperatureObservation} related by \\textsc{isAt}\nto the constant \\textsc{\\_130degrees}\nare also an instance of \\textsc{TractionTotalMissionImpactSymptom},\nwhich is a subclass of \\textsc{Symptom}. Therefore, a reasoner \ncan infer that $s$ belongs to \n\\textsc{MissionRelatedSymptom}\n\n\n\\begin{table}[t!]\n\\caption{\\label{tab:results} Results with \n (a) lazy and (b) eager implementations of the CA.}\n\\begin{center}\n\\tiny\n \\begin{tabular}{ | c | c | c | c | }\n \\hline\t\t\t\n Scenario & Memory Consumption [MB] & CPU Time [ms] & Amortized CPU Time [ms] \\\\\n \\hline\n 1a & 38 & 90 & ND \\\\\n 2a & 74 & 25373 & 25373 \\\\\n 3a & 106 & 1053656 & 210731 \\\\\n 4a & OUT OF MEMORY & 3253637 & 191390 \\\\\n \\hline\n 1b & 37 & 90 & ND \\\\\n 2b & 72 & 21506 & 21506 \\\\\n 3b & 104 & 86938 & 17387 \\\\\n 4b & 105 & 279523 & 16442 \\\\\n \\hline\n \\end{tabular}\n\\end{center}\n\\end{table}\n\nOut of the three sets of experiments performed in~\\cite{AmbrosiGT09},\nwe report just those to ensure that the CA\nimplementation fits the constraints. To this end, we ran several tests\nusing different fault scenarios\\footnote{Sets \n of multidimensional time series (3600 samples at 1Hz) corresponding\n to 52 process variables are generated.\n \n \n \n Simulations run on EN50155-compliant embedded devices \n with 1GHz Socket 370 FC-PGA Celeron Processor with 256MB \n of main memory and a 1GB SSD running Linux Blue Cat (kernel 2.6) and\n Sun Java Virtual Machine implementation (JRE 1.6). The DL reasoner is \\textsc{Pellet}{}~\\cite{sirin07}.}.\nTable~\\ref{tab:results} shows the results obtained by running the CA\non four different scenarios --- the first includes no\nfault, the second includes only one fault, the third includes five\ncontemporary faults, and the last 17 contemporary faults --- \nusing two different configurations.\nConfiguration (a) is ``lazy'', i.e., it keeps all the individuals,\nwhile configuration (b) is ``eager'', i.e., it \ndeletes individuals as soon as possible.\nAs we can see in Table~\\ref{tab:results}, the eager version results in\na great improvement over the lazy one, both in terms of memory\nconsumption and in terms of computation time.\nIn particular, in the second column of Table~\\ref{tab:results} we can\nnotice that the eager version performs reasonably well, even in the\nfourth test case (worst-case scenario). In the same scenario,\nthe lazy version exceeds the amount of available memory.\nAs we can see in the rightmost column of Table~\\ref{tab:results}, the\namortized computation time over a single scenario\ndecreases with the number of concurrent\nobservations detected in the round.\nManaging a round of samples without detected observations\ntakes only 90 ms, which leaves enough time for other activities, and\nallows the CA to process all the incoming signals in due course.\n\n\n\n\n\n\n\n\n\n\n\\section{Monitoring of intermodal systems}\n\\label{sec:ilog}\n\\begin{figure}[t!]\n\\centerline{\\scalebox{.26}{\\includegraphics{iLogDot}}}\n\\caption{\\label{fig:iLog_dot} ILS ontology describing the design of the\n OBDA solution. Ellipses denote concepts with datatype properties;\n directed edges are object properties; dotted edges are concept\n inclusions.} \n\\end{figure}\n\nIn~\\cite{CasuCT13} we provided evidence that ontology-based data\naccess (OBDA)~\\cite{cal05} \nis of practical use in the context of \\emph{Intermodal Logistics\n Systems} (ILSs). The investigation focuses on the opportunity to build a monitoring information\nsystem (MIS) using OBDA instead of relational databases\n(RDBs). The application scenario is an ILS relying on a\nlogic akin to computer networks, i.e., frequent short-distance trains\nwith a fixed composition and a predefined daily schedule to cover some\ngeographical area. \\emph{Intermodal Transport Units} (ITUs) enter\nthe network at some terminal and travel to their destination according\nto a predefined route, usually boarding more than one train along the\nway. Terminals collect ITUs from areas of approximately 150Km in\nradius in order to minimize road transport. The MIS is a key\nenabler to minimize delivery time, maximize rolling-stock and network\nutilization and, ultimately, reduce the economic overhead of\ntransportation for the final customer. The main goal of the MIS is to\ncompute \\emph{Key Performance Indicators} (KPIs) to perform tactical and \nstrategical decision making about the network.\n\nIn Figure~\\ref{fig:iLog_dot} we present a graphical outline of the\nontology at the heart of our OBDA solution to monitor the ILS.\nThe ontology --- ILS ontology in the following --- is compliant with\nthe OWL 2 QL profile described in the official W3C's recommendation\nas \\emph{``[the sub-language of OWL 2] aimed at applications that use \n very large volumes of instance data, and where query answering is\n the most important reasoning task.''}. Given the ILS application\ndomain, OWL 2 QL guarantees that conjunctive query answering and\nconsistency checking can be implemented efficiently\nwith respect to the size of data and ontology, respectively. The\nrestrictions that OWL 2 QL implies did not hamper the modeling\naccuracy of our ILS ontology. In Figure~\\ref{fig:iLog_dot} we can\npinpoint classes related to freight forwarding such as \n\\textbf{Customer}, i.e., companies forwarding their goods through the\nnetwork, \\textbf{RequestForWork}, i.e., the main document witnessing\nthat a given customer has issued a request for transporting a number\nof ITUs, \\textbf{TransportOrder}, i.e., the ``bill of transit''\nassociated to each ITU, as well as entities related to physical\nelements such as \\textbf{ITU}, \\textbf{Terminal} and \\textbf{Train}. Also\n``logical'' entities are modeled such as \\textbf{Route}, i.e., a\nsequence of terminals and railway connections serviced regularly by\none or more scheduled trains and \\textbf{ScheduledStop}, i.e.,\nterminals associated to a given route with a given\nschedule. \\textbf{Event} is the main monitoring entity, as the\ncalculation of most KPIs relies on the exact recording of events at\nspecific locations. \n\n\\begin{figure}[t!]\n\\begin{center}\n \\scalebox{.27}{\\includegraphics{q3heavy.pdf}}\n\\end{center}\n\\caption{\\label{fig:results} Computation time of a KPI with different\n query processors: SQL (square), \\textsc{ARQ}{} (circle), \\textsc{Pellet}{}\n (hourglass), \\textsc{Quest}{} (triangle). In each plot, the $x$ axis\n displays the number of simulation days from 1 to 15, the $y$ axis\n displays the CPU time (in milliseconds on a logarithmic\n scale).} \n\\end{figure}\n\nTo assess OBDA performances, in~\\cite{CasuCT13} we obtained different\nartificial utilization scenarios by changing \nthe number of ITUs shipped daily from each terminal. Considering \ntypical usage patterns, we postulated that a\nprovision of 10 to 50 ITUs is to be shipped daily from each terminal,\nwith 40 to 50 ITUS corresponding to a heavy utilization.\nScenarios are simulated for an increasing number of days to\nevaluate scalability, and all of them share common settings as far as\nnumber of train travels, number of cars per train, and timetabling are\nconcerned. Unexpected delays as well as the number of customers per\nterminal follow a probabilistic model --- see~\\cite{CasuCT13} for more\ndetails. In Figure~\\ref{fig:results} we display the results\\footnote{\n All results are obtained on a family of identical\n Intel-based PCs, featuring a Core2Duo 2.13 GHz CPU, 4GB of RAM and\n running Ubuntu Linux 10.04 (64 bit edition).} obtained\nin the case of an heavy utilization scenario to compute a specific\nKPI, namely the average number of ITUs unloaded per hour.\nThe performance of four different query-answering systems\nare reported: a SQL query on a native RDB implementation, and a SPARQL\nquery on the ontology store. The SPARQL query can be answered by three\ndifferent systems, namely \\textsc{ARQ}{} (the default query processor in the\n\\textsc{Jena}{} library), \\textsc{Pellet}{} (the same DL reasoner that we consider in\nSection~\\ref{sec:condition}) and \\textsc{Quest}{}~\\cite{mur12}. The latter is the only\nreasoner exploiting the fact that SPARQL queries can be compiled\non-the-fly into SQL queries for an equivalent RDB representation of\nthe ontology stored in the main memory. As we can observe in\nFigure~\\ref{fig:results}, OBDA-based solutions show higher overall\ncomputation times than the RDB-based solution --- from 1 to 2 orders\nof magnitude --- together with an apparently growing trend associated\nto the time span of the simulation. However, as we have shown\nin~\\cite{CasuCT13}, a trend test performed on the results obtained\nwith the best OBDA solutions for various KPIs, displays no statistically\nsignificant increase in the CPU time required to answer various\nqueries with respect to the number of days. Considering that for most\nKPIs we can adopt an ``eager'' solution similar to that considered in\nSection~\\ref{fig:results}, we can conclude that OBDA is practically\nfeasible for monitoring medium-to-large scale systems.\n\n\n\n\\section{Diagnostic support systems generation}\n\\label{sec:ondaBrief}\n\\begin{figure}[!t]\n\\centering\n\\scalebox{0.34}{\\includegraphics{ondaFramework.png}}\n\\caption{\\label{fig:ondaModel}Functional architecture and work-flow of DiSeGnO framework.}\n\\end{figure}\n\nIn~\\cite{CicalaLOT16} we introduced an approach to compile ontology-based\ndescriptions of equipment into diagnostic decision support systems\n(DDSSs). The tool DiSeGnO, whose functional architecture and work-flow\nis sketched in Figure~\\ref{fig:ondaModel}, fulfills this task in\nthree phases: in the \\textsf{USER} phase, a domain ontology and\ndiagnostic rules model are designed by the user; in the\n\\textsf{DiSeGnO} phase, the system reads and analyzes the ontology\nand the rules to output the actual DDSS; in the \\textsf{DDSS} phase,\ninput web services receive data from the observed physical system and\nrecord them in the generated data store. According to the ISO 13374-1\nstandard a DDSS consists of six modules of which DiSeGnO implements three:\n\\textit{Data Manipulation} to perform signal analysis and \ncompute meaningful descriptors, \\textit{State Detection}\nto check conformity to reference patterns, and \\textit{Health\n Assessment} to diagnose faults and rate the current \nhealth of the equipment or process.\nAs shown in Figure~\\ref{fig:ondaModel}, the ontology description is\ncreated by a system architect in the \\textsf{USER} phase. \nThe ontology must be written using OWL 2 QL language\\footnote{While\n this can be accomplished in several ways, the tool\n \\textsc{prot\\'eg\\'e}{}~\\cite{gennari2003evolution} is suggested because it \nis robust, easy to use, and it provides, either directly or through\nplug-ins, several add-ons that facilitate ontology design and\ntesting.} as in the case study shown in Section~\\ref{sec:ilog}. \nThe diagnostic computation model must be a sound\nactor diagram generated by \\textsc{Ptolemy II}{}~\\cite{Ptolemy} which describes\nthe processing to be applied to incoming data in order to generated\ndiagnostic events --- here we focus on the ontology part, but more\ndetails on the rule handling part can be found in~\\cite{CicalaLOT16}.\nThe \\textsf{DiSeGnO} phase contains the actual DDSS generation\nsystem which consists of the \\textsf{Data Store Generator}, i.e.,\na piece of software that creates a relational database \nby mapping the domain ontology to suitable tables, \nand the \\textsf{Web Services Generator}, i.e., a module \nthat creates interface services for incoming and outgoing events.\nFinally, in the \\textsf{DDSS} phase, \ndata is acquired and stored in the internal database,\nthe rules engine processes data and generates diagnostic events which\nare then served to some application. \n\n\\begin{figure}[t!]\n\\centering\n\\scalebox{0.25}{\\includegraphics{ontology.pdf}}\n\\caption{\\label{fig:hvac_onto} Domain ontology for HVAC monitoring.\n Formalism is the same as in Figure~\\ref{fig:iLog_dot}.}\n\\end{figure}\n\nAn example of a DiSeGno-compliant equipment description \nis shown in Figure~\\ref{fig:hvac_onto}. The ontology is related to a \nHeating Ventilation and Air Conditioning (HVAC) appliance and it is\ndivided into a \\emph{static} and a \\emph{dynamic} part. In the static\npart, which is not updated while monitoring, the ontology contains a\ndescription of the observed physical system. In the\nHVAC ontology we have \\textbf{System} and\n\\textbf{DataSource}, \nrelated by the \\textbf{isInSystem} property. \\textbf{hasSubsystem}\nrelationship indicates that one \\textbf{System} could be composed by\none or more \\textbf{SystemComponent} which are themselves subclasses\nof \\textbf{System}. Finally, \\textbf{DataSource} is the \nclass of elements that can generate diagnostic-relevant information. \nThe dynamic part describes \\emph{events}, including both\nthe ones generated by the observed system and its components, and\nthose output by the DDSS. An event is always associated to a\ntime-stamp and it can be either \\emph{incoming} to the DDSS from the\nobserved system, or \\emph{outgoing} from the DDSS\\footnote{This\n distinction is fundamental, because DiSeGnO must know which \nevents have to be associated with input and output web services,\nrespectively.}.\nThe main concepts in the dynamic part of the HVAC\nontology are \\textbf{DDSS} which \\textbf{receives} instances of\n\\textbf{IncomingEvent} and \\textbf{sends} instances of\n\\textbf{OutgoingEvent}. Notice that \\textbf{IncomingEvent} instances\nare connected to \\textbf{DataSource} instances by the role\n\\textbf{generates}, denoting that all incoming events\nare generated by some data source.\nAlso every \\textbf{OutgoingEvent} instance, i.e., every\ndiagnostic event, \\textbf{relatesTo} some instance of\n\\textbf{DataSource}, because the end user must be able to\nreconstruct which data source(s) provided information that caused\ndiagnostic rules to fire a given diagnostic event.\n\\textbf{OutgoingEvent} specializes to \\textbf{AlarmEvent}, \\textbf{FaultEvent}\nand \\textbf{DescriptorEvent}. Every \\textbf{OutgoingEvent} instance is\nconnected to one of \\textbf{DiagnosticIndicator} instances, \ni.e. \\textbf{Alarm}, \\textbf{Fault} and \\textbf{Descriptor} sub-concepts,\n by \\textbf{reports} relation, in order to have a reference message\n about the diagnostic rules.\n\n\n\\section{Computer-automated design of elevators}\n\\label{sec:elevator}\n\\begin{figure}[t!]\n\\begin{center}\n\\scalebox{.22}{\\includegraphics{Elevator.png}}\\\\\n\\scalebox{.26}{\\includegraphics{HydraulicElevator.png}}\n\\end{center}\n\\caption{\\label{fig:elev_onto} Ontologies describing the\n implements handled by \\textsc{LiftCreate}{} (top) and \n the components of \\textsf{OnePistonHydraulicElevator}\n (bottom). Concepts are rectangles, concept inclusion is denoted by\n solid arrows, and HAS-A object properties are denoted by diamond-based arrows.} \n\\end{figure}\n\nOur latest ontology-based application is in the field of\ncomputer-automated design (CautoD) which differs\nfrom ``classical'' computer-aided design (CAD) in that it\nis oriented to replace some of the designer's capabilities and not just\nto support a traditional work-flow with computer graphics\nand storage capabilities. Nevertheless, CautoD programs most\noften include CAD facilities to visualize technical drawings related\nto the implements of interest. \nIn particular, our \\textsc{LiftCreate}{} program is oriented to automating\ndesign of elevators, taking the designer from the very first\nmeasurements to a complete project which guarantees feasibility within\na specific normative framework. \\textsc{LiftCreate}{} works in three steps. In\nthe first step, the user is asked to enter relevant parameters\ncharacterizing the project, and an overall ``design philosophy'' to be\nimplemented. For instance, if the size of the elevator's shaft is\nknown and fixed in advance, \\textsc{LiftCreate}{} can generate solutions which\nmaximize payload, door size, or car size. A design philosophy is just\na set of heuristics which, e.g., prioritize door size over other\nelements, still keeping into account hard constraints, e.g., payload\nand car size should not fall below some threshold. In the second\nphase, \\textsc{LiftCreate}{} retrieves components from a database of parts and\nexplores the (combinatorial) space of potential solutions, either\nusing heuristic search techniques, or resorting to optimizations\ntechniques --- like those suggested, e.g., in~\\cite{ByeOPHS16}. In the\nthird phase, a set of feasible designs is proposed to the user, sorted\naccording to decreasing relevance considering the initial design\nphilosophy. For instance, if door size is to be maximized, the first\nalternatives shown to the user are those with the widest doors,\nperhaps at the expense of payload or car size.\n\nThe main issue with \\textsc{LiftCreate}{} work-flow is that even simple\nversions of elevators consists of a large number of components,\nincluding car frame, car, doors (car and landing doors), emergency\nbrakes, pistons or cables, motors and control logic. In order to\nexplore the space of potential designs, components cannot be\nsolely available as drawing elements, like in classical CAD solutions,\nbut they must be handled as first class data inside \\textsc{LiftCreate}{}\nlogic. This aspect required us to organize the taxonomy related to\ndifferent kinds of elevators and, for each elevator kind, to structure\nthe components in a part-whole hierarchy. In\nFigure~\\ref{fig:elev_onto} we show a fragment of the taxonomy for\nelevators and an example of part-whole structure for a specific\nelevator kind. In particular, in\nFigure~\\ref{fig:elev_onto} (top), we see that \\textsc{LiftCreate}{} classifies\n\\textbf{Elevator} individuals in two main subclasses corresponding to\nhydraulic-based (\\textbf{HydraulicElevator}) and rope-based\n(\\textbf{RopeElevator}) designs. Both subclasses feature additional\npartitions to handle specific design requirements, e.g., rope\nelevators can be provided with a reduction gearbox or not, and the\ndrive can be direct of reeved. For one leaf class of the taxonomy,\nnamely \\textbf{OnePistonDirectHydraulicElevator}, in\nFigure~\\ref{fig:elev_onto} (bottom) we show the detailed part-whole\ndiagram, from which we learn that, e.g., the only peculiar aspects of\nsuch subclass is to have only one \\textbf{Piston}, whereas the\nremaining components are common to \\textbf{HydraulicElevator} or\n\\textbf{Elevator}. Also we can see that the car frame is specific of hydraulic elevators (\\textbf{CarFrameHydra}) and it is comprised of several parts, including\n\\textbf{CarRails}, \\textbf{Buffer} and \\textbf{Ropes}. The\nrelationships encoded in such part-whole hierarchy are\ninstrumental to \\textsc{LiftCreate}{} when it comes to handle drawing, storage\nand retrieval of designs, but also to reason about the various\ntrade-offs of a design when searching in the space of potential solutions.\n\n\n\\section{Conclusions}\n\\label{sec:concl}\nConsidering the experiences herein outlined, we summarize some\nlessons learned in applying ontologies for systems engineering. First\nand foremost, while ontologies provide an effective tool for\nconceptualizing scenarios as diverse as those considered, \nsome ontology-based tools, e.g., DL reasoners, are untenable\nunless small-to-medium scale systems are considered. In the case of\nE414 ontology reasoning with an expressive ontology required us to\nimplement strategies to ``forget'' data to avoid cluttering\nthe reasoner. In the ILS ontology, where SPARQL queries for KPIs are\nthe only reasoning requested and the usage of OWL 2 QL profile banned\nexpressive but hard-to-compute constructs, scaling \nstill requires discarding data using a recency approach. On the other hand, \nin DiSeGnO and \\textsc{LiftCreate}{}, ontologies merely provide means for\nconceptualizing data and, as such, flexibility is gained without\nsacrificing performances. The second take-home message is that\nsublanguages of OWL 2 are adequate for most modeling purposes. \nWith the only exception of E414 ontology, the ones herein\nconsidered fit OWL 2 QL constraints which allowed us\nto combine in a natural way subclassing (``IS-A'' relationships) with \nother kind of object properties (including ``HAS-A''). However, the fact that OWL 2 QL \nontologies can be compiled to relational databases --- as in the case\nof DiSeGnO --- or handled trough an object-persistency module --- as\nin the case of \\textsc{LiftCreate}{} --- makes their use transparent to other\nsystem components. Third, and final point, with the exception of ILS\nmonitoring, none of our applications required the integration of\ndifferent data sources which is indeed one of the main tasks which\nontologies are advocated for. Nevertheless, our experience witnesses\nthat even in single-source data modeling, ontologies provide an\nexcellent mean to bridge the gap between domain experts and\ncomputer software designers. \n\n\n\n\n\n\n\\bibliographystyle{unsrt}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section{Introduction}\n\nProprioception, or the ability to perceive the relative positioning of\nneighboring body parts as well as the muscle effort deployed to\nproduce it, is a fundamental human sense. Together with vision and\ntactile sensing, it plays a unique and important role in human hand\nperception \\cite{blanchard2013differential}. For robotic manipulation,\nproprioception is translated as the combination of joint position and\ntorque sensing (assuming a hand comprised exclusively of revolute joints).\n\nCompared to vision and tactile sensing, both of which have been\nstudied extensively in the context of manipulation, we believe that\ngrasping with proprioception is still an\nimportant area to advance. On one hand, vision and tactile sensing\nhave intrinsic limitations, such as occlusion for vision and hardware\ncomplexity for tactile sensing. On the other hand, when all these\nsenses are available, they can still complement each other. Demonstrating\nmanipulation capabilities based exclusively on proprioception becomes\na useful exercise: we believe the more a hand can do with only one\nsensing modality, the more versatile it will be when multi-modal\nsensory information gets integrated.\n\nAs we show here, proprioception is promising in providing the hand\nwith the ability to adapt to the previously unknown shape of the\nobject, and to execute stable grasps. We note that there are multiple\nways for hands to adapt to an object: while fully-actuated\nhands use sensor information (such as proprioception here) to perform\nactive adaptation, underactuated hands are good examples of passive\nadaptation without the use of sensing. However, the former have the\nadvantage of versatility: proprioceptive grippers can provide\ncompliance similar to passive mechanisms, but can also change behavior\nat runtime and selectively execute different types of grasps. Of\ncourse, the price paid for the additional versatility is the increased\ncomplexity of the sensory setup.\n\nIn this study, we explore the problem of \\textit{grasping using only\nproprioceptive feedback}, without any contact information or\nknowledge of object pose and properties. To the best of our knowledge,\nwe are the first to show that a robot hand can perform all the\nfollowing tasks using proprioception exclusively:\n\\begin{itemize}\n\\item execution of fingertip grasps for unknown objects;\n\\item execution of enveloping grasps for unknown objects;\n\\item on-demand transitions between fingertip and enveloping grasps.\n\\end{itemize}\nOur main contributions are to provide methods for the tasks above,\nand, in the process, demonstrate their effectiveness by\nexperiment. Our results indicate that the proprioceptive gripper is\nmore versatile in the range of fingertip grasps it can perform,\ncompared to our two baselines: an emulated underactuated gripper\ncommanding fixed torques to the joints, as well as a physically\nconstructed underactuated gripper. In addition, our gripper also\ndisplays the ability to execute enveloping grasps and to transition to\nthem from fingertip grasps. Both examples of increased versatility\nwere achieved using proprioception as the only available sensing\nmodality.\n\n\\section{Related Work}\n\nResearchers have been exploring real-time sensing and control as\nan alternative to vision-based planning in manipulation. Assuming\nobject information is available, model-based controllers can perform\ngrasping or in-hand manipulation. For example, Yoshikawa et\nal. presented studies (e.g. \\cite{yoshikawa2000control}) on hybrid\nforce-position control for manipulation. Arimoto et\nal. \\cite{arimoto2000dynamics} derived the dynamics of a dual-finger\ngripper and proposed a controller which can regulate the object\nposition and orientation. Caccavale et al. \\cite{caccavale2013grasp}\nproposed an impedance controller to keep track of desired object\ntrajectory and ensure the grasp quality simultaneously. Unlike our\napproach, these methods require complete information of the\nhand-object system.\n\nWhen the models of the objects are not available, researchers either\nrelied on assumption about the contacts, or used sensor-based\ntechniques for grasping. For example, Schneider and\nCannon \\cite{schneider1992object} studied object impedance control\nusing multiple manipulators. Arimoto et al. \\cite{arimoto2005two} and Yoshida et al. \\cite{yoshida2007blind}\nstudied ``blind grasping'' using two fingertips. However, these studies\nassume the contacts only happen at the end points or the end hemispheres\nof the fingertips. Wang et al. \\cite{wang2007switching} proposed a\ncontroller that can search appropriate finger contact locations using\nhaptic feedback and can switch between control modes for different\nsurfaces. Platt et al. \\cite{platt2010null} presented a study on\nchanging the contact configuration by following the gradient of\ngrasping objective functions using six-axis loadcell data. Hsiao et\nal. \\cite{hsiao2010contact} proposed a contact-reactive method using\ntactile sensing to deal with uncertainty. However, these methods\nrequire contact sensing methods, such as tactile sensors or in-finger\nload cells. In contrast, our approach does\n not make any assumptions about contact location or state, and does not\nrequire tactile sensing data.\n\nTorque measurement is often used for grasp control. Researchers have\ndeveloped several robotic hands with force or torque sensing. For\nexample, the Robonaut Hand \\cite{lovchik1999robonaut} and the DLR Hand\nII \\cite{butterfass2001dlr} have strain gauges or force-torque sensors\nembedded in their fingers. The hand of the DOMO robot\n\\cite{edsinger2004domo} and the hand of the Obrero robotic platform\n\\cite{torres2005obrero}\nmake use of the Series Elastic Actuators (SEA), which are a type of\nactuators with elastic components in series with the motor to sense\nthe torque \\cite{pratt1995series}. Furthermore, the DLR Hand-Arm\nSystem \\cite{grebenstein2011dlr} incorporates the Variable Impedance\nActuators, which are SEAs whose spring stiffnesses are actively\ncontrolled. These hardware designs offer high performance, but at the\ncost of high complexity and large overall packages.\n\nAs an alternative, researchers have developed underactuated hands that do\nnot require sophisticated sensing and control, and this types of hands are good baselines to compare against. Underactuated hands can\nadapt to the object and make a grasp by the virtue of\ncarefully-designed torque ratios between joints. The Harvard Hand\n\\cite{dollar2010highly}, iHY Hand \\cite{odhner2014compliant}, Robotiq\nHand \\cite{birglen2004kinetostatic}, and Velo Gripper\n\\cite{ciocarlie2014velo} are good examples in this\ncategory. However, even though underactuation simplifies control, it\ngenerally does not provide as much dexterity as full actuation. Many\nof the hands above can only perform certain types of grasps, or lack the flexibility to choose the\nconfiguration after making the grasp.\n\n\n\n\\section{Hardware Platform}\n\nWhile joint position sensing is ubiquitous for fully-actuated robot\nhands, torque sensing and control is not common in commercially available\nmanipulators. This compelled us to design our own hardware testbed. We\nimplemented torque sensing and control with Series Elastic Actuators\n(SEA), a method known for high-fidelity torque control, shock\nprotection, and human-safety~\\cite{pratt1995series}. \n\n\\subsubsection{SEA Module}\n\nSimilar to the design from Ates et al. \\cite{ates2014servosea}, we developed a simple and compact SEA module (Fig.~\\ref{fig:sea}). A position-driven servo (gray) is used as the driving motor, which receives position commands and returns the current position (measured by the built-in potentiometer), i.e., $\\theta_{motor}$ can be measured. A torsion spring (orange) is used as the elastic component, connecting the motor shaft (purple) and the pulley shaft (blue). A Spectra cable is tied on the pulley to transmit the force to the finger joint. An absolute magnetic encoder (in green) is mounted on the end of the pulley shaft to measure $\\theta_{pulley}$. In steady-state, the force in the tendon can be calculated as the product of spring stiffness and deflection divided by pulley radius: \n\\begin{equation} \\label{eq:sea}\nF_{tendon} = K_{spring} \\cdot (\\theta_{pulley} - \\theta_{motor}) \/ R_{pulley}\n\\end{equation}\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width = 80 mm]{images\/hand.pdf}\\\\\n\\caption{Schematics of the gripper.}\n\\label{fig:hand}\n\\vspace{-4mm}\n\\end{figure}\n\n\n\n\\subsubsection{Gripper Design}\n\nThe gripper consists of two fingers and each finger has two links, shown in Fig.~\\ref{fig:hand}. \nIn each joint, flexion is\npowered by the tendon (shown as colored dash lines in\nFig.~\\ref{fig:hand}) connected to the SEA pulley, and the extension is\ndriven by a restoring spring. The tendon connected to the\ndistal joint (the red dash line) goes right through the axis of the\nproximal joint so that the torques of proximal and distal joints are\nfully decoupled.\n\n\n\\subsubsection{SEA-level Control}\n\nThere are three SEA-level control modes: motor position control, pulley position control and torque control, and they can be switched online. \nWe note that we built joint-level or hand-level controllers on top of these SEA-level controllers. For example, joint position and torque control can be achieved by SEA pulley position and torque control with a simple linear conversion.\n\n\n\n\\section{Fingertip Grasping}\n\nFingertip grasps commonly refer to grasps where only the most\ndistal links of each finger make contact with the object. We note that it does not necessarily mean the contacts are located in the very end of the fingers, so contact locations are still unknown. This type of\ngrasp is important not only for precision tasks, but also for cases\nwhere a more stable enveloping grasp is not immediately available\nbecause of the environment (an object laying on a table, against a\nbackdrop, etc.). In this section, we introduce a control algorithm\nwhich can perform stable fingertip grasps for unknown objects.\n\n\\subsection{Problem Statement}\n\nThe objective of our algorithm is to\nincrease the torques applied at the joints (and implicitly the contact forces) in a ``stable'' fashion after making initial contacts. In other words, we need to find an\nincrease in joint torques that produces no net wrench on the\nobject. For a hand with multi-link fingers, this is not straightforward given that the joint torques need to be coordinated in the absence of information on object\nshape and contact locations. It is necessary to note that we are not\nsolving contact planning problem, and we wish to develop reactive control strategies that do not require pre-planning.\n\nOur insight is that proprioception alone can characterize grasp stability. For an SEA-powered proprioceptive gripper, an\nunbalanced net wrench will produce object movement against the\ncompliant elements, which we can measure. Therefore, \\textit{we\n formulate the goal of grasp stability as the one of minimizing\n object movement while applying forces}. Since we do not have a direct measure of object\nmovement in Cartesian coordinates by using only proprioception, we use the change of joint position in joint space (measured by SEA)\nduring grasping as a proxy for object movement.\n\nFor intuition, consider the analogies shown in Fig.~\\ref{fig:analogy}:\nFigure (a) shows a (simplified) scenario in which the motors (red dots) are\ndriving the torsion springs, and the springs are pushing the fingers\n(blue dots) to make a grasp (gray dots). (b) shows a simpler\none-dimensional abstraction using linear springs, with similar\ncolor-coding as in (a). Here, we actively control the positions\n$x_{m1}$ and $x_{m2}$ (which translate to motor positions\n$\\theta_{motor}$ on the real gripper) to apply forces, and measure\n$x_{j1}$ and $x_{j2}$ (which translate to pulley positions\n$\\theta_{pulley}$ linearly mapped to joint positions). We aim to keep\n$x_{j1}$ and $x_{j2}$ constant as we squeeze.\n\n\\subsection{MIMO Grasping Controller}\n\nOur key insight is that the problem of grasping unknown objects can be solved even in the absence of\ncontact information, by using proprioception as inputs for a\nmulti-input-multi-output (MIMO) proportional-integral (PI)\ncontrol. Without knowledge of object geometry, contact locations and contact states, it\nis impossible to fully model the dynamic system analytically. However,\na proprioceptive platform still provides sensory access to the\nvariables that characterize the grasp stability, and the PI control framework provides ways to regulate these variables even though the analytical relationship is not constructed . We\nthus aim to use a feedback scheme operating exclusively in\nthe sensory space of the robot, without explicitly modeling the physics of the gripper and the object.\n\nFig.~\\ref{fig:mimo} shows the block diagram of the MIMO control loop. Here, the controller is constructed on top\nof the low-level sensing, so we consider the joint angles and torques\nare already obtained from SEA measurements. The reference vector\n$\\bm{u}$ consists of desired joint angle values ($\\theta^{p1}_{des}$,\n$\\theta^{d1}_{des}$, $\\theta^{p2}_{des}$, $\\theta^{d2}_{des}$, where\nthe superscripts $p$ represent proximal and $d$ represent distal\njoints) and reference joint torques ($\\tau^{p1}_{des}$,\n$\\tau^{d1}_{des}$, $\\tau^{p2}_{des}$, $\\tau^{d2}_{des}$ ). The feedback vector $\\bm{y}$ has the same structure, but contains actual measured\nmeasured values. The desired joint angles (first half of the reference\nvector $\\bm{u}$) are extracted by a feedforward matrix $\\bm{F}$ and\nused as a feedforward term. The error between the reference $\\bm{u}$\nand feedback $\\bm{y}$ is fed into a MIMO PI block (a combination of\nmany P and PI controllers) which is a $4\\times8$ matrix. The output of\nthe PI block and the feedforward term are summed up as motor position\ncommand $\\bm{c}$ (a $ 4 \\times 1$ vector) and sent to the motors:\n\\begin{equation} \\label{eq:mimo}\n\\bm{c}(t)=\\bm{F u}(t)+ {\\bm{K}_{p}} \\bm{e}(t)+ {\\bm{K}_{i}} \\int_{0}^{t}{ \\bm{e}(\\tau) d\\tau} \n\\end{equation}\nHere,\n$\n\\bm{F}=\\left[ \\begin{matrix} {\\bm{I}_{4\\times 4}} & {\\bm{O}_{4\\times 4}}\\end{matrix} \\right]\n$\nis the feedforward matrix,\n$\\bm{K}_p$ ($4\\times 8$ matrix with all entries being non-zero) and \n$\\bm{K}_i=\\left[ \\begin{matrix} {\\bm{K}_{4\\times 4}} & {\\bm{O}_{4\\times 4}}\\end{matrix} \\right]$ (where $\\bm{K}$ is a matrix with all entries being non-zero)\nare proportional and integral gain matrices, and $\\bm{e}$ is the $ 8 \\times 1$ error vector between $\\bm{y}$ and $\\bm{u}$. After that, the actual motor position vector $\\bm{d}$ goes to the black-box system of the gripper and the unknown object.\n\nIn the reference vector $\\bm{u}$, the desired joint angle values ($\\theta^{p1}_{des}$, $\\theta^{d1}_{des}$, $\\theta^{p2}_{des}$, $\\theta^{d2}_{des}$) \nare equal to those in the initial touch configuration, while the reference torques ($\\tau^{p1}_{des}$, $\\tau^{d1}_{des}$, $\\tau^{p2}_{des}$, $\\tau^{d2}_{des}$ ) \nare chosen using the maximum motor torques. A special design of this controller is that we require the joint angles to be regulated exactly to the set points, but do not require the torques to be so. We allow and make use of the steady-state error of pure proportional control (we note that entries in the right half of the integral gain $\\bm{K}_i$ are set to be zeros). In this way, the reference torques do not need to be a legal set of torques that result in equilibrium --- actually, we are not able to design such a legal set of torques due to the absence of contact or object information. We let the law of dynamics decide the steady-state values for torques, and let the system balance itself automatically. The effectiveness of increasing joint torques is shown in section VI.\n\nFrom a practical standpoint, the tuning process of the MIMO PI controller is not\nas complicated as it would seem based on the number of parameters. First, due to gripper symmetry, the number of parameters is cut by half. Second, we formulate\nevery gain as a product of a baseline value ($b_i$ in (\\ref{eq:kp})(\\ref{eq:ki}) ) and a weight\ncoefficient ($w_i$ in (\\ref{eq:kp})(\\ref{eq:ki}) ). The baseline values are set to be the same if the input\nentries corresponding to those gains have same physical\ndimensionality, and the weight coefficients are tuned based on its\nrelative importance. Third, conventional tuning heuristics for the gains of single-input-single-output systems also apply here. The structures of the gain matrices are as follows:\n\\begin{equation} \\label{eq:kp}\n\\bm{K}_p=\\left[ \\begin{smallmatrix}\n{w_1 b_1} & {w_2 b_2} & {w_2 b_1} & {w_2 b_2} & {w_3 b_3} & {w_4 b_4} & {w_4 b_3} & {w_4 b_4} \\\\\n{w_2 b_1} & {w_1 b_2} & {w_2 b_1} & {w_2 b_2} & {w_4 b_3} & {w_3 b_4} & {w_4 b_3} & {w_4 b_4} \\\\\n{w_2 b_1} & {w_2 b_2} & {w_1 b_1} & {w_2 b_2} & {w_4 b_3} & {w_4 b_4} & {w_3 b_3} & {w_4 b_4} \\\\\n{w_2 b_1} & {w_2 b_2} & {w_2 b_1} & {w_1 b_2} & {w_4 b_3} & {w_4 b_4} & {w_4 b_3} & {w_3 b_4} \\\\\n\\end{smallmatrix} \\right]\n\\end{equation}\n\\begin{equation} \\label{eq:ki}\n\\bm{K}_i=\\left[ \\begin{smallmatrix}\n{w_1 b_5} & {w_2 b_6} & {w_2 b_5} & {w_2 b_6} & ~~0~~ & ~~0~~ & ~~0~~ & ~~0~~ \\\\\n{w_2 b_5} & {w_1 b_6} & {w_2 b_5} & {w_2 b_6} & 0 & 0 & 0 & 0\\\\\n{w_2 b_5} & {w_2 b_6} & {w_1 b_5} & {w_2 b_6} & 0 & 0 & 0 & 0 \\\\\n{w_2 b_5} & {w_2 b_6} & {w_2 b_5} & {w_1 b_6} & 0 & 0 & 0 & 0 \\\\\n\\end{smallmatrix} \\right]\n\\end{equation}\nWe pick $b_1 = 0.2$, $b_2 = 0.5$, $b_3 = 4.0$, $b_4 = 8.0$, $b_5 = 1.0$, $b_6 = 1.0$, $w_1 = 1.0$, $w_2 = 0.3$, $w_3 = 1.0$, $w_4 = 0.5$ for our hardware.\n\n\n\\section{Enveloping Grasping and Transitions}\n\nAn enveloping grasp is the one where both distal and proximal links\nmake contact with the object around its circumference. This type of\ngrasp is generally considered more stable than a fingertip grasp\nbecause it can resist disturbances in a wider range of directions. The\ninability to envelop is also one of the main shortcomings of simple\nparallel grippers. In contrast, some of the more recent underactuated\nhands are optimized explicitly for effective enveloping grasps of a\nwide range of objects (e.g. \\cite{ciocarlie2014velo}).\n\nWhen using our proprioceptive gripper, we found that stable\nenveloping grasps for unknown objects are easier to obtain than fingertip grasps. The\nmechanism is generally fully constrained and all the links are\ncounterbalancing each other. A simple joint torque control scheme, or the MIMO Grasping Controller, can fulfill this task.\n\nA very important ability of this gripper, further underlining its\nversatility, is to \\textit{transition} between grasp types when holding unknown objects. After\nexecuting a stable fingertip grasp (using the MIMO Grasping\nController), the gripper can switch to joint torque control with the\ntorque ratio (between distal and proximal joints) being 0.5 to 1.0,\nthus bringing the object into the hand and creating an enveloping\ngrasp. We illustrate this behavior with several experiments in the\nfollowing section.\n\n\n\n\n\\section{Experiments and Results}\nIn this section, we demonstrate the merits of the\nproprioception-enabled gripper by several experiments. We validated\nits capability of performing fingertip grasps, enveloping grasps, and\nthe transition from the former to the latter. We note that in this\nstudy we only consider a two-dimensional scenario, in which the\nobjects are confined to move only in the plane of the fingers.\n\n\\subsection{Fingertip Grasp}\nThe goal of this experiment is to test the hypothesis that the MIMO Grasping Controller is effective in fingertip grasping for unknown objects. We compare against two baselines: a (fully-actuated) gripper running a Fixed Torque Ratio Controller, and a physical underactuated gripper. \n\nThe Fixed Torque Ratio Controller, where the torques applied to proximal and distal joints always follow a certain ratio, can be thought of as an emulation of a common type of underactuated grippers (tendon-pulley-driven, without special designs such as stoppers or clutches). When this kind of grippers make grasps, the configuration-dependent torques from the extension springs can be ignored, as they are usually much smaller than the flexing torques from the tendons. Therefore, the net joint torques in proximal and distal joint have a configuration-independent and design-time-fixed ratio, which is the ratio between the joint pulley radii.\n\nFurthermore, we understand that this emulation is subject to limited control bandwidth and may have unrealistic behavior compared to its physical counterpart. Thus, we also built a physically underactuated gripper testbed for comparison. This testbed has same specs as the fully-actuated proprioceptive gripper, except that the proximal and distal joints are driven by a single tendon wrapping around the joint pulleys. In this design, we can alter the torque ratio by physically changing the pulleys between experiments. We perform torque control for proximal joints in our experiments, thus the torques on the distal joints are defined by the physically determined ratios. However, in this setup, we lose the ability to measure joint positions by SEA readings because, in underactuated mechanisms, joint positions are determined not only by actuator positions but also by contact forces which here are unknown. \n\n\n\n\\subsubsection{Experiment Protocol}\n\nOur experiment proceeds as follows. We execute the grasping in two phases. In the first phase (approaching and touching), the fingers are set in torque control mode with very low reference torques so that they stop when they touch the object. In the second phase(squeezing), the gripper executes the MIMO Grasping Controller or the Fixed Torque Ratio Controller in the fully-actuated testbed, or the joint torque control on proximal joints in the underactuated testbed for comparison.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=55 mm]{images\/experiment_setup.pdf}\\\\\n\\caption{Object sizes, object locations and initial touch poses.}\n\\label{fig:expsetup}\n\\vspace{-3mm}\n\\end{figure}\n\n\\begin{figure}[t]\n\\hspace{-6mm}\\includegraphics[width=100 mm]{images\/pose_photo.pdf}\n\\caption{Pose changes in different controllers.}\n\\label{fig:pose}\n\\vspace{-3mm}\n\\end{figure}\n\nThere are a lot of factors that may influence the performance. To have a well-rounded comparison, we swept the following dimensions:\n\\begin{itemize}\n\\item \\textit{Controllers.} The torque ratio is a key parameter for both the Fixed Torque Ratio Controller, and the physically underactuated gripper. We tested the MIMO Grasping Controller against the other two baselines with three different ratios between the distal and proximal joint: 0.3, 0.4 and 0.5.\n\\item \\textit{Objects.} We selected four objects for the test: a big cylinder (diameter: 67mm), a big box (side length: 57mm), a small cylinder (diameter: 47mm) and a small box (side length: 39 mm). All objects have negligible friction with the table.\n\\item \\textit{Object locations.} We swept three locations along the center line of the gripper within the range of fingertip grasp: 100 mm, 120 mm and 140 mm from the palm.\n\\item \\textit{Initial touch poses.} We tested three different distal joint angles for the initial touch: 0, 30 and 60 degrees. We note that we only include this dimension for MIMO Grasping Controller and Fixed Torque Ratio Controller test, and not for the physically underactuated hand because the distal joint angles of initial touch cannot be explicitly controlled in runtime.\n\\item \\textit{Friction coefficient.} We tested the controllers with\n two fingertip materials: rubber (high friction, $\\mu = 1.2$) and\n vinyl plastic (low friction, $\\mu = 0.4$).\n\\end{itemize}\n\nTo sum up, we swept all five dimensions and conducted 360 grasping experiments. Fig.~\\ref{fig:expsetup} shows the three object locations (shown as the crosshairs), three initial touch poses (colored fingers), and the sizes of the objects relative to the gripper (orange shapes).\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics{images\/torque_increase.pdf}\n\\caption{Increases of torque magnitude during squeezing}\n\\label{fig:trq}\n\\vspace{-3mm}\n\\end{figure}\n\n\\subsubsection{Performance Metric}\n\n\\begin{itemize}\n\\item \\textit{Success rate.} The success rate is our primary performance metric. We define a ``success\" if the gripper finally settles down in equilibrium with the object in hand after squeezing. This definition includes three scenarios: (1) the gripper keeps the object in fingertips near initial touch pose, without converting to enveloping grasp or reaching joint limits, (2) the gripper holds the object but reconfigures to an enveloping grasp, and (3) the gripper keeps the object in fingertips but reaches a mechanical joint limit (thus the joint torque ratio changes). These cases are all considered successful but still need to be distinguished. Case (1) is the most desirable, while (2) and (3) mean the grasp is not stable at initial pose and relies on reconfiguration to be balanced.\n\n\\item \\textit{Gripper pose change.} We believe it is also useful to keep the object in the same pose as when first contact is made. We thus use a secondary performance metric that evaluates how much the object moves in the hand during the squeezing process, with less movement considered better. Without access to object pose in Cartesian space, we measure this as the change in gripper pose between initial touch and final grasp (Euclidian distance in four-dimensional joint space). This metric is only calculated and averaged for the successful cases. Besides, it is not computed for physically underactuated gripper because the joint angles during grasping are not accessible for the reasons mentioned above.\n\\end{itemize}\n\n\\begin{figure*}[t]\n\\centering\n\\includegraphics[width=1.0\\textwidth]{images\/result.pdf}\n\\caption{Experiment results of fingertip grasping compared to Fixed Torque Ratio Controller.}\n\\label{fig:result}\n\\vspace{-3mm}\n\\end{figure*}\n\n\n\\subsubsection{Results}\nFig.~\\ref{fig:pose} shows the photos of some typical scenarios in the experiments. (a) and (b) shows the resting pose and the initial touch. (c) shows the successful grasp near initial touch pose. The other images show cases in which the object was kept in fingertip grasp but a joint limit was reached (d), the grasp was transformed into an enveloping one (e), and the object was squeezed out of the hand (f). \n\nFig.~\\ref{fig:trq} shows that all controllers are effective in increasing joint torques. The horizontal axis shows different controllers (or grippers), the vertical axis is the magnitude of the four-dimensional torque vector ($\\tau^{p1}_{meas}$, $\\tau^{d1}_{meas}$, $\\tau^{p2}_{meas}$, $\\tau^{d2}_{meas}$) which indicates how ``strong'' the grasp is, and bar colors distinguish between initial touch and final grasp. We can see that there is a significant increase in the torque magnitude, and the torque levels in different cases are similar.\n\n\nThe results of the experiments comparing against Fixed Torque Ratio Controller are visualized as multiple bar charts in Fig.~\\ref{fig:result}. In each plot, the bar colors show four different controllers,\nthe vertical axis is one of the performance metrics \nand the horizontal axis represents another dimension which is different in each plot (from (a) (f) to (d) (i): different objects, object locations, initial poses, and friction coefficients). In each bar in the first row showing the success rate, the pure-color area, the dotted area, and the line-shaded area represent, respectively, successful fingertip grasp without reaching joint limit, successful grasps but converted to enveloping, as well as successful fingertip grasp but joint angles reached limits.\n\nSimilarly, the results comparing against physically underactuated grippers are shown in Fig.~\\ref{fig:result_ua}. Here, the initial touch pose dimension is not available, so there are three dimensions (from (a) to (c): different objects, object locations, and friction coefficients). Also, the pose change metric is not available due to the absence of joint angle information. All other plotting rules are the same as Fig.~\\ref{fig:result}.\n\n\n\n\nAs shown in Fig.~\\ref{fig:result} (e)(j) and Fig.~\\ref{fig:result_ua} (d), the overall success rates are 91.67\\% for MIMO Grasping Controller, 72.22\\%, 76.39\\%, 66.67\\% for Fixed Torque Ratio Controller with torque ratio of 0.3, 0.4, 0.5, respectively, and 87.50\\%, 75.00\\%, 87.50\\% for physically underactuated gripper with torque ratio of 0.3, 0.4, 0.5, respectively. Even when the overall success rates are close (for example, Fig.~\\ref{fig:result_ua} (d)), the types of the resulting grasps are significantly different. Besides, the gripper pose change metric for the MIMO controller is 9.96, compared to 41.55, 31.93, and 81.14 (degrees) respectively for the Fixed Torque Ratio controllers. \n\n\n\\subsection{Enveloping Grasps and Transitions}\n\nWe performed a second experiment to show that this gripper can perform enveloping grasp with either MIMO Grasping Controller or Fixed Torque Ratio Controller. We tested on two objects (big cylinder and big box), two object locations (60mm and 80mm from the palm), and two controllers mentioned above. The success rate is 100\\%.\n\nThe last experiment is to show the performance of the transition from fingertip grasp to enveloping grasp. We first created fingertip grasps using the MIMO Grasping Controller, and then switched to Fixed Torque Ratio Controller with a ratio of 0.5. We tested on two objects (big cylinder and big box), three object locations (100, 120 and 140mm), three initial poses (0, 30 and 60 degrees) with the low friction fingertips. We found the success rate was 83.33\\%.\n\n\n\\begin{figure*}[t]\n\\centering\n\\includegraphics[width=0.8 \\textwidth]{images\/result_ua.pdf}\\\\\n\\caption{Experiment results of fingertip grasping compared to physically underactuated gripper.}\n\\label{fig:result_ua}\n\\vspace{-2mm}\n\\end{figure*}\n\n\\section{Discussion and Conclusion}\n\nOverall, the results of the previous section support our hypotheses: the proprioceptive gripper running MIMO Grasping Controller is effective at executing stable fingertip grasps in a variety of situations and outperforms the baselines. Furthermore, the proprioceptive gripper exhibits versatility in being able to perform multiple types of grasps and also to transition between them on-demand.\n\nBased on Fig.~\\ref{fig:result} and \\ref{fig:result_ua}, the MIMO Grasping Controller outperforms the baselines in fingertip grasping, and usually succeeds without transforming to an enveloping grasp or reaching joint limits. In contrast, the emulated and physical underactuated gripper often transform to an enveloping grasp or reach joint limits, thus relying on gripper reconfiguration. The second row of Fig.~\\ref{fig:result} ((f) to (j)) gives similar intuition: for the Fixed Torque Ratio Controller, most cases have a large pose change. While the end-result is stable, it is different from the originally intended grasp. This might be unimportant or detrimental depending on the application.\n\nIt is also interesting to notice that, in different conditions, the optimal torque ratio for the emulated or physical underactuated gripper is different. We take this to mean that there is no one clearly preferable pre-set torque ratio, which could be physically implemented in a mechanical design, in order to obtain ideal performance in all these cases. In contrast, the proprioception-enabled gripper has the flexibility to alter torques at run-time.\n\nLooking at how specific variables affect performance we can gain additional insights. From Fig.~\\ref{fig:result} and~\\ref{fig:result_ua} (a) and (b) we can see that the success rates for Fixed Torque Ratio Controller are low if the objects are small and close to the palm. This is because the emulated or physical underactuated gripper tends to transform the initial unstable fingertip grasps to enveloping when contacts are close to distal joints, but cannot cage the object if it is small because the distal links are fighting against each other --- a common issue for underactuated grippers. In contrast, the MIMO Grasping Controller does not suffer from this because it does not perform the conversion.\n\n\nIn the transitioning experiment, the high success rate shows the\nproprioceptive gripper can indeed perform the conversion between grasp types\n\\textit{on-demand}. Though underactuated grippers also\noccasionally perform such transitions, they occur unintentionally\nand without giving the user an option to select the desired type of\ngrasp.\n\nIt is important to also highlight the limitations of this\nstudy. Due to high dimensionality of the brute-force sweep in\nour experiment, we cannot afford to cover a larger range with a finer\nresolution for each dimension. In particular, we are unable to explore\nmore possibilities for physically implemented torque ratios. The\nevaluation of the controller is primarily experimental and would\nbenefit from additional stability analysis, carried out for example\nfor representative cases and grasps.\n\nOverall, we claim that proprioceptive manipulators, using active\nsensing and control such as the MIMO Grasping Controller, represent a\npromising way towards more versatile grasping and manipulation for\nunknown objects. Future work will include the extension of the operation to three-dimensional cases, optimization \/ learning of the control gains, and the inclusion of hand\nposition to our set of actively controlled variables. We are aiming to\nfurther explore these possibilities.\n\n\\section*{APPENDIX}\n\n\n\n\n\n\n\\bibliographystyle{bib\/IEEEtran} \n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\nOver the past decade, advances in data collection and increasing access to computational resources have led to a revolution in the use of data-driven techniques for the solution of complex inverse problems. One such problem is that of turbulence, the multiscale nature of which causes extreme computational demands for most practical systems. As a result, turbulence requires the use multiple modeling approximations for the higher wavenumbers which remain unsupported by computational degrees of freedom. One such modeling approach is that of large eddy simulation (LES) \\cite{sagaut2006large}, which attempts to simulate the evolution of the smaller wavenumbers while the unresolved frequencies are modeled by an algebraic or differential equation. As such, the basic premise of LES is extendable to many partial differential equation systems with quadratic non-linearities. The procedure of modeling these smaller scales is often denoted \\emph{closure} due to insufficient knowledge about higher-order wavenumber interactions with the coarse-grained system \\cite{berselli2006mathematics} and remains vital for the accurate computation of many applications \\cite{hickel2014subgrid,yu2016dynamic,zhou2018structural}. From an LES point of view, the closure problem may be considered to be dominated by commutative errors in the calculation of the non-linear term as well as the defects associated with commutative errors stemming from the dynamic term. In this study, we focus on the former.\n\nThere are two main schools of thought when it comes to the LES of the Navier-Stokes equations. The first of these promotes the use of explicit closures. Explicit LES argues for the utilization of closures in the form of sub-grid models specified as algebraic or differential equations for the unresolved scales. These are built on intuitive reasoning of how the losses of coarse graining the Navier-Stokes equations may be incorporated into an LES deployment. Some of the most notable sub-grid closure strategies are those given by the eddy-viscosity hypothesis. Within the context of the Navier-Stokes equations, it is generally accepted that the finer scales are dissipative at the Kolmogorov length scales \\cite{kolmogorov1941local} and therefore, most turbulence models seek to specify a sub-grid dissipation \\cite{frisch1995turbulence}. Most sub-grid models can be traced back to the seminal work of Smagorinsky \\cite{smagorinsky1963general}, where a model was proposed based on the concepts of an effective eddy-viscosity determined by an \\emph{a priori} specified mixing length and a $k^{-5\/3}$ scaling recovery for the kinetic energy content in the wavenumber domain. Similar hypotheses have also been used for two-dimensional turbulence \\cite{leith1968diffusion} (often utilized as a test-bed for geophysical scenarios, for instance see works by Pearson \\textit{et al.}\\cite{pearson2018log,pearson2017evaluation}), for approximating the $k^{-3}$ cascade in two-dimensional turbulence and generally have their roots in dimensional analysis related to the cascade of enstrophy. These models may also be classified as \\emph{functional} due to the phenomenological nature of their deployment and represent the bulk of explicit LES turbulence models used in practical deployments. Explicit LES closures may also be specified through the specification of a low-pass spatial filter to account for the unresolved scales \\cite{bardina1980improved,stolz1999approximate,layton2003simple,mathew2003explicit} where phenomenology is bypassed but ansatz are provided for the bulk dissipative nature of the smaller scales through the control of a characteristic filter-width. In either scenario, (i.e., whether structural or functional), the choice of the phenomenology (or dissipation control parameter) plays a key role in the successful calculation of accurate \\emph{a posteriori} statistics. In contrast, the implicit LES (or ILES) approach utilizes numerical dissipation to model the unresolved scales in a turbulent flow \\cite{grinstein2007implicit,el2017investigation,Margolin2018}. In essence, the predominantly dissipative effects of the smallest scales are replicated through an artificial numerical dissipation via a biased discretization used in the calculation of the non-linear advective term \\cite{thornber2007implicit,debonis2013solutions}. The ILES approach is popular due to reduced algorithmic complexity and represents a union of turbulence modeling and shock capturing mechanisms but is often criticized due to the difficulties involved in quantifying the correct amount of dissipation in a turbulent flow evolution. This results in ILES methods often proving robust and stable but overly dissipative. In this work, we propose a machine learning algorithm to enable selective dissipation within an ILES deployment through the use of explicit LES concepts during the training of the learning framework. \n\n\n\nThe past few years have seen a rapid increase in the use of data-driven techniques for the spatio-temporal modeling of dynamical systems \\cite{schmidt2009distilling,bright2013compressive,xiao2015non,ma2015using,gautier2015closed,brunton2016discovering,schaeffer2017learning,raissi2017machine,mohan2018deep,raissi2018hidden,rudy2018deep,san2018neural,wan2018data,kim2018deep,muravleva2018application,jin2018prediction}. When it comes to turbulence, some widely used strategies for inference include symbolic regression \\cite{weatheritt2016novel,weatheritt2017development,weatheritt2017hybrid}, where functional model-forms for Reynolds-averaged Navier-Stokes (RANS) deployments were generated through evolutionary optimization against high-fidelity data. Other techniques incorporating Bayesian ideologies have also been used, for instance by Xiao \\textit{et al.}\\cite{xiao2016quantifying} where an iterative ensemble Kalman method was used to assimilate prior data for quantifying model form uncertainty in RANS models. In Wang \\textit{et al.}\\cite{wang2017physics,wang2017comprehensive} and Wu \\textit{et al.}\\cite{wu2018data}, random-forest regressors were utilized for RANS turbulence-modeling given direct numerical simulation (DNS) data. In Singh and Duraisamy \\cite{singh2016using} and Singh \\textit{et al.}\\cite{singh2017machine}, an ANN was utilized to predict a non-dimensional correction factor in the Spalart-Allmaras turbulence model through a field-inversion process using experimental data. Bypassing functional formulations of a turbulence model (a focus of this study) was also studied from the RANS point of view by Tracey \\textit{et al.} \\cite{tracey2015machine}. Ling and Templeton \\cite{ling2015evaluation} utilized support vector machines, decision trees and random forest regressors for identifying regions of high RANS uncertainty. A deep-learning framework where Reynolds-stresses would be predicted in an invariant subspace was developed by Ling \\textit{et al.} \\cite{ling2016reynolds}. Machine learning of invariance properties has also been discussed in the context of turbulence modeling \\cite{ling2016machine}. The reader is directed to a recent review by Duraisamy \\textit{et al.}\\cite{duraisamy2018turbulence}, for an excellent review of turbulence modeling using data-driven ideas.\n\nAs shown above, the use of data-driven ideologies and in particular artificial neural networks (ANNs) has generated significant interest in the turbulence modeling community for addressing long-standing challenges (also see \\cite{sotgiu2018turbulent,zhang2018machine,zhu2019machine,zhang2019application,raissi2019deep} for recent progress). One motivation for the popularity of ANNs is that a multilayered ANN may be optimally trained to universally approximate any non-linear function \\cite{hornik1989multilayer}. In addition, the deployment of ANNs is amenable to integration within existing computational frameworks. Greater accessibility to data and ever-improving computing capabilities has also motivated the development of advanced ANN architectures for large-scale learning of complicated physical phenomena such as turbulence. Within the context of LES (and associated with the scope of this paper) there are several investigations into sub-grid modeling using data-driven techniques. In one of the first studies of the feasibility of using learning from DNS based high-fidelity data, Sarghini \\textit{et al.}\\cite{sarghini2003neural} utilized ANNs for estimating Smagorinsky model-form coefficients within a mixed sub-grid model for a turbulent channel flow. ANNs were also used for wall-modeling by Milano and Koumotsakos \\cite{milano2002neural} where it was used to reconstruct the near wall field and compared to standard proper-orthogonal-decomposition techniques. An alternative to ANNs for sub-grid predictions was proposed by King \\textit{et al.}\\cite{king2016autonomic} where \\emph{a priori} optimization was utilized to minimize the $L^2$-error between true and modeled sub-grid quantities in a least-squares sense using a parameter-free Volterra series. Maulik and San \\cite{maulik2017neural} utilized an extreme-learning-machine (a variant of a single-layered ANN) to obtain maps between low-pass spatially filtered and deconvolved variables in an \\emph{a priori} sense. This had implications for the use of ANNs for turbulence modeling without model-form specification. A more in-depth investigation was recently undertaken by Fukami \\textit{et al.}\\cite{fukami2018super} where convolutional ANNs were utilized for reconstructing from downsampled snapshots of turbulence. Maulik \\textit{et al.} \\cite{maulik2018deconvolution} also deployed a data-driven convolutional and deconvolutional operation to obtain closure terms for two-dimensional turbulence. Gamahara and Hattori \\cite{gamahara2017searching}, utilized ANNs for identifying correlations with grid-resolved quantities for an indirect method of model-form identification in turbulent channel flow. The study by Vollant \\textit{et al.} \\cite{vollant2017subgrid} utilized ANNs in conjuction with optimal estimator theory to obtain functional forms for sub-grid stresses. In Beck \\textit{et al.}\\cite{beck2018neural}, a variety of neural network architectures such as convolutional and recurrent neural networks are studied for predicting closure terms for decaying homogeneous isotropic turbulence. A least-squares based truncation is specified for stable deployments of their model-free closures. Model-free turbulence closures are also specified by Maulik \\textit{et al.}\\cite{maulik2018deconvolution,maulik2019subgrid} and Wang \\textit{et al.}\\cite{wang2018investigations}, where sub-grid scale stresses are learned directly from DNS data and deployed in \\emph{a posteriori} assessments. King \\textit{et al.}\\cite{king2018deep} studied generative-adversarial networks and the LAT-NET \\cite{hennigh2017lat} for \\emph{a priori} recovery of statistics such as the intermittency of turbulent fluctuations and spectral scaling. A detailed discussion of the potential benefits and challenges of deep learning for turbulence (and fluid dynamics in general) may be found in the article by Kutz \\cite{kutz2017deep}.\n\nWhile a large majority of the LES-based frameworks presented above utilize a least-squares error minimization technique for constructing maps to sub-grid stresses \\emph{directly} for theoretically optimal LES \\cite{langford1999optimal,moser2009theoretically,labryer2015framework}, this work is novel in that it utilizes sub-grid statistics (pre-computed from DNS data) to train a classifier. This classifier determines whether a location requires dissipation or not through \\emph{a priori} experience in the learning phase. Once classified, the non-linear term at this particular point is evaluated using one of two schemes. If it is determined that the point requires no sub-grid closure, a symmetric and second-order accurate, energy and enstrophy conserving Arakawa-scheme \\cite{arakawa1981potential} is utilized for the non-linear term computation. If dissipation is necessary, an upwinding scheme is utilized instead. Therefore this study may be interpreted as a machine learning framework for devising hybrid schemes for non-linear term computation with a view to reconstructing turbulence statistics in a superior fashion. Therefore, this study is similar to that employed by Ling and Kurzawski \\cite{ling2017data} for adaptively determining RANS corrections. We note that the classification framework devised in this study is also deployed in an aligned work to switch between functional and structural explicit LES hypotheses spatio-temporally \\cite{maulik2018online} thus proving that high-fidelity DNS statistics may be qualitatively utilized to inform modeling strategies through conditional probability predictions. The article shall describe how the proposed framework is effective in moderating the larger dissipation of an upwinded-scheme through assessments on the Kraichnan turbulence test-case. \n\n\\section{Turbulence modeling equations}\n\nThe governing equations for two-dimensional turbulence are given by the Navier-Stokes equations in the vorticity-stream function formulation. In this formulation, our non-dimensional governing equation for incompressible flow may be represented as\n\\begin{align}\n\\label{eq1}\n\\frac{\\partial \\omega}{\\partial t} + J(\\omega,\\psi) = \\frac{1}{Re} \\nabla^2 \\omega,\n\\end{align}\nwhere $Re$ is the Reynolds number, $\\omega$ and $\\psi$ are the vorticity and stream function respectively connected to each other through the Poisson equation given by\n\\begin{align}\n\\label{eq2}\n\\nabla^2 \\psi = - \\omega.\n\\end{align}\nIt may be noted that the Poisson equation implicitly ensures a divergence-free flow evolution. The non-linear term (denoted the Jacobian) is given by\n\\begin{align}\n\\label{eq3}\nJ(\\omega,\\psi) = \\frac{\\partial \\psi}{\\partial y} \\frac{\\partial \\omega}{\\partial x} - \\frac{\\partial \\psi}{\\partial x} \\frac{\\partial \\omega}{\\partial y}.\n\\end{align}\nThe stream function and the two-dimensional velocity components are related as \n\\begin{align}\n\\label{eq3a}\nu &= \\frac{\\partial \\psi}{\\partial y}, \\quad v = -\\frac{\\partial \\psi}{\\partial x}.\n\\end{align}\n\nA reduced-order implementation of the aforementioned governing laws (i.e., an LES) is obtained through\n\\begin{align}\n\\label{eq4}\n\\frac{\\partial \\bar{\\omega}}{\\partial t} + J(\\bar{\\omega},\\bar{\\psi}) = \\frac{1}{Re} \\nabla^2 \\bar{\\omega},\n\\end{align}\nwhere the overbarred variables are now evolved on a grid with far fewer degrees of freedom. Due to the reduction in supported frequencies, the non-linear Jacobian fails to capture inter-eddy interactions at different wavenumbers. If it is assumed that the finer scales of vorticity are generally dissipative in nature for two-dimensional turbulence (based on Kraichnan's cascade of enstrophy \\cite{kraichnan1967inertial}), dissipative models may be embedded into the coarse-grained evolution of the vorticity evolution equation to recover some portion of the effect of the finer scales. Explicit LES closures embed dissipation into the vorticity evolution in the form of eddy-viscosity phenomenology or through structural arguments of scale-similarity. However ILES manipulates the computation of the non-linear Jacobian term to add numerical dissipation to mimic that of the unresolved frequencies. The latter framework, while numerically robust, suffers from difficulties associated with \\emph{directed} dissipation where it is often very easy to be over-dissipative in regions where sub-grid dissipation may not be as pronounced. In this article, we introduce a hybrid ILES framework that focuses upwinding at areas where high probability of sub-grid dissipation necessity is detected.\n\n\n\\section{Non-linear Jacobian computation}\n\nThe study utilizes two types of non-linear term computation schemes. Our first choice is symmetric, second-order accurate and conserves energy and enstrophy to minimize numerical dissipation. This is given by the well-known second-order Arakawa scheme \\cite{arakawa1981potential} as detailed below. The non-linear term in Equation \\ref{eq4} may be numerically calculated on a coarse grid using \n\\begin{align}\nJ^A (\\bar{\\omega},\\bar{\\psi}) = \\frac{1}{3} \\left( J_1 (\\bar{\\omega}, \\bar{\\psi}) + J_2 (\\bar{\\omega}, \\bar{\\psi}) + J_3 (\\bar{\\omega}, \\bar{\\psi}) \\right)\n\\end{align}\nwhere $J^A(\\bar{\\omega},\\bar{\\psi})$ will henceforth refer to the Arakawa discretization. The individual terms on the right hand side of the above equation are given as \n\\begin{align}\n\\begin{split}\n J_1 (\\bar{\\omega},\\bar{\\psi}) & = \\frac{1}{4 \\Delta x \\Delta y} \\left[ (\\bar{\\omega}_{i+1,j}-\\bar{\\omega}_{i-1,j}) (\\bar{\\psi}_{i,j+1} - \\bar{\\psi}_{i,j-1}) \\right. \\\\ \n& \\left. - (\\bar{\\omega}_{i,j+1}-\\bar{\\omega}_{i,j-1}) (\\bar{\\psi}_{i+1,j} - \\bar{\\psi}_{i-1,j}) \\right],\n\\end{split}\n\\end{align}\n\n\\begin{align}\n\\begin{split}\n & J_2 (\\bar{\\omega},\\bar{\\psi}) = \\frac{1}{4 \\Delta x \\Delta y} \\left[ \\bar{\\omega}_{i+1,j} (\\bar{\\psi}_{i+1,j+1}-\\bar{\\psi}_{i+1,j-1}) \\right. \\\\ \n & \\left. - \\bar{\\omega}_{i-1,j} (\\bar{\\psi}_{i-1,j+1}-\\bar{\\psi}_{i-1,j-1}) - \\bar{\\omega}_{i,j+1} (\\bar{\\psi}_{i+1,j+1}-\\bar{\\psi}_{i-1,j+1})\\right. \\\\\n & \\left. + \\bar{\\omega}_{i,j-1} (\\bar{\\psi}_{i+1,j-1}-\\bar{\\psi}_{i-1,j-1}) \\right],\n\\end{split}\n\\end{align}\n\n\\begin{align}\n\\begin{split}\n& J_3 (\\bar{\\omega},\\bar{\\psi}) = \\frac{1}{4 \\Delta x \\Delta y} \\left[ \\bar{\\omega}_{i+1,j+1} (\\bar{\\psi}_{i,j+1} - \\bar{\\psi}_{i+1,j}) \\right. \\\\\n& \\left. - \\bar{\\omega}_{i-1,j-1} (\\bar{\\psi}_{i-1,j}-\\bar{\\psi}_{i,j-1}) - \\bar{\\omega}_{i-1,j+1} (\\bar{\\psi}_{i,j+1}-\\bar{\\psi}_{i-1,j}) \\right. \\\\\n& \\left. + \\bar{\\omega}_{i+1,j-1} (\\bar{\\psi}_{i+1,j}-\\bar{\\psi}_{i,j-1}) \\right].\n\\end{split}\n\\end{align}\nThe aforementioned scheme is utilized when our proposed classifier recognizes that no dissipation is necessary. \n\nA numerically dissipative computation of the non-linear term allows for that stabilization of noise accumulation at the grid cut-off wavenumbers. Although there are many different methodologies for upwind based dissipation with varying degrees of complexity, in this article, we utilize a conventional upwind-biased scheme as detailed in the following \\cite{hoffmann2000computational}. Our ILES Jacobian is computed as \n\\begin{align}\n\\begin{split}\nJ^I(\\bar{\\omega},\\bar{\\psi}) =& \\bar{u}_{i,j} \\frac{\\bar{\\omega}_{i+1,j} - \\bar{\\omega}_{i-1,j}}{2 \\Delta x} + \\frac{1}{2} (\\bar{u}^{+} \\bar{\\omega}_x^{-} + u^{-} \\bar{\\omega}_x^{+}) \\\\\n& + \\bar{v}_{i,j} \\frac{\\bar{\\omega}_{i,j+1} - \\bar{\\omega}_{i,j-1}}{2 \\Delta y} + \\frac{1}{2} (\\bar{v}^{+} \\bar{\\omega}_y^{-} + \\bar{v}^{-} \\bar{\\omega}_y^{+}),\n\\end{split}\n\\end{align}\nwhere \n\\begin{alignat}{2}\n\\bar{u}^{-} &= \\min(\\bar{u}_{i,j},0), \\quad \\bar{u}^{+} &= \\max(\\bar{u}_{i,j},0), \\\\\n\\bar{v}^{-} &= \\min(\\bar{v}_{i,j},0), \\quad \\bar{v}^{+} &= \\max(\\bar{v}_{i,j},0).\n\\end{alignat}\nIn addition,\n\\begin{align}\n\\begin{split}\n\\bar{\\omega}_x^{-} &= \\frac{\\bar{\\omega}_{i-2,j} - 3 \\bar{\\omega}_{i-1,j} + 3 \\bar{\\omega}_{i,j} - \\bar{\\omega}_{i+1,j} }{3 \\Delta x}, \\\\\n\\bar{\\omega}_x^{+} &= \\frac{\\bar{\\omega}_{i-1,j} - 3 \\bar{\\omega}_{i,j} + 3 \\bar{\\omega}_{i+1,j} - \\bar{\\omega}_{i+2,j} }{3 \\Delta x}, \\\\\n\\bar{\\omega}_y^{-} &= \\frac{\\bar{\\omega}_{i,j-2} - 3 \\bar{\\omega}_{i,j-1} + 3 \\bar{\\omega}_{i,j} - \\bar{\\omega}_{i,j+1} }{3 \\Delta y}, \\\\\n\\bar{\\omega}_y^{+} &= \\frac{\\bar{\\omega}_{i,j-1} - 3 \\bar{\\omega}_{i,j} + 3 \\bar{\\omega}_{i,j+1} - \\bar{\\omega}_{i,j+2} }{3 \\Delta y}.\n\\end{split}\n\\end{align}\nNote that velocity components are recovered using\n\\begin{align}\n\\begin{split}\n\\bar{u}_{i,j} &= \\frac{\\bar{\\psi}_{i,j+1}-\\bar{\\psi}_{i,j-1}}{2 \\Delta y} \\\\\n\\bar{v}_{i,j} &= -\\frac{\\bar{\\psi}_{i+1,j}-\\bar{\\psi}_{i-1,j}}{2 \\Delta x},\n\\end{split}\n\\end{align}\nwhere the second-order accurate reconstruction of the velocity leads to overall second-order accuracy for non-linear Jacobian reconstruction using the upwinded procedure outlined above. We also note that our Poisson equation given by Equation \\ref{eq2} is solved using a spectrally-accurate scheme. \n\nWith the choice of one of the two aforementioned schemes, a point in space-time may or may not have an artificial dissipation imparted to it numerically. However, we mention the caveat that switching between these two schemes would mean that the kinetic energy and enstrophy preserving property of the Arakawa scheme is lost. \n\n\\section{Machine learning for scheme selection}\n\nWe now discuss the procedure of utilizing DNS data for learning to classify one of the two dissipation scenarios. Of these two options, one is given by the choice of the Arakawa scheme and the other by our upwinded computation of the Jacobian (i.e., when the classification framework has determined that the point does not require sub-grid dissipation or vice-versa respectively). This switching between scenarios is spatio-temporally dynamic. We proceed by outlining our training strategy through the utilization of DNS data. Five equidistant snapshots of DNS data at $Re=32000$ (i.e., at $t=0,1,2,3,4$) and at $N^2 = 2048^2$ degrees of freedom (from 40000 available snapshots) are utilized to compute the grid-filtered variables (denoted FDNS) (at $N^2 = 256^2$ degrees of freedom) through the application of a spectral cut-off filter. Perfect closure values \n\\begin{align}\n\\Pi = J(\\bar{\\omega},\\bar{\\psi})-\\overline{J(\\omega,\\psi)}\n\\end{align}\nare then obtained (the reader is directed to \\citep{maulik2019subgrid} for details related to the calculation of these quantities). Note here, that the Kraichnan turbulence problem is transient with the evolution of vorticity represented in Figure \\ref{Fig1} representing different closure needs over time evolution. \n\nWe proceed by introducing the \\emph{a priori} eddy-viscosity given by\n\\begin{align}\n\\nu_e^a = \\frac{\\Pi}{\\nabla^2 \\bar{\\omega}}\n\\end{align}\nwhere the right-hand side of the above equation may be calculated from DNS snapshots. The \\emph{a priori} eddy-viscosity is centered at zero (corresponding to where closure modeling is unnecessary) and spreads out in the negative and positive directions (a hallmark of isotropic turbulence). We segregate this \\emph{a priori} estimate of sub-grid effects into three categories as follows. The \\emph{a priori} eddy-viscosities calculated from the DNS data are compared with a Gaussian distribution where values lying less than a distance of 1\\% of the standard-deviation from the mean (which is zero) are labeled as those requiring no dissipation (due to the low strength of the \\emph{a priori} eddy-viscosity). For posterity, we label these points as $k=1$. Positive values lying beyond this range are labeled as those requiring sub-grid dissipation and are labeled $k=2$. Negative values less than 1\\% of the standard-deviation are also considered to require no dissipation and are labeled $k=3$. This three-category segregation stems from a learning hypothesis that seeks to identify regions in a flow evolution that require structural, functional or no-closure modeling hypothesis. We link labels of negative or nearly-zero eddy-viscosities to the Arakawa classification and positive eddy-viscosities to the upwinded classification. The positive eddy-viscosity prediction would indicate that the sub-grid term at a point is predominantly dissipative in nature at which point the numerical dissipation of the upwinded scheme would be utilized. We note here that the concept of an \\emph{a priori} eddy-viscosity lies firmly within the explicit LES hypothesis. The classifier is therefore instrumental in moderating ILES deployments through a decision making process that recognizes the dissipative (or forcing) nature of the sub-grid quantities. \n\nWe note that the choice of 1\\% as the decision parameter for switching between hypothesis is motivated by a sensitivity study that showed the highest classification accuracy for the ANN framework. Larger choices of this hyper-parameter would result in a classifier that would be prone to classify most points in the `no-model' zone. However, we clarify that the choice of this value is also correlated with the architecture of the ANN. A potential extension of the proposed hypothesis is to combine architecture search algorithms with varying value of the decision hyper-parameters for larger classification accuracies. In addition, the three-category framework is derived from an aligned study \\cite{maulik2018online} where sub-grid models are determined according to negative, positive and nearly-zero \\emph{a priori} eddy-viscosities and utilizes the same learning. This enables use to determine a unified framework for switching between turbulence model hypotheses as well as numerical dissipation scenarios. However, we would like to emphasize that, for the purpose of switching between the Arakawa and upwinded Jacobian computation, a simple two-class framework would also suffice. \n\n\\begin{figure*}\n\\centering\n\\includegraphics[width=\\textwidth]{Figure_1-eps-converted-to.pdf}\n\\caption{Time evolution of the Kraichnan turbulence case with DNS ($N^2 = 2048^2$) contours for vorticity of $t=1$ (top-left), $t=2$ (top-right), $t=3$ (bottom-left), $t=4$ (bottom-right). One can discern the dissipation of vorticity as the system evolves.}\n\\label{Fig1}\n\\end{figure*}\n\nA one-hot labeling of our eddy-viscosity classes is utilized for a classification deployment and a schematic for this hypothesis segregation and labeling is shown in Figure \\ref{Segregation}. The labels indicate the conditional probability of a point belonging to each possible class. As such, the training labels are given by a value of 1 for the particular class that a point belongs to and zeros for other choices. This is because there is no ambiguity in the class a training sample belongs to. Each label for the \\emph{a priori} eddy-viscosity is also associated with a corresponding input kernel of grid-resolved quantities. This kernel is given by a local stencil of vorticity and stream function. There are 9 inputs each for vorticity and stream function given by a query of the field quantity at a point on the coarse grid, 4 adjacent points in each dimension ($x,y$) and the 4 diagonally adjacent points. Each sample of our training data thus consists of 18 inputs of vorticity and stream function and outputs given by one-hot labels for the choice of closure modeling strategy. We then utilize an ANN to establish a relationship between these inputs and outputs. Mathematically, if our input vector $\\mathcal{P}$ resides in a $P$-dimensional space and our desired output $\\mathcal{Q}$ resides in a $Q$-dimensional space, this framework establishes a map $\\mathbb{M}$ as follows:\n\\begin{align}\n\\label{eq6}\n\\mathbb{M} : \\{ \\mathcal{P}_1, \\mathcal{P}_2, \\hdots, \\mathcal{P}_P\\} \\in \\mathbb{R}^P \\rightarrow \\{ \\mathcal{Q}_1, \\mathcal{Q}_2, \\hdots, \\mathcal{Q}_Q\\} \\in \\mathbb{R}^Q.\n\\end{align}\nAccordingly, the framework utilized in this article leads to the following relation:\n\\begin{align}\n\\label{eq7}\n\\mathbb{M} : \\{ \\textbf{p} \\} \\in \\mathbb{R}^{18} \\rightarrow \\{ P(\\textbf{q}|\\textbf{p})\\} \\in \\mathbb{R}^3,\n\\end{align}\nwhere \n\\begin{align}\n\\begin{gathered}\n\\textbf{p}_{i,j} = \\{ \\bar{\\omega}_{i,j}, \\bar{\\omega}_{i,j+1}, \\bar{\\omega}_{i,j-1}, \\hdots, \\bar{\\omega}_{i-1,j-1}, \\\\ \\bar{\\psi}_{i,j}, \\bar{\\psi}_{i,j+1}, \\bar{\\psi}_{i,j-1}, \\hdots, \\bar{\\psi}_{i-1,j-1} \\}\n\\end{gathered}\n\\end{align}\nis our input vector for each query of the machine learning framework and where \n\\begin{align}\nP(\\textbf{q}|\\textbf{p})_{i,j} = \\{ P(J^k(\\bar{\\omega},\\bar{\\psi})_{i,j}| \\textbf{p}_{i,j})\\},\n\\end{align}\nis the conditional probability of a Jacobian computation (given by a connection to the explicit closure hypothesis). Note that $i,j$ refer to the spatial indices on the coarse-grid (i.e., the point of deployment). The indices $k=1$ and $k=3$ refer to the Arakawa non-linear Jacobian computation and $k=2$ refers to the upwinded computation instead (see Figure \\ref{Segregation}). Our optimal map $\\mathbb{M}$ is then trained by minimizing the categorical cross-entropy loss-function\n\\begin{align}\nE(\\textbf{w}) = -\\sum_{n=1}^{N} \\sum_{k=1}^{K} \\{ t_{nk} \\log(y_{nk}) + (1-t_{nk})\\log(1-y_{nk})\\},\n\\end{align}\nwhere $\\textbf{w}$ are the variable weight and bias parameters of the network, $N$ refers to the total number of samples and $K=3$ is the total number of classification scenarios (i.e., negative, positive or nearly-zero \\emph{a priori} eddy-viscosities). Here, $t_{nk}$ refers to the true label of class $k$ and sample $n$ and $y_{nk}$ refers to a corresponding prediction of the learning framework. One-hot encoding ensures that $t_{nk}$ values are always binary \\cite{Bishop:2006:PRM:1162264} and the outputs of the ANN may be interpreted as conditional-probabilities. Our optimal architecture is given by five 40-neuron hidden layers (obtained via grid-search hyper-parameter tuning). All hidden layers utilize ReLU units to impart non-linearity to the layer-wise transformations. For reference, our architecture is trained using the open-source deep learning software Tensorflow and is optimized with the use of ADAM, a popular gradient-descent based optimizer \\cite{kingma2014adam}. Figure \\ref{Loss_history} shows the progress to convergence for our framework with our optimally trained network displaying approximately 79\\% accuracy in classifying points to their correct labels. To summarize this section, we train a deep ANN to estimate probabilities of negative, positive or nearly-zero eddy-viscosities which are utilized to decide the choice of the Jacobian computation. We clarify that the decision to deploy a particular hypothesis is obtained by utilization of the classification scenario which has the highest conditional probability.\n\n\\begin{figure}\n\\centering\n\\includegraphics[trim={3cm 16cm 6cm 1cm},clip,width=\\columnwidth]{Schematic-eps-converted-to.pdf}\n\\caption{Hypothesis segregation and one-hot labeling for our proposed framework. The learning predicts conditional probabilities for the three segregated \\emph{a priori} eddy-viscosity classes which are utilized for Jacobian calculation decisions spatio-temporally.}\n\\label{Segregation}\n\\end{figure}\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{Loss_history-eps-converted-to.pdf}\n\\caption{Learning rate and convergence of our classification framework training. 2000 epochs were sufficient for converged validation loss.}\n\\label{Loss_history}\n\\end{figure}\n\n\\section{Results}\n\n\\subsection{\\emph{A posteriori} deployment}\n\nIn this section, we detail the results from an \\emph{a posteriori} deployment of the classification framework (denoted ML henceforth) for the Kraichnan test-case. In the LES evolution of the problem, a considerably coarser grid is used (at $N^2=256^2$). We remark that the forward deployment of our framework needs to overcome the challenge of numerical errors and is a robust test of the generalizability and robustness of our learning. Our LES results are assessed using angle-averaged kinetic energy spectra and through structure functions of vorticity. In addition, qualitative comparisons are also provided through visual examinations of the vorticity contours. We remark that the LES deployment is performed from $t=0$ to $t=4$ which spans the training regime data obtained from DNS. In what follows we note that DNS refers to a high-fidelity evolution of the governing equations (i.e., at $N^2=2048^2$ degrees of freedom), UNS refers to results obtained using the Arakawa scheme alone and ILES refers to a simulation where the non-linear Jacobian at all points in space and time are upwinded. Figure \\ref{Fig2} shows the \\emph{a posteriori} performance of the proposed framework at $Re=32000$ in terms of energy spectra predictions. The reader may find an exact definition of the kinetic-energy spectra in Maulik and San \\cite{maulik2017stable}. We note that the training data was obtained for the same Reynolds number as well. The prediction of the proposed framework is seen to agree remarkably well with DNS. It is apparent that the switching of schemes using the classifier has obtained an optimal balance between both techniques.\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{Figure_2-eps-converted-to.pdf}\n\\caption{The \\emph{a posteriori} performance of proposed framework (ML) for $Re=32000$ and at $t=4$ in terms of angle-averaged kinetic energy spectra. Comparisons with DNS, the Arakawa scheme (UNS) and the upwinded scheme (ILES) show that ML provides directed dissipation adequately.}\n\\label{Fig2}\n\\end{figure}\n\nVorticity contours for LES resolution assessments are shown in Figure \\ref{Fig3}, where it is apparent that the proposed framework optimally balances the energy-conserving and dissipative natures of the Arakawa and upwinded schemes respectively. This is verified by qualitative examination with FDNS contours obtained by spectrally filtering the DNS snapshot for $Re=32000$ at $t=4$.\n\n\\begin{figure*}\n\\centering\n\\includegraphics[width=\\textwidth]{Figure_3-eps-converted-to.pdf}\n\\caption{Contours for the vorticity at LES resolution and at $t=4$. In the top-left, we have predictions from the ML approach. The top-right field has been obtained using ILES, the bottom-left field is obtained from UNS and the bottom right shows FDNS contours obtained by spectral cut-off filtering of DNS. }\n\\label{Fig3}\n\\end{figure*}\n\nA second statistically significant quantity of interest studied in this investigation is the vorticity structure function \\cite{grossmann1992structure} given by\n\\begin{align}\nS_\\omega^x = \\langle |\\omega(x+r,y) - \\omega(x,y)|^2 \\rangle \\\\\nS_\\omega^y = \\langle |\\omega(x,y+r) - \\omega(x,y)|^2 \\rangle,\n\\end{align}\nwhere the angle-brackets indicate ensemble averaging and $x,y$ indicate a position on the grid with $r$ being a certain distance from this location. Figures \\ref{Fig4} and \\ref{Fig5} show the structure functions obtained from \\emph{a posteriori} deployments of the UNS, ILES and ML frameworks compared against those obtained from the final time FDNS snapshot. It is clear that the proposed framework balances between UNS and ILES deployments well to recover appropriate trends. We can thus claim that our learning is appropriate for hybrid deployments of dissipative and conservative frameworks for two-dimensional turbulence. Before moving on, we would like to point out to the reader here that the proposed methodology for closure does not require any post-processing prior to deployment in the forward simulation as utilized in several data-driven turbulence modeling studies\\cite{beck2018neural,maulik2019subgrid}.\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{Figure_4-eps-converted-to.pdf}\n\\caption{\\emph{A posteriori} vorticity structure functions in $x$ direction of our proposed framework (ML), the Arakawa scheme (UNS) and the upwind scheme (ILES) with statistics obtained from an FDNS snapshot at $t=4$. It is apparent that the ML method stabilizes the UNS result optimally.}\n\\label{Fig4}\n\\end{figure}\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{Figure_5-eps-converted-to.pdf}\n\\caption{\\emph{A posteriori} vorticity structure functions in $y$ direction of our proposed framework (ML), the Arakawa scheme (UNS) and the upwind scheme (ILES) with statistics obtained from an FDNS snapshot at $t=4$. It is apparent that the ML method stabilizes the UNS result optimally.}\n\\label{Fig5}\n\\end{figure}\n\n\n\\subsection{Validation of learning}\n\nIn this section, we proceed with a rigorous validation of our learning for deployment in regimes that are not a part of the training data. This is to ensure that the framework has truly learnt a classification based on the underlying physical hypothesis used for data segregation and is not memorizing data. This ensures that our classifier can be used in a more generalizable fashion. Figure \\ref{Fig6} shows kinetic energy spectra obtained from the forward deployment of the ML framework for a $Re=64000$ which represents a classification task that the framework has not previously seen (although the physics of the test-case remains similar). As observed, the proposed method performs quite well in this out-of-training data range as well. We note that a similar resolution ($N^2=256^2$) is utilized for this deployment. In contrast, Figure \\ref{Fig7} shows the performance of the ML technique for a reduced resolution of $N^2=128^2$ but utilizing the same Reynolds number of 32000. The kinetic energy spectra show a successful stabilization of the flow evolution at this reduced resolution although some forcing to the large scales is observed. This suggests that the classification framework may be improved by sampling from different resolutions. \n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{Figure_6-eps-converted-to.pdf}\n\\caption{The \\emph{a posteriori} performance of proposed framework (ML) for $Re=64000$ and at $t=4$ in terms of energy spectra. This represents deployment of our learning at a different Reynolds number than that used for generating training data.}\n\\label{Fig6}\n\\end{figure}\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{Figure_7-eps-converted-to.pdf}\n\\caption{The \\emph{a posteriori} performance of proposed framework (ML) for $Re=32000$, $t=4$ and at $N^2 = 128^2$ in terms of energy spectra. This represents deployment of our learning at a different resolution than that used for generating training data.}\n\\label{Fig7}\n\\end{figure}\n\n\n\\section{Concluding remarks}\n\nIn this article, we have proposed a neural network based classifier that enables us to take decisions on the choice of non-linear term computation in the LES evolution of the Kraichnan turbulence test-case. The classifier outputs conditional probabilities for the presence (or absence) of eddy-viscosities within three different ranges during deployment and is used to switch between the Arakawa and upwind computation of the non-linear Jacobian for a hybrid upwinded deployment that optimally directs dissipation on the coarse-grained flow field. Our machine learning framework is trained by calculating \\emph{a priori} eddy-viscosities which are projected onto a Gaussian distribution and segregated into three categories. Each category is devised to capture a unique behavior of the underlying sub-grid terms with negative and nearly-zero eddy-viscosity classes signifying absence of sub-grid dissipation. An optimally trained classifier is then utilized to identify if a point requires sub-grid dissipation based on if it is placed in the positive eddy-viscosity category. If so, the upwind Jacobian is calculated for imparting numerical dissipation. \n\nWe perform \\emph{a posteriori} assessments on the Kraichnan turbulence test-case through statistical quantities such as the angle-averaged kinetic energy spectra and the vorticity structure functions. It is observed that the proposed framework is successful in balancing the dissipative nature of the upwind scheme and the energy-conserving Arakawa scheme to give excellent agreement with DNS statistics. Validation for out-of-training regimes also indicate that the framework is able to learn the link between grid-resolved quantities at a coarse resolution and the nature of the sub-grid forcing. \n\nOur conclusions therefore point toward the possibility of using classifiers for the unified deployment of numerical schemes with varying dissipation through the decision making process described above. A key strength of our hypothesis stems from the fact that an ILES deployment is moderated by concepts drawn from the explicit LES ideology (i.e., that of an \\emph{a priori} eddy-viscosity). The successful deployment of our method thus points towards the possibility of deploying directed numerical dissipation that preserves the statistics of turbulence without sacrificing the shock-capturing ability of many non-oscillatory schemes. Our future work lies in that particular direction. \n\n\n\\begin{acknowledgements}\nThis material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number DE-SC0019290. OS gratefully acknowledges their support. Disclaimer: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.\n\\end{acknowledgements}\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzfmgg b/data_all_eng_slimpj/shuffled/split2/finalzzfmgg new file mode 100644 index 0000000000000000000000000000000000000000..579c38a922e39e4af4730953265f095a0b5eb48f --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzfmgg @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nWe study the jet kinematics and jet ridge line evolution of the Caltech-Jodrell Bank flat-spectrum (CJF) sample. The sample consists of 293 radio-loud active galaxies \\citep{Taylor1996}, spanning a large redshift range ($z_{avg}=1.254$; $z_{max}=3.889$). Each source has at least 3 epochs of radio interferometric (VLBA) observations and has been imaged and studied kinematically \\citep{Britzen2007b,Britzen2008}.\n\n\\section{Radio Jets in the CJF}\n\\subsection{Apparent Speeds and $\\mathbf{\\gamma}$-ray Properties}\n We correlate the kinematic information \\citep{Britzen2008} with the $\\gamma$-ray properties of the sample. In total 25 sources have been detected in $\\gamma$-rays. 18 CJF sources are included in the 3 month bright AGN source list of the Fermi-LAT \\citep{Abdo2009}, with 7 additional sources detected by EGRET \\citep{Hartman1999}. The maximum of the apparent speed distribution for the $\\gamma$-ray non-detected sources is $3\\le\\beta_{tot,max}\\le5$. For the detected sources the distribution shows two maxima ($\\beta_{tot,max;1}=0$ and $\\beta_{tot,max;2}=15$). The detected sources dominate the high speed tail of the total distribution (Fig. \\ref{fig:gamma_speeds}).\n\\begin{figure*}[!ht]\n\\begin{center}\n \\includegraphics[width=0.7\\textwidth]{Karouzos_M_fig1.eps}\n \\caption{Distribution of the maximum total apparent speed $\\beta_{tot,max}$ for $\\gamma$-ray detected (shaded) and non-detected (non-shaded) sources (data from \\citealt{Britzen2008}, \\citealt{Hartman1999}, and \\citealt{Abdo2009}).}\n \\label{fig:gamma_speeds}\n\\end{center}\n\\end{figure*}\n\\subsection{AGN Jet Ridge Lines}\nWe define the ridge line of a jet as the line that linearly connects all component positions at a given epoch. We build the jet ridge lines for all CJF sources (for S5 1803+784, see Britzen et al. 2009, A\\&A, in press) and study their properties and evolution across all available epochs.\n\n\\subsubsection{Ejection Angle Evolution}\nWe define the angle between the innermost jet component and the core, $theta$, as the approximate ejection angle of the jet (the core being at the beginning of the coordinates system). We study the change of this angle across all available epochs. Most sources exhibit a maximum ejection angle change $<5^{\\circ}$. 5.1\\% of the sources show ejection angle changes $>15^{\\circ}$, with two sources at $\\Delta\\theta_{max}\\approx45^{\\circ}$.\n\\subsubsection{Jet Ridge Lines and $\\mathbf{\\gamma}$-ray properties}\nWe quantify the jet ridge line evolution as the total component position change across all epochs. $\\gamma$-ray variable sources show stronger ridge line evolution than non-variable ones. Sources with stronger ridge line evolution tend to be more luminous in the $\\gamma$-rays.\n\n\\section{Conclusions}\nWe find that $\\gamma$-ray detected sources show higher apparent speeds than non-detected ones. $\\gamma$-variable sources show stronger jet ridge line evolution. $5.1\\%$ show considerable jet ejection angle changes ($>15^{\\circ}$). Such jet ejection angle changes might be connected to precessing or helical jets.\n\\acknowledgements\nM. Karouzos was supported for this research through a stipend from the International Max Planck Research School (IMPRS) for Astronomy and Astrophysics. M.K. wants to thank C.S. Chang for proofreading this poster and for insightful comments on various points of this work.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\chapter{Introduction}\n\nNowadays, Computer Science has become predominant in many fields of science.\nData storage, data analysis and visualization are only a few examples where it plays a fundamental role.\nThe amount of available data is increasing every day which encourages the improvement of computers and the exploration of more complex problems.\nTherefore, there is an enormous flow of information in terms of quantity and dimensionality.\nSound, images and videos are common example of large multidimensional data.\nMany areas of science need methods to reliably extract specific information from these growing amount of data.\nThis is usually done through analysis, visualization or a combination of both.\n\nComputer Vision (CV) is a field addressing the problem of analyzing visual multimedias by automatic processing.\nImage classification and pattern recognition are two examples of problems studied in Computer Vision.\nMany state-of-the-art solutions for these problems are combining the advance of Machine Learning to find data-driven models, instead of hand-designing algorithms, which usually requires expert knowledge of the domain.\nAmong these methods, {\\em Convolutional Neural Networks} (CNN), are a variant of {\\em Neural Network}s (NN) used in vision tasks and are now widely employed for Computer Vision with great success\\cite{krizhevsky2012imagenet}\\cite{rowley1998neural}\\cite{prechelt1994proben1}.\nThey are trained in a supervised way to perform a systematic processing task, for instance to classify images into a number of fixed categories (e.g.: handwritten digits between 0 and 9).\nTheir {\\em features} are the internal representation of the input inside NN that is being optimized during training to give better results.\nIn common classification tasks, the features are meant to lie in a low dimensional space and should provide a more compact representation of the input.\nTherefore, these NN can be seen as two embedded parts: a dimensionality reduction part that extracts important informations and a classifier that takes a decision based on these features.\nIn practical applications, image distortions of various kind are present in the data: shearing, noise, camera viewpoint, spatial positioning and lighting condition.\nIn the case of classification, distortions can require putting extra efforts to train a robust model able to classify correctly the input in the presence of such deformations.\nMost implementations decide to nullify the distortion signal with data-augmentation which assigns the original label on distorted samples.\nThis can be done when the application does not require this information.\nHowever, there are practical cases, for instance in Biomedical Imaging and Face Detection, which may require to have a feature representation able to quantify and possibly predict the distortions.\n\nThe first step of this work is to better understand NN embeddings and explore if conventional tools can be used to extract distortion features reliably.\nMoreover, visual inspection of the feature space using a human-friendly representation can help to gain insights and to improve models.\nOne way to analyze the features is to apply dimensionality reduction techniques to project highly-dimensional points into lower dimensional space.\nMany differences characterize dimensionality reduction methods such as: properties, flexibility and final goals.\nA brief overview is describing: PCA, MDS, LLE, Isomap and the more recent SNE.\nHowever many of them lack flexibility by representing only linearity, while the others don't preserve hierarchical structures accurately\\cite{t-SNE}.\nResearches have shown that t-SNE, a variant of SNE, has greatly improved on this regard: it is capable of keeping global clusters and fine-grained coherence at small scales.\nMoreover it was used multiple times successfully to visualize the feature space of NN\\cite{donahue2013decaf}\\cite{yu2014visualizing}.\n\nNonetheless, several limitations of t-SNE are encountered in this work.\nIt cannot be controlled directly because t-SNE is only given unlabelled points like unsupervised methods.\nTherefore, as shown later, the resulting embedding can have an unexpected shape unfit for the target application.\nSecondly, this method works directly on the representations of the points without creating a mapping that computes the relation between inputs and outputs.\nThus it is impossible to compute the representation of points that were not part of the optimization process at the beginning.\nIt would be necessary to recompute the whole representation from scratch with these new points but t-SNE is expensive.\nIn this work, a first try is made by combining CNN and t-SNE to produce an embedding quantifying distortions.\nHowever, the results are not predictable and difficult to interpret.\nDistortions are the primary factor of clustering, while classes are not well separated.\nMoreover, problems were faced to scale this method on larger datasets.\nt-SNE could be probably improved to leverage external prior-informations such as labels, but because of the major shortcomings faced, we opted for another solution.\n\nAn alternative dimensionality reduction method consists of using the NN to directly compute an embedding similar to t-SNE but trained in a supervised manner.\nThe network is used to learn a mapping from the image space to a lower embedding which does not have the previous shortcomings.\nAs NN have the capacity to represent any non-linear continuous functions\\cite{csaji2001approximation}, it is suitable to compute a very powerful dimensionality reduction method.\n\nTo achieve this goal, {\\em Dimensionality Reduction by Learning an Invariant Mapping} ({\\em DrLIM}) combines concepts similar to the ones employed in t-SNE but expressed in an equivalent adapted formulation for NN\\cite{hadsell2006dimensionality}.\nThe key elements are: the contrastive loss function with the Siamese network architecture for training and a pairing strategy directly describing the embedding.\nThe current formulation allows to express a one-dimensional relation between two pair of images based on the similarity: similar pairs of images should be close together whereas dissimilar pairs should be far from each other.\nIn this work, an extension of DrLIM is proposed with a generalized loss function.\nIt allows to express richer similarity relations between image pairs for training.\nMoreover, each of these similarities is expressed in the embedding by dimensions selected in advance and is associated feature components.\n\nTherefore, thanks to this extension, it becomes possible to have a better control on the final embedding of a NN.\nThe experiments with DrLIM are reproduced on the data-augmentation of the MNIST dataset\\cite{lecun1998mnist} with translations or rotations and likewise on the NORB dataset\\cite{lecun2004learning}.\nThe resulting embeddings are studied in details with multiple comparisons to the original work on DrLIM.\nWe find it is possible to embed distortion information in one extra dimension while preserving the characteristics of DrLIM on MNIST.\nWe also find that this new solution can represent the DrLIM's coherent and cyclic space of the camera viewpoint on NORB.\nThe successful application on these two datasets demonstrates the effectiveness of this method.\n\n\\section{Thesis Outline}\nIn Chapter~\\ref{chap:related}, the previous papers related to this work are presented.\nAn overview of the prior art is describing dimensionality reduction methods and the advance of Computer Vision with NN.\nThen, the reference paper introducing DrLIM models is discussed.\n\nIn Chapter~\\ref{chap:neural_network}, a summary is given for the background knowledge necessary to understand the networks used in this work.\nThe general architecture of NN, their similarity with CNN and some important definitions are also discussed.\n\nThis is followed by Chapter~\\ref{chap:dim_red} where the standard dimensionality reduction methods are reviewed in more details.\nThe major advantages and drawbacks of t-SNE, compared to other methods are explained.\nLater in the chapter, the basic theoretical knowledge behind t-SNE optimisation problem is provided.\nThen the necessary tools are explained to achieve dimensionality reduction with NN and how to extend the contrastive loss to N-dimensional similarity.\n\nIn Chapter~\\ref{chap:results}, the experiment environment is established in terms of technical choices (software, network architecture and datasets).\nThis chapter also includes all the practical results, the hypothesises and also the early conclusions of our work.\n\nIn Chapter~\\ref{chap:conclusion}, some final remarks are made about the results and we encourage to continue from our contribution by providing some propositions of future works.\n\n\n\\chapter{Related Work}\n\\label{chap:related}\n\nMore than 20 years ago, researchers discovered that NN is a highly flexible model architecture to solve many classification problems.\nMost of today's NN are using convolutions in CNN which are a powerful and specialized variant for visual tasks like MNIST \\cite{mnist_web} and ImageNet \\cite{krizhevsky2012imagenet}, face detection as well \\cite{rowley1998neural} and many more \\cite{prechelt1994proben1}.\nThis architecture is more efficient for Computer Vision problems, partly because the convolutions share their weights in the first layers to extract spatial cues.\n\nCurrent researches are mainly focused on advancing into more complex classification problems using Deep Learning.\nThis field suggests improving the current results by deepening the architecture in terms of number of layers to express more abstract and higher-level concepts.\nUnfortunately, more layers require more computations than before due to the increase of parameters but researchers started to overcome this challenge.\nWays to speed up the training and the classification appeared and were greatly beneficial for the development of this field in recent years\\cite{ciresan2011flexible}\\cite{schmidhuber2015deep}\\cite{nasse2009face}.\nThe use of GPUs parallelism and cloud computing allowed scaling up to much deeper architecture (with many more parameters) than before\\cite{coates2013deep}.\nBoth researchers and professional programmers built various frameworks to train and to use NN with many different goals such as: performance, accessibility or composability.\nThe most populars are: Theano\\cite{bastien2012theano}, Caffe\\cite{jia2014caffe}, Torch7\\cite{collobert2011torch7}, Pylearn2\\cite{goodfellow2013pylearn2}.\nThe efficiency and modularity proposed by Caffe were leveraged in this work for several reasons described later.\n\nEven though CNN models are now widely used in Computer Vision with great success thanks to these frameworks, current research exposed the ignorance of these networks behaviors.\nSome papers work on reliable ways to fool networks using adverserial attacks which exploits their unintuitive properties\\cite{szegedy2013intriguing}.\nMost publications do not try to formally justify their good results because of the non-linear and complex relations between units.\nMoreover, this problem is greater with the addition of distortions in the inputs.\nIt is not possible to directly look at the high-dimensional embedding, because humans cannot easily plot nor interpret so many dimensions.\nThe manifestation of this relation can be looked visually using methods to produce an alternative human-friendly representation.\n\nDimensionality reduction methods is a popular tool in such a case\\cite{dai2014document}\\cite{taylor2011learning} and several methods exist that are specialized for visualization.\nStandard methods, like PCA and MDS\\cite{cox2000multidimensional}, are used for reducing dimensions usually as a preprocessing step and they are limited to linearity in the data.\nTheir primary characteristic is to maximize the variance of the original data which is important for reconstruction but not necessarily for visualization.\nOther non-linear dimensionality reduction methods, for instance: Isomap\\cite{tenenbaum2000global}, LLE\\cite{roweis2000nonlinear} and SNE\\cite{SNE}, solve an optimisation problem with a loss function which improve their flexibility for purposes like visualization and preserve local structure\/cluster.\nHowever, recent papers show that SNE has better potential to keep global clusters and local details at the same time unlike the other previously cited methods\\cite{SNE}.\n\nSNE has introduced a better maximization problem to preserve point neighborhood and general clusters with an important emphasis on distances\\cite{SNE}.\nHowever it suffers from ``center-crowdedness'' and faces difficulties in optimisation\\cite{t-SNE}.\nThis is the reason why recent works are now using t-SNE, a variant of SNE, to reduce CNN embeddings into a 2D human-friendly manifold.\nThis method exhibits an easier optimisation formulation and preserves more structures at diverse scales.\nMore convincing examples were created with t-SNE directly applied on popular datasets\\cite{van2009new} like MNIST\\cite{t-SNE}.\nA more recent work proposed an alternative method, called Barnes-Hut SNE, that approximates t-SNE with a much faster algorithm but it wasn't useful for this work\\cite{van2013barnes}.\n\nMany more details in CNN embeddings emerge by using t-SNE.\nFor instance, how samples are grouped depending on multiple factors: essentially based on similarity of the digits (strokes, thickness and shapes) and natural variance (rotation).\nMany papers use dimensionality reduction to create a human viewable representation of the output embedding \\cite{donahue2013decaf}\\cite{yu2014visualizing}\\cite{yaotiny}.\nOnly a few papers actually try to formalize the input-output relations\\cite{goodfellow2009measuring}.\nSome papers proposes to learn transformation-invariant embeddings, by means of modelling distortions directly into their models\\cite{gens2014deep} or using data-augmentation\\cite{hadsell2006dimensionality}.\n\nThe latter proposes to create a dimensionality reducing mapping, called {\\em Dimensionality Reduction by Learning an Invariant Mapping} ({\\em DrLIM}), by training a CNN using a new loss function inspired by Energy-Based modeling, called the {\\em contrastive loss}.\nThis paper will be mentioned in this work as the {\\em reference paper} or DrLIM's paper.\nTheir results on the MINST dataset\\cite{lecun1998mnist} shows that CNN can learn a mapping that distinguish labels, and group alike digits even when they are translated artificially.\nThey experimented with the NORB dataset\\cite{lecun2004learning} as well with intriguing results: images are mapped on a 3D cylinder whose axes quantify the orientation in 2D and the azimuth angle in 1D.\nThe network was successfully trained to ignore lighting conditions, which is a strongly non-linear distortion.\nThey use the Siamese training architecture to present image pairs which are optimized to be close if similar or far otherwise\\cite{bromley1993signature}\\cite{chopra2005learning}.\nThere are several advantages of such a method: the cost penalty is very low and present only during training.\nThe usage of standard CNN also allows more freedom for experimenting with different architectures and the proposed loss function is effective while simple.\n\nHowever the input-output relations of distorted images is usually damped instead of being quantified in a predictable way.\nThe distortion information should be kept because they can be valuable later on.\nBesides, the reference paper provides subjective comments of the embedding coherence (descriptions and figures) but they do not offer nor propose a formal way to measure and compare its quality objectively.\n\nIn this work, a few steps are presented towards predictable embedding with respect to distortions and a simple qualitative measure is presented to compare similar methods.\n\n\n\\chapter{Theory: Neural Networks}\n\\label{chap:neural_network}\nThe general layout of neural networks and their mathematical foundations is now introduced.\nThe important differences between {\\em Neural Networks} (NN) and {\\em Convolutional Neural Networks} (CNN) are discussed and a justification is given why CNN is so predominant in Computer Vision tasks.\nAn intuitive explanation of embeddings is given and will be used later for dimensionality reduction.\n\n\\section{NN and CNN Classifiers}\n\nLet us quickly recall the definition of a Neural Network and its workings for classification.\nBasically, NN are composed of $L$ layers which are made of inter-connected neurons (see figure~\\ref{fig:neural_network} for an example).\nA connection $l_{i \\rightarrow j}$ in the $k$th layer is weighted by a parameter $w^k_{i,j}$ which is learned.\nClassification is done by feed-forwarding the input onto the first layer whose output is propagated layers by layers through the network.\nThe output of one layer is forwarded onto the next through their neuronal connections.\nThis process is repeated until the last layer, whose output is considered as the network output.\nEach layer does a single particular computation over its input.\nUsual NN have a repetition of the following pattern: one layer computing inner-products with their neuronal weights then adding biases, followed by a non-linear layer using an activation function.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics{thesis_figures\/NN.jpg}\n \\end{center}\n \\caption{An example of a Neural Network with three layers -- Source: neuralnetworksanddeeplearning.com}\n \\label{fig:neural_network}\n\\end{figure}\n\nThe figure~\\ref{fig:artificial_neurons} presents the computation involving a particular neuron and its activation.\nLet us define the $j$th neuron in the $k$th layer for the inner-product case, then its output $z^k_j$ is defined:\n\\begin{eqnarray}\n z^k_j = \\sum_{i=1}^{U^{k-1}} w^k_{i,j} z^{k-1}_i + b^k_j\n\\end{eqnarray}\nwhere $U^k$ is the number of neurons in layer $k$.\nThen we define the $j$th neuron in the $k$th layer for the activation case:\n\\begin{eqnarray}\n z^k_j = g(z^{k-1}_j, w^k_{j})\n\\end{eqnarray}\nwhere $g$ is the activation function with its parameter.\nMost of them use the sigmoid activation function and connect each neuron to all the next layer neurons.\nTraining the weights generally involves gradient descent to minimize the loss function of such networks.\nThis loss represents the error between the prediction of the network (its output) versus the expected output (in classification: the class label).\nThe training involves an iterative process where: the input is first feed-forwarded into the network to compute the error, then this latter is back-propagated up to the first layer, while each unit weights is adjusted to minimize the unit error.\nThis process should be repeated until the error on the validation set has converged to a local optimum.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics{thesis_figures\/800px-ArtificialNeuronModel_english.jpg}\n \\end{center}\n \\caption{A neuron computing an inner-product with a link to its activation neuron -- Source: Wikipedia}\n \\label{fig:artificial_neurons}\n\\end{figure}\n\nOne particular architecture is the CNN, a special case of NN with certain restrictions.\nCNN models learn very effective solutions for many Computer Vision problems in image classification.\nThe three key ideas of CNN are described below: local receptive fields, weight sharing and subsampling.\nIn CNN, a convolutional layer can model receptive fields by computing multiple trainable 2D kernels which is convoluted with its entire input whose result is called its feature maps.\nA kernel convolution can be seen as the replication of a single unit along the dimensions of the input (a 2D grid for images), thus all weights are constrained to be equal by definition.\nA convolutional layer contains multiple units where each has its own kernel.\nTherefore different kernels are applied to the image where a single kernel convolution is called a feature map.\nSuch layer naturally computes filters which can be seen like data-driven feature extractors.\nThe benefit of weight sharing in convolutional layers is to reduce the number of global parameters to learn general purpose filters which can increase generalization.\nUsually, CNNs have subsampling layers right after convolutional layers to reduce the dimensionality of the feature maps so that more concise and high-level information are extracted.\nMost CNN architectures puts multiple convolutional layers connected to the input to extract visual features after which they have regular inner-product layers (see figure~\\ref{fig:convnet}).\nCNN can be seen as two parts: a trainable feature extractor made by the convolutional network followed by a neural network classifier.\nAlthough CNNs have more constraints, it has been shown they generally outperform NN in multiple Computer Vision problems because they learn more invariance\\cite{simard2003best}\\cite{mnist_web}\\cite{lawrence1997face}\\cite{krizhevsky2012imagenet}.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics[width=\\textwidth]{thesis_figures\/conv-net2.jpg}\n \\end{center}\n \\caption{Architecture of a CNN where the computation flow is explicitly shown -- Source: code.flickr.net}\n \\label{fig:convnet}\n\\end{figure}\n\nMoreover, research found interesting structures in the last-layer embedding of classification NN using t-SNE.\nThe last layer tends to cluster samples of the same class together while separating the rest\\cite{donahue2013decaf}\\cite{yu2014visualizing}.\nThis is explained by the fact that deeper layers extract more high-level information, which is necessary to separate classes. Moreover the prediction is a direct product with the last layer, which encourages the network to have a simple structure directly clustered by classes.\n\n\n\\chapter{Theory: Dimensionality reduction}\n\\label{chap:dim_red}\nDimensionality reduction is an important method in machine learning that maps points in a high-dimensional space into a space with a lower number of dimensions.\nThis lower subspace is later called an {\\em embedding}.\nRepresentations suitable for human visualization should keep important relations between points of the original space.\nIn the experiments, we will apply dimensionality reduction on the neural networks' outputs.\nThis allows to quickly compare qualitatively the impact of controlled distortions applied on images over the networks' outputs.\nThe structure inferred by distances between points such as clusters, intra-cluster neighbors and outsiders, reveals important properties of neural network models.\nThus, accurate low-dimensional representations of embeddings that preserve local distances are desirable to compare different models.\nIn the following sections, dimensionality reduction methods are introduced where some are disregarded because they were not justified for this project, then the one used here, called t-SNE, is introduced in details.\n\n\\section{Optimization Problem}\nVarious algorithms differentiate themselves by several properties: their goal (e.g.: interpolation, compression, visualization), by what they preserve (e.g.: variance, distances), how they model point correlation (e.g.: linearly or not) or whether they can model new points (e.g.: a representation or a mapping).\n\nFirst, the primary factor for the selection of a method is guided by the requirements, which is here to visualize.\nThus it should produce a 2D or 3D embedding, preferably in 2D, for easily interpretable scatter plots.\nSecondly, we need an optimization problem that keeping certain relations between points such that clusters are equivalently represented.\nThis can be implemented by preserving relative distances to close neighborhood.\nConsidering the previous works discussed above, t-SNE is the best candidate in the current state-of-the-art.\nThe definition of the objective functions, the probabilities and the intuitive properties behind this method are now established.\n\nLet us define the dataset $D$ formed by points in the input-space $X$ of dimension $N$: $D = \\left\\{ x_1, x_2, \\dots, x_n \\right\\}$, each $x_i \\in \\mathbb{R}^N$, and one representation $D^\\ast$ in the output-space $Y$ of dimension $M$: $D^\\ast = \\left\\{ y_1, y_2, \\dots, y_n \\right\\}$, each $y_i \\in \\mathbb{R}^M$.\nThe process of dimensionality reduction is to find the best representation $D^\\ast$ that best preserves the most ``important'' information between $x_i$ and $y_i$ for each point.\nSNE uses two Gaussian distributions for each point expressing the neighbor distances in $X$ and its equivalent in $Y$.\nThe Kullback-Leibler divergence is used to compute the objective function, which represents the mismatch of these two distribution for each pair:\n\\begin{eqnarray}\n L = \\sum_{i=1}^n KL(P_i || Q_i) = \\sum_{i=1}^n \\sum_{j=1}^n p_{j|i} \\log\\left(\\frac{p_{j|i}}{q_{j|i}}\\right)\n\\end{eqnarray}\nThe probability for a pair of point $x_i$ electing $x_j$ in $X$ follows a Gaussian is as follows:\n\\begin{eqnarray}\n p_{j|i} = \\frac{\\exp(-|x_i - x_j|^2 \/ 2 \\sigma_i^2)}{\\sum_{k \\not = i} \\exp(-|x_i - x_k|^2 \/ 2 \\sigma_i^2 )}\n \n \n\\end{eqnarray}\nThe equivalent probability $q_{j|i}$ in $Y$ is the same except that $\\sigma = 0$ and $x$ is replaced by $y$.\nTherefore, a closer pair implies a higher neighbor-election probability because the distance is low.\nThis cost function gives an asymmetrical importance to the distances: nearby points in $X$ are greatly penalized if they are far in $Y$; whereas a small cost is incurred for pairs far in $X$ but close in $Y$.\n\nAs said SNE suffers from crowdedness problems in the middle of the embedding and the optimisation is harder due to the asymmetrical nature of the objective function.\nBoth problems were addressed in t-SNE which give very good results in practice.\nThe two major differences with t-SNE is the symmetrization of the cost function and replaces the distributions of the embedding by student variants.\nIn t-SNE, a single objective function is minimized:\n\\begin{eqnarray}\n L = \\sum_{i=1}^n KL(P || Q) = \\sum_{i=1}^n \\sum_{j=1}^n p_{ij} \\log\\left(\\frac{p_{ij}}{q_{ij}}\\right)\n\\end{eqnarray}\nwhere:\n\\begin{eqnarray}\n p_{ij} = \\frac{p_{j|i} + p_{i|j}}{2 n}\n\\end{eqnarray}\nwhich forces outliers points to contribute more to the loss.\nAnd:\n\\begin{eqnarray}\n q_{ij} = \\frac{1 \/ (1 + |y_i - y_j|^2)}{\\sum_{k \\not = l} 1\/(1 + |y_k - y_l|^2)}\n\\end{eqnarray}\nwhich replaces the $q$ distribution in SNE by a Student with a heavier tail: distances in high dimensional spaces spread across more dimensions; in low dimensional space, the accurate equivalent distance needs to be much higher per dimension (thus more points end up further in $Y$, a heavier tail than in $X$).\nAs before, a point cannot elect itself: $p_{ii} = q_{ii} = 0$ and probabilities are symmetric for both distributions: $p_{ij} = p_{ji}$ and $q_{ij} = q_{ji}$.\nAs stated previously the input space $X$ has much more dimensions were distances can be expressed than the 2 dimensions of $Y$.\nIn summary, t-SNE uses Kullback-Leibler divergence to minimize the mismatch of these two spaces by means of probabilities, therefore the chance of important local structures (frequent patterns) being preserved is higher than with other methods (mosts do not express this goal through an objective function).\nGlobal structures is encouraged by the coherence of the local structures as the divergence decreases and the system stabilizes.\n\n\\section{CNN for Dimensionality Reduction}\nUsual dimensionality reductions like t-SNE are helpful for many visualizing tasks but it has important drawbacks as well.\nThe most important ones are: the computational cost, the incapacity of mapping new points and the indirect control over the resulting embedding.\nCurrent implementations of t-SNE are still rare, unpolished and require tricks to make them tractable in practice (e.g.: reducing first with PCA).\nAs there is currently no way with t-SNE to map new points (not in $D$), it is necessary to optimize the whole system from scratch.\nBesides, t-SNE parameters like perplexity and learning rate are not simple to chose.\nFortunately there are new promising alternatives directly harnessing the power of NN.\nSuch models are introduced in the following section.\n\nCNN are mostly used for classification but it can be optimized for other purposes as well.\nInstead of the SoftMax loss function used in classification, a special training architecture with a suitable loss can be used to optimize topological constraints.\nFor example to create an embedding with particular properties in the output layer.\nMoreover the ideas presented by reduction methods like t-SNE can be formulated in different terms to be applicable to NN.\nIn this work, the most important idea is to keep similar points together and dissimilar far away from each other.\n\nThe Siamese network combined with a contrastive loss is a good practical solution to train such networks\\cite{bromley1993signature}\\cite{chopra2005learning}.\nLet us define $G_W$, the function that computes the network output, with parameters $W$ (see figure~\\ref{fig:siamese_network}).\nThen the Siamese network put two weight-sharing instances of $G_W$ side by side, each having their own input.\nOn top of this Siamese network is placed the ``cost module'', the contrastive loss, which will compute a loss proportional to the difference between the two output.\nThe complete network takes a pair of images as input, each image fed into a single $G_W$ instance, and the output is computed over their outputs.\nThe idea is to optimize the metric between points represented by $G_W$ and to spread contributions between the weights because they are shared.\nAt the end, when the network is used, only a single instance of $G_W$ is required to feed an input and $G_W$ gives the dimensionality-reduced point.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics[width=0.5\\textwidth]{thesis_figures\/siamese_network.jpg}\n \\end{center}\n \\caption{Schematic representation of a Siamese network -- Source:~\\cite{bromley1993signature}}\n \\label{fig:siamese_network}\n\\end{figure}\n\nThe contrastive loss function takes its root from Energy-Based Models (figure~\\ref{fig:contrastive_spring}).\nThe loss is constructed as follows: an attractive term is used to assemble similar points together.\nInsufficient alone because it does not prevent degenerate solutions where all points are merged.\nAn opposing term is added to push dissimilar pairs to allow the system to converge towards a structured embedding.\n\nMore formally, the definition of the contrastive loss for a pair is as follows:\n\\begin{eqnarray}\n L = \\frac{1}{2} Y (D_W)^2 + \\frac{1}{2} (1-Y) \\max(0, m - D_W)^2\n\\end{eqnarray}\nwhere $Y \\in \\{0,1\\}$ is the label: $1$ for similar pairs, $0$ otherwise; $D_W \\in \\mathbb{R}^N$ is the difference between the two networks' outputs and the parameter $m \\in \\mathbb{R}$ defines the minimal distance between dissimilar points.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics[width=0.5\\textwidth]{thesis_figures\/contrastive_spring.jpg}\n \\end{center}\n \\caption{A schematic representation of the contrastive loss using physical springs. Consult \\cite{hadsell2006dimensionality} for more details. (a) shows the points connected to similar points with {\\em attract-only} springs. (b) the loss function and its gardient associated with similar pairs. (c) the point connected only with dissimilar points inside the circle of radius $m$ with {\\em m-repulse-only} springs. (d) shows the loss function and its gradient associated with dissimilar pairs. (e) shows the situation where a point is pulled by other points in different directions, creating equilibrium -- Source \\cite{hadsell2006dimensionality}}\n \\label{fig:contrastive_spring}\n\\end{figure}\n\nIn the contrastive loss, a label means whether a pair should be close in the embedding but the definition of similarity is left open for the application.\n\n\\section{CNN for Predictable Reduction}\nThe method above can learn an embedding which groups points or separates them based on the pairing strategy but there is still room for improvement.\nThe shape of the embedding is determined by the dataset labels which helps to form local structures.\nHowever, there is no guarantee that this system will converge to an intended global structure with so many dimensions, such as: the ordering of the deformations (e.g.: from lowest to highest) or their distribution (e.g.: a line, plane or circle).\nSecondly, the training required to make the embedding converge to a desirable structure can happen or not depending on the training time or specific initialization seeds, and this is partially due to the lack of constraints in the task.\nWhen the number of embedding dimensions, $M$, is higher than the dimension of the pairing, $1$, the model can become unpredictable because more dimensions give more freedom in the way to represent these pairs.\n\nTo improve this situation, a solution is to add more constraints on such dimensions directly into the optimisation process.\nWe propose to give more than one information per pair and this allows to structure the embedding directly through the loss function.\nThis new method allows to control separately the usage of each dimension to express simultaneously different properties of the dataset.\nTo accomplish that, a generalization to the contrastive loss is proposed to work on training pairs with $p$-dimensional labels and an embedding with $M$ dimensions, where $p \\leq N$ by definition.\nIn this framework, an embedding dimension is allocated to one particular component of the label and one component can be expressed through one or more embedding dimensions.\nThe allocation of the embedding dimensions and the pairing strategy are left open to the use-case.\n\nThis new loss function is now introduced with its formal definition.\nLet us define $M$ as the number of embedding dimensions, $p$ the dimensions of the labels and $p \\leq M$.\nWe define $D \\in \\mathbb{N}_+^p$ as the number of embedding dimensions assigned for each label component.\nOnly problems where each dimension is assigned to a single label and no dimension is left unconstrained are considered in this work: $\\sum_i^p D_i = M$.\nThen it follows that the definition of the generalized $p$-dimensional contrastive loss for a pair is:\n\\begin{eqnarray}\n L = \\frac{1}{2} \\sum_{i=1}^p \\left( Y_i (D_{Wi})^2 + (1-Y_i) \\max(0, m_i - D_{Wi})^2 \\right)\n\\end{eqnarray}\nwhere $Y \\in \\{0,1\\}^p$ with $Y_i$ being the $i$th component of $Y$, $D_{Wi} \\in \\mathbb{R}^{D_i}$ is the difference between the two points in the sub-embedding for dimensions of the $i$th component, and $m_i \\in \\mathbb{R}$ is the minimal distance for dimension $i$.\n\n\n\\chapter{Methodology and Results}\n\\label{chap:results}\n\nIn this chapter, the following sections describe the general environment used to make the experiments, the settings to reproduce them and their outcomes.\nThe different models are introduced in details with their architecture, then the implementation using Caffe and Python is explained.\nThe generation of the datasets by combining samples from MNIST or NORB is also discussed.\nFinally, the idea of quantitative measures is presented and used in the results for comparisons with the previous work.\nThe source code is available on GitHub at: \\url{https:\/\/github.com\/axel-angel\/master-project}.\n\n\\section{Models and parameters}\n\nAs previously said, the work in the reference paper \\cite{hadsell2006dimensionality} is closely followed here.\nTherefore, we employ the same two models to experiment on the respective datasets MNIST and NORB.\n\nThe {\\bf first model} is {\\em LeNet 5}, a multi-layer neural networks that is characterized by an architecture designed for handwritten characters as illustrated in figure~\\ref{fig:siamese_cnn}.\nIn the experiments with the MNIST dataset, a variant is used with some minor changes described below.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics{thesis_figures\/siamese_cnn.jpg}\n \\end{center}\n \\caption{Architecture of the CNN used for the experiments on MNIST (based on LeNet 5). The network contains two convolutions layer which are connected by a subsampling layer and the final output is computed by a fully-connected layer. The computation flow is illustrated for a region of an image. -- Source: \\cite{hadsell2006dimensionality}}\n \\label{fig:siamese_cnn}\n\\end{figure}\n\nThis network architecture is comprised of a total of 7 layers with trainable parameters.\nThe first part of this network contains two pairs of convolution-pooling layers, where each convolution shares its weights defined as a kernel.\nAs explained earlier, each convolution is applied to the entire image to create one feature map and the ensemble of convolutions of a layer creates the feature map of this layer.\nThe first convolutional layer has 20 different trainable $5 \\times 5$ kernels, which is followed by a trainable $2 \\times 2$ max-pooling layer (with pixel stride of $2$).\nThe second convolutional layer has 50 trainable $5 \\times 5$ kernels, followed again by a $2 \\times 2$ max-pool layer (same stride).\nThese two pairs of layers form the convolutional network ({\\em convnet}) part of this network which can be seen as {\\em feature extractors}.\nThey will extract features encoding high-level cues (e.g.: shape of strokes: straight or curved) in this application to discriminate between digits.\n\nThe second part of this network is made of two fully-connected layers which are connected through a non-linear activation layer.\nThe first layer has 500 trainable units computing an inner-product, followed by a non-trainable Rectified Linear Unit (ReLU) activations\\cite{nair2010rectified}, followed by 10 trainable output units computing an inner-product which gives the digit class (1 versus rest).\nThese three layers form the neural network part used for classification based on the prior features.\n\nThe {\\bf second model} is only made of two fully-connected inner-product layers.\nThis network is much simpler than the one above because it is trained on a subset of the NORB dataset, which exhibits very few variability compared to MNIST.\nIndeed, only a single 3D object will be used and its shape in 3D is easily recognizable from any angle.\nBack to the network, its two layers are made of 20 and 3 trainable units respectively, without non-linearity between them.\n\nIn the following experiments, the variant of LeNet is trained in two different ways: for digit classification to analyze its ``natural'' embedding then with a Siamese training architecture with the contrastive loss function to analyze a ``constrained'' embedding.\nIn the second version, a single fully-connected layer is left in the NN part and reshape its number of units to match the embedding dimensions (e.g.: $2$ or $3$).\nThe motivation is that the classification task is built on top of the network stack generating this embedding but in the second experiment this output needs to be projected, like in the reference paper.\nRegarding this second model, a ``constrained'' embedding is directly trained with the Siamese architecture for NORB.\n\nIn the case of digit classification, the model is trained using Stochastic Gradient Descend (SGD) with a learning rate of 0.01 optimizing a SoftMax loss function.\nSiamese models are trained with SGD with a learning rate of 0.01 or 0.001 optimizing the contrastive loss.\n\nFor this work, the {\\bf Caffe} Deep Learning framework was chosen to make models.\nThere are several reasons such as: simplicity to express, train and manipulate networks and the inclusion of several practical architectures: LeNet on MNIST, Siamese on MNIST, AlexNet on ImageNet.\nMoreover Caffe is well optimized in C++ for CPU and CUDA for GPU, flexible with official bindings for Python and MatLab and its community is very active and helpful.\n\nThe provided LeNet is easily adapted to match the architecture design discussed above for MNIST.\nAs the second model is very simple, it was trivial to write its definition ourself.\nThe training stage needs many important parameters related to the SGD solver: learning rate, batch size, momentum, weight decay (gamma and power) and number of epoch.\nThe default parameters were mostly used as defined in Caffe because the community already fine-tuned them.\nThe experiments are limited to $10'000$ iterations for MNIST and $100'000$ for NORB, with a batch size of $64$.\nAll networks were trained using the Caffe built-in solver started using the {\\tt caffe train} command.\nThe alternative loss function was implemented in a new loss layer inspired by the built-in contrastive loss.\nThe implementation was straightforward to make in Python with NumPy and the Caffe bindings to integrate this layer into the network architecture.\n\nThe set of tools to generate distorted training sets were created ourself for the t-SNE experiments and to generate the distorted pairs training sets for the two Siamese experiments.\nThe scikit-learn Python library ({\\em sklearn}) implements most of the standard algorithms for machine learning including: PCA and t-SNE\\cite{pedregosa2011scikit}.\nThe performance of t-SNE depends heavily on the number of input dimensions and the usage of PCA on the datasets was necessary and followed other papers suggestions\\cite{t-SNE}.\nLikewise sklearn recommendation is to reduce to $50$ dimensions with PCA before applying t-SNE\\footnote{t-SNE documentation page: \\url{http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.manifold.TSNE.html}}.\n\nFor the visualization of the resulting embeddings, a solution based on the web library {\\em CanvasJS} was developed for this project.\nIts main advantage is to allow to interactively visualize with scatter plots directly from the output of t-SNE or the neural networks.\nSeveral features were already provided: coloring points, fast plotting for interactivity but some were added by ourselves: zooming and moving the viewport, display the image of any sample and filtering\/highlighting capabilities for the experiments.\nAll these functionalities provide the necessary tools to inspect the results and to come with the presented insights.\n\n\\subsection{Datasets}\nTo perform the experiments, two different datasets were used: {\\bf MNIST}\\cite{lecun1998mnist} (a popular handwritten digit dataset) and {\\bf NORB}\\cite{lecun2004learning} (a popular dataset of photographed 3D objects).\nEach dataset is briefly discussed then a few characteristics is established regarding their variability and their usual usage in Computer Vision.\n\nThe {\\bf MNIST} dataset ({\\em Modified National Institute of Standards and Technology}) is a gathering of multiple databases of handwritten digits.\nOne of the goal of MNIST is to provide a unified benchmark for digit recognition and it is widely used in Computer Vision for many years.\nIt is composed of $60'000$ training and $10'000$ testing images of digits between $0$ and $9$.\nA few examples of the training set are illustrated in figure~\\ref{fig:mnist}.\nThe samples are heavily post-processed: uniform black background, white-shaded digits with bold strokes.\nThe digits are centered such that the gravity center is in the middle and size-normalized so one digit lies inside a restrained sub-region of the $28 \\times 28$ bitmap.\nThe variability of this dataset lies in the different strokes, handwritting style, natural rotations, thickness, curve roundedness and such.\nThe MNIST dataset is considered nowadays extremely simple due to the lack of natural variability and because state-of-the-art achieved extremely good results.\nHowever it is still a good subject of experiments to try and validate new ideas.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics{thesis_figures\/mnist.jpg}\n \\end{center}\n \\caption{A few samples taken from the MNIST training set to illustrate the dataset. Images were concatenated to save space.}\n \\label{fig:mnist}\n\\end{figure}\n\nThe {\\bf NORB} dataset ({\\em NYU Object Recognition Benchmark}) is a collection of photos of toys taken from continuous poses.\nThis dataset has two variants and the normalized-uniform set was retained.\nIt is also intended for benchmark usages, in ``large-scale invariant object categorization''.\nThe set is composed of $48'600$ processed photos of toys, as illustrated by figure~\\ref{fig:norb}.\nThe dataset is made by combining: $9$ elevation views, $18$ azimuth views, $6$ illumination conditions and $5$ toys categories, each containing $10$ toys.\nThe categories are: humans, animals, airplanes, trucks and cars.\nThe usual training task is to recognize the toy category whichever viewpoint or illumination is captured.\nThe DrLIM paper only selected a particular toy of a plane for its experiments and they infer the coherent representation of the camera viewpoint in 3D.\nThis same toy is used in this work to make the results comparable with DrLIM.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics{thesis_figures\/norb.jpg}\n \\end{center}\n \\caption{A few samples taken from the NORB training set to illustrate the dataset. Images were concatenated to save save.}\n \\label{fig:norb}\n\\end{figure}\n\nThe experiments require to process the images in a systematic and coherent way.\nPython scripts were used to automatically derive the training and testing sets in a predictable way based on the original dataset (MNIST and NORB).\nThe training architecture apply sequentially SGD over the samples in batch and loops over the whole dataset until the number of iterations reaches zero.\nThe generation of these datasets depends on multiple parameters related to the experiment.\nIn the case of the t-SNE visualization, a distorted dataset was created containing each sample plus its distorted versions varying in strength, where each image has only a single transformation at a time.\nThe samples are all distorted using the same set of intensities, quantified as follows: translations and shearing using pixel displacement, rotations using positive and negative angles and blurring with averaging radius (examples in figure~\\ref{fig:mnist_transfo_tsne}.\nIn the case of Siamese training, paired datasets were created containing two distorted images with their similarity (1 or 0).\nImages in a pair can have different distortions and different classes (digit for MNIST, elevation\/azimuth for NORB) and still be paired (label 1), depending on the experiment.\nAn example of pairs for MNIST is illustrated in figure~\\ref{fig:mnist_pairs}.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics{thesis_figures\/mnist_transfo_tsne.jpg}\n \\end{center}\n \\caption{Illustration of some distortions applied to a random sample included in the data-augmented dataset used in the t-SNE visualization experiment. The following distortions are shown: translations, rotations, blurring and linear deformation}\n \\label{fig:mnist_transfo_tsne}\n\\end{figure}\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics{thesis_figures\/mnist_pairs.jpg}\n \\end{center}\n \\caption{Illustration of some pairs generated for a random sample with its distortions, neighbors and non-neighbors. The pairs are included in the training dataset for the Siamese network.}\n \\label{fig:mnist_pairs}\n\\end{figure}\n\n\\section{Evaluation Metrics}\nQualitative results can provide insights regarding the model behavior or the quality of embeddings but it has drawbacks.\nFirst it comes from human judgement which requires manual efforts, thus it cannot be systematic and automatic.\nSecondly the subjective nature of qualitative measures is subject to different interpretation or can be biased towards certain aspect that people value differently.\nWe think qualitative judgements are unreliable and should not be used alone to asses the work in NN.\nHowever, DrLIM results are lacking objective measures, for example the final loss or any sense of scale of dimensions to relate in the plots nor does it mention any kind of measure to quantify the quality.\nBy lacking any form of measures, we think that DrLIM paper does not promote the continuation of its work because there is no common way to measure ``good''.\nEvaluation metrics are very important to permit objective comparisons between several results to see how they perform.\n\nThus we propose a very simple quantitative measure that allows to compare DrLIM and the new models.\nModels are optimized with respect to their loss functions and indeed the loss is an objective measure that can be directly used to compare models on certain conditions.\nGiven the same loss, if its parameters are the same then it is logically fair to compare them.\nTherefore, the contrastive loss of DrLIM is used on its test set using the original pairing strategy.\nHowever, DrLIM paper does not provide the margin which is problematic to reproduce their work.\nIn the following, replicated versions of the DrLIM's models are trained ourself and thus a common margin is chosen per dataset for the two models.\n\n\\section{Results and Discussion}\n\nThis chapter is concluded by the presentation of the results and insights, including the plots and quantitative measures.\nThe first experiment is the visualization of the LeNet embedding on the MNIST classification task.\nThe structural differences are compared between models between trained with and without data-augmentation.\nHowever the resulting embedding has an unexpected structures that does not fit the needed requirements.\nThen, this method is left aside to learn directly optimized embedding with models using the contrastive loss.\nThe DrLIM's methods are applied by replicating the experiments on MNIST and NORB.\nFollowed by a throughout detailed discussion of the new methods in the same conditions to compare the two.\n\n\n\\subsection{t-SNE on LeNet}\n\nIn this experiment, two models for classification are trained on MNIST which is once unmodified, the second time it is data-augmented with translations.\nThe {\\em data augmentation} process grows a dataset artificially by applying various distortions to train invariant models.\nIn this case, it contains each original digit plus many translated copies which are all labeled identically.\nAfter the models are trained, their underlying embedding are inspected by using t-SNE.\nThe goal of this experiments is two folds.\nFirst we would like to understand classifier embeddings and its interaction with t-SNE in a practical manner: testing various data-augmentation methods to see how it impacts the model, its accuracy and how t-SNE adapts.\nSecondly, this experiment tests whether it is possible to get an embedding with the prerequisites properties of predictability with a state-of-the-art method specialized for projecting in 2D.\n\nTo create the first model, the training is done on the MNIST classification task to predict digits without any modification.\nAt this point, all images of the training set are used without data-augmentation, thus there is little invariance.\nThe error rate of this model is 1.0\\% which is computed over the MNIST test set.\nAccording to the published results on the MNIST website, this is a reasonable value (LeNet-5: 0.95)\\cite{mnist_web}.\nIn the following visualization with t-SNE, the classification layers of this this network are removed (SoftMax and 10-outputs inner-product).\nTherefore the last remaining layer is a ReLU which follows the last inner-product of 500 dimensions.\nThe hypothesis at this stage is as follows: this last layer expresses high-level concepts of digits.\nTo understand the behavior of the system, a few distorted samples are included in the test set: for each digit, five samples are selected and their distortions included.\nThis modified test set is feed-forwarded into the network, apply PCA, then apply t-SNE to create the final embedding.\nThe whole process takes around 45 minutes on a single core.\nThe resulting embedding is illustrated in the figures~\\ref{fig:mnist_nda_tsne} and ~\\ref{fig:mnist_nda_tsne2}.\nMost of the original digits are well separated on the first figure where each cluster represents a single class.\nThe major exception is the 1s spreading on a thin vertical curve over a small part of he 6s.\nThe hypothesis seems validated by the structure on this plot: class separation and coherence inside clusters.\nMost of the distorted samples on figure~\\ref{fig:mnist_nda_tsne2} are part of their respective clusters however they start to drift towards other classes as their distortion increases and the most extreme examples are at the opposite side.\nAt this point, it should be noted that the model is very sensitive to translations and many reside in the wrong clusters.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/mnist_nda_tsne.jpg}\n \\caption{The t-SNE representation of the CNN embedding for the MNIST test set using the interactive tool.\n This figure shows a general view of the non-distorted samples.\n Some random samples are shown for each cluster.\n Each color represent a digit class.\n The original digits are well separated into clusters for each class.\n The major exception is the 1s spreading on a thin vertical curve over a small part of he 6s.\n Inside clusters, digits are spread coherently to their characteristics.\n }\n \\label{fig:mnist_nda_tsne}\n\\end{figure}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/mnist_nda_tsne2.jpg}\n \\caption{The t-SNE representation of the CNN embedding for the MNIST test set using the interactive tool.\n This figure shows a close view of the cluster for 0s including this time the distorted samples.\n A unique color is given to each digit class as before plus one unique color for each sample's distortions.\n Translated samples quickly end up outside the digit cluster whereas other distortions like rotations are structured into curved lines.\n Most illustrated images are rotations of a few selected samples.\n }\n \\label{fig:mnist_nda_tsne2}\n\\end{figure}\n\nIn the next experiment, the same process is applied with a translation-invariant model.\nOne simple way to achieve invariance is to train the model over a data-augmented training set to classify correctly translated digits as well.\nThe augmentation consists of a range of translations applied to all samples which are included into the training set.\nIn this case, all translations in $x$ between $-5$ to $+5$ plus translations in $y$ between $-10$ and $+10$ are generated.\nThe error rate on the original test set increased up to $3.5\\%$, but there is no comparison for this value.\nHowever this model has a strong translation invariance because its error rate is only $8.5\\%$ on the data-augmented test set (it contains extreme translations).\nThe second hypothesis is as follows: As the features contain many different characteristics, it should also detect translations which was heavily present in the training set.\nThe embeddings of this new model can be now compared (see figure~\\ref{fig:mnist_da_tsne}) versus the one above.\nFrom a general perspective, translations are dominating the global structure of the clusters.\nMany centered classes are still in their own clusters but most of the translated digits are grouped disregarding their class.\nHowever the ``translated'' clusters exhibits coherence: sub-clusters represent digit classes but they are heavily overlapping.\nFor example, the right-displaced digits are exclusively appearing in the top-left corner where all digits are present and mostly stacked together in the center.\nThis is actually a general problem with this plot: many clusters appear but their delimitations are fuzzy which suggests it would be hard to quantify translations and digits.\n\n\\begin{figure}[t]\n \\begin{center}\n \\includegraphics[width=\\textwidth]{thesis_figures\/mnist_da_tsne.jpg}\n \\end{center}\n \\caption{The t-SNE representation of the CNN embedding for the data-augmented MNIST test set using the interactive tool.\n A unique color is attributed to each digit class and one for each sample's distortions (as before).\n The illustrated digits were manually picked to represent local trends.\n Global structures are dominated by translations but most of the translated digits are grouped disregarding their class.\n Local structures exhibit partial coherence by packing digit classes together but the overlapping is too important to ignore.\n }\n \\label{fig:mnist_da_tsne}\n\\end{figure}\n\nFrom these two experiments, important knowledge emerges about this classification tasks, the data-augmentations and t-SNE as well.\nManual experiments confirmed that LeNet is sensitive to the employed distortions and can easily misclassify if no form of data-augmentation is used during training.\nAs previously said, nearly no natural distortion is present in the original dataset and this is partly due to the heavy prior processing applied to the MNIST dataset.\nHowever small rotations are still present in MNIST which explains why the new model is less prone to errors on artificially rotated samples.\nData-augmentation is an effective way to reduce the error rate on certain type of distortions but it has a cost.\nIn this case, t-SNE embedding looks ``overcrowded'' and most clusters are not well separated anymore.\nWe think it is due to the important difference of energy in the pixel space, e.g.: the center of mass is translated.\nOne explanation is that the network learned to separate this source of variance and acquired position-specific features to distinguish translated digits which impacts characteristics of the last layer.\nThen t-SNE is impacted by clustering translations first, then digits as its second major structure.\nOne important insight is that t-SNE seem to represent hierarchical structures with decreasing importance thus the most discriminative features are weighted more.\n\nThese results are unexpected: the clusters are not well separated and they are lacking in coherence even though the task require to separate the digits.\nThis experiment was meant to create an invariant model for classification but the representation does not naturally split digits and translations into different axes.\nIt should be noted again that t-SNE is oriented towards visualization and the goals of visualization are not formally and universally well defined.\nThe current formulation of this method is to preserve distances between the features which is not sufficient for the application.\nThere is currently no way to add prior information like supervised methods to guide the optimization to associate particular dimensions to particular variance in the data.\nA possible solution is to modify the objective function of t-SNE but it has the important limitations previously discussed.\nThe lack of end-to-end optimization in this method is also important and this can be solved by using layers handling the dimensionality reducing into a single NN.\n\nIn the following part, results of an alternative method is presented based on end-to-end and supervised training of a single CNN.\n\n\\subsection{Contrastive LeNet (MNIST)}\nIn the two following experiments, Siamese models were trained based on DrLIM settings on both MNIST and NORB so that the results are comparable between the reference paper and the networks with the extension as described below for two-label pairs.\nThe goal of DrLIM is to train a model where points are close together if they are ``visually'' similar without considering distortions depending on the dataset.\nThe implementation of their pairing strategy is as follows.\nFirst they group each sample into a common ``neighborhood'' relation: the 5 most similar ones using the Euclidean distance in pixel space.\nHowever in practice, neighbors with different digit class would appear and DrLIM's paper did not mention if they considered legit or not.\nIn this work, only neighborhood with the same class is used because a manual look unveils that they are in fact quite dissimilar.\nThe sample and its $n$ translations are all paired with both its 5 neighbors and all its $n$ translations (this sums up to $n \\times 5n$ pairs per sample).\nAll other pairs are considered dissimilar: when the two samples are not part of the same common translated neighborhood.\nThis strategy uses the regular contrastive loss function in 1D with a single similarity per pair.\n\nThe objective is comparable in the sense that staying invariant is desirable but only in certain dimensions priorly picked.\nThe new implementation can still quantify distortions by expression them in the other dimensions.\nOne important advantage is to allow more control, especially in how dimensions are used.\nAs said, a single model is trained which is less expensive and it learns reusable features for multiple pairings.\nThe extension of the contrastive loss function can be used with $p$ labels for each pair.\nThe definitions of $p$ and $D$ for the two Siamese experiments are described and justified later.\n\nIn the case of MNIST, a 3-dimensional embedding space is used: where the first two dimensions express neighborhood similarity and the last dimension expresses the distortion similarity.\nThis case only needs two dimensions but to make it comparable to DrLIM's 2D embedding, a 2D neighborhood space will be used as well and distortions are separated into its own space.\nMaking comparisons between 2D space (DrLIM) and 1D neighborhood (this work) would be unfair due to distances increasing very quickly as the number of dimensions increases.\nIn this settings: $M=3$ (embedding dimensions), $p = 2$ (two labels) and $D = \\left\\{ 2, 1 \\right\\}$ (2D + 1D).\nThe network is trained using a pair strategy inspired by DrLIM but with important differences.\nThe first label is called the ``neighborhood'' similarity and considers: a sample with its translations to be similar, the sample with its neighbors also similar if they have the same translations, and the sample with any non-neighbor sample to be dissimilar with or without translation.\nThe second label is called the ``transformation'' similarity and considers: a sample with any other sample with the same translation similar, and any pair without the same translation dissimilar.\nPairs not mentioned in this descriptions above are not included in the datasets.\nMoreover the size and the time for training is reduced with a low impact by randomly taking a subset of dissimilar pairs to balance the label ratio instead of including all of them.\n\nIn the case of NORB, a 3D embedding is used as well.\nThe first two dimensions are allocated for the cyclic azimuth (horizontal angle of the viewpoint) and the last dimension for the non-cyclic elevation (vertical angle).\nDrLIM's work defined similar pairs when the two images are from contiguous elevation or azimuth, this work will continue likewise.\nThe justification of DrLIM's dimension allocation is as follows: the azimuth viewpoint is cyclic and the shape, to represent such a structure without overlapping, is an ellipsis and this requires at least two dimensions.\nThe elevation viewpoint is not cyclic because NORB has only a smaller subset of values, thus only a single dimension is allocated.\nA direct comparison with DrLIM's embedding is possible in this case as the models work on the same problem.\nOnce again: $M=3$ (embedding dimensions), $p = 2$ (two labels) and $D = \\left\\{ 2, 1 \\right\\}$ (2D + 1D).\nThe network is trained based on DrLIM's pairing strategy where the azimuth and elevation are separated into two labels (instead of having them merged).\n\nThe first dataset is a subset of MNIST containing only 4s and 9s digits which are used to derive the training and testing set based on the 2D pairing strategy.\nTherefore the resulting dataset is composed of each sample plus its translations (\u00b13, \u00b16 pixels) which is then paired.\nThe models are trained until the loss on the respective test set has attained a local minima, which takes around 10 minutes.\nAfter around $100$ iterations, both models have reasonably converged to a stable solution (see figure~\\ref{fig:mnist_cl2d_loss}).\nHowever, the new model has reached a lower solution than DrLIM: comparing them directly is not possible because the two datasets are different but the difference in the loss in this model is more important and converge as quickly.\nIntuitively this means that the new problem formulation is easier to solve and the model finds a ``better'' solution, in the sense it could satisfy more the new constraints described by the loss.\nThe resulting embeddings are shown in figure ~\\ref{fig:mnist_cl_drlim} for DrLIM and figures ~\\ref{fig:mnist_cl2d_1} and ~\\ref{fig:mnist_cl2d_2} for this model.\nThe DrLIM embedding reproduced in this work is very similar to the expected result: a coherent grouping based on translation-invariant similarity but the separation between digits is more pronounced.\nThe DrLIM embedding can be considered as a top-down projection of the new 3D embedding where the additional vertical dimension quantify the displacement.\nThe different intensity of translations are well separated into their own cluster as demonstrated by a meticulous manual inspection and remarkably they are ordered vertically by their intensity.\nOnce again, the space inside clusters is well organized by digit features like DrLIM.\n\n\\begin{figure*}[h]\n \\centering\n \\begin{subfigure}{0.45\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/final_loss_testset_2bv7.jpg}\n \n \\end{subfigure}\n \\begin{subfigure}{0.45\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/final_loss_testset_3Dc3.jpg}\n \n \\end{subfigure}\n \\caption{The model losses for 500 iterations of DrLIM and the new model on MNIST.\n The two losses cannot be compared directly but the speed of convergence and the final difference are key.\n Both models show sign of convergence after $100$ iterations but the new model has reached a simpler solution due to the different pairing strategy chosen in this work.\n An important point to emphasis is that DrLIM is in 2D whereas the new model is in 3D.\n }\n \\label{fig:mnist_cl2d_loss}\n\\end{figure*}\n\nIn order to compare the two methods, quantitative measures are presented where the contrastive loss is computed on the DrLIM paired test set as described in the methodology.\nThe evolution of this measure during training is plotted in figure~\\ref{fig:loss_mnist_test_common} and the final losses after 10'000 iterations are: $0.160023$ (DrLIM) and 0.145418 (the new model).\nThe two loss series shown in this figure are statistically indistinguishable according to a t-test with a null hypothesis of $\\alpha = 0.05$.\nTherefore it can be concluded that the new model learns an equivalent representation in its 2D digit-projection to DrLIM with a slightly different formulation and it can expresses distortions without loosing information in its 3rd dimension at the same time.\n\n\\begin{figure}[h]\n \\centering\n \\includegraphics{thesis_figures\/mnist_cl_drlim.jpg}\n \\caption{DrLIM embedding for the MNIST translation-augmented test set where some random samples are shown for illustration.\n This figure is very similar to the own present in DrLIM paper and also exhibits coherent groupings based on translation-agnostic similarity.\n }\n \\label{fig:mnist_cl_drlim}\n\\end{figure}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/mnist_cl2d.jpg}\n \\caption{The new model embedding on the MNIST translation-augmented test set projected into its last two dimensions.\n Two samples of different class were picked at random and their distortions are marked as triangles in the figure.\n This 3D embedding has an additional vertical dimension compared to DrLIM that quantifies the displacement and the other top-down projection of this embedding would give an identical plot as above for DrLIM.\n Clusters are formed for each translation intensity and each digit class in a well separated manner and remarkably they are logically ordered.\n }\n \\label{fig:mnist_cl2d_1}\n\\end{figure}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/mnist_cl2d2.jpg}\n \\caption{The new model embedding on the MNIST translation-augmented test set projected into its last two dimensions.\n General view where many random sample's distortions are displayed to represent the local tendencies.\n The space inside each cluster appears to be also coherent.\n }\n \\label{fig:mnist_cl2d_2}\n\\end{figure}\n\n\\begin{figure}[h]\n \\begin{center}\n \\includegraphics{thesis_figures\/final_loss_test2bv7.jpg}\n \\end{center}\n \\caption{Comparison of quantitative measures between DrLIM and the new model.\n The measures are taken during training iterations over the translation-augmented MNIST test set.\n The two loss series are statistically indistinguishable and it can be concluded that both models learned an equivalent representation whereas the new model can also expresses distortions without noticeable impact.\n }\n \\label{fig:loss_mnist_test_common}\n\\end{figure}\n\nThe new model was tested on a data-augmentation of MNIST based on rotations as well to show it works on non-linear deformations as well.\nClusters are still arranged in a linear fashion although rotations are non-linear but the cluster separations are smaller, see figure~\\ref{fig:mnist_cl2d_rotate}.\nThe results are similar to the previous experiment and the figure is provided for demonstration as it will not be discussed further.\n\n\\begin{figure}[h]\n \\begin{center}\n \\includegraphics{thesis_figures\/mnist_cl2d_rotate.jpg}\n \\end{center}\n \\caption{The new model embedding on the MNIST rotation-augmented test set.\n Clusters are remarkably arranged in a linear fashion although rotations are non-linear.\n }\n \\label{fig:mnist_cl2d_rotate}\n\\end{figure}\n\n\\subsection{Contrastive LeNet (NORB)}\nOnce again, the network is trained following the new conventions, this time on a subset of the NORB dataset with a single plane.\nAs previously described, this subset is composed of all viewpoint of a single plane which is later called the 1-plane NORB dataset.\nThis subset is split into two parts: 660 training samples and 312 test samples.\nThese two sets are both separately paired according to the strategy described in the methodology to form the training and testing sets.\nThe models are trained until the loss on the respective test set has attained a reasonable local minima, which takes around 10 minutes.\nAfter $2'000$ iterations, both models have reasonably converged to a stable solution (see figure~\\ref{fig:norb_cl2d_loss}).\nTo get the most convincing results, a margin $m=1$ was picked for DrLIM and $m=10$ for the new model, therefore their loss on the test set should not be compared directly, instead the quantitative measures below should be used instead.\nHowever, it can seen that DrLIM in figure \\ref{fig:norb_drlim_embedding} has an easier formulation this time (smaller margin) and the embedding is very similar to the one presented in the reference paper.\nThe most important features of the embedding to represent NORB are: the cyclic structures for the azimuth angle, the continuity along the two axes disregarding lighting illumination and the sharp separation along the axes.\nIndeed, this embedding is a thin cylinder whose long axis represent the elevation and the radius is the azimuth angle.\nHowever, the cylinder is not perfect: multiples parts are more flat along the elevation axis (like a plane) and the radius is fuzzier than presented in the reference paper.\nThe major difference with the new solution, as shown in figures \\ref{fig:norb_cl2d_embedding_1} and \\ref{fig:norb_cl2d_embedding_2}, is the alignment along the axes, a more stable radius and a better separation inside the cylinder.\nThe predictability of the solution is improved by these properties compared to DrLIM.\n\n\\begin{figure*}[h]\n \\centering\n \\begin{subfigure}{0.45\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/final_loss_testset_3d.jpg}\n \n \\end{subfigure}\n \\begin{subfigure}{0.45\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/final_loss_testset_2D1g2.jpg}\n \n \\end{subfigure}\n \\caption{The model losses for 10'000 iterations of DrLIM and the new model $m=10$ on NORB.\n After $2'000$ iterations, both models have reasonably converged to a stable solution.\n DrLIM model has a very low loss and the new model faces problems to correctly represent the azimuth dimensions.\n }\n \\label{fig:norb_cl2d_loss}\n\\end{figure*}\n\n\\begin{figure*}[h]\n \\centering\n \\begin{subfigure}{0.65\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/norb_drlim1.jpg}\n \\end{subfigure}\n \\begin{subfigure}{0.65\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/norb_drlim2.jpg}\n \\end{subfigure}\n \\caption{The DrLIM embedding in 3D on the NORB 1-plane complete dataset after $100'000$ iterations.\n The colors indicate the elevation angle.\n The result found that a thin cylinder whose long axis represent the elevation and the radius is the azimuth angle.\n Such results are remarkable because the model found this shape on its own.\n However it is not perfect, some parts are flat along the elevation axis and the radius is fuzzy.\n }\n \\label{fig:norb_drlim_embedding}\n\\end{figure*}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/norb_cl2d.jpg}\n \\caption{The new model embedding on the NORB 1-plane complete after $100'000$ iterations.\n 2D projection of the first two axes.\n Colors indicate the azimuth angle.\n The major difference with DrLIM is the alignment along the plot axes and a better separation inside the cylinder.\n The mapping is also invariant to the lighting conditions.\n }\n \\label{fig:norb_cl2d_embedding_1}\n\\end{figure}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\textwidth]{thesis_figures\/norb_cl2d2.jpg}\n \\caption{The new model embedding on the NORB 1-plane complete after $100'000$ iterations.\n 2D projection of the last two axes.\n Colors indicate the elevation angle.\n The relation between elevation and azimuth is well organized in the embedding.\n }\n \\label{fig:norb_cl2d_embedding_2}\n\\end{figure}\n\nTo compare the two models, the contrastive loss was computed on the test set of the 1-plane NORB with the same parameters ($m=1$) as with MNIST.\nThe loss evolution is shown in figure \\ref{fig:loss_norb_test_common} where a version of the new model was also added with $m=1$.\nThe embedding of this model has many aspects of the $m=10$ model and the main difference is instead of having a round shape for the azimuth, it is similar to a heart which is also cyclic but less desirable.\nA major difference can be found with the $m=10$ model compared to both DrLIM and the $m=1$ model.\nBy definition of the loss function, the model is not penalized to put dissimilar pairs further than the margin, therefore it means the $m=10$ model has decided it is better to put similar neighbors further than the other two models.\nMoreover, the loss suggests that the $m=1$ model is superior but $m=10$ looks better, although it has a higher loss.\nUnfortunately, the visual quality is not reflected by the loss function in this case.\n\n\\begin{figure}[h]\n \\begin{center}\n \\includegraphics{thesis_figures\/final_loss_testv3.jpg}\n \\end{center}\n \n \\caption{Quantitative measures to compare between DrLIM and the new model during training iterations on the 1-plane NORB test set.\n The $m=10$ model put less importance to the proximity of similar neighbors than the other two models.\n This plot suggests that the $m=1$ model is better but $m=10$ is qualitatively better, although it has a higher loss.\n The loss function does not seem to reflect the visual quality in this case.\n }\n \\label{fig:loss_norb_test_common}\n\\end{figure}\n\n\\subsection{Remarks}\nTo conclude this chapter, a few remarks about the general results and some difficulties during the experiments should be noted.\nThe primary goal of the experiments using contrastive losses is to show more predictability can be expected when given more control, over each dimension and over the clustering, with a simple extension of the original formulation.\nOur qualitative judgement is positive about the success of the experiments.\nThe first experiment demonstrated that models based on DrLIM can include more informations concerning the distortions with a minor impact over the original feature space.\nIn our second experiment it was shown that these models can also be guided to represent the NORB space by using dimensions in a certain way with minimal efforts.\nMoreover some difficulties were faced to reproduce DrLIM, especially on NORB due to the lack of directives for parameters and due to the initial randomness of model initialization which changes the final embedding consequently.\nDuring these experiments, one if not the most important factor for training a Siamese network comes from the pairing strategy.\nOn this regard, many small factors can have a big impact over the final results such as: the ratio of similar\/dissimilar pairs, their ordering (shuffled or not) and the addition\/removal of certain pairs.\nThe new models are easier to train because their convergence to the above results required nearly no effort: the default weights and margin were safely used and the models converged to the expected solutions unlike DrLIM in our experiments.\n\nHowever there is still room for improvements, especially for the quantitative measures which does not always reflect the structure quality quantified by a visual inspection.\nIt is important to keep in mind that the visual quality is not necessarily representative of its real usability as features for further layers in a NN.\nNonetheless, it shows that the new model can create quite interesting and human-friendly representation by simple methods.\n\n\n\\chapter{Conclusion}\n\\label{chap:conclusion}\n\nNeural Networks are widely used in many applications of Computer Vision and their usage is growing every day.\nThe recent usage of NN in dimensionality reduction is an important step to create better embeddings.\nIndeed they learn powerful mappings whose relation seems more deeply tied to the dataset.\nIn particular they can learn invariance to non-linear distortions while preserving relations between the other distortions.\nIn the experiments, NN were found to be much faster and reliable for our application than even the state-of-the-art methods such as t-SNE and the only prior-knowledge is the labels or the viewpoint angles.\nMoreover, many tricks developed during the past years of researches on NN have helped in this task: CNN architectures for images, Siamese networks and the contrastive loss function to train the new specialized networks.\nThanks to these findings and the current advance in Deep Learning, this work benefited from their improvement in the automatic derivation of better features to represent the data.\n\nIn this thesis, the performance of t-SNE on data-augmented datasets were presented with its advantages and shortcomings due to its unsupervised nature.\nAnother solution was then chosen based on NN because they have many advantages, in particular that they can represent very complex mappings.\nTo achieve more predictable embeddings, an extension to DrLIM was designed in this work.\nThen, several NN models were experimented on different datasets to compare the state-of-the-art with this new solution which demonstrated similar results, while being more general.\nIn particular, with the new method it is possible to quantify distortions in a consistent way, by just looking at the components of the learned feature representation.\nThe qualitative results were presented using plots which were analyzed to understand the models.\nBut quantitative measures were also introduced based on the loss function to compare more objectively with the previous work.\nAs said, there is still room for improvements in this regard as current measures does not seem completely reliable to measure the quality.\nMoreover, a better metric to give a more accurate score was a subject of our research to compare clustering and the predictability of the new models.\nHowever, such a solution is non-trivial to find and this would require comparing the new models with a simple baseline, which, to the best of our knowledge, does not exist at this day.\n\nThe current direction of dimensionality reduction started with DrLIM looks very similar to regression problems and this is also the case for the proposed generalization for N-dimensions.\nHowever, regression is more constrained than the current formulation because: (1) in this work, clusters are not constrained to be at a particular position, but only their relative positions matter whereas a regression based approach would set them in absolute positions; (2) the margin formulation allows to put extra gap to make space for clusters without cost whereas regression penalizes in every directions.\nThus, the current formulation allows more freedom in placement than regression and a possibility is that regression would perform worse due to the addition of these artificial constraints but the opposite may be true.\nFuture work could compare the new models with regression models to see if further restriction would actually help instead.\n\nAnother aspect that needs to be explored in future research is to precisely test the predictability of the embeddings.\nFor instance, the learned features could be used in a larger classification network to predict the distortion intensity and the digit class in MNIST.\nAnother useful contribution would find a measure that quantifies the ``predictability'' of the new model for certain application.\nOne simple example would be to use the accuracy of the classification model above but it would not represent the effective usability in practical applications which combine both distortions and application-specific features.\nA better experiment is to train a regression model, for instance, to predict the distorted inputs' features given a single input's features.\nThe accuracy of such a model would serve as a score for predictability.\n\n\nAn application is possible in Biomedical Imaging for classification tasks (e.g.: segmentation) which need to detect orientation-dependent structures in images.\nThe classification of such images begins by a normalization step with a rotation and a translation to normalize the structure of interest into a centered slice.\nThis slice is used to extract orientation-dependant features which are then fed into a classifier like a CNN.\nIt is important to understand that the normalization step to extract features is costly especially for 3D images and even specialized FPGAs were designed to compute 3D expensive rotations.\nThe presented contribution allows theoretically to skip this normalization step to directly create enhanced features.\nIn this case, the model learns features predictable with respect to rotations and the features for other distortions are easily derived and classified.\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe Galerkin-Finite Element (FE) method applied to partial differential equations (PDEs) with advection terms dominating over diffusion ones may suffer of numerical instability \\cite{hughes2012finite,Quarteroni_2017}. These numerical instabilities cause the numerical solution to exhibit oscillations that increase in amplitude with a local increment of the transport dominance over diffusion, i.e. as soon as the local P\\`eclet number becomes larger than one. In order to eliminate (or at least mitigate) numerical instabilities, a general employed strategy is to consider the generalized Galerkin method, i.e. the Galerkin method with additional stabilization terms \\cite{Quarteroni_2017}. Examples of stabilization methods to reduce numerical oscillations in the advection dominated regimes are for instance the upwind and streamline-diffusion method, both methods that are not strongly consistent. Instead, examples of strongly consistent methods are the Galerkin-Least-Squares, the Streamline Upwind Petrov-Galerkin (SUPG), and the Douglas-Wang methods \\cite{Roos_1996,Brooks_1982,Quarteroni_2017}. In particular, for these strongly consistent stabilization methods, the formulation depends on the residual of the PDE (in strong formulation), other than on a parameter -- called \\textit{stabilization parameter} -- whose definition is crucial for the success of the stabilization strategies. Determining the values of such stabilization parameter is not straightforward, especially for 2D and 3D problems. In addition, a universal formulation of such parameter is lacking, especially as it is strongly dependent on data of the model and the numerical setting used for the FE approximation of the PDEs, e.g. low and high order FE methods, spectral element methods, isogeometric methods, etc. \\cite{Schwab, spectralbook, cottrell2009isogeometric}. Some of the formulations for the stabilization parameter have been derived from analytical considerations on the differential problem in 1D, others are suitable only for a specific numerical approximation method, while the most comprehensive ones incorporate some (empirical) dependence on numerical setting, as e.g. the order of the FE \\cite{Bochev,Brooks_1982, CODINA200061,CODINA2008264,CODINA20072413,franca1992stabilized,galeao2004finite,REBOLLO2015406,ScovazziRossi,SCOVAZZI2007923,tezduyar2000finite}.\n\nIn this work, we propose using Machine Learning (ML) \\cite{mitchell1997machine,Yegnanarayana,goodfellow2016deep} to make computers learn autonomously the values of the stabilization parameter for the FE approximation of advection dominated PDEs. Artificial Neural Newtorks (ANNs) \\cite{university1988continuous,Kutyniok_2021}, are widely popular in ML and Deep Learning for a wide array of applications: they are indeed very versatile tools that are increasingly finding their way in scientific computing \\cite{Neittaanm\u00e4ki202227}, especially in the context of numerical approximation of PDEs \\cite{Mishra_2018}. For instance, as substitute to standard numerical methods, ANNs can be employed as meshless methods in Physics Informed Neural Networks (PINNs) to directly approximate the solution of the PDE as it is trained by minimizing the (strong) residual of the PDE \\cite{raissi2017machine,raissi2018hidden,raissi2019physics}. ANNs are also largely employed in a data-driven fashion in the context of model order reduction for parametric PDEs \\cite{fresca2021comprehensive, regazzoni2019machine, hesthaven2018non, guo2019data, zancanaro2021hybrid} and to enhance the stability properties of numerical methods for PDEs \\cite{discacciati2020controlling}. In fluid dynamics modelling, ANNs are massively adopted for flow features extractions, modelling, optimization and control. For flow features extractions through clustering and classification to classify wake topologies \\cite{annurev_wake}; for modeling fluid dynamics by reconstructing specific flows such as the near wall field in a turbulent flow \\cite{annurev_turbulent_wall} and for flow optimization \\cite{annurev_gliding}, and control for aerodynamics applications \\cite{annurev_control}. ANNs are also used in a data-driven framework as a manner for providing alternative closure models for RANS' stress tensor \\cite{annurev_rans}, for sub-grid scale models in LES turbulence models \\cite{janssensadvancing, janssenthesis, xie2019artificial, zhou2019subgrid, xie2020modeling}, or for model learning input-output relationships in complex physical processes \\cite{REGAZZONI2020113268}. \n\nIn this work, we use ANNs to learn the optimal stabilization parameter in advection dominated PDEs that are discretized by means of FE method: the goal is to enhance the accuracy of the SUPG FE method by optimally selecting the stabilization parameter under different data of the PDE and numerical settings of the FE method. We found that the proposed ANN-enhanced stabilization method allows to improve accuracy and stabilization properties of the numerical solution compared to those results obtained by analytical expressions of the stabilization parameter. The numerical results obtained shed light also on the possibility to apply the presented strategy to learn closure laws for stabilization and turbulence models of fluid dynamics, as for instance to learn the stabilization parameters in the Variational Multiscale--LES model \\cite{forti2015semi, bazilevs2007variational}.\n\nThis work is organized as follows: in Section~\\ref{section:advection-diffusion}, we recall the SUPG stabilization method for advection-diffusion equations; in Section~\\ref{sec:strategy}, we present our numerical strategy to compute an optimal SUPG stabilization parameter through a feed-forward fully connected ANN. In Section~\\ref{sec:validation} we validate our method by comparing the ANN results with those obtained with the 1D advection-diffusion problem from which the expression of the theoretical stabilization parameter has been derived. In Section~\\ref{sec:results} we first show the ANN's training and we present our numerical results on the 2D advection-diffusion problem used for training and we finally generalize our findings using the ANN's prediction on a different advection-diffusion problem. Finally, in Section~\\ref{sec:conclusions} we draw our conclusions highlighting possible future developments.\n\n\\section{The SUPG method for advection-diffusion problems}\n\\label{section:advection-diffusion}\nWe briefly recall the advection-diffusion differential problem and the SUPG stabilization method for the advection dominated regime.\n\nLet $\\Omega \\in \\mathbb{R}^d, \\; d = 1, 2, 3$ be the physical domain with $\\partial \\Omega$ being its boundary. We consider the following problem in the unknown function $u$:\n\\begin{equation}\n \\label{eq:transport_diffusion}\n \\left\\{ \\; \\begin{aligned}\n - \\nabla \\cdot (\\mu \\nabla u) + \\boldsymbol{\\beta} \\cdot \\nabla u &= f & \\qquad \\text{in} \\; \\Omega, \\\\\n u &= g & \\qquad \\text{on} \\; \\partial \\Omega,\n \\end{aligned} \\right.\n\\end{equation}\nwhere $\\mu$, $\\boldsymbol{\\beta}$, and $f$ are assigned functions or constants, with $\\mu \\in L^\\infty(\\Omega)$, $\\bm \\beta \\in [L^\\infty(\\Omega)]^d$, with $\\nabla \\cdot \\bm \\beta \\in L^2(\\Omega)$, and $f \\in L^2(\\Omega)$. \nThe Dirichlet datum on the boundary is $g\\in H^{1\/2}(\\partial \\Omega)$.\n\nLet $V_g = \\{ v \\in H^1(\\Omega): \\, v|_{\\partial \\Omega} = g \\}$ and $V_0 = H_0^1(\\Omega)$, the weak formulation of Eq.~\\eqref{eq:transport_diffusion} reads\n\\begin{equation}\n \\label{eq:weak_bilinear}\n \\text{find} \\; u \\in V_g \\ : \\ a(u,v) = F(v) \\quad \\text{for all } v \\in V_0.\n\\end{equation}\nwith the bilinear form $a(u,v)$ and the linear functional $F(v)$ respectively defined as:\n\\begin{equation*}\n \\label{eq:bilinearForm}\n \\begin{aligned}\n a(u,v) &:= \\int_\\Omega \\mu \\, \\nabla u \\cdot \\nabla v \\, d\\Omega + \\int_\\Omega v \\, \\boldsymbol{\\beta} \\cdot \\nabla u \\, d\\Omega, \\\\\n F(v) &:= \\int_\\Omega f \\, v \\, d\\Omega.\n \\end{aligned}\n\\end{equation*}\nWe consider a family of function spaces $V_h \\subset V$ (either for $V_g$ and $V_0$) dependent on a parameter $h$ such that $\\dim(V_h) = N_h < \\infty$. Let $X_r^h = \\{ v^h \\in C^0(\\overline{ \\Omega}):\\, v^h|_K \\in \\mathbb{P}_r,\\, \\, \\text{for all } \\, K \\in \\mathcal T_h\\}$ be the function space of the FE discretization with piecewise Lagrange polynomials of degree $r \\geq 1$, $\\mathcal T_h$ a triangulation of $\\Omega$ and $h$ the characteristic size of the mesh, comprised of elements $K \\in \\mathcal T_h$. By setting $V_h = \\mathring{X}_h^r \\bigcap V_0$, the Galerkin FE method applied to Eq.~\\eqref{eq:bilinearForm} reads\n\\begin{equation}\n \\label{eq:galerkin_1}\n \\text{find} \\; u_h \\in V_{g,h} \\ : \\ a(u_h,v_h) = F(v_h) \\quad \\text{for all } v_h \\in V_{h},\n\\end{equation}\nwhere $V_{g,h}= \\mathring{X}_h^r \\bigcap V_g$.\nThe standard Galerkin-FE method in Eq.~\\eqref{eq:galerkin_1}, can generate numerical oscillations on $u_h$ if the problem is dominated by the advection term. In particular, these numerical instabilities can arise if the local P\\'eclet number is $\\Peclet_h > 1$, where\n\\begin{equation}\n \\label{eq:peclet}\n \\Peclet_h = \\frac{|\\boldsymbol{\\beta}| h}{2 \\mu}.\n\\end{equation}\nThe {generalized Galerkin} method (\\cite{QV94}) allows eliminating or mitigating numerical oscillations by adding stabilization terms to the standard Galerkin formulation as\n\\begin{equation}\n \\label{eq:generalized_galerkin}\n \\text{find} \\; u_h \\in V_{g,h} \\ : \\ a(u_h,v_h) + b_h(u_h,v_h) = F(v_h) \\quad \\text{for all } v_h \\in V_{h}.\n\\end{equation}\nIn the SUPG method, the additional stabilization term reads:\n\\begin{equation}\n \\label{eq:supg}\n b_h(u_h,v_h) = \\int_\\Omega R(u_h) \\; \\tau \\; \\frac{1}{2} \\left( \\nabla \\cdot (\\boldsymbol{\\beta} v_h) + \\boldsymbol{\\beta} \\cdot \\nabla v_h \\right) \\, d\\Omega, \n\\end{equation}\nwhere $R(u_h)$ stands as the residual in strong formulation of Eq.~\\eqref{eq:transport_diffusion}, which is defined as:\n\\begin{equation*}\n \n R(u_h) := - \\nabla \\cdot (\\mu \\nabla u_h) + \\boldsymbol{\\beta} \\cdot \\nabla u_h - f.\n\\end{equation*}\nThe term $\\tau$, appearing in Eq.~\\eqref{eq:supg}, is the \\textit{stabilization parameter}, which is the focus of this work. A universal and optimal definition of $\\tau$ in terms of the problem data, numerical settings like the mesh size and FE degree is lacking. An extensive review of stabilization parameters for the SUPG method is reported in \\cite{john2007spurious}. The most common choices for $\\tau$ come from an analytic derivation made for the advection-diffusion problem in 1D with $f=0$ and approximated by means of linear finite elements, which reads:\n\\begin{equation}\n \\label{eq:tau_theory}\n \\widetilde{\\tau}_1 = \\frac{h}{2 |\\boldsymbol{\\beta}|} \\, \\xi(\\Peclet_h),\n\\end{equation}\nwhere $\\xi(\\theta)$ is the upwind function:\n\\begin{equation*}\n \\xi(\\theta) = \\coth({\\theta}) - \\frac{1}{\\theta}, \\quad \\theta>0.\n\\end{equation*}\nThe stabilization parameter $\\widetilde{\\tau}_1$ is generally defined locally, i.e. mesh element by element. If a uniform mesh is used, as we consider in this paper, then the value of $h$ is uniform over $\\mathcal T_h$; if in addition $\\boldsymbol{\\beta}$ is a constant, then this implies that $\\tau$ is uniform over $\\mathcal T_h$. The choice of $\\tau$ made in Eq.~\\eqref{eq:tau_theory} represents an optimal choice of the stabilization parameter as it yields a nodally exact numerical solution for the 1D advection-diffusion problem if $f = 0$ and the FE polynomial order is $r = 1$ \\cite{Quarteroni_2017, galeao2004finite}. \nThus, the stabilization parameter of Eq.~\\eqref{eq:tau_theory} may not be fully effective to provide the optimal stabilization for a general advection-diffusion problem, e.g. to guarantee a nodally exact solution in 2D\/3D or when using FE degrees larger than $r=1$. \nA commonly used generalization of the formula of Eq.~\\eqref{eq:tau_theory} to higher FE degrees ($r>1$) is presented in \\cite{galeao2004finite} and reads:\n\\begin{equation}\n \\label{eq:tau_theory_r}\n \\widetilde{\\tau}_r = \\frac{h}{2 |\\boldsymbol{\\beta}| r} \\, \\xi \\left( \\frac{\\Peclet_h}{r} \\right).\n\\end{equation}\nDifferently from Eq. \\eqref{eq:tau_theory}, the stabilization parameter $\\widetilde{\\tau}_r$ in Eq.~\\eqref{eq:tau_theory_r} takes into account the contribute of higher polynomial degrees $r$. Still, this formula is not optimal as it does not guarantee a nodally exact numerical solution. \nOur goal is to find a general and optimal expression of the stabilization parameter holding for advection-diffusion problems and FE approximations of degree $r\\geq 1$ to be dependent on: the dimension $d$, the FE degree $r$, the mesh size $h$, the forcing term $f$, the diffusion coefficient $\\mu$ and the transport coefficient $\\beta$.\n\n\\section{ANN-based approach for determining the optimal stabilization parameter}\n\\label{sec:strategy}\n\nWe present our approach to determine the optimal stabilization parameter $\\tau$ for the SUPG stabilization method by using an ANN. We consider as {features} (inputs) of our ANN: the degree of finite element $r$, the mesh size $h$ and the global P\\'eclet number $\\mathbb Pe_g := | \\boldsymbol{\\beta}| L \/ (2\\mu)$ of the advection-diffusion problem, where $L$ is the characteristic length of the problem. As we consider $\\boldsymbol{\\beta}$ to be uniform and fixed, using $\\Peclet_g$ as input corresponds to varying the value of the diffusion coefficient $\\mu$. The features (input) of the ANN read:\n\\begin{equation}\n\\mathbf x^{(i)} = \\left [ r, \\, h,\\, \\Peclet_g \\right ]; \n\\label{input}\n\\end{equation}\nthe \\textit{target} (output) of interest is the optimal SUPG stabilization parameter that we denote with $\\tau^*$:\n\\begin{equation}\n\\mathbf y^{(i)} = \\left [ \\tau^* \\right ].\n\\label{output}\n\\end{equation}\nThe first step consist in generating the dataset, i.e. pairs of inputs and outputs to be used for training the ANN (\\textit{data generation} step). This consists in choosing repeatedly and randomly values of the features $\\mathbf x^{(i)}$ in given ranges. These features are used to feed an \\textit{optimization problem} that, through an optimizer and a suitable error measure, provide an optimal stabilization parameter $\\mathbf y^{(i)}$, i.e. the target of the given feature. Such optimization problem considers as error measure $E(\\tau)$: the mismatch between the numerical solution $u_h$ and the exact one $u_\\mathrm{ex}$ over the nodes of the FE mesh. $E(\\tau)$ reads as:\n\\begin{equation}\n \\label{eq:td_error_definition}\n E(\\tau) = \\sum_{k=1}^{K_h} | \\, e(\\bm x_k, \\tau) \\, |, \\qquad e(\\bm x_k, \\tau) = u_h(\\bm x_k;\\tau) - u(\\bm x_k).\n\\end{equation}\nbeing $\\bm x_k$ the $k$-th node of the FE mesh $\\mathcal T_h$ and $K_h$ its number. To find the minimum of $E(\\tau)$ for different problem configurations we solve, for each instance of the input parameters $\\mathbf x^{(i)}$, the following optimization problem:\n\\begin{equation}\n \\label{eq:td_findtau}\n \\text{find} \\; \\tau^* \\ : \\quad \\min_\\tau \\, E(\\tau).\n\\end{equation}\nThus, the dataset generated consists of $m$ pairs of inputs-outputs $(\\mathbf x^{(i)}, \\mathbf y^{(i)})$, with $i=1, \\dots, m$. The latter is used for \\textit{training} the ANN. The latter will be then used to predict the optimal stabilization parameter $\\mathbf y^{(j)}= \\tau_\\mathrm{ANN}$ to be used for the FE approximation of the advection-diffusion problem in the settings provided by $\\mathbf x^{(j)}$.\n\n\\begin{figure}[t!]\n\\centering\n\\includegraphics[width=\\singlefigureXL]{figures\/ad\/sketch_strategy_az.png}\n\\caption{Representation of the strategy used for learning the the relation among $\\tau$ and the advection-diffusion parameters and numerical settings}\n\\label{fig:td_scheme}\n\\end{figure}\n\n\nWe report in Figure~\\ref{fig:td_scheme} a sketch of the procedure used to build the ANN for the prediction of the optimal stabilization parameter for any new feature $\\mathbf x^{(j)}$.\n\n\n\n\\section{Numerical validation}\n\\label{sec:validation}\nIn this section, we validate the proposed numerical strategy by means of a 1D advection-diffusion problem from which the expression of $\\widetilde{\\tau}_1$ in Eq.~\\eqref{eq:tau_theory} has been derived. In particular, we consider the advection-diffusion problem in Eq.~\\eqref{eq:transport_diffusion} with $\\Omega = (0, 1)$, $f = 0$ and with Dirichlet BCs $ u = 0$ on $x = 0$ and $ u = 1$ on $x = 1$. It admits the following exact solution:\n\\begin{equation*}\n \n u(x) = \\frac{\\exp{\\left( \\frac{\\beta}{\\mu} x \\right)} - 1}{\\exp{\\left( \\frac{\\beta}{\\mu} \\right)} - 1}.\n\\end{equation*}\nWe recall that the stabilization parameter $\\widetilde{\\tau}_1$ of Eq.~\\eqref{eq:tau_theory} yields a nodally exact numerical solution if Eq.~\\eqref{eq:transport_diffusion} is solved by linear FE ($r = 1$).\n\n\n\n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/minimumErr_Pe12.5_r1-eps-converted-to.pdf}\n \\caption{$r = 1$}\n \\label{fig:td_minimumErr_r1}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/minimumErr_Pe12.5_r3-eps-converted-to.pdf}\n \\caption{$r = 3$}\n \\label{fig:td_minimumErr_r3}\n \\end{subfigure}\n \\caption{Error (\\ref{eq:td_error_definition}) of numerical solutions with SUPG method vs. the stabilization parameter $\\tau$ for the 1D problem in Section~\\ref{sec:validation} with $h = 1\/20$ and $\\Peclet_h = 12.5$; comparison between the theoretical value $\\widetilde{\\tau}_r$ of Eq.~\\eqref{eq:tau_theory_r} and the optimal one $\\tau^*$}\n \\label{fig:td_minimumErr}\n\\end{figure}\n\nWe plot in Figure~\\ref{fig:td_minimumErr} the error measure $E(\\tau)$ against the value of the stabilization parameter $\\tau$, with $h = 1\/20$, $\\Peclet_h = 12.5$ and by using $r = 1$ (Figure \\ref{fig:td_minimumErr_r1}) and $r = 3$ (Figure \\ref{fig:td_minimumErr_r3}). We observe that $E(\\tau)$ shows a minimum in $\\tau^*$, thus suggesting the possibility to use an optimization algorithm to solve the problem. Specifically, we employ the L-BFGS-B optimization algorithm from {\\tt SciPy}, an open source library for {\\tt Python} \\cite{scipy}. Instead, the advection-diffusion problem has been solved using the FE open source library {\\tt FEniCS} \\cite{fenics}, by applying the SUPG method on a uniform mesh.\nParticularly, the optimal value $\\tau^*$ found for the case $r=1$ corresponds to the one provided by the theory in Eq.~\\eqref{eq:tau_theory}. For the case $r=3$, we observe a optimal value of $\\tau^*$ for which the error is minimized: in this case, it does not corresponds to the stabilization parameter $\\widetilde{\\tau}_r$ provided by the theory in Eq.~\\eqref{eq:tau_theory_r}: as a matter of fact, the latter arises from empirical considerations to extend the parameter $\\widetilde{\\tau}_1$ in Eq.~\\eqref{eq:tau_theory} to polynomials degrees $r>1$. However, this does not ensure a nodally exact numerical solution. \n\nWe solve the optimization problem for different values of the local P\\'eclet number $1 \\leq \\Peclet_h \\leq 250$ and we plot in Figure~\\ref{fig:td_1d_comparison} the optimal stabilization parameter $\\tau^*$ against the local P\\'eclet number for $r = 1, 2$, and $3$.\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/1D_comparison_r1-eps-converted-to.pdf}\n \\caption{$r = 1$}\n \\label{fig:td_1d_comparison_r1}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/1D_comparison_r2-eps-converted-to.pdf}\n \\caption{$r = 2$}\n \\label{fig:td_1d_comparison_r2}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/1D_comparison_r3-eps-converted-to.pdf}\n \\caption{$r = 3$}\n \\label{fig:td_1d_comparison_r3}\n \\end{subfigure}\n \\caption{Comparison of the optimal stabilization parameter $\\tau^*$ (full line) and the theoretical one $\\widetilde{\\tau}_r$ of Eq.~(\\ref{eq:tau_theory_r}) (dashed line) against $\\Peclet_h$, with $h = 1\/20$. Results are referred to the 1D advection-diffusion problem described Section~\\ref{sec:validation}}\n \\label{fig:td_1d_comparison}\n\\end{figure}\n\nFigure~\\ref{fig:td_1d_comparison_r1} shows that, by employing a linear FE space $r=1$, the optimal stabilization parameter $\\tau^*$ obtained by minimizing $E(\\tau)$ exactly matches the theoretical one $\\widetilde{\\tau}_1$ for every value of $\\Peclet_h$, thus confirming the findings of Figure~\\ref{fig:td_minimumErr_r1}. This also serves as validation of the optimization procedure that we proposed. By recalling that $\\widetilde{\\tau}_1$ of Eq.~\\eqref{eq:tau_theory} guarantees the numerical solution to be exact at nodes for this particular advection-diffusion problem and $r=1$, we can infer that the optimization procedure is meaningful and it can be therefore exploited further, for example for $r>1$. On the other hand, Figures~\\ref{fig:td_1d_comparison_r2} and~\\ref{fig:td_1d_comparison_r3} show instead a mismatch between the theoretical $\\widetilde{\\tau}_r$ of Eq.~\\eqref{eq:tau_theory_r} and the optimal one $\\tau^*$, thus confirming the findings reported in Figure~\\ref{fig:td_minimumErr_r3}.\n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/1D_error_r2-eps-converted-to.pdf}\n \\caption{$r = 2$}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/1D_error_r3-eps-converted-to.pdf}\n \\caption{$r = 3$}\n \\end{subfigure}\n \\caption{Comparison of the error of Eq.(\\ref{eq:td_error_definition}) obtained with the optimal $\\tau^*$ (full line) and theoretical $\\widetilde{\\tau}_r$ (dashed line) for varying $\\Peclet_h$ with $h = 1\/20$. Results referred to the 1D advection-diffusion problem of Section~\\ref{sec:validation}}\n \\label{fig:td_1d_error}\n\\end{figure}\n\nIn order to better appreciate the differences in the numerical solutions due to the choice of the stabilization parameters, we report, in Figure~\\ref{fig:td_1d_error}, a comparison of the error $E(\\tau)$ obtained by means of the optimal $\\tau^*$ and theoretical $\\widetilde{\\tau}_r$ stabilization parameters for $r=2$ and $3$. Moreover, we report in Figure~\\ref{fig:td_boundary_layer_r3} a comparison among the exact solution $u$, the SUPG-stabilized numerical solution $u^*_h$ obtained with the optimal stabilization parameter $\\tau^*$, and the numerical solution $\\widetilde{u}_h$ obtained with theoretical one $\\widetilde{\\tau}_r$. Specifically, we consider FE of degree $r=3$ and two different values of $\\Peclet_h$. In both these cases, the optimal parameter $\\tau^*$ leads to a more accurate solution with respect to using the theoretical parameter $\\widetilde{\\tau}_r$. In particular, when $\\Peclet_h$ is ``small\", $\\widetilde{\\tau}_r$ leads to overshooting in the numerical solution, while if $\\Peclet_h$ is ``large\" the theoretical stabilization parameter leads to undershooting. Conversely, the optimal parameter $\\tau^*$ accurately intercepts the exact solution at the nodes.\n\n\n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/solution_Pe2.5_r3-eps-converted-to.pdf}\n \\caption{$\\Peclet_h = 2.5$}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/solution_Pe12.5_r3-eps-converted-to.pdf}\n \\caption{$\\Peclet_h = 12.5$}\n \\end{subfigure}\n \\caption{Boundary layers of the numerical solutions $u^*_h$ and $\\widetilde{u}_h$ obtained with the SUPG method with optimal $\\tau^*$ and theoretical one $\\widetilde{\\tau}_r$ for $h = 1\/20$ and $r = 3$, respectively; comparison with the exact solution $u$ ($u^*_h$ is nodally exact at the node in $x=0.95$). Results are referred to the 1D advection-diffusion problem of Section~\\ref{sec:validation}}\n \\label{fig:td_boundary_layer_r3}\n\\end{figure}\n\n\\newpage\n\n\\section{Numerical results}\n\\label{sec:results}\nWe first introduce the training set of our problem and we detail the setup of the ANN that we use in this work. Then, we show the prediction of the stabilization parameter by means of the ANN on different advection-diffusion problems.\n\n\\subsection{Training the ANN for a 2D advection-diffusion problem}\n\\label{sec:training_2D}\nWe apply the strategy presented so far to a 2D advection-diffusion problem to generate the dataset for the training of the ANN. Specifically, we consider in Eq.~\\eqref{eq:transport_diffusion}: $\\Omega = (0, 1)^2$, $f = 0$, and $\\boldsymbol{\\beta} = (1,1)$. We prescribe the following exact solution on the whole boundary $\\partial \\Omega$:\n\\begin{equation}\n \\label{eq:td_2d_exact}\n u(x, y) = \\frac{e^{(x \/ \\mu)} - 1}{e^{(1 \/ \\mu)} - 1} + \\frac{e^{(y \/ \\mu)} - 1}{e^{(1 \/ \\mu)} - 1}.\n\\end{equation}\n\n\n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{0.32\\textwidth}\n \\includegraphics[width=\\textwidth]{figures\/ad\/mesh10-eps-converted-to.pdf}\n \\caption{$h = \\sqrt{2} \/ 10$}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{0.32\\textwidth}\n \\includegraphics[width=\\textwidth]{figures\/ad\/mesh20-eps-converted-to.pdf}\n \\caption{$h = \\sqrt{2} \/ 20$}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{0.32\\textwidth}\n \\includegraphics[width=\\textwidth]{figures\/ad\/mesh40-eps-converted-to.pdf}\n \\caption{$h = \\sqrt{2} \/ 40$}\n \\end{subfigure}\n \\caption{Structured meshes used for the FE approximation of the 2D advection-diffusion problem}\n \\label{fig:td_2d_mesh}\n\\end{figure}\n\n\\begin{figure}[t!]\n \\centering\n \\includegraphics[width=0.65\\textwidth]{figures\/ad\/dataset_ann2-eps-converted-to.pdf}\n \\caption{Visualization of the dataset used for the ANN training: target $\\tau^*$ against feature $\\Peclet_g$ colored by feature $r=1$ (red), $2$ (blue), and $3$ (green) for different values of the feature $h$ (increasing values of $h$ from bottom to top) as listed in Table~\\ref{tab:dataset}}\n \\label{fig:td_ann2_dataset}\n\\end{figure}\n\nWe generate in $\\Omega$ a structured mesh $\\mathcal T_h$ of triangles with {\\tt FEniCS} \\cite{fenics}, as shown in Figure~\\ref{fig:td_2d_mesh}.\nWe generate the dataset by repeatedly solving the optimization problem of Eq.~(\\ref{eq:td_findtau}) for varying set of features as described in Section~\\ref{sec:strategy}; specifically, we choose the features as reported in Eq.~\\eqref{input}. The complete dataset contains $m = \\, 900$ examples with $r = \\left \\{1, 2, 3 \\right \\}$, $h = \\left \\{ \\frac{\\sqrt{2}}{10}, \\frac{\\sqrt{2}}{20}, \\frac{\\sqrt{2}}{40} \\right \\}$ and values of $\\Peclet_g$ randomly chosend in the range $[7, 70'710]$ (uniform distribution), which yields $\\mu \\in [10^{-5}, 10^{-1}]$. We summarize the details for generating the dataset in Table~\\ref{tab:dataset}. Figure~\\ref{fig:td_ann2_dataset} provides an overview of the dataset used to the training of the ANN, specifically the features $\\mathbf x^{(i)}=[r, \\, h,\\, \\mathbb Pe_g]$ and the target $\\mathbf y^{(i)}=[\\tau^*]$. In this case, the characteristic length $L$ is fixed and set equal to $L=1$.\n\n\n\n\\begin{table}[t!]\n\\begin{tabular}{cccccc}\n\\hline\n\\# Data & \\# Training & \\# Validation & \\multirow{2}{*}{$r$} & \\multirow{2}{*}{$h$} & \\multirow{2}{*}{$\\Peclet_g$} \\\\\nset ($m$) & set & set & & & \\\\\n\\hline\n\\multirow{2}{*}{$900$} & \\multirow{2}{*}{$720\\, (80 \\%)$} & \\multirow{2}{*}{$180\\, (20 \\%)$} & \\multirow{2}{*}{$\\left \\{1, 2, 3 \\right \\}$} &\\multirow{2}{*}{ $\\left \\{ \\frac{\\sqrt{2}}{10}, \\frac{\\sqrt{2}}{20}, \\frac{\\sqrt{2}}{40} \\right \\} $}\n& randomly \\\\\n& & & & & in $[7, 70'710]$.\n\\end{tabular}\n\\caption{Details of the dataset used for the ANN training}\n\\label{tab:dataset}\n\\end{table}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.6\\textwidth]{figures\/ad\/ann.png}\n \\caption{Architecture of the feed-forward fully-connected ANN}\n \\label{fig:ann_architecture}\n\\end{figure}\n\nWe train a fully-connected feed-forward ANN on the generated dataset by using the open source library {\\tt Keras} \\cite{keras} built on top of {\\tt TensorFlow} \\cite{tensorflow}. We divide the dataset into two parts: a training dataset that takes $80\\%$ of the examples to be used for the ANN training and a validation dataset that takes the remaining $20\\%$. We choose the loss function as the mean squared error that measures, for each training feature, the squared mismatch between the prediction of the ANN $\\widehat{y}^{(j)}$ and the actual target $y^{(j)}$. Specifically, the loss function is defined as\n\\begin{equation}\n \\mathcal J = \\frac{1}{2m} \\sum_{j = 1}^m \\left ( \\widehat{y}^{(j)} - y^{(j)}\\right )^2.\n \\label{cost}\n\\end{equation}\nWe normalize the features by subtracting their mean and dividing by their standard deviation in order to help the weights to better adapt to the different scales of the features. Moreover, the targets and the feature $\\Peclet_g$ need special care as built on a logarithmic scale; specifically, the features are normalized by applying base $10$ logarithm. The normalized features and targets read:\n\\begin{equation*}\n \n \\widetilde{\\mathbf x} = \\left[ \n \\begin{array}{c}\n \\frac{r - \\overline{r}}{\\sigma_r}, \\; \\frac{h - \\overline{h}}{\\sigma_h}, \\; \\frac{\\log_{10}(\\Peclet_g) - \\overline{\\log_{10}(\\Peclet_g)}}{\\sigma_{\\log_{10}(\\Peclet_g)}}\n \\end{array} \\right], \\qquad\n \\widetilde{\\mathbf y} = \\left[ \n \\begin{array}{c}\n - \\log_{10} (\\tau^*)\n \\end{array} \\right],\n\\end{equation*}\nwhere $\\overline{r}$, $\\overline{h}$, $\\overline{\\log_{10}(\\Peclet_g)}$ are the mean of the training features $r$, $h$ and the logarithm of $\\Peclet_g$ respectively, while $\\sigma_r$, $\\sigma_h$ and $\\sigma_{\\log_{10}(\\Peclet_g)}$ are their standard deviations.\n\n\n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/td_ann2_predictions_r1-eps-converted-to.pdf}\n \\caption{$\\tau_{\\mathrm{ANN}}$ for $r = 1$}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/td_theory_predictions_r1-eps-converted-to.pdf}\n \\caption{$\\widetilde{\\tau}_r$ for $r = 1$}\n \\end{subfigure}\n \n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/td_ann2_predictions_r2-eps-converted-to.pdf}\n \\caption{$\\tau_{\\mathrm{ANN}}$ for $r = 2$}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/td_theory_predictions_r2-eps-converted-to.pdf}\n \\caption{$\\widetilde{\\tau}_r$ for $r = 2$}\n \\end{subfigure}\n \n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/td_ann2_predictions_r3-eps-converted-to.pdf}\n \\caption{$\\tau_{\\mathrm{ANN}}$ for $r = 3$}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/td_theory_predictions_r3-eps-converted-to.pdf}\n \\caption{$\\widetilde{\\tau}_r$ for $r = 3$}\n \\end{subfigure}\n \\caption{Stabilization parameter $\\tau_{\\mathrm{ANN}}$ predicted by the ANN and theoretical one $\\widetilde{\\tau}_r$ for varying $\\Peclet_g$ and $h$ at different FE degrees $r=1,2$, an $3$ for the 2D advection-diffusion problem of Section~\\ref{sec:training_2D}}\n \\label{fig:td_2d_predictions_comparison}\n\\end{figure}\n\n\n\nIn order to find the best performing ANN architecture, we carried out a study by testing different numbers of hidden layers, nodes per layer, optimization algorithm, its learning rate, and batch size. Finally, we choose an ANN with $3$ hidden layers, $64$ nodes per layer and an output layer with a single node, all using a rectified linear unit (\\textit{ReLU}) activation function. We display the ANN architecture in Figure~\\ref{fig:ann_architecture}. We trained the ANN with the SGD optimization algorithm, a constant learning rate of $0.01$, and mini-batch size of $32$ examples. Moreover, we employed a momentum of $0.9$ in the optimization algorithm to update the weights. The trained ANN is available in the {\\tt GitLab} repository \\cite{ann_repo}.\n\n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/td_ann2_predictions_r4-eps-converted-to.pdf}\n \\caption{ANN's predicted $\\tau$ at $r = 4$}\n \\end{subfigure}\n \n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/td_theory_predictions_r4-eps-converted-to.pdf}\n \\caption{Theoretical $\\tau$ at $r = 4$}\n \\end{subfigure}\n \\caption{Stabilization parameter $\\tau_{\\mathrm{ANN}}$ predicted by the ANN and theoretical one $\\widetilde{\\tau}_r$ for varying $\\Peclet_g$ and $h$ with FE degree $r=4$ for the 2D advection-diffusion problem of Section~\\ref{sec:training_2D}}\n \\label{fig:td_2d_predictions_comparison_r4}\n\\end{figure}\n\n\\subsection{Predictions of the stabilization parameter by ANN}\nNow, we compare the predictions of the ANN with the theoretical stabilization parameter $\\widetilde{\\tau}_r$ of Eq.~\\eqref{eq:tau_theory_r} \\cite{Quarteroni_2017,galeao2004finite}. We show in Figure~\\ref{fig:td_2d_predictions_comparison} (left) the stabilization parameter $\\tau_{\\mathrm{ANN}}$ predicted by the ANN by varying mesh size $h$ and global P\\'eclet number $\\mathbb Pe_g$. For comparison, we report in Figure~\\ref{fig:td_2d_predictions_comparison}(right) the corresponding theoretical stabilization parameter $\\widetilde{\\tau}_r$. We observe that the overall behavior of the trained ANN's predictions are qualitatively similar of the theoretical stabilization parameter $\\widetilde{\\tau}_r$. Nevertheless, it can be inferred that the values of the stabilization parameters are almost completely matched with linear FE ($r=1$), while they quantitatively differ for $r=2$ and $r=3$. This was expected as $\\widetilde{\\tau}_r$ for $r>1$ is an empirical extension of the formula for the case $r=1$.\n\nThe ANN allows to make predictions with features outside the range of values for which it has been trained, that is for unseen values of such features. With this aim, we report in Figure~\\ref{fig:td_2d_predictions_comparison_r4} the comparison of the ANN's predictions $\\tau_\\mathrm{ANN}$ with $\\widetilde{\\tau}_r$ for the FE degree $r = 4$. This comparison shows a clear difference between the theoretical and ANN's $\\tau$ for $r = 4$ even though the general trend is maintained similar. \n\n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/sol1_compare-eps-converted-to.pdf}\n \\caption{$\\Peclet_h = 2$, $h = \\sqrt{2}\/10$, $r = 1$}\n \\label{fig:td_2d_solutions_extracted1}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/sol2_compare-eps-converted-to.pdf}\n \\caption{$\\Peclet_h = 500$, $h = \\sqrt{2}\/20$, $r = 3$}\n \\label{fig:td_2d_solutions_extracted2}\n \\end{subfigure}\n \\caption{Comparison of the solutions $u$ (blue), $\\widetilde{u}_h$ (green), and $u_h^\\mathrm{ANN}$ (red) of the 2D advection-diffusion problem along the line $(1-h,y)$ for any $y \\in [0,1]$}\n \\label{fig:td_2d_solutions_extracted}\n\\end{figure}\n\n\\begin{table}[t!]\n\\centering\n\\begin{tabular}{ccc|cc|cc|}\n& & & \\multicolumn{2}{c|}{$\\widetilde{\\tau}_r$ ($e=\\widetilde u_h - u$)}\n& \\multicolumn{2}{c|}{${\\tau}_\\mathrm{ANN}$ ($e=u^\\mathrm{ANN}_h - u$)}\n\\\\\n$r$ & $h$ & $\\Peclet_h$ & $||e||_{L^2(\\Omega)}$ & $||e||_{H^1(\\Omega)}$ & $||e||_{L^2(\\Omega)}$ & $||e||_{H^1(\\Omega)}$ \n\\\\\n\\hline\n1 & $\\sqrt{2}\/10$ & 2 & $9.56 \\cdot 10^{-2}$ & $7.93 \\cdot 10^{-1}$ & $6.49 \\cdot 10^{-2}$ & $5.31 \\cdot 10^{-1}$\\\\\n2 & $\\sqrt{2}\/10$ & 2 & $6.48 \\cdot 10^{-2}$ & $5.31 \\cdot 10^{-1}$ & $6.50 \\cdot 10^{-2}$ & $5.33 \\cdot 10^{-1}$\\\\\n3 & $\\sqrt{2}\/10$ & 2 & $6.50 \\cdot 10^{-2}$ & $5.33 \\cdot 10^{-1}$ & $6.49 \\cdot 10^{-2}$ & $5.32 \\cdot 10^{-1}$\\\\\n1 & $\\sqrt{2}\/20$ & 500 & $2.99 \\cdot 10^{-4}$ & $5.30 \\cdot 10^{-3}$ & $3.11 \\cdot 10^{-5}$ & $5.50 \\cdot 10^{-4}$\\\\\n2 & $\\sqrt{2}\/20$ & 500 & $2.71 \\cdot 10^{-2}$ & $4.86 \\cdot 10^{-1}$ & $2.50 \\cdot 10^{-2}$ & $4.48 \\cdot 10^{-1}$\\\\\n3 & $\\sqrt{2}\/20$ & 500 & $1.05 \\cdot 10^{-2}$ & $1.90 \\cdot 10^{-1}$ & $1.68 \\cdot 10^{-3}$ & $3.46 \\cdot 10^{-2}$\\\\\n\\end{tabular}\n\\caption{Comparison of the errors in norms $L^2$ and $H^1$ between the numerical solutions ($u_h^\\mathrm{ANN}$ and $\\widetilde{u}_h$) and the exact one $u$ of the 2D advection-diffusion problem used for the training (Section~\\ref{sec:training_2D}) for different values of the features}\n\\label{tab:errors}\n\\end{table}\n\n\n\\subsubsection{Predictions for the problem used in the ANN's training}\nWe compare the numerical solutions $u^\\mathrm{ANN}_h$ and $\\widetilde u_h$ obtained by means of the SUPG stabilization method with the parameter $\\tau_\\mathrm{ANN}$ predicted by the ANN and the theoretical one $\\widetilde{\\tau}_r$, respectively; the comparison also involves the exact solution $u$~(\\ref{eq:td_2d_exact}) of the 2D advection-diffusion problem used for the training of the ANN. In particular, we display the comparison of the former 2D solutions in Figure~\\ref{fig:td_2d_solutions_extracted} along the line $(1-h,y)$ for any $y \\in [0,1]$ with: (a) $\\Peclet_h = 2$, $r=1$, and $h=\\sqrt{2}\/10$ (Figure~\\ref{fig:td_2d_solutions_extracted1}); (b) $\\Peclet_h = 500$, $r=3$, and and $h=\\sqrt{2}\/20$ (Figure~\\ref{fig:td_2d_solutions_extracted2}). We notice that \nin both the cases, the numerical solution $u_h^\\mathrm{ANN}$ involving the stabilization parameter $\\tau_\\mathrm{ANN}$ provides more accurate results than with the theoretical stabilization paramter $\\widetilde{\\tau}_r$. In particular, in the case (a), $\\widetilde{u}_h$ involves a much smoother boundary layer and overshoots the exact solutions $u$, conversely to the nearly nodally exact numerical solution $u_h^\\mathrm{ANN}$. In the case~(b),\n$u_h^\\mathrm{ANN}$ provides a much better representation of the bundary layer, without the undershooting of the solution $u$ exhibited by $\\widetilde u_h$. Furthermore, we report in Table ~\\ref{tab:errors} absolute errors between the numerical solutions ($u_h^\\mathrm{ANN}$ and $\\widetilde{u}_h$) and the exact one $u$. The errors, computed in $L^2(\\Omega)$ and $H^1(\\Omega)$ norms, show that the ANN-based SUPG stabilization method very often produces more accurate results than the ones obtained with theoretical stabilization parameter $\\widetilde{\\tau}_r$, especially for $r=3$.\n\n\n\\subsubsection{Predictions for an unseen problem}\n\\label{sec:newadvection-diffusion}\nWe check the model generalization of the ANN trained for the 2D advection-diffusion problem with exact solution~$u$ of Eq.~\\ref{eq:td_2d_exact} by predicting $\\tau_\\mathrm{ANN}$ for an unseen 2D advection-diffusion problem. In particular, we consider the advection-diffusion problem of Eq.~\\eqref{eq:transport_diffusion} in $\\Omega=(0,1)^2$, with $f=1$ and $\\boldsymbol{\\beta} = (1, 1)$. We prescribe the following exact solution on the whole boundary $\\partial \\Omega$:\n\\begin{equation}\n \\label{eq:td_2d_exact_new}\n u(x, y) = \\frac{1}{2}(x+y) + \\frac{1 - \\frac{1}{2} (e^{(x \/ \\mu)} + e^{(y \/ \\mu)})}{e^{(1 \/ \\mu)} - 1}.\n\\end{equation}\n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/sol1_compare_new-eps-converted-to.pdf}\n \\caption{$\\Peclet_h = 2$, $h = \\sqrt{2}\/10$, $r = 1$}\n \\label{fig:td_2d_solutions_extracted_new_1}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/sol2_compare_new-eps-converted-to.pdf}\n \\caption{$\\Peclet_h = 500$, $h = \\sqrt{2}\/20$, $r = 3$}\n \\label{fig:td_2d_solutions_extracted_new_2}\n \\end{subfigure}\n \\caption{Comparison of the solutions $u$~(\\ref{eq:td_2d_exact_new}) (blue), $\\widetilde{u}_h$ (green), and $u_h^\\mathrm{ANN}$ (red) of the \\emph{unseen} 2D advection-diffusion problem along the line $(1-h,y)$ for any $y \\in [0,1]$}\n \\label{fig:td_2d_solutions_extracted_new}\n\\end{figure}\n\nWe compare in Figure~\\ref{fig:td_2d_solutions_extracted_new} the numerical solutions $u^\\mathrm{ANN}_h$ and $\\widetilde u_h$ with the novel exact solution $u$. As for the previous numerical tests, the stabilization parameter $\\tau_\\mathrm{ANN}$ predicted by the network provides more accurate numerical solutions $u_h^\\mathrm{ANN}$ than with the theoretical stabilization parameter $\\widetilde{\\tau}_r$. In particular, the boundary layers and overall solution behaviours are better represented.\n\n\nWe better investigate assess the former qualitative consideration, by computing the error $E(\\tau)$ associated with the numerical solutions with SUPG stabilization for the parameters $\\tau_\\mathrm{ANN}$ and $\\widetilde{\\tau}_r$; different values of $\\Peclet_g$ and $r$ are considered. In particular, Figure~\\ref{fig:td_error_comparison_new} compares $E(\\tau)$ against $\\Peclet_g$ (in logarithmic scale) for different values of $r$: the error obtained by using $\\tau_\\mathrm{ANN}$ is always lower than the one achieved with $\\widetilde{\\tau}_r$. These results indicate that $\\tau_\\mathrm{ANN}$, although trained on a specific advection-diffusion problem, can be used to make predictions of the optimal $\\tau$ for an unseen advection-diffusion problem in place of the theoretical stabilization parameter. \n\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/error2_r1-eps-converted-to.pdf}\n \\caption{$r = 1$}\n \\end{subfigure}\n \n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/error2_r2-eps-converted-to.pdf}\n \\caption{$r = 2$}\n \\end{subfigure}\n \\par\\bigskip\n \\begin{subfigure}[b]{\\doublefigure}\n \\includegraphics[width=\\textwidth]{figures\/ad\/error2_r3-eps-converted-to.pdf}\n \\caption{$r = 3$}\n \\end{subfigure}\n \\caption{Comparison of errors $E(\\tau)$ in Eq.~(\\ref{eq:td_error_definition}) obtained with $\\tau_\\mathrm{ANN}$ (blue, full line) and $\\widetilde{\\tau}_r$ (orange, ashed line) applied to the useen 2D advection-diffusion problem with the exact solution $u$~(\\ref{eq:td_2d_exact_new}); $E(\\tau)$ against $\\Peclet_g$ with $h = \\sqrt{2}\/10$ and FE degrees $r=1,2$, and $3$}\n \\label{fig:td_error_comparison_new}\n\\end{figure}\n\n\n\n\\section{Conclusions}\n\\label{sec:conclusions}\n\nIn this work, we presented an approach based on Machine Learning and ANN to compute the optimal stabilization parameter to be used in the SUPG FE approximation of advection-diffusion problems. Indeed, albeit the expression of the stabilization parameter is available for 1D problems and FE of degree $r=1$, its extension to more general advection-diffusion problems (2D and 3D) and FE degrees $r>1$ is still lacking.\n\nWe validated our approach against the 1D case, for which the ANN-stabilization parameter matches the already optimal, theoretical one for $r=1$, leading to nodally exact numerical solutions Instead, for higher polynomials degrees $r>1$, remarkable differences are observed among the theoretical and optimal stabilization parameter: we observed better accuracy and stabilization properties of the numerical solution with the optimized stabilization parameter with respect to the theoretical one. \n\nThen, we generated the dataset on a 2D advection-diffusion problem, and we used it to train a fully-connected feed-forward ANN. We applied the predictions of the network on the original advection-diffusion problem for unseen input values and we also apply it to an unseen advection-diffusion problem to check for model generalization.\nOur numerical results showed that the proposed ANN-based approach provides more accurate numerical solutions than using the theoretical stabilization parameter for the SUPG method.\n\n\nThis work represents a step towards the enhancements of stabilization methods for the FE approximation of advection-dominated differential problems. In particular, this work is limited to few features for the ANN-based stabilization parameter and provides as output a single optimal stabilization parameter meant for the whole mesh FE $\\mathcal T_h$. Natural extensions of this work will therefore involve local stabilization parameters, i.e. element by element over the mesh $\\mathcal T_h$, to better account for non-uniform meshes, differential problems with varying coefficients, and capturing the local behaviour of the solution. We envision extending the proposed Machine Learning approach to SUPG and variational multiscale stabilization methods for the FE approximation of the Navier-Stokes equations. \n\n\n\\section*{Acknowledgements}\nThe authors acknowledge Prof. C. Canuto, Politecnico di Torino, for the fruitful discussions and useful suggestions. L.D. and A.Z. are members of the INdAM Research group GNCS. L.D. has been partially funded by the research project PRIN 2020, n.20204LN5N5, by MIUR.\n\n\n\\printbibliography\n\\clearpage\n\n\n\n\\end{document}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nHigh mass X-ray binaries (HMXBs) are among the brightest X-ray \nsources in the sky. They consist of an accreting compact object (a neutron star \nor black hole) in orbit with an early type star (O or B star).\nThey represent an important stage in the evolution of binary stars with\nearly-type components, and they dominate the X-ray output of galaxies with\nyoung stellar populations. HMXBs emit X-rays \nvia accretion onto the compact object by gas from the strong wind from the companion star, \nor from Roche lobe overflow. The X-rays interact with the accretion flow and stellar \nwind, producing observable signatures in timing and spectra. This provides\na means to study the hot star wind and the accretion flow and the interaction\nof X-rays with these structures. One consequence of this interaction is the\nproduction of polarized X-rays via scattering. Polarization provides unique\ninformation into the geometry of the stars, wind and scattering region.\nIn this paper, we \nexplore the polarization signatures associated with this interaction.\n\nThere are $\\simeq$10 HMXB sources \nwith properties of their stellar winds and binary orbit which are well enough determined\nfor quantitative modeling of the interaction of the accretion flow with X-rays \\citep{Kape98}.\nThe number of known HMXBs is many times greater when sources with less well constrained properties are included\n\\citep{Liu06,Walt15}; these include sources discovered by INTEGRAL which are seen primarily at hard X-ray energies\nowing to obscuration, and transient sources which are detected primarily via long time-baseline\nmonitoring. The most thoroughly studied systems include the objects Cyg X-1, Vela X-1, and Cen X-3. \nThe majority of HMXBs contain pulsating neutron stars. If so, orbital eclipses, X-ray pulse timing and primary radial velocities\n\\citep{vand07} provides strong constraints on the orbital \nseparations and masses. Simple estimates suggest that the \naccretion rate onto the compact object can be provided by the wind from the \ncompanion star in fewer than half the sources, while Roche lobe overflow is \nrequired in the remainder \\citep{Cont78, Whit85,Kape98}. Though they are outnumbered \nby low mass X-ray binaries in our galaxy, these sources dominate the X-ray \noutput from galaxies with larger star formation rates \\citep{Fabb06}.\nTheir mass transfer can be very rapid; in a system in which the primary has a radiative envelope\nand fills its Roche lobe, the primary radius will shrink as a consequence of mass transfer,\nbut if the mass transfer is conservative the Roche lobe will shrink more rapidly, potentially\nleading to a common envelope phase. If the primary is an evolved star with a convective envelope\nthis outcome is more probable \\citep{Taam00}. Common envelope evolution when the primary is an evolved star\nis likely to lead to ejection of much of the envelope of the primary, leaving a binary consisting of a\nneutron star and the core of the primary. \nWhether due to nuclear evolution or to unstable mass transfer, their evolutionary lifetimes \nmust be short ($\\leq 10^5$ yrs). \n\nThe wind from the companion absorbs and scatters the X-rays from the accreting \ncompact object in HMXBs, and the column densities traversed by the X-rays \nrange from $\\sim 10^{21} - 10^{24}$cm$^{-2}$. The X-ray source luminosities \nrange from $\\sim 10^{35} - 10^{38}$ erg s$^{-1}$. In sources where the X-ray \nsource is luminous and the wind is weak, the wind is ionized throughout most \nof the region between the two stars. In systems with more massive winds \nand weaker X-ray sources X-ray-ionization effects are a perturbation \non the wind properties.\nKey questions about HMXBs include the nature of the compact object: its mass, \nvariability and intrinsic radiation pattern. Also of interest are the properties of \nthe companion star wind and its uniformity in density and temperature.\nThe X-ray source can provide a probe for the \nstudy of wind regions which are only weakly affected by ionization or the \ngravity of the compact object. \n\n\nMany of the X-ray properties of HMXBs are affected by the stellar wind and accretion flow. \nThis includes the variability around the orbit, which shows gradual eclipse transitions due \nto photoelectric absorption in the wind \\citep{Clar88}, enhanced absorption at late \norbital phases due to a wake or stream trailing the compact object \\citep{Kall82, Wata06}. \nThe wind and accretion flow also provide torque to the compact object and regulate its \nspin or angular momentum. The structure of the wind and accretion flow is \nuncertain; winds from comparable single O or B stars have radiatively driven winds \\citep{Lame99}\nwhich are affected by instabilities \\citep{Owocki88}, shocks \\citep{LucyWhite80} and clumping \\citep{Cohen06};\nobservations provide the most reliable means for understanding these processes, via fitting \nof parameterized models. In addition, the compact object affects the gas flow, via its gravity\nand also X-ray heating and ionization. \n\nThe next new astrophysical window will be the advent of measurements \nof X-ray polarization \\citep{Jaho14,Weis13}. Among other things, polarization allows for potentially \nsensitive tests of the geometry of astrophysical sources, on scales which are \nfar too small to be imaged directly. \nThere is only one source in the sky whose X-ray polarization is known, the \nCrab nebula \\citep{Weis78}. Owing to visibility constraints, it is likely \nthat the first astronomical X-ray polarimetry observations will be of objects \nwhich have never before been observed with this technique. This motivates \nthorough and accurate modeling of the polarization properties of the brightest \nand (otherwise) best understood X-ray sources, for use as calibrators and test sources for X-ray polarimetry. \nHMXBs are among the sources best suited for this. Their orbital elements are relatively well understood\nand their orbital variability provides a predictably changing view with respect to an important source\nof polarization: the strong stellar wind from the companion star. \nX-ray polarimetry provides a means to probe the structure and the physical processes \noccuring in and near compact objects, on length scales too small to be imaged directly.\nIn spite of scanty astrophysical detections so far \\citep{Weis78} it is of interest to \nconsider the possible signals and diagnostic use of X-ray polarization in known classes \nof cosmic sources, as it is likely that instruments with improved sensitivity\nwill become available eventually.\n\nPolarization properties of HMXBs have been discussed previously in the context \nof scattering of optical and UV from the primary star \\citep{Brow77, Brow78, Rudy78}. \nThese authors predicted that the linear polarization has a characteristic variability around the \nbinary orbit, associated with scattering in the stellar envelope which is distorted by \nthe gravity of the compact object. This variability can be used as a diagnostic of the \nshape of the star, and also of the inclination of the binary orbit. These effects are observed \nat the predicted level in some systems; their absence in other cases suggests that the optical light \nmay be affected by contributions from structures whose shape is not symmetric around the line \nof centers. Related work has been carried out by \\citet{Alma99,Igna09,Nofi14}.\nUseful formalism for the calculation of polarization has been provided by \\citet{Matt96}.\nThe polarization properties of the accretion columns in X-ray pulsars have been calculated by\n\\citep{Mesz88}. Predictions of X-ray polarization by the large scale structures in binaries has been carried out for\ndistorted stars \\citep{Ange69}; for Compton scattering within the accretion column of magnetic cataclysmic variables\n\\citep{Mcna08}, following up on pioneering work by \\cite{Matt04}.\n\nX-rays have an important advantage over optical light which is that the source is almost certainly \ncompact compared with the size of the binary system. That is, we expect X-rays to be emitted from \na region smaller than the Alfven shell, defined as the distance from the compact object where \nthe pressure due to a dipole magnetic field balances the ram pressure of accretion. \nFor fields $\\sim 10^{12}$ Gauss this region has a size $\\sim 10^8$ cm \\citep{Lamb73}.\nFar outside this region the influence of the field on the dynamics is negligible.\nThe Alfven shell size can be compared with the primary star radius and orbital separation\nwhich are $\\sim 10^{12}$ cm. This disparity in sizes greatly simplifies the geometrical \nconsiderations associated with the use of X-ray polarization since the X-ray source is\neffectively a point source when considering the effect of the scattering by the stellar wind.\n Calculating the polarization from these structures is straightforward, \ngiven certain simplifying assumptions, though it is important to take into account the \neffects of atomic absorption and resonance line scattering, which in turn depends on the \ngas dynamics, along with electron scattering. \n\n\nThe polarization properties of HMXBs will be affected by the intrinsic polarization \nof the compact X-ray source, and by the polarization imprinted by the stellar \nwind and accretion flow. These can be distinguished by their differing \nvariability behavior: the compact source generally varies on a pulsation timescale for\nsources containing a pulsar, which is $\\sim$ seconds -- minutes.\nThe very few sources which contain black holes also have their strongest intrinsic variability\non comparably short timescales. Wind variability, on the other hand,\nis associated with the orbital timescale ($\\sim$ days) or possibly on the wind flow timescale which is\n$\\geq$hours. In some HMXBs the X-ray source is luminous enough to ionize the wind almost completely, \nso the light observed during and near eclipse, and its polarization, is \ndominated by electron scattering.\n\nIn this paper \nwe present a general discussion of the behavior of X-ray polarization from HMXBs \nas a function of the parameters of the system and the viewing position. \nWe present calculations of the polarization \nsignature for various simple analytic models for the wind density and ionization structure,\nand discuss the dependence of these on parameters: wind optical depth, inclination, and\nviewing direction or orbital phase. \nWe also explore the effects of wind hydrodynamics, i.e. departures from spherical symmetry due\nto the effects of the X-ray source gravity and heating. We utilize sample three-dimensional\ndynamical models. We take the \nintrinsic polarization of the compact object as unpolarized and explore the combined effects of\ngeometry and scattering physics on the predicted linear polarization in the 0.1 - 10 keV X-ray band.\n\n In section \\ref{sec1} we present numerical calculations \nfor spherical winds using only electron scattering. The effects of resonance line scattering\nare discussed in section \\ref{sec3}.\nIn section \\ref{sec3b} we present models which include photoelectric absorption \nand an ensemble of resonance scatterers.\nIn section \\ref{sec4} we present models utilizing three-dimensional hydrodynamic models for the \nwind density and velocity field. We use these to derive approximate predictions for the polarization\nlevels expected for several well known systems in section \\ref{sec5}. The Appendix presents\nsimple analytic estimates of the polarization for idealized HMXB conditions;\nmany of these mirror earlier results. The computational techniques and level of detail we employ \nare similar to those of our previous work \\citep{DoraKall10} on the polarization from warm absorbers in \nSyefert galaxies.\n\n\n\\section{Spherical Winds}\n\\label{sec1}\n\nThe basic geometry of an HMXB is illustrated in figure \\ref{fig1}.\nThis shows a schematic of an HMXB with a typical orbital separation and a sample \nX-ray ionized zone. It also shows the geometry we use when calculating the\npolarization: the X-ray source is at the origin and the primary orbits in a plane\ninclined by an angle $i$ to the line of sight. The orbital phase is described by the angle $\\Theta_V$ relative\nto the line of centers. The system is viewed along the y axis.\n\nIn this section we will consider the simple case in which the wind density is spherically\nsymmetric about the primary star, and the only interaction between the X-rays and\nthe wind is electron scattering. If so, the scattering phase function follows the\nRayleigh form \\citep{ChandraRadTransfer}. The scattered emissivity at each point\ndepends only on the local gas density and on the flux from the X-ray source.\nWe assume the X-ray source radiates isotropically with a constant total luminosity\n$L_0$ and is unpolarized.\n\nWe calculate the polarization signatures in the form of the three Stokes parameters for linearly polarized \nlight using the formal solution to the equation of transfer \\citep{Miha78}.\n\n\\begin{equation}\n\\label{eq1}\n\\left\\{\n\\begin{matrix}\nL(\\varepsilon) \\\\\nQ(\\varepsilon) \\\\\nU(\\varepsilon) \n\\end{matrix}\n\\right\\}\n= \\int dV \\kappa(\\varepsilon,{\\bf r}) S(\\varepsilon,{\\bf r}) e^{-\\tau(\\varepsilon,{\\bf r})}\n\\left\\{\n\\begin{matrix}\n1 + \\cos^2\\chi \\\\\n\\sin^2\\chi \\cos(2\\gamma) \\\\ \n\\sin^2\\chi \\sin(2\\gamma) \n\\end{matrix}\n\\right\\}\n\\end{equation}\n\n\\noindent where $\\varepsilon$ is the photon energy, $S(\\varepsilon,{\\bf r})$, is the source function, $\\kappa(\\varepsilon,{\\bf r})$\nis the opacity, $\\tau(\\varepsilon,{\\bf r})=\\int{\\kappa(\\varepsilon,{\\bf r}) d\\zeta}$ is the optical\ndepth from a point ${\\bf r}$ to a distant viewer,\n$\\chi$ is the scattering angle and $\\gamma$ is the angle between the scattering \nplane and a reference direction on the sky. Here and in what follows we describe the Stokes parameters in terms of the\nluminosity seen by a distant observer, i.e. the total energy (in ergs s$^{-1}$ sr$^{-1}$) radiated by the\nsystem in that direction. Equation \\ref{eq1} defines the scattered luminosity, $L$\n(polarized plus unpolarized); observations are also affected by an\nunscattered component, $L_u(\\varepsilon)=L_0(\\varepsilon)e^{-\\tau(\\varepsilon,{\\bf r_x})}$\nwhere ${\\bf r_x}$ is the position of the X-ray source. For the purpose of evaluating these\nquantities we adopt a cylindrical coordinate system with the X-ray source at the origin \nand the viewing direction along the $\\hat{\\bf y}$ axis. The angle $\\gamma$ corresponds \nto the azimuthal angle on the plane of the sky and is measured relative\nto a line which is the intersection between the orbital plane and the plane of the sky.\nIn the case of pure electron scattering the source function is $S=L_0\/(4\\pi r_x^2)$ where \n$r_x$ is the distance from the X-ray source and the opacity is $\\kappa(\\varepsilon,{\\bf r})=n({\\bf r}) \\sigma_{Th}$.\n\nSome useful results are presented in the Appendix. The fractional polarization relevant to observation is\n$P=\\sqrt{(Q^2+U^2)}\/(L+L_u)$. This quantity is zero when the system is viewed at inclination\n$i=\\pi\/2$ and at either conjunction, i.e. orbital phase 0.5 or 1, $\\Theta_V=0$ or $\\pi$.\nThe polarization at $i=\\pi\/2$ is a maximum at quadrature, phase 0.25 or 0.75.\nWe can also define the polarization of the scattered radiation only,\ni.e. $P_s=\\sqrt{(Q^2+U^2)}\/L$, and the value of this quantity at quadrature and $i=\\pi\/2$ depends only on\nthe orbital separation and on the distribution of gas density in the wind.\nThe value of $P$ is less than the value of $P_s$ by a factor proportional to the Thomson depth \nwhenever the X-ray source is not eclipsed. \nAt $i=0$, i.e. when the system is viewed face-on, the polarization rotates with orbital phase at a rate\ntwice the orbital rotation rate.\n\nAs an illustration of these results, we calculate the polarization produced by a single-scattering\ncalculation by numerically evaluating equation \\ref{eq1}. The wind velocity is assumed to be radial\nrelative to the star with a speed given by $v(r)=v_0+(v_{\\infty}-v_0)(1-r_{*}\/r)$\nwhere the terminal velocity is $v_{\\infty}$=1000 km s$^{-1}$ and $v_0$=100 km s$^{-1}$, and\n$r_{*}=10^{12}$ cm. The wind mass loss rate is $\\dot M_{wind}=10^{-8} M_{\\odot} {\\rm yr}^{-1}$.\nThe orbital separation is $a=1.5 r_{\\*}$.\n\nIn the remainder of this paper we discuss quantitative calculations of polarization produced by wind scattering. These\ninclude calculations based on both the very simple spherical wind with parameters given in the previous paragraph\nand also on hydrodynamic calculations of the wind density structure which make no assumptions about spherical symmetry.\nIn the former case, it is illuminating to estimate the accretion rate onto the compact object if the accretion is supplied\nsolely by the wind. This can be done using simple estimates based on \\citet{Bond44} and \\citet{Davi73}, i.e.\n$\\dot M_{acc}=\\pi R_{acc}^2 n_x v_x m_H$ where the accretion radius is $R_x=2GM\/v_x$, $M$ is the mass of the compact\nobject and $v_x$ is the speed of the wind at the X-ray source. This corresponds to\n$\\dot M_{acc}\/\\dot M_{wind}= 1.8 \\times 10^{-4} (M\/M_\\odot)^2 v_{x8}^{-4} a_{12}^{-2}$ where $v_{x8}=v_x\/(1000 {\\rm km s}^{-1})$,\n $a_{12}=a\/(10^{12} {\\rm cm})$ or an X-ray luminosity $L_x\\simeq 1.8 \\times 10^{35} {\\rm erg s}^{-1}$ using the\nparameters given in the previous paragraph and assuming an efficiency of converting accreted mass into energy of 0.1.\nThis luminosity is less than the time averaged luminosities of most HMXBs by factors $\\sim$5 -- 100, which likely reflects\nthe fact that wind mass loss rates may be greater, additional mass can be supplied by Roche lobe overflow, and the\nwind and accretion flow dynamics can be enhanced by the influence of the compact object gravity and ionization.\nThe results in what follows, those which are based on the simple spherical wind approximation, must\ninterpreted subject to this caveat.\n\nAn additional caveat which applies to essentially all of the results presented in this paper is the assumption of single\nscattering. The validity of this assumption depends on the wind optical depth from the X-ray source;\nmultiple scattering is important when this quantity approaches or exceeds unity. For parameters similar\nto those discussed so far, this quantity for a distant observer corresponds to\n$\\tau_{Th} \\sim n_x a \\simeq 2 \\times 10^{-4} \\dot M_8 a_{12}^{-1} v_{x8}^{-1}$,\nwhere $\\dot M_8= \\dot M\/(10^{-8}M_{\\odot}{\\rm yr}^{-1})$. Comparing this expression\nwith the accretion luminosity estimate given above implies that multiple scattering can be important when\nthe accretion luminosity exceeds $\\sim10^{38} {\\rm erg s}^{-1}$ for a 1 $M_\\odot$ compact object. The likely effect of\nmultiple scattering is to produce smaller net polarization than for single scattering, since the second and subsequent\nscatterings have a wider range of scattering angles and planes than for single scattering. If the\ndepth is moderate, i.e. $\\leq$ 10, there will still be a significant fraction of photons which reach the\nobserver after a single scattering. If so the net polarization will likely be less than predicted here,\nby factors of order unity, and our results should be modified to include multiple scattering effects for such sources. \n\nMaps of polarization projected against the sky\nare shown in figure \\ref{fig2}. This shows contours of constant intensity\nas solid colors separated by solid black curves, along with lines corresponding to polarization vectors,\nwhich appear as dashed curves. These are plotted vs position in units of $10^{12}$ cm.\nThese illustrate many of the results presented in the Appendix: At high inclination, $i=\\pi\/2$,\nand at orbital phase angles 0 and $\\pi$ (orbital phases 0 and 0.5) polarization vectors are perfectly\ncircumferential, contours of constant intensity are also circular and the net polarization is zero.\nAt orbital phase angles $\\pi\/2$ and $3\\pi\/2$ (orbital phases 0.25 and 0.75) the net polarization is\nmaximum. \nAt inclination $i=0$ the star always influences the polarization,\nso that the net polarization fraction is constant, but the position rotates with the orbit.\n\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=0, scale=0.5]{fig1.ps\n\\caption{\\label{fig1} Schematic of high mass X-ray binary (HMXB) as viewed from above the orbital plane,\n showing typical orbital separation and companion \nstar size. Coordinates are labeled according to their use in the Appendix.}\n\\end{figure*} \n\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=0, scale=0.5]{fig2.ps\n\\caption{\\label{fig2} Map of intensity and polarization vectors projected on the sky\n for four orbital phases and two inclinations. Colors correspond to scattered intensity\n according to the color bar, in which the labeled quantities correspond to log$_{10}$ of the specific\n intensity. Short black lines correspond to polarization vectors.\n Spherically symmetric wind is assumed.} \n\\end{figure*} \n\nMore insight comes from the Stokes quantities as a function of the orbital phase. These\nare shown in figure \\ref{fig3} for inclinations $i=0$ and $i=\\pi\/2$. U is green while Q is shown\nin red; solid is $i=\\pi\/2$ and dashed is $i=0$. This further illustrates the results from the\nprevious figure: at $i=0$ the polarization oscillates between Q and U along orbital phase, and the\nvector sum is constant. At $i=\\pi\/2$ U is always small (in this convention) and Q is a maximum\nat phase angles $\\pi\/2$ and $3\\pi\/2$.\nAnother way of displaying the same thing is shown in\nfigure \\ref{fig4}, which shows U plotted vs Q, for $i=0$ in green, and $i=\\pi\/2$ in red. This\nshows that the trajectory of U vs. Q is circular at $i=0$ and becomes linear at $i=\\pi\/2$. As shown by\n\\citet{Brow78}, this trajectory is an ellipse for intermediate inclinations, and the cases\nin figure \\ref{fig3} are the extremes of eccentricity. This has been suggested as a means\nfor measuring inclination \\citep{Rudy78}. \n\n\\begin{figure*}[p] \n\\includegraphics*[angle=90, scale=0.3]{fig3.ps\n\\caption{\\label{fig3} Stokes parameters for spherical wind, Q (red ) and U (green) vs. phase\n for inclinations $i=0$ (dashed) and $i=\\pi\/2$ (solid). The total intensity I is shown as dashed black for\n $i=0$. At $i=0$ the polarization oscillates between\nQ and U along orbital phase, and the vector sum is constant.\nAt $i=\\pi\/2$ U is always small (in this convention) and Q is a maximum \nat phase angles $\\pi\/2$ and $3\\pi\/2$.}\n\\end{figure*} \n\n\n\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=90, scale=0.4]{fig4.ps\n\\caption{\\label{fig4} Stokes parameters for spherical wind, Q vs U $i=0$ (green)\n and $i=\\pi\/2$ (red). This again illustrates the circular path of the net polarization\ndirection with orbital phase at $i=0$ and the constancy of the direction at $i=\\pi\/2$.}\n\\end{figure*} \n\nFigure \\ref{fig5} shows the scattered polarization fraction vs. orbital phase,\ni.e. $P_s=\\sqrt{(Q^2+U^2)}\/L$. This shows that the\nmaximum polarization fraction is $\\simeq$10$\\%$ for the parameters chosen here, when the\npolarized component is compared with the total scattered component. This is comparable to the\nresult in the Appendix, calculated for a thin shell at the star rather than for an extended wind.\nWe emphasize that this value depends primarily on geometric quantities: the extent of the wind, and\norbital separation relative to the stellar radius. It does not depend on the wind\ndensity or column density since it is a comparison of scattered quantities. The difference between\n$i=0$ and $i=\\pi\/2$ is clearly apparent: the former produces approximately constant polarization fraction\nand the latter oscillates between 0 and a value comparable to the $i=\\pi\/2$ value.\nFigure \\ref{fig6} shows the polarization fraction measured relative to the total radiation, scattered\nplus direct, i.e. i.e. $P=\\sqrt{(Q^2+U^2)}\/(L+ L_u)$. This illustrates the diluting effect of the direct\nradiation for this model, which has $\\tau_{Th}\\simeq 0.25$. Maximum linear polarization at $i=\\pi\/2$occurs\njust following the eclipse transition, where the departures from circular symmetry on the sky are not negligible,\nand where the direct radiation is blocked by the primary star. At mid-eclipse the polarization at $i=\\pi\/2$ is\nzero due to symmetry.\nFigure \\ref{fig7} shows the polarization angles from the same set of models. The\nangle sweeps through 180 degrees twice per orbital period for $i=0$, while the\nangle is constant (though undefined near conjunctions) for $i=\\pi\/2$.\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=270, scale=0.4]{fig5.ps\n\\caption{\\label{fig5} Polarization fraction of scattered radiation i.e.\n $P_s=\\sqrt{(Q^2+U^2)}\/L$ for spherical wind, $i=0$ (red)\n and $i=\\pi\/2$ (black).}\n\\end{figure*} \n\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=270, scale=0.4]{fig6.ps\n\\caption{\\label{fig6} Polarization fraction relative to total radiation, scattered plus direct,\n for spherical wind, $i=0$ (red)\n and $i=\\pi\/2$ (black).}\n\\end{figure*} \n\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=270, scale=0.5]{fig7.ps\n\\caption{\\label{fig7} Polarization angle for spherical wind, $i=0$ (red)\n and $i=\\pi\/2$ (black).}\n\\end{figure*} \n\n\n\\section{Resonance Line Scattering: Single Line}\n\\label{sec3}\n\nThe models discussed in section \\ref{sec1} include solely electron scattering;\nthey do not include resonant scattering in bound-bound transitions. This\nprocess (when associated with UV and optical transitions) is the dominant driving\nmechanism for the winds from early type stars. Here we consider\nscattering in the X-ray band. In the absence of X-ray ionization, the ions\nwhich are most abundant in early-type star winds are in charge states from\n$\\sim$1 -- 5 times ionized, and do not have many strong X-ray resonance\nline transitions. Ionization by a compact X-ray source can produce\nions of arbitrary charge state, depending on the X-ray flux and gas density,\nand so resonance scattering can affect the scattered X-ray intensity and\npolarization. The ionization structure in HMXB winds has been explored by\n\\citet{Hatc77} who showed that the surfaces of constant ionization in a spherical\nwind are either nested spheres surrounding the X-ray source, or else open surfaces\nenclosing the primary star. The ionization distribution depends on a parameter\n$q=\\xi\/\\xi_x$ where $\\xi_x=L_x\/(n_xa^2)$ where $L_x$ is the ionizing X-ray luminosity,\n$n_x$ is the gas density at the X-ray source and $a$ is the orbital separation.\nThis is a particular example\nof the scaling of ionization in optically thin photoionized gas, which depends on\nthe ionization parameter $\\xi=L_x\/(n(r) r_x^2)$ \\citep{Tart69,KallmanBautista01}.\nThe properties of resonance scattering as applied to X-ray polarization\nin HMXBs are dominated by the fact that,\nowing to the restricted regions where ionization is suitable,\nmost lines can only have strong opacity\nover a fraction of the wind. The regions where this occurs most often resemble\nspherical shells surrounding the X-ray source.\n\nResonance scattering differs from electron scattering in its sensitivity to the velocity\nstructure of the wind. At a given observed photon energy, scattering can occur only\nover a resonant surface with shape determined by the wind velocity law; in a\nspherical wind with monotonic velocity law these surfaces are open surfaces of revolution\nsymmetric about the line of sight to the X-ray source.\n\nResonance scattering affects polarization by \nredistributing the polarization among the components\nof the Stokes vector according to the phase matrix which is a linear\n combination of the Rayleigh and isotropic phase matrices; coefficients \nof the matrix depend on the angular momentum quantum \nnumbers of the initial and final states of the transition, $j$ \\citep{LeeBlandfordWestern94}. \nThe matrices describing this process have been calculated by \\citet{Hamilton47}. \nIn practice most of the strongest X-ray resonance lines have initial and final values\n$j_{lower}=1\/2$ and $j_{upper}=3\/2$, which correspond to the Rayleigh phase matrix,\nand we adopt this in our calculations. Similar assumptions were employed in \n\\citet{DoraKall10}.\n\nPolarization also depends on the scattering angle, and the resonance\ncondition constrains the scattering geometry, thereby imposing a relation\nbetween the energy of the scattered photon and its polarization.\nResonance scattering can have a cross section which is much greater than\nelectron scattering, but only over the relatively narrow energy band spanned by the\nresonance line, including the effects of Doppler broadening associated with the\nwind motion. Within this band, the polarization can be greater than that produced by\nelectron scattering, owing to the greater cross section, given suitable geometry. \n\nIn the remainder of this section we illustrate these effects by generalizing\nthe single scattering spherical wind calculations to include resonance\nscattering. We consider a single resonance line, chosen to crudely resemble\na line such as O VIII L$\\alpha$. The parent ion is assumed to exist over\na range of ionization parameter $1\\leq {\\rm log}(\\xi) \\leq 3$.\nWe calculate the optical depth using the Sobolev expression \\citep{Castor}:\n\n\\begin{equation}\n\\label{equation2}\n \\tau_{line}(\\varepsilon,{\\bf r})=\\frac{\\pi e^2}{mc} f n({\\bf r})x_{ij}y_j \\frac{\\lambda r}{v(r)(1+\\frac{z^2}{r^2}\\left(\\frac{d{\\rm ln}v(r)}{d{\\rm ln}r}-1\\right)}\n \\end{equation}\n\n\\noindent where $f$ is the oscillator strength, $n({\\bf r})$ is the gas number density, $x_{ij}$ is the ion fraction, $y_j$ is the\nelement abundance, $z$ is the position along the line of sight, and $\\lambda$ is the line wavelength. For the source\nfunction we adopt the same expression used for electron scattering $S=L_0\/(4\\pi r_x^2)$ since thermalization is unimportant and the\nsize of the X-ray source is small compared with the other length scales of interest for HMXBs.\n\n\n\nResults of our simple single line calculation are shown in figure \\ref{fig8}.\nThis shows the line profile as a function of orbital phase for a system viewed edge on ($i=\\pi\/2$).\nFor each phase we display the luminosity, with the transmitted luminosity shown in black and the\nscattered luminosity shown in green (bottom panel) and polarization fraction (top panel).\nThe polarization angle is not shown because there is no significant dependence on energy\nor orbital phase when the system is viewed at high inclination; the polarization is always\nperpendicular to the orbital plane. The horizontal axis is energy in units of the wind terminal\nvelocity.\n\nAt phase 0 (superior conjunction of the X-ray source) the shape of the profile in\nluminosity is similar to P-Cygni profiles familiar from UV resonance\nlines in hot stars: the outflow produces blue-shifted absorption (shown as negative energy in these\nfigures) of the continuum. This absorption is offset from the zero of energy owing to the fact that\nthe continuum source in this case is the compact X-ray source, and the wind speed at the X-ray source\nis approximately half the terminal speed. In addition, the X-ray source creates an ionized region\nwithin which line scattering cannot occur. As a result,\nthe absorption occurs in a relatively narrow region of energy near the energy corresponding to the\nwind terminal velocity. The scattered emission, on the other hand, is essentially symmetric in energy, since\nscattered emission comes from a region which is not necessarily along the line of sight to the X-ray source. \nProjection effects make the scattered emission appear at all energies $|\\varepsilon| \\leq \\varepsilon_0 v_\\infty\/c$\nwhere $\\varepsilon_0$ is the line rest energy. The scattered emission is unpolarized at phase 0 for the same\nreason as in the pure electron scattering case.\nAt phase 0.25 (quadrature) the wind is viewed perpendicular to its velocity vector at the X-ray source,\nand therefore the absorption covers a large fraction of the energy spanned by the wind.\nThe scattered emission is polarized up to $\\sim$50$\\%$; the degree of polarization is symmetric around\nthe center of the line, owing to the fact that the wind velocity structure is symmetric around the line\nof centers. At phase 0.5 (inferior conjunction of the X-ray source)\nthere is no transmitted flux (due to occultation by the star) and the emission is predominantly red-shifted\nowing to the location of the X-ray ionized zone in the receding part of the wind. In this case,\neven though the flux is all scattered, the polarization fraction is negligible owing to the circular\nsymmetry of the scattering region of the wind as viewed in the plane of the sky.\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=270, scale=0.5]{fig8.ps\n\\caption{\\label{fig8} Spectrum, polarization fraction and angle in a resonance line vs. energy\n in units of the terminal velocity at orbital phase 0.25 for inclination $i=\\pi\/2$}\n \\end{figure*} \n\n\n\\section{Absorption Effects}\n\nThe ionization balance in an HMXB wind is determined at each point by the\nX-ray flux and by the gas density. The X-ray flux in turn depends\non the effects of geometrical dilution and on attenuation. \nIn order to do so, we utilize results from {\\sc xstar} \\citep{KallmanBautista01} at each point\nin the wind in order to determine the opacity. This is done along\nradial rays originating from the X-ray source, so that the opacity\nat each point is calculated using an ionization parameter which corresponds\nto the flux transmitted along the corresponding ray.\n\nThis single stream\ntransfer treatment is similar to the transfer treatment used by {\\sc xstar}\nwith one difference: {\\sc xstar} uses the local spectral energy distribution (SED)\nto calculation the ionization along a ray; here we use the same unattenuated\nSED everywhere for the ionization calculation (so we can use a stored table),\nbut we calculate the ionization parameter self-consistently, i.e. by calculating the \ntransmitted flux at each position and then using that value to calculate the ionization parameter. \nThis simplification allows for more efficient computation without unduly sacrificing physical realism.\nThis approximation will be least accurate when the column density is highest, i.e.\n$\\geq 10^{23}$ cm$^{-2}$. In such regions, the ionization is expected to be low,\nowing to attenuation, and X-ray scattering will be relatively unimportant. Our\napproximation will tend to produce less attenuation at a given column density\nthan a self-consistent calculation would.\n\n\\section{Resonance Line Scattering: Ensemble of Lines}\n\\label{sec3b}\n\nX-ray ionization creates an ensemble of resonance lines in an HMXB wind from the many\ntrace elements such as C, N, O, Ne, Mg, Si, S, and Fe. At\nany point in the wind the ionization depends most sensitively on the ionization parameter,\ndefined in the previous section. Figure \\ref{fig9} shows the distribution of ionization\nparameter produced in a wind with a density distribution which is spherically symmetric\naround the primary star, as discussed so far. \nWe adopt a spherical wind with mass loss rate $10^{-5} M_\\odot {\\rm yr}^{-1}$, corresponding to a\ntotal wind column density of $3 \\times 10^{23}$ cm$^{-2}$, and an X-ray source luminosity of $10^{38}$ erg s$^{-1}$.\nEach spatial region with a distinct ionization parameter will have a different distribution of ion abundances,\nand hence a unique distribution of resonance line opacity. These can be calculated\nunder the assumption of ionization equilibrium. Figure \\ref{fig10} \nshow examples of the spectrum and the polarization fraction produced by\nsuch a distribution from the spherical wind shown in figure \\ref{fig9}.\nIonization and line opacities were calculated as a function\nof ionization parameter using the {\\sc xstar} code \\citep{KallmanBautista01}.\nThese were binned into 1000 energy bins over energy from 0.1 eV to 10$^4$ eV.\nSince the Doppler shifts associated with the wind speed is less than the energy bin size,\nwe adopt an approximate form for the source function each resonance line:\n\n\\begin{equation}\n S(\\varepsilon,{\\bf r})=\\frac{L_0}{4\\pi r_x^2} \\frac{v_{\\infty}-v_0}{\\Delta \\varepsilon c\/\\varepsilon}\n\\end{equation}\n\n\\noindent where the quantity\n$(v_{\\infty}-v_0)\/(\\Delta \\varepsilon c\/\\varepsilon)$ takes into account the fact that the line\ndoes not conver the entire energy bin. This treatment assumes that the line optical depths are not large,\nwhich is an adequate approximation for our situation. \nDepolarization effects at large Sobolev optical depths associated with multiple scatterings are not taken into account.\nThese calculations serve to illustrate the magnitude of the polarization effects expected from\nresonance scattering in HMXBs. Simulations suitable for quantitatively diagnosing the wind or the X-ray source properties\nwill require reexamination of these assumptions.\n\nResults of spectra and polarization fractions are shown in figure \\ref{fig10}.\nThese demonstrate that the resonance lines cover a significant fraction of the X-ray energy\nband and that they can scatter a significant flux of X-rays, and create polarization fractions\nas high as 0.5, much greater than would be produced by electron scattering alone.\nA difference between resonance scattering and electron scattering is that resonance\nscattering in a given line occurs within a relatively narrow spatial region where the parent\nion is most abundant. For X-ray lines, these are most likely to be approximately spherical\nsurface surrounding the X-ray source. This region tends to produce a small polarization which\nis almost independent of the viewing angle. Thus resonance scattering produces\nweaker modulation of the polarization with orbital phase than does electron scattering.\n\nIt is also worth noting that the effects of scattering can be either polarizing or not polarizing,\ndepending on the scattering angle. Also, lines which appear in absorption in the\nspectrum can have enhanced polarization in their troughs owing to the presence of scattered light\nin the residual intensity. In the spectrum shown in figure \\ref{fig10} the polarization\nin the absorption lines is generally lower than in the emission features, or in the adjacent\ncontinuum. This is due to the fact that the scattered light in the residual intensity is\nforward scattered, and so is not polarized because of the scattering angle. \n\n\\begin{figure*}[p] \n\\includegraphics*[angle=90, scale=0.5]{fig9.ps\n\\caption{\\label{fig9}Distribution of ionization parameter in the orbital plane\n for a spherical wind with $\\dot M_{wind}=10^{-5} M_\\odot {\\rm yr}^{-1}$\n illuminated by a 10$^{38}$ erg s$^{-1}$ X-ray source. Contours are\n labeled with log$\\xi$.}\n\\end{figure*} \n\n\\begin{figure*}[p] \n\\includegraphics*[angle=270, scale=0.5]{fig10.ps\n\\caption{\\label{fig10} Spectrum and polarization fraction vs energy\nfor spherical wind in units log(E\/keV) at orbital phase 0.25 for inclination $i=\\pi\/2$}\n \\end{figure*} \n\n\n\n\n\n\n\n\\section{Hydrodynamic Models}\n\\label{sec4}\n\nThe stellar wind in HMXBs is not spherically symmetric. Physical processes \nwhich affect the wind and accretion flow in HMXBs include: the three-dimensional \ngeometry of the flow (i.e. the flow both in and out of the \norbital plane), rotational forces, the influence of gravity and radiation \npressure from both stars in the binary, transport of the radiation \nfrom the stars into the flow and the reprocessed radiation out of the \nflow, X-ray heating and ionization, and departures from thermal equilibrium \ndue to advection and adiabatic heating and cooling. The dynamics\nof X-ray heated winds have been discussed by \\citet{Fran80}, and \\citet{Hatc77}, \nand multi-dimensional models have been \ncalculated by \\citet{Blon90, Blondin94, Mauc08}\n\nIn this section we illustrate the effects of the wind hydrodynamics on the \npolarization. We use two sample wind hydrodynamic models calculated using \na numerical model similar to that described in \\citet{Blon09}\nbut extended to three dimensions. The hydrodynamic models \nare computed on one hemisphere of a spherical grid, assuming reflection \nsymmetry about the orbital plane. A non-uniform grid of 448 (r) by \n128 ($\\theta$) by 512($\\phi$) zones is used with highest resolution near the \nsurface of the primary star and in the vicinity of the accreting neutron \nstar. This model calculates the wind dynamics in three dimensions taking \ninto account the radiative driving by the UV\/optical radiation from \nthe primary, and also the gravity of the star and the compact X-ray source. \nA fixed X-ray luminosity of 10$^{36}$ erg\/s is used to calculate X-ray heating \nusing the approximate formulae given in Blondin (1994) and the effects \nof X-ray ionization on the dynamics via changes in the UV radiation \nforce multiplier. The two models have identical dimensions:\nprimary radius 2.4 $\\times 10^{12}$ cm and orbital separation 3.6 $\\times 10^{12}$ cm.\nThey differ in their mass loss rates, which are 4 $\\times 10^{-7} M_\\odot {\\rm yr}^{-1}$ and\n1.7 $\\times 10^{-6} M_\\odot {\\rm yr}^{-1}$. The maximum wind speed in\nboth models is approximately 1600 km s$^{-1}$.\nA plot showing the density contours and velocity vectors\nis shown in figure \\ref{fig11}. This\nclearly shows the influence of the gravity of the compact object in creating a region of higher\ndensity and non-radial wind flow in the vicinity of the X-ray source.\n\nThe distorted stellar wind of the hydrodynamic models is illustrated with velocity \nvectors in the orbital plane and a series of transparent density isosurfaces.\nRight panel is the model with a higher mass loss rate (Mdot = 1.7e-6); left panel\nis a lower mass loss rate (Mdot = 4.0e-7). The lowest density isosurface corresponds\nto a density of 4e9 cm$^{-3}$ in both panels. Velocity vectors are not shown for values\nless than 800 km\/s.\n\nThe high mass loss rate model has a denser spherical component of the wind, but a\nless pronounced accretion wake. The low mass loss rate model has a larger volume\nof wind moving at low velocity due to photoionization of the wind in the vicinity \nof the X-ray source. The result is\na denser wind coming off the primary along the line of centers of the binary system\nand a larger, denser accretion wake that wraps more tightly around the primary star.\n\n\n\nWe apply the same calculation of X-ray scattering to the hydrodynamic wind\nas was done in section \\ref{sec3}. We assume an X-ray source\nluminosity of $10^{36}$ erg s$^{-1}$ and a $\\Gamma$=2 power law ionizing spectrum.\nThe distribution of ionization parameter in the orbital plane is shown in figure\n\\ref{fig12}. This shows a spiral structure, owing to a corresponding structure\nin the gas density.\n\nFigures \\ref{fig13} shows the spectra and polarization produced\nby the hydrodynamic wind model with $\\dot M=4.0 \\times 10^{-7} M_\\odot {\\rm yr}^{-1}$\nat orbital phases 0, 0.25 and 0.5. Comparison\nwith figure \\ref{fig10} shows that the hydrodynamic model produces a spectrum which is \n similar to that from \na smooth spherical wind. However, the degree of polarization is significantly \ngreater, due to the departure from spherical symmetry created by the X-ray \nsource. In particular, the spectrum during eclipse is greater, owing to the \nfact that the wake structure extends beyond the disk of the primary star \nduring eclipse. The spherical wind produces fractional polarization averaged \nover the 0.1 \u2013 10 keV energy band, of at most 10$\\%$ during eclipse. The spherical \nwind produces polarization values which are much less than this at mid-eclipse. \nThe hydrodynamic wind produces fractional polarization of approximately 21$\\%$ at mid-eclipse.\n\nPolarization calculations for hydrodynamic models have been carried out for\nthe two mass loss rates shown in figure \\ref{fig11}, and for several choices\nof ionizing X-ray luminosity. Note that the hydrodynamic models assume a fixed X-ray \nluminosity (used to calculate X-ray heating and ionization within the simulation) \nof 10$^{36}$ erg\/s, independent of the X-ray luminosity used to calculate the \nspectrum and polarization. Moreover, the prescribed X-ray luminosity is \nnot generally consistent with the mass accretion rate derived from the hydrodynamic \nsimulation, and thus these models are not fully self-consistent. Nonetheless, \nthey serve to illustrate the behavior of the polarization and its dependence \non wind density and X-ray luminosity. \n\nValues for polarization fractions during\neclipse are shown in table \\ref{table1}. These are averages of the linear polarization\nover energy in the range from 0.1 -- 10 keV. Polarization values reflect competing effects.\nFor highly ionized gas, the wind is essentially transparent and the polarization is due to\nThomson scattering. The polarization fraction is proportional to the Thomson depth (when small)\nand also to departures from circular symmetry of the scattering material in the plane of the sky.\nPartially ionized gas can produce larger polarization, owing to the greater cross section associated\nwith resonance scattering, although each line has very limited spectral range and the ensemble of\nlines for any given model seldom has a width which exceeds $\\Delta \\varepsilon\/\\varepsilon \\sim 0.1$. On the other hand,\npartially ionized and near-neutral gas also is affected by photoelectric absorption. This limits\nthe size of the X-ray scattering region, and tends to reduce the net polarization.\nTable \\ref{table1} shows that, for the limited range of parameters spanned by our models,\nmore highly ionized models tend to produce greater polarization. That is, the effects of photoelectric\nabsorption offset the effects of resonance scattering in partially ionized gas, leading to very weak\ndependence of the polarization on X-ray luminosity when the luminosity is not large.\nWe expect that at very low X-ray luminosities this effect will be even stronger, since the\npartially ionized zone containing the resonance scattering gas will shrink and become more round,\nand photoelectric absorption will remove a larger fraction of photons.\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=0, scale=0.4]{fig11a.ps\n\\includegraphics*[angle=0, scale=0.4]{fig11b.ps\n\\caption{\\label{fig11} Density contour and vector plot of wind hydrodynamic\n models. Right panel is model with\n $\\dot M=1.7 \\times 10^{-6} M_\\odot {\\rm yr}^{-1}$\n left panel is model with\n $\\dot M=4.0 \\times 10^{-7} M_\\odot {\\rm yr}^{-1}$. Spatial scale is\n the same for both panels. Yellow contour corresponds to a density of\n $6 \\times 10^8 {\\rm cm}^{-3}$. }\n\\end{figure*} \n\n\\begin{figure*}[p] \n\\includegraphics*[angle=90, scale=0.5]{fig12.ps\n\\caption{\\label{fig12}Distribution of ionization parameter in the orbital plane\n for hydrodynamic wind illuminated by a 10$^{36}$ erg s$^{-1}$ X-ray source. Contours are labeled with log$\\xi$.}\n\\end{figure*} \n\n\\begin{figure*}[p] \n\\includegraphics*[angle=270, scale=0.5]{fig13.ps\n\\caption{\\label{fig13} Spectrum and polarization fraction vs energy\n at phase 0, 0.25, 0.5 for hydrodynamic wind with $\\dot M=4.0 \\times 10^{-7} M_\\odot {\\rm yr}^{-1}$\n illuminated by a 10$^{36}$ erg s$^{-1}$ X-ray source.}\n\\end{figure*} \n\n\n\\begin{figure*}[p] \n\\includegraphics*[angle=90, scale=0.6]{fig14.ps\n\\caption{\\label{fig14} Diagram showing estimated polarization fractions in lines and continuum at conjunction vs. quadrature for\nthe objects in table \\ref{table2}.}\n\\end{figure*} \n\n\n\\section{Discussion}\n\\label{sec5}\n\nOur results so far on the polarization produced by wind scattering in HMXBs can be summarized as follows:\nPolarization depends on inclination: high inclination produces variable polarization fraction plus\nconstant angle; low inclination produces constant polarization fraction plus variable polarization position angle.\nThe maximum attainable continuum polarization fraction scales approximately proportional to electron scattering optical depth, for\ndepths less than unity; this is partly a consequence of our single scattering assumption and must be suitably modified\nif multiple scattering is important.\nAt high inclination, polarization fraction of the scattered radiation ($P_S$) at phase 0 and 0.5 is less than \nat phases 0.25 or 0.75; for a spherical wind the phase 0 and 0.5 polarization is zero.\nPolarization fraction of the total radiation, including unscattered, is small for the parameters considered\nhere for all orbital phases out of eclipse. A spherical wind thus produces narrow intervals of high polarization\nduring eclipse but away from phase 0.\nResonance line optical depths are greater than for electron scattering, and so can produce greater\nlinear polarization. On the other hand, the optical depths are often large, and the\nX-ray scattering regions tend to be small, i.e. nearly circular around the compact object, producing\nsmaller orbital phase modulation. The hydrodynamics of the interaction between the wind and the compact object breaks\nthe spherical symmetry and increases the net polarization. \n\nPredictions of the polarization signatures for particular known HMXBs require\nhydrodynamic simulations for each system incoporating known physical parameters: the\nsizes and masses of the components, the primary wind mass loss rate and the X-ray source\nluminosity and spectrum. These would then need to be analyzed using a transfer calculation\nsuch as the single scattering calculations we have presented.\nIn this paper we have presented generic models for the wind densities and X-ray source properties,\nboth for spherical winds and incoporating the hydrodynamic effects of the\ntwo gravitating centers. These generic models can be used to infer, in a very simple way, the\npolarization signatures expected from various sources.\n\nTable \\ref{table2} shows the known parameters of the 5 brightest and best-studied HMXBs. The\nrelevant quantities are the source X-ray luminosity, primary type, mass loss rate, and average\nX-ray luminosity. These quantities are taken from \\citet{Cont78} and from\n\\citet{Kape98}. We do not include several systems which generally have smaller observed X-ray fluxes\nor less well constrained properties.\n\nWith these quantities we can use our spherical wind models to\ncrudely predict the polarization fractions and orbital phase modulation of the polarization for these\nsources. We do this in the following way: we use the results in table \\ref{table1}\nto construct an approximate scaling of mid-eclipse net polarization with the wind mass loss rate\nand X-ray luminosity. We then apply this to the known HMXBs in table 1. The results\nare shown graphically in figure \\ref{fig14}. The most relevant quantities for each of the table 1 HMXBs, X-ray\nluminosity and wind mass-loss rate, are plotted on the axes and the values for each object are plotted\nas points. Contours of constant predicted mid-eclpise polarization are shown as solid curves, and labeled. This\nshows that high polarizations are expected for some objects, those with the strongest winds and weakest\nX-rays generally. This demonstrates that polarization fractions in the range\nfrom 5 -- 30 $\\%$ are expected at mid-eclipse for these systems. More detailed information is contained in the\nspectra and the time variation of the net polarization during eclipse; interpretation of these signals\nrequires modeling which is tailored to each particular system.\n\n\\acknowledgements Support was provided through grant 10-ATP10-0171 through the NASA astrophysics theory program. \n\n\n\\begin{deluxetable}{crrr}\n\\tabletypesize{\\scriptsize}\n\\tablecaption{Hydrodynamic Model Results. Values for the polarization fraction are given for several hydrodynamic\n models described in the text. \\label{table1}}\n\\tablewidth{0pt}\n\\tablehead{\n\\colhead{Model}&\\colhead{$\\dot M$}&\\colhead{L$_{x}$}&\\colhead{P$_{\\phi=0.5}$} }\n\\startdata\n &$M_\\odot {\\rm yr}^{-1}$ & erg s$^{-1}$&\\\\\n1& 4 $\\times 10^{-7}$ & 10$^{38}$&0.25\\\\\n2& 4 $\\times 10^{-7}$ & 10$^{37}$&0.25\\\\\n3& 4 $\\times 10^{-7}$ & 10$^{39}$&0.29\\\\\n4& 1.7 $\\times 10^{-6}$ & 10$^{38}$&0.22\\\\\n\\enddata\n\\end{deluxetable}\n\n\\begin{deluxetable}{crrr}\n\\tabletypesize{\\scriptsize}\n\\tablecaption{Sample HMXBs and Properties Needed for Predicting Wind Polarization. \\label{table2}}\n\\tablewidth{0pt}\n\\tablehead{\n\\colhead{object}&\\colhead{Sp. type}&\\colhead{$\\dot M_{wind}$}&\\colhead{L$_x$} }\n\\startdata\n & &$M_\\odot {\\rm yr}^{-1}$&erg s$^{-1}$\\\\\nVela X-1&B0.5 Iab&7 $\\times 10^{-6}$&0.05\\\\\nCen X-3&O6.5 II-III&2 $\\times 10^{-7}$&0.3\\\\\nCyg X-1&O9.7 Iab&2.5 $\\times 10^{-6}$&0.1\\\\\n4U1700-37&O6.5 Iaf+&1 $\\times 10^{-5}$&0.01\\\\\nsmc x-1&B0.6 Iab&5 $\\times 10^{-7}$&3 \\\\\n\\enddata\n\\end{deluxetable}\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{secintro}\n\\IEEEPARstart{T}HE Internet of Things (IoT) is the interconnection of network-enabled devices communicating with each other over the Internet ~\\cite{Da14,Oteafy17,Deng18,Adhatarao18}. Hence, the purpose of IoT is to integrate the physical world and the virtual world to attain a self-sustaining system ~\\cite{Da14,Oteafy17,Deng18,Adhatarao18}. To achieve this integration in the near future, research into the application of IoT for the creation of smart-cities, smart-homes, smart-energy, smart-transportation and many more is growing ~\\cite{Da14,Oteafy17,Deng18,Adhatarao18}. In IoT, access and interaction between devices are seamless due to the use of unique identifiers, sensors and communication technologies ~\\cite{Ciuonzo12,Ciuonzo14,Da14,Oteafy17,Deng18,Adhatarao18}. The evolution of IoT started from wireless sensor networks (WSN) and developed into different heterogeneous networks interacting with each other over the internet ~\\cite{Ciuonzo12,Ciuonzo14,Rossi16,Deng18}. A wireless network of spatially distributed autonomous devices acting as sensors to monitor and record physical or environmental conditions (e.g.s humidity, pressure, temperature, power-line voltages, and vital body functions) is called a WSN ~\\cite{Raghavendra06,Yick08,Ciuonzo12,Ciuonzo14,Rossi16,Yetgin17}. These sensor nodes may range from a few hundreds to thousands in a sensor network ~\\cite{Raghavendra06,Yick08,Yetgin17}. Each sensor node is equipped with an antenna, microcontroller, interfacing electric circuit, and an energy source ~\\cite{Raghavendra06,Yick08,Yetgin17}. \n\nWhile a WSN and other sensor networks operate in their own private networks, IoT integrates the various sensor networks into an IoT network by providing them access to the internet ~\\cite{Adhatarao18}. For an IoT network, by the use of routing schemes, a gateway supports communication between the sensor nodes and a central system where actions are taken based on the information received from the sensor network ~\\cite{Raghavendra06,Yick08,Ciuonzo12,Ciuonzo14,Rossi16,Yetgin17}. The gateway of a sensor network within an IoT network communicates with the central system over the internet. The routing schemes facilitates either direct or relayed communication between a gateway and a particular sensor node ~\\cite{Raghavendra06,Yick08,Yetgin17}. Relays not only facilitate information forwarding, but may lead to an increase in a communication system's throughput ~\\cite{Bai15,Simmons16,Guo16,Song17}. Relays, however, depend on their resources (i.e., power and computational components) to aid the processing and transfer of signals ~\\cite{Bi15,Lu15,Guo16}. However, sensor nodes in IoT networks have limited resources, especially power ~\\cite{Raghavendra06,Yick08,Yetgin17,Guo16,Adhatarao18}. Energy harvesting (EH) can be used to reduce the strain on the power resource of the relaying sensor nodes. By harvesting energy from a portion of the received radio frequency (RF) signal at the relay node, the harvested energy can be a source of power for information signal relaying ~\\cite{Ulukus15,Ding15,Guo16}. \n\nThe technique used for EH from RF signals is known as wireless power transfer (WPT) ~\\cite{Zhou16,Song17,Mahama17}. WPT involves the wireless transfer of electrical energy from a power source to a load ~\\cite{Mahama17}. EH is the process of scavenging energy from an external energy source ~\\cite{Zhou16,Song17,Mahama17}. Typical traditional energy sources include hydro, wind, solar and wind, however, these sources of energy are less stable due unruly factors such as weather ~\\cite{Mahama17}. Therefore, RF energy signals can serve as a more stable source of electrical energy for self-sustaining cellular systems ~\\cite{Zhou16,Song17,Mahama17}. This implies that, EH from RF via WPT can be used in sensor network to aid sensor nodes with power constraints when operating as relays ~\\cite{Guo16}. WPT implementation in cellular communication systems is accomplished using two main approaches, namely, wireless powered communication networks (WPCN) and simultaneous wireless information and power transfer (SWIPT) \\cite{Tang18,Asiedu18,Mahama17,Lee16,Yin17,Lee18}. SWIPT involves the transmission of wireless information signal and wireless power signal concurrently ~\\cite{Mukhlif18,Jameel17,Perera17}. Time switching (TS) and power splitting (PS) are the two main techniques by which SWIPT is implemented ~\\cite{Mukhlif18,Jameel17,Perera17}. The successive transmission of wireless information signal and wireless power signal is the technique used to accomplish WPCN ~\\cite{Ramezani17,Niyato16}. \n\nA few investigations on dual-hop SWIPT relay node system configurations are presented in ~\\cite{Mahama17,Asiedu18,Do17} and ~\\cite{Liu17}. Using outage probability and throughput as performance matrices, the research in ~\\cite{Mahama17} optimizes the PS ratio for a single amplify-and-forward (AF) relay node facilitating communication between two nodes. In ~\\cite{Mahama17}, it is assumed that each relay node's battery is charged with the energy it harvests from a portion of the received RF signal. In addition, all communicating nodes were equipped with a single antenna. The work in ~\\cite{Mahama17} also included an extension into AF-SWIPT relay node selection. In ~\\cite{Asiedu18}, the authors considered a single antenna AF dual-hop configuration with multiple relay nodes. Both the power control factors and the PS ratios for each relay node were optimized with the objective of maximizing the achievable rate. The work presented in ~\\cite{Do17} focused on a non-orthogonal multiple access (NOMA) system where a MIMO base station (BS) communicates with a single antenna cell-center user and a single antenna cell-edge user. The authors proposed three cooperative downlink (DL) transmission schemes based on hybrid SWIPT and BS antenna selection. The hybrid SWIPT powered relaying between the BS and cell-edge user is done by the cell-center user. The authors in ~\\cite{Do17} acquired closed-form solutions for outage probability for their proposed schemes. The schemes were then compared to both orthogonal multiple access and non-NOMA systems in their simulation results. In addition, the authors presented a discussion on the diversity gains and complexity requirements of the various proposed schemes. A two-hop multi-antenna decode-and-forward (DF) SWIPT relay scenario is investigated in ~\\cite{Liu17}. The PS ratio and power allocation at the DF-SWIPT relay node were optimized to maximize the end-to-end system throughput. The authors in ~\\cite{Liu17} proposed an optimal clustering algorithm and a greedy clustering algorithm which grouped the multiple antennas of the DF-SWIPT relay node into information detection and energy harvesting antenna sets. \n\nSWIPT schemes in multi-hop relay systems are researched in ~\\cite{Chen17}, ~\\cite{Xu17}, ~\\cite{Mao15}, and ~\\cite{Liu19}. The work in ~\\cite{Chen17} presents a novel multi-hop relay transmission strategy, where energy is harvested by the source and the relay nodes from the co-channel interference. The PS ratio for the source and AF-SWIPT multi-hop relay nodes were optimized based on a given outage probability threshold. In ~\\cite{Chen17}, the authors identified the maximum number of AF-SWIPT multi-hop nodes that can support information transfer between a source node and a destination node. The research in ~\\cite{Xu17} studied the coexistence of primary users (PUs) and secondary users (SUs). The PUs and SUs interacted in an energy harvesting cognitive radio network implemented using time division multiple access (TDMA) technique. The multi-hop relaying scenario in ~\\cite{Xu17} occurs during data transmission between the SU nodes. The SUs harvest energy from the PU's RF signals. By employing an iterative algorithm, the end-to-end throughput of the SUs is maximized based on the time and power resource optimization in ~\\cite{Xu17}. A DF-SWIPT and an AF-SWIPT multi-hop relay configurations considering both the TS ratio and the PS ratio techniques are investigated in ~\\cite{Mao15}. The authors in ~\\cite{Mao15} aimed to find the maximum number of nodes which support communication between a source and a destination given a rate threshold constraint. A SWIPT AF multi-hop system with multiple-input-multiple-output (MIMO) relay nodes is investigated in ~\\cite{Liu19}. In ~\\cite{Liu19}, the achievable rate was maximized by optimizing the source and relay beamforming vectors, and the power resource of each node. \n \nIn this paper, we investigate a DF sensor network, where a single antenna source node communicates with a single antenna destination node through multi-hop single antenna relay nodes. By adopting the PS ratio scheme, the multi-hop relay nodes operate in the SWIPT mode. Each relay node uses a portion of the RF signal it receives for EH, and it is saved in a supercapacitor. Unlike ~\\cite{Chen17} and ~\\cite{Xu17} which harvest energy from their co-channel interference and primary users, our model considers energy harvesting from only a portion of the RF signal received from the previous node. The harvested energy in the supercapacitor is used to both decode the rest of the RF signal, and forward the decoded information signal to the next node. An example in which our system model occurs is when the BS is requesting data from the destination wireless sensor node ~\\cite{Raghavendra06,Sabor17,Djiroun17,Sandeep17}. The BS sends the request command to the destination wireless sensor via routing through the DF wireless sensor relay nodes. Another application of our system model involves device-to-device (D2D) communication between nodes facilitated by energy harvesting. Instead of relaying information, each node harvests energy from the RF signal it received from a previous node to power its own information signal transmission to the next node. Unlike the work in ~\\cite{Mao15}, where the maximum number of hops is determined based on an available source power and throughput constraint, we solve the DF system throughput maximization and the source power minimization problems. In addition, we found a closed-form solution for determining the maximum number of DF-SWIPT relay nodes which can support communication between the source and the destination nodes given an SNR threshold constraint and an available power source value. Our close-form solution differs from the approach presented in ~\\cite{Chen17} and ~\\cite{Mao15}, which uses an algorithm to determine the maximum number of relay nodes. The main contributions of this paper are as follows.\n\n\\begin{itemize}\n\\item First, we seek to identify the minimum amount of source transmit power which supports communication between the source node and destination node via the DF-SWIPT multi-hop relay nodes. By tackling this problem, we reduce the strain (i.e., the depletion of the source power resource) on the source power during routing of information in the IoT network. From our source power minimization problem, we derive closed-form solutions for determining the minimum required transmit power at the source and the PS ratios for the DF-SWIPT relay nodes. \n\\item We further consider the maximization of the minimum system achievable throughput also based on an already specified number of DF-SWIPT relay nodes and a source transmit power constraint. The maximization of the minimum system achievable rate is to aid in improving the system rate with different individual quality-of-service (QoS) constraint (i.e., the SNR thresholds) within the IoT network. We then derive closed-form solutions for the optimal PS ratios for each DF-SWIPT relay node, and the optimum system achievable throughput.\n\\item Using the optimal solutions for the source transmit power minimization problem and the optimal system achievable throughput problem, we show in this work that with a general SNR threshold constraint for all nodes within the IoT network, our two optimization solutions become equivalent. We treat equivalency of our solution due to the general SNR threshold consideration as a special scenario. \n\\item From the special case, we present a closed-form solution to determine the number of relay nodes which support communication between the source node and destination node for a homogeneous sensor network. This closed-form solution can be used in a routing algorithm for our proposed system model. The prediction of the DF-SWIPT number can aid in resolving coverage, connectivity and routing issues in the IoT sensor networks ~\\cite{Yetgin17,Mhatre04}.\n\\item Using the closed-form optimal PS ratio solution, we propose a centralized and a distributed method by which the PS ratio of each node can be determined in a real-world sensor network. In addition, we compare our two PS ratio determination methods in terms of complexity.\n\\item Since in practical systems channel estimation may not be perfect, within the simulation section of this paper we discuss the effects of imperfect channel estimation on the performance of our discussed optimization problems. \n\\end{itemize}\nFrom the remarks of authors in ~\\cite{Yick08} and ~\\cite{Yetgin17} concerning major issues on sensor networks, we can state the following advantages of implementing our proposed system model in a sensor network\\footnote{The current issues as stated in ~\\cite{Yick08} and ~\\cite{Yetgin17} concerning sensor networks are with the limitations on the node powers, the computational power of each sensor node, the storage capacity of each sensor node, and the QoS requirement for the system.}. The application of our proposed sensor network scenario and optimization solutions presented in this work can be implemented in large or small sensor node network types. This is due to the low computational complexity of the closed-form solutions presented in this paper which is important considerations for sensor networks. Another advantage of this work is the possibility of lengthening network lifetime for a sensor network using energy harvesting. We also covered QoS requirement constraint (i.e. throughput), and resource limitation constraint (i.e. transmit power, computational and data storage capabilities) in this work. To the best of our knowledge, this is the first work to analyze both the source transmit power minimization problem and the optimization of the system achievable throughput problem for a DF-SWIPT multi-hop relay system model. We compare the optimal closed-form solutions to the fixed PS ratio scheme in our simulation results. The results affirmed that the optimal scheme outperformed the suboptimal scheme in terms of the minimum source power and the system achievable throughput. \n\nThe rest of the paper is organized as follows: Section ~\\ref{secsystem_model} presents the system model and optimization problem statement. Closed-form solutions for the optimization problems are discussed in Section ~\\ref{secoptimization}. Section ~\\ref{secsimulation} discusses our numerical results, and conclusions are drawn in Section ~\\ref{secconclusion}.\n\n\\emph{Notations}: $n \\sim \\mathcal{CN}(0,\\delta^{2})$ denotes a circularly symmetric complex Gaussian random variable, $n$, with zero mean and a variance of $\\delta^{2}$. $\\mathop{\\mathbb{E}}_{X}[f(X)]$ is the expectation operation over random variable $X$. $f(X)$ and $R(X)$ represent a general function and a rate function respectively which are dependent on the variable $X$. \n\\section{System Model and Problem Formulation}\n\\label{secsystem_model}\nA sensor network consisting of a source, $K$ multi-hop DF-SWIPT relay nodes, and a destination is investigated in this paper as shown in Fig. ~\\ref{figSystems}. Each node is equipped with a single antenna. The source is a BS which may consist of the data gateway and the external\/central systems of the sensor network ~\\cite{Raghavendra06,Sabor17,Ciuonzo14,Rossi16,Djiroun17,Sandeep17}. The multi-hop DF-SWIPT relay nodes are the wireless sensor nodes found in the mesh network. This is due to the wireless sensor nodes being able to act as repeaters and relays ~\\cite{Raghavendra06,Sabor17,Djiroun17,Sandeep17}. The destination node is also a wireless sensor node within the network ~\\cite{Raghavendra06,Sabor17,Djiroun17,Sandeep17}. To reduce the strain on the relay node's resource (i.e., the power resource) during the relaying process, the relay nodes operate in the SWIPT mode. During the SWIPT mode, each relay utilizes their EH interfacing electrical circuit and a supercapacitor to facilitate the EH process from a portion of the RF signal it receives. Each relay node then facilitates the information decoding and forwarding process of the rest of the RF signal with the harvested energy.\n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.3in]{systemmodel}\n\\end{center}\n\\caption{Multi-hop DF relay systems with SWIPT architecture.}\n\\label{figSystems}\n\\end{figure}\n\nThe SWIPT architecture of our DF relay nodes in the sensor network system model is presented in Fig. ~\\ref{figSWIPTarch}. The source, $\\mathcal{S}$, and the destination, $\\mathcal{D}$, already possess their own energy source for communication. Since each DF-SWIPT relay node uses a supercapacitor, each relay node dissipates all its harvested energy for information decoding (ID) and retransmission. Each DF-SWIPT relay node undergoes EH via the PS technique and operates in the half-duplex mode. We assume that the source node knows the channel state information (CSI) for all nodes communicating, while each DF-SWIPT relay node and destination node have knowledge of only the CSI for their communicating channels\\footnote{The CSI for the sensor network can be acquired during the training phase for channel gain estimation when sensor nodes send pilots to each other and the BS (i.e., gateway and central system unit) ~\\cite{Zhao04,Taricco12,Wang18}.}. It is assumed that, there is no direct link between the source and the destination nodes. Also, we assume that there is no direct link between the relay nodes. For example, from Fig. ~\\ref{figSystems}, no direct link exists between $\\mathcal{R}_{1}$ and $\\mathcal{R}_{3}$. The assumption of no direct link holds for the worst case scenario, that is, when the distance between nodes is large. This assumption can be justified by virtue of routing from using a routing algorithm in the sensor network ~\\cite{Liu19,Qiao13,Wu18}. The detailed operation of the considered system model will now be presented.\n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.4in]{SWIPTArchitecture}\n\\end{center}\n\\caption{Multi-hop DF relay node SWIPT architecture.}\n\\label{figSWIPTarch}\n\\end{figure}\n\nThe received RF signal at node $k$ from the previous node is given as\n\\begin{equation}\ny_{k}=\\sqrt{E_{k-1}}h_{k}\\hat{x}_{k}+n_{k}, \\text{ }k=1,\\ldots,K+1,\n\\end{equation}\nwhere $h_{k}$ is the channel coefficient between the current node and the previous node, and $n_{k}\\sim \\mathcal{CN}(0,\\delta^{2}_{k})$ represents the antenna noise at the current node. $\\hat{x}_{k}$ stands for information signal from the preceding node. $K+1$ denotes the total number of subsequent nodes after the source node (i.e. the total number of DF-SWIPT relay nodes and the destination node). The channel $h_{k}$ is modeled as $h_{k}=\\sqrt{\\xi_{k}}\\tilde{h}_{k}$, where $\\xi_{k}$ is the large-scale fading coefficient and $\\tilde{h}_{k}\\sim \\mathcal{CN}(0,1)$ represents the small-scale fading component with Rayleigh distribution ~\\cite{Zhang13,Lee17}. The large-scale fading coefficient is modeled as $\\xi_{k}=C_{k}\\Big(\\frac{d_{k}}{d_{0}}\\Big)^{-\\alpha_{k}}$, where $C_{k}$ is the constant attenuation for a reference distance $d_{0}$ ~\\cite{Zhang13,Lee17}. $\\alpha_{k}$ and $d_{k}$ indicate the pathloss exponent, and the distance between the transmit and receive nodes respectively ~\\cite{Zhang13,Lee17}.\n\nNext, the received RF signal at node $k$ is then split into two based on the PS ratio, $\\rho_{k}$, for EH and ID. The EH and ID signals at node $k$ are respectively written as\n\\begin{equation}\n\\label{eq1}\ny^{EH}_{k}=\\sqrt{\\rho_{k}}\\Big(\\sqrt{E_{k-1}}h_{k}\\hat{x}_{k}+n_{k}\\Big),\n\\end{equation}\nand\n\\begin{equation}\n\\label{eq2}\ny^{ID}_{k}=\\sqrt{1-\\rho_{k}}\\Big(\\sqrt{E_{k-1}}h_{k}\\hat{x}_{k}+n_{k}\\Big)+z_{k},\n\\end{equation}\nwhere $z_{k}\\sim \\mathcal{CN}(0,\\sigma^{2}_{k})$ is the additional noise introduced by the ID circuitry.\n\nFrom (~\\ref{eq1}), the harvested energy, $E_{k}$, at node $k$ is expressed as\n\\begin{equation}\n\\begin{aligned}\n\\label{eq3}\nE_{k}&\\quad=\\beta_{k}\\mathop{\\mathbb{E}}_{\\hat{x}_{k},n_{k}}\\Big[\\vert y^{EH}_{k}\\vert^{2}\\Big]\\backsimeq\\beta_{k}\\rho_{k}E_{k-1}\\vert h_{k}\\vert^{2}\\\\&\\quad=E_{0}\\Gamma_{k}\\prod^{k}_{j=1}\\rho_{j}, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\n\\end{equation}\nwhere $\\Gamma_{k}\\triangleq\\prod^{k}_{j=1}\\beta_{j}\\vert h_{j}\\vert^{2}$, $0<\\beta_{k}\\leq 1$ is the energy conversion efficiency of the $k$th node, and $E_{0}$ is the source transmit power. Also, from (~\\ref{eq2}), the achievable rate, $R_{k}$, at node $k$ becomes\n\\begin{equation}\n\\begin{aligned}\n\\label{eq4}\nR_{k}&\\quad=\\log_{2}\\Bigg(1+\\frac{E_{k-1}\\vert h_{k} \\vert^2(1-\\rho_{k})}{(1-\\rho_{k})\\delta^{2}_{k}+\\sigma^{2}_{k}}\\Bigg)\\\\&\\quad\\backsimeq\\log_{2}\\Bigg(1+\\frac{E_{k-1}\\vert h_{k} \\vert^2}{\\sigma^{2}_{k}}(1-\\rho_{k})\\Bigg)\\\\&\\quad=\\log_{2}\\Bigg(1+\\frac{E_{0}\\Gamma_{k}}{\\sigma^{2}_{k}\\beta_{k}}\\prod^{k-1}_{j=1}\\rho_{j}(1-\\rho_{k})\\Bigg),\\\\&\\quad \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\n\\end{equation}\nwhere (~\\ref{eq4}) results from $\\delta^{2}_{k}\\ll \\sigma^{2}_{k}$, that is, the antenna noise is negligible compared to the ID circuit noise power ~\\cite{Liu13,Lee018,Zhang13}. \n\nIn this paper, we consider two different optimization methods for the proposed multi-hop DF-SWIPT networks by jointly optimizing the PS ratio $\\{\\rho_{k}\\}^{K}_{k=1}$ at the relay nodes. First, we aim to minimize the source transmit power, $E_{0}$, under the individual SNR constraint and PS ratio for each of multi-hop links as\\footnote{To further emphasize the importance of solving the source power minimization problem, we will shortly present two possible scenarios in which our source power minimization problem is applicable as well as the DF multi-hop relay systems. The first deals with the system model where several node pairs perform SWIPT D2D communication with neighboring nodes (i.e., $\\mathcal{R}_{k-1}\\text{-to-}\\mathcal{R}_{k}$ and $\\mathcal{R}_{k}\\text{-to-}\\mathcal{R}_{k+1}$, $k=1,\\ldots,K$ using Fig. ~\\ref{figSystems} as a reference). Here, within the $\\mathcal{R}_{k-1}\\text{-to-}\\mathcal{R}_{k}$ D2D nodes, $\\mathcal{R}_{k}$ uses SWIPT PS ratio technique to decode and harvest energy from the RF signal it receives from $\\mathcal{R}_{k-1}$. $\\mathcal{R}_{k}$ then uses its harvested energy to forward its own information signal to $\\mathcal{R}_{k+1}$ in the preceding D2D communication (i.e., $\\mathcal{R}_{k}\\text{-to-}\\mathcal{R}_{k+1}$). The second application involves a SWIPT PS ratio D2D communication between the BS and $\\mathcal{R}_{1}$ node. $\\mathcal{R}_{1}$ then transfer energy to $\\mathcal{R}_{2}$ to recharge its battery. The D2D WET occurs between $\\mathcal{R}_{k}\\text{-to-}\\mathcal{R}_{k+1}$ for all subsequent nodes after $\\mathcal{R}_{1}$ to recharge their batteries.}\n\\begin{equation}\n\\label{eq7}\n\\begin{aligned}\n& \\underset{E_{0},\\{\\rho_{k}\\}^{K}_{k=1}}{\\text{min}}\\text{ }E_{0}\\\\\n& \\text{subject to} \n\\begin{aligned} \n& & \\frac{E_{0}\\Gamma_{k}}{\\sigma^{2}_{k}\\beta_{k}}\\prod^{k-1}_{j=1}\\rho_{j}(1-\\rho_{k})\\geq \\bar{\\gamma}_{k}, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\\\\\n& \\text{ } \n\\begin{aligned} \n& & & & & & & & & & 0 \\leq \\rho_{k}\\leq 1, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\\\\\n&\\text{ } \n\\begin{aligned} \n& & & & & & & & & & E_{0} \\geq 0,\n\\end{aligned}\n\\end{aligned}\n\\end{equation}\nwhere $\\bar{\\gamma}_{k}$ is the SNR threshold constraint of node $k$. \n\nNext, we consider the maximization of the overall DF system rate which can be formulated as\n\\begin{equation}\n\\label{eq5}\n\\begin{aligned}\n& \\underset{\\{\\rho_{k}\\}^{K}_{k=1}}{\\text{max}}\\text{ }\\text{ }\nR\\\\\n& \\text{subject to} \n\\begin{aligned} \n& & &0 \\leq \\rho_{k}\\leq 1, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\n\\end{aligned}\n\\end{equation}\nwhere $R=\\underset{\\{1\\leq k\\leq K+1\\}}{\\text{min}}\\text{ }\\text{ }R_{k}$, and $R_{k}$ is defined in equation (~\\ref{eq4}). With problem (~\\ref{eq5}), the source transmit power is fixed. It is not considered as a constraint because we assume the source will transmit the RF signal with full power. \n\nProblem (~\\ref{eq7}) is always feasible for any given set of the SNR constraints $\\bar{\\gamma}_{k}$ since the initial energy $E_{0}$ is included as an optimization variable in (~\\ref{eq7}). For instance, it is obvious that the problem in (~\\ref{eq7}) is feasible when $\\gamma_{k}=0, \\text{ }\\forall k$. Also, even though some $\\bar{\\gamma}_{k}$ become large, we can always find a feasible PS ratio $\\rho_{k}\\in [0,1]$ by setting $E_{0}$ to a sufficiently large number. In addition, it is not difficult to show that the feasibility of the problem in (~\\ref{eq5}) is always guaranteed since no constraints on the system throughput $R$ is included. Hence, both (~\\ref{eq7}) and (~\\ref{eq5}) are always feasible in practice. In the next section, we provide the globally optimal $\\{\\rho^{\\star}_{k}\\}^{K}_{k=1}$, $E^{\\star}_{0}$ and $R^{\\star}$ solutions for both problems. \n\n\\section{Multi-Hop Relay Joint Optimal Design}\n\\label{secoptimization}\nIn this section, we solve the source transmit power minimization problem and the minimum system rate maximization problems in subsections ~\\ref{subsecpowermin} and ~\\ref{subsecrateminmax} respectively. We also discuss the physical (i.e., real-world) implementation of our optimal solutions and its influence on power constraint, computational constraint and QoS constraint in subsection ~\\ref{subsecsecapplic}.\n\\subsection{Source Transmit Power Minimization}\n\\label{subsecpowermin}\nIn this subsection, the optimum values for the source transmit power and the PS ratio are presented in the Theorem ~\\ref{thmoptimal_min}. We then provide a few remarks based on the optimal solutions for the source transmit power and the PS ratio of each DF-SWIPT relay node. \n\\begin{theorem} \\label{thmoptimal_min}\nFor the joint optimization problem given in (~\\ref{eq7}), the optimal source transmit power, $E^{\\star}_{0}$, is deduced as $$E^{\\star}_{0}=\\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}_{k}\\sigma^{2}_{k}\\beta_{k}}{\\Gamma_{k}},$$ with the optimal PS ratio, $\\rho^{\\star}_{k}$, at node $k$ defined as\n$$\\rho^{\\star}_{k}=\\begin{cases}\n1-\\frac{1}{\\prod^{k-1}_{j=1}\\rho_{j}}\\frac{\\frac{\\bar{\\gamma}_{k}\\beta_{k}}{\\Gamma_{k}}}{\\sum^{K+1}_{j=1}\\frac{\\bar{\\gamma}_{j}\\beta_{j}}{\\Gamma_{j}}},\\text{ }\\text{ }k=1,\\ldots,K \\\\\n0,\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }k=K+1.\n\\end{cases}$$\n\\end{theorem}\n\n\\textit{Proof:} See Appendix ~\\ref{app1}.\n\nFrom Theorem ~\\ref{thmoptimal_min}, we can infer that $E^{\\star}_{0}$ is dependent on $\\beta_{k}$, $\\sigma_{k}$, $\\Gamma_{k}$, and $\\bar{\\gamma}_{k}$, and independent of $\\rho_{k}$. This implies that, the optimal source power can be determined at the source node without knowledge of each relay node's PS ratio. \n\nSecondly, we can further state that, increasing the number of DF-SWIPT relay nodes, increases the minimum source transmit power needed to support communication. Using an example to illustrate this deduction, for simplicity, we assume $\\beta_{k}$, $\\vert h_{k}\\vert^{2}$, $\\bar{\\gamma}_{k}$ and $\\sigma^{2}_{k}$ are the same for all DF-SWIPT relay nodes, that is, all the relays have similar properties\\footnote{Similar properties here mean all the relays have the same channel structure, equal distances between each node, energy harvesting efficiency, and noise variance. This implies that, $\\beta_{K+1}=1$, $\\beta_{1}=\\beta_{2}=\\ldots=\\beta_{K}=\\beta$, $\\vert h_{1}\\vert^{2}=\\vert h_{2}\\vert^{2}=\\ldots=\\vert h_{K+1}\\vert^{2}=\\vert h \\vert^{2}$, $\\bar{\\gamma}_{1}=\\bar{\\gamma}_{2}=\\ldots=\\bar{\\gamma}_{K+1}=\\bar{\\gamma}$, and $\\sigma^{2}_{1}=\\sigma^{2}_{2}=\\ldots=\\sigma^{2}_{K+1}=\\sigma^{2}$. Therefore, at the $k$th RF signal hop, we have $\\Gamma_{k}$ becoming $\\Gamma^{k}$ because $\\Gamma_{1}=\\beta\\vert h \\vert^{2}=\\Gamma$, $\\Gamma_{2}=\\beta^2\\vert h \\vert^{4}=\\Gamma^{2}$, and so on.}. $E^{\\star}_{0}$ becomes $E^{\\star}_{0}\\thickapprox \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{k}}$. As the number of nodes after the source node increases, $\\Gamma^{k}$ reduces further below $1$, hence, leading $E^{\\star}_{0}$ also increases.\n\nFrom the inspection of $\\rho^{\\star}_{k}$ in Theorem ~\\ref{thmoptimal_min}, it can be established that $\\rho^{\\star}_{k}$ is dependent on the product of all previous nodes' PS ratio $\\rho_{j}$ (i.e., $j=1,\\ldots,k-1$) and independent of the optimal minimum source power. This implies that, the current $\\rho^{\\star}_{k}$ can not be determined without knowledge of the previous relay nodes' PS ratios. Since $\\rho_k \\propto \\frac{1}{\\prod^{k-1}_{j=1}\\rho_{j}}$, this means the current DF-SWIPT relay node harvests less energy from the RF signal it receives from the previous node. Therefore, more portion of the received signal will be dedicated to ID at the current DF-SWIPT node. \n\\subsection{Achievable System Rate Optimization}\n\\label{subsecrateminmax}\nIn this subsection, we will first reformulate the system rate optimization problem. From the reformulated problem, propose a theorem for finding the optimum system rate and the PS ratio of each relay node. Now, the system rate optimization problem for the DF-SWIPT relaying protocol is formulated as\n\\begin{equation}\n\\label{eq20}\n\\begin{aligned}\n& \\underset{\\{\\rho_{k}\\}^{K}_{k=1}}{\\text{max}}\\text{ }\\underset{\\{1\\leq k\\leq K+1\\}}{\\text{min}}\\text{ }\nR_{k}\\\\\n& \\text{subject to} \n\\begin{aligned} \n& & &0 \\leq \\rho_{k}\\leq 1, \\text{ }k=1,\\ldots,K+1, \n\\end{aligned}\n\\end{aligned}\n\\end{equation}\nwhere $R_{k}=\\log_{2}(1+\\gamma_{k})$, and $\\gamma_{k}$ represents the received SNR at node $k$ represented in equation (~\\ref{eq4}). As $\\gamma_{k}$ increases or decreases, the rate $R_{k}$ also increases and decreases. Hence, the system rate maximization problem (~\\ref{eq20}) can be rewritten as \n\\begin{equation}\n\\label{eq21}\n\\begin{aligned}\n& R^{\\star}=\n\\log_{2}\\Bigg(1+\\underset{\\{\\rho_{k}\\}^{K}_{k=1}}{\\text{max}}\\text{ }\\underset{\\{1\\leq k\\leq K+1\\}}{\\text{min}}\\text{ }\\gamma_{k}\\Bigg)\\\\\n& \\text{subject to} \n\\begin{aligned} \n& & &0 \\leq \\rho_{k}\\leq 1, \\text{ }k=1,\\ldots,K+1.\n\\end{aligned}\n\\end{aligned}\n\\end{equation}\nBy introducing a new system SNR constraint variable, $\\hat{\\gamma}$ (i.e., $\\hat{\\gamma}\\leq \\text{min}\\text{ }\\{\\gamma_{k}\\}^{K+1}_{k=1}$), the optimization problem (~\\ref{eq21}) is redefined as \n\\begin{equation\n\\label{eq22}\n\\begin{aligned}\n& \\underset{\\{\\rho_{k}\\}^{K}_{k=1},\\hat{\\gamma}}{\\text{max}}\\text{ }\\hat{\\gamma}\\\\\n& \\text{subject to} \n\\begin{aligned} \n& & & \\frac{E_{0}\\Gamma_{k}}{\\sigma^{2}_{k}\\beta_{k}}\\prod^{k-1}_{j=1}\\rho_{j}(1-\\rho_{k})\\geq \\hat{\\gamma}, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\\\\\n& \\text{ } \n\\begin{aligned} \n& & & & & & & & & & 0 \\leq \\rho_{k}\\leq 1, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\n\\end{aligned}\n\\end{equation}\nFrom problem (~\\ref{eq22}), it can be deduced that $\\hat{\\gamma}$ is the minimum achievable SNR threshold of the system, hence, the solution to (~\\ref{eq22}) should give us the optimal $\\rho_{k}$ solution for which each hop SNR constraint is not less than $\\hat{\\gamma}$ ~\\cite{Xu17,Liu17}. By solving (~\\ref{eq22}), we obtain the following theorem.\n\n\\begin{theorem} \\label{thmoptimal_minmax\nThe optimal rate, $R^{\\star}$, of the DF-SWIPT system is expressed as \n$$R^{\\star}=\\log_{2}\\Bigg(1+\\frac{E_{0}}{\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}}{\\Gamma_{k}}}\\Bigg),$$ \nand the optimal PS ratio, $\\rho^{\\star}_{k}$, deduced as \n$$\\rho^{\\star}_{k}=\\begin{cases}\n1-\\frac{1}{\\prod^{k-1}_{j=1}\\rho_{j}}\\frac{\\frac{\\beta_{k}}{\\Gamma_{k}}}{\\sum^{K+1}_{j=1}\\frac{\\beta_{j}}{\\Gamma_{j}}},\\text{ }\\text{ }\\text{ } k=1,\\ldots,K \\\\\n0,\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }k=K+1.\n\\end{cases}$$\n\\end{theorem}\n\n\\textit{Proof:} See Appendix ~\\ref{app2}.\n\nFrom Theorem ~\\ref{thmoptimal_minmax}, we make a few assertions. First, the current optimal PS ratio in (~\\ref{eq34}) is dependent on the product of the PS ratios of all the preceding relay nodes. This means that the current node's PS ratio is smaller than the previous node's PS ratio. Hence, there may be more information decoded at the current node as compared to the previous node. Also, the optimal PS ratio is independent of the achievable SNR threshold. The achievable SNR threshold is also independent of the optimal PS ratio but depends on $\\beta_{k}$, $\\sigma_{k}$, $\\Gamma_{k}$, and $E_{0}$. The optimal SNR threshold for the system reduces with increasing number of DF-SWIPT relay nodes between the source and the destination. \n\n\\subsection{Implementation and Analysis}\n\\label{subsecsecapplic}\nIn this subsection, we discuss how the proposed protocols can be implemented. We discuss a special scenario where both the source power minimization and the minimum system rate maximization are equivalent. We also present a simple closed-form solution for the determination of the number of relay nodes need to support communication between the source and the destination. This solution is for the special scenario where the sensor network is homogeneous and the inter-node distance are equivalent (i.e., the distance between nodes are equal). The closed-form solution for determining the number of relay nodes can be used in a routing algorithm for our proposed system model. For the protocol implementation, we will delve into how the PS ratio can be calculated for each relay node based on the computation constraints of each sensor node. \n\nFor the source power minimization problem, if each relay node has the same SNR threshold constraint (i.e., $\\bar{\\gamma}_{1}=\\bar{\\gamma}_{2}=\\ldots=\\bar{\\gamma}_{K}=\\bar{\\gamma}_{K+1}$), then the PS ratio solution for both problems (~\\ref{eq8}) and (~\\ref{eq20}) are the same. That is, for both cases, the optimal PS ratio is expressed as\n\\begin{equation}\n\\label{eqgenrho}\n\\rho^{\\star}_{k}=\\begin{cases}\n1-\\frac{1}{\\prod^{k-1}_{j=1}\\rho_{j}}\\frac{\\frac{\\beta_{k}}{\\Gamma_{k}}}{\\sum^{K+1}_{j=1}\\frac{\\beta_{j}}{\\Gamma_{j}}},\\text{ }\\text{ }k=1,\\ldots,K \\\\\n0,\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }k=K+1.\n\\end{cases}\n\\end{equation}\nIn addition, (~\\ref{eq18}) and (~\\ref{eq32}) become equivalent. This implies that, given either a QoS constraint (i.e., the system required SNR threshold) or a source power constraint (i.e., the minimum available source power), we can calculate either the minimum source power or the maximum system achievable rate, respectively, from the generalized equation,\n\\begin{equation}\n\\label{eqgeneraleq}\n\\frac{E_{0}}{\\hat{\\gamma}}=\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}}{\\Gamma_{k}}.\n\\end{equation} \n\nKnowing $E_{0}$ and $\\hat{\\gamma}$, we can estimate the number of relay nodes needed to support communication between the source node and the destination node from (~\\ref{eqgeneraleq}). By manipulating the (~\\ref{eqgeneraleq}), we can obtain the estimated maximum number of relay nodes as\n\\begin{equation}\n\\label{eqnodenumb}\nK\\thickapprox \\begin{cases}\n\\frac{\\ln\\Big[1-\\Big(\\frac{E_{0}}{\\bar{\\gamma}\\sigma^{2}\\beta}+1\\Big)\\Big(1-\\frac{1}{\\Gamma}\\Big)\\Big]}{-\\ln \\Gamma}-1,\\text{ }\\text{ }\\Gamma > 1 \\\\\n\\frac{\\ln\\Big[1+\\Big(\\frac{E_{0}}{\\bar{\\gamma}\\sigma^{2}\\beta}+1\\Big)\\Big(\\frac{1}{\\Gamma}-1\\Big)\\Big]}{-\\ln \\Gamma}-1,\\text{ }\\text{ }\\Gamma <1,\n\\end{cases}\n\\end{equation}\nwhere $\\Gamma =0.5 \\beta C\\Big(\\frac{d}{d_{0}}\\Big)^{-\\alpha}$, and the variables $C$, $d$, $\\alpha$ are the inter-node attenuation constant, distance, and pathloss exponent. The $\\Gamma$ and $K$ are predetermined at the source node and used in a routing algorithm. The detailed derivation of (~\\ref{eqnodenumb}) is presented in Appendix ~\\ref{app3}. \n\nThe two possible methods for determining the PS ratio are by a centralized method and a distributed method. For the centralized method, since the source node knows all the CSI for all communicating channels in the network, it can calculate the PS ratio for each DF-SWIPT node. The source node calculates the PS ratio for each node using equations (~\\ref{eq19}) and (~\\ref{eq34}) for the source power minimization case and the system throughput maximization case, respectively. After calculating all the PS ratios, $\\{\\rho^{\\star}_{k}\\}^{K}_{k=1}$, it transmits the PS ratios with its information signal to the first DF-SWIPT node. With the centralized system, the relay node $k$ must transmit its decoded information along with $\\{\\rho^{\\star}_{j}\\}^{K}_{j=k+1}$ to the next relay node. However, with the distributed system, since each relay node knows its own CSI for the channels it communicates on, it can calculate the PS ratio for the next node. In the distributed system, the source node calculates the PS ratio of the first relay node as \n\\begin{equation}\n\\rho_{1}=1-\\tilde{\\psi}_{1},\n\\end{equation}\nwhere $\\tilde{\\psi}_{1}=\\frac{1}{\\vert h_{1} \\vert^2\\sum^{K+1}_{j=1}\\frac{\\beta_{j}}{\\Gamma_{j}}}$. The source node then transmits its information signal, $\\rho_{1}$ and $\\tilde{\\psi}_{1}$ to the first relay node. The $k$th relay node calculates the $k+1$ relay's PS ratio as \n\\begin{equation}\n\\rho_{k+1}=1-\\tilde{\\psi}_{k+1},\n\\end{equation} \nwhere $\\tilde{\\psi}_{k+1}=\\frac{1}{\\rho_{k}\\beta_{k}\\vert h_{k+1} \\vert^2}\\tilde{\\psi}_{k}$, and $k=1,\\ldots,K-1$. The current $k$ DF-SWIPT node transmits its decoded information, $\\rho_{k+1}$ and $\\tilde{\\psi}_{k+1}$ to the next $k+1$ DF-SWIPT node. The advantage of the centralized system is with the relay nodes not having any computational burden concerning the PS ratio calculation. However, the first few relay nodes would have a large amount of data bits to process and forward depending on the number of preceding relay nodes' PS ratios transmitted to it. The DF process may be affected if the relay nodes do not have enough memory (i.e., computational processing power). But, with the distributed system, each node receives fewer information bits to DF compared to the centralized system. The drawback of the distributed system is the need for the DF-SWIPT relay node to compute variables $\\rho_{k+1}$ and $\\tilde{\\psi}_{k+1}$ before retransmission to the next node.\n\nA comparison of the centralized and distributed methods is summarized in Table ~\\ref{tabsummary}. For the computational complexity comparison, let $\\mathcal{O}(J)$ represent a single arithmetic operation. For the centralized system, the source node performs $\\mathcal{O}((K+1)J)$ arithmetic operations, that is, it calculates the $E_{0}$ and $\\{\\rho_{k}\\}^{K}_{k=1}$ values. The relay nodes do not perform any arithmetic operations in the centralized system. However, with the distributed method, the source node performs $\\mathcal{O}(J)$ arithmetic operations to determine $E_{0}$, $\\tilde{\\psi}_{1}$ and $\\rho_{1}$, while the $k-$th relay node performs $\\mathcal{O}(J)$ arithmetic operations to determine $\\tilde{\\psi}_{k+1}$ and $\\rho_{k+1}$. \n\n\\begin{table*}\n\\centering\n\\caption{Comparison of PS Ratio Determination Methods}\n\\label{tabsummary}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nPS ratio Scheme & \\multicolumn{2}{c|}{Centralized Method} & \\multicolumn{2}{c|}{Distributed Method} \\\\ \\hline\\hline\nCommunicating Node & Source & $k-$th Relay Node & Source & $k-$th Relay Node\\\\ \\hline\nComputational Complexity & $\\mathcal{O}((K+1)J)$ & -- & $\\mathcal{O}(J)$ & $\\mathcal{O}(J)$\\\\ \\hline\nTransmit Bits & $K (i_{0}\\log_{2} K + B) + F $ & $(K-k) \\{i_{0} \\log_{2}(K-k) + 2B\\} + F $ & $2B + F$ & $ 2B + F $ \\\\ \\hline\nCSI Requirement & Global CSIs & -- & Global CSIs & Local CSIs \\\\ \\hline\n\\end{tabular}\n\\end{table*}\n\nNext, we discuss the number of bits needed to be transmitted at each node. First, we assume the actual transmitted information bit, real number bits (i.e. either $\\rho_{k+1}$ or $\\tilde{\\psi}_{k+1}$ value), and relay index bits broadcast from the current node to the next node to be processed are defined as $F$, $B$, and $i_{0}$, respectively. With the centralized system, the source node transmits the information signal, the $K$ relay indexes and $K$ PS ratios to the first relay node as $K (i_{0}\\log_{2} K + B) + F$ bits. Each $k-$th relay node then transmit its decoded information signal, the $K-k$ relay indexes and $K-k$ PS ratios to the next relay node. For the distributed method, each node including the source node transmits its information signal, and the next node's PS ratio and $\\tilde{\\psi}_{k+1}$ value to the next relay node as $2B + F$ bits.\n\n\\section{Simulation Results}\n\\label{secsimulation}\nThis section provides simulation results to demonstrate the system performance of multi-hop DF-SWIPT sensor network. Unless otherwise stated, the following parameters are utilized for the simulations: for the model channel model presented in paragraph three of Section ~\\ref{secsystem_model}, for the large-scale fading component, the attenuation constant $C_{0}=-10$dB, the pathloss exponent $\\alpha = 3$ and the inter-node distance is set as $d=d_{1}=d_{2}=\\ldots=d_{K}=2$m. Please, note that by considering $d_{1}=d_{2}=\\ldots=d_{K}=2$m, the distance between the source node and the destination node increases with increasing number of DF-SWIPT relay nodes. Hence, the total distance between the source node and destination node for $K$ DF-SWIPT relay nodes is $2\\times (K+1)\\times d$. The antenna noise variance $\\sigma^{2}_{1}=\\sigma^{2}_{2}=\\ldots=\\sigma^{2}_{K+1}=-80$dBm, and the energy conversion efficiency $\\beta_{1}=\\beta_{2}=\\ldots=\\beta_{K}=0.7$. A suboptimal naive scheme with a fixed PS ratio ($\\rho_{k}=0.5$, $\\forall k$) is adopted for comparison with the optimal DF-SWIPT scheme. The simulation results are obtained over $10^{4}$ random channel realizations. \n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.4in]{results5}\n\\end{center}\n\\caption{Comparison of various schemes based on harvested power at each relay node, $K=3$ and $d_{k}=2$m.}\n\\label{figresults5}\n\\end{figure}\n \nFig. ~\\ref{figresults5} shows a comparison of the energy harvested by the optimal DF-SWIFT scheme and suboptimal DF-SWIFT scheme at each node. The plot of each relay node's harvested energy is presented for $K=3$, a set of SNR threshold constraints (i.e. $\\bar{\\gamma}=-10$dB, $0$dB, $10$dB and $20$dB). For each SNR threshold in the figure, the optimal source transmit power is calculated for $K=3$, and used in determining how much energy is harvested at each node. From the figure, it can be seen that the fixed $\\rho_{k}$ scheme harvests lesser energy at each node as compared to the optimal PS ratio. There is a reduction in the amount of harvested energy as the RF signal moves from one node to the other. This may lead to a subset of all nodes being able to harvest enough energy to support communication between the source and destination for the suboptimal scheme as shown in the first plot of Fig. ~\\ref{figresults5}(a). Therefore, to achieve the same QoS, the source needs to transmit more power for the suboptimal scheme. Hence, the suboptimal scheme puts a higher strain on the source power. \n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.2in]{Results11}\n\\end{center}\n\\caption{The maximum number of DF-SWIPT nodes against increasing source transmit power, $\\bar{\\gamma}=20$dB and $d_{k}=2$m.}\n\\label{figresults11}\n\\end{figure}\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=3.2in]{Results10}\n\\end{center}\n\\caption{The maximum number of DF-SWIPT nodes against increasing system required SNR, $E_{0,max}=50$dBm and $d_{k}=2$m.}\n\\label{figresults10}\n\\end{figure}\nFigs. ~\\ref{figresults11} and ~\\ref{figresults10} show the implementation of (~\\ref{eqnodenumb}) in determining the number of relay nodes. Fig. ~\\ref{figresults11} shows the plot of the number of relay nodes against varying $E_{0}$, while, Fig. ~\\ref{figresults10} is a plot on the number of relay nodes against varying $\\bar{\\gamma}$. From both plots, we can observe that the closed-form solution for determining the number of nodes gives an excellent approximation of the number of relay nodes.\n\\subsection{Transmit Power Minimization}\n\\label{subsecsulenergymin}\n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.2in]{results1}\n\\end{center}\n\\caption{Average minimum transmit power with respect to varying $\\bar{\\gamma}$ plot for $K=2$ and $3$, and $d_{k}=2$m.}\n\\label{figresults1}\n\\end{figure}\nFig. ~\\ref{figresults1} shows a graph of the minimum source transmit power, $E_{0}$, against the SNR threshold constraint range, $\\bar{\\gamma}$, for different number of relay nodes, i.e., $K=2$ and $K=3$. The optimal PS scheme outperforms the fixed PS suboptimal scheme in terms of the $E_{0}$ needed to support the source to destination communication. The optimal scheme achieves a $15$dBm gain over the suboptimal scheme when $K=2$ and $20$dBm gain with $K=3$. An increase in the SNR threshold generally results in a corresponding increase in the minimum source power required for successful communication. Also, a rise in the number of relay nodes results in an increase in the minimum source power. This conclusion is consistent with the analysis of Theorem ~\\ref{thmoptimal_min}. It is observed that there is a loss of about $60$dBm and $50$dBm of the source transmit power in the optimal and the suboptimal PS schemes, respectively, with just a single increase in the number of relay nodes. This increase in $E_{0}$ is due to the increase in the distance and the number of relay nodes between the source and the destination nodes.\n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.2in]{results3}\n\\end{center}\n\\caption{Average minimum transmit power for increasing distance between nodes with $K=2$ and $3$, and $\\bar{\\gamma}=-10$dB.}\n\\label{figresults3}\n\\end{figure}\n \nFig. ~\\ref{figresults3} shows a plot of the minimum source power , $E_{0}$, against the inter-node distance, $d_{k}$. The average minimum amount of source transmit power needed to support the end-to-end communication in the sensor network rises with increasing $d_{k}$ for both the optimal and suboptimal PS schemes. There is a constant difference in the $E_{0}$ for the optimal and suboptimal for all values of $d_{k}$. A performance degradation of about $15$dBm and $25$dBm occurs between the optimal and suboptimal schemes for both $K=2$ and $K=3$, respectively as $d_{k}\\geq 2$m. By increasing the number of relays, there is a significant rise in the $E_{0}$ needed to facilitate communication. This phenomenon is due to the increase in distance between the source and destination nodes which in-turn influences the signal attenuation over the increasing inter-node distance. \n\n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.2in]{results4}\n\\end{center}\n\\caption{Average minimum transmit power against rate threshold for $K=2$ and $3$.}\n\\label{figresults4}\n\\end{figure}\nA plot of the average minimum $E_{0}$ against the system rate threshold constraint is presented in Fig. ~\\ref{figresults4}. Similarly, by increasing the system rate threshold, the system's minimum source transmit power, $E_{0}$, requirement increases. From the system rate threshold of $1$bps\/Hz upward, there is an improvement of about $15$dBm in terms of $E_{0}$ for the optimal PS scheme, and a $20$dBm improvement for the suboptimal PS scheme.\n\nThis behavior is well appreciated in Fig. ~\\ref{figresults2}, where we plot the minimum source power against the number of relay nodes. Here, we set $d_{k}=2$m. The distance between the source and destination node increases from $4$m to $14$m for $K=1$ and $K=6$, respectively. Increasing the DF-SWIPT nodes produces a constant increase in the minimum source transmit power needed to support the system QoS. \n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.2in]{results2}\n\\end{center}\n\\caption{Average minimum transmit power against the number of DF-SWIPT relay nodes at $\\bar{\\gamma}=-10$ and $0$dB, and $d_{k}=2$m.}\n\\label{figresults2}\n\\end{figure}\n\\subsection{System Rate Maximization}\n\\label{subsecsulratemaxmin}\nFig. ~\\ref{figresults7} shows the impact of the source transmit power on the achievable rate for different numbers of relay nodes. The optimal PS scheme has better achievable rate as compared to the suboptimal PS scheme. The lesser the number of relay nodes, the higher the achievable rate. This is because the distance between the source and destination node also reduces. For the $E_{0}$ range of $30$dBm to $50$dBm, the optimal and suboptimal schemes have a performance gap of about $4$bps\/Hz and $6$bps\/Hz for $K=2$ and $K=3$, respectively. There is a performance improvement for both schemes of about $5$bps\/Hz when relay node is reduced (i.e. from $K=3$ to $K=2$).\n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.2in]{results7}\n\\end{center}\n\\caption{Average achievable system rate against available source power, $K=2$ and $3$, and $d_{k}=2$m.}\n\\label{figresults7}\n\\end{figure}\n\nTo further appreciate the effect of how increasing the number of DF-SWIPT relay nodes has on the achievable rate, we consider Fig.~\\ref{figresults8}. The plot shows the average achievable rate against the number of relays for $E_{0}=30$ and $60$dBm. From Fig. ~\\ref{figresults8}, the achievable rate deteriorates with increasing number of relay nodes. For $K>5$, the achievable rate approaches zero for both schemes. A similar behavior can be seen in Fig. ~\\ref{figresults9} where the achievable rate reduces with an increase in the inter-node distance. For a fixed number of relays, as the distance between the relays increases, the performance gap between the suboptimal and optimal schemes narrows. This is due to the widening of the distance between the source and destination node by a total distance of $d_{k}\\times(K+1)$. \n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.2in]{results8}\n\\end{center}\n\\caption{Average achievable system rate against available source power, $E_{0}=30$ and $60$dBm, and $d_{k}=2$m.}\n\\label{figresults8}\n\\end{figure}\n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.2in]{Results9}\n\\end{center}\n\\caption{Average achievable system rate against available source power, $E_{0}=30$dBm, and $K=2$ and $3$.}\n\\label{figresults9}\n\\end{figure}\n\n\n\\subsection{Imperfect Channel State Information}\n\\label{subsecimpcsicon}\nWe now consider imperfect channel state information (ICSI) for calculating the optimal PS ratio since channel estimation in practical systems may not be accurate. This inaccuracy may be due: (i) inherent delay occurring between the channel estimation and actual data transmission, and (ii) limited feedback in frequency division multiplexing or imperfect reciprocity in time division multiplexing ~\\cite{Yoo06,Lee09,Xiang12}. The source node is assumed to have ICSI of each relay node ~\\cite{Yoo06,Lee09,Xiang12}. With the ICSI, the channel estimation error is modeled as $\\tilde{h}_{k}=\\hat{h}_{k}+e_{k}$, where $\\hat{h}_{k}\\sim \\mathcal{CN}(0,1-\\sigma^{2}_{E})$ and $e_{k}\\sim \\mathcal{CN}(0,\\sigma^{2}_{E})$ represent the estimated and the error channel coefficients, respectively. Here, $\\sigma^{2}_{E}$ is the estimation error variance, which is assumed to be $0$, $0.1$, $0.2$, and $0.3$ for our simulation. \n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.35in]{ResultsImp1}\n\\end{center}\n\\caption{The effect of estimated channel errors for $K=2$ and $3$, and $d_{k}=2$m.}\n\\label{figresultsimp1}\n\\end{figure}\n\nThe effect of ICSI on our achievable rate problem is presented in Fig. ~\\ref{figresultsimp1}. As expected, when $\\sigma^{2}_{E}$ increases, the maximum achievable rate reduces. It can be observed from Fig. ~\\ref{figresultsimp1} that the fixed PS ratio scheme remains unchanged for all $\\sigma^{2}_{E}$ considered in the simulation, since it does not exploit the channel state information to compute the PS ratio. In addition, by reducing the estimation error variance, the curves in both plots approach their perfect CSI curves (i.e., $\\sigma^{2}_{E}=0$) for optimal schemes. This result shows that the proposed rate maximization solution can be used in practical environment. \n\n\\section{Conclusion}\n\\label{secconclusion}\nThis paper investigated a DF-SWIPT multi-hop relay configuration in an IoT sensor network scenario. The DF-SWIPT relay nodes facilitated communication between a source and a destination node. All the nodes possess a single antenna. Two optimization problems have been studied in this work. We first optimized the PS ratio with the aim of minimizing the source transmit power. We then optimized the PS ratio with the objective of maximizing the achievable rate. Closed-form solutions were found for the minimum source power, maximum system achievable rate and the PS ratio at each DF-SWIPT relay node. From our research, we found a single equation linking the source energy to the system achievable rate. We have also shown that the source power minimization problem is equivalent to the system throughput maximization. The optimal PS ratio scheme was compared to a fixed PS ratio suboptimal scheme. The optimal scheme outperformed the suboptimal scheme for both the source power minimization problem and the system throughput maximization problem in the simulation section. \n\nIn this work, we consider the simple case of single antenna system for our initial research analysis and presentation of our DF-SWIPT proposed communication model. A multi-antenna systems as in ~\\cite{Liu19} and imperfect channel estimation at each node can be considered as extension of this work. Unlike the works presented in ~\\cite{Mao15} and ~\\cite{Liu16} which considered both AF and DF relay configuration, we focus on only DF relay configuration in this paper. The AF relay configuration can be considered as an extension of this work. Another approach or extension which can be researched is the use of rechargeable battery instead of a supercapacitor as considered in ~\\cite{Mahama17} and ~\\cite{Lee016}. We also considered DF-SWIPT multi-hop relay system using the PS SWIPT technique in this work. Hence, the TS SWIPT technique can be considered as an extension of the work presented in this paper. \n\n\n\\appendices \n\\section{Proof of Theorem 1}\n\\label{app1}\nFirst of all, problem (~\\ref{eq7}) is non-convex with respect to both $E_{0}$ and $\\{\\rho_{k}\\}^{K}_{k=1}$. By introducing four new variable definitions, we reformulate problem (~\\ref{eq7}) into an equivalent convex problem as follows; \n\\begin{equation}\n\\label{eq8}\n\\begin{aligned}\n& \\underset{Q,\\{A_{k}\\}^{K+1}_{k=1}}{\\text{minimize}}\\text{ }\\frac{1}{Q}\\\\\n& \\text{subject to} \n\\begin{aligned} \n& & & A_{k-1}-A_{k} \\geq Q\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\\\\\n& \\text{ } \n\\begin{aligned} \n& & & & & & & & & & A_{k} \\leq A_{k-1}, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\\\\\n&\\text{ } \n\\begin{aligned} \n& & & & & & & & & & Q \\geq 0,\n\\end{aligned}\n\\end{aligned}\n\\end{equation}\nwhere $A_{k}\\triangleq\\prod^{k}_{j=1}\\rho_{j}$, $A_{0}\\triangleq 1$, $A_{K+1}\\triangleq 0$, and $Q\\triangleq \\frac{1}{E_{0}}$. It is obvious that, if the first constraint is satisfied $k=1,\\ldots,K+1$, then the second constraint is also satisfied. Thus, we can remove the second constraint $k=1,\\ldots,K+1$.\n\nThe Lagrangian of problem (~\\ref{eq8}) is defined as\n\\begin{equation}\n\\label{eq9}\n\\begin{aligned}\n\\mathcal{L}\\Big\\{Q,\\{A_{k}\\}^{K+1}_{k=1},\\{\\lambda_{k}\\}^{K+1}_{k=0}\\Big\\}&\\quad=\\frac{1}{Q}+Q\\sum^{K+1}_{k=1}\\lambda_{k-1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}\\\\&\\quad +\\sum^{K+1}_{k=1}(\\lambda_{k-1}-\\lambda_{k})A_{k}-\\lambda_{0},\n\\end{aligned}\n\\end{equation}\nwhere $\\lambda_{k}\\geq 0$ and $\\lambda_{K+1}\\geq 0$ are the dual variables corresponding to the constraints $A_{k-1}-A_{k} \\geq Q\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}, k=1,\\ldots,K+1$ and $A_{K+1}$, respectively. \nFollowing (~\\ref{eq9}), we can express the KKT conditions as \n\\begin{equation}\n\\label{eq11a}\n-\\frac{1}{Q^{\\star 2}}+\\sum^{K+1}_{k=1}\\lambda^{\\star}_{k-1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}=0,\n\\end{equation}\n\\begin{equation}\n\\label{eq12a}\n\\sum^{K+1}_{k=1}\\lambda^{\\star}_{k-1}\\Bigg(Q^{\\star}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}-(A^{\\star}_{k-1}-A^{\\star}_{k})\\Bigg)=0. \n\\end{equation}\nIf we have $\\lambda^{\\star}_{k-1}-\\lambda^{\\star}_{k}>0$, then the optimal $A^{\\star}_{k}$ minimizing the Lagrangian is given by $A^{\\star}_{k}=-\\infty$, and the dual function is unbounded. Also, if $\\lambda^{\\star}_{k-1}-\\lambda^{\\star}_{k}<0$, then we have $A^{\\star}_{k}=\\infty$, which also yields an unbounded dual function. Therefore, $\\lambda^{\\star}_{k-1}-\\lambda^{\\star}_{k}=0, k=1,\\ldots,K+1$, this implies that, $\\lambda^{\\star}_{0}=\\lambda^{\\star}_{1}=\\ldots=\\lambda^{\\star}_{K+1}$. Hence, the KKT conditions are rewritten as\n\\begin{equation}\n\\label{eq11}\n-\\frac{1}{Q^{\\star 2}}+\\lambda^{\\star}_{0}\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}=0,\n\\end{equation}\n\\begin{equation}\n\\begin{aligned}\n\\label{eq12}\n\\lambda^{\\star}_{0}\\Bigg(Q^{\\star}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}-(A^{\\star}_{k-1}-A^{\\star}_{k})\\Bigg)=0,\\text{ } k=1,\\ldots,K+1,\n\\end{aligned}\n\\end{equation}\n\\begin{equation}\n\\begin{aligned}\n\\label{eq12b}\n-\\lambda^{\\star}_{0}A^{\\star}_{K+1}=0.\n\\end{aligned}\n\\end{equation}\nThe optimal minimum source power, $E^{\\star}_{0}$, can be acquired from (~\\ref{eq11}) as\n\\begin{equation}\n\\label{eq14}\nE^{\\star}_{0}=\\frac{1}{Q^{\\star}}=\\sqrt{\\lambda_{0}^{\\star}\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}}.\n\\end{equation}\n\nFrom the complementary slackness condition for $k=1$ in (~\\ref{eq12}), we have the following two cases: \n\\begin{equation}\n\\label{eq15}\n\\begin{aligned}\n&\\quad\\textit{Case 1:}\\text{ }\\lambda^{\\star}_{0}=0,\\text{ and }1-A^{\\star}_{1}>Q^{\\star}\\frac{\\sigma^{2}_{1}\\beta_{1}\\bar{\\gamma}_{1}}{\\Gamma_{1}},\\\\&\\quad\\textit{Case 2:}\\text{ }\\lambda^{\\star}_{0}>0,\\text{ and }1-A^{\\star}_{1}=Q^{\\star}\\frac{\\sigma^{2}_{1}\\beta_{1}\\bar{\\gamma}_{1}}{\\Gamma_{1}}.\n\\end{aligned}\n\\end{equation}\nFor case 1, $\\frac{1}{Q}=\\sqrt{\\lambda^{\\star}_{0}\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}}=\\infty$, this implies $E_{0}=0$. Obviously, this is not the feasible solution for the problem in equation (~\\ref{eq8}) with arbitrary given $\\bar{\\gamma}_{k}>0$. Thus, we focus on case 2, which yields $\\frac{1}{Q}=\\sqrt{\\lambda^{\\star}_{0}\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}}<\\infty$, hence $\\lambda^{\\star}_{0}$ is always positive. Since $\\lambda^{\\star}_{0}=\\lambda^{\\star}_{1}=\\ldots=\\lambda^{\\star}_{K+1}$, all the optimal dual variables are positive and equal. From the complementary slackness condition in (~\\ref{eq12}), it follows that\n\\begin{equation}\n\\label{eq16}\n\\begin{aligned}\n&\\quad Q^{\\star}\\frac{\\sigma^{2}_{k+1}\\beta_{k+1}\\bar{\\gamma}_{k+1}}{\\Gamma_{k+1}}=(A^{\\star}_{k}-A^{\\star}_{k+1}), A^{\\star}_{K+1}=0,\\\\&\\quad \\Bigg(\\lambda^{\\star}_{0}\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}\\Bigg)^{-\\frac{1}{2}}\\frac{\\sigma^{2}_{k+1}\\beta_{k+1}\\bar{\\gamma}_{k+1}}{\\Gamma_{k+1}}=(A^{\\star}_{k}-A^{\\star}_{k+1}),\\\\&\\quad \\text{ }k=1,\\ldots,K+1.\n\\end{aligned}\n\\end{equation}\nSince $\\sum^{K}_{k=0}(A^{\\star}_{k}-A^{\\star}_{k+1})=1$, the optimal dual variable $\\lambda^{\\star}_{0}$ is given by\n\\begin{equation}\n\\label{eq17}\n\\lambda^{\\star}_{0}=\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}, \n\\end{equation}\nwhich is obtained from (~\\ref{eq12a}). Now, substituting (~\\ref{eq17}) into (~\\ref{eq14}) yields \n\\begin{equation}\n\\label{eq18}\nE^{\\star}_{0}=\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\bar{\\gamma}_{k}}{\\Gamma_{k}}.\n\\end{equation}\nBy inserting $E^{\\star}_{0}$ into SNR constraint of the optimization problem (~\\ref{eq7}), $\\rho^{\\star}_{k}$ is derived as \n\\begin{equation}\n\\label{eq19}\n\\rho^{\\star}_{k}=\\begin{cases}\n1-\\frac{1}{\\prod^{k-1}_{j=1}\\rho_{j}}\\frac{\\frac{\\bar{\\gamma}_{k}\\beta_{k}}{\\Gamma_{k}}}{\\sum^{K+1}_{j=1}\\frac{\\bar{\\gamma}_{j}\\beta_{j}}{\\Gamma_{j}}},\\text{ }\\text{ }\\text{ }k=1,\\ldots,K \\\\\n0,\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }k=K+1.\n\\end{cases}\n\\end{equation}\nThe $\\rho^{\\star}_{k}$ is found by simply making $\\rho_{k}$ the subject at equality. $\\blacksquare$\n\\section{Proof of Theorem 2}\n\\label{app2}\nSimilar to the proof of Theorem ~\\ref{thmoptimal_min}, we apply the change of variables where $A_{k}\\triangleq\\prod^{k}_{j=1}\\rho_{j}$, $A_{0}\\triangleq 1$ and $A_{K+1}\\triangleq 0$, since problem (~\\ref{eq22}) is also non-convex with respect to $\\hat{\\gamma}$ and $\\{\\rho_{k}\\}^{K}_{k=1}$. Then, the non-convex problem in (~\\ref{eq22}) can be equivalently transformed to the following convex formulation\n\\begin{equation}\n\\label{eq23}\n\\begin{aligned}\n& \\underset{\\{A_{k}\\}^{K+1}_{k=1}}{\\text{maximize}}\\text{ }\\hat{\\gamma}\\\\\n& \\text{subject to} \n\\begin{aligned} \n& & & A_{k-1}-A_{k} \\geq \\hat{\\gamma}\\frac{\\sigma^{2}_{k}\\beta_{k}}{E_{0}\\Gamma_{k}}, \\text{ }k=1,\\ldots,K+1,\n\\end{aligned}\\\\\n& \\text{ } \n\\begin{aligned} \n& & & & & & & & & & A_{k} \\leq A_{k-1}, \\text{ }k=1,\\ldots,K+1.\n\\end{aligned}\n\\end{aligned}\n\\end{equation}\n\nBy solving the first, we solve the second constraint of problem (~\\ref{eq23}), therefore, the second constraint is removed (i.e., it is not considered in the Lagrangian and solution). The Lagrangian for problem (~\\ref{eq23}) is thus expressed as\n\\begin{equation}\n\\label{eq24}\n\\begin{aligned}\n\\mathcal{L}\\Big\\{\\hat{\\gamma},\\{A_{k}\\}^{K+1}_{k=1},\\{\\lambda_{k}\\}^{K+1}_{k=0}\\Big\\}&\\quad= \\hat{\\gamma}+\\hat{\\gamma}\\sum^{K+1}_{k=1}\\lambda_{k-1}\\frac{\\sigma^{2}_{k}\\beta_{k}}{E_{0}\\Gamma_{k}}\\\\&\\quad+\\sum^{K+1}_{k=1}(\\lambda_{k-1}-\\lambda_{k})A_{k}-\\lambda_{0},\n\\end{aligned}\n\\end{equation}\nwith its KKT conditions given as \n\\begin{equation}\n\\label{eq25}\n1+\\sum^{K+1}_{k=1}\\lambda^{\\star}_{k-1}\\frac{\\sigma^{2}_{k}\\beta_{k}}{E_{0}\\Gamma_{k}}=0,\n\\end{equation}\n\\begin{equation}\n\\label{eq26}\n\\lambda^{\\star}_{k-1}-\\lambda^{\\star}_{k}=0,\n\\end{equation}\nand\n\\begin{equation}\n\\label{eq27}\n\\sum^{K+1}_{k=1}\\lambda^{\\star}_{k-1}\\Bigg(\\hat{\\gamma}^{\\star}\\frac{\\sigma^{2}_{k}\\beta_{k}}{E_{0}\\Gamma_{k}}-(A^{\\star}_{k-1}-A^{\\star}_{k})\\Bigg)=0.\n\\end{equation}\n\nFrom (~\\ref{eq26}), we deduce that $\\lambda^{\\star}_{k-1}=\\lambda^{\\star}_{k}$, hence, $\\lambda^{\\star}_{0}=\\lambda^{\\star}_{1}=\\ldots=\\lambda^{\\star}_{K+1}$. The KKT conditions are modified to give,\n\\begin{equation}\n\\label{eq28}\n1+\\lambda^{\\star}_{0}\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}}{E_{0}\\Gamma_{k}}=0,\n\\end{equation}\n\\begin{equation}\n\\label{eq29}\n\\lambda^{\\star}_{0}\\Bigg(\\hat{\\gamma}^{\\star}\\frac{\\sigma^{2}_{k}\\beta_{k}}{E_{0}\\Gamma_{k}}-(A^{\\star}_{k-1}-A^{\\star}_{k})\\Bigg)=0,\\text{ }\n-\\lambda^{\\star}_{0}A^{\\star}_{K+1}=0.\n\\end{equation}\nThe optimal $\\lambda^{\\star}_{0}$ can be derived from (~\\ref{eq28}) as\n\\begin{equation}\n\\label{eq31}\n\\lambda^{\\star}_{0}=-\\frac{1}{\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\hat{\\gamma}^{\\star}}{E_{0}\\Gamma_{k}}}=-\\frac{E_{0}}{\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\hat{\\gamma}^{\\star}}{\\Gamma_{k}}}.\n\\end{equation}\nNoting that $\\lambda^{\\star}_{0}=\\lambda^{\\star}_{1}=\\ldots=\\lambda^{\\star}_{K+1}$, the optimal dual variables are negative and equal based on (~\\ref{eq31}). To find the optimal $\\hat{\\gamma}^{\\star}$, (~\\ref{eq31}) is substituted into (~\\ref{eq29}) to produce ,\n\\begin{equation}\n\\begin{aligned}\n\\label{eq33}\n&\\quad\\frac{E_{0}}{\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\hat{\\gamma}^{\\star}}{\\Gamma_{k}}}\\sum^{K+1}_{k=1}(A^{\\star}_{k-1}-A^{\\star}_{k})=\\Bigg(\\frac{\\sum^{K+1}_{k=1}\\frac{\\hat{\\gamma}^{\\star}\\sigma^{2}_{k}\\beta_{k}E_{0}}{E_{0}\\Gamma_{k}}}{\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}\\hat{\\gamma}^{\\star}}{\\Gamma_{k}}}\\Bigg)\\\\&\\quad=1.\n\\end{aligned}\n\\end{equation} \nSince $\\sum^{K+1}_{k=1}(A^{\\star}_{k-1}-A^{\\star}_{k})=1$, the optimal $\\hat{\\gamma}^{\\star}$ is obtained as \n\\begin{equation}\n\\label{eq32}\n\\hat{\\gamma}^{\\star}=\\frac{E_{0}}{\\sum^{K+1}_{k=1}\\frac{\\sigma^{2}_{k}\\beta_{k}}{\\Gamma_{k}}}.\n\\end{equation}\nBy placing $\\hat{\\gamma}^{\\star}$ into the SNR constraint of problem (~\\ref{eq23}), making $\\rho_{k}$ the subject at equality, the optimal $\\rho^{\\star}_{k}$ can be found as \n\\begin{equation}\n\\label{eq34}\n\\rho^{\\star}_{k}=\\begin{cases}\n1-\\frac{1}{\\prod^{k-1}_{j=1}\\rho_{j}}\\frac{\\frac{\\beta_{k}}{\\Gamma_{k}}}{\\sum^{K+1}_{j=1}\\frac{\\beta_{j}}{\\Gamma_{j}}},\\text{ }\\text{ }k=1,\\ldots,K \\\\\n0,\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }k=K+1.\n\\end{cases}\n\\end{equation}\n\n\\begin{figure}[t!]\n\\begin{center}\n\\includegraphics[width=3.4in]{proofmin}\n\\end{center}\n\\caption{A graphical explanation of how the optimal system rate is achieved: (a) shows the achievable rate by each of the DF-SWIPT relay nodes and the destination node, (b) the node rates are rearranged in ascending order and the minimum rate's PS ratio is optimized, (c) the first and second node adjust their individual rates based on their PS ratio until they achieve the same rate,(d) each successive node's rate is adjusted based on their PS ratio until the whole system achieves the optimal system rate.}\n\\label{figproofmin}\n\\end{figure}\n\nWe will now show that $\\hat{\\gamma}^{\\star}$ is the optimal SNR threshold with the aid of both simple arithmetic and the diagrams presented in Fig. ~\\ref{figproofmin}. We know that each of the $K+1$ nodes (i.e., the DF-SWIPT relay nodes and the destination node) achieve different individual rates, \\{$R(\\rho_{k})\\}^{K+1}_{k=1}$ depending on their current $\\rho_{k}$ value as shown in Fig. ~\\ref{figproofmin}(a). These rates can be sorted in an ascending order as $\\hat{R}(\\rho_{\\bar{1}})\\leq\\hat{R}(\\rho_{\\bar{2}})\\leq\\ldots\\leq\\hat{R}(\\rho_{\\overline{K+1}})$, where $\\bar{k}=\\bar{1},\\bar{2},\\ldots,\\overline{K+1}$ is the new ordering number for each of the $K+1$ nodes based on the achieved rates. Also, its obvious that a node's rate is a function of its $\\rho_{\\bar{k}}$. A node's rate increases or decreases by reducing or increasing its $\\rho_{\\bar{k}}$, respectively. The system throughput at this first stage is constrained by $\\hat{R}(\\rho_{\\bar{1}})$ since it is the minimum achievable rate as seen in Fig. ~\\ref{figproofmin}(b). In Fig. ~\\ref{figproofmin}(c), by reducing $\\rho_{\\bar{1}}$ and increasing $\\rho_{\\bar{2}}$, the system can achieve a state where $\\hat{R}(\\rho_{\\bar{1}})=\\hat{R}(\\rho_{\\bar{2}})\\leq\\ldots\\leq\\hat{R}(\\rho_{\\overline{K+1}})$. Now the system throughput is constrained by the minimum rate $\\hat{R}(\\rho_{\\bar{1}})=\\hat{R}(\\rho_{\\bar{2}})$. Following the same procedure for the preceding node, the system achieves a rate of $\\hat{R}(\\rho_{\\bar{1}})=\\hat{R}(\\rho_{\\bar{2}})=\\hat{R}^(\\rho_{\\bar{3}})\\leq\\ldots\\leq\\hat{R}(\\rho_{\\overline{K+1}})$. By continuously repeating and applying this same process to all the subsequent nodes, the system will achieve a rate where $\\hat{R}^{\\star}(\\rho^{\\star}_{\\bar{1}})=\\hat{R}^{\\star}(\\rho^{\\star}_{\\bar{2}})=\\ldots=\\hat{R}^{\\star}(\\rho^{\\star}_{\\overline{K+1}})$ as shown in Fig. ~\\ref{figproofmin}(d). From our solution, we are able to acquire closed-form solutions for our PS ratio at each of the $K+1$ nodes. These optimal PS ratios achieve the maximized set system SNR (i.e., $\\{\\hat{\\gamma}\\}^{K+1}_{k=1}=\\hat{\\gamma}^{\\star}$), this implies that, each node attains the maximized system rate established as $R^{\\star}=\\log_{2}(1+\\hat{\\gamma}^{\\star})$. $\\blacksquare$\n\\section{Number of Relay Nodes Estimation}\n\\label{app3}\nFrom (~\\ref{eqgeneraleq}), we have the estimated source power as \n\\begin{equation}\n\\label{app11}\n\\begin{aligned}\nE_{0}&\\quad\\approx \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}_{k}\\beta_{k}}{\\mathop{\\mathbb{E}}[\\Gamma_{k}]}= \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}_{k}\\beta_{k}}{\\mathop{\\mathbb{E}}\\Big[\\prod^{k}_{j=1}C_{j}\\Big(\\frac{d_{j}}{d_{0}}\\Big)^{-\\alpha_{j}}\\beta_{j}\\vert h_{j}\\vert^{2}\\Big]}\\\\&\\quad = \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}_{k}\\beta_{k}}{\\prod^{k}_{j=1}C_{j}\\Big(\\frac{d_{j}}{d_{0}}\\Big)^{-\\alpha_{j}}\\beta_{j}\\mathop{\\mathbb{E}}\\Big[\\prod^{k}_{j=1}\\vert h_{j}\\vert^{2}\\Big]}\\\\&\\quad = \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}_{k}\\beta_{k}}{\\prod^{k}_{j=1}C_{j}\\Big(\\frac{d_{j}}{d_{0}}\\Big)^{-\\alpha_{j}}\\beta_{j}\\prod^{k}_{j=1}\\mathop{\\mathbb{E}}\\big[\\vert h_{j}\\vert^{2}\\big]}.\n\\end{aligned}\n\\end{equation}\nAssuming the sensor network is a homogeneous sensor network, let $\\beta_{1}=\\beta_{2}=\\ldots=\\beta_{K}$, $\\beta_{K+1}=1$, $\\sigma_{1}=\\sigma_{2}=\\ldots=\\sigma_{K}=\\sigma_{K+1}$, and $d_{1}=d_{2}=\\ldots=d_{K}=d_{K+1}$, then, \n\\begin{equation}\n\\label{app12}\n\\begin{aligned}\nE_{0}&\\quad\\approx \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\mathop{C^{k}\\Big(\\frac{d}{d_{0}}\\Big)^{-k\\alpha_{j}}\\beta^{k}\\prod^{k}_{j=1}\\mathbb{E}}\\big[\\vert h_{j}\\vert^{2}\\big]}.\n\\end{aligned}\n\\end{equation}\nSince $\\vert h_{j}\\vert^{2}$ is a random variable with an exponential distribution, hence, $\\mathop{\\mathbb{E}}\\big[\\vert h_{j}\\vert^{2}\\big]=1\/2=0.5$, because $h_{j} \\sim \\mathcal{CN}(0,1)$. This expectation solution is acquired from the fact that, the expectation of an exponential distribution acquired from a the square of Rayleigh distribution is the variance of the Rayleigh distribution divided by $2$, hence, we have $1\/2$. Therefore the optimal source power becomes\n\\begin{equation}\n\\label{app12a}\n\\begin{aligned}\nE_{0}&\\quad\\approx \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{C^{k}\\Big(\\frac{d}{d_{0}}\\Big)^{-k\\alpha_{j}}\\beta^{k}0.5^{k}}\\\\&\\quad= \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Big[ 0.5 C\\Big(\\frac{d}{d_{0}}\\Big)^{-\\alpha}\\beta\\Big]^{k}}.\n\\end{aligned}\n\\end{equation}\nLet $\\Gamma =0.5 C\\Big(\\frac{d}{d_{0}}\\Big)^{-\\alpha}\\beta$, then, the approximated source power becomes, \n\\begin{equation}\n\\label{app12b}\n\\begin{aligned}\nE_{0}\\approx \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{k}}.\n\\end{aligned}\n\\end{equation}\nUsing the approximation $E_{0}\\approx \\sum^{N}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{k}}$, we present the stepwise derivation of the estimated maximum number of DF-SWIPT relay nodes supported by a given source power. By expanding the (~\\ref{app12b}), we have, \n\\begin{equation}\n\\label{app13}\n\\begin{aligned}\nE_{0}&\\quad\\approx \\sum^{K+1}_{k=1}\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{k}}= \\sum^{K+1}_{k=0}\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{k}}-\\bar{\\gamma}\\sigma^{2}\\beta\\\\&\\quad=\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{0}}+\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{1}}+\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{2}}+\\ldots+\\frac{\\bar{\\gamma}\\sigma^{2}\\beta}{\\Gamma^{K+1}}-\\bar{\\gamma}\\sigma^{2}\\beta.\n\\end{aligned}\n\\end{equation}\nIt is obvious from (~\\ref{app11}) that the $E_{0}$ equation is similar to the geometric progression sum equation, that is,\n\\begin{equation}\n\\label{app14}\n\\begin{aligned}\nE_{0}&\\quad\\approx \\alpha_{0}r^{0}+\\alpha_{0}r^{1}+\\alpha_{0}r^{2}+\\ldots+\\alpha_{0}r^{K+1}-\\alpha_{0}\\\\&\\quad = \\alpha_{0}\\Big(\\frac{1-r^{K+1}}{1-r}\\Big)-\\alpha_{0}=\\alpha_{0}\\Big(\\frac{1-r^{K+1}}{1-r}-1\\Big),\n\\end{aligned}\n\\end{equation}\nwhere $\\alpha_{0}=\\bar{\\gamma}\\sigma^{2}\\beta$ and $r=\\frac{1}{\\Gamma}$ ~\\cite{Bird14,Stroud13}. By making $K+1$ the subject, we have,\n\\begin{equation}\n\\label{app15}\nE_{0}\\approx\\alpha_{0}\\Big(\\frac{1-r^{K+1}}{1-r}-1\\Big),\n\\end{equation}\n\\begin{equation}\n\\label{app16}\n1-\\Big(\\frac{E_{0}}{\\alpha_{0}}+1\\Big)\\Big(1-r\\Big)\\approx r^{K+1},\n\\end{equation}\n\\begin{equation}\n\\label{app17}\n\\ln\\Big[1-\\Big(\\frac{E_{0}}{\\alpha_{0}}+1\\Big)\\Big(1-r\\Big)\\Big]\\approx(K+1)\\ln r,\n\\end{equation}\n\\begin{equation}\n\\label{app18}\nK+1\\approx\\frac{\\ln\\Big[1-\\Big(\\frac{E_{0}}{\\alpha_{0}}+1\\Big)\\Big(1-r\\Big)\\Big]}{\\ln r}.\n\\end{equation}\nThe above solution is acquired from the geometric progression sum equation is for when $0 1$. Following similar steps used above, when $r > 1$, that is, $\\Gamma < 1$, the solution becomes,\n\\begin{equation}\n\\label{app19}\nK+1\\approx\\frac{\\ln\\Big[1+\\Big(\\frac{E_{0}}{\\alpha_{0}}+1\\Big)\\Big(r-1\\Big)\\Big]}{\\ln r}.\n\\end{equation}\nNow, we replace $\\alpha_{0}$ and $r$ by their defined values to acquire the approximation of $K$ DF-SWIPT nodes as\n\\begin{equation}\n\\label{appnodenumb}\nK\\approx\\begin{cases}\n\\frac{\\ln\\Big[1-\\Big(\\frac{E_{0}}{\\bar{\\gamma}\\sigma^{2}\\beta}+1\\Big)\\Big(1-\\frac{1}{\\Gamma}\\Big)\\Big]}{-\\ln \\Gamma}-1,\\text{ }\\text{ }0 1 \\\\\n\\frac{\\ln\\Big[1+\\Big(\\frac{E_{0}}{\\bar{\\gamma}\\sigma^{2}\\beta}+1\\Big)\\Big(\\frac{1}{\\Gamma}-1\\Big)\\Big]}{-\\ln \\Gamma}-1,\\text{ }\\text{ }r>1,\\Gamma <1.\n\\end{cases}\n\\end{equation}\nIt can be observed that equation ($~\\ref{appnodenumb}$) is always positive and depends on the value of $\\Gamma$. $\\blacksquare$\n\\ifCLASSOPTIONcaptionsoff\n \\newpage\n\\fi\n\\bibliographystyle{IEEEtr}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzhagu b/data_all_eng_slimpj/shuffled/split2/finalzzhagu new file mode 100644 index 0000000000000000000000000000000000000000..a8c12cad5b40f82ac42a093cd2ebfa57fd93a441 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzhagu @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nThe Weyl anomaly in quantum field theory has a long history (see e.g. \\cite{Duff:1993wm} for a historical overview). It states that the Weyl transformation of the metric in the curved space-time may not be a symmetry of the system even though the quantum field theory under consideration has the conformal symmetry in the flat space-time. Indeed, in most conformal field theories, the Weyl anomaly is non-vanishing, and we say that the Weyl symmetry is quantum mechanically broken in curved space-time.\n\nThe Weyl anomaly has played many important roles in theoretical physics.\nThe existence of the Weyl anomaly gives a constraint on the expectation values of energy-momentum tensor in curved background \\cite{Page:1982fm}, and may be related to the nature of Hawking radiation \\cite{Robinson:2005pd}\\cite{Iso:2006wa}\\cite{Kawai:2017txu}\\cite{Kawai:2014afa}. The fact that vanishing of the Weyl anomaly happens only in a limited class of theories dictates the number of space-time dimensions in critical string theory. \nMore recently, we find that the universal terms in the entanglement entropy of conformal field theories are given by the coefficient of the Weyl anomaly, suggesting a deep relation between geometry and information \\cite{Ryu:2006ef}.\n\nWhat we would like to study in this paper is to find a way to cancel the Weyl anomaly from the other source, e.g. from the position dependent coupling constant.\\footnote{Sorry for the oxymoron. It is no longer constant. Probably it is Dirac who openly advocated this idea in the early days \\cite{Dirac:1938mt}.} Once we put a quantum field theory on a curved manifold, it is natural to assume that coupling constants are position dependent. The position dependent coupling constants then provide an extra contribution to the Weyl anomaly so that we may attempt to cancel the entire Weyl anomaly on the curved manifold. We would like to find under which condition such a cancellation is possible.\n\nThe similar idea of cancelling more general anomalies have been implicitly assumed in many places. For example, if we try to introduce background gauge fields for chiral current operators in the curved background (e.g. in the context of supersymmetric localization), then they may be mutually inconsistent due to the 't Hooft anomaly. One way to avoid this is to cancel the anomaly of the background gauge field from the space-time curvature and vice versa. Similarly, if preserving the Weyl anomaly is the critical issue (e.g. if we try to gauge it), our new way of doing it may be another option to be considered.\n\nThe organization of the paper is as follows. In section 2, we study the cancellation of the Weyl anomaly from position dependent coupling constant in two dimensional conformal field theories. In section 3, we study it in three dimensions and in section 4, we study it in four dimensions. We supplement the holographic viewpoint in section 5 and conclude with some discussions in section 6.\n\n\\section{Two dimensions}\nLet us consider a two-dimensional conformal field theory with an exactly marginal deformation denoted by $g$. For instance, we may take the Gaussian $c=1$ boson with the compactification radius as the exactly marginal deformation here.\nWe put the theory on a curved background with the metric $g_{\\mu\\nu}(x)$ and then vary the coupling constant $g(x)$ over the manifold: schematically we consider the action $S = S_0 + \\int d^2x\\sqrt{g} g(x) O(x)$. Even though the theory is conformal invariant in the Euclidean space $g_{\\mu\\nu} = \\delta_{\\mu\\nu}$ with $g(x) = g$, it is not necessarily so after turning on the background metric and position dependent coupling constant.\\footnote{We define the scale transformation by the change of the difference of the coordinate as in \\cite{Osborn:1991gm} rather than the coordinate itself \\cite{Dong:2012ua}.} \nThis obstruction is known as the Weyl anomaly under the infinitesimal Weyl rescaling: $\\delta g_{\\mu\\nu}(x) = 2\\delta \\sigma g_{\\mu\\nu}$. \n\nIn terms of the free energy functional $e^{-F[g_{\\mu\\nu}(x),g(x)]} = \\int \\mathcal{D} \\Phi e^{-S[\\Phi]}$, the Weyl anomaly for a two-dimensional conformal field theory is given by (e.g. see \\cite{Osborn:1991gm})\\footnote{In this paper, we always assume that the conformal field theories under consideration preserve the CP symmetry.}\n\\begin{align}\n\\delta F_{\\sigma} = \\int d^2x\\sqrt{g} \\delta \\sigma(x) (cR -\\frac{1}{2} \\partial^\\mu g(x) \\partial_\\mu g(x)) \\ \\label{twoa}\n\\end{align}\nin a certain renormalization scheme so that the exactly marginal deformation has a flat line metric. Otherwise, we can always redefine the coupling constant or the renormalization scheme so that it is flat. It is clear that when $g(x) = g$, the only way to cancel the Weyl anomaly is to require $c=0$, which is typically what we demand in critical string theory.\n\nHowever, we see that this is not the only available option. Now, given a positive curvature $R(x)\\ge 0$, we may try to cancel the curvature term in the Weyl anomaly \\eqref{twoa} against the second term originating from the position dependent coupling constant by solving the equation\n\\begin{align}\nc R = \\frac{1}{2} \\partial^\\mu g(x) \\partial_\\mu g(x) \\ . \\label{twoc}\n\\end{align}\nThis is possible for positive curvature $R(x) \\ge 0$ (assuming $c>0$ in unitary conformal field theories). \n\n\nFor example, if we take the Fubini-Study metric on the sphere with the complex coordinate $z$ and $\\bar{z}$:\n\\begin{align}\nds^2 = \\frac{dzd\\bar{z}}{(1+|z|^2)^2} \\ , \n\\end{align}\nthe solution of \\eqref{twoc} is \n\\begin{align}\ng(x) = \\sqrt{c} \\cdot \\mathrm{arctan}(|z|) + \\mathrm{const} \\ .\n\\end{align}\nIn this way, one can cancel the Weyl anomaly on the sphere by introducing the position dependent coupling constant. Note, however, that the position dependence of the coupling constant reduces the symmetry of the sphere from $SO(3)$ down to $SO(2)$. The idea here is we gained extra ``Weyl symmetry\" at the sacrifice of the rotational symmetry.\\footnote{This is not necessarily a bad idea: for examle, in the supersymmetric localization, we often do not keep the full isometry of the sphere but only the $U(1)$ subgroup of it.}\n\nThe above cancellation works both for infinitesimal generic Weyl transformation $\\delta g_{\\mu\\nu}(x) = 2\\delta \\sigma(x) g_{\\mu\\nu}(x)$ or finite but constant Weyl transformation $g_{\\mu\\nu}(x) \\to e^{2\\bar{\\sigma}} g_{\\mu\\nu}(x)$, where $\\bar{\\sigma}$ is a finite constant. The latter is because the equation to be solved in \\eqref{twoc} trivially scales under the constant Weyl transformation, so once it is solved then it is also solved after finite but constant Weyl transformation.\nFor finite generic Weyl transformation, however, the cancellation may not persist. The point is that the curvature term in the Weyl anomaly is non-trivially transforms under the Weyl transformation:\n\\begin{align}\nR \\to e^{-2\\sigma(x)} (R -2D^2 \\sigma) \\ , \n\\end{align}\nwhere $D^2$ is the Laplacian, \nwhile the Weyl transformation of the second term from the position dependent coupling constant $\\partial^\\mu g(x) \\partial_\\mu g(x)$ is trivial:\n\\begin{align}\n\\partial^\\mu g(x) \\partial_\\mu g(x) \\to e^{-2\\sigma(x)} \\partial^\\mu g(x) \\partial_\\mu g(x)\n\\end{align}\nThus, even though one may solve the cancellation condition for a given $g_{\\mu\\nu}(x)$ with a certain position dependent coupling constant $g(x)$, the cancellation does not persist for the Weyl transformed geometry. \n\n Nevertheless, we realize that the cancellation is still intact if we restrict\\footnote{A similar restriction on the Weyl transformation has been studied in \\cite{Edery:2014nha}.} ourselves to the harmonic Weyl transformation, which satisfies $D^2 \\sigma = 0$. Thus, we may construct a quantum field theory which is exactly invariant under the harmonic Weyl transformation by cancelling the Weyl anomaly from the position dependent coupling constant.\n\n\nMore generically, one may consider theories with several exactly marginal deformations. The Weyl anomaly has the generalized form\n\\begin{align}\n\\delta F_{\\sigma} = \\int d^2x\\sqrt{g} \\delta\\sigma(x) (cR - \\chi_{ij}(g) \\partial^\\mu g^i(x) \\partial_\\mu g^j(x)) + \\partial_\\mu \\delta\\sigma(x) w_i(g) \\partial^\\mu g^i(x) \\ , \\label{twog}\n\\end{align}\nwhere $\\chi_{ij}(g)$ and $w_i(g)$ may depend on the exactly marginal deformations $g^i(x)$. \nFor a constant Weyl transformation, the condition for the cancellation is essentially the same as before since the last term in \\eqref{twog} drops out.\n\nFor infinitesimal generic Weyl transformation, however, we have to think about the cancellation of the third term proportional to $\\partial_\\mu \\delta \\sigma(x)$. We did not talk about it in the single coupling case because we were able to remove it from the local counterterm $\\int d^2x\\sqrt{g} b(g) R$, but we have to discuss it now with several coupling constants when it has the non-trivial curvature $\\partial_i w_j - \\partial_j w_i$.\nWhile the Wess-Zumino consistency condition does not say anything about the (non-)existence of this term \\cite{Osborn:1991gm}, the recent analysis in \\cite{Gomis:2015yaa} tells that on the conformal manifold spanned by the exactly marginal deformations, the curvature is trivial (i.e. $\\partial_i w_j - \\partial_j w_i = 0$) and can be removed by the local counterterm $\\int d^2x\\sqrt{g} b(g^i) R$,\n so we actually do not have to worry about its cancellation. The non-existence of the curvature $\\partial_i w_j -\\partial_j w_i$ is related to the gradientness of the beta functions and it may have a deep implication in renormalization group flows \\cite{Osborn:1991gm}\\cite{Friedan:2009ik}\\cite{Gukov:2016tnp}.\n\n\n\n\\section{Three dimensions}\nThere is no curvature dependent Weyl anomaly in three dimensions. The position dependent exactly marginal deformations do not introduce the additional Weyl anomaly either under the assumption of the CP symmetry \\cite{Nakayama:2013wda}. Thus there is no interesting scenario we can imagine in three dimensions.\n\n\\section{Four dimensions}\nLet us consider a four-dimensional conformal field theory with an exactly marginal deformation denoted by $g$. We put the theory on a curved background with the metric $g_{\\mu\\nu}(x)$ and then vary the coupling constant over the manifold $g(x)$. \nIn a certain renormalizaiton scheme, the first order Weyl transformation (i.e. the Weyl anomaly) is given by \\cite{Osborn:1991gm} (See also \\cite{Nakayama:2013is}\\cite{Jack:2013sha}.)\n\\begin{align}\n\\delta F_{\\sigma} = \\int d^4x \\sqrt{g} \\delta\\sigma(x) &\\left( c(g) \\mathrm{Weyl}^2 - a \\mathrm{Euler} + (D^2 g D^2g - 2G_{\\mu\\nu} \\partial^\\mu g \\partial^\\nu g -\\frac{R}{3}\\partial^\\mu g\\partial_\\mu g) \\right. \\cr\n& \\left. + \\chi_4(g) \\partial_\\mu g \\partial^\\mu g \\partial_\\nu g \\partial^\\nu g \\right) \\ . \\label{foura}\n\\end{align}\nHere $G_{\\mu\\nu} = R_{\\mu\\nu} - \\frac{R g_{\\mu\\nu}}{2}$ is the Einstein tensor and $D^\\mu$ is the covariant derivative. In addition, we have introduced $\\mathrm{Weyl}^2 = R_{\\mu\\nu\\rho\\sigma}^2 -2R_{\\mu\\nu}^2 + \\frac{1}{3}R^2$ and $\\mathrm{Euler} = R_{\\mu\\nu\\rho\\sigma}^2 - 4R_{\\mu\\nu}^2 + R^2$. \nIn principle $c(g)$ can depend on $g$, but the only such theories known are constructed in somewhat artificial holographic realization \\cite{Nakayama:2017oye}.\n\n \nTo simplify the analysis, let us focus on the regime in which the last quartic term in \\eqref{foura} i.e. $\\chi_4(g) \\partial_\\mu g \\partial^\\mu g \\partial_\\nu g \\partial^\\nu g$ can be neglected (e.g. in the small coupling regime). \nNeglecting the quartic term, we try to solve the equation\n\\begin{align}\n-c\\mathrm{Weyl}^2 + a \\mathrm{Euler} = (D^2 g D^2g - 2G_{\\mu\\nu} \\partial^\\mu g \\partial^\\nu g -\\frac{R}{3}\\partial^\\mu g\\partial_\\mu g) \\ . \\label{fourc}\n\\end{align}\nIn particular, suppose that the metric $g_{\\mu\\nu}(x)$ is Ricci flat. Then the equation \\eqref{fourc} becomes\n\\begin{align}\n\\sqrt{(a-c) R_{\\mu\\nu\\rho\\sigma}^2} = D^2 g \\ , \n\\end{align}\nwhich may be solved by using Green's function for the Laplacian \n\\begin{align}\ng(x) = \\int d^4x' G(x,x') \\sqrt{(a-c) R_{\\mu\\nu\\rho\\sigma}^2(x')}\n\\end{align}\nwhen the manifold is non-compact (otherwise the regular solution does not exist).\n\n\n\nAs in two-dimensions, the above argument works both for infinitesimal Weyl transformation or finite but constant Weyl transformation.\nFor finite generic Weyl transformation, one may define the analogue of harmonic Weyl transformation. For this purpose, it is more convenient to choose a different renormalization scheme so that the Weyl anomaly takes the form (e.g. \\cite{Gomis:2015yaa})\n\\begin{align}\n\\delta F_{\\sigma} = \\int d^4x \\sqrt{g} \\delta\\sigma(x) &\\left( (c(g)-a) \\mathrm{Weyl}^2 - 4a Q + g \\Delta_4 g \\right. \\cr\n& \\left. + \\chi_4(g) \\partial_\\mu g \\partial^\\mu g \\partial_\\nu g \\partial^\\nu g \\right) \\ , \\label{alt} \n\\end{align}\nwhere $\\Delta_4$ is the Fradkin-Tseytlin-Riegert-Paneitz conformal operator \\cite{Fradkin:1982xc}\\cite{Fradkin:1981jc}\\cite{Riegert:1984kt}\\cite{PA}\n\\begin{align}\n\\Delta_4 = (D^2)^2 + 2G_{\\mu\\nu}D^\\mu D^\\nu + \\frac{1}{3}(D^\\mu R) D_\\mu + \\frac{1}{3}R D^2, \n\\end{align}\nwhich is Weyl covariant $\\Delta_4 \\to e^{-4\\sigma} \\Delta_4$, \nand $Q$ is what is called the Q-curvature \\cite{Q}:\n\\begin{align}\nQ = \\frac{-1}{6} D^2 R -\\frac{1}{2}R^{\\mu\\nu}R_{\\mu\\nu} + \\frac{1}{6}R^2 \\ \n\\end{align}\nwhich has a nice mathematical property under the Weyl transformation\n\\begin{align}\nQ \\to e^{-4\\sigma} (Q + \\Delta_4 \\sigma)\n\\end{align}\n\nThe advantage of this rewriting or a choice of the particular local counterterm is as follows. Suppose we cancelled the Weyl anomaly at a particular background by demanding\n\\begin{align}\n0 = (c(g)-a) \\mathrm{Weyl}^2 - 4a Q + g \\Delta_4 g + \\chi_4(g) \\partial_\\mu g \\partial^\\mu g \\partial_\\nu g \\partial^\\nu g \n\\end{align}\nThen, we are still able to cancel the Weyl anomaly on the Weyl transformed manifold whenever the Weyl rescaling is annihilated by the Fradkin-Tseytlin-Riegert-Paneitz operator:\n\\begin{align}\n\\Delta_4 \\sigma = 0 \\ . \\label{ann}\n\\end{align}\nThis is because all the terms in \\eqref{alt} except for the Q-curvature transform covariantly under the finite Weyl transformation.\nIf the Weyl scaling factor satisfies \\eqref{ann}, the cancellation of the Weyl anomaly therefore persists even for finite Weyl transformation. This is precisely analogous to the special role of harmonic Weyl transformation in two dimensions.\n\n\nLet us move on to the most generic cases with multiple coupling constants. The Weyl transformation is given by\n\\begin{align}\n\\delta F_{\\sigma} = \\int d^4x \\sqrt{g} \\delta \\sigma(x) &( c(g) \\mathrm{Weyl}^2 - a \\mathrm{Euler} + \\chi_{ij}(g) (D^2 g^i D^2g^j - 2G_{\\mu\\nu} \\partial^\\mu g^i \\partial^\\nu g^j -\\frac{R}{3}\\partial^\\mu g^i \\partial_\\nu g^j) \\cr\n& \\left. + \\chi_{ijkl}(g) \\partial_\\mu g^i \\partial^\\mu g^j \\partial_\\nu g^k \\partial^\\nu g^l \\right) \\cr\n&+ \\partial_\\mu \\delta\\sigma G^{\\mu\\nu} w_i(g) \\partial_\\nu g^j \\ . \\label{fourm}\n\\end{align}\nFor finite but constant Weyl transformation, we only have to cancel the first two lines in \\eqref{fourm}, which is essentially equivalent to what we have done in the above. On the other hand, for infinitesimal but generic Weyl transformation, we have to cancel the third line as well, which requires either $\\partial_i w_j - \\partial_j w_i =0$ or $G_{\\mu\\nu} = 0$ in the background.\n\nTo conclude the analysis, we would like to mention the other obstructions to the Weyl transformation if the dimension two operator $O(x)$ exist in the theory. If this is the case, there is a further operator Weyl anomaly such as \n\\begin{align}\n\\int d^4x \\sqrt{g} \\delta\\sigma(x) (\\eta(g) R O(x) + \\epsilon(g) \\partial_\\mu g \\partial^\\mu g O + \\tau(g) D^2 O + \\delta(g) D^2 g O) + \\partial_\\mu \\delta \\sigma(x) \\theta(g) \\partial^\\mu g O \\ .\n\\end{align}\nIt has been shown that such Weyl anomaly can be removed when $g(x) = g$ \\cite{Jack:2013sha} (see also \\cite{Nakayama:2013wda}\\cite{Farnsworth:2017tbz} for similar analysis), but with the space-time dependent coupling, we need the extra cancellation to get the consistent picture. Schematically, the Wess-Zumino consistency condition demands $\\eta = 0$ and one can always remove $\\theta$ and $\\tau$ by local counterterms. Then we need to cancel $\\epsilon$ term against the $\\delta$ term. Since the existence of dimension two operator is non-generic, we will not pursue the cancellation in further details.\n\n\n\\section{Holographic models}\nWe revisit the cancellation mechanism we have studied in previous sections from the holographic perspective. For definiteness we consider the case of four dimensional conformal field theories with the five dimensional bulk. Let us study the Einstein gravity coupled with a scalar field $\\phi$ given by the minimal action\n\\begin{align}\nS = \\int d^5x \\sqrt{g} \\left( R + \\Lambda + \\frac{1}{2}\\partial^M \\phi \\partial_M \\phi \\right) .\n\\end{align}\n\nIn the AdS\/CFT correspondence, we compute the on-shell action for a given boundary condition at $\\rho= \\epsilon$ (i.e. $\\phi_{(0)}(x)$ and $g_{(0)\\mu\\nu}(x)$ below) with the expansion \n\\begin{align}\n\\phi &= \\phi_{(0)}(x) + \\rho \\phi_{(1)}(x) + \\rho^2 \\phi_{(2)}(x) + \\cdots \\cr\ng_{\\mu\\nu} &= g_{(0)\\mu\\nu}(x) + \\rho g_{(1)\\mu\\nu}(x) + \\rho^2 g_{(2) \\mu\\nu}(x) + \\cdots\n\\end{align}\nin the Graham-Fefferman gauge \n\\begin{align}\nds^2 = G_{MN} dx^M dx^N = \\frac{d\\rho^2}{\\rho^2} + \\frac{g_{\\mu\\nu} dx^\\mu dx^\\nu}{\\rho} \\ .\n\\end{align}\n\nThe resulting on-shell action is generically divergent in the limit $\\epsilon \\to 0$ from the $\\rho$ integration as $\\int_{\\epsilon} d\\rho \\rho^{-1} S_{\\log} = \\log \\epsilon S_{\\log}$, leading to the holographic Weyl anomaly \\cite{Henningson:1998gx}. Explicitly \\cite{Nojiri:1998dh}, we have\n\\begin{align}\nS = \\log \\epsilon \\int d^4x &\\left( \\frac{1}{8} R_{\\mu\\nu (0)}^2 -\\frac{1}{24} R_{(0)}^2 + \\frac{1}{4} (D^2 \\phi_{(0)})^2 \\right. \\cr \n& \\left. - \\frac{1}{2} R_{(0)}^{\\mu\\nu} \\partial_\\mu \\phi \\partial_\\nu \\phi + \\frac{1}{6} R_{(0)} \\partial^\\mu \\phi_{(0)} \\partial_\\nu \\phi_{(0)} + \\frac{1}{3}(\\partial_\\mu \\phi_{(0)} \\partial^\\mu\\phi_{(0)})^2 \\right) \\ \n\\end{align}\nwhich is exactly what we had in section 4 for the constant Weyl transformation.\nThus cancelling the Weyl anomaly from the position dependent coupling constant corresponds to the choice of the boundary values of $\\phi(x)$ such that the on-shell gravity action is finite without the logarithmic divergence. The choice of such boundary conditions make the AdS\/CFT correlation functions better behaved, so classifying such supergravity background may be of theoretical interest.\n\n\\section{Discussions}\nIn this paper, we have studied a novel way to cancel the Weyl anomaly from the position dependent coupling constant. Here we would like to mention further possibilities to cancel the Weyl anomaly.\n\nFirst of all, if the theory under consideration possesses a conserved current $J_\\mu$, one may introduce the background field strength by the coupling $\\int d^4x \\sqrt{g} A^\\mu J_\\mu$. This gives another contribution to the Weyl anomaly as $\\int d^4x \\delta\\sigma(x) b_0 F_{\\mu\\nu}(x) F^{\\mu\\nu}(x)$, where $b_0$ is the coefficient of the one-loop beta function determined from the current two-point function (which is positive in unitary conformal field theories) and $F_{\\mu\\nu} = \\partial_\\mu A_\\nu - \\partial_\\nu A_\\mu$. Then we may try to cancel the Weyl anomaly from this contribution.\nActually, simultaneous use of the gauge field and the position dependent coupling constant may not be a good idea because of the existence of the vector beta functions \\cite{Nakayama:2013ssa}. Again we have to think about the cancellation of the extra operator Weyl anomaly such as $\\int d^4x \\delta \\sigma \\rho(g) \\partial^\\mu g J_\\mu$. To avoid the appearance of the vector beta functions, we may only introduce the position dependent coupling constant which is neutral under the symmetry generated by $J_\\mu$.\n\nIt could have been extremely interesting if we were able to find a novel class of Weyl gauging without demanding $c=0$ in two dimensions, and $c=a=0$ in four dimensions.\\footnote{See e.g. \\cite{Fradkin:1983tg} for a possibility in the context of supersymmetric Weyl gravity.} \nCurrently, the closest way to do this is to demand all the non-trivial Weyl anomalies vanish, say $a=0$ in four dimensions, and then try to cancel the $c \\mathrm{Weyl}^2$ term against the space-time dependent coupling constants. Here, we should further employ the non-unitariness of the model (since $a=0$ from the beginning suggests it must be so) to obtain the cancellation in the Weyl anomalies. This is because unitarity demands the positivity of the both terms and the cancellation only happens by using the non-unitary property. Whether this is better than just demanding $c=a=0$ is yet to be seen in the context of quantum Weyl gravity in which we would like to gauge the Weyl symmetry exactly.\\footnote{Note, however, that the introduction of the position dependent coupling constant modifies the conservation of the energy-momentum tensor, and the consistecy with the dynamical gravity must be discussed more carefully. We stress that in the main part of this paper, we have focused on the fixed gravitational background, so there is no inconsistency. The author would like to thank S.~Deser for the discussions.}\n\nIn two dimensions, we did not obtain any new possibilities to gauge the entire Weyl symmetry than demanding $c=0$ from the beginning. We are still able to gauge the harmonic Weyl symmetry, but the physical interest in such gauging (e.g. whether it defines new class of quantum gravity in two dimensions) should be discussed more in detail.\n\nFinally, we point out that there is an alternative option. Once we know how to solve $g(x)$ to cancel the Weyl anomaly in a given metric $g_{\\mu\\nu}(x)$, one may introduce the extra transformation on $g(x)$ so that the Weyl anomaly is always cancelled (irrespective of the obstructions we have discussed above). This possibility requires further investigation if such transformation can be defined systematically and then whether such a generalized notion of the Weyl transformation is useful or not.\n\n\n\\section*{Acknowledgements}\nThis work is in part supported by JSPS KAKENHI Grant Number 17K14301. \n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Effects of the code improvements on the wind}\nIn this section, we evaluate the effect on the wind of the three improvements that we presented to the treatment of the radiation field: (i) radial dependence of $f_\\text{\\tiny UV}$, (ii) improved radiation transport, and (iii) relativistic corrections.\n\\begin{figure*}\n \\centering\n \\includegraphics[width=\\textwidth]{figures\/comparisons.pdf}\n \\caption{Effects of our improvements to the treatment of the radiation field on the trajectories of gas blobs. All simulations have been done with $M_\\mathrm{BH} = 10^8\\mathrm{M}_\\odot$, $\\dot m =0.5$, and $R_\\mathrm{in}=50R_\\mathrm{g}$.}\n \\label{fig:comparisons}\n\\end{figure*}\nThe impact of each improvement is shown in \\autoref{fig:comparisons}. In the first panel (blue), we calculate the optical depth $\\tau_\\text{\\tiny UV}$ from the centre, rather than attenuating each individual light ray coming from the disc. We also take a constant $f_\\text{\\tiny UV}=0.85$ and we do not include relativistic corrections. Calculating $\\tau_\\text{\\tiny UV}$ from the centre overestimates the attenuation of the UV radiation field in the outer parts of the wind, since it assumes that the radiation is crossing all the inner gas. This produces a failed wind in the outer regions of the disc (which is on too small a scale to be seen in \\autoref{fig:comparisons}), since the inner gas is optically thick to the UV radiation. In the next panel (green), we calculate $\\tau_\\text{\\tiny UV}$ by attenuating each individual light ray as explained in \\autoref{sec:radiation_transport}, consequently, most of the wind escapes from the disc since the UV attenuation is less strong, with the average wind velocity being slightly lower because we are now averaging over the slower trajectories in the outer part of the wind. The next improvement we evaluate is the inclusion of the radial dependence of $f_\\text{\\tiny UV}$ (third panel, orange). The change in wind geometry can be explained by considering the distribution of the UV emissivity, which is skewed towards the centre of the disc thus pushing the outer wind along the equator. This also leads to a more powerful wind, due to the increase in UV luminosity at small radii, where the fastest and most massive streamlines originate. Lastly, the effect of including relativistic corrections is shown in the 4th panel (red). As expected, the velocity of the wind is considerably lower, and hence also its kinetic luminosity. Furthermore, the wind flows at a slightly higher polar angle, as the vertical component of the radiation force gets weaker where the wind has a high vertical velocity. \n\\section{Conclusions and future work}\n\nIn this paper, we have presented \\textsc{Qwind3}, a model that builds upon the non-hydrodynamical code \\textsc{Qwind} first introduced in \\cite{risaliti_non-hydrodynamical_2010} to model UV line-driven winds in the context of AGNs. In this new version, we generalise the CAK formalism for stellar winds to AGNs, which we use to calculate the initial density and velocity with which the wind is launched at the surface of the accretion disc. We have highlighted the importance of correctly accounting for the fraction of luminosity emitted in the UV by each disc annulus, which in other numerical codes is assumed to be a constant over the whole disk. We have also introduced an algorithm to do ray-tracing calculations throughout the wind, allowing us to compute the radiation transfer of the system taking into account the full geometry of the wind and the spatial distribution of the X-ray and UV radiation sources. Furthermore, we have also included special relativistic corrections to the calculation of the radiation flux, which are important for ensuring that the wind does not achieve superluminal speeds. We note two important assumptions that still remain: the simplified dependence of the X-ray opacity on the ionisation parameter and the X-ray luminosity fraction, which we here assume is constant at $f_X=0.15$, independent of black hole mass and mass accretion rate. We will address these issues in the next {\\sc Qwind} release. \n\nWe have used the new code to explore under what conditions UV line-driven winds are successfully launched from accretion discs, studying how the normalised mass loss rate, kinetic luminosity, momentum outflow rate, and final velocity change as a function of black hole mass, mass accretion rate, and initial wind launching radius. We find that winds can carry a mass loss rate up to 30\\% of the mass accretion rate,\nso this can have a moderate impact on the mass accretion rate at radii below the wind launching radius. The next {\\sc Qwind} release will address this by reducing the mass accretion rate through the disc, and hence reducing the UV flux produced at small radii, to make the model self-consistent (see also \\citealt{nomura_line-driven_2020}).\nThe current code produces winds where the \nkinetic power and especially the momentum outflow rate can \nbe comparable to that of the radiation power at high $\\dot{m}$, and even exceed it which is unphysical. This shows that the effect of line blanketing must be important in reducing the UV flux driving the wind below that predicted by electron scattering opacity alone. We will address this in the next {\\sc Qwind} release, but \noverall, the wind clearly carries sufficient power to meet the criteria for an efficient feedback mechanism in galaxy formation \\citep{hopkins_stellar_2016}.\n\nOur results here show that the outflow velocity is mildly relativistic across a broad parameter space, with velocity $0.1-0.3$~c even excluding the unphysically efficient winds. Thus UV line-driven winds can reach UFO velocities even when special relativistic corrections (radiation drag) are included. This contrasts with \\cite{luminari_speed_2021}, who conclude that UV line-driving is not capable of generating such high velocity winds. \nWe caution that the two codes make different assumptions about the initial conditions and ray tracing (both of which \\textsc{Qwind} does more accurately and self-consistently) as well as the opacity (which their code does better), so it is premature to rule out UV line-driving in favour of other mechanisms such as magnetic driving \\citep{blandford_hydromagnetic_1982, fukumura_magnetic_2017} as the origin of UFOs until these factors are all incorporated together. We will explore this more fully in the next \\textsc{Qwind} release.\n\nThe normalised wind mass loss rate and normalised kinetic luminosity vary substantially as a function of black hole mass and accretion rate, the latter being the most significant factor. The ratio of wind kinetic power to mass accretion rate scales steeply with $\\dot{m}$, in contrast to the constant ratio normally assumed in the AGN feedback models currently implemented in cosmological simulations of galaxy formation. Implementing this new more physically-based AGN feedback prescription in simulations will therefore change how galaxies are predicted to evolve across cosmic time.\n\n\n\\section{Calculating the gas trajectories}\n\\label{sec:gas_trajectories}\n\\subsection{Initial radii of gas trajectories}\n\nThe first thing to consider when calculating the gas trajectories is the initial location of the gas blobs. The innermost initial position of the trajectories is taken as a free parameter $R_\\mathrm{in}$, and the outermost initial position, $R_\\mathrm{out}$, is assumed to be the self-gravity radius of the disc, where the disc is expected to end \\citep{laor_massive_1989}, which for our reference BH corresponds to 1580 $R_g$. We initialise the first trajectory at $R_\\mathrm{in}$, the next trajectory starts at $R=R_\\mathrm{in} + \\Delta R$, where $\\Delta R$ is the distance between adjacent trajectories. We determine $\\Delta R$ by considering two quantities: (i) the change in optical depth between two adjacent trajectories along the base of the wind and (ii) the mass loss rate along a trajectory starting at $R$ and at a distance $\\Delta R$ to the next one. Regarding (i), the change in optical depth $\\Delta\\tau$ between two trajectories initially separated by $\\Delta R$ is given by\n\\begin{equation}\n \\Delta\\tau = \\int_R^{R+\\Delta R_1} n(R')\\, \\sigma_{\\text{\\tiny T}} \\,\\mathrm{d} R'.\n\\end{equation}\nWe consider\n\\begin{equation}\n \\Delta\\tau = \\begin{cases} \n 0.05 & \\mathrm{ if } \\;\\tau(R) < 5,\\\\\n 0.5 & \\mathrm{ if } \\;\\tau(R) < 10,\\\\\n 5 & \\mathrm{ if } \\;\\tau(R) < 100,\\\\\n 20 & \\mathrm{ if } \\;\\tau(R) > 100,\\\\\n \\end{cases}\n\\end{equation}\nwhere $\\tau(R) = \\sum_i \\Delta\\tau_i$. This guarantees that the spacing between trajectories resolves the transition from optically thin to optically thick for both the UV and X-ray radiation. The quantity (ii) is given by\n\\begin{equation}\n \\Delta \\dot M_\\mathrm{wind} = \\int_R^{R+\\Delta R_2}2\\pi R'\\rho(R')v(R') \\mathrm{d} R',\n\\end{equation}\nwhere we consider $\\Delta \\dot M_\\mathrm{wind}=0.01\\dot M$, so that no streamline represents more than $1\\%$ of the accreted mass rate. The $\\Delta R$ step is thus given by \n\\begin{equation}\n \\Delta R = \\min (\\Delta R_1, \\Delta R_2)\n\\end{equation}\nand we repeat this process until $R=R_\\mathrm{out}$\n\n\\subsection{Solving the equation of motion}\n\nThe equation of motion of the wind trajectories is the same as in \\citetalias{quera-bofarull_qwind_2020}, and we solve it analogously by using the Sundials IDA integrator \\citep{hindmarsh_sundials_2005}. The wind trajectories are calculated until they either exceed a distance from the centre of $10^4 R_g$, fall back to the disc, or self intersect. To detect when a trajectory self intersects we use the algorithm detailed in Appendix \\ref{app:intersections}.\n\nBy considering the full wind structure for the radiation ray tracing, we run into an added difficulty: the interdependence of the equation of motion with the density field of the wind. To circumvent this, we adopt an iterative procedure in which the density field of the previous iteration is used to compute the optical depth factors for the current iteration. We first start assuming that the disc's atmosphere is void, with a vacuum density of $n_\\text{vac} = 10^2$ cm$^{-3}$. Under no shielding of the X-ray radiation, all the trajectories fall back to the disc in a parabolic motion. After this first iteration, we calculate the density field of the resulting failed wind and use it to calculate the wind trajectories again, only that this time the disc atmosphere is not void. We keep iterating until the mass loss rate and kinetic luminosity do not significantly change between iterations. It is convenient to average the density field in logarithmic space between iterations, that is, the density field considered for iteration $k$, $n_k$, is given by\n\\begin{equation}\n \\log_{10}(n_{k}(R, z)) = \\frac{1}{2}\\, \\left(\\log_{10}(n_{k-1}(R,z)) + \\log_{10}(n_{k-2}(R,z))\\right).\n\\end{equation}\nWe do not take the average for the first two iterations. The number of iterations required depends on the initial radius of the wind, but we typically find convergence after $\\sim 20$ iterations, although we run several more to ensure that the standard deviation of the density field between iterations is small. The normalised mass loss rate and kinetic luminosity for our fiducial model at each iteration are shown in \\autoref{fig:iterations}.\n\n\\begin{figure}\n \\centering\n \\includegraphics{figures\/iterations.pdf}\n \\caption{Normalised mass loss rate and kinetic luminosity for each iteration for our fiducial case.}\n \\label{fig:iterations}\n\\end{figure}\n\nFor a given iteration, solving the equation of motion for the different gas trajectories is an embarrassingly parallel problem, and so our code's performance scales very well upon using multiple CPUs, allowing us to quickly run multiple iterations, and scan the relevant parameter spaces. The computational cost of running one iteration is $\\sim 5$ CPU hours, so one is able to obtain a fully defined wind simulation after $\\sim 100$ CPU hours.\n\n\\section{Critical point derivation}\n\\label{app:initial_conditions}\n\nIn this appendix we aim to give a detailed and self-contained derivation of the initial conditions for launching the wind from the surface of the accretion disk. Let us first start with the 1D wind equation (now including gas pressure forces),\n\\begin{equation}\n \\label{eq:ic_1d}\n \\rho \\frac{\\mathrm{d} v_z}{\\mathrm{d} t} = \\rho (a_\\mathrm{rad}^z - a_\\mathrm{grav}^z) - \\frac{\\partial P}{\\partial z}.\n\\end{equation}\nThe left-hand side of the equation can be expanded to\n\\begin{equation}\n \\rho \\frac{\\mathrm{d} v_z}{\\mathrm{d} t} = \\rho \\frac{\\partial v_z}{\\partial t} + \\rho v_z \\frac{\\partial v_z}{\\partial z} ,\n\\end{equation}\nwhere we note that $\\partial v_z \/ \\partial t$ = 0, since we are only interested in steady solutions that do not depend explicitly on time. For simplicity, we drop the partial derivative notation going forward, since only derivatives along the $z$ direction are involved. We assume that the wind is isothermal, with an equation of state given by\n\\begin{equation}\n P = c_s^2 \\rho\n\\end{equation}\nwhere $c_s$ is the isothermal sound speed, which is assumed constant throughout the wind. Using the mass conservation equation (\\autoref{eq:mass_conservation}), $\\dot M = A\\, \\rho\\, v_z$, where $A = 2\\pi r \\Delta r$, we can write\n\\begin{equation}\n \\frac{\\mathrm{d} P}{\\mathrm{d} z} =c_s^2\\frac{\\mathrm{d} \\rho}{\\mathrm{d} z} = -\\rho c_s^2 \\left(\\frac{\\mathrm{d} A \/ \\mathrm{d} z}{A} + \\frac{\\mathrm{d} v_z \/ \\mathrm{d} z}{v_z}\\right),\n\\end{equation}\nso we can rewrite \\autoref{eq:ic_1d} as\n\\begin{equation}\n v_z \\frac{\\mathrm{d} v_z}{\\mathrm{d} z}\\left(1-\\frac{c_s^2}{v_z^2}\\right) = a_\\mathrm{rad}^z - a_\\mathrm{grav}^z + c_s^2\\frac{\\mathrm{d} A \/ \\mathrm{d} z}{A}.\n\\end{equation}\nWe now focus on the radiation force term, which is the sum of the electron scattering and line-driving components,\n\\begin{equation}\n a_\\mathrm{rad}^z = a_\\mathrm{rad}^{\\mathrm{es}, z} + \\mathcal M \\, a_\\mathrm{rad}^{\\mathrm{es}, z}.\n\\end{equation}\nWhen the wind is rapidly accelerating, as it is the case at low heights, the force multiplier is well approximated by its simpler form (see section 2.2.3 of \\citetalias{quera-bofarull_qwind_2020}),\n\\begin{equation}\n \\label{eq:fm_simple}\n \\mathcal M(t) = k \\, t^{-\\alpha},\n\\end{equation}\nwhere $\\alpha$ is fixed to $0.6$, $k$ depends on the ionisation level of the gas (in the \\citetalias{stevens_x-ray_1990} parameterisation), and $t = \\kappa_{\\text{\\tiny e}} \\, \\rho \\, v_\\text{th} \\left | \\mathrm{d} v\/\\mathrm{d} z \\right | ^{-1}$. As discussed in section 3.1.3 of \\citetalias{quera-bofarull_qwind_2020}, the thermal velocity, $v_\\mathrm{th}$ (\\autoref{eq:thermal_velocity}), is computed at a fixed temperature of $T=2.5 \\times 10^4$ K. Once again making use of the mass conservation equation we can write\n\\begin{equation}\n \\mathcal M = k\\, t^{-\\alpha} = \\frac{k}{(\\kappa_{\\text{\\tiny e}}\\, v_\\text{th}\\, \\rho)^\\alpha}\\left |\\frac{dv}{dz}\\right|^\\alpha = \\frac{k}{\\left(\\kappa_{\\text{\\tiny e}} v_\\text{th} \\, \\right)^\\alpha}\\left(\\frac{A}{\\dot M} v_z \\left|\\frac{dv_z}{dz}\\right|\\right)^\\alpha\n\\end{equation}\nand so the full equation to solve with all derivatives explicit is\n\\begin{equation}\n \\begin{split}\n v_z \\frac{\\mathrm{d} v_z}{\\mathrm{d} z}\\left( 1- \\frac{c_s^2}{v_z^2}\\right) &= a_\\mathrm{rad}^{\\mathrm{es}, z} (z) -a_\\mathrm{grav}^z (z) \\; \\\\\n &+ a_\\mathrm{rad}^{\\mathrm{es}, z}(z)\\frac{k}{\\left(\\kappa_{\\text{\\tiny e}} v_\\text{th} \\, \\right)^\\alpha}\\left(\\frac{A(z)}{\\dot M} v_z \\left|\\frac{dv_z}{dz}\\right|\\right)^\\alpha \\\\\n &+ c_s^2\\frac{\\mathrm{d} A(z) \/ \\mathrm{d} z}{A(z)}.\n \\end{split}\n\\end{equation}\nWe assume that $\\mathrm{d} v_z \/ \\mathrm{d} z >0$ always, so the absolute value can be dropped. It is convenient to introduce new variables to simplify this last equation. The first change we introduce is $W=v_z^2\/2$, so that $v_z \\, \\mathrm{d} v_z \/ \\mathrm{d} z = \\mathrm{d} W \/ \\mathrm{d} z$. Next, we aim to make the equation dimensionless, so we consider $R$ as the characteristic length, $B_0 = GM_\\mathrm{BH}\/R^2$ as the characteristic gravitational acceleration value, $W_0=GM_\\mathrm{BH}\/R = B_0 R$ as the characteristic $W$ value, $A_0 = 2\\pi R \\Delta R$ as the characteristic $A$ value, and finally we take the characteristic radiation force value to be \n\\begin{equation}\n\\label{eq:gamma_0}\n \\gamma_0 = \\frac{k}{(\\kappa_{\\text{\\tiny e}} v_\\text{th})^\\alpha} \\; a_\\mathrm{rad, 0}^{\\mathrm{es}, z},\n\\end{equation}\nwhere $a_\\mathrm{rad, 0}^{\\mathrm{es}, z}$ is \\autoref{eq:radiation_acceleration_approx} without considering the $\\tau_\\text{\\tiny UV}$ factor, since we do not include attenuation in this derivation. With all this taken into account, we can write\n\\begin{equation}\n \\label{eq:half_way}\n \\begin{split}\n B_0 \\frac{dw}{dx} \\left(1 - \\frac{s}{w}\\right) & = -a_\\mathrm{grav}^z + a_\\mathrm{rad}^{\\mathrm{es}, z} \\left(1+ \\frac{k}{(\\kappa_{\\text{\\tiny e}} v_\\text{th} )^\\alpha}\\left(\\frac{A}{\\dot M} B_0\\frac{dw}{dx}\\right)^\\alpha\\right) \\\\\n & + c_s^2\\frac{\\mathrm{d} A \/ \\mathrm{d} z}{A},\n\\end{split}\n\\end{equation}\nwhere we have defined $x=z\/R$, $w=W\/W_0$, and $s=c_s^2 \/ (2 W_0)$. We also define $a=A\/A_0$, and $\\varepsilon = \\dot M \/ \\dot M_0$ with\n\\begin{equation}\n \\dot M_0 = \\alpha (1-\\alpha)^{(1-\\alpha) \/ \\alpha} \\frac{(\\gamma_0 A_0)^{1 \/ \\alpha}}{(B_0 A_0)^{(1-\\alpha) \/ \\alpha}}.\n \\label{eq:Mdot0_def}\n\\end{equation}\nThis last definition may seem a bit arbitrary, but it is taken such that $\\varepsilon = 1$ corresponds to the classical \\citetalias{castor_radiation-driven_1975} $\\dot M$ value for O-stars. \\autoref{eq:half_way} then becomes\n\\begin{equation}\n \\begin{split}\n \\frac{\\mathrm{d} w}{\\mathrm{d} x} \\left(1-\\frac{s}{w}\\right) &= \\frac{(-a_\\mathrm{grav}^z + a_\\mathrm{rad}^{\\mathrm{es}, z})}{B_0} + \\frac{1}{\\alpha^\\alpha(1-\\alpha)^{1-\\alpha}}\\frac{a_\\mathrm{rad}^{\\mathrm{es}, z}}{a_\\mathrm{rad,0}^{\\mathrm{es}, z}} \\left(\\frac{a}{\\varepsilon}\\frac{\\mathrm{d} w}{\\mathrm{d} x}\\right)^\\alpha \\\\\n &+ \\frac{4sx}{a},\n \\end{split}\n\\end{equation}\nwhere we have used\n\\begin{equation}\n a = \\frac{A}{A_0} = \\frac{2\\pi r \\Delta r}{2\\pi r_0 \\Delta r_0} = \\frac{r^2}{r_0^2},\n\\end{equation}\nso that\n\\begin{equation}\n \\frac{c_s^2}{B_0}\\frac{\\mathrm{d} A \/ \\mathrm{d} z}{A} = \\frac{4sx}{a}.\n\\end{equation}\nWe note that we have assumed that the area $A$ changes with $z$ like the 2D solution, despite the fact that we are considering here a 1D wind. This small correction guarantees that we find critical-point like solutions for all initial radii, since it guarantees that the \\citetalias{castor_radiation-driven_1975} conditions for the existence of a critical point are satisfied.\nFinally, we define\n\\begin{equation}\n\\label{eq:nozzle_f}\n f = \\frac{1}{\\alpha^\\alpha (1-\\alpha)^{1-\\alpha}}\\frac{a_\\mathrm{rad}^{\\mathrm{es}, z}}{a_\\mathrm{rad, 0}^{\\mathrm{es}, z}},\n\\end{equation}\nand \n\\begin{equation}\n\\label{eq:nozzle_h}\n h = \\frac{(a_\\mathrm{grav}^z - a_\\mathrm{rad}^{\\mathrm{es}, z})}{B_0} - 4sxa,\n\\end{equation}\nso that\n\\begin{equation}\n \\frac{\\mathrm{d} w}{\\mathrm{d} x} \\left(1-\\frac{s}{w}\\right) = -h(x) + f(x) \\left(\\frac{a}{\\varepsilon}\\frac{\\mathrm{d} w}{\\mathrm{d} x}\\right)^\\alpha,\n\\end{equation}\nwhich is the dimensionless wind equation. The choice of sign in $h(x)$ is to make the further steps clearer.\nIt is useful to interpret this equation as an algebraic equation for $w'=\\mathrm{d} w \/ \\mathrm{d} x$,\n\\begin{equation}\n F(x,w,w') = w' \\left(1-\\frac{s}{w}\\right) + h(x) - f(x) \\left(\\frac{a}{\\varepsilon}w'\\right)^\\alpha = 0.\n \\label{eq:CAK_F_wprime}\n\\end{equation}\nThis has the same general form as Equation~26 in \\citetalias{castor_radiation-driven_1975}, which applies to a spherical wind from a star, but the functions $h(x)$ and $f(x)$ are different. We note that $f(x)>0$ and $0<\\alpha<1$, but $h(x)$ can have either sign. (Also note that we have chosen the opposite sign for $h(x)$ to CAK, for later convenience.)\nGiven values of $x$ and $\\varepsilon$, we can distinguish 5 different regions of solutions for $w'$ (we follow the original enumeration of regions by \\citetalias{castor_radiation-driven_1975}):\n\n\\begin{itemize}\n \\item Subsonic stage ($w0$, $F(w'=0)$ is positive and as $w'$ increases, $F$ goes to negative values crossing $F=0$ once, so there is one solution for $w'$.\n \\end{itemize}\n \\item Supersonic stage ($w >s$): We have three regions\n \\begin{itemize}\n \\item Region III: If $h(x)<0$, $F(w'=0)$ is negative and as $w'$ increases, $F$ goes to positive values crossing $F=0$ once, so there is one solution for $w'$.\n \\item If $h(x) > 0$, $F(w'=0)$ is positive, then $F$ initially decreases until its minimum, $w_\\mathrm{min}'$, and then increases again. If $F(w_\\mathrm{min}') >0$, we have no solution for $w'$ and if $F(w_\\mathrm{min}') \\leq 0$ we have two solutions, which are the same if $F(w_\\mathrm{min}') = 0$.\n The minimum can be found by solving\n \\begin{equation}\n \\frac{\\partial F}{\\partial w'} = 0,\n \\end{equation}\n which gives\n \\begin{equation}\n \\label{eq:w_min}\n w_\\mathrm{min}' = \\left( \\frac{1-s\/w}{\\alpha f (a\/\\varepsilon)^\\alpha}\\right)^\\frac{1}{\\alpha -1},\n \\end{equation}\n so we have\n \\begin{itemize}\n \\item Region IV: If $F(w_\\mathrm{min}') > 0$, there is no solution.\n \\item Region II: If $F(w_\\mathrm{min}') \\leq 0$, there are two solutions, one with $w' < w_\\mathrm{min}'$ and the other with $w' > w_\\mathrm{min}'$\n \\end{itemize}\n \\end{itemize}\n\\end{itemize}\n\nWe assume that the wind starts subsonic ($ws) and extends to $x\\to\\infty$, this means that the wind must end at Region~III. However, because $h(x)>0$ in Region~I and $h(x)<0$ in Region~III, these two regions must be connected by Region~II in between. At the boundary between Regions~I and II, $w=s$ and $h(x)>0$, while at the boundary between Regions~II and III, $w>s$ and $h(x)=0$.\n\nConsidering first the boundary between Regions~I and II, setting $w=s$ in \\autoref{eq:CAK_F_wprime} gives\n\\begin{equation}\n h - f \\left(\\frac{a}{\\varepsilon} w'\\right)^\\alpha = 0,\n\\end{equation}\nwith $h>0$. Considering \\autoref{eq:w_min} for $w_\\mathrm{min}'$ as $w \\to s^{+}$, we find that the wind solution at this boundary must lie on the branch with $w' < w_\\mathrm{min}'$.\n\nConsidering next the boundary between Regions~II and III, setting $h=0$ in \\autoref{eq:CAK_F_wprime} (neglecting the $w'=0$ solution) gives\n\\begin{equation}\n 1-\\frac{s}{w} - f \\left(\\frac{a}{\\varepsilon}\\right)^\\alpha w'^{\\alpha-1} = 0,\n\\end{equation}\nAgain using \\autoref{eq:w_min} and $w>s$, we find that the wind solution at this boundary must lie on the branch with $w' > w_\\mathrm{min}'$.\n\n\nHowever, we assume that $w'$ must be continuous throughout the wind, which would not be the case if the two branches of region II were not connected. Therefore, both branches of region II must coincide at some point, so the condition\n\\begin{equation}\n F(w_\\mathrm{min}') = 0\n\\end{equation}\nmust hold at that point, with $w' = w_\\mathrm{min}'$ for both branches. This point is in fact the critical point of the solution, and $\\partial F\/\\partial w' = 0$ there. Upon substitution of $w_\\mathrm{min}'$, this condition is equivalent to\n\\begin{equation}\n \\label{eq:nozzle_function_sonic}\n \\varepsilon \\, \\left(1 - \\frac{s}{w} \\right) = \\alpha (1-\\alpha)^\\frac{1-\\alpha}{\\alpha} \\, \\frac{f^{1\/\\alpha} a}{h^\\frac{1-\\alpha}{\\alpha}}.\n\\end{equation}\nThe right-hand side of the equation is usually referred to as the Nozzle function $\\mathcal N$,\n\\begin{equation}\n\\label{eq:nozzle_function}\n \\mathcal N(x) = \\alpha (1-\\alpha)^\\frac{1-\\alpha}{\\alpha} \\frac{f^{1\/\\alpha}a}{h^\\frac{1-\\alpha}{\\alpha}}.\n\\end{equation}\nWe define $x=x_c$ to be the position of the critical point. We now assume that at the critical point the wind is highly supersonic ($w \\gg s$). We verify this assumption in \\autoref{subsection:verify_critical_point}. Then $1-s\/w \\approx 1$, and so the normalised mass loss rate is given by\n\\begin{equation}\n \\varepsilon = \\mathcal N(x_c).\n \\label{eq:eps_n_xc}\n\\end{equation}\n\nWe now show that the location of the critical point $x_c$ is at the minimum of $\\mathcal N(x)$. Let us consider the total derivative of $F$ with respect to $x$ taken along a wind solution. Since $F=0$ at all points along the solution,\n\\begin{equation}\n \\frac{\\mathrm{d} F}{\\mathrm{d} x} = \\frac{\\partial F}{\\partial x} + \\frac{\\partial F}{\\partial w}w' + \\frac{\\partial F}{\\partial w'}\\frac{\\mathrm{d} w'}{\\mathrm{d} x} = 0.\n\\end{equation}\nBecause of our assumption $w\\gg s$, we have $\\frac{\\partial F}{\\partial w} = 0$, and, at the critical point, $\\frac{\\partial F}{\\partial w'} = 0$, so that\n\\begin{equation}\n \\left .\\frac{\\mathrm{d} F}{\\mathrm{d} x}\\right |_{w'_\\mathrm{min}, x_c} = \\left .\\frac{\\partial F}{\\partial x}\\right |_{w'_\\mathrm{min}, x_c} = 0,\n\\end{equation}\nwhich gives (a prime denotes $\\mathrm{d} \/ \\mathrm{d} x$)\n\\begin{equation}\n\\label{eq:dF_dx}\n h' - \\left(\\frac{w'_\\mathrm{min}}{\\varepsilon}\\right)^\\alpha \n \\frac{\\mathrm{d}}{\\mathrm{d} x} \\left( f a^{\\alpha} \\right) = 0.\n\\end{equation}\nNow using \\autoref{eq:w_min} for $w'_\\mathrm{min}$ along with \\autoref{eq:eps_n_xc} and \\autoref{eq:nozzle_function}, we obtain $w'_\\mathrm{min} = \\frac{\\alpha}{(1-\\alpha)} h$. Substituting in \\autoref{eq:dF_dx} and using \\autoref{eq:eps_n_xc} and \\autoref{eq:nozzle_function} again, we obtain \n\\begin{equation}\n(1-\\alpha) \\frac{h'}{h} - \\frac{1}{f a^{\\alpha}} \\frac{\\mathrm{d}}{\\mathrm{d} x} \\left( f a^{\\alpha} \\right) = 0.\n\\end{equation}\nComparing to \\autoref{eq:nozzle_function}, we see that this is equivalent to the condition $\\mathrm{d} \\mathcal{N}\/\\mathrm{d} x =0$. Therefore the critical point happens at an extremum of $\\mathcal{N}(x)$, but since the nozzle function is not upper bounded, the extremum has to be a minimum. We have therefore shown that the critical point $x_c$ corresponds to the minimum of the nozzle function $\\mathcal{N}(x)$.\n\n\n\nIn \\autoref{fig:nozzle_function}, we plot the nozzle function for $M_\\mathrm{BH} = 10^8\\mathrm{M}_\\odot$, $\\dot m =0.5$, $k=0.03$, and $R=20R_\\mathrm{g}$ and we highlight the position of the critical point. The value of the nozzle function at the critical point determines the mass-loss rate along the streamline, through $\\dot M = \\varepsilon \\dot M_0$, so that the surface mass loss rate is given by\n\\begin{equation}\n \\dot \\Sigma = \\frac{\\dot M}{A}.\n\\end{equation}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/nozzle_function.pdf}\n \\caption{Nozzle function for $\\protectM_\\mathrm{BH}=10^8\\mathrm{M}_\\odot$, $\\protect\\dot m =0.5$, $k=0.03$, and $R=20 R_\\mathrm{g}$} \n \\label{fig:nozzle_function}\n\\end{figure}{}\n\nIt is interesting to point out that the position of the critical point and the value of $\\epsilon$ do not depend on the chosen value of $k$ in the force multiplier parametrisation (\\autoref{eq:fm_simple}), however, \nthe mass loss rate does depend on $k$ through the value of $\\dot{M}_0$.\nIn the \\citetalias{stevens_x-ray_1990} parametrisation, $k$ is a function of the ionisation state of the gas, $k=k(\\xi)$, so the mass loss rate directly depends on the ionisation conditions at the critical point location. Since this would make our results dependent on the modelling of the vertical structure of the disc, which is out of scope for the purpose of this work, we assume that the gas is always ionised when it reaches the critical point, so we take $k=0.03$, corresponding to the minimum value of $k$ in \\citetalias{stevens_x-ray_1990}. Similarly, we set the initial height of the wind to $z=0$ to avoid dependencies on the disc vertical structure.\n\n\n\\subsection{Scaling of the initial conditions with BH properties}\n\\label{subsection:ic_scaling}\n\n\\begin{figure}\n \\centering\n \\includegraphics{figures\/nozzle_scaling.pdf}\n \\caption{Value of the nozzle function at the critical point ($\\varepsilon = \\mathcal N(x_c)$) as a function of radius for varying $M_\\mathrm{BH}$ (left panel) with $\\dot m =0.5$ and varying $\\dot m$ (right panel) with $M_\\mathrm{BH}=10^8 \\mathrm{M}_\\odot$}\n \\label{fig:nozzle_scaling}\n\\end{figure}\n\nSince we explore the BH parameter space in \\autoref{sec:results_bh}, it is useful to assess how the initial number density and velocity of the wind scale with $M_\\mathrm{BH}$ and $\\dot m$. To this end, we need to determine how the values of $\\varepsilon$ and $\\dot \\Sigma_0$ change with $M_\\mathrm{BH}$ and $\\dot m$. For this analysis, we ignore the dependence of $f_\\text{\\tiny UV}(R)$ on $M$ and $\\dot m$. We also have $v_\\text{th}(R) \\propto T^{1\/2} \\propto (M_\\mathrm{BH} \\dot{M}\/R^3)^{1\/8}$ (using \\autoref{eq:radiation_flux}). Accounting for the fact that the disk size scales as $R \\propto R_\\mathrm{g} \\propto M_\\mathrm{BH}$, this gives $v_0 = v_\\text{th} \\propto (\\dot{m}\/M_\\mathrm{BH})^{1\/8}$, where $v_0$ is the initial velocity. This is a weak dependence, so we ignore it here. Looking at \\autoref{eq:gamma_0}, and using \\autoref{eq:radiation_acceleration_approx}, we get $\\gamma_0 \\propto M_\\mathrm{BH} \\dot{M}\/R^3 \\propto \\dot m \/ M_\\mathrm{BH}$. Similarly, $B_0 \\propto M_\\mathrm{BH}\/R^2 \\propto 1 \/ M_\\mathrm{BH}$. Using \\autoref{eq:Mdot0_def}, we then have $\\dot \\Sigma_0 \\propto \\dot{M}_0\/A_0 \\propto \\gamma_0^{1\/\\alpha}\/B_0^{(1-\\alpha)\/\\alpha} \\propto \\dot m^{1\/\\alpha} \/ M_\\mathrm{BH}$.\n\nThe scaling of $\\varepsilon = \\mathcal{N}(x_c)$ is a bit more complicated, since it depends on the exact position of the critical point for each value of $M_\\mathrm{BH}$, $\\dot m$, and $R$. In \\autoref{fig:nozzle_scaling}, we plot the values of $\\varepsilon$ as a function of radius for varying $M_\\mathrm{BH}$ (left panel) and $\\dot m$ (right panel), ignoring the dependence of $f_\\text{\\tiny UV}$ with $M_\\mathrm{BH}$ and $\\dot m$ (we set $f_\\text{\\tiny UV} = 1$). We note that $\\varepsilon$ does not scale with $M_\\mathrm{BH}$, and changes very little with $\\dot m$. Including the dependence of $f_\\text{\\tiny UV}$ with $M_\\mathrm{BH}$ and $\\dot m$, effectively reduces the value of $\\varepsilon$ at the radii where $f_\\text{\\tiny UV}$ is small, but it does not change substantially in the radii that we would expect to launch an escaping wind. We can then conclude that $\\varepsilon$ has a very weak scaling with $M_\\mathrm{BH}$ and $\\dot m$ so that\n\\begin{equation}\n \\label{eq:sigma_scaling}\n \\dot\\Sigma \\propto \\dot\\Sigma_0 \\propto \\frac{\\dot m^{\\frac{1}{\\alpha}}}{M_\\mathrm{BH}},\n\\end{equation}\nwhich is the same result that \\cite{pereyra_steady_2006} found in applying the \\citetalias{castor_radiation-driven_1975} formalism to cataclysmic variables.\n\n\\subsection{Verification of the critical point conditions}\n\\label{subsection:verify_critical_point}\n\nFor our fiducial case (\\autoref{sec:results}), we plot the critical point location compared to the wind trajectories in \\autoref{fig:verify_critical_point}. All escaping trajectories are vertical at the critical point, so our treatment of the wind as a 1D flow for the initial conditions derivation is justified. Nonetheless, we emphasise again that this treatment does not hold for the inner failed wind. The wind is highly supersonic (~$10^3$ times the sound speed) at the critical point, as shown in the bottom panel of \\autoref{fig:verify_critical_point}, so our assumption $w\\gg s$ is validated.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/verify_critical_point.pdf}\n \\caption{Results for fiducial case $\\protectM_\\mathrm{BH}=10^8\\mathrm{M}_\\odot$ and $\\protect\\dot m =0.5$. Top panel: wind trajectories compared to the critical point position, plotted on a linear scale. Bottom panel: Velocity at the critical point plotted on a log scale}\n \\label{fig:verify_critical_point}\n\\end{figure}\n\\section{Initial conditions}\n\\label{sec:initial_conditions}\n\nAs initial conditions, we determine the density and velocity at the base of the wind following the \\citetalias{castor_radiation-driven_1975} formalism. Let us consider a wind originating from the top of an accretion disc. At low heights, a gas blob is mostly irradiated by the local region of the disc that is just below it. Since this local disc area can be considered to be at a uniform temperature, the direction of the radiation force is mostly upwards, and thus the wind flows initially vertically and can be considered a 1D wind. The corresponding equation describing the vertical motion is\n\n\\begin{equation}\n \\rho \\frac{\\mathrm{d} v_z}{\\mathrm{d} t} = \\rho\\, (a_\\mathrm{grav}^z + a_\\mathrm{rad}^z) - \\frac{\\partial P}{\\partial z},\n\\end{equation}\nEven though the \\textsc{Qwind} model does not include hydrodynamic forces when solving the 2D trajectories of gas parcels, we do include the force term due to gas pressure here, since it is necessary for deriving critical point like solutions (see Appendix \\ref{app:initial_conditions}). \n\nThe study of 1D line-driven winds was pioneered by \\citetalias{castor_radiation-driven_1975}, who defined a framework to find steady state solutions of the 1D wind equation. Their methodology can be extended to any particular geometry of the gravitational and radiation fields, in particular, \\cite{pereyra_steady_2006} (hereafter \\citetalias{pereyra_steady_2006}) apply the \\citetalias{castor_radiation-driven_1975} formalism to the study of cataclysmic variables (CVs). We here aim to further extend this approach to our case, by using the \\citetalias{castor_radiation-driven_1975} formalism to calculate the properties of the 1D wind solutions from an accretion disk as initial conditions for the global 2D wind solution.\n\nThe core result of the \\citetalias{castor_radiation-driven_1975} approach is that if a steady state solution of the 1D wind equation satisfies the following conditions:\n\n\\begin{itemize}\n \\item the velocity increases monotonically with height,\n \\item the wind starts subsonic,\n \\item the wind extends towards arbitrarily large heights,\n \\item the wind becomes supersonic at some height,\n \\item the velocity gradient is a continuous function of position,\n\\end{itemize}\nthen the wind must pass through a special point called the critical point $z_c$, which can be derived without solving the wind differential equation, and thus the global properties of the wind such as its mass loss rate can be determined without resolving the full wind trajectory. To keep the main text concise, we refer the reader to Appendix \\ref{app:initial_conditions} for a detailed derivation.\n\nThe previously specified conditions for the existence of a critical point solution may not be satisfied for all of the wind trajectories that we aim to simulate. For instance, a wind trajectory that starts in an upward direction and falls back to the disc because it failed to achieve the escape velocity does not have a velocity that increases monotonically with height. Furthermore, eventually the wind trajectory is no longer vertical, and the 1D approach breaks down. Having considered these possibilities, and only for the purpose of deriving the initial conditions of the wind, we assume that these conditions hold, so that we can derive the wind mass loss rate at the critical point, which we in turn use to determine the initial conditions of the wind. The full 2D solution of the wind may then not satisfy these conditions. \n\nThe location of the critical point as a function of radius is plotted in \\autoref{fig:critical_points}, where we also plot the height of the disc,\n\\begin{equation}\n z_\\mathrm{h}(R) = \\frac{\\kappa_{\\text{\\tiny e}} \\mu_e \\mathcal F(R) R^3}{GM_\\mathrm{BH} c},\n\\end{equation}\ndefined as the point of equality between the vertical gravitational and radiation force. Overall, we notice that the critical point height increases slowly with radius, except for a bump at $R\\sim 20R_\\mathrm{g}$ which is caused by the UV fraction dependence with radius (see \\autoref{fig:uv_fractions}). For radii $R \\lesssim 50\\, R_\\mathrm{g}$ the critical point height is comparable to the disc radius, so our approximation that streamlines are vertical at that point may not be applicable. We assess the validity of this assumption in \\autoref{subsection:verify_critical_point}. \n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/critical_points.pdf}\n \\caption{Position of the critical point $z_c$ as a function of radius, for $M_\\mathrm{BH} = 10^8 \\mathrm{M}_\\odot$ and $\\dot m =0.5$.} \n \\label{fig:critical_points}\n\\end{figure}{}\n\nWe assume that the wind originates from the disc surface with an initial velocity $v_0$ equal to the thermal velocity (or isothermal sound speed) at the local disc temperature, \n\\begin{equation}\n \\label{eq:thermal_velocity}\n v_\\text{th}(R) = \\sqrt{\\frac{k_\\mathrm{B} T(R)}{\\mu \\, m_p}}.\n\\end{equation}\nGiven that the critical point is close to the disc surface, and that the wind is supersonic at the critical point (see Appendix \\ref{app:initial_conditions}), this is a good starting point. Since mass conservation holds, the initial number density of the wind can then be calculated as\n\\begin{equation}\n n_0 (R) = \\frac{\\dot \\Sigma (R)}{v_\\text{th}(T(R)) \\, \\mu \\, m_\\mathrm{p}},\n\\end{equation}\nwhere $\\dot \\Sigma(R)$ is the mass loss rate per unit area at the critical point. In \\autoref{fig:initial_conditions}, we plot the initial number density and velocity for $M_\\mathrm{BH} =10^8\\, \\mathrm{M}_\\odot$, $\\dot m =0.5$. We note that the initial velocity stays relatively constant, only varying by a factor of $\\sim 3$ across the radius range. However, the initial number density varies by more than 5 orders of magnitude, showing that the assumption of a constant density at the base of the wind used in the previous versions of the model was poor. \n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/initial_conditions.pdf}\n \\caption{Initial conditions at the base of the wind as a function of radius. The original \\textsc{Qwind} code assumed \n constant initial density and velocity across the disk, and these were free parameters. We now calculate these from first principles. The left panel shows that the initial density changes by a factor of $10^6$, very different to the original assumption, while the right panel (note change in y axis scale) shows that the initial velocity changes by less than a factor 10. } \n \\label{fig:initial_conditions}\n\\end{figure}{}\n\n\\subsection{Comparison to other models}\n\nAs a partial test of this new section of the code, we can compare our findings with \\cite{nomura_modeling_2013} (hereafter \\citetalias{nomura_modeling_2013}), in which the authors also use the sonic velocity as the initial velocity of the wind, and derive the initial density profile by assuming the same functional form for the mass loss rate as in \\citetalias{castor_radiation-driven_1975}, but using the AGN $M_\\mathrm{BH}$ and $\\dot m$ instead. We note that the use of the CAK formula directly for accretion discs may not be appropriate because the geometry of the system is very different from stellar winds. Additionally, in \\citetalias{nomura_modeling_2013} the dependence of $f_\\text{\\tiny UV}$ on the disc radius is not considered. Hence we first hardwire $f_\\text{\\tiny UV}$ for the comparison. This comparison is shown in the upper panels of \\autoref{fig:nomura_comparison} for different $M_\\mathrm{BH}$ (left, all at $\\dot{m}=0.5$), and (right) for $M_\\mathrm{BH}$ fixed at $10^8\\,\\mathrm{M}_\\odot$ with different $\\dot{m}$. It is clear that the initial density now derived in \\textsc{Qwind3} (dashed lines) has a steeper decrease with radius than in \\citetalias{nomura_modeling_2013}. This is probably due to their use of the direct CAK formula, which assumes a spherical geometry rather than an accretion disc. However, the inferred densities are within an order of magnitude of each other, and both approaches give a linear scaling of the initial number density profile with $M_\\mathrm{BH}$, but \\textsc{Qwind3} gives an almost quadratic scaling with $\\dot m$, compared to a linear one for \\citetalias{nomura_modeling_2013} (see \\autoref{subsection:ic_scaling}).\n\nThe lower panels of \\autoref{fig:nomura_comparison} show instead the comparison of the \\textsc{Qwind} models using the self consistent $f_\\text{\\tiny UV}$ with its radial dependence (solid lines) instead of assuming $f_\\text{\\tiny UV}=1$ (dashed lines). There is a very strong drop in the initial density at radii where $f_\\text{\\tiny UV}$ drops (see Fig. \\ref{fig:uv_fractions}). This shows the importance of including the self-consistent calculation of $f_\\text{\\tiny UV}$ in \\textsc{Qwind3}. \n\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/nomura_comparison.pdf}\n \\caption{Initial density as a function of radius. In the top panels we compare with the results of \\protect\\citetalias{nomura_modeling_2013} (solid lines), the only other diskwind code which used UV line driving to calculate the initial density. That code assumes constant $f_{UV}$, so we fix $f_\\text{\\tiny UV}=1$ in \\textsc{Qwind} to compare results (dashed lines). In the bottom panels we compare the \n \\textsc{Qwind} results with $f_\\text{\\tiny UV}=1$ (dashed lines) with the full \\textsc{Qwind} results for $f_\\text{\\tiny UV}(R_\\mathrm{d})$. Left and right panels show results for fixed $\\dot m = 0.5$, and vary $\\protectM_\\mathrm{BH} \\in (10^7, 10^8, 10^9) \\, \\mathrm{M}_\\odot$. Right panels: We fix $M_\\mathrm{BH} = 10^8\\,\\mathrm{M}_\\odot$ and vary $\\protect \\dot m \\in(0.05, 0.1, 0.5)$.}\n \\label{fig:nomura_comparison}\n\\end{figure}{}\n\n\n\n\n\n\\section{Introduction}\n\nAGN feedback is a very important process in shaping the growth of galaxies, but the prescriptions for it that are included in current cosmological simulations are generally highly simplified and \nnot based on any deeper understanding of the physical processes involved. Jets from AGN are poorly understood, but some types of AGN winds can be calculated ab initio from the fundamental parameters of black hole mass, mass accretion rate and spin. Observations show the existence of ultra-fast outflows (UFOs) in AGN, likely originating from the accretion disc close to the central supermassive black hole (BH). These outflows can reach velocities of $v$ $\\sim (0.03-0.3)\\,c$ \\citep{weymann_comparisons_1991, pounds_evidence_2003, pounds_high-velocity_2003, reeves_compton-thick_2009, crenshaw_feedback_2012, tombesi_evidence_2010, fiore_agn_2017}. UV line driving is a mechanism which is especially likely to be present in luminous AGN, with their accretion disc spectrum peaking in the UV, where there are multiple strong atomic transitions in low ionisation material. These transitions absorb the photon momentum, producing the strong winds seen from similar temperature material in O star photospheres \\citep{howarth_stellar_1989}, which were first extensively studied by \\cite{castor_radiation-driven_1975} (hereafter \\citetalias{castor_radiation-driven_1975}) and \\cite{abbott_theory_1980}. In the context of AGN, the study of UV line-driven winds started with analytical studies \\citep{murray_accretion_1995}, and continued with the use of radiation-hydrodynamic simulations \\citep{proga_radiation-driven_1998, nomura_radiation_2016}. However, the computational complexity of the radiation-hydrodynamics codes prevents us from efficiently exploring the input parameter space. Even more importantly, the complexity of these codes can obscure the effect of some of the underlying assumptions, e.g. the lack of scattered emission, on setting the radiation environment \\citep{higginbottom_line-driven_2014}, or the effect of wind mass loss on the net accretion rate and hence the disc emission \\citep{nomura_line-driven_2020}. \n\nTo circumvent this, we build on the pioneering approach of \\textsc{Qwind} \\citep{risaliti_non-hydrodynamical_2010} in developing a non-hydrodynamic code. This calculates ballistic trajectories, ignoring pressure forces (which should be negligible in a supersonic flow) but including gravity and radiation forces, to obtain the streamlines, making the computer code much faster, and simpler, so that it can be used to explore the parameter space much more fully. In \\cite{quera-bofarull_qwind_2020} (hereafter \\citetalias{quera-bofarull_qwind_2020}) we released a modern version of this code (\\textsc{Qwind2}), but this was still based on some underlying, arbitrary parameter choices, including for the launching of the wind from the accretion disk, and used simplified radiation transport. \nHere, we aim to significantly improve the predictive power of the \\textsc{Qwind} code. We present a model to derive the initial conditions of the wind, a radiative transfer algorithm that takes into account the wind geometry and density structure, and we include special relativistic corrections to our calculations of the radiation force on the wind. We then use this new model, which we refer as \\textsc{Qwind3}, to study the dependence of the mass loss rate and kinetic power of the wind on the BH mass and mass accretion rate. These results can form the basis for a physical prescription for AGN feedback that can be used in cosmological simulations to explore the coeval growth of galaxies and their central black holes across cosmic time. \n\n\\section*{Acknowledgements}\n\nAQB acknowledges the support of STFC studentship (ST\/P006744\/1) and the JSPS London Pre\/Postdoctoral Fellowship for Foreign Researchers. CD and CGL acknowledge support from STFC consolidated grant ST\/T000244\/1. CD acknowledges support for vists to Japan from Kavli Institute for the Physics\nand Mathematics of the Universe (IPMU) funding from the National\nScience Foundation (No. NSF PHY17-48958).\nThis work used the DiRAC@Durham facility managed by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The equipment was funded by BEIS capital funding via STFC capital grants ST\/K00042X\/1, ST\/P002293\/1, ST\/R002371\/1 and ST\/S002502\/1, Durham University and STFC operations grant ST\/R000832\/1. DiRAC is part of the National e-Infrastructure. This work was supported in part by JSPS Grant-in-Aid for Scientific Research (A) JP21H04488 (KO), Scientific Research (C) JP18K03710 (KO), Early-Career Scientists JP20K14525 (MN). This work was also supported by MEXT as \"Program for Promoting Researches on the Supercomputer Fugaku\" (Toward a unified view of the universe: from large scale structures to planets, JPMXP1020200109) (KO), and by Joint Institute for Computational Fundamental Science (JICFuS, KO). This paper made use of the Matplotlib \\citep{hunter_matplotlib_2007} and SciencePlots \\citep{garrett_scienceplots_2021} software packages.\n\n\n\n\n\n\\bibliographystyle{mnras}\n\n\\section{Review of Qwind}\n\nThe \\textsc{Qwind} code of Q20 is based on the approach of \\cite{risaliti_non-hydrodynamical_2010}, which calculates ballistic trajectories of gas blobs launched from the accretion disc. These blobs are subject to two forces: the gravitational pull of the BH, and the outwards pushing radiation force, which can be decomposed into an X-ray and a UV component, with the later being dominant. On the one hand, the X-ray photons couple to the accretion disc material via bound-free transitions with outer electrons, ionising the gas. On the other hand, the UV opacity is greatly enhanced when the material is not over-ionised, since the UV photons can then excite electrons to higher states while transferring their momentum to the gas in the process. If sufficient momentum is transferred from the radiation field to the gas, the latter may eventually reach the gravitational escape velocity, creating an outgoing flow. This mechanism for creating a wind is known as UV line-driving. The conditions under which the wind can escape depend on the density and velocity structure of the flow, where part of the material can be shielded from the X-ray radiation while being illuminated by the UV emitting part of the accretion disc. \n\n\\subsection{Radiation force}\n\nUsing a cylindrical coordinate system $(R, \\phi, z)$, with $r^2 = R^2 + z^2$, let us consider a BH of mass $M_\\mathrm{BH}$, located at $r=0$, accreting mass at a rate $\\dot M$, and an accretion disc located at the $z=0$ plane. We use the gravitational radius, $R_g = G M_\\mathrm{BH} \/ c^2$, as our natural unit of length. The total luminosity of the system is related to the accreted mass through\n\\begin{equation}\n L_\\text{bol} = \\eta \\dot M c^2\n\\end{equation}\nwhere $\\eta$ is the radiation efficiency. We set $\\eta = 0.057$ throughout this work, as we only consider non-rotating BHs \\citep{thorne_disk-accretion_1974}. We frequently refer to the Eddington fraction $\\dot m = \\dot M \/ \\dot M_\\text{Edd}$, where $\\dot M_\\text{Edd}$ is the mass accretion rate corresponding to the Eddington luminosity,\n\\begin{equation}\n \\dot M_\\text{Edd} = \\frac{L_\\text{Edd}}{\\eta c^2} = \\frac{4\\pi G M_\\mathrm{BH}}{\\eta c \\kappa_{\\text{\\tiny e}}},\n\\end{equation}\nwhere $\\kappa_{\\text{\\tiny e}}$ is the electron scattering opacity, related to the electron scattering cross section $\\sigma_{\\text{\\tiny T}}$ through\n\\begin{equation}\n \\kappa_{\\text{\\tiny e}} = \\frac{\\sigma_{\\text{\\tiny T}}}{m_p \\, \\mu_e},\n\\end{equation}\nwhere $m_p$ is the proton mass, and $\\mu_e$ is the mean molecular weight per electron. We set $\\mu_e = 1.17$ corresponding to a fully ionised gas with solar chemical abundance \\citep{asplund_chemical_2009}.\n\nThe emitted UV radiated power per unit area by a disc patch located at $(R_\\mathrm{d}, \\phi_\\mathrm{d}, 0)$ is given by \\citep{shakura_black_1973}\n\\begin{equation}\n \\label{eq:radiation_flux}\n \\mathcal F_{\\rm UV} = \\frac{3 G M_\\mathrm{BH} \\dot M}{8\\pi R_\\mathrm{d}^3} f_\\text{\\tiny UV}(R_\\mathrm{d}) f_\\text{\\tiny NT}(R_\\mathrm{d}, R_\\mathrm{isco}),\n\\end{equation}\nwhere $f_\\text{\\tiny NT}$ are the Novikov-Thorne relativistic factors \\citep{novikov_astrophysics_1973}, and $f_\\text{\\tiny UV}$ is the fraction of power in the UV band, which we consider to be (200--3200) \\AA. (The total power radiated per unit area $\\mathcal F$ is given by setting $f_\\text{\\tiny UV}=1$ in the above equation.) The force per unit mass exerted on a gas blob at a position $(R, 0, z)$ due to electron scattering is (see \\citetalias{quera-bofarull_qwind_2020})\n\\begin{equation}\n \\label{eq:radiation_acceleration}\n \\bmath{a}_\\mathrm{rad}^\\mathrm{es}(R,z) = \\mathcal C \\,z\\int\\int\\frac{f_\\text{\\tiny UV} f_\\text{\\tiny NT}}{R_\\mathrm{d}^2 \\, \\Delta^4} e^{-\\tau_\\text{\\tiny UV}} \\begin{pmatrix}R-R_\\mathrm{d}\\cos\\phi_\\mathrm{d}\\\\ -R_\\mathrm{d} \\sin \\phi_\\mathrm{d}\\\\ z \\end{pmatrix} \\, \\mathrm{d} R_\\mathrm{d} \\mathrm{d} \\phi_\\mathrm{d},\n\\end{equation}\nwhere \n\\begin{equation}\n \\mathcal C = \\frac{3 G M_\\mathrm{BH} \\dot M \\kappa_{\\text{\\tiny e}}}{8 \\pi^2 c},\n\\end{equation}\n$\\Delta^2 = R^2 + z^2 - 2 R R_\\mathrm{d} \\cos\\phi_\\mathrm{d}$, and $\\tau_\\text{\\tiny UV}$ is the UV optical depth measured from the disc patch to the gas blob. We note that it is enough to consider the case $\\phi=0$ due to the axisymmetry of the system, and, furthermore, the $\\phi$ component of the force vanishes upon integration. The total radiation force can be greatly amplified by the contribution from the line opacity, which we parameterise as $\\kappa_\\mathrm{line} = \\mathcal{M} \\,\\kappa_{\\text{\\tiny e}}$, such that the total radiation opacity is $(1 + \\mathcal{M}) \\kappa_{\\text{\\tiny e}}$, implying that\n\\begin{equation}\n \\bmath{a}_\\mathrm{rad} = (1 + \\mathcal M) \\; \\bmath{a}_\\mathrm{rad}^\\mathrm{es}.\n\\end{equation}\nThe parameter $\\mathcal M$ is known as the force multiplier, and we use the same parametrisation as \\citetalias{quera-bofarull_qwind_2020} \\citep{stevens_x-ray_1990} (hereafter \\citetalias{stevens_x-ray_1990}). A limitation of our assumed parametrisation is that we do not take into account the dependence of the force multiplier on the particular spectral energy distribution (SED) of the accretion disc \\citep{dannen_photoionization_2019}. Furthermore, the force multiplier is also expected to depend on the metallicity of the gas \\citep{nomura_radiation_2021}. A self consistent treatment of the force multiplier with relation to the accretion disc and its chemical composition is left to future work. \n\nIt is useful to consider that, close to the disc's surface, the radiation force is well approximated by considering the radiation force produced by an infinite plane at a temperature equal to the local disc temperature,\n\\begin{equation}\n \\label{eq:radiation_acceleration_approx}\n \\bmath{a}_\\mathrm{rad, 0}^\\mathrm{es}(R) = \\frac{3 GM_\\mathrm{BH} \\dot M \\kappa_{\\text{\\tiny e}}}{8\\pi^2 R^3 c} f_\\text{\\tiny UV} f_\\text{\\tiny NT}\\, e^{-\\tau_\\text{\\tiny UV}}\\; \\bmath{\\hat{z}},\n\\end{equation}\nwhere $\\tau_\\text{\\tiny UV}$ is calculated along a vertical path. The radiation force is then vertical and almost constant at small heights \n($z \\lesssim 0.1 R_\\mathrm{g}$).\nTo speed up calculations and minimise numerical errors, we use this expression when $z < 0.01 R_\\mathrm{g}$.\n\n\n\\subsection{Equations of motion}\n\nThe equations of motion of the gas blob trajectories are\n\n\\begin{equation}\n\\label{eq:trajectory_ode}\n \\begin{split}\n &\\frac{\\mathrm{d} R}{\\mathrm{d} t} &=& \\; v_R,\\\\\n \n &\\frac{\\mathrm{d} z}{\\mathrm{d} t} &= &\\; v_z,\\\\\n &\\frac{\\mathrm{d} v_R}{\\mathrm{d} t} &= &\\; a^\\mathrm{grav}_R + a^\\mathrm{rad}_R + \\frac{\\ell^2}{R^3},\\\\\n \n &\\frac{\\mathrm{d} v_z}{\\mathrm{d} t} &= &\\; a^\\mathrm{grav}_z + a^\\mathrm{rad}_z,\n \\end{split}\n\\end{equation}\nwhere $\\ell$ is the specific angular momentum (assumed constant for a given blob), and $\\bmath{a}_\\mathrm{grav}$ is the gravitational acceleration,\n\\begin{equation}\n\\label{eq:gravity}\n\\bmath{a}_\\mathrm{grav}\\,(R,z) = -\\frac{GM_\\mathrm{BH}}{r^2}\\,\\begin{pmatrix}R\/r \\\\ 0 \\\\ z \/ r\\end{pmatrix}.\n\\end{equation}\nWe assume that initially the gas blobs are in circular orbits around the BH, so that $\\ell = \\sqrt{G M_\\mathrm{BH} R_0}$, where $R_0$ is the launch radius, and thus the azimuthal velocity component at any point is $v_\\phi = \\ell \/ r$.\n\nAs in \\citetalias{quera-bofarull_qwind_2020}, we ignore contributions from gas pressure (except when calculating the launch velocity and density) as these are negligible compared to the radiation force, especially since we focus our study on the supersonic region of the wind. We assume that the distance between two nearby trajectories at any point, $\\Delta r$, is proportional to the distance to the centre $\\Delta r \\propto r$, so that the surface mass loss rate along a particular streamline is $\\dot \\Sigma = \\dot M_\\mathrm{streamline} \/ (2 \\pi \\, r_0 \\, \\Delta r_0)$. This both captures the fact that streamlines are mostly vertical close to the disc, and diverge in a cone-like shape at large distances. The gas blob satisfies the approximate mass conservation equation along its trajectory (\\citetalias{quera-bofarull_qwind_2020}),\n\\begin{equation}\n \\label{eq:mass_conservation}\n \\dot M_\\mathrm{streamline} = 2 \\, \\pi \\, r \\, \\Delta r \\, \\rho \\, v,\n\\end{equation}\nwhere $\\rho$ is the density of the wind, related to the number density $n$ through $\\rho = \\mu \\, m_p \\, n$, and $\\Delta r = (r \/ r_0) \\Delta R_0$. We set the mean molecular weight $\\mu$ to $\\mu = 0.61$, corresponding to a fully ionised gas with solar abundance \\citep{asplund_chemical_2009}. We use \\autoref{eq:mass_conservation} to determine the gas density at each point along a trajectory.\n\n\\subsection{Improvements to \\textsc{Qwind}}\n\nIn \\citetalias{quera-bofarull_qwind_2020}, a series of assumptions are made to facilitate the numerical solution of the presented equations of motion. Furthermore, the initial conditions of the wind are left as free parameters to explore, limiting the predictive power of the model. In this work, we present a series of important improvements to the \\textsc{Qwind} code. \nFirstly we calculate $f_\\text{\\tiny UV}$ from the disc spectrum rather than have this as a free parameter (\\autoref{sec:fuv}).\nSecondly, we derive the initial conditions of the wind in \\autoref{sec:initial_conditions}, based on the methodology introduced in \\citetalias{castor_radiation-driven_1975}, and further developed in \\cite{abbott_theory_1982, pereyra_steady_2004, pereyra_further_2005, pereyra_steady_2006}. This removes the wind's initial velocity and density as degrees of freedom of the system. Thirdly, we vastly enhance the treatment of the radiative transfer in the code, reconstructing the wind density and velocity field from the calculated gas trajectories. This allows us to individually trace the light rays coming from the accretion disc and the central X-ray source, correctly accounting for their attenuation. This is explained in detail in \\autoref{sec:radiation_transport}. Lastly, we include the relativistic corrections from \\cite{luminari_importance_2020} in the calculation of the radiation force, solving the issue of superluminal winds, and we later compare our findings to \\cite{luminari_speed_2021}.\nReaders interested only in the results should skip to Section \\ref{sec:results}. \n\nThe improvement in the modelling of the physical processes comes at the expense of added computational cost. We have ported the \\textsc{Qwind} code to the Julia programming language \\citep{bezanson_julia_2017}, which is an excellent framework for scientific computing given its state of the art performance, and ease of use. The new code is made available to the community under the GPLv3 license on GitHub\\footnote{https:\/\/github.com\/arnauqb\/Qwind.jl}.\n\n\\section{Updates to the radiation transport}\n\\label{sec:radiation_transport}\n\nIn \\citetalias{quera-bofarull_qwind_2020}, the radiation transfer is treated in a very simple way. The disc atmosphere (i.e. the wind) is assumed to have constant density, and so the line of sight absorption does not take into consideration the full geometry and density structure of the wind (see section 2 of \\citetalias{quera-bofarull_qwind_2020}). Furthermore, the UV optical depth is measured from the centre of the disc, and assumed to be the same for radiation from all disc patches, regardless of the position and angle relative to the gas parcel. In this section, we improve \\textsc{Qwind}'s radiative transfer model, by reconstructing the wind density from the gas blob trajectories, thus accounting fully for the wind geometry. The disc is assumed to be flat and thin, with constant height $\\bar z_\\mathrm{h} = 0$, thus we do not model the effect of the disc itself on the radiation transfer. To illustrate the improvements, we consider our fiducial model with $M_\\mathrm{BH}=10^8\\mathrm{M}_\\odot$, and $\\dot m=0.5$, and present the ray tracing engine of the code in an arbitrary wind solution. We discuss particular physical implications and results in \\autoref{sec:results}.\n\n\\begin{figure}\n \\includestandalone[width=\\columnwidth]{diagram}\n \\caption{Diagram of the disc-wind geometry. The blue line corresponds to the light path an X-ray photon takes, while the violet line corresponds to an example of a UV light ray from the accretion disc.}\n \\label{fig:geometry_diagram}\n\\end{figure}\n\n\\subsection{Constructing the density interpolation grid}\n\\label{sec:interp_grid}\n\nGiven a collection of trajectories, we aim to obtain the wind density field at every point in space. The first step is to delimit where the wind is spatially located by computing the concave hull that contains all the points of all wind trajectories. We use the algorithm described in \\cite{moreira_concave_2007} and implemented in \\cite{stagner_lstagnerconcavehulljl_2021}. The resulting concave set is illustrated in \\autoref{fig:wind_hull}. Outside the concave hull, the density is set to the vacuum density which is defined to be $n_\\text{vac} = 10^2$ cm$^{-3}$. \nSince the density varies by orders of magnitude within the wind, we compute the density at a point by linearly interpolating $\\log_{10} n$ in logarithmic space ($\\log R$ - $\\log z$) from the simulated wind trajectories. We use the interpolation algorithm \\textsc{LinearNDInterpolator} from \\textsc{SciPy} \\citep{virtanen_scipy_2020}. The resulting density map is shown in \\autoref{fig:density_grid}. We note that using a concave hull envelope is important, since the interpolation algorithm we use restricts the interpolation space to the convex hull of the input points, which, due to non-convexity of the wind geometry, would otherwise lead us to overestimate the obscuration in certain regions.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/wind_hull.pdf}\n \\caption{Gas parcel trajectories encapsulated by the concave hull containing the points. Left panel on linear scale and right panel on logarithmic scale. Note that the non-convexity of the wind prevents us from using a simpler convex hull envelope.}\n \\label{fig:wind_hull}\n\\end{figure}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/density_grid.pdf}\n \\caption{Interpolated density grid from the simulated wind positions and densities. Left panel: position in linear scale. Right panel: position in logarithmic scale.}\n \\label{fig:density_grid}\n\\end{figure}\n\nOnce we have built the interpolator, we construct a rectilinear grid with the interpolated values. This allows us to implement an efficient ray tracing algorithm to compute the UV and X-ray optical depths. The vertical coordinates of the grid nodes are logarithmically spaced from $10^{-6}$ $R_g$ to the wind's maximum height. The horizontal coordinates are taken at the initial positions of the wind's trajectories, plus an additional range logarithmically spaced from the initial position of the last streamline to the highest simulated $R$ coordinate value.\n\n\n\\subsection{Ray tracing}\n\nTo compute the optical depth along different lines of sight, we need to calculate an integral along a straight path starting at a disc point $(R_\\mathrm{d}, \\phi_\\mathrm{d}, 0)$ to a point $(R, \\phi, z)$. Due to the axisymmetry of the system, the radiation acceleration is independent of $\\phi$ so we can set $\\phi = 0$. We can parametrise the curve in the Cartesian coordinate system with a single parameter $t\\in [0,1]$, (see \\autoref{fig:geometry_diagram}),\n\\begin{equation}\n \\begin{split}\n x(t) &= R_\\mathrm{d} \\cos\\phi_\\mathrm{d} \\, (1-t) + t \\, R , \\\\\n y(t) &= R_\\mathrm{d} \\sin\\phi_\\mathrm{d} \\, (1-t),\\\\\n z(t) &= t \\, z\\\\\n \\end{split}\n\\end{equation}\nso that the cylindrical radius varies along the path as\n\\begin{equation}\n R_t^2(t) = R_\\mathrm{d}^2(1-t)^2 + t^2 R^2 + 2R_\\mathrm{d} R \\cos\\phi_\\mathrm{d} \\,t (1-t) ,\n\\end{equation}\nand $\\phi(t) = \\arctan{(y(t) \/ x(t))}$. In this parametrisation, $t=0$ points to the disc plane point, and $t=1$ to the illuminated wind element. The integral to compute is thus\n\\begin{equation}\n \\tau = \\Delta \\, \\int_0^1 \\sigma(t)\\; n(t)\\, \\mathrm{d} t,\n\\end{equation}\nwhere $\\Delta$ is the total path length defined earlier.\nGiven a rectilinear density grid in the R-z plane, we compute the intersections of the light ray with the grid lines, $\\{ R_i, z_i\\}$, such that we can discretise the integral as,\n\\begin{equation}\n \\tau \\approx \\sum_i \\sigma(R_i, z_i) \\, n(R_i, z_i)\\Delta d_i,\n\\end{equation}\nwhere $\\Delta d_i$ is the 3D distance between the $i$-th intersection point and the $(i-1)$-th,\n\\begin{equation}\n \\Delta d_i = \\sqrt{R_i^2 + R_{i-1}^2 + (z_i - z_{i-1})^2 -2 R_i R_{i-1} \\cos(\\phi_i-\\phi_{i-1})},\n\\end{equation}\nTo find the intersections, we need to calculate whether the light ray crosses an $R_i$ grid line or a $z_i$ grid line. We start at the initial point $(R_\\mathrm{d}, \\phi_\\mathrm{d}, 0)$, and compute the path parameter $t_R$ to hit the next $R$ grid line $R_i$ by solving the second degree equation $R(t_R) = R_i$, and similarly for the next $z_i$ line, $t_z = z_i \/ z$. The next intersection is thus given by the values of $R(t_m)$ and $z(t_m)$ where $t_m = \\min(t_R, t_z)$. Geometrically, the projection of the straight path onto the $(R-z)$ grid is in general a parabola as we can see in \\autoref{fig:tikz:ray_tracing}. \n\n\\begin{figure}\n \\includestandalone[width=\\columnwidth]{ray_tracing_diagram}\n \\caption{Projections of two typical light rays onto the $R-z$ interpolation grid. The X-ray radiation (path with blue dots) is assumed to come from the centre of the grid $(0,0)$, so the projection of the light curve onto the $R-z$ grid is always a straight line. However, for the UV case (purple dots), the light ray can originate from any $\\phi_\\mathrm{d}$, so the path on the interpolation grid is, in general, a parabola.}\n \\label{fig:tikz:ray_tracing}\n\\end{figure}\n\n\\subsection{X-ray optical depth}\n\nThe X-ray opacity depends on the ionisation level of the gas and is assumed to have the same functional form as \\citetalias{quera-bofarull_qwind_2020},\n\\begin{equation}\n \\label{eq:xray_opacity}\n \\sigma_\\text{\\tiny X}(\\xi) = \\begin{cases}\\sigma_{\\text{\\tiny T}} & \\text{ if } \\xi > 10^5 \\text{erg cm s}^{-1}\\\\ 100 \\sigma_{\\text{\\tiny T}} & \\text{ if }\\xi \\leq 10^5 \\text{erg cm s}^{-1}\\end{cases},\n\\end{equation}\nwhere $\\sigma_{\\text{\\tiny T}}$ is the Thomson scattering cross section, $\\xi$ is the ionisation parameter,\n\\begin{equation}\n \\xi = \\frac{4\\pi F_\\text{\\tiny X}}{n},\n\\end{equation}\nand $F_\\text{\\tiny X}$ is the X-ray radiation flux, $F_\\text{\\tiny X} = L_\\text{\\tiny X} \\exp(-\\tau_\\text{\\tiny X}) \/ (4\\pi r^2)$. We notice that to compute the value of $\\tau_\\text{\\tiny X}$ we need to solve an implicit equation, since the optical depth depends on the ionisation state of the gas which in turn depends on the optical depth. One thus needs to compute the distance $d_\\text{\\tiny X}$ at which the ionisation parameter drops below $\\xi_0 = 10^5 \\text{ erg cm s}^{-1}$,\n\\begin{equation}\n \\xi_0 - \\frac{L_\\text{\\tiny X}}{n d_\\text{\\tiny X}^2} \\exp{(-\\tau_\\text{\\tiny X})} = 0.\n\\end{equation}\nThis equation needs to be tested for each grid cell along the line of sight as it depends on the local density value $n$. Therefore for a cell at a distance $d_{0_i}$ from the centre, density $n_i$, and intersection length $\\Delta d_i$ with the light ray (see \\autoref{fig:tikz:ray_tracing}), the contribution $\\Delta \\tau_\\text{\\tiny X}$ to the optical depth is\n\\begin{equation}\n \\Delta \\tau_\\text{\\tiny X} = \\sigma_{\\text{\\tiny T}} \\, n_i \\cdot \\left[\\max(0, d_\\text{\\tiny X}-d_{0_i}) + 100 \\cdot \\max(0, \\Delta d_i - (d_\\text{\\tiny X} - d_{0_i}))\\right],\n\\end{equation}\nwhere $d_\\text{\\tiny X}$ is calculated from\n\\begin{equation}\n \\xi_0 - \\frac{L_\\text{\\tiny X}}{n_i d_\\text{\\tiny X}^2} \\exp{\\left(-\\tau_{0_i} - \\sigma_{\\text{\\tiny T}} \\cdot n_i \\cdot (d_\\text{\\tiny X} - d_{0_i})\\right)} = 0,\n\\end{equation}\nwhere $\\tau_{0_i}$ is the accumulated optical depth from the centre to the current position. We solve the equation numerically using the bisection method. In \\autoref{fig:xray_grid} we plot the X-ray optical depth grid for our example wind. We observe that there is a region where $\\tau_\\text{\\tiny X} \\gg 1$ at very low heights. This shadow is caused by the shielding from the inner wind, which makes the ionisation parameter drop below $\\xi_0$, substantially increasing the X-ray opacity. As we will later discuss in the results section, the shadow defines the acceleration region of the wind, where the force multiplier is very high.\n\n\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/xray_tau_grid.pdf}\n \\caption{X-ray optical depth as a function of position, measured from $R=0$ and $z=0$. Left panel: position in linear scale. Right panel: position in logarithmic scale.}\n \\label{fig:xray_grid}\n\\end{figure}\n\n\\subsection{UV optical depth}\n\nThe UV opacity calculation is significantly simpler than that for the X-rays, since we assume that the line shift due to the Doppler effect in an accelerating wind is sufficient to always reveal fresh, unabsorbed continuum, so that the opacity is constant at the Thomson (electron scattering) value, $\\sigma(R_i, z_i) = \\sigma_{\\text{\\tiny T}}$. \nIn \\autoref{fig:uv_grid}, we plot the UV optical depth as a function of $R$ and $z$ for light rays originating at the disc position $R_\\mathrm{d} = 500$, $\\phi_\\mathrm{d}=0$. Nevertheless, there are many more sight-lines to consider as the UV emission is distributed over the disc, making this ray tracing calculation the highest contributor to the computational cost of the model. The total UV flux and its resultant direction at any given position in the wind have to be calculated as the sum over each disc element (see \\autoref{eq:radiation_flux}) where now $\\tau_\\text{\\tiny UV} = \\tau_\\text{\\tiny UV}(R_d, \\phi_d, R, z)$. \n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/uv_tau_grid.pdf}\n \\caption{UV optical depth as a function of position, measured from $R=500R_\\mathrm{g}$ and $z=0$. Left panel: position in linear scale. Right panel: position in logarithmic scale.}\n \\label{fig:uv_grid}\n\\end{figure}\n\nAs already noted in \\citetalias{quera-bofarull_qwind_2020}, the integral in \\autoref{eq:radiation_acceleration} is challenging to calculate numerically, and in this case the computational cost is further increased by the refined UV ray tracing. In spite of that, by using the adaptive integration scheme presented in \\cite{berntsen_adaptive_1991} and implemented in \\cite{johnson_juliamathcubaturejl_2021}, the integration typically converges after $\\mathcal O(10^4)$ integrand evaluations, which results in a computation time of a few milliseconds, keeping the simulation tractable. At low heights, $z\\sim 0$, the trajectory solver requires several evaluations of the radiation force to correctly compute the adaptive time-step, which makes the computation particularly slow. Fortunately, the approximation in \\autoref{eq:radiation_acceleration_approx} comes in very handy at reducing the overall computational cost at low heights.\n\n\n\\section{Special relativity effects}\n\\label{sec:relativistic}\nWhen the gas trajectory approaches the speed of light, one should consider special relativistic effects such as relativistic beaming and Doppler shifting \\citep[see eg][chapter 4]{rybicki_radiative_1986}. The importance of taking these effects into account is highlighted in \\cite{luminari_importance_2020}. We include a correction to the radiation flux seen by the gas (\\autoref{eq:radiation_flux}),\n\\begin{equation}\n \\mathrm{d} F_\\text{relativistic}= \\Psi(R_d, \\phi_d, R, z, v_R, v_z) \\; \\mathrm{d} F,\n\\end{equation}\nwhere $v_R$ and $v_z$ are the radial and vertical velocity components of the gas at the position $(R, 0, z)$. We ignore the contribution from the angular velocity component, $v_{\\phi}$ for simplicity, as its inclusion would break our assumption that angular momentum is conserved along a gas blob trajectory. The correction $\\Psi$ is given by \\citep{luminari_speed_2021},\n\\begin{equation}\n \\Psi = \\frac{1}{\\gamma^4 (1+\\beta\\cos\\theta)^4},\n\\end{equation}\nwhere $\\gamma$ is the Lorentz factor, $\\beta = \\sqrt{v_R^2 + v_z^2} \/ c$, and $\\theta$ is the angle between the incoming light ray and the gas trajectory,\n\\begin{equation}\n \\cos\\theta = \\frac{(R - R_d\\cos\\phi_d) v_R + z v_z}{\\beta \\Delta}.\n\\end{equation}\nIntuitively, when the incoming light ray is parallel to the gas trajectory, $\\cos\\theta=1$, so the correction reduces to $\\Psi = \\left(\\frac{1-\\beta}{1+\\beta}\\right)^2$, which is 0 when $\\beta=1$ and 1 when $\\beta=0$, as expected. \n\nIt is worth noting that this is a local correction which needs to be integrated along all the UV sight-lines (see \\autoref{eq:radiation_acceleration}). Nonetheless, the computational cost of calculating the radiation force is heavily dominated by ray tracing and the corresponding UV optical depth calculation, so this relativistic correction does not significantly increase the computation time. \n\nThe X-ray flux, which determines the ionisation state of the gas, is also likewise corrected for these special relativistic effects, but there is only one such sight-line to integrate along for any position in the wind, as the X-ray source is assumed to be point-like. \n\n\n\\section{Results}\n\\label{sec:results}\n\nHere, we evaluate the dependence of the wind properties on the initial wind radius, BH mass, and mass accretion rate. We also study the impact of the relativistic corrections on the wind velocity and structure. All of the parameters that are not varied are specified in \\autoref{table:fixed_parameters}.\n\n\\begin{table}\n\\centering\n\\begin{tabular}{ c c c }\n Parameter & Value \\\\ \n \\hline\\hline \n $f_\\mathrm{\\tiny X}$ & 0.15 \\\\\n $z_0$ & 0 \\\\\n $R_\\text{out}\/ R_\\mathrm{g}$ & 1580 \\\\\n $\\mu$ & 0.61 \\\\ \n $\\mu_e$ & 1.17 \\\\ \n $\\alpha$ & 0.6 \\\\\n $k_\\text{ic}$ & 0.03 \\\\\n\\end{tabular}\n\\caption{\\label{table:fixed_parameters} Fixed parameters for the results section. Note that $k_\\text{ic}$ refers to the value of $k$ used to compute the initial conditions of the wind (\\autoref{eq:fm_simple}), but we use the SK90 parametrisation $k=k(\\xi)$ elsewhere.}\n\\end{table}\n\n\\subsection{The fiducial case}\n\nTo gain some intuition about the structure of the wind trajectories solutions, we first have a close look at our fiducial simulation with $M_\\mathrm{BH} = 10^8\\, \\mathrm{M}_\\odot$, $\\dot m =0.5$, and $R_\\mathrm{in} = 50\\,R_\\mathrm{g}$. We run the simulation iterating 50 times through the density field, to make sure that our density grid has converged (see \\autoref{sec:interp_grid}). \n\nIn \\autoref{fig:failed_wind}, we plot the wind streamline shapes, zooming in on the innermost region where we also show the ionisation state of the gas. \\autoref{fig:initial_conditions} shows that the initial density should be $\\sim 3\\times 10^{12}$~cm$^{-3}$. Hence the initial ionisation parameter at $R_\\mathrm{in}$ is $\\xi=f_\\text{\\tiny X}\\, 0.5\\, L_\\mathrm{Edd}\/(n_\\mathrm{in} R_\\mathrm{in}^2)\\sim 800$ so the base of the wind is already in the regime where the X-ray opacity is high. \nThe wind starts, but the drop in density as the material accelerates means it reaches a high ionisation parameter where the force multiplier is low before it reaches escape velocity. Hence the material falls back to the disc as a failed wind region. \n\nThe failed wind region has a size characterised by $\\tau_x \\lesssim 5$, acting as a shield to the outer wind from the central X-ray source. The X-ray obscuration is especially large in the failed wind shadow, due to the jump in X-ray opacity at $\\xi_0 = 10^5$ erg cm s$^{-1}$ (see contour shown by turquoise line in the left panel of \\autoref{fig:failed_wind}), but its opening angle may be small (\\autoref{fig:xray_grid}). The shadow region defines the acceleration region of the wind, where the force multiplier is greatly enhanced and the wind gets almost all of its acceleration. Eventually this acceleration is enough that the material reaches the escape velocity before it emerges from the shadow, and is overionised by the X-ray radiation. The left panel of \nFig. \\ref{fig:failed_wind} shows these first escaping streamlines (blue) which are close to $R_\\mathrm{in}$. \n\n\\autoref{fig:ufo_pros} shows the resulting wind parameters at \na distance $r=5000 \\, R_\\mathrm{g}$. We plot the wind column density, and density-weighted mean velocity and ionisation parameter as a function of the polar angle $\\theta = \\arctan(R\/z)$. The column is almost constant at $N_H\\sim 2\\times 10^{23}$~cm$^{-2}$ (optical depth of $\\sim 0.1$ to electron scattering) across the range $25^\\circ<\\theta<85^\\circ$. For $\\theta > 85^\\circ$, the sight-line intercepts the inner failed wind and the column density increases to $N_H\\sim 10^{24}$~cm$^{-2}$. The\ntypical wind velocity at this point is $\\simeq (0.1-0.4)\\,c$\nbut it is always very ionised ($\\xi > 10^5$ erg cm s $^{-1}$) at these large distances. This is too ionised to allow even H- and He-like iron to give visible atomic features in this high velocity gas, although these species may exist at smaller radii where the material is denser. We will explore the observational impact of this in a future work, specifically assessing whether UV line-driving can be the origin of the ultra-fast outflows seen in some AGN\n(see also \\citealt{mizumoto_uv_2021}). \n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/failed_wind.pdf}\n \\caption{Simulated wind trajectories for our fiducial system ($M_\\mathrm{BH} = 10^8\\mathrm{M}_\\odot$, $\\dot m =0.5$, and $R_\\mathrm{in} = 50 R_\\mathrm{g}$) zooming in on the failed wind region, where we also colour-plot the ionisation parameter $\\xi$, showing the contour at $\\xi = 10^5$ erg cm s$^{-1}$ as the light turquoise line. We plot the failed trajectories in green and the escaping trajectories in blue.}\n \\label{fig:failed_wind}\n\\end{figure}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/ufo_pros.pdf}\n \\caption{Wind properties for a system with $M_\\mathrm{BH} = 10^8\\mathrm{M}_\\odot$, $\\dot m =0.5$, $R_\\mathrm{in}=50R_\\mathrm{g}$ measured along a sight-line at angle $\\theta$ at a distance $r=5000R_\\mathrm{g}$ from the centre. First panel: column density. Second panel: outward mean velocity weighted by density. Third panel: mean ionisation parameter weighted by density. Fourth panel: kinetic luminosity per unit angle. Fifth panel: wind momentum rate per unit angle.}\n \\label{fig:ufo_pros}\n\\end{figure}\n\nThe escaping wind carries a mass loss rate of $\\dot M_\\mathrm{wind} \\simeq 0.26$ $\\mathrm{M}_\\odot \/$ yr, corresponding to $\\dot M_\\mathrm{wind} \/ \\dot M \\simeq 11\\%$ of the mass accretion rate, and a kinetic luminosity of $L_\\mathrm{kin} \\approx 7 \\times 10^{44}$ erg \/ s, which is equal to 10\\% of the bolometric luminosity. As the two bottom panels of \\autoref{fig:ufo_pros} show, most of the energy and momentum of the wind is located at small polar angles ($\\sim 20^\\circ$), which is consistent with the initial density profile since the innermost streamlines carry the largest amount of mass.\n\n\\subsection{Dependence on the initial radius \\texorpdfstring{$R_\\mathrm{in}$}{Rin}}\n\n\\begin{figure*}\n \\centering\n \\includegraphics[width=\\textwidth]{figures\/rin_scan.pdf}\n \\caption{Mass loss rate (first panel), kinetic luminosity (second panel), momentum loss rate (third panel) and average velocity (fourth panel) for different values of $R_\\mathrm{in}$, at a fixed $M_\\mathrm{BH}=10^8\\mathrm{M}_\\odot$ and $\\dot m =0.5$. The mass loss rate is normalised to the system's mass accretion rate, while we normalise the luminosity to the bolometric luminosity. The average velocity is taken as \\protect$v_\\mathrm{r} = \\sqrt{2 L_\\mathrm{kin} \/ \\dot M_\\mathrm{wind}}$.}\n \\label{fig:rin_scan}\n\\end{figure*}\n\nAs we already mentioned in \\autoref{sec:gas_trajectories}, the initial radius of the innermost trajectory ($R_\\mathrm{in}$) is left as a free parameter to explore. This parameter is likely dependent on the structure of the accretion flow, which we do not aim to model here. As we increase $R_\\mathrm{in}$, the amount of mass that can potentially be lifted from the disc decreases, both because of the reduction in the extent of the launching region and the decrease in initial density with radius (\\autoref{fig:initial_conditions}). Furthermore, increasing $R_\\mathrm{in}$ also narrows the failed wind shadow, since it reduces its subtended angle, thus reducing the accelerating region of the wind. We therefore expect the wind to flow at higher polar angles and smaller velocities when increasing $R_\\mathrm{in}$. In \\autoref{fig:rin_scan}, we plot the predicted normalised mass loss rate, kinetic luminosity, momentum loss rate, and average velocity of the wind as a function of $R_\\mathrm{in}$. As we expected, both the mass loss rate and the kinetic luminosity decrease with $R_\\mathrm{in}$, with the latter decreasing much faster. This difference in scaling is not surprising, since the fastest part of the wind originates from the innermost part of the disc, where most of the UV radiation is emitted. To further illustrate this, we plot the average velocity of the wind for each simulation in the rightmost panel of \\autoref{fig:rin_scan}, observing that the maximum velocity decreases with initial radius. We note that the wind successfully escapes for $R_\\mathrm{in} \\gtrsim 175 R_\\mathrm{g}$, which is a consequence of the initial number density profile (\\autoref{fig:initial_conditions}) sharply declining after $R \\gtrsim 100 R_\\mathrm{g}$. There is a physically interesting situation happening at $R_\\mathrm{in} \\sim 12R_\\mathrm{g}$, where the average velocity of the wind drops. This is due to the failed wind being located where most of the UV emission is, thus making the wind optically thick to UV radiation (with respect to electron scattering opacity). Lastly, we note that the wind consistently reaches velocities $\\gtrapprox 0.3$ $c$ for $R_\\mathrm{in} \\lesssim 50\\, R_\\mathrm{g}$.\n\n\n\\subsection{Dependence on BH mass and mass accretion rate}\n\\label{sec:results_bh}\n\n\\begin{figure*}\n \\centering\n \\includegraphics[width=\\textwidth]{figures\/bh_scan.pdf}\n \\caption{Wind mass loss rate normalised by the mass accretion rate (first panel column), kinetic luminosity normalised by bolometric luminosity (second panel column), momentum loss rate normalised by $L_\\mathrm{bol} \/ c$ (third panel column), and average velocity (fourth panel column) as functions of the Eddington fraction $\\dot m$ for different $M_\\mathrm{BH}$.The average velocity is taken as \\protect $v_\\mathrm{r} = \\sqrt{2 L_\\mathrm{kin} \/ \\dot M_\\mathrm{wind}}$.}\n \\label{fig:bh_scan}\n\\end{figure*}\n\nWe now investigate the dependence of the wind mass loss rate, kinetic luminosity, momentum loss rate, and average velocity on $M_\\mathrm{BH}$, $\\dot m$, and $R_\\mathrm{in}$. We scan the BH parameter range for $M_\\mathrm{BH}\\in(10^6 - 10^{10})$, $\\dot m \\in(0.01-0.5)$, and $R_\\mathrm{in} = (10, 25, 50)R_\\mathrm{g}$. We fix $f_\\mathrm{X} = 0.15$ and recognise that keeping it constant throughout the parameter scan is a limitation, since in reality it depends on $M_\\mathrm{BH}$ and $\\dot m$. The results are shown in \\autoref{fig:bh_scan}, where all of the quantities have been normalised to their characteristic scales. The red dashed line denotes the limit when quantities become unphysical since the wind is carrying more mass, energy, or momentum than the disc can provide. The first thing to note is that we do not obtain any wind for $\\dot m \\lesssim 0.06$, regardless of $M_\\mathrm{BH}$. This is initially surprising, as the force multiplier is of order $1000$ for cool material, apparently allowing a wind to escape for $\\dot{m}>0.001$. However, the initial density drops as $\\dot{m}^{1\/\\alpha}$ (see \\autoref{subsection:ic_scaling}) so the X-ray shielding drops dramatically, strongly suppressing the wind. Overall, the weakest winds are seen from the highest ($M_\\mathrm{BH}=10^{10}\\,\\mathrm{M}_\\odot$) and lowest ($M_\\mathrm{BH}=10^6\\,\\mathrm{M}_\\odot$) black hole masses. This can be explained by the behaviour of the UV fraction (\\autoref{fig:uv_fractions}). For the $M_\\mathrm{BH}=10^6\\,\\mathrm{M}_\\odot$ case, the UV bright disc annuli are located at large radii, where the disc luminosity is lower; for the $M=10^{10}\\mathrm{M}_\\odot$ case, $f_\\text{\\tiny UV}$ is only high at very small radii, and overall small in the wind launching region. Furthermore, the high disc temperatures expected for the lowest mass systems ($M_\\mathrm{BH} = 10^6\\,\\mathrm{M}_\\odot$), especially at high $\\dot{m}$, mean that the disc contributes to the ionising X-ray flux. This effect is not considered in our work here, but makes it likely that even our rather weak UV line-driven wind is an overestimate for these systems.\n\nFor the values of $M_\\mathrm{BH}$ where the wind is robustly generated across the rest of the parameter space, $M_\\mathrm{BH} \\in (10^7, 10^8, 10^9)\\mathrm{M}_\\odot$, we find a weak dependence of the normalised wind properties on $M_\\mathrm{BH}$. This is expected, since the initial density profile scales with $M_\\mathrm{BH}$ and $\\dot m$ as (\\autoref{subsection:ic_scaling})\n\\begin{equation}\n n_0 \\propto \\frac{\\dot m^{\\frac{1}{\\alpha}}}{M_\\mathrm{BH}},\n\\end{equation}\nwhere we ignore the dependence of $f_\\text{\\tiny UV}$ on $M_\\mathrm{BH}$. The initial wind velocity $v_0 = v_\\text{th} \\propto T^{1\/2}$, hence\n\\begin{equation}\n v_0 \\propto \\left( \\frac{\\dot m}{M_\\mathrm{BH}} \\right)^{1\/8} \n\\end{equation}\n(see \\autoref{eq:radiation_flux}.)\nThis then implies\n\\begin{equation}\n \\dot M_\\mathrm{wind} \\propto n_0 \\; v_0 \\; R^2 \\propto \\left(\\frac{\\dot m^{\\frac{1}{\\alpha}}}{M_\\mathrm{BH}}\\right) \\left(\\frac{\\dot m}{M_\\mathrm{BH}}\\right)^{1\/8}\\left( M_\\mathrm{BH}^2\\right)\n\\end{equation}\nSince scaling due to the dependence on $v_0$ is particularly weak (it depends on the 1\/8-th power), we choose to ignore it so that we can write\n\\begin{equation}\n \\frac{\\dot M_\\mathrm{wind}}{\\dot M_\\mathrm{acc}} \\propto \\frac{\\dot m^{\\frac{1}{\\alpha}} \\, M_\\mathrm{BH}}{\\dot m M_\\mathrm{BH}}\\propto \\dot m^{\\frac{1}{\\alpha} -1},\n\\end{equation}\nwhere we have used the fact that $\\dot M_\\mathrm{acc} = \\dot m \\dot M_\\mathrm{Edd} \\propto \\dot m M_\\mathrm{BH}$. Similarly,\n\\begin{equation}\n \\frac{L_\\mathrm{kin}}{L_\\mathrm{bol}} \\propto \\frac{\\dot M_\\mathrm{wind} v_f^2}{\\dot M_\\mathrm{acc}} = \\dot m^{1 + \\frac{1}{\\alpha}},\n\\end{equation}\nwhere $v_f$ is the final wind velocity and we have assumed $v_f \\propto \\dot m$. This last assumption is verified (for $0.1 \\lesssim \\dot m \\lesssim 0.5$) in the rightmost panel of \\autoref{fig:bh_scan}. Consequently, ignoring the scaling of $f_\\text{\\tiny UV}$, both the normalised mass loss rate and normalised kinetic luminosity are independent of $M_\\mathrm{BH}$. For our value of $\\alpha=0.6$, we find $L_\\mathrm{kin} \\propto \\dot m^{2.7} L_\\mathrm{bol}$. This scaling is significantly different from the one often assumed in models of AGN feedback used in cosmological simulations of galaxy formation, where the energy injection rate is assumed to be proportional to the mass accretion rate and hence to the bolometric luminosity \\citep{schaye_eagle_2015, weinberger_simulating_2017, dave_simba_2019}. However, it is consistent with the results found in the hydrodynamical simulations of \\cite{nomura_line-driven_2017} and with current observational constraints \\citep{gofford_suzaku_2015, chartas_multiphase_2021}.\n\nFor the $R_\\mathrm{in} = 10 R_\\mathrm{g}$ case, many parameter configurations of $M_\\mathrm{BH}$ and $\\dot m$ give rise to winds that are unphysical, since they carry more momentum than the radiation field. This is caused by us not considering the impact that the wind mass loss would have on the disc SED, and an underestimation of the UV opacity in our ray tracing calculation of the UV radiation field, in which we only include the Thomson opacity. We also observe that for the lower and upper ends of our $M_\\mathrm{BH}$ range the existence of a wind for different $\\dot m$ values depends on the value of $R_\\mathrm{in}$. This is a consequence of a complex dependence of the failed wind shadow on the initial density and $R_\\mathrm{in}$.\n\nWe now investigate the geometry of the wind: where it originates and at which angles it flows outwards. Despite the strong dependence of the kinetic luminosity on $\\dot m$, the wind launching region does not vary significantly with $\\dot m$, as is shown in \\autoref{fig:bh_scan_radii}, where we plot the average launch radius, weighted by mass loss rate or kinetic luminosity. The exception is the case $R_\\mathrm{in} =10R_\\mathrm{g}$, where the lower inner density at $R_\\mathrm{in}=10R_\\mathrm{g}$ (see \\autoref{fig:initial_conditions}), and its decrease with $\\dot m$ produce a larger failed wind region for $\\dot m \\lesssim 0.15$. The dependence of the mass weighted average radius with $M_\\mathrm{BH}$ is a consequence of the dependence of $f_\\text{\\tiny UV}$ on $M_\\mathrm{BH}$. Larger $M_\\mathrm{BH}$ black holes have the $f_\\text{\\tiny UV}$ peak closer in, so the wind carries more mass at smaller radii.\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/bh_scan_avg_radius.pdf}\n \\caption{Average launch radius weighted by the trajectories' mass loss rate (left panels) and kinetic luminosity (right panels) as a function of $M_\\mathrm{BH}$ and $\\dot m$ for the 3 values of $R_\\mathrm{in}$.}\n \\label{fig:bh_scan_radii}\n\\end{figure}\n\nFinally, we plot the wind opening angle for the scanned parameter space in \\autoref{fig:wind_angle}. We again find a small dependence on $M_\\mathrm{BH}$, except for near the boundaries of our $M_\\mathrm{BH}$ range, where the angle is quite sensitive to $\\dot m$. The wind flows closer to the equator as we decrease $\\dot m$, hence whether the wind is more polar or equatorial depends on the mass accretion rate of the system. This can be explained by considering that lower disc luminosities do not push the wind as strongly from below, and thus the wind escapes flowing closer to the equator.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/wind_angle.pdf}\n \\caption{Wind opening angle, measured as the smallest polar angle, $\\theta_\\mathrm{min}$, of all escaping streamlines as a function of $M_\\mathrm{BH}$ and $\\dot m$, for the 3 studied $R_\\mathrm{in}$ values.}\n \\label{fig:wind_angle}\n\\end{figure}\n\n\\subsection{Can UV line-driven winds be UFOs?}\n\nIn \\citetalias{quera-bofarull_qwind_2020}, wind trajectories could achieve arbitrarily large velocities, often surpassing the speed of light, due to the neglect of relativistic effects. With the introduction of relativistic corrections (\\autoref{sec:relativistic}), the wind is always sub-luminal, as we show in \\autoref{fig:rin_scan} and \\autoref{fig:bh_scan}. Nonetheless, throughout the simulated parameter space, outflows consistently achieve speeds of $(0.1 - 0.8)$ $c$, scaling approximately as $\\dot{m}$\nwith little dependence on \n$M_\\mathrm{BH}$. If we limit ourselves to the simulations that conserve the overall momentum and energy of the system, then the simulated wind still achieve speeds in the range $(0.1 - 0.4)$c. This implies that UV line-driving is a feasible mechanism to produce UFOs even when relativistic corrections are included. The final velocity of the wind depends on how much a gas blob can be accelerated while it is shadowed from the X-ray radiation. In \\autoref{fig:velocity_dependence}, we plot the velocity profile for a trajectory starting at $R=100R_\\mathrm{g}$ in our fiducial simulation density grid for different values of the initial velocity. We find that the final velocity of the trajectory is independent of the initial velocity and that the wind is able to drastically accelerate (up to 6 orders of magnitude in velocity) over a very small distance ($\\lesssim 1 R_\\mathrm{g}$), which suggests that line-driving can be very effective even when the X-ray shadowed region is very small.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/velocity_dependence.pdf}\n \\caption{Velocity as a function of $z$ for a trajectory starting at $R=100R_\\mathrm{g}$ for our fiducial model. Different colours correspond to different initial velocities. }\n \\label{fig:velocity_dependence}\n\\end{figure}\n\nWe thus find that UV line-driving is sufficient to reproduce the range of observed UFO velocities, as opposed to the findings of \\cite{luminari_speed_2021}. \nWe note that their code assumes an initial density (similar to \\citetalias{quera-bofarull_qwind_2020}) rather than calculating it from first principles, and does not include the full ray tracing of both UV and X-rays that are considered here. On the other hand, our treatment of the force multiplier is simplified compared to their calculation, where they use the radiative transfer code XSTAR \\citep{kallman_photoionization_2001} to compute the radiation flux absorbed by the wind. Nonetheless, our results show that \na small shaded region can produce a very fast wind (see \n\\autoref{fig:velocity_dependence}), \nand the range of $R_\\mathrm{in}$ that gives rise to velocities $\\geq 0.3 c$ is quite wide (see \\autoref{fig:bh_scan_radii}). It then seems\nquite likely that UV line-driven winds can indeed reach these velocities and hence be the origin of the majority of UFOs seen. \nThere are even higher velocities claimed for a few absorption features in the literature, but these are generally low signal-to-noise detections. \n\\section{Intersection of trajectories}\n\\label{app:intersections}\n\nOnce we have solved the equations of motion of the different gas blobs, it is common for the resulting trajectories to cross each other. Trajectories of gas elements computed using our ballistic model should not be confused with the streamlines of the actual wind fluid, since the latter cannot cross each other as it would imply the presence of singular points where the density and the velocity fields are not well defined.\n\nNonetheless, if we aim to construct a density and velocity field of the wind, we need to define the density and velocity at the crossing points. To circumvent this, once two trajectories intersect, we terminate the one that has the lowest momentum density at the intersection point.\n\nTo determine at which, if any, point two trajectories cross, we consider a trajectory as a collection of line segments $\\{s_i\\}$. Two trajectories $\\{s_i\\}$, and $\\{t_j\\}$ cross each other if it exists $i, j$ such that $s_i \\cap t_j \\neq 0$.\n\nSuppose the line segment $s_i$ is bounded by the points $\\bmath{p_1}$ and $\\bmath{p_2}$ such that $\\bmath{p_2} = \\bmath{p_1} + \\alpha' (\\bmath{p_2} - \\bmath{p_1})$ with $\\alpha' \\in [0,1]$. Similarly, $t_j$ is limited by $\\bmath{q_1}$ and $\\bmath{q_2}$ such that $\\bmath{q_2} = \\beta' (\\bmath{q_2} - \\bmath{q_1})$ with $\\beta' \\in [0,1]$. The condition that $s_i$ intersects $t_j$ is equivalent to finding $\\alpha$, $\\beta$ $\\in [0,1] \\times [0,1]$ such that\n\\begin{equation}\n \\bmath{p_1} + \\alpha' (\\bmath{p_2} - \\bmath{p_1}) = \\bmath{q_1} + \\beta' (\\bmath{q_2} - \\bmath{q_1}),\n\\end{equation}\nwhich corresponds to the linear system $\\mathbfss{A}\\bmath{x} = \\bmath{b}$ with\n\\begin{equation}\n \\mathbfss{A} = \\left(\\bmath{p_2}-\\bmath{p_1}, \\bmath{q_1}-\\bmath{q_2}\\right),\n\\end{equation}\n$\\bmath{x} = \\left(\\alpha', \\beta'\\right)^\\intercal$, and $\\bmath{b} = \\bmath{q_1} - \\bmath{p_1}$. The segments intersect if this linear system is determined with solution inside the unit square.\n\n\\section{Radial dependence of \\texorpdfstring{$f_\\text{\\tiny UV}$}{fuv}}\n\\label{sec:fuv}\n\nWe first address the validity of assuming a constant emitted UV fraction with radius. We can calculate its radial dependence using\n\\begin{equation}\n f_\\text{\\tiny UV}(R_\\mathrm{d}) = \\frac{\\int_{E_1}^{E_2} B(E, T(R_\\mathrm{d}))\\, \\mathrm{d} E}{\\int_{0}^{\\infty} B(E, T(R_\\mathrm{d}))\\, \\mathrm{d} E},\n\\end{equation}\nwhere $B(E,T)$ is the blackbody spectral radiance, $E_1 = 0.0038$ keV and $E_2=0.06$ keV (the standard definition of the UV transition band: (3200-200) \\AA), and $T^4(R_\\mathrm{d})=\\mathcal{F} \/ \\sigma_\\text{\\tiny SB}$ (where $\\mathcal F$ is defined in \\autoref{eq:radiation_flux}). In \\autoref{fig:uv_fractions}, we plot the UV fraction as a function of radius for different $M_\\mathrm{BH}$ and $\\dot m$. The disc temperature is related to the total flux (given by setting $f_\\text{\\tiny UV}=1$ in \\autoref{eq:radiation_flux}) so $T^4\\propto (M\\dot{M}f_\\text{\\tiny NT}\/R^3)\\propto \\dot{m}\/M (R\/R_\\mathrm{g})^3$.\nThus the disc temperature increases with decreasing $R\/R_\\mathrm{g}$, and for the fiducial case of $M_\\mathrm{BH}=10^8 \\mathrm{M}_\\odot$ this leads to the majority of the disc emission in the UV coming from $R\/R_\\mathrm{g}\\le 100$ (left panel of \\autoref{fig:uv_fractions}: orange line).\nHowever, the increase in disc temperature at a given $R\/R_\\mathrm{g}$ for decreasing mass means that the same $\\dot{m}$ for $M_\\mathrm{BH}=10^6 \\mathrm{M}_\\odot$ gives a UV flux which peaks at $R\/R_\\mathrm{g}>100$, as the inner regions are too hot to emit within the defined UV bandpass (left panel of \\autoref{fig:uv_fractions}: blue line). Conversely, for the highest BH masses of $M_\\mathrm{BH}=10^{10}\\mathrm{M}_\\odot$ the disc is so cool that it emits UV only very close to the innermost stable circular orbit (left panel of \\autoref{fig:uv_fractions}: purple line). The universal upturn at $R=10R_\\mathrm{g}$ is caused by the the temperature sharply decreasing at the inner edge of the accretion disc due to the viscuous torque dropping to zero there.\n\nSimilarly, the right panel of \\autoref{fig:uv_fractions} shows the impact of changing $\\dot{m}$ for the fiducial mass of $10^8\\mathrm{M}_\\odot$. The dashed orange line shows the case $\\dot{m}=0.5$, as before, \nand the disc temperature decreases with decreasing $\\dot{m}$ to $0.1$ (dashed green line) and $0.05$ (dashed blue line), reducing the radial extent of the UV-emitting zone. \n\nWe note that the assumption used in \\citetalias{quera-bofarull_qwind_2020} (and many other UV line driven disc wind codes) of $f_\\text{\\tiny UV}$ being a constant value is poor overall, highlighting the importance of including the radial dependence of the UV flux.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/uv_fractions.pdf}\n \\caption{UV fraction as a function of disc radius. Left panel: dependence on $M_\\mathrm{BH}$ for fixed $\\dot m =0.5$. Right panel: dependence on $\\dot m$ for fixed $M_\\mathrm{BH} = 10^8\\mathrm{M}_\\odot$.}\n \\label{fig:uv_fractions}\n\\end{figure}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nCryptocurrencies are notoriously known to attract financial speculators who often seek to multiply their potential monetary upside and financial gains through leverage. Leverage is realized by borrowing assets to perform trades~---~commonly referred to as margin trading. It is apparent that margin trading, speculating with borrowed assets in general, is an incredibly risky endeavor. Yet, the borrowing and lending markets on blockchains are thriving and have reached a collective~$39.88$B USD of {\\em total value locked (TVL)} at the time of writing\\footnote{\\url{https:\/\/defipulse.com\/}}.\n\nLoans on a blockchain typically operate as follows. Lenders with a surplus of money provide assets to a lending smart contract. Borrowers then provide a security deposit, known as collateral, to borrow cryptocurrency. Because the lending and borrowing on blockchains lacks compulsory means on defaults,\nthe amount of debt borrowers can take on is typically inferior to the security deposit in value~---~resulting in \\emph{over-collateralized loans}. Over-collateralized loans are interesting from a financial perspective, as they enable borrowers to take on leverage.\n\nIf the collateral value decreases under a specific threshold (e.g., below $150$\\% of the debt value~\\cite{makerdao}), the associated debt can be recovered through three means: \\emph{(1)} a loan can be made available for liquidation by the smart contract. Liquidators then pay back the debt in exchange for receiving the collateral at a discount (i.e., \\emph{liquidation spread}), or the collateral is liquidated through an auction. \\emph{(2)} Debt can also be rescued by ``topping up'' the collateral, such that the loan is sufficiently collateralized. \\emph{(3)} Finally, the borrower can repay parts of their debt. While users can repay their debts manually, this appears impractical for the average user, as it requires infrastructure to constantly monitor the blockchain, collateral price, and transaction fee fluctuations. For example, even professional liquidation bots from MakerDAO failed to monitor and act upon price variations during blockchain congestion~\\cite{maker-fail}. \n\nIn this paper we make the following contributions. \n\n\\begin{enumerate}\n \\item {\\bf Liquidation Models and Insights:} We provide the first longitudinal study of the four major lending platforms MakerDAO, Aave, Compound, and dYdX, capturing collectively over $85$\\% of the borrowing\/lending market on the Ethereum blockchain. By focusing on the protocol's liquidation mechanisms, we systematize their liquidation designs. MakerDAO, for instance, follows an auction-based liquidation process, while Aave, Compound, and dYdX operate under a fixed spread liquidation model.\n \n \\item {\\bf Data Analytics:} We provide on-chain data analytics covering the entire existence of the four protocols ($2$ years). Our findings show how the accumulative liquidation proceeds amount to~\\empirical{$347.62$M}~USD\\xspace, we identify~\\empirical{$1,600$}\\xspace unique liquidator addresses and~\\empirical{$21,792$}\\xspace liquidation events, of which~\\empirical{$265$}\\xspace} \\newcommand{\\empirical{$\\numprint{467.44}$K}~USD\\xspace}{\\empirical{$12.20$k}~USD\\xspace auction liquidations are not profitable for the liquidators. We show how~\\empirical{$72.85\\%$}\\xspace of the liquidations pay an above average transaction fee, indicating competitive behavior. We find the existence of bad debts, the borrowing positions that do not financially incentivize borrowers to perform a close. Notably, Aave V2 has accumulated up to~$87.4$K~USD of bad debts by the end of April,~2021.\n \n We quantify how sensitive debt behaves to collateral price declines and find that, for example, a~$43$\\% reduction of the ETH price (analogous to the ETH price decline on the~13th of March,~2020) would engender liquidatable collateral volume of~$1.07$B USD on MakerDAO.\n \\item {\\bf Objective Liquidation Mechanism Comparison:} We provide a methodology to compare quantitatively whether a liquidation mechanism favors a borrower or a liquidator. We find evidence that fixed spread liquidation mechanisms favor liquidators over borrowers. That is, because existing DeFi systems are parameterized to allow more collateral than necessary to be liquidated.\n \n \\item {\\bf Optimal Fixed Spread Liquidation Strategy:} We propose an optimal fixed spread liquidation strategy. This strategy allows liquidators to lift the restrictions of the close factor (the upper limit of repaid debts in a single liquidation, cf.\\ Section~\\ref{sec:terminology}) within two successive liquidations. We provide a case study of a past liquidation transaction and show that the optimal strategy could have increased the liquidation profit by \\empirical{$53.96$K}~USD\\xspace (\\empirical{$1.36\\%$}\\xspace), validated through concrete execution on the real blockchain state. This optimal strategy can further aggravate the loss of borrowers.\n\\end{enumerate}\n\nThe remainder of the paper is organized as follows. Section~\\ref{sec:background} outlines the background on blockchain and lending, while we systematize existing liquidation mechanisms in Section~\\ref{sec:existing-protocols}. Section~\\ref{sec:insights} provides liquidation data insights from empirical data.\nWe discuss how to objectively compare liquidation mechanisms and the optimal liquidation strategy in Section~\\ref{sec:better-liquidation}.\nWe outline related work in Section~\\ref{sec:related-work} and conclude the paper in Section~\\ref{sec:conclusion}.\n\n\\section{Lending on the Blockchain}\\label{sec:background}\nWe proceed by outlining the required background on blockchain and DeFi for the remainder of the paper.\n\n\\subsection{Blockchain \\& Smart Contract}\nBlockchains are distributed ledgers that enable peers to transact without the need to entrust third-party intermediaries. There exist two categories of blockchains: \\textit{(i)} permissionless blockchains, where any entity is able to join and leave without permission; \\textit{(ii)} permissioned blockchains, which are typically composed of a group of authenticated participants. In this work, we only focus on permissionless blockchains, on top of which DeFi is built.\n\nAt its core, a blockchain is a hash-linked chain of blocks operating over a peer-to-peer (P2P) network~\\cite{bonneau2015sok}. A block is a timestamped data structure aggregating transactions, which record, e.g., asset transfers. To transfer assets, users need to broadcast digitally signed transactions through the P2P network. The so-called miners then collect transactions, pack transactions into blocks, and append blocks to the blockchain. The whole network follows a consensus protocol (e.g., Nakamoto consensus~\\cite{bitcoin}) allowing honest participants to agree on a consistent version of the blockchain. Transactions waiting to be confirmed on-chain are stored in the so-called mempool\\footnote{Note that there is no universal mempool across all network participants. Every node maintains its own mempool depending on the received transactions.}. We refer the reader to~\\cite{bonneau2015sok,bano2019sok} for a more thorough background on blockchains.\n\nSome blockchains, for example, Ethereum~\\cite{wood2014ethereum}, offer generic computation capabilities through smart contracts.\nIn essence, an Ethereum smart contract is an account controlled by an immutable program (i.e., bytecode). One can trigger the execution of the bytecode by sending a transaction, which contains the executing parameters specified by the transaction sender, to the smart contract account. The EVM, a quasi Turing-complete state machine~\\cite{atzei2017survey}, provides the runtime environment to the contract execution. Solidity~\\cite{dannen2017introducing}, which can be compiled into bytecode, is to date the most prevalent high-level language for implementing Ethereum smart contracts. Smart contracts are widely used to create cryptocurrencies (also known as tokens) on Ethereum in addition to the native coin ETH. Notably, WETH is a one-to-one equivalent token of ETH.\n\nTo submit a transaction on-chain, a user is required to pay a transaction fee. On Ethereum, the transaction fee equals the product of the gas (i.e., an integer measuring the computation complexity of a transaction) and the gas price (i.e., the amount of ETH that the transaction sender is willing to pay for a single unit of gas). Due to the limited space of an Ethereum block (i.e., the total amount of gas consumed in on block), a financially rational miner may include the transactions with the highest gas prices from the mempool into the next block. The blockchain network congests when the mempool grows faster than the transaction inclusion speed due to, for example, traders place substantial orders in a market collapse. Under such circumstances, users have to increase gas prices or wait longer than average to confirm their transactions.\n\n\\subsection{Decentralized Finance (DeFi)}\nSmart contracts allow, not only the creation of tokens, but further the construction of sophisticated on-chain financial systems, namely Decentralized Finance (DeFi). In DeFi, any entity can design a financial protocol, implement in smart contracts, and deploy on-chain. Compared to traditional finance, DeFi presents promising peculiarities, e.g., non-custody and public verifiability~\\cite{qin2021cefi}. Although most DeFi protocols are mirrored services from traditional finance (e.g., exchanges), a proper redesign appears to be necessary considering the special settlement mechanisms of the underlying blockchains. For instance, due to the limited computation capacity, a limit order book with a matching engine, which has been adopted in centralized exchanges for decades, is, however, inefficient on blockchains. This leads to the invention of the automated market maker, where traders, instead trading against other traders, only need to interact with a pool of assets reserved in a smart contract. Since the rise of DeFi, we have observed numerous such innovative DeFi design, most of which, however, have not been thoroughly studied. As a result, the risks and threats that DeFi users are exposed to are still unclear, necessitating empirical research to provide objective insights.\n\nAt the time of writing, Ethereum is the dominating permissionless blockchain hosting DeFi. The DeFi ecosystem on Ethereum reached a TVL of over $80$B~USD\\footnote{In comparison, the Binance Smart Chain (BSC), ranked the second in terms of TVL at the time of writing, reaches~$20$B USD (cf.\\ \\url{https:\/\/debank.com\/ranking\/locked_value}). We omit BSC in this work because BSC starts to grow from early 2021, which has not accumulated sufficient data.}, with more than $50\\%$ contributed by lending protocols. Lending and borrowing is a popular way to realize a leverage (amplifying the profit) in DeFi. A typical use case is outlined as follows. A trader collateralizes $\\numprint{5000}$~USDT (a USD-pegged stablecoin, cf.\\ Section~\\ref{sec:stablecoinbackground}) to borrow~$1$ ETH, when the ETH\/USDT price is $\\numprint[ETH]{1} = \\numprint[USDT]{3000}$. The borrower then sells the borrowed $\\numprint[ETH]{1}$ for $\\numprint[USDT]{3000}$. If the ETH price declines to, for example, $\\numprint[ETH]{1} = \\numprint[USDT]{2000}$, the trader can purchase~$1$ ETH with $\\numprint[USDT]{2000}$, repay the debt, redeem the collateral, and finally realize a profit of $\\numprint[USDT]{1000}$. The trader at the same time bears the liquidation risk if the ETH price increases and the USDT collateral is insufficient to back the $\\numprint[ETH]{1}$ debt. In a liquidation, a liquidator repays the ETH debt for the trader and acquires the USDT collateral. The acquired collateral exceeds the rapid debt in value incurring a loss to the trader. Such repayment-acquisition liquidation mechanisms are adopted by most DeFi lending platforms. However, the incentives, risks (e.g., to what extend borrowers have lost in liquidation events), and stabilities of these protocols have not been thoroughly studied, which motivates this work.\n\nWe outline the details of the liquidation mechanisms in Section~\\ref{sec:existing-protocols}. In the following, we introduce the essential components of DeFi that are relevant to lending protocols.\n\n\\subsubsection{Price Oracle}\nBecause lending protocols aim to liquidate collateralized assets upon collateral price declines, the lending smart contract is required to know the price of the collateral asset. Prices can either be provided through an on-chain oracle, such as smart contract based exchanges (e.g., Uniswap~\\cite{uniswap2018}), or via an off-chain oracle (such as Chainlink~\\cite{arijuel2017chainlink}). On-chain price oracles are known to be vulnerable to manipulation~\\cite{qin2020attacking}.\n\n\\subsubsection{Flash Loan} The atomicity of blockchain transactions (executions in a transaction collectively succeed or fail) enables flash loans. A flash loan represents a loan that is taken and repaid within a single transaction~\\cite{qin2020attacking,allen2020design}. A borrower is allowed to borrow up to all the available assets from a flash loan pool and execute arbitrary logic with the capital within a transaction. If the loan plus the required interests are not repaid, the whole transaction is reverted without incurring any state change on the underlying blockchain (i.e., the flash loan never happened). Flash loans are shown to be widely used in liquidations~\\cite{qin2020attacking}.\n\n\\subsubsection{Stablecoin}\\label{sec:stablecoinbackground}\nStablecoins are a class of cryptocurrencies designed to provide low price volatility~\\cite{clark2020demystifying}. The price of a stablecoin is generally pegged to some reference point (e.g., USD). The typical stablecoin mechanisms are reserve of the pegged asset (e.g., USDT and USDC), loans (e.g., DAI), dual coin, and algorithmic supply adjustments~\\cite{moin2020sok}.\n\n\\subsection{Terminology}\\label{sec:terminology}\nWe adhere to the following terminologies in this paper.\n\\begin{description}\n\\item[Loan\/Debt:] A borrower, secured by a collateral deposit, temporarily takes capital from a lender. The collateral is the insurance of the lender against defaults.\n\\item[Interest Rate:] A loan is repaid by repaying the lent amount, plus a periodic percentage of the loan amount. The interest rate can be governed by the scarcity\/surplus of the available asset supply within the lending smart contract.\n\\item[Over\/Under-collateralization:] Blockchain based loans are typically over-collateralized, i.e., the borrower has to provide collateral assets of higher total value than the granted loan. A loan is under-collateralized when the value of the collateral is inferior to the debt.\n\\item[Position:] In this work, the collateral and debts are collectively referred to as a position. A position may consist of multiple-cryptocurrency collaterals and debts.\n\\item[Liquidation:] In the event of a negative price fluctuation of the debt collateral (i.e., a move below the liquidation threshold), a position can be liquidated. In permissionless blockchains, anyone can repay the debt and claim the collateral.\n\\item[Liquidation Threshold ($\\mathbf{LT}$):] Is the percentage at which the collateral value is counted towards the borrowing capacity (cf. Equation~\\ref{eq:borrowing-capacity}).\n\\item[Liquidation Spread ($\\mathbf{LS}$):] Is the bonus, or discount, that a liquidator can collect when liquidating collateral (cf.\\ Equation~\\ref{eq:ls}). This spread incentivises liquidators to act promptly once a loan crosses the liquidation threshold.\n\n\\begin{equation}\\label{eq:ls}\n\\begin{aligned}\n &Value\\ of\\ Collateral\\ to\\ Claim \\\\&= Value\\ of\\ Debt\\ to\\ Repay\\times(1+\\mathbf{LS})\n\\end{aligned}\n\\end{equation}\n\\item[Close Factor ($\\mathbf{CF}$):] Is the maximum proportion of the debt that is allowed to be repaid in a single liquidation.\n\\item[Collateralization Ratio ($\\mathsf{CR}$):] Is the ratio between the total value of collateral and debt (cf.\\ Equation~\\ref{eq:collateralizationratio}) where $i$ represents the index of collateral or debt if the borrower owns collateral or owes debt in multiple cryptocurrencies.\n\n\\begin{equation}\\label{eq:collateralizationratio}\n \\mathsf{CR}=\\frac{\\sum{Value\\ of\\ Collateral_i}}{\\sum{Value\\ of\\ Debt_i}}\n\\end{equation}\nA debt is under-collateralized if $\\mathsf{CR}<1$, otherwise the debt is over-collateralized.\n\\item[Borrowing Capacity ($\\mathsf{BC}$):] Refers to the total value that a borrower is allowed to request from a lending pool, given its collateral amount.\nFor each collateral asset $i$ of a borrower, its borrowing capacity is defined in Equation~\\ref{eq:borrowing-capacity}.\n\n\\begin{equation}\\label{eq:borrowing-capacity}\n \\mathsf{BC} = \\sum{Value\\ of\\ Collateral_i\\times \\mathbf{LT}_i}\n\\end{equation}\n\\item[Health Factor ($\\mathsf{HF}$):] The health factor measures the collateralization status of a position, defined as the ratio of the borrowing capacity over the outstanding debts (cf.\\ Equation~\\ref{eq:health-factor}).\n\n\\begin{equation}\\label{eq:health-factor}\n\\mathsf{HF}=\\frac{\\mathsf{BC}}{\\sum{Value\\ of\\ Debt_i}}\n\\end{equation}\nIf $\\mathsf{HF}<1$, the collateral becomes eligible for liquidation.\n\\end{description}\n\n\n\n\n\n\n\\section{Systematization of Lending and Liquidation Protocols}\\label{sec:existing-protocols}\nIn this section, we systematize how the current borrowing mechanisms and their specific liquidation processes operate. \n\\subsection{Borrowing and Lending System Model}\\label{sec:borrowingandlendingsystemmodel}\n\n\\begin{figure}[tb!]\n \\centering\n \n \n \n \n \n \n \n \n \n \n \n \n \n \\includegraphics[width=0.9\\columnwidth]{figures\/high_level_system_diagrams.pdf}\n \\caption{High-level system diagram of a lending pool system.\n }\n \\label{fig:liquidation_system_diagrams}\n\\end{figure}\n\nThe following aims to summarize the different actors engaging in borrowing and lending on a blockchain (cf.\\ Figure~\\ref{fig:liquidation_system_diagrams}).\n\nA \\textbf{lender} is an actor with surplus capital who would like to earn interest payments on its capital by lending funds to a third party, e.g., a borrower.\n\nA \\textbf{borrower} provides collateral to borrow assets from a lender. The borrower is liable to pay regular interest fees to the lender (typically measured as percentage of the loan). As lending in blockchains is typically performed without KYC, the borrower has to collateralize a value that is greater than the borrowed loan. In pure lending\/borrowing platforms such as Aave, Compound and dYdX, the collateral from borrowers is also lent out as loans. Borrowers hence automatically act as lenders.\n\nWhile lending could be performed directly on a peer-to-peer basis, blockchain-based lending protocols often introduce a \\textbf{lending pool} governed by a smart contract. A pool can hold several cryptocurrencies, and users can interact with the pool to deposit or withdraw assets according to the rules defined by the smart contract code.\n\nA \\textbf{liquidator} observes the blockchain for unhealthy positions (i.e., the health factor is below~$1$) to be liquidated. Liquidators typically operate bots, i.e., automated tools which perform a blockchain lookup, price observation, and liquidation attempt, if deemed profitable. Liquidators are engaging in a competitive environment, where other liquidators may try to front-run each other~\\cite{daian2019flash}. Notably, an atomic liquidation (e.g., a fixed spread liquidation) is settled in one blockchain transaction, while non-atomic liquidation mechanisms (e.g., auctions) generally require liquidators to interact with the lending pool in multiple transactions.\n\n\n\\subsection{Systematization of Liquidation Mechanisms}\\label{sec:liquidationmechanisms}\nWe observe that existing lending platforms mainly adopt two distinct liquidation mechanisms. One mechanism is based on a non-atomic \\emph{English auction}~\\cite{krishna2009auction} process, and the other follows an atomic fixed spread strategy. We formalize the two existing dominating mechanisms as follows.\n\n\n\\subsubsection{Auction Liquidation}\nAn auction based liquidation mechanism follows the subsequent methodology:\n\\begin{enumerate}\n \\item A loan becomes eligible for liquidation (i.e., the health factor drops below $1$).\n \\item A liquidator starts the auction process (which can last several hours).\n \\item Interested liquidators provide their bids (e.g., the highest bid receives the loan collateral).\n \\item The auction ends according to the rules set forth in the auction contract.\n\\end{enumerate}\n\n\\begin{figure}[ht!]\n \\centering\n \\includegraphics[width=\\columnwidth]{figures\/makerdao.pdf}\n \\caption{Two-phase liquidation auction of MakerDAO. Any actor can invoke the public function \\emph{bite} to initiate the collateral auction and invoke the public function \\emph{deal} to finalize the liquidation after the auction terminates.}\n \\label{fig:maker-dao-auction}\n\\end{figure}\n\n\n\\paragraph{MakerDAO tend-dent auction} Specifically, MakerDAO employs a two-phase auction process, which we term \\emph{tend-dent auction} (cf.\\ Figure~\\ref{fig:maker-dao-auction}). \nWhen a position with $D$ value of debt and $C$ value of collateral is eligible for liquidation, i.e., $\\mathsf{HF}<1$, a liquidator is able to initiate the tend-dent auction. A liquidator is required to bid at a higher price than the last bid in the auction. The two-phase workflow is described as follows.\n\\begin{description}[leftmargin=10pt]\n \\item[Tend:] In the \\emph{tend} phase, liquidators compete by bidding to repay parts of the debt in exchange for the entire collateral. We denote the amount of debt committed to repay in each bid by $d_i$, s.t.~$d_i \\leq D$ and $d_i>d_{i-1}$. If the auction terminates in the tend phase, the winning bidder receives all the collateral (i.e., $C$). When $d_i$ reaches $D$, the auction moves into the \\emph{dent} phase.\n \\item[Dent:] In the dent phase, liquidators compete by bidding to accept decreasing amounts of collateral in exchange for the full debt (i.e., $D$) they will end up repaying. We denote the amount of collateral committed in each bid by $c_i$, s.t.~$c_i \\leq C$ and $c_i 1$}\\;\n}\n\\BlankLine\n\\Fn{\\Liquidate{$\\mathcal{POS}$, $repay$}}{\n $\\mathcal{POS}'\\leftarrow\\left \\langle C - repay\\times (1+\\mathbf{LS}) , D - repay \\right \\rangle$\\;\n \\KwRet{$\\mathcal{POS}'$}\\;\n}\n\\BlankLine\n$repay_1 \\leftarrow \\operatorname{argmax}_r$ \\IsLiquidatable{\\Liquidate{$\\mathcal{POS}, r$}}\\;\n$\\mathcal{POS}'\\leftarrow$ \\Liquidate{$\\mathcal{POS}, repay_1$}\\;\n$repay_2 \\leftarrow \\mathcal{POS}'.D \\times \\mathbf{CF}$\\;\n\n\\caption{Optimal fixed spread liquidation strategy.}\n\\label{alg:optimalfixedspreadliquidation}\n\\end{algorithm}\n\n\\subsubsection{Optimality Analysis} Given a liquidatable borrowing position with $C$ collateral value and $D$ debt (cf.\\ Equation~\\ref{eq:position}), we proceed to analyze the profit of our optimal strategy.\n\n\\begin{equation}\\label{eq:position}\n \\mathcal{POS} = \\left \\langle C, D \\right \\rangle\n\\end{equation}\n$\\mathbf{LT}$, $\\mathbf{LS}$, $\\mathbf{CR}$ denote the liquidation threshold, liquidation spread and close factor respectively (cf.\\ Section~\\ref{sec:terminology}).\n\nFollowing Algorithm~\\ref{alg:optimalfixedspreadliquidation}, the repaid debt amounts in the two successive liquidations are given in Equation~\\ref{eq:repay1} and~\\ref{eq:repay2}. Note that the $D-\\mathbf{LT}\\cdot C > 0$ because $\\mathcal{POS}$ is liquidatable (i.e., the debt is greater than the borrowing capacity). We show in Appendix~\\ref{app:reasonalconfiguration} that a reasonable fixed spread liquidation configuration satisfies $1-\\mathbf{LT}(1+\\mathbf{LS})>0$.\n\n\\begin{equation}\\label{eq:repay1}\n\\begin{aligned}\n repay_1 &= \\operatorname{argmax}_r \\frac{\\mathbf{LT}(C-r(1+\\mathbf{LS}))}{D-r}\\geq 1\\\\\n &=\\frac{D-\\mathbf{LT}\\cdot C}{1-\\mathbf{LT}(1+\\mathbf{LS})}\n\\end{aligned}\n\\end{equation}\n\n\\begin{equation}\\label{eq:repay2}\n repay_2 = \\mathbf{CF}\\left(D-repay_1\\right)= \\mathbf{CF}\\left(D-\\frac{D-\\mathbf{LT}\\cdot C}{1-\\mathbf{LT}(1+\\mathbf{LS})}\\right)\n\\end{equation}\nThe overall profit of the two liquidations is shown in Equation~\\ref{eq:overallprofit}.\n\\begin{equation}\\label{eq:overallprofit}\n\\begin{aligned}\n profit_{o} &= (repay_1 + repay_2)\\times \\mathbf{LS}\\\\\n &=\\mathbf{LS}\\cdot\\mathbf{CF}\\cdot D+\\mathbf{LS}(1-\\mathbf{CF})\\left(\\frac{D-\\mathbf{LT}\\cdot C}{1-\\mathbf{LT}(1+\\mathbf{LS})}\\right)\n\\end{aligned}\n\\end{equation}\n\nIf the liquidator instead chooses to perform the up-to-close-factor strategy, the repay amount is $\\mathbf{CF}\\cdot D$ and the profit hence is $profit_{c}=\\mathbf{LS}\\cdot\\mathbf{CF}\\cdot D$. Therefore, the optimal strategy can yield more profit than the up-to-close-factor strategy. The increase rate of the liquidation profit is shown in Equation~\\ref{eq:increaserate}.\n\n\\begin{equation}\\label{eq:increaserate}\n \\Delta R_{profit} = \\frac{profit_o-profit_c}{profit_c}=\\frac{\\mathbf{CF}}{1-\\mathbf{CF}}\\cdot\\frac{1-\\mathbf{LT}\\cdot\\mathsf{CR}}{1-\\mathbf{LT}(1+\\mathbf{LS})}\n\\end{equation}\nwhere $\\mathsf{CR}=\\frac{C}{D}$ is the collateralization ratio (cf.\\ Section~\\ref{sec:terminology}). We notice that the optimal strategy is more effective when $\\mathsf{CR}$ is low. \n\n\\subsubsection{Case Study}\nIn the following, we study the most profitable fixed spread liquidation transaction (\\empirical{$4.04$M}~USD\\xspace)\\footnote{Transaction hash: \\etherscantx{0x53e09adb77d1e3ea593c933a85bd4472371e03da12e3fec853b5bc7fac50f3e4}} we detect and showcase how the optimal fixed spread liquidation strategy increases the profit of a liquidator. In this Compound liquidation, the liquidator first performs an oracle price update\\footnote{Compound allows any entity to update the price oracle with authenticated messages signed by, for example, off-chain price sources.}, which renders a borrowing position liquidatable. The liquidator then liquidates the position within the same transaction. In Table~\\ref{tab:casestudystatus}, we present the status change of the position following the price update. Before the price update (block~\\block{11333036}), the position owns a total collateral of $135.07$M~USD (with a borrowing capacity of $101.30$M~USD), and owes a debt of~$101.18$M~USD. After the price of DAI increases from $1.08$ to $1.095299$~USD\/DAI, the total debt reaches $102.61$M~USD, while the borrowing capacity is only $102.55$M~USD. The health factor drops below~$1$, and hence the position becomes liquidatable.\n\n\\begin{table}[tb!]\n\\centering\n\\caption{Status of the borrowing position (\\account{0x909b443761bbD7fbB876Ecde71a37E1433f6af6f}). Note we ignore the tiny amount of collateral and debt in USDT that the borrower owns and owes. The liquidation thresholds (i.e., $\\mathbf{LT}$) of DAI and USDC are both~$0.75$.}\n\\resizebox{\\columnwidth}{!}{%\n\\begin{tabular}{@{}ccc|c|c@{}}\n\\toprule\n\\multirow{2}{*}{\\bf Token} & \\multirow{2}{*}{\\bf Collateral} & \\multirow{2}{*}{\\bf Debt} & \\multicolumn{2}{c}{\\bf Price (USD)} \\\\ \\cmidrule(l){4-5} \n& & & Block \\block{11333036} & After Price Update \\\\ \\midrule\nDAI & $108.51$M & $93.22$M & $1.08$ & $1.095299$ \\\\\nUSDC & $17.88$M & $506.64$k & $1$ & $1$ \\\\ \\midrule\\midrule\n\\multicolumn{3}{c|}{\\bf Total Collateral (USD)} & $135.07$M & $136.73$M \\\\\n\\multicolumn{3}{c|}{\\bf Borrowing Capacity (USD)} & $101.30$M & $102.55$M \\\\\n\\multicolumn{3}{c|}{\\bf Total Debt (USD)} & $101.18$M & $102.61$M \\\\ \\bottomrule\n\\end{tabular}%\n}\n\\label{tab:casestudystatus}\n\\end{table}\n\n\\begin{table}[tb!]\n\\centering\n\\caption{The depiction of liquidation strategies. At the time of the original liquidation, the price of DAI is $1.095299$~USD\/DAI. The close factor is $50\\%$. The optimal strategy is the most profitable liquidation mechanism for the liquidator.}\n\\resizebox{\\columnwidth}{!}{%\n\\begin{tabular}{@{}c|l|l@{}}\n\\toprule\n\\textbf{Original liquidation} & \\multicolumn{2}{l}{\\begin{tabular}[c]{@{}l@{}}Repay $46.14$M~USD\\\\ Receive $49.83$M~DAI\\\\ \\emph{Profit} $3.69$M~DAI\\end{tabular}} \\\\ \\midrule\\midrule\n\\textbf{Up-to-close-factor strategy} & \\multicolumn{2}{l}{\\begin{tabular}[c]{@{}l@{}}Repay $46.61$M~DAI\\\\ Receive $50.34$M~DAI\\\\ \\emph{Profit} $3.73$M~DAI\\end{tabular}} \\\\ \\midrule\\midrule\n\\multirow{4}{*}{\\textbf{Optimal strategy}} & Liquidation 1 & Liquidation 2 \\\\ \\cmidrule(l){2-3} \n & \\begin{tabular}[c]{@{}l@{}}Repay $296.61$K~DAI\\\\ Receive $320.34$K~DAI\\\\ \\emph{Profit} $23.73$K~DAI\\end{tabular} & \\begin{tabular}[c]{@{}l@{}}Repay $46.46$M~DAI\\\\ Receive $50.18$M~DAI\\\\ \\emph{Profit} $3.72$M~DAI\\end{tabular} \\\\ \\bottomrule\n\\end{tabular}%\n}\n\\label{tab:liquidation-strategy-comparisons}\n\\end{table}\n\nTo evaluate the up-to-close-factor strategy and our optimal liquidation strategy, we implement the original liquidation and the two liquidation strategies\\footnote{We publish the smart contract code at \\url{https:\/\/anonymous.4open.science\/r\/An-Empirical-Study-of-DeFi-Liquidations-Anonymous\/CompoundLiquidationCaseStudy.sol}.} in Solidity v0.8.4\\footnote{\\url{https:\/\/docs.soliditylang.org\/en\/v0.8.4\/}}.\nWe execute them on the corresponding blockchain states\\footnote{We fork the Ethereum mainchain locally from block~\\block{11333036} and apply all the transactions executed prior to the original liquidation transaction in block~\\block{11333037}. We then execute the liquidation strategies to ensure that they are validated on the exact same state of the original liquidation.} and present the results in Table~\\ref{tab:liquidation-strategy-comparisons}. We find that the optimal strategy is superior to the up-to-close-factor strategy and can generate an additional profit of $49.26$K~DAI (\\empirical{$53.96$K}~USD\\xspace) compared to the original liquidation.\n\n\n\\subsubsection{Mitigation}\nThe aforementioned optimal strategy defeats the original intention of a close factor, which incurs undesirably additional losses to borrowers. A possible mitigation solution is that for every position only one liquidation is permitted within one block. Such a setting enforces a liquidator adopting the optimal strategy to settle the two liquidations in two blocks, which decreases the success probability.\n\nWe proceed to assume the existence of a mining liquidator with a mining power $\\alpha$. Given a liquidation opportunity, the up-to-close-factor strategy produces a profit of $profit_c$, while the optimal strategy yields $profit_{o_1}$ and $profit_{o_2}$ respectively in the two successive liquidations. We further assume that there is no ongoing consensus layer attack (e.g., double-spending), implying that a miner with an $\\alpha$ fraction mining power mines the next block is with a probability of $\\alpha$. We hence derive the the expected profit of the two strategies as shown in Equation~\\ref{eq:expected-profit-close} and~\\ref{eq:expected-profit-optimal}\\footnote{To ease understanding, we assume other competing liquidators adopt the up-to-close-factor strategy.}.\n\n\\begin{equation}\\label{eq:expected-profit-close}\n \\mathbb{E}[\\text{up-to-close-factor}] = \\alpha\\cdot profit_c\n\\end{equation}\n\\begin{equation}\\label{eq:expected-profit-optimal}\n \\mathbb{E}[\\text{optimal}] = \\alpha\\cdot profit_{o_1} + \\alpha^2\\cdot profit_{o_2}\n\\end{equation}\nThe liquidator is incentivized to perform the optimal strategy only when $\\mathbb{E}[\\text{optimal}] > \\mathbb{E}[\\text{up-to-close-factor}]$, leading to Equation~\\ref{eq:optimal-rational-condition}.\n\n\\begin{equation}\\label{eq:optimal-rational-condition}\n\\alpha > \\frac{profit_c-profit_{o_1}}{profit_{o_2}}\n\\end{equation}\nIntuitively, $profit_{o_1}$ is relatively small compared to $profit_{c}$ and $profit_{o_2}$ because the liquidator needs to keep the position unhealthy after the first liquidation. The expected profit in the second liquidation then should be sufficient to cover the opportunity cost in the first one, which is typically unattainable.\nInstantiating with our case study liquidation (cf.\\ Table~\\ref{tab:casestudystatus}), we show that a rational mining liquidator would attempt the optimal strategy in two consecutive blocks only if its mining power is over $99.68\\%$. Therefore, we conclude that the one liquidation in one block effectively reduces the expected profit of the optimal liquidation strategy, protecting borrowers from a further liquidation losses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Related Work}\\label{sec:related-work}\n\n\\point{Blockchains and DeFi}\nThere is a growing body of literature on blockchains and DeFi. Qin \\textit{et al.}~\\cite{qin2020attacking} study flash loan attacks and present an optimization approach to maximize the profit of DeFi attacks. Zhou \\textit{et al.}~\\cite{zhou2020high} analyze sandwich attacks in decentralized exchanges. Eskandari \\textit{et al.}~\\cite{eskandari2019sok} provide an overview of the blockchain front-running attacks. Daian \\textit{et al.}~\\cite{daian2019flash} investigate the front-running attacks in decentralized exchanges and propose the concept of \\emph{Miner Extractable Value} (MEV), a financial revenue miners can extract through transaction order manipulation. Qin \\textit{et al.}~\\cite{qin2021quantifying} quantify the extracted MEV on the Ethereum blockchain, including fixed spread liquidations, and present a generalized front-running algorithm, transaction replay. Zhou \\textit{et al.}~\\cite{zhou2021just} propose a framework called \\textsc{DeFiPoser} that allows to automatically create profit-generating transactions given the blockchain state.\n\n\\point{Blockchain Borrowing and Lending Markets}\nDarlin \\textit{et al.}~\\cite{darlin2020optimal} study the MakerDAO liquidation auctions. The authors optimize the costs for participating in the auctions and find that most auctions conclude at higher than optimal prices. The work appears real-world relevant, as it considers the transaction fees, conversion costs and cost of capital, yet it does not consider potential gas bidding contests by the end of MakerDAO auctions~\\cite{daian2019flash}. Kao \\textit{et al.}~\\cite{kao2020analysis} and ZenGo~\\cite{zengo-compound} are to our knowledge the first to have investigated Compound's liquidation mechanism (the third biggest lending protocol in terms of USD at the time of writing). Perez~\\textit{et al.}~\\cite{perez2020liquidations} follow up with a report that focuses on additional on-chain analytics of the Compound protocol. DragonFly Research provides a blog post~\\cite{medium-liquidation} about the liquidator profits on Compound, dYdX and MakerDAO. Minimizing financial deposit amounts in cryptoeconomic protocols, while maintaining the same level of security is studied in Balance~\\cite{harzbalance}.\n\n\\point{Liquidations in Traditional Finance} Liquidations are essential to traditional finance (TradFi) and are well studied in the related literature~\\cite{titman1984effect,shleifer1992liquidation,alderson1995liquidation,almgren1999value,reinhart2011liquidation}. We remark that liquidations in blockchain systems are fundamentally different from those in TradFi in terms of high-level designs and settlement mechanisms.\n\n\n\\section{Conclusion}\\label{sec:conclusion}\nDue to their significant volatility when compared to alternative financial vehicles cryptocurrencies are attracting speculators. Furthermore, because speculators seek to further their risk exposure, non-custodial lending and borrowing protocols on blockchains are thriving. The risks of borrowing, however, manifests themselves in the form of liquidation profits claimed by liquidators.\n\nIn this paper we study the lending platforms that capture $85$\\% of the blockchain lending market. We systematize the most prevalent liquidation mechanisms and find that many liquidations sell excessive amounts of borrower's collateral. In this work we provide extensive data analytics covering over $2$ years the prevalent $4$ lending protocols. We systematize their respective liquidation mechanisms and show that most liquidation systems are unfavorable to the borrowers. We finally show an optimal liquidation strategy which we have not yet observed in the wild.\n\n\n\n\n\\bibliographystyle{ACM-Reference-Format}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nA \\emph{hole} in a graph is an induced cycle of length at least four. A \\emph{proper coloring} of a graph is a function that assigns to each vertex a color with the constraint that two adjacent vertices are not given the same color. The \\emph{chromatic number} of $G$, denoted by $\\chi(G)$, is the smallest number of colors needed to color the graph properly. All the colorings considered in the sequel are proper, so we just call them colorings. \nThe size of the largest clique of $G$ is denoted $\\omega(G)$.\nWe obviously have $\\omega(G)\\leq \\chi(G)$, and one may wonder whether the equality holds. In fact, it does not hold in the general case, and the simplest counter-example is any \\emph{odd hole}, i.e. any hole of odd length, for which $\\omega(G)=2$ but $\\chi(G)=3$. Graphs for which the equality $\\chi(G')=\\omega(G')$ holds for every induced subgraph $G'$ of $G$ are called \\emph{perfect}, and the Strong Perfect Graph Theorem \\cite{SPGT} proved that \na graph if perfect if and only if it is\n\\emph{Berge}, that is to say there is no odd hole in $G$ nor in its complement.\nIn order to get some upper bound on $\\chi(G)$, Gy\\'arf\\'as \\cite{G54} introduced the concept of $\\chi$-bounded class: a family $\\mathcal{G}$ of graphs is called $\\chi$\\emph{-bounded} if there exists a function $f$ such that $\\chi(G')\\leq f(\\omega(G'))$ whenever $G'$ is an induced subgraph of $G\\in \\mathcal{G}$.\n\nThis notion has widely been studied since then, in particular in hereditary classes (\\emph{hereditary} means closed under taking induced subgraph). \nA classical result of Erd\\H{o}s \\cite{E40} asserts that there exist graphs with arbitrarily large \\emph{girth} (that is, the length of the shortest induced cycle) and arbitrarily large chromatic number. Thus forbidding only one induced subgraph $H$ may lead to a $\\chi$-bounded class only if $H$ is acyclic. It is conjectured that this condition is also sufficient \\cite{G32, S52}, but it is proved only if $H$ is a path, a star \\cite{G54} or a tree of radius two \\cite{TreeRadius2} (or three, with additional conditions \\cite{TreeRadius3}). Scott \\cite{ScottSubdivTree} also proved it for any tree $H$, provided that we forbid every induced subdivision of $H$, instead of just $H$ itself.\n\nConsequently, forbidding holes in order to get a $\\chi$-bounded class is conceivable only if we forbid infinitely many hole lengths. \nTwo parameters should be taken into account: first, the length of the holes, and secondly, the parity of their lengths. \nIn this respect, Gy\\'arf\\'as \\cite{G54} made a famous series of three conjectures. The first one asserts the class of graphs with no odd hole is $\\chi$-bounded. The second one asserts that, for every $k$, the class of graphs with no hole of length at least $k$ is $\\chi$-bounded. The last one generalizes the first two conjectures and asserts that for every $k$, the class of graphs with no odd hole of length at least $k$ is $\\chi$-bounded. After several partial results \\cite{RS99, ScottCycles, CSSGyarfas}, the first and the second conjectures were recently solved by Chudnovsky, Scott and Seymour \\cite{SSOddHoles, CSSLongHoles}. Moreover, we learned while writing this article that Scott and Seymour claimed\\footnote{Their article is still in preparation.} to have proved a very general result implying the triangle-free case of the third conjecture (which would also imply the result of this paper): for every $k\\geq 0$, every triangle-free graph with large enough chromatic number admits a sequence of holes of $k$ consecutive lengths.\n\n\nThe class of even-hole-free graphs has extensively been studied from a structural point of view. A decomposition theorem together with a recognition algorithm have been found by Conforti, Cornu\\'ejols, Kapoor and Vu\\v{s}kovi\\'c \\cite{EvenHole1, EvenHole2,ChangLu}. Reed conjectured \\cite{R58} that every even-hole-free graphs has a vertex whose neighborhood is the union of two cliques (called a \\emph{bisimplicial} vertex), which he and his co-authors proved \\cite{EvenHoleBisimplicial} a few years later. As a consequence, they obtained that every even-hole-free graph $G$ satisfies $\\chi(G)\\leq 2\\omega(G)-1$.\n\nForbidding $C_4$ is in fact a strong restriction since $C_4$ can also be seen as the complete bipartite graph $K_{2,2}$: K\\\"uhn and Osthus \\cite{KuhnOsthus} proved that for every graph $H$ and for every integer $s$, every graph of large average degree (with respect to $H$ and $s$) with no $K_{s,s}$ as a (non-necessarily induced) subgraph contains an induced subdivision of $H$, where each edge is subdivided at least once. This strong result implies that $\\chi$ is bounded in any class $\\mathcal{C}$ defined as graphs with no triangles, no induced $C_4$ and no cycles of length divisible by $k$, for any fixed integer $k$. Indeed, let $G\\in \\mathcal{C}$ be a minimal counter-example to $\\chi(G)\\leq t$ (with $t$ chosen large enough with respect to $k$), then it has large minimum degree. Moreover it has neither induced $C_4$ nor triangles, consequently it has no $C_4$ subgraphs. By K\\\"uhn and Osthus' theorem, there exists an induced subdivision $H$ of $K_\\ell$ for some well-chosen integer $\\ell$ depending on $k$. Consider $K_\\ell$ as an auxiliary graph where we color each edge with $c\\in\\{1,\\ldots, k \\}$ if this edge is subdivided $c$ times modulo $k$ in $H$. By Ramsey's theorem \\cite{Ramsey}, if $\\ell$ is large enough, then we can find a monochromatic clique $K$ of size $k$. Let $C_0$ be a Hamiltonian cycle through $K$ and call $C$ the corresponding cycle in the subdivided edges in $H$. Since $K$ was monochromatic in $K_\\ell$, the edges used in $C_0$ are subdivided the same number of times modulo $k$, consequently $C$ has length divisible by $k$. Moreover, it is an induced cycle since each edge is subdivided at least once in $H$.\n\n\nThis is why we are interested in finding a $\\chi$-boundedness result when every even hole except $C_4$ is forbidden, which was conjectured by Bruce Reed \\cite{ReedPrivate}. In this paper, we achieve a partial result by forbidding also triangles. This is a classical step towards $\\chi$-boundedness, and Thomass\\'e \\emph{et al.} \\cite{FPTBullFree} even asked whether this could always be sufficient, namely: does there exist a function $f$ such that for every class $\\mathcal{C}$ of graphs and any $G\\in \\mathcal{C}$, $\\chi(G)\\leq f(\\chi_T(G), \\omega(G))$, where $\\chi_T(G)$ denotes the maximum chromatic number of a triangle-free induced subgraph of $G$?\n\n\n\nThe result of this paper is closely related to the following recent one, by Bonamy, Charbit and Thomass\\'e, answering to a question by Kalai and Meshulam on the sum of Betti numbers of the stable set complex (see \\cite{Thomasse0mod3} for more details):\n\n\\begin{theorem}[\\cite{Thomasse0mod3}]\nThere exists a constant $c$ such that every graph $G$ with no induced cycle of length divisible by 3 satisfies $\\chi(G) < c$.\n\\end{theorem}\n\nIndeed, the so-called Parity Changing Path (to be defined below) is directly inspired by their Trinity Changing Path. The structure of the proofs also have several similarities.\n\n\n\n\n\\bigskip\n\n\\paragraph{Contribution} We prove the following theorem:\n\n\\begin{theorem} \\label{th: C chi borne}\nThere exists a constant $c$ such that every graph $G$ with no triangle and no induced cycle of even length at least 6 satisfies $\\chi(G) < c$.\n\\end{theorem}\n\nThe outline is to prove the result when the 5-hole is also forbidden (see Lemma \\ref{lem: sans C5} below), which should intuitively be easier, and then deduce the theorem for the general case.\n\n\n\nTo begin with, let us introduce and recall some notations:\nthe class under study, namely graphs with no triangle and no induced $C_{2k}$ with $k\\geq 3$ (meaning that every even hole is forbidden except $C_4$) will be called \\noindent {\\bf Definitions. }{$\\mathcal{C}_{3, 2k\\geq 6}$} for short. Moreover, we will consider in Subsection \\ref{sec: sans C5} the subclass \\noindent {\\bf Definitions. }{$\\mathcal{C}_{3, 5, 2k\\geq 6}$} of $\\mathcal{C}_{3, 2k\\geq 6}$ in which the 5-hole is also forbidden. For two subsets of vertices $A, B \\subseteq V$, $A$ \\noindent {\\bf Definitions. }{dominates} $B$ if $B\\subseteq N(A)$.\nA \\noindent {\\bf Definitions. }{major connected component} of $G$ is a connected component $C$ of $G$ for which $\\chi(C)=\\chi(G)$. Note that such a component always exists. \nFor any induced path $P=x_1x_2\\cdots x_\\ell$ we say that $P$ is a path from its \\noindent {\\bf Definitions. }{origin} $x_1$ to its \\noindent {\\bf Definitions. }{end} $x_\\ell$ or an \\noindent {\\bf Definitions. }{$x_1x_\\ell$-path}. Its \\noindent {\\bf Definitions. }{interior} is $\\{x_2, \\ldots, x_{\\ell-1}\\}$ and its \\noindent {\\bf Definitions. }{length} is $\\ell-1$. \n\nMoreover, we use a rather common technique called a \\emph{levelling} \\cite{SSOddHoles, CSSGyarfas} :\ngiven a vertex $v$, the \\noindent {\\bf Definitions. }{$v$-levelling} is the partition $(N_0, N_1, \\ldots, N_k, \\ldots)$ of the vertices according to their distance to $v$: $N_k$ is the set of vertices at distance exactly $k$ from $v$ and is called the \\noindent {\\bf Definitions. }{$k$-th level}. In particular, $N_0=\\set{v}$ and $N_1=N(v)$. We need two more facts about levellings: \nif $x$ and $y$ are in the same part $N_k$ of a $v$-levelling, we call an \\noindent {\\bf Definitions. }{upper $x y$-path} any shortest path from $x$ to $y$ among those with interior in $N_0\\cup \\cdots \\cup N_{k-1}$. Observe that it always exists since there is an $xv$-path and a $vy$-path (but it may take shortcuts; in particular, it may be just one edge). Moreover, in any $v$-levelling, there exists $k$ such that $\\chi(N_k)\\geq \\chi(G)\/2$: \nindeed, if $t$ is the maximum of $\\chi(N_i)$ over all levels $N_i$, one can color $G$ using $2t$ colors by coloring $G[N_{i}]$ with the set of colors $\\{1, \\ldots, t\\}$ if $i$ is odd, and with the set of colors $\\{t+1, \\ldots , 2t\\}$ if $i$ is even. Such a level with chromatic number at least $\\chi(G)\/2$ is called a \\noindent {\\bf Definitions. }{colorful} level.\n\n\n\nLet us now introduce the main tool of the proof, called \\noindent {\\bf Definitions. }{Parity Changing Path} (\\noindent {\\bf Definitions. }{PCP} for short) which, as already mentioned, is inspired by the \\emph{Trinity Changing Path (TCP)} appearing in \\cite{Thomasse0mod3}: intuitively (see Figure \\ref{fig: PCP} for an unformal diagram), a PCP is an induced sequence of induced subgraphs and paths $(G_1, P_1, \\ldots, G_\\ell, P_\\ell, H)$ such that each block $G_i$ can be crossed by two possible paths of different parities, and the last block $H$ typically is a stock of big chromatic number.\nFormally, a \\noindent {\\bf Definitions. }{PCP of order $\\ell$} in $G$ is a sequence of induced subgraphs $G_1, \\ldots, G_{\\ell}, H$ (called \\noindent {\\bf Definitions. }{blocks}; the $G_i$ are the \\noindent {\\bf Definitions. }{regular} blocks) and induced paths $P_1, \\ldots , P_{\\ell}$ such that the origin of $P_i$ is some vertex $y_i$ in $G_i$, and the end of $P_i$ is some vertex $x_{i+1}$ of $G_{i+1}$ (or of $H$ if $i=\\ell$). Apart from these special vertices which belong to exactly two subgraphs of the PCP, the blocks and paths $G_1, \\ldots, G_\\ell, H, P_1, \\ldots, P_\\ell$ composing the PCP are pairwise disjoint. The only possible edges have both endpoints belonging to the same block or path.\nWe also have one extra vertex $x_1\\in G_1$ called the \\noindent {\\bf Definitions. }{origin} of the PCP. \nMoreover in each block $G_i$, there exists one induced $x_i y_i$-path of odd length, and one induced $x_i y_i$-path of even length (these paths are not required to be disjoint one from each other). In particular $x_i \\neq y_i$ and $x_i y_i$ is not an edge. For technical reasons that will appear later, we also require that $H$ is connected, every $G_i$ has chromatic number at most 4 and every $P_i$ has length at least 2. Finally the chromatic number of $H$ is called the \\noindent {\\bf Definitions. }{leftovers}. \n\nIn fact in Subsection \\ref{sec: Yes C4 avec C5}, we need a slightly stronger definition of PCP: a \\noindent {\\bf Definitions. }{strong PCP} is a PCP for which every $G_i$ contains an induced $C_5$.\n\n\\begin{figure}\n\\center\n\\includegraphics[scale=1]{fig\/PCP}\n\\caption{An informal diagram for a PCP of order 3. Grey curved lines stand for the even and odd length $x_iy_i$-paths.}\n\\label{fig: PCP}\n\\end{figure}\n\n\\bigskip\n\nWe first bound the chromatic number in $\\mathcal{C}_{3, 5, 2k\\geq 6}$ (see Lemma \\ref{lem: sans C5} below), which is easier because we forbid one more cycle length, and then deduce the theorem for $\\mathcal{C}_{3, 2k\\geq 6}$. The proofs for $\\mathcal{C}_{3, 2k\\geq 6}$ and $\\mathcal{C}_{3, 5, 2k\\geq 6}$ follow the same outline, which we informally describe here:\n\n\\begin{enumerate}\n\\item If $\\chi(G)$ is large enough, then for every vertex $v$ we can grow a PCP whose origin is $v$ and whose leftovers are large (Lemmas \\ref{lem: debut PCP sans C5}, \\ref{lem: existence PCP sans C5} and then Lemma \\ref{lem: debut PCP}).\n\\item Using (i), if $\\chi(G)$ is large enough and $(N_0, N_1, \\ldots)$ is the $v$-levelling,\nwe can grow a \\noindent {\\bf Definitions. }{rooted} PCP:\nit is a PCP in a level $N_k$, which has a \\noindent {\\bf Definitions. }{root}, \\emph{i.e.} a vertex in the previous level $N_{k-1}$ whose unique neighbor in the PCP is the origin (Lemma \\ref{lem: rooted PCP sans C5} and then Lemma \\ref{lem: rooted PCP}).\n\\item Given a rooted PCP in a level $N_k$, if a vertex $x\\in N_{k-1}$ has a neighbor in the last block $H$, then it has a neighbor in every regular block $G_i$ (Lemma \\ref{obs: sommets riches sans C5}).\n\\item Given a rooted PCP of order $\\ell$ in a level $N_k$ and a stable set $S$ in $N_{k-1}$, the set of neighbors of $S$ inside $N_k$ can not have a big chromatic number. Consequently, the \\noindent {\\bf Definitions. }{active lift} of the PCP, defined as $N(G_\\ell)\\cap N_{k-1}$, has high chromatic number (Lemmas \\ref{lem: shadow stable bornee sans C5}, \\ref{lem: active lift gros si neighb stable gros} and \\ref{lem: shadow borne bornee sans C5} and then Lemmas \\ref{lem: active lift gros si neighb stable gros}, \\ref{lem: shadow stable bornee}, \\ref{lem: shadow borne bornee}).\n\\item The final proofs put everything together: consider a graph of $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ (resp. $\\mathcal{C}_{3, 2k\\geq 6}$) with chromatic number large enough. Then pick a vertex $v$, let $(N_0, N_1, \\ldots)$ be the $v$-levelling and $N_k$ be a colorful level. By (ii), grow inside $N_k$ a rooted PCP $P$. Then by (iv), get an active lift $A$ of $P$ inside $N_{k-1}$ with big chromatic number. Grow a rooted PCP $P'$ inside $A$, and get an active lift $A'$ of $P'$ inside $N_{k-2}$ with chromatic number big enough to find an edge $xy$ (resp. a 5-hole $C$) in $A'$ . Then ``clean\" $P'$ in order to get a stable set $S$ inside the last regular block of $P'$, dominating this edge (resp. hole). Now find an even hole of length $\\geq 6$ in $\\set{x,y}\\cup S\\cup P$ (resp. $C\\cup S\\cup P$), a contradiction.\n\\end{enumerate}\n\n\n\\section{Forbidding 5-holes}\n\\label{sec: sans C5}\n\nThis section is devoted to the proof of the following lemma :\n\n\\begin{lemma} \\label{lem: sans C5}\nThere exists a constant $c'$ such that every graph $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ satisfies $\\chi(G)< c'$.\n\\end{lemma}\n\n\nWe follow the outline described above. Let us start with step (i):\n\n\\begin{lemma} \\label{lem: debut PCP sans C5}\nLet $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ be a connected graph and $v$ be any vertex of $G$. For every $\\delta$ such that $\\chi(G)\\geq \\delta \\geq 18$, there exists a PCP of order 1 with origin $v$ and leftovers at least $h(\\delta)=\\delta\/2 -8$.\n\\end{lemma}\n\n\\begin{proof}\nThe proof is illustrated on Figure \\ref{fig: build PCP overview}. Let $(N_0, N_1, \\ldots)$ be the $v$-levelling and $N_k$ be a colorful level.\nLet $N'_k$ be a major connected component of $G[N_k]$, so $\\chi(N'_k)\\geq \\delta\/2$.\n Let $xy$ be an edge of $N'_k$, and $x'$ (resp. $y'$) be a neighbor of $x$ (resp. $y$) in $N_{k-1}$.\nLet $Z' = N(\\{x',y',x,y\\})\\cap N'_k$ and $Z=Z' \\setminus \\{x,y\\}$. \n Let $z\\in Z$ be a vertex having a neighbor $z_1$ in a major connected component $M_1$ of $N'_k \\setminus Z'$. Observe that $N'_k \\setminus Z'$ is not empty since $\\chi(Z')\\leq 6$ (the neighborhood of any vertex is a stable set).\n The goal is now to find two $vz$-paths $P$ and $P'$ of different parities with interior in $G[N_0 \\cup \\ldots \\cup \\{ x', y' \\} \\cup \\{x,y\\}]$. Then we can set $G_1=G[P\\cup P']$, $P_1=G[\\{z,z_1\\}]$ and $H=G[M_1]$ as parts of the wanted PCP. In practice, we need to be a little more careful to ensure the condition on the length of $P_1$ and the non-adjacency between $z$ and $H$, which is described after finding such a $P$ and a $P'$.\n\nLet $P_0$ (resp. $P'_0$) be a $vx'$-path (resp. $vy'$-path) of length $k-1$ (with exactly one vertex in each level).\nBy definition of $Z$, $z$ is connected to $\\{x',y',x,y\\}$.\n\\begin{enumerate}\n\\item (see Figure \\ref{fig: build PCP case 1}) If $z$ is connected to $x$ or $y$, say $x$, then $z$ is connected neither to $x'$ nor to $y$, otherwise it creates a triangle. We add the path $x'xz$ to $P_0$ to form $P$. Similarly, we add either the edge $y'z$ if it exists, or else the path $y' y x z$ to $P'_0$ to form $P'$. Observe that $P'$ is indeed an induced path since there is no triangle.\n\\item (see Figure \\ref{fig: build PCP case 2}) Otherwise, $z$ is connected neither to $x$ nor to $y$, thus $z$ is connected to exactly one of $x'$ and $y'$, since otherwise it would either create a triangle $x', y', z$ or a 5-hole $z x' x y y'$, so say $zx'\\in E$ and $zy'\\notin E$. We add the edge $x'z$ to $P_0$ to form $P$. We add the path $y' x' z$ if $y'x'\\in E$, otherwise add the path $y' y x x' z$ to $P'_0$ to form $P'$. Observe that this is an induced path since $G$ has no triangle and no 5-hole.\n\\end{enumerate}\nNow come the fine tuning.\nChoose in fact $z_1\\in M_1\\cap N(z)$ so that $z_1$ is connected to a major connected component $M_2$ of $M_1 \\setminus N(z)$. Choose $z_2$ a neighbor of $z_1$ in $M_2$ such that $z_2$ is connected to a major connected component $M_3$ of $M_2\\setminus N(z_1)$. We redefine $H=G[\\{z_2 \\cup M_3\\}]$ and $P_1=G[\\{z,z_1,z_2\\}]$. Then $P_1$ is a path of length 2, $G_1$ is colorable with 4 colors as the union of two induced paths, and $H$ is connected. Moreover $H$ has chromatic number at least $\\chi(N'_k)-\\chi(Z')-\\chi(N(z))-\\chi(N(z_1))$. Since the neighborhood of any vertex is a stable set, $\\chi(Z')\\leq 6$ and $\\chi(N(z)), \\chi(N(z_1))\\leq 1$. Thus $\\chi(H)\\geq \\delta\/2- 8$.\n\n\\end{proof}\n\n\\begin{figure}\n\\center\n\n\\subfigure[Overview of the situation\\label{fig: build PCP overview}]{\\includegraphics[scale=1, page=1]{fig\/buildPCP}}\n\\subfigure[Case (i)\\label{fig: build PCP case 1}]{\\includegraphics[scale=1, page=2]{fig\/buildPCP}}\n\n\\subfigure[Case (ii)\\label{fig: build PCP case 2}]{\\includegraphics[scale=1, page=3]{fig\/buildPCP}}\n\\caption{Illustrations for the proof of Lemma \\ref{lem: debut PCP sans C5}. Dashed edges stand for non-edges, and grey edges stand for edges that may or may not exist.}\n\\label{fig: build PCP}\n\\end{figure}\n\nWe can iterate the previous process to grow some longer PCP. In the following, for a function $f$ and an integer $k$, $f^{(k)}$ denotes the $k$-th iterate of $f$, that is to say that $f^{(k)}(x)=\\underbrace{(f \\circ \\ldots \\circ f)}_{k \\text{ times}} (x)$.\n\n\\begin{lemma}\n\\label{lem: existence PCP sans C5}\nLet $h(x)=x\/2-8$ be the function defined in Lemma \\ref{lem: debut PCP sans C5}. For every positive integers $\\ell, \\delta\\in \\mathbb{Z}_+$, if $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ is connected and satisfies $\\chi(G)\\geq \\delta$ and $h^{(\\ell-1)}(\\delta)\\geq 18$, then from any vertex $x_1$ of $G$, one can grow a PCP of order $\\ell$ with leftovers at least $h^{(\\ell)}(\\delta)$.\n\\end{lemma}\n\n\\begin{proof}\nWe prove the result by induction on $\\ell$. For $\\ell=1$, the result follows directly from Lemma \\ref{lem: debut PCP sans C5}. Now suppose it is true for $\\ell-1$, and let $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ be such that $\\chi(G)\\geq \\delta$ and $h^{(\\ell-1)}(\\delta)\\geq 18$. Then $\\delta\\geq h^{(\\ell-1)}(\\delta)\\geq 18$, so we can apply Lemma \\ref{lem: debut PCP sans C5} to get a PCP of order 1 and leftovers at least $h(\\delta)$ from any vertex $x_1$. Let $x_2$ be the common vertex between the last block $H$ and the first path $P_1$ of the PCP (as in the definition). Now apply the induction hypothesis to $H$, knowing that $H$ is connected, $\\chi(H)\\geq h(\\delta)=\\delta'$ and $h^{(\\ell-2)}(\\delta')\\geq 18$. Then we obtain a PCP of order $\\ell-1$ with origin $x_2$ and leftovers at least $h^{(\\ell-2)}(\\delta')$, which finishes the proof by gluing the two PCP together.\n\\end{proof}\n\n\n\nNow we grow the PCP in a level $N_k$ of high chromatic number, and we want the PCP to be rooted (\\emph{i.e.} there \nexists a root $u'\\in N_{k-1}$ that is adjacent to the origin $u$ of the PCP, but to no other vertex of the PCP). This is step (ii).\n\n\n\\begin{lemma}\n\\label{lem: rooted PCP sans C5}\nLet $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ be a connected graph, $v\\in V(G)$ and $(N_0, N_1, \\ldots)$ be the $v$-levelling. Let $h$ be the function defined in Lemma \\ref{lem: debut PCP sans C5}. For every $k, \\delta$ such that $\\chi(N_k)\\geq \\delta+1$ and $h^{(\\ell-1)}(\\delta)\\geq 18$, there exists a rooted PCP of order $\\ell$ in $N_k$ with leftovers at least $h^{(\\ell)}(\\delta)$.\n\\end{lemma}\n\n\\begin{proof}\nLet $N'_k$ be a major connected component of $N_k$ and $u\\in N'_k$. Consider a neighbor $u'$ of $u$ in $N_{k-1}$. Since there is no triangle, $N'_k\\setminus N(u')$ still has big chromatic number (at least $\\delta$), and let $N''_k$ be a major connected component of $N'_k\\setminus N(u')$. Let $z$ be a vertex of $N(u)\\cap N'_k$ having a neighbor in $N''_k$. Then we apply Lemma \\ref{lem: existence PCP sans C5} in $\\{z\\}\\cup N''_k$ to grow a PCP of order $\\ell$ from $z$ with leftovers at least $h^{(\\ell)}(\\delta)$. Now $u'$ has an only neighbor $z$ on the PCP, which is the origin.\n\\end{proof} \n \nLet us observe the properties of such a rooted PCP. We start with step (iii):\n\n\\begin{lemma} \\label{obs: sommets riches sans C5}\nLet $v$ be a vertex of a graph $G\\in \\mathcal{C}_{3, 2k\\geq 6}$, $(N_0, N_1, \\ldots)$ be the $v$-levelling. Let $P$ be a rooted PCP $(G_1, P_1, \\ldots, G_\\ell, P_\\ell, H)$ of order $\\ell$ in a level $N_k$ for some $k$. If $x'\\in N_{k-1}$ has a neighbor $x$ in $H$, then $x$ has a neighbor in every $G_i$ for $1\\leq i \\leq \\ell$.\n\\end{lemma}\n\n\n\n\\begin{proof}\nLet $u$ be the origin of the PCP and $u'$ its root. Since $x'\\neq u'$ by definition of the root, there exists an upper $x'u'$-path $P_{up}$ of length at least one. \nConsider a $ux$-path $P$ inside the PCP. Let $v_1, \\ldots, v_r$ be the neighbors of $x'$ on this path, different from $x$ (if any), in this order (from $u$ to $x$).\nNow we can show that any regular block $G_i$ contains at least one $v_j$: suppose not for some index $i$, let $j$ be the greatest index such that $v_j$ is \\emph{before} $x_i$, \n\\emph{i.e.} $v_j\\in G_1\\cup P_1 \\cup \\cdots \\cup G_{i-1} \\cup P_{i-1}$.\n\nIf such an index does not exist (\\emph{i.e.} all the $v_j$ are after $G_i$), then there is an odd and an even path from $u$ to $v_1$ of length at least 3 by definition of a regular block,\nand this path does not contain any neighbor of $x'$. Close them to build two induced cycles by going through \n$x'$, $P_{up}$ and $u'$: one of them is an even cycle, and its length is at least 6.\n\nIf $j=r$ (\\emph{i.e.} all the $v_j$ are before $G_i$), then we can use the same argument with a path of well-chosen parity from $v_r$ to $x$, crossing $G_i$. \n\n\nOtherwise, there is an odd and an even path in the PCP between $v_j$ and $v_{j+1}$, crossing $G_i$, and its length is at least 4 because $x_i$ and $y_i$ are at distance at least 2 one from each other. We can close the even path path by going back and forth to $x$: this gives an even hole of length at least 6.\n\\end{proof}\n\nNote that, in the lemma above, $G$ is taken in $\\mathcal{C}_{3, 2k\\geq 6}$ and not in $\\mathcal{C}_{3, 5, 2k\\geq 6}$. In particular, we will use Lemma \\ref{obs: sommets riches sans C5} in the next section as well. Let us now continue with step (iv):\n \n\\begin{lemma} \\label{lem: shadow stable bornee sans C5}\nLet $v$ be a vertex of a graph $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ and $(N_0, N_1, \\ldots)$ be the $v$-levelling. \nLet $S\\subseteq N_{k-1}$ be a stable set. Then $\\chi(N(S)\\cap N_k)\\leq 52$.\n\n\\end{lemma}\n\n\\begin{proof}\nLet $\\delta=\\chi(N(S)\\cap N_{k-1})-1$. Suppose by contradiction that $\\delta\\geq 52 $, then $h(\\delta)\\geq 18$ hence by Lemma \\ref{lem: rooted PCP sans C5}, we can grow a rooted PCP of order 2 inside $N(S)\\cap N_k$. Let $u$ be the origin of the PCP and $u'$ its root. Observe in particular that $S$ dominates $G_2$. Let $xy$ be an edge of $G_2$, and let $x'$ (resp. $y'$) be a neighbor of $x$ (resp. $y$) in $S$. \nBy Lemma \\ref{obs: sommets riches sans C5}, both $x'$ and $y'$ have a neighbor in $G_1$.\nThis gives an $x'y'$-path $P_{down}$ with interior in $G_1$. In order not to create an even hole nor a 5-hole by closing it with $x' x y y'$, we can ensure that $P_{down}$ is an even path of length at least 4. Moreover, there exists an upper $x'y'$-path $P_{up}$. Then either the hole formed by the concatenation of $P_{up}$ and $x' x y y'$, or the one formed by the concatenation of $P_{up}$ and $P_{down}$ is an even hole of length $\\geq 6$, a contradiction.\n\\end{proof}\n\nThe previous lemma allows us to prove that one can \\emph{lift} the PCP up into $N_{k-1}$ to get a subset of vertices with high chromatic number. We state a lemma that will be reused in the next section:\n\n\n\n\\begin{lemma}\n\\label{lem: active lift gros si neighb stable gros}\nLet $v$ be a vertex of a graph $G\\in \\mathcal{C}_{3, 2k\\geq 6}$ and $(N_0, N_1, \\ldots)$ be the $v$-levelling. Let $P$ be a rooted PCP of order $\\ell$ in a level $N_k$ (for some $k\\geq 2$) with leftovers at least $\\delta$. Let $A=N(G_\\ell)\\cap N_{k-1}$ (called the active lift of the PCP).\nSuppose that for every stable set $S\\subseteq A$, we have $\\chi(N(S)\\cap N_{k})\\leq \\gamma$,\n then $\\chi(A)\\geq {\\delta}\/{\\gamma}$.\n\\end{lemma}\n\n\\begin{proof}\nLet $r=\\chi(A)$, suppose by contradiction that $r<\\delta\/\\gamma$ and decompose $A$ into $r$ stable sets $S_1, \\ldots, S_r$. Then $N(A)\\cap N_k$ is the (non-necessarily disjoint) union of $r$ sets $N(S_1)\\cap N_k, \\ldots, N(S_r)\\cap N_k$, and each of them has chromatic number at most $\\gamma$ by assumption. Consequently $\\chi(N(A)\\cap N_k)\\leq r \\gamma < \\delta$ and hence $\\chi(H\\setminus N(A)) \\geq \\chi(H) - \\chi(N(A)\\cap N_k) \\geq 1$.\nLet $x$ be any vertex of $H\\setminus N(A)$ and $x'$ be a neighbor of $x$ in $N_{k-1}$. By construction, $x'\\notin A$ so $x'$ has no neighbor in $G_\\ell$. This is a contradiction with Lemma \\ref{obs: sommets riches sans C5}.\n\\end{proof}\n\nBy Lemmas \\ref{lem: shadow stable bornee sans C5} and \\ref{lem: active lift gros si neighb stable gros} with $\\gamma=52$, we can directly deduce the following:\n\n\\begin{lemma} \\label{lem: shadow borne bornee sans C5}\nLet $v$ be a vertex of a graph $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ and $(N_0, N_1, \\ldots)$ be the $v$-levelling. Let $P$ be a rooted PCP of order $\\ell$ in a level $N_k$ (for some $k\\geq 2$) with leftovers at least $\\delta$. Let $A=N(G_\\ell)\\cap N_{k-1}$ be the active lift of the PCP, then we have $\\chi(A)\\geq g(\\delta)={\\delta}\/{52}$.\n\\end{lemma}\n\n\n\n\nWe can now finish the proof, this is step (v). Recall that a sketch was provided, and it may help to understand the following proof.\n\n\n\n\n\\begin{proof}[Proof of Lemma \\ref{lem: sans C5}] \nLet $c'$ be a constant big enough so that \n$$g\\left(h^{(2)}\\left(g\\left(h^{(2)}\\left(\\frac{c'}{2}-1\\right)\\right)-1\\right)\\right)\\geq 5 \\ .$$\nSuppose that $\\chi(G)\\geq c'$. Pick a vertex $v$, let $(N_0, N_1, \\ldots)$ be the $v$-levelling and $N_k$ be a colorful level, so $\\chi(N_k)\\geq \\chi(G)\/2\\geq c_1+1$ where $c_1=c'\/2-1$. By Lemma \\ref{lem: rooted PCP sans C5}, grow a rooted PCP $P=(G_1, P_1, G_2, P_2, H)$ inside $N_k$ of order 2 with leftovers at least $c_2=h^{(2)}(c_1)$. Then apply Lemma \\ref{lem: shadow borne bornee sans C5} and get an active lift $A$ of $P$ inside $N_{k-1}$ with chromatic number at least $c_3=g(c_2)$. Since $h(c_3 -1)\\geq 18$, apply again Lemma \\ref{lem: rooted PCP sans C5} to get a rooted PCP $P'=(G'_1, P'1, G'_2, P'_2, H')$ of order 2 inside $N_{k-1}$ with leftovers at least $c_4=h^{(2)}(c_3 -1)$. Now apply Lemma \\ref{lem: shadow borne bornee sans C5} to get an active lift $A'$ of $P'$ inside $N_{k-2}$ with chromatic number at least $c_5=g(c_4)$.\n\nBecause of the chromatic restriction in the definition of the PCP, one can color $G'_2$ with 4 colors. Moreover, $G'_2$ dominates $A'$ by definition. Thus there exists a stable set $S\\subseteq G'_2$ such that $\\chi(N(S)\\cap A')\\geq c_6=c_5\/4$ (since $A'$ is the union of the $N(S')\\cap A'$ for the four stable sets $S'$ that partition $G_2'$).\n\nNow $c_6>1$ so there is an edge $xy$ inside $N(S) \\cap A'$. Call $x'$ (resp. $y'$) a vertex of $S$ dominating $x$ (resp. $y$). Both $x'$ and $y'$ have a neighbor in $G_2$ by definition of $A$ and, by Lemma \\ref{obs: sommets riches sans C5}, both $x'$ and $y'$ also have a neighbor in $G_1$. This gives an $x'y'$-path $P_1$ (resp. $P_2$) with interior in $G_1$ (resp. $G_2$). Due to the path $x'x y y'$ of length 3, $P_1$ and $P_2$ must be even paths of length at least 4. Thus the concatenation of $P_1$ and $P_2$ is an even hole of length at least 6, a contradiction.\n\\end{proof}\n\n\\section{General case}\n\\label{sec: Yes C4 avec C5}\n\nThis section aims at proving Theorem \\ref{th: C chi borne}, using the result of the previous section. As already mentioned, we follow the same outline, except that we now need the existence of a $C_5$ several times. Let us start by a technical lemma to find both an even and an odd path out of a 5-hole and its dominating set:\n\n\n\n\\begin{lemma}\n\\label{lem: C5 chemin pair chemin impair}\nLet $G$ be a triangle-free graph inducing a 5-hole $C$. Let $S\\subseteq V(G)$ be a minimal dominating set of $C$, assumed to be disjoint from $C$. If we delete the edges with both endpoints in $S$, then for every vertex $t\\in S$, there exists a vertex $t'\\in S$ such that \none can find an induced $tt'$-path of length 4 and an induced $tt'$-path of length 3 or 5, both with interior in $C$.\n\\end{lemma}\n\n\\begin{proof}\nLet $t\\in S$, call $v_1$ a neighbor of $t$ on the cycle and number the others vertices of $C$ with $v_2, \\ldots , v_5$ (following the adjacency on the cycle). Since $G$ is triangle-free, $t$ can not be adjacent to both $v_3$ and $v_4$, so up to relabeling the cycle in the other direction we assume that $t$ is not adjacent to $v_3$. Let $t'\\in S$ be a vertex dominating $v_3$. Then $tv_1v_2v_3t'$ is an induced path of length 4 between $t$ and $t'$. Moreover, $tv_1v_5v_4t'$ is a (non-necessarily induced) path of length 5 between $t$ and $t'$. If this path is not induced, the only possible chords are $tv_4$ and $t'v_5$ since $G$ is triangle-free, which in any case gives an induced $tt'$-path of length 3.\n\\end{proof}\n\n\n\n\nRecall that in this section, we are interesting in strong PCP, \\emph{i.e.} PCP, all regular blocks $G_i$ of which contain an induced $C_5$. We start with step (i):\n\n\n\\begin{lemma} \\label{lem: debut PCP}\nLet $c'$ be the constant of Lemma \\ref{lem: sans C5}, let $G\\in \\mathcal{C}_{3, 2k\\geq 6}$ and $v$ be any vertex of $G$. For every $\\delta \\in \\mathbb{N}$ such that $\\chi(G)\\geq \\delta \\geq 2c'$, there exists a strong PCP of order 1 with origin $v$ and leftovers at least $f(\\delta)=\\delta\/2-15$.\n\n\\end{lemma}\n\n\\begin{proof}\n\n\nLet $(N_0, N_1, \\ldots)$ be the $v$-levelling, $N_k$ be a colorful level and let $N'_k$ be a major connected component of $G[N_k]$, so $\\chi(N'_k)\\geq c'$. Using Lemma \\ref{lem: sans C5}, there exists a 5-hole $C$ in $G[N'_k]$. Consider a minimum dominating set $D$ of $C$ inside $N_{k-1}$. \n\n\nFrom now on, the proof is very similar to the one of Lemma \\ref{lem: debut PCP sans C5}. Similarly, we define $Z'=N(D\\cup C)\\cap N'_k$ and $Z=Z' \\setminus C$. Let $z\\in Z$ be a vertex having a neighbor $z_1$ in a major connected component $M_1$ in $N'_k \\setminus Z'$. The goal is now to find two $vz$-paths $P$ and $P'$ of different parity with interior in $N_0 \\cup \\ldots \\cup D \\cup C$, then we can set $G_1=G[P\\cup P'\\cup C]$, $P_1=G[\\{z,z_1\\}]$ and $H=G[M_1]$ as parts of the wanted PCP. In practice, we need to be a little more careful to ensure the condition on the length of $P_1$ and the non-adjacency between $z$ and $H$.\n\n\nLet us now find those two paths $P$ and $P'$. By definition of $Z$, $z$ also has a neighbor in $D$ or in $C$. \n\\begin{enumerate}\n\\item If $z$ has a neighbor $x\\in C$, let $y\\in C$ be a vertex adjacent to $x$ on the hole. Let $x'$ and $y'$ be respectively a neighbor of $x$ and a neighbor of $y$ in $D$. Observe that $z$ is connected neither to $x'$ nor to $y$, otherwise it creates a triangle. We grow $P$ by starting from an induced path of length $k-1$ from $v$ to $x'$ and then add the path $x'x z$. Similarly, we grow $P'$ by starting from an induced path of length $k-1$ from $v$ to $y'$, and then add the edge $y'z$ if it exists, or else the path $y' y x z$. Observe that $P'$ is indeed an induced path since there is no triangle.\n\\item If $z$ has no neighbor in $C$, then it has at least one neighbor $x'$ in $D$. Apply Lemma \\ref{lem: C5 chemin pair chemin impair} to get a vertex $y'\\in D$ such that there exists an $x'y'$-path of length 3 or 5, and another one of length 4, both with interior in $C$. Observe that \n$x'$ and $y'$ cannot have a common neighbor $u$ in $N_{k-2}\\cup \\{z\\}$, otherwise there would be either a triangle $x', u, y'$ (if $x'y'\\in E$), or a $C_6$ using the \\mbox{$x'y'$-path} of length 4 with interior in $C$. Now we grow $P$ by starting from an induced path of length $k-1$ from $v$ to $x'$, and add the edge $x'z$. We grow $P'$ by starting from an induced path of length $k-1$ from $v$ to $y'$, and then add the edge $x'y'$ if it exists, otherwise add the $x'y'$-path of length 3 or 5 with interior in $C$, and then finish with the edge $x'z$.\n\\end{enumerate}\n\n\nNow come the fine tuning.\nChoose in fact $z_1\\in M_1\\cap N(z)$ so that $z_1$ is connected to a major connected component $M_2$ of $M_1 \\setminus N(z)$. Choose $z_2$ a neighbor of $z_1$ in $M_2$ such that $z_2$ is connected to a major connected component $M_3$ of $M_2\\setminus N(z_1)$. We redefine $H=G[\\{z_2 \\cup M_3\\}]$ and $P_1=G[\\{z,z_1,z_2\\}]$. Then $P_1$ is a path of length 2, $H$ is connected and $G_1$ is colorable with 4 colors (it is easily 7-colorable as the union of a 5-hole and two paths; a careful case analysis shows that it is 4-colorable). Moreover $H$ has chromatic number at least $\\chi(N'_k)-\\chi(Z')-\\chi(N(z))-\\chi(N(z_1))$. Since the neighborhood of any vertex is a stable set, $\\chi(Z')\\leq |D|+|C| +\\chi(C)\\leq 13$ and $\\chi(N(z)), \\chi(N(z_1))\\leq 1$. Thus $\\chi(H)\\geq \\delta\/2- 15$.\n\n\\end{proof}\n\nWe go on with step (ii): find a strong rooted PCP.\nThe following lemma is proved in the same way as Lemma \\ref{lem: rooted PCP sans C5} by replacing the use of Lemma \\ref{lem: existence PCP sans C5} by Lemma \\ref{lem: debut PCP}, so we omit the proof here.\n\n\\begin{lemma}\n\\label{lem: rooted PCP}\nLet $G\\in \\mathcal{C}_{3, 2k\\geq 6}$ be a connected graph, $f$ be the function defined in Lemma \\ref{lem: debut PCP}, $v$ be a vertex of $G$ and $(N_0, N_1, \\ldots)$ be the $v$-levelling. For every $k, \\delta$ such that $\\chi(N_k)\\geq \\delta+1\\geq 2c'+1$, there exists a strong rooted PCP of order 1 in $N_k$ with leftovers at least $f(\\delta)$.\n\\end{lemma}\n\nStep (iii) is proved by Lemma \\ref{obs: sommets riches sans C5} from the previous section, and was valid not only for $G\\in \\mathcal{C}_{3, 5, 2k\\geq 6}$ but also for $G\\in \\mathcal{C}_{3, 2k\\geq 6}$. So we continue with step (iv):\n\n\\begin{lemma} \\label{lem: shadow stable bornee}\nLet $v$ be a vertex of a graph $G\\in \\mathcal{C}_{3, 2k\\geq 6}$, $(N_0, N_1, \\ldots)$ be the $v$-levelling. \nLet $S$ be a stable set inside $N_{k-1}$. Then $\\chi(N(S)\\cap N_k)\\leq 2c'$.\n\\end{lemma}\n\n\\begin{proof}\nSuppose by contradiction that $\\chi(N(S)\\cap N_k)\\geq 2c'+1$. By Lemma \\ref{lem: rooted PCP}, we can grow in $N(S)\\cap N_k$ a rooted PCP of order 1, and in particular $S$ dominates $G_1$.\nBy definition of a strong PCP, there is a 5-hole $C$ in $G_1$. Since $S$ is a dominating set of $C$, we can apply Lemma \\ref{lem: C5 chemin pair chemin impair} to get two vertices $t, t'\\in S$ such that one can find both an even and an odd $tt'$-path with interior in $C$ and length at least 3. Then any upper $tt'$-path close a hole of even length $\\geq 6$.\n\\end{proof}\n\nIn fact, as in previous section, we can directly deduce from Lemmas \\ref{lem: active lift gros si neighb stable gros} and \\ref{lem: shadow stable bornee} that one can lift the PCP up into $N_{k-1}$ to get a subset of vertices with high chromatic number:\n\n\n\n\\begin{lemma} \\label{lem: shadow borne bornee}\nLet $G\\in \\mathcal{C}_{3, 2k\\geq 6}$, $v\\in V(G)$ and $(N_0, N_1, \\ldots)$ be the $v$-levelling. Let $P$ be a strong rooted PCP of order $1$ in a level $N_k$ (for some $k\\geq 2$) with leftovers $\\delta$. Let $A=N(G_1)\\cap N_{k-1}$ be the active lift of the PCP. If $\\delta \\geq 2c'$, then $\\chi(A)\\geq \\varphi(\\delta)=\\frac{\\delta}{2c'}$.\n\\end{lemma}\n\n\n\n\nWe are now ready to finish the proof, this is step (v). Recall that a sketch was given and may be useful to have a less technical overview of the proof.\n\n\\begin{proof}[Proof of Theorem \\ref{th: C chi borne}] \nLet $c$ be a constant such that \n$$\\varphi\\left(f\\left(\\varphi\\left(f\\left(\\frac{c}{2} -1\\right)\\right)-1\\right)\\right)\\geq 4c' \\ .$$ \nSuppose that $G\\in \\mathcal{C}_{3, 2k\\geq 6}$ has chromatic number $\\chi(G)\\geq c$. Then pick a vertex $v$, let $(N_0, N_1, \\ldots)$ be the $v$-levelling and $N_k$ be a colorful level, consequently $\\chi(N_k)\\geq c_1+1=c\/2$.\nApply Lemma \\ref{lem: rooted PCP} and grow inside $N_k$ a strong rooted PCP $P=(G_1, P_1, H)$ of order 1 with leftovers at least $c_2=f(c_1)$. Then apply Lemma~\\ref{lem: shadow borne bornee} and get an active lift $A=N(G_1)$ of $P$ inside $N_{k-1}$ with chromatic number at least $c_3=\\varphi(c_2)$. By Lemma \\ref{lem: rooted PCP}, we can obtain a strong rooted PCP $P'=(G'_1, P'_1, H')$ inside $A$ with leftovers at least $c_4=f(c_3-1)$, and by Lemma~\\ref{lem: shadow borne bornee} we obtain an active lift $A'$ of $P'$ inside $N_{k-2}$ with chromatic number at least $c_5=\\varphi(c_4)$. Because of the chromatic restriction in the definition of the PCP, one can color $G'_1$ with 4 colors. Moreover, $G'_1$ dominates $A'$ by definition. Thus there exists a stable set $S\\subseteq P'$ such that $\\chi(N(S)\\cap A')\\geq c_6=c_5\/4$.\nNow $c_6\\geq c'$ thus Lemma \\ref{lem: sans C5} proves the existence of a 5-hole $C$ inside $N(S) \\cap A'$. Let us give an overview of the situation: we have a 5-hole $C$ inside $N_{k-2}$, dominated by a stable set $S$ inside $N_{k-1}$, and every pair of vertices $t,t'$ of $S$ can be linked by a $tt'$-path $P_{down}$ with interior in $G_1\\subseteq N_k$. Lemma \\ref{lem: C5 chemin pair chemin impair} gives the existence of two vertices $t, t'\\in S$ linked by both an odd path and an even path of length $\\geq 3$ with interior in $C$. Closing one of these paths with $P_{down}$ gives an induced even hole of length $\\geq 6$, a contradiction.\n\\end{proof}\n\n\n\n\\section*{Acknowledgment}\n\nThe author would like to sincerely thank St\\'ephan \\textsc{Thomass\\'e} for bringing this problem to her knowledge and for useful discussions about Trinity\/Parity Changing Paths.\n\n\n\n\n\n\\bibliographystyle{plain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nGiven a spin manifold $M$ of dimension $n$, we consider the universal spinor bundle $\\Sigma M$. This is the bundle whose sections consist of pairs of metrics $g \\in \\Gamma(\\odot^2_+ T^*M)$ and spinor fields $\\varphi \\in \\Gamma(\\Sigma_g M)$.\nWe denote by $\\mathcal{N}$ the set\n$$\\{ (g, \\varphi) \\in \\Gamma( \\Sigma M) : |\\varphi| = 1\\}$$\nand define the spinorial energy functional\n$$\\mathcal{E}: \\mathcal{N} \\to \\mathbb R$$\n$$\\mathcal{E}(g, \\varphi) = \\frac{1}{2}\\int_M |\\nabla^g \\varphi|^2 \\operatorname{vol}_g.$$\nIf the dimension of $M$ is at least three, the only critical points of $\\mathcal{E}$ are absolute minimizers. This implies $\\nabla^g \\varphi = 0$ for a critical point $(g, \\varphi)$, i.e. $\\varphi$ is a parallel spinor with respect to the metric $g$. Existence of parallel spinors is a strong constraint on the metric $g$. Indeed, such a metric is necessarily Ricci flat and of special holonomy. Conversely, Ricci flat manifolds with special holonomy admit a parallel spinor. Given that manifolds with such metrics are difficult to construct, it is natural to consider the negative gradient flow\n$$\\partial_t \\Phi_t = Q(\\Phi_t)$$\nof $\\mathcal{E}$ to find such a metric. Here $Q: \\mathcal{N} \\to T \\mathcal{N}$ is the negative gradient of $\\mathcal{E}$ with respect to the natural $L^2$ metric on $\\mathcal{N}$. It turns out that $Q$ is weakly elliptic and has negative symbol.\nThe spinorial energy and the associated negative gradient flow, called spinor flow, were first examined in \\cite{Ammann2015}. There, short time existence of this flow on closed manifolds was established.\nFrom here on we assume $M$ to be a closed manifold and $\\dim M = n \\geq 3$.\nWe will prove that critical points of $\\mathcal{E}$, i.e. pairs of Ricci flat metrics and parallel spinor fields, are stable with respect to the spinor flow, that is:\n\\begin{thm}\nSuppose $\\bar{\\Phi} = (\\bar{g}, \\bar{\\varphi})$ is a critical point of $\\mathcal{E}$ and suppose $\\bar{g}$ has no Killing fields. Then there exists a $C^{\\infty}$ neighborhood $U$ of $\\bar{\\Phi}$, such that a solution of the negative gradient flow $\\Phi_t$ with initial condition $\\Phi_0 = \\Phi$ smoothly converges to a critical point. In any $C^k$ norm the speed of convergence is exponential.\n\\end{thm}\nA Ricci flat manifold is up to a finite covering a product of irreducible Ricci-flat manifolds and a flat torus. Hence the condition on the Killing fields can also be read as saying that $\\bar{g}$ has no torus factor. This can for instance be ruled out by the topological condition that the fundamental group of $M$ be finite.\nThe strategy of the proof will be roughly as follows: first, we establish a \u0141ojasiewicz-Simon type inequality for the spinorial energy. This inequality implies exponential decay of the energy along the flow. We will then show that this implies convergence to a critical point. The inequality depends in its optimal form on the fact that the critical set of $\\mathcal{E}$ is smooth. This was shown in \\cite{Ammann2015b}.\n\nWe will also consider the stability of volume constrained critical points. A section $\\Phi \\in \\mathcal{N}$ is a {\\em volume constrained critical point}, if\n$$\\frac{d}{dt}\\Bigr|_{t=0} \\mathcal{E}(\\Phi_t) = 0$$\nfor all volume preserving variations $\\Phi_t$ of $\\Phi$. Such a critical point evolves under the spinor flow by rescaling. A {\\em volume constrained minimizer} $\\Phi = (g, \\varphi)$ is a volume constrained critical point, such that for any $\\Psi \\in \\mathcal{N}$ close to $\\Phi$ we have $\\mathcal{E}(\\Phi) \\leq \\mathcal{E}(\\Psi)$, provided the metrics induced by $\\Phi$ and $\\Psi$ have equal volume.\n\\begin{thm}\nLet $\\bar{\\Phi} = (\\bar{g}, \\bar{\\varphi})$ be a volume constrained minimizer of $\\mathcal{E}$. Suppose that the critical set near $\\bar{\\Phi}$ is a manifold and suppose $\\bar{g}$ has no Killing fields. Then there exists a $C^{\\infty}$ neighborhood $U$ of $\\bar{\\Phi}$, such that the volume normalized spinor flow converges smoothly to a volume constrained minimizer, if the initial condition is in $U$. The convergence speed in $C^k$ is exponential. \n\\end{thm}\nThe strategy for the proof is essentially the same as in the case of critical points. However, here both the condition on Killing fields and the assumption that the critical set near $\\bar{\\Phi}$ is a manifold are strong restrictions. Indeed, suppose $(g, \\varphi)$ is such that\n$$\\nabla^g_X \\varphi = \\lambda X \\cdot \\varphi \\text{ for all } X \\in \\Gamma(TM)$$\nwith $\\lambda \\in \\mathbb R$.\nThen $(g,\\varphi)$ is a volume constrained critical point. The spinor $\\varphi$ is called a Killing spinor.\nIf $g$ carries a Killing spinor, then the cone $((0,\\infty)\\times M, dr^2 + r^2g)$ carries a parallel spinor.\nA large class of metrics with Killing spinors is supplied by Sasaki--Einstein manifolds.\nSince the Reeb vector field is a Killing vector field, all Sasaki--Einstein manifolds carry Killing fields.\nFurthermore, the moduli space of Sasaki--Einstein manifolds is not known to be smooth in general.\nWhat's more, in contrast to the space of parallel spinors, whose dimension is locally constant under Ricci flat deformations of the metric, the dimension of the space of killing spinors can jump under Einstein deformations of the metric. Indeed, a 3-Sasakian manifold admits three linearly independent Killing spinors. Van Coevering found that a toric 3-Sasakian manifold has Einstein deformations $g_t$, such that the space of Killing spinors is two-dimensional for any $t\\neq 0$.\n\nSince the spinor flow is a generalization of the heat flow for $G_2$-structures introduced in \\cite{Weiss2012}, our result is a generalization of the stability result proven there. However, the arguments of our proof are closer in spirit to the proofs in \\cite{Haslhofer2011}, \\cite{Haslhofer2014}, \\cite{Krncke2014}, where stability of Ricci-flat and Einstein metrics with respect to the Ricci flow is shown.\n\n\\section*{Acknowledgements}\nThe author thanks Hartmut Wei\u00df for posing the problem and numerous discussions related to it.\n\n\n\\section{The universal spinor bundle and the spinor flow}\nFor convenience and completeness, we recall the precise definitions of the spinor flow as well as results on short time existence of the flow. Details may be found in \\cite{Ammann2015}. \nWe defined the spinor energy to be a functional on sections of the universal spinor bundle. We will now construct this universal spinor bundle.\nBefore we do this, let us first recall the ordinary spinor bundle on a spin manifold. \nThe orientation preserving component of the general linear group $\\operatorname{GL}_+(n)$ has fundamental group $\\mathbb Z_2$ and hence there exists a universal double covering group $\\widetilde{\\operatorname{GL}}_+(n)$ together with a covering map $\\xi: \\widetilde{\\operatorname{GL}}_+(n) \\to \\operatorname{GL}_+(n)$, which is also a homomorphism.\nLet $M$ be a spin manifold of dimension $n$. By this we mean a manifold $M$ and a $\\widetilde{\\operatorname{GL}}_+(n)$ principal bundle $\\tilde{P}$ which covers the $\\operatorname{GL}_+(n)$ frame bundle $P$, $\\pi: \\tilde{P} \\to P$, so that for any $g \\in \\widetilde{\\operatorname{GL}}_+(n), p \\in \\tilde{P}$ we have\n$$\\pi(p \\cdot g) = \\pi(p) \\cdot \\xi(g).$$\nNow let $g$ be a metric on $M$. The metric induces a reduction of the structure group of $P$ to the oriented orthonormal frame bundle $P_{\\operatorname{SO}(n)}$. The group $\\operatorname{Spin}(n) = \\xi^{-1}(\\operatorname{SO}(n))$ is called the spin group. Thus the structure group of $\\tilde{P}$ reduces to $\\operatorname{Spin}(n)$ and we call this bundle $P_{\\operatorname{Spin}(n)}$, which double covers $P_{\\operatorname{SO}(n)}$.\nNow we define the (complex) spinor bundle as the associated vector bundle\n$$\\Sigma_g M = P_{\\operatorname{Spin}(n)} \\times_{\\Delta_n} \\Sigma_n$$\nwhere $\\Delta_n : \\operatorname{Spin}(n) \\to \\operatorname{End}(\\Sigma_n)$, $\\Sigma_n = \\mathbb C^{2^{[n\/2]}}$, is the standard complex spin representation. Up to scaling, there exists one $\\operatorname{Spin}(n)$ invariant Hermitian product on $\\Sigma_n$. This turns $\\Sigma_g M$ into a Hermitian bundle.\nThe universal spinor bundle gives us a way to compare spinors over different metrics.\nRecalling that\n$$\\faktor{\\operatorname{GL}_+(n)}{\\operatorname{SO}(n)} \\cong \\odot^2_+ \\mathbb R^n,$$\nwe conclude\n$$\\odot^2_+ T^*M = P \\times_{\\operatorname{GL}_+(n)} \\faktor{\\operatorname{GL}_+(n)}{\\operatorname{SO}(n)} = \\tilde{P} \\times_{\\widetilde{\\operatorname{GL}}_+(n)} \\faktor{\\widetilde{\\operatorname{GL}}_+(n)}{\\operatorname{Spin}(n)} = \\faktor{\\tilde{P}}{\\operatorname{Spin}(n)}.$$\nWe define\n$$\\Sigma M = \\tilde{P} \\times_{\\Delta_n} \\Sigma_n.$$\nThis is a vector bundle over $\\odot^2_+ T^*M$, i.e. we have the structure of two nested fibrations:\n$$\\Sigma M \\xrightarrow{\\pi_{\\Sigma}} \\odot^2_+ T^*M \\xrightarrow{\\pi_{\\mathcal{M}}} M.$$\nGiven a metric $g$ we can identify $\\pi_{\\Sigma}^{-1}(g)$ and $\\Sigma_g M$. Using this identification any element $\\Phi \\in\\Sigma M$ can be considered as a pair of a metric $g_{\\Phi} = \\pi_{\\Sigma}(\\Phi)$ and a spinor $\\varphi_{\\Phi} \\in \\Sigma_{g_{\\Phi}} M$.\nAs above we also get a Hermitian inner product $h$ on $\\Sigma M$.\nWe denote by $\\langle \\cdot, \\cdot \\rangle = \\operatorname{Re} h$ the real part of $h$ and $| \\cdot |$ the associated norm.\nNow the definition\n$$\\mathcal{N} = \\{\\Phi \\in \\Gamma(\\Sigma M) : |\\Phi| = 1\\}$$\nfrom the introduction is fully explained. To make sense of the gradient of $\\mathcal{E}$ we need to compute the tangent spaces of $\\mathcal{N}$. For this we need to compare spinors in different fibers $\\Sigma_{g_1} M$ and $\\Sigma_{g_2} M$. This can be done using the Bourgignon--Gauduchon connection.\n\nSuppose we have a vector space $V$ and two inner products $\\langle \\cdot, \\cdot \\rangle_1$ and $\\langle \\cdot, \\cdot \\rangle_2$. Then there exists a unique endomorphism $A^2_1 : V \\to V$, such that\n$$\\langle v, w \\rangle_2 = \\langle A_1^2 v, w \\rangle_1 \\text{ for all } v,w \\in V.$$\nDenote by $B_1^2$ the square root of $A_1^2$. The operator $B_1^2$ maps orthonormal bases of $(V, \\langle \\cdot, \\cdot \\rangle_1)$ to orthonormal bases of $(V, \\langle \\cdot, \\cdot \\rangle_2)$. Since no choices are involved and $B_1^2$ depends smoothly on the inner products, we can transfer this construction to Riemannian manifolds $(M,g_i)$, $i=1,2$, and consequently obtain a smooth principal bundle isomorphism\n$$P^{g_2}_{\\operatorname{SO}(n)} \\to P^{g_1}_{\\operatorname{SO}(n)}.$$\nThis map lifts to the spinor bundle and hence induces a isomorphism $\\hat{B}^{g_2}_{g_1}:\\Sigma_{g_2} M \\to \\Sigma_{g_1} M$.\nSince the metric on $\\Sigma_n$ is $\\operatorname{Spin}(n)$-invariant, this is an isometry.\nNotice that the restriction of $\\hat{B}_{g_1}^{g_2}$ to a fibre over a point $x \\in M$ only depends on the scalar products $g_1(x), g_2(x)$ on $T_xM$.\nWe now have a canonical isometry between two spinor bundles over the same manifold with two distinct metrics. From this we can derive a horizontal distribution\n$$\\mathcal{H}_{\\Phi} = \\left\\{ \\frac{d}{dt}\\Bigr|_{t=0} \\hat{B}^{g}_{g_t} \\varphi \\; \\Big| \\; g: (-\\epsilon, \\epsilon) \\to \\odot^2_+ T_x^*M, g(0) = g \\right\\} \\subset T_{\\Phi} \\Sigma M $$\nwhere $\\Phi = (g, \\varphi) \\in \\Sigma M_x$. By construction, $\\mathcal{H}_{\\Phi} \\cong \\odot^2 T_x^*M$.\nThis distribution yields a splitting of the tangent bundle\n$$T_{\\Phi} \\Sigma M_x = \\mathcal{H}_{\\Phi} \\oplus \\Sigma_g M \\cong \\odot^2 T_x^*M \\oplus \\Sigma_g M.$$\nTurning to sections of the universal spinor bundle, this implies that for $\\Phi = (g, \\varphi) \\in \\Gamma (\\Sigma M)$ we have the splitting\n$$T_{\\Phi} \\Gamma(\\Sigma M) = \\Gamma(\\odot^2 T^* M) \\oplus \\Gamma( \\Sigma_g M)$$\nand if $\\Phi \\in \\mathcal{N}$\n$$T_{\\Phi} \\mathcal{N} = \\Gamma(\\odot^2 T^* M) \\oplus \\Phi^{\\perp},$$\nwhere\n$$\\Phi^{\\perp} = \\{ \\psi \\in \\Gamma(\\Sigma_g M) : \\langle \\varphi, \\psi\\rangle \\equiv 0 \\}.$$\nNow we define for $\\Phi \\in \\Gamma (\\Sigma M)$ and $\\Psi_1, \\Psi_2 \\in T_{\\Phi} \\Gamma( \\Sigma M)$\n$$\\left( \\Psi_1, \\Psi_2 \\right)_{L^2} = \\int_M g(h_1, h_2) \\operatorname{vol}_g + \\int_M \\langle \\psi_1, \\psi_2 \\rangle_{\\Sigma_g M} \\operatorname{vol}_g$$\nwhere $(h_i, \\psi_i) \\in \\Gamma(\\odot^2_+ T^*M) \\oplus \\Gamma (\\Sigma_g M)$ are the sections corresponding to $\\Psi_i$ according to the isomorphisms above. From now on we will use these isomorphisms implicitly.\nNow the negative gradient\n$$Q: \\mathcal{N} \\to T\\mathcal{N}$$\nis defined by the property\n$$\\left(Q(\\Phi), \\Psi \\right) = - \\frac{d}{dt}\\Bigr|_{t=0} \\mathcal{E}(B^g_{g+th} (\\varphi + t \\psi)),$$\nwhere $\\Phi = (g, \\varphi) \\in \\mathcal{N}$ and $\\Psi = (h, \\psi) \\in T_{\\Phi} \\mathcal{N}$.\n\n\\section{Diffeomorphism invariance, the gauged spinor flow and a slice theorem}\nWe denote by $\\operatorname{Diff}_s(M)$ the group of spin diffeomorphisms, i.e. the orientation preserving diffeomorphisms of $M$, which lift to $\\tilde{P}$. To be more precise, by a lift of a orientation preserving diffeomorphism $f: M \\to M$ to $\\tilde{P}$, we mean a lift of the map\n$$ P_x \\ni [e_1, ..., e_n] \\mapsto [Df e_1,..., Df e_n] \\in P_{f(x)}$$\ninduced by $f$ on the oriented frame bundle $P$ to the topological spin bundle $\\tilde{P}$.\nSince $\\tilde{P}$ is a $\\mathbb Z_2$ bundle over $P$, there is a choice of lift and the group of lifts of spin diffeomorphisms $\\widehat{\\operatorname{Diff}}_s(M)$ fits into an exact sequence\n$$0 \\to \\mathbb Z_2 \\to \\widehat{\\operatorname{Diff}}_s(M) \\to \\operatorname{Diff}_s(M) \\to 0.$$\nThe group $\\widehat{\\operatorname{Diff}}_s(M)$ acts on $\\Gamma( \\Sigma M)$ in the following way. Let $F \\in \\widehat{\\operatorname{Diff}}_s(M)$, $\\Phi = (g, \\varphi) \\in \\Gamma(\\Sigma M)$. The map $F : \\tilde{P} \\to \\tilde{P}$ is a lift of a diffeomorphism $f: M \\to M$. Restricting $\\tilde{P}$ to $P_{\\operatorname{Spin}(n)}^g$ we obtain an isomorphism\n$$F : P_{\\operatorname{Spin}(n)}^g \\to P^{f_* g}_{\\operatorname{Spin}(n)}.$$\nThen we define locally\n$$F_*\\varphi = [F \\circ b \\circ f^{-1}, \\varphi \\circ f^{-1}] \\in \\Gamma(\\Sigma_{f_* g} M),$$\nif $\\varphi = [b, \\varphi]$, $b$ a local section of $P_{\\operatorname{Spin}(n)}^g$, $\\varphi$ a $\\Sigma_n$ field. The push forward preserves the metric in the following sense:\n$$|F_* \\varphi|_{f_* g} (x) = |\\varphi|_g (f^{-1}(x)).$$\nIn particular $F_*$ preserves $\\mathcal{N}$. Moreover, we have\n$$\\mathcal{E}(F_* \\Phi) = \\mathcal{E}(\\Phi),$$\n$$Q(F_* \\Phi) = F_* Q(\\Phi).$$\nIn particular, the spinor flow is not strongly parabolic, since $Q$ is invariant under an infinite dimensional group. This invariance is reflected on the infinitesimal level by the following Bianchi-type identity\n$$\\lambda_{g,\\varphi} Q(g,\\varphi) = 0$$\nwhere\n$$\\lambda_{g, \\varphi} : \\Gamma(\\odot^2 T^*M) \\oplus \\Gamma(\\Sigma_g M) \\to \\Gamma(TM)$$\nis defined as the formal adjoint of\n$$\\lambda_{g,\\varphi}^* : \\Gamma(TM) \\to \\Gamma(\\odot^2 T^*M) \\oplus \\Gamma(\\Sigma_g M)$$\n$$X \\mapsto \\left(2 \\delta^*_g X^{\\flat}, \\nabla^g_X \\varphi - \\frac{1}{4} dX^{\\flat} \\cdot \\varphi\\right) = \\left( \\mathcal{L}_X g, \\tilde{\\mathcal{L}}_X \\varphi\\right) =: \\tilde{\\mathcal{L}}_X \\Phi.$$\nIndeed, the tangent space of the orbit $\\widehat{\\operatorname{Diff}}_s(M).(g,\\varphi)$ is the image of $\\lambda_{g,\\varphi}^*$.\nAt a critical point $(g,\\varphi) \\in \\Gamma( \\Sigma M)$, we get the following exact sequence\n$$0 \\to \\Gamma(TM) \\xrightarrow{\\lambda_{g, \\varphi}^*} \\Gamma(\\odot^2 T^* M) \\oplus \\Gamma(\\varphi^{\\perp}) \\xrightarrow{L_{g,\\varphi}} \\Gamma(\\odot^2 T^* M) \\oplus \\Gamma(\\varphi^{\\perp}) \\xrightarrow{\\lambda_{g, \\varphi}} \\Gamma(TM) \\to 0,$$\nwhere $L_{g,\\varphi} = DQ (g,\\varphi)$. It turns out that with\n$$X_{\\bar{g}} : \\Gamma(\\odot^2 T^*M) \\to \\Gamma(TM)$$\n$$g \\mapsto -2 (\\delta_{\\bar{g}} g)^{\\sharp}$$\nand $\\bar{g}$ any given metric the operator\n$$\\tilde{Q}_{\\bar{g}}(\\Phi) = Q(\\Phi) + \\lambda^*_{g,\\varphi}(X_{\\bar{g}}(\\Phi))$$\nis strongly parabolic for any $\\Phi = (\\bar{g}, \\varphi) \\in \\mathcal{N}_{\\bar{g}}$ and hence the flow\n$$\\partial_t \\Phi_t = \\tilde{Q}(\\Phi_t)$$\nexists for short time. We call this flow {\\em gauged spinor flow} or {\\em spinor-DeTurck flow}.\nMoreover, the spinor flow and the gauged spinor flow differ only by a family of diffeomorphisms, i.e.\nif $\\Phi_t = (g_t, \\varphi_t)$ is a solution of the spinor flow and $\\tilde{\\Phi}_t = (\\tilde{g}_t, \\tilde{\\varphi}_t)$ is a solution of the gauged spinor flow with $\\Phi_0 = \\tilde{\\Phi}_0$, then there exists a family $F_t \\in \\widehat{\\operatorname{Diff}}_s(M)$, induced by $f_t \\in \\operatorname{Diff}(M)$, such that\n$$\\tilde{\\Phi}_t = F_{t*} \\Phi_t.$$\nThis family obeys the partial differential equation\n$$\\partial_t f_t = P_{g_t, \\bar{g}}(f_t)$$\nwith initial condition $f_0 = \\operatorname{id}_M$, where\n$$P_{g,\\bar{g}} : \\mathcal{C}^{\\infty}(M,M) \\to T \\mathcal{C}^{\\infty}(M,M)$$\n$$f \\mapsto -df (X_{f^* \\bar{g}} (g)).$$\nFor future reference we note that the linearization of $P_{\\bar{g},\\bar{g}}$ at $\\operatorname{id}_M$ is given by\n$$\\Gamma(TM) \\ni X \\mapsto -4 (\\delta_{\\bar{g}} \\delta^*_{\\bar{g}} X^{\\flat})^{\\sharp} \\in \\Gamma(TM).$$\nBecause\n$$T_{\\Phi} \\Gamma(\\Sigma M) = \\ker \\lambda_{\\Phi} \\oplus \\operatorname{im} \\lambda_{\\Phi}^* = \\ker \\lambda_{\\Phi} \\oplus T_{\\Phi} \\widehat{\\operatorname{Diff}}_s(M).\\Phi,$$\nwe can consider $\\ker \\lambda_{\\Phi}$ to be an infinitesimal slice to the diffeomorphism action.\n Indeed, we will prove that, in a weak sense, $\\ker \\lambda_{g,\\varphi}$ parametrizes a slice in a simple way. To see this we first need a parametrization of $\\Gamma(\\Sigma M)$ by the set $T_{(g,\\varphi)} \\Gamma(\\Sigma M) = \\Gamma(\\odot^2 T^*M) \\oplus \\Gamma(\\Sigma_g M)$ near $(g,\\varphi)$. This will be frequently useful and throughout the rest of the article $\\Xi = \\Xi_{g,\\varphi}$ denotes this parametrization.\nWe define\n$$\\Xi_{g,\\varphi}: (U_g \\subset \\Gamma(\\odot^2 T^*M)) \\times \\Gamma(\\Sigma_g M) \\to \\Gamma(\\Sigma M)$$\n$$(h, \\psi) \\mapsto (g+h, \\hat{B}^g_{g+h}(\\varphi + \\psi))$$\nand its inverse\n$$\\Xi^{-1}: \\Gamma(\\Sigma M) \\to U_g \\times \\Gamma(\\Sigma_g M)$$\n$$(g', \\varphi') \\mapsto (g'-g, \\hat{B}^{g'}_g (\\varphi') - \\varphi).$$\nHere $U_g = \\{h \\in \\Gamma(\\odot^2 T^*M) : g+h \\text{ is a metric}\\}$.\nIn terms of this parametrization we can formulate the following slice theorem:\n\\begin{prop}\nLet $\\Phi = (g,\\varphi) \\in \\Gamma(\\Sigma M)$ and assume $g$ has no Killing fields. Then there exists a $C^{k+1,\\alpha}$ neighborhood $U$ of $\\Phi$, such that for any $\\tilde{\\Phi} \\in U$, there exists a $C^{k+2,\\alpha}$ diffeomorphism $f: M\\to M$, such that\n$$\\lambda_{\\Phi}(\\Xi^{-1}(F^* \\tilde{\\Phi})) = 0.$$\n\\end{prop}\n\\begin{proof}\nWe base the proof on \\cite{Viaclovsky13}, theorem 3.6.\nConsider the map\n$$G: \\Gamma^{k+1, \\alpha}(\\odot^2 T^*M \\oplus \\Sigma_g M) \\times \\Gamma^{k+2, \\alpha}(TM) \\to \\Gamma^{k,\\alpha}(TM)$$\n$$((h, \\psi), X) \\mapsto \\lambda_{\\Phi}(\\phi^{X*}_1(g+h, B^g_{g+h}(\\varphi + \\psi)))$$\nThen the derivative of $G$ at $((0,0), 0)$ in $X$ direction is given by\n$$\\frac{d}{dt}\\Bigr|_{t=0} \\lambda_{\\Phi}(\\phi^{tV*}_1 \\Phi) = \\lambda_{\\Phi}(\\tilde{\\mathcal{L}}_V \\Phi) = \\lambda_{\\Phi} \\lambda_{\\Phi}^* V$$\nfor $V \\in \\Gamma^{k+2, \\alpha}(TM)$. Since $g$ posesses no Killing fields, $\\lambda_{\\Phi} \\lambda_{\\Phi}^*$ is injective, because in the first component $\\lambda_{\\Phi} \\lambda_{\\Phi}^* X$ is just $\\delta_g \\delta_g^* X^{\\flat}$. Additionally, $\\lambda_{\\Phi} \\lambda_{\\Phi}^*$ is an elliptic operator. It is selfadjoint and hence it must also be surjective. Thus we may apply the implicit function theorem and we find that there exists a neighborhood $U \\subset \\Gamma^{k+1, \\alpha}(\\odot^2 T^*M \\oplus \\Sigma_g M)$ of $(0,0)$ and a map $H: U \\to \\Gamma^{k+2, \\alpha}(TM)$, such that $G((h,\\psi), H(h,\\psi)) = 0$.\nNow let $\\tilde{\\Phi} \\in \\Xi(U)$.\nThen denote by $f$ the time-$1$ map of the vector field $H(\\Xi^{-1}(\\tilde{\\Phi}))$. Then\n$$\\lambda_{\\Phi}(F^*(\\Xi^{-1}(\\tilde{\\Phi}))) = 0$$\nby construction.\nThe statement then follows, because\n$$F^*(\\Xi^{-1}(\\tilde{\\Phi})) = \\Xi^{-1}(F^*(\\tilde{\\Phi})).$$\n\\end{proof}\n\n\n\\section{Volume normalized spinor flow}\nVolume constrained critical points evolve by rescaling under the spinor flow. We expect similar behavior near such a point. To address convergence questions in this situation, it is thus useful to rescale the solutions to a fixed volume. In this section, we introduce the volume normalized spinor flow and describe its evolution equation.\nLet $\\Phi_t = (g_t, \\phi_t)$ be a solution to the spinor flow. We denote by $\\mu(t)$ the normalizing factor $\\left(\\int_M \\operatorname{vol}_{g_t}\\right)^{-2\/n}$. Then $\\int_M \\operatorname{vol}_{\\mu(t) g_t} = 1$.\nNow let $\\tilde{\\Phi}(t) = (\\tilde{g}(t), \\tilde{\\varphi}(t))$, where\n$$\\tilde{g}(t) = \\mu (\\tau(t)) g_{\\tau(t)},$$\n$$\\tilde{\\varphi}(t) = \\hat{B}^{g_{\\tau(t)}}_{\\mu(\\tau(t)) g_{\\tau(t)}} (\\varphi_{\\tau(t)}),$$\nwhere $\\tau: I \\subset \\mathbb R \\to J \\subset \\mathbb R$ is some time reparametrization.\nThen we have\n$$\\partial_t \\tilde{g}_t = \\dot{\\mu}(\\tau(t)) \\tau'(t) g_{\\tau(t)} + \\mu(\\tau(t)) \\dot{g}_{\\tau(t)} \\tau'(t),$$\n$$\\partial_t \\tilde{\\varphi}_t = \\hat{B}^{g_{\\tau(t)}}_{\\mu(\\tau(t)) g_{\\tau(t)}} (\\dot{\\varphi}_{\\tau(t)}) \\tau'(t).$$\nSolving a separable ordinary differential equation, we can arrange $\\tau'(t) \\mu(\\tau(t)) = 1$.\nWe call $\\tilde{\\Phi}_t$ with this choice of time rescaling the {\\em volume normalized spinor flow}.\nFor any $h\\in \\Gamma(\\odot^2 T^*M)$, we denote by $\\mathring{h}$ the tensor \n$$h - \\frac{\\int_M \\operatorname{tr}_g h \\operatorname{vol}_g}{n \\int_M \\operatorname{vol}_g} g.$$\nSince $\\tilde{g}_t$ has constant volume $1$, it follows that $\\int_M \\partial_t g_t \\operatorname{vol}_{g_t} = 0$. \nThus we have\n$$\\partial_t \\tilde{g}_t = \\mathring{Q}_1(g_{\\tau(t)}, \\varphi_{\\tau(t)}).$$\nBy corollary 4.5 in \\cite{Ammann2015}, we moreover have $Q_1(c^2 g, \\hat{B}^g_{c^2 g}(\\varphi)) = Q_1(g, \\varphi)$, which implies\n$$\\partial_t \\tilde{g}_t = \\mathring{Q}_1(g_{\\tau(t)}, \\varphi_{\\tau(t)}) = \\mathring{Q}_1\\left(\\mu (\\tau(t)) g_{\\tau(t)}, \\hat{B}^{g_{\\tau(t)}}_{\\mu(\\tau(t)) g_{\\tau(t)}} (\\varphi_{\\tau(t)})\\right) = \\mathring{Q}_1(\\tilde{\\Phi}_t).$$\nAgain by corollary 4.5 in op. cit., we have $Q_2(c^2 g, \\hat{B}^g_{c^2 g} (\\varphi)) = c^{-2} \\hat{B}^g_{c^2 g} Q_2(g, \\varphi)$. Thus\n$$\\partial_t \\tilde{\\varphi}_t = \\mu(t)^{-1} \\hat{B}^{g_{\\tau(t)}}_{\\mu(\\tau(t)) g_{\\tau(t)}} (Q_2(g_{\\tau(t)}, \\varphi_{\\tau(t)})) = Q_2(\\tilde{\\Phi}_t).$$\nWe define\n$$\\mathring{Q}(\\Phi) = (\\mathring{Q}_1(\\Phi), Q_2(\\Phi))$$\nand can rewrite the evolution of $\\tilde{\\Phi}_t$ as\n$$\\partial_t \\tilde{\\Phi}_t = \\mathring{Q}(\\tilde{\\Phi}_t).$$\nSince $\\mathring{Q}$ is the negative gradient of $\\mathcal{E}$ restricted to the set\n$$\\mathcal{N}^1 = \\left\\{ \\Phi = (g, \\varphi) \\in \\mathcal{N} : \\int_M \\operatorname{vol}_g = 1\\right\\},$$\nwe conclude that the volume normalized spinor flow coincides with the negative gradient flow of $\\mathcal{E}$ restricted to $\\mathcal{N}^1$.\n\n\\section{Analytical setup}\nIn the following proof of stability we will analyze three flows: the spinor flow, the gauged spinor flow and the mapping flow. Each of these flows is defined on an infinite dimensional manifold rather than a vector space and we feel it is appropiate to clarify our analytic setup, so that we can proceed in a somewhat more formal manner later on without bypassing rigor altogeher. \n\nThe set of unit spinors $\\mathcal{N}$ forms a Fr\u00e9chet manifold with the $\\mathcal{C}^{\\infty}$ topology. We will however never use this topology directly. Instead, we will typically restrict to a chart and work with the Sobolev or $C^{k,\\alpha}$ topologies. We do this as follows.\nFix $\\Phi_0 = (g_0, \\varphi_0) \\in \\Gamma(\\Sigma M)$. We already constructed the chart \n$$\\Xi^{-1}_{\\Phi_0} : U \\subset \\Gamma(\\Sigma M) \\to V \\subset \\Gamma(\\odot^2 T^*M) \\oplus \\Gamma(\\Sigma_{g_0} M).$$\nThe metric $g_0$ then induces the usual $H^k$ and $C^{k,\\alpha}$ norms on $\\Gamma(\\odot^2 T^*M) \\oplus \\Gamma(\\Sigma_g M)$ and we simply pull them back via the chart.\nLocally we can now consider the spinor energy $\\mathcal{E}$ as a map $V \\to \\mathbb R$ and $Q$ as a map $V \\to V$.\nWhenever we use a $C^k$ or $H^s$ norm we implicitly use this construction. In particular, when we write $\\|\\Phi - \\Phi_0\\|_{X}$ for a fixed $\\Phi_0$ and a nearby $\\Phi$, we mean $\\|\\Xi^{-1}_{\\Phi_0} (\\Phi)\\|_{X}$, where $X$ is one of the discussed Banach spaces.\n\nFor the mapping flow we proceed in a similar manner. Note first that for $f_0 \\in \\mathcal{C}^{\\infty}(M,M)$, there is a local chart around $f_0$ given by\n$$U \\subset \\mathcal{C}^{\\infty}(M,M) \\to V \\subset \\Gamma(f_0^* TM)$$\n$$f \\mapsto (x \\mapsto (\\exp_{f_0(x)})^{-1}(f(x))),$$\nwhere $\\exp$ is the exponential map of some Riemannian metric on $g$ and $V$ is a neighborhood of the $0$ section in $TM$, such that $exp_x$ is a diffeomorphism from $V_x = T_x M \\cap V$ to $\\exp (V_x)$ for every $x \\in M$. \nThen we define\n$$U = \\{f: M\\to M \\Big| (f_0,f)(M) \\subset \\exp(V)\\}.$$\nWe can define appropiate norms in the standard manner using some Riemannian metric on $M$, for example\n$$\\left(X,Y\\right)_{L^2} = \\int_M g_{f_0(p)}(X(p), Y(p)) \\operatorname{vol}_g$$\nfor $X,Y \\in \\Gamma(f_0^* TM)$.\n\nFor future reference we also quote a standard parabolic estimate and prove an interior estimate following from this.\n\\begin{thm}\nSuppose $A_t$ is an elliptic differential operator of order $m$, uniformly elliptic in $t$, with $\\mathcal{C}^{\\infty}$ coefficients in $x$ and $t$.\nThen for any $s \\in \\mathbb R$ and $T > 0$, there exists $C > 0$ such that\n$$\\|u_t\\|_{H^s}^2 + \\int_0^T \\|u_{t'}\\|_{H^{s+m'}}^2 dt' \\leq C \\left( \\|u_0\\|_{H^s}^2 + \\int_0^T \\|\\partial_t u_{t'} - A_{t'} u_{t'}\\|^2_{H^{s-m'}} dt' \\right)$$\nfor any $t \\in [0, T]$ and $u \\in C^1([0,T], H^s) \\cap C^0([0,T], H^{s+m'})$, where $m' = m\/2$.\n\\end{thm}\nFor a proof, see 6.5.2 in \\cite{Chazarain1982}.\nWe will need the following estimate for solutions, derived from this inequality:\n\\begin{cor}\n\\label{PE}\nFor any $\\delta > 0$ and any \n $A_t$ as above, there exists $C, \\tilde{C}>0$, such that for any $u_t$ a solution of\n$$\\partial_t u_t = A_t u_t,$$\nwe have\n$$\\int_{\\delta}^T \\|u_{\\tau}\\|_{H^r}^2d \\tau \\leq C \\int_0^T \\|u_{\\tau}\\|_{H^{s}}^2 d\\tau,$$\nas well as\n$$\\|u_{t}\\|_{H^r}^2 \\leq \\tilde{C} \\int_0^T \\|u_{\\tau}\\|_{H^{s}}^2 d\\tau$$\nfor any $r,s \\in \\mathbb R$ and any $t \\in [\\delta, T]$.\n\\end{cor}\n\\begin{proof}\nFor $r < s$ the inequality is trivial.\nFor $r > s$ the claim follows inductively from\n$$\\int_{\\delta}^T \\|u_{\\tau}\\|^2_{H^{s+m'}} d\\tau \\leq C \\int_0^T \\|u_{\\tau}\\|_{H^{s-m'}}^2 d\\tau.$$\nFor this consider $f:[0,T] \\to [0,1]$ smooth such that $f(0) = 0, f(\\delta) = 1$.\nThen\n$$\\partial_t (f(t) u_t) - A_t u_t = (\\partial_t f(t)) u_t.$$\nHence the above estimate yields\n$$\\int_{\\delta}^T \\|u_{\\tau}\\|_{s+m'}^2 d\\tau \\leq C \\int_0^T \\|u_{\\tau}\\|_{s-m'}^2 d\\tau,$$\nwhere $C = \\max |\\partial_t f|$.\n\nWe have shown that\n$$\\int_{\\delta}^T \\|u_{\\tau}\\|_{H^r}^2 d\\tau \\leq \\tilde{C} \\int_0^T \\|u_{\\tau}\\|_{H^s}^2 d\\tau.$$\nSince $\\partial_t u_t = A_t u_t$ and $u_t$ is a differential operator of order $m$ this implies\n$$\\int_{\\delta}^T \\|\\partial_{\\tau} u_{\\tau}\\|^2_{H^{r-m}} d\\tau \\leq \\tilde{C} \\int_0^T \\|u_{\\tau}\\|^2_{H^s} d\\tau$$\nand hence by the Sobolev embedding $W^{1,2}([a,b]; H^{l+1}, H^l) \\hookrightarrow C^0([a,b]; H^l)$ (cf. \\cite{Cherrier12}, Theorem 1.7.4 and (1.7.62)) we conclude\n$$\\|u_t\\|_{H^{r-m}} \\leq \\hat{C} \\int_0^T \\|u_{\\tau}\\|^2_{H^s} d\\tau.$$\n(Here\n$$W^{1,2}([a,b]; H^{l+1}, H^l) = L^2([a,b]; H^{l+1}) \\cap \\{u: [a,b] \\to H^l : \\partial_t u \\in L^2([a,b]; H^l) \\}$$\nwith the obvious norm.)\n\\end{proof}\n\n\n\\section{The \u0141ojasiewicz inequality and gradient estimates}\nThe \u0141ojasiewicz inequality relates the norm of the gradient of a differentiable function to its value near a critical point in a way that allows us to show convergence of the gradient flow. There are two situations when \u0141ojasiewicz inequalities are known to hold. The optimal situation is when the function is a Morse function or less restrictively a Morse--Bott function. Then we have\n$$|f(x) - f(x_0)| \\leq C \\|\\operatorname{grad} f(x)\\|^2$$\nfor $x_0$ a critical point of $f$ and some constant $C > 0$. This can be easily seen by applying the Morse--Bott lemma: near a critical manifold we may write a Morse--Bott function as\n$$f(x_1, ..., x_n) = c + x_1^2 + ... + x_r^2 - x_{r+1}^2 - ... - x_s^2,$$\nwhere $(x_1, ..., x_n)$ are coordinates with $x_0$ at the origin and critical manifold $\\{x_{s+1} = ... = x_{n} = 0\\}$.\nBecause in a small neighborhood the Riemannian metric is very close to being Euclidean, we get the inequality\n$$|f(x) - c| \\leq C |\\operatorname{grad} f(x)|^2$$\nfor some $C > 0$.\nThe other case is that $f$ is analytic. Then there exists $\\theta \\in (1,2)$, such that\n$$|f(x) - f(x_0)| \\leq \\|\\operatorname{grad} f(x)\\|^{\\theta}.$$\nWe will make use of both versions. The inequality for analytic functions is a difficult theorem in the theory of semianalytic sets, due to \u0141ojasiewicz. The first version will be employed to demonstrate stability of parallel spinors, since there we know $\\mathcal{E}$ to be Morse--Bott. For volume constrained critical points we do not know this and instead use the weaker inequality for analytic functions.\nBoth inequalities are known in this general form only for functions on finite dimensional domains. We will spend most of the rest of the section justifying these inequalities for the spinor energy functional.\n\n\\begin{prop}[Optimal \u0141ojasiewicz inequality for parallel spinors]\n\\label{LIP}\nLet $\\bar{\\Phi}$ be a critical point of $\\mathcal{E}$. (Hence $\\bar{\\Phi}$ is an absolute minimiser with $\\mathcal{E}(\\bar{\\Phi}) = 0$.) Then there exists a $C^{2,\\alpha}$ neighborhood $U$ of $\\bar{\\Phi}$ and some constant $C>0$, such that for any $\\Phi \\in U$ we have\n$$\\mathcal{E}(\\Phi) \\leq C \\|Q(\\Phi)\\|_{L^2}^2.$$\n\\end{prop}\n\n\\begin{prop}[\u0141ojasiewicz inequality for volume constrained critical points]\n\\label{LIVC}\nLet $\\bar{\\Phi} = (\\bar{g},\\bar{\\varphi})$ be a volume constrained critical point of $\\mathcal{E}$. Then there exists a $C^{2,\\alpha}$ neighborhood $U$ of $\\bar{\\Phi}$ and some constant $\\theta \\in (1,2)$, such that for any $\\Phi=(g,\\varphi)$ with $\\int_M \\operatorname{vol}_g = \\int_M \\operatorname{vol}_{\\bar{g}}$ we have\n$$|\\mathcal{E}(\\Phi) - \\mathcal{E}(\\bar{\\Phi})| \\leq \\|\\mathring{Q}(\\Phi)\\|_{L^2}^{\\theta}.$$\nIf the set of volume constrained critical points near $\\bar{\\Phi}$ is a manifold, this can be improved to\n$$|\\mathcal{E}(\\Phi) - \\mathcal{E}(\\bar{\\Phi})| \\leq C \\|\\mathring{Q}(\\Phi)\\|_{L^2}^2.$$\n\\end{prop}\nThe proofs of both propositions rely on the following infinite-dimensional form of the \u0141ojasiewicz inequality, due to Colding and Minicozzi II, see \\cite{Colding2013}.\n\\begin{thm}\n\\label{CML}\n\\begin{enumerate}\n\\item Suppose $E \\subset L^2$ is a closed subspace, $U$ is an open neighborhood of $0$ in $C^{2, \\beta} \\cap E$.\n\\item Suppose $G: U \\to \\mathbb R$ is an analytic function or that there is a neighborhood $V$ of $0$, such that $\\{x \\in V : \\operatorname{grad} G (x) = 0\\}$ is a finite dimensional submanifold.\n\\item Suppose the gradient $\\operatorname{grad} G : U \\to C^{\\beta} \\cap E$ is $C^1$, $\\operatorname{grad} G (0) = 0$ and\n$$\\|\\operatorname{grad} G(x) - \\operatorname{grad} G(y) \\|_{L^2} \\leq C \\|x - y\\|_{H^2}$$\n\\item $L = D \\operatorname{grad} G (0)$ is symmetric, bounded from $C^{2, \\beta} \\cap E$ to $C^{\\beta} \\cap E$ and from $H^2 \\cap E$ to $L^2 \\cap E$ and Fredholm from $C^{2, \\beta} \\cap E$ to $C^{\\beta} \\cap E$.\n\\end{enumerate}\nThen there exists $\\theta \\in (1,2)$ so that for all $x\\in E$ sufficiently small\n$$|G(x) - G(0)| \\leq \\|\\operatorname{grad} G(x)\\|_{L^2}^\\theta$$\nIf there is a neighborhood $V$ of $0$, such that $\\{x \\in V : \\operatorname{grad} G (x) = 0\\}$ is a finite dimensional submanifold, we get the stronger inequality\n$$|G(x) - G(0)| \\leq C \\|\\operatorname{grad} G(x)\\|_{L^2}^2$$\nfor some $C>0$.\n\\end{thm}\n{\\em Remark.} Colding and Minicozzi II prove this for $G$ analytic. The alternative condition we give is essentially that $G$ is Morse--Bott at $0$. The proof in that case is the same except that when the finite dimensional \u0141ojasiewicz inequality is used, we instead invoke the stronger inequality for Morse--Bott functions.\n\nSince this theorem requires the linearisation of the gradient to be Fredholm we will be working on a slice of the spin diffeomorphism group. \n\\begin{lemma}\nLet $\\bar{\\Phi} = (\\bar{g}, \\bar{\\varphi})$ be a critical point. Let $\\iota: \\ker \\lambda_{\\bar{\\Phi}} \\to \\Gamma(\\Sigma M)$ be the inclusion. $f = \\mathcal{E} \\circ \\Xi_{\\bar{\\Phi}} \\circ \\iota$ fulfills the conditions of theorem \\ref{CML}.\nIn particular we have\n$$|f(x)| \\leq C \\|\\operatorname{grad} f(x)\\|_{L^2}^2$$\n\\end{lemma}\n\\begin{proof}\nWe equip $\\Gamma(\\odot^2 T^* M) \\oplus \\Gamma(\\Sigma_g M)$ with the $L^2$ metric induced by $\\bar{g}$, and similarly we define the $C^{2,\\alpha}$ norm in terms of $\\bar{g}$.\nThen clearly $\\mathcal{E} \\circ \\Xi_{\\bar{\\Phi}} \\circ \\iota$ is a smooth function and by \\cite{Ammann2015b} its critical set is smooth, thus the second condition in theorem \\ref{CML} is fulfilled.\nMoreover $0$ corresponds to $\\bar{\\Phi}$ and hence is a critical point, i.e. $\\operatorname{grad} f(0) = 0$.\nThe gradient of $f$ can be considered as a nonlinear second order differential operator. In fact, it is a smooth map \n$$\\operatorname{grad} f: \\Gamma^{2,\\alpha}(\\odot^2 T^*M \\oplus \\Sigma_g M) \\to \\Gamma^{\\alpha}(\\odot^2 T^*M \\oplus \\Sigma_g M).$$\nOn any bounded $C^{2,\\alpha}$ neighborhood $U$ of $0$ we have\n$$\\|\\operatorname{grad} f(x) - \\operatorname{grad} f(y)\\|_{L^2} \\leq C \\|x - y\\|_{H^2}.$$\nThis is a simple consequence of the fact that $Q(g,\\varphi)$ can be locally represented as a polynomial expression\nin the coordinate expressions of $g$ and $\\varphi$ and their first and second derivatives. In a bounded $C^{2,\\alpha}$ neighborhood we then estimate terms as needed to get an expression which is bounded by $\\|(g,\\varphi)\\|_{H^2}$.\nThis concludes the argument for conditions 1,2 and 3.\n\nSince $DQ(\\bar{\\Phi})$ is symmetric (by \\cite{Ammann2015}), so is $L$. Since $L$ is a linear second order differential operator, it induces continuous maps $C^{2,\\alpha} \\to C^{\\alpha}$ and $H^2 \\to L^2$.\nIt remains to be shown that $L$ is Fredholm.\nTo see this, remember that we have a splitting\n$$T_{\\bar{\\Phi}} \\mathcal{N} = \\ker \\lambda_{\\bar{\\Phi}} \\oplus \\operatorname{im} \\lambda^*_{\\bar{\\Phi}}.$$\nWith respect to these operators, we know the two identities\n$$DQ(\\bar{\\Phi}) \\circ \\lambda^*_{\\bar{\\Phi}} = 0 \\text{ and } \\lambda_{\\bar{\\Phi}} \\circ DQ(\\bar{\\Phi}) = 0,$$\nboth of which reflect diffeomorphism invariance of $Q$.\nMoreover, we introduced the perturbed gradient $\\tilde{Q}_{\\bar{\\Phi}}$, which we know is strongly elliptic and thus its linearization is Fredholm. Its linearization is also symmetric.\nThus we conclude that $DQ(\\bar{\\Phi})$ has the form\n$$\\bordermatrix{\n & \\ker \\lambda_{\\bar{\\Phi}} & \\operatorname{im} \\lambda_{\\bar{\\Phi}}^* \\cr\n\\ker \\lambda_{\\bar{\\Phi}} & P & 0 \\cr\n\\operatorname{im} \\lambda_{\\bar{\\Phi}}^* & 0 & 0 \\cr\n},$$\nwhereas $D\\tilde{Q}_{\\bar{\\Phi}}(\\bar{\\Phi})$ has the form \n$$\\bordermatrix{\n & \\ker \\lambda_{\\bar{\\Phi}} & \\operatorname{im} \\lambda_{\\bar{\\Phi}}^* \\cr\n\\ker \\lambda_{\\bar{\\Phi}} & P & 0 \\cr\n\\operatorname{im} \\lambda_{\\bar{\\Phi}}^* & 0 & R \\cr\n}.$$\nSince $D\\tilde{Q}_{\\bar{\\Phi}}(\\bar{\\Phi})$ is Fredholm, so is $P = \\pi \\circ DQ(\\Phi) \\circ \\iota$, where $\\pi: T_{\\bar{\\Phi}} \\mathcal{N} \\to \\ker \\lambda_{\\bar{\\Phi}}$ denotes the orthogonal projection.\nWe compute\n$$D\\operatorname{grad} f (0) = D(\\Xi \\circ \\iota)(0)^* DQ(\\Xi \\circ \\iota(x)) = \\pi \\circ D\\Xi(0)^* DQ(\\bar{\\Phi}).$$\nSince the domain is restricted to $\\ker \\lambda_{\\bar{\\Phi}}$ and $D\\Xi(0) = \\operatorname{id}$, we conclude that\n$$D \\operatorname{grad} f(0) = P,$$\nand hence $L = D\\operatorname{grad} f(0)$ is Fredholm as required. Thus we have checked all conditions in theorem \\ref{CML}, and the inequality holds.\n\\end{proof}\n\n\\begin{proof}[Proof of proposition \\ref{LIP}]\nWhat remains to be shown is that the inequality\n$$|f(x)| \\leq \\|\\operatorname{grad} f(x)\\|_{L^2}^2$$\nimplies the inequality\n$$|\\mathcal{E}(\\Phi)| \\leq C \\|Q(\\Phi)\\|^2_{L^2}.$$\nFirst, by the slice theorem there exists a $C^{k+1,\\alpha}$ neighborhood $U$ of $\\bar{\\Phi}$, such that for any $\\Phi \\in U$ there exists a diffeomorphism $f: M\\to M$, such that\n$$\\lambda_{\\bar{\\Phi}}(\\Xi^{-1}(F_* \\Phi)) = 0.$$\nSince\n$$\\mathcal{E}(F_* \\Phi) = \\mathcal{E}(\\Phi), \\quad F_* Q(\\Phi) = Q(F_* \\Phi)$$\nand since the $L^2$ metric is diffeomorphism invariant, we can assume that $\\Phi$ lies in the slice, i.e. $\\lambda_{\\bar{\\Phi}}(\\Xi^{-1}(\\Phi)) = 0$. Then we have\n$$|\\mathcal{E}(\\Phi)| = f(\\Xi^{-1}(\\Phi)) \\leq \\|\\operatorname{grad} f(\\Xi^{-1}(\\Phi))\\|_{L^2}^2.$$\nHence we must show\n$$\\|\\operatorname{grad} f (\\Xi^{-1}(\\Phi))\\|_{L^2}^2 \\leq \\|Q(\\Phi)\\|_{L^2}.$$\nFirst note that the metric on $\\ker \\lambda_{\\bar{\\Phi}}$ is the metric induced by $\\bar{\\Phi}$. By making the neighborhood smaller if necessary, we can assume that all $L^2$ metrics in that neighborhood are uniformly equivalent. We have\n$$\\operatorname{grad} f(\\Xi^{-1}(\\Phi)) = D(\\Xi \\circ \\iota) (\\Xi^{-1}(\\Phi))^* Q(\\Phi).$$\nSince $D(\\Xi \\circ \\iota)$ is clearly Lipschitz, we obtain our estimate. This concludes the proof of the \u0141ojasiewicz inequality in this case.\n\\end{proof}\n\\begin{proof}[Proof of proposition \\ref{LIVC}]\nFor the purposes of the following discussion, read the spaces of smooth mappings as the spaces of $C^{2,\\alpha}$ mappings, so that they are Banach spaces or Banach manifolds.\nBy the analytic regular value theorem, we can find an analytic parametrization of\n$$\\left\\{ g \\in \\Gamma(\\odot^2_+ T^*M) : \\int_M \\operatorname{vol}_g = 1\\right\\}$$\nby\n$$\\left\\{ h \\in \\Gamma(\\odot^2_+ T^* M) : \\int_M \\operatorname{tr}_g h \\operatorname{vol}_g = 0\\right\\}.$$\n(For a treatment of the implicit function theorem in the analytic category on Banach spaces, take for example \\cite{Hajek14}, theorem 174.)\nWe combine this parametrization with $\\Xi_{g,\\varphi}$ to obtain an analytic parametrization\n$$\\Psi: U \\subset V_0 \\to \\mathcal{N}^1$$\nwhere\n$$V_0 = \\left\\{(h, \\psi) \\in \\ker \\lambda_{g,\\varphi} : \\int_M \\operatorname{tr}_g h \\operatorname{vol}_g = 0\\right\\}$$\nand\n$$\\mathcal{N}^1 = \\left\\{\\Phi = (g,\\varphi) \\in \\mathcal{N} : \\int_M \\operatorname{vol}_g = 1\\right\\}.$$\nDefine $f = \\mathcal{E} \\circ \\iota \\circ \\Psi$, with $\\iota: \\mathcal{N}^1 \\to \\mathcal{N}$ the inclusion.\nThen $f$ fulfills the conditions of theorem \\ref{CML}, which can be shown as in the previous lemma. \nApplying the theorem, we thus obtain\n$$|f(x) - f(0)| \\leq \\|\\operatorname{grad} f (x)\\|_{L^2}^{\\theta},$$\nwhere $\\theta \\in (1,2)$. If the critical set is a manifold near $\\bar{\\Phi}$, we use the optimal version theorem of theorem \\ref{CML} and obtain\n$$|f(x) - f(0)| \\leq C \\|\\operatorname{grad} f(x)\\|_{L^2}^2$$\nfor some $C > 0$.\n What remains to be shown is\n$$\\|\\operatorname{grad} f(\\Psi^{-1}(\\Phi))\\|_{L^2} \\leq C \\|\\mathring{Q}(\\Phi)\\|_{L^2}.$$\nAs in the previous proposition, we compute\n$$\\operatorname{grad} f(\\Psi^{-1}(\\Phi)) = (D\\Psi )^*(\\Psi^{-1}(\\Phi)) \\operatorname{grad} (\\mathcal{E} \\circ \\iota)(\\Phi).$$\nThen the claim follows, since, on the one hand, $D\\Psi$ is Lipschitz by the regular value theorem, and on the other hand\n$$\\operatorname{grad}(\\mathcal{E} \\circ \\iota)(\\Phi) = D\\iota(\\Phi)^* \\operatorname{grad} \\mathcal{E}(\\Phi) = D\\iota(\\Phi)^* Q(\\Phi).$$\nSince $D\\iota(\\Phi)^* : T_{\\Phi} \\mathcal{N} \\to T_{\\Phi} \\mathcal{N}^1$ is the orthogonal projection,\nthis implies\n$$\\operatorname{grad}(\\mathcal{E} \\circ \\iota)(\\Phi) = \\mathring{Q}(\\Phi).$$\n\\end{proof}\n\n\n\\begin{thm}[Energy decay]\n\\label{L2cv}\nSuppose $M$ is a compact manifold.\n\\begin{enumerate}\n\\item Suppose $\\bar{\\Phi}$ is a critical point of $\\mathcal{E}$. Then there exists a $C^{2,\\alpha}$ neigborhood $U$ of $\\bar{\\Phi}$, such that for any $\\Phi \\in U$ the following inequalities hold\n$$\\mathcal{E}(\\Phi_t) \\leq C e^{-\\alpha t},$$\n$$\\int_T^{\\infty} \\|Q(\\Phi_t)\\|^2_{L^2} dt \\leq C e^{-\\alpha T},$$\nand\n$$\\int_T^{\\infty} \\|Q(\\Phi_t)\\|_{L^2} dt \\leq C e^{-\\alpha T},$$\nwhere $C, \\alpha>0$ and $\\Phi_t$ is the solution of\n$$\\partial_t \\Phi_t = Q(\\Phi_t), \\Phi_0 = \\Phi.$$\n\\item Suppose $\\bar{\\Phi}$ is a volume constrained minimizer of $\\mathcal{E}$. Then there exists a $C^{2,\\alpha}$ neighborhood $U$ of $\\bar{\\Phi}$, such that for any $\\Phi \\in U$ it holds\n$$|\\mathcal{E}(\\Phi_t) - \\mathcal{E}(\\bar{\\Phi})| \\leq \\frac{C}{1+T^{\\beta}},$$\n$$\\int_T^{\\infty} \\|\\mathring{Q}(\\Phi_t)\\|^2_{L^2} dt \\leq \\frac{C}{1 + T^{\\beta}}$$\nand\n$$\\int_T^{\\infty} \\|\\mathring{Q}(\\Phi_t)\\|_{L^2} dt \\leq \\frac{C}{1+T^{\\gamma}},$$\nfor some $C, \\beta > 1$. If the set of volume constrained critical sets is a manifold near $\\bar{\\Phi}$, we can instead choose exponential bounds as in the first case. Here we assume $\\Phi_t$ is the volume normalized spinor flow with initial condition $\\Phi_0 = \\Phi \\in U$.\n\\end{enumerate}\nThe integrals are to be read as the integral from $T$ to the maximal time of existence in the neighborhood $U$. The constants $C, \\alpha, \\beta$ only depend on the constants $C$ and $\\theta$ in the \u0141ojasiewicz inequalities.\n\\end{thm}\n{\\em Remark. } The constants $\\beta$ and $\\gamma$ can be computed from the constant $\\theta$ in the \u0141ojasiewicz inequality as $\\beta = \\frac{\\theta }{2 - \\theta}$ and $\\gamma = \\frac{\\theta - 1}{2 - \\theta}$. As $\\theta$ tends to $2$, $\\beta$ tends to infinity, i.e. the convergence rate improves. As $\\theta$ tends to $1$, $\\beta$ tends to $1$, i.e. the convergence rate gets worse. Likewise, $\\gamma$ tends to $\\infty$ if $\\theta$ tends to $2$, but $\\gamma$ tends to $0$ as $\\theta$ tends to $1$. \n\\begin{proof}\nFirst we note that\n$$\\frac{d}{dt} \\mathcal{E}(\\Phi_t) = -\\|Q(\\Phi_t)\\|_{L^2}^2$$\nimplies that the integral of the gradient over all future time is controlled by the energy at a fixed time.\nNow applying the optimal \u0141ojasiewicz inequality, we obtain \n$$\\frac{d}{dt} \\mathcal{E}(\\Phi_t) \\leq -\\frac{1}{C} \\mathcal{E}(\\Phi_t).$$\nIntegrating this differential inequality, we obtain\n$$\\mathcal{E}(\\Phi_t) \\leq \\mathcal{E}(\\Phi_0) e^{-(1\/C) t}.$$\nChoosing the neighborhood so that $\\mathcal{E}$ is bounded, we obtain the desired inequality.\n\nFor the second case consider\n$$\\frac{d}{dt} |\\mathcal{E}(\\Phi_t) - \\mathcal{E}(\\bar{\\Phi})| = -\\|Q(\\Phi_t)\\|_{L^2}^2 \\leq -|\\mathcal{E}(\\Phi_t) - \\mathcal{E}(\\bar{\\Phi})|^{2\/\\theta}.$$\nIntegrating this differential inequality, we obtain\n$$|\\mathcal{E}(\\Phi_t) - \\mathcal{E}(\\bar{\\Phi})| \\leq \\left(\\frac{2}{\\theta} - 1\\right) \\frac{1}{(C + t)^{\\beta}}$$\nwhere $\\beta = \\frac{1}{2\/\\theta - 1}$ and $C = |\\mathcal{E}(\\Phi_0) - \\mathcal{E}(\\bar{\\Phi})|^{1-2\/\\theta}$.\nBy continuity of $\\mathcal{E}$ we can find a lower bound for $C$ on a small neighborhood, and using this lower bound we obtain the desired inequality. The bound for the integral of $\\|\\mathring{Q}(\\Phi_t)\\|_{L^2}$ follows as above.\n\nFor the estimates of $\\int_T^{\\infty} \\|Q(\\Phi_t)\\| dt$, notice that the \u0141ojasiewicz inequality implies $\\mathcal{E}(\\Phi)^{-1\/\\theta} \\geq C \\|Q(\\Phi)\\|^{-1}$. (Here we actually have $\\theta = 2$. The case of volume constrained minimizers is analogous with $\\theta \\neq 2$ in general.)\nThis implies\n\\begin{eqnarray*}\n-\\frac{d}{dt}\\mathcal{E}(\\Phi_t)^{1-1\/\\theta} & = & (1-1\/\\theta) \\mathcal{E}(\\Phi_t)^{-1\/\\theta} \\|Q(\\Phi_t)\\|^2\\\\\n& \\geq & C \\|Q(\\Phi_t)\\| \n\\end{eqnarray*}\nHence\n$$\\int_T^{\\infty} \\|Q(\\Phi_t)\\|_{L^2} dt \\leq C \\mathcal{E}(\\Phi_T)^{1-1\/\\theta}.$$\nPlugging in the estimate for $\\mathcal{E}(\\Phi_T)$ then gives the desired result.\n\\end{proof}\n\n\\section{Mapping flow estimates}\nSuppose $\\Phi_t$ solves\n$$\\partial_t \\Phi_t = Q(\\Phi_t).$$\nIn the previous section we proved a strong estimate of the gradient along the flow in the $L^2$ norm, provided $\\Phi_t$ is near a critical point. We would now like to improve this to an estimate in some higher regularity norm. Since the gradient $Q_t = Q(\\Phi_t)$ satisfies the linear parabolic equation\n$$\\partial_t Q_t = DQ(\\Phi_t) Q_t,$$\nthis is reasonable by parabolic regularity. Unfortunately, this equation is only weakly parabolic and hence we can not directly apply parabolic regularity. However, we recall that $\\tilde{\\Phi}_t = F_{t*} \\Phi_t$ obeys the strongly parabolic equation\n$$\\partial_t \\tilde{\\Phi}_t = \\tilde{Q}(\\tilde{\\Phi}_t)$$\nif $f_t$ satisfies the mapping flow equation\n$$\\partial_t f_t = P_{g_t, g_0}(f_t), f_0 = \\operatorname{id}_M.$$\nThe gauged gradient $\\tilde{Q}_t = \\tilde{Q}(\\tilde{\\Phi}_t)$ satisfies the linear strongly parabolic equation\n$$\\partial_t \\tilde{Q}_t = D\\tilde{Q}(\\tilde{\\Phi}_t) \\tilde{Q}_t.$$\nParabolic regularity applies to $\\tilde{Q}_t$, but we have no estimate of $\\tilde{Q}_t$! To obtain such an estimate, we will now show how to control $\\partial_t f_t$ along the mapping flow. In the next section, we will combine this estimate with the gradient estimate of the previous section to obtain an estimate of $\\tilde{Q}_t$.\n\n\\begin{lemma}\n\\label{MFE}\nLet $\\tilde{g} \\in \\Gamma(\\odot^2_+ T^* M)$ and $k > \\frac{n}{2} + 2$. Suppose $\\tilde{g}$ has no Killing fields.\nThen there exists a $H^k$ neighborhood $U \\times V$ of $(\\operatorname{id}_M, \\tilde{g})$ and constants $C, \\lambda > 0$, such that for a solution $f_t$ and a metric $g_t \\in V$, $g_t$ once differentiable in time, of an initial value problem\n$$f_0 = \\operatorname{id}_M$$\n$$\\dot{f_t} = P_{g_t, \\tilde{g}}(f_t)$$\nwe have\n$$\\int_{t_1}^{t_2} \\|P_{g_t, \\tilde{g}}\\|_{H^{-2}} dt \\leq C \\left( \\int_0^{t_1} \\|\\dot{g}_t\\|_{L^2} e^{\\lambda (t - t_1)} dt + \\int_{t_1}^{t_2} \\|\\dot{g}_t\\|_{L^2} dt + e^{-\\lambda t_1}\\right)$$\nfor some $C, \\lambda > 0$, provided the flow exists until time $t_2$ in the neighborhood $U \\times V$.\n\\end{lemma}\n\\begin{proof}\nAs computed in \\cite{Ammann2015},\n$$DP_{\\tilde{g}, \\tilde{g}}(\\operatorname{id}_M) X = -4 (\\delta_{\\tilde{g}} \\delta_{\\tilde{g}}^* X^{\\flat})^{\\sharp}.$$\nA computation of the symbol then shows that this operator is strongly elliptic. \nFurthermore, this formula implies\n$$\\left(DP_{\\tilde{g}, \\tilde{g}}(\\operatorname{id}_M) X, X\\right)_{L^2} = -4 \\left(\\delta^*_{\\tilde{g}} X^{\\flat}, \\delta^*_{\\tilde{g}} X^{\\flat} \\right) = -4 \\left(\\mathcal{L}_X \\tilde{g}, \\mathcal{L}_X \\tilde{g} \\right)_{L^2}.$$\nSince we assume $\\tilde{g}$ has no Killing fields, this implies $DP_{\\tilde{g}, \\tilde{g}}(\\operatorname{id}_M)$ is strictly negative definite, i.e. there exists $\\mu > 0$, such that\n$$\\left(DP_{\\tilde{g}, \\tilde{g}}(\\operatorname{id}_M) X, X\\right)_{L^2} \\leq -\\mu \\left(X,X\\right)_{L^2}.$$\nSince the coefficients of the operator $P_{g_1, g_2}(f)$ are continuous in $f$ and the first derivatives of $g_1$ and $g_2$ and recalling that by the Sobolev embedding theorem $H^k$ continuously embeds in $C^2$, we conclude that there is a $H^k$ neighborhood $U$ of $\\tilde{g}$, a neighborhood $V$ of $\\operatorname{id}_M$ and a constant $0 < \\lambda < \\mu$, such that $DP_{g, \\tilde{g}}(f)$ is strongly elliptic and strictly negative definite with a constant $\\lambda$.\n\nSince $L = DP_{\\tilde{g}, \\tilde{g}}(\\operatorname{id}_M)$ is strictly negative definite, it induces an invertible operator from $H^{s+2} \\to H^s$. We have, up to equivalence,\n$$\\|f\\|_{H^{-2}} = \\|L^{-1}f\\|_{L^2}.$$\nThis implies, in particular, that $DP_{g, \\tilde{g}}(f)$ is also strictly negative definite with respect to the Sobolev inner product $\\langle \\cdot, \\cdot \\rangle_{H^{-2}}$.\n\nWe will now derive a differential inequality for $\\|\\dot{f}_t\\|_{H^{-2}}^2$, where\n$$\\dot{f}_t = P_{g_t, \\tilde{g}}(f_t).$$\nFor brevity, we let $P_{g_t, \\tilde{g}}(f_t) = P_{g_t}(f_t)$.\nIn what follows, we tacitly assume $g_t \\in U$, $f_t \\in V$ for all $t$, as per the statement of the lemma. \nWe calculate\n\\begin{eqnarray*}\n\\frac{1}{2} \\frac{d}{dt} \\langle P_{g_t}(f_t), P_{g_t}(f_t) \\rangle_{H^{-2}} & = & \\langle \\frac{d}{dt} P_{g_t}(f_t), P_{g_t}(f_t) \\rangle_{H^{-2}}\\\\\n& = & \\langle P_{\\dot{g_t}}(f_t) + DP_{g_t}(f_t) \\dot{f}_t, P_{g_t}(f_t) \\rangle_{H^{-2}} \\\\\n& = & \\langle P_{\\dot{g_t}}(f_t), P_{g_t}(f_t) \\rangle + \\langle DP_{g_t}(f_t) P_{g_t}(f_t), P_{g_t}(f_t) \\rangle_{H^{-2}}.\n\\end{eqnarray*}\nThe map\n$$g \\mapsto P_g(f) = 2 df(\\delta_{f^*\\tilde{g}} g),$$\nis a linear first order differential operator with bounds dependent on $\\|f\\|_{C^1}$ and $\\|\\tilde{g}\\|_{C^1}$. As such we can estimate, using that bound and the Cauchy-Schwarz inequality\n$$|\\langle P_{\\dot{g_t}}(f_t), P_{g_t}(f_t) \\rangle_{H^{-2}}| \\leq \\|P_{\\dot{g_t}} (f_t)\\|_{H^{-2}} \\|P_{g_t}(f_t)\\|_{H^{-2}} \\leq C \\|\\dot{g}_t\\|_{L^2} \\|P_{g_t}(f_t)\\|_{H^{-2}}.$$\nThen we obtain for\n$$a(t) = \\langle P_{g_t}(f_t), P_{g_t}(f_t) \\rangle_{H^{-2}}$$\nthe inequality\n$$\\frac{1}{2} \\dot{a}(t) \\leq C \\|\\dot{g}_t\\|_{L^2} \\sqrt{a(t)} - \\lambda a(t).$$\nLet $b(t) = \\sqrt{a(t)}$. The function $b$ then satisfies the following differential inequality\n$$\\dot{b}(t) \\leq -\\lambda b(t) + \\|g_t\\|_{L^2}.$$\nDefine\n$$\\beta(t) = e^{-\\lambda t} \\left( b(0) + \\int_0^t e^{\\lambda s} \\|\\dot{g}_s\\|_{L^2} ds \\right).$$\nThen we have\n$$\\dot{\\beta}(t) = -\\lambda \\beta(t) + \\|\\dot{g}_t\\|_{L^2}.$$\nWe deduce\n$$\\frac{d}{dt} (b-\\beta) \\leq -\\lambda (b-\\beta),$$\nand since $b(0) = \\beta(0)$, $b(t) \\leq \\beta(t)$ follows.\nTo obtain the claim of the lemma, we will now estimate the integral of $\\beta(t)$. For brevity, we denote $\\gamma(t) = \\|\\dot{g}_t\\|_{L^2}$. Define $\\chi(s,t) = 1$ if $0 \\leq s \\leq t$ and $\\chi(s,t) = 0$ otherwise. Then we calculate\n\\begin{eqnarray*}\n\\int_{t_1}^{t_2} e^{-\\lambda t} \\int_0^t e^{\\lambda s} \\gamma(s) ds dt\n& = & \\int_{t_1}^{t_2} \\int_0^t e^{\\lambda(s-t)} \\gamma(s) ds dt \\\\\n& = & \\int_{t_1}^{t_2} \\int_0^{t_2} \\chi(s,t) e^{\\lambda(s-t)} \\gamma(s) ds dt\\\\\n& = & \\int_0^{t_2} \\gamma(s) \\int_{t_1}^{t_2} \\chi(s,t) e^{\\lambda(s-t)} dt ds\\\\\n& = & \\int_0^{t_2} \\gamma(s) \\int_{\\max\\{s, t_1\\}}^{t_2} e^{\\lambda(s-t)} dt ds\\\\\n& = & \\int_0^{t_1} \\gamma(s) \\int_{t_1}^{t_2} e^{\\lambda(s-t)} dt ds + \\int_{s}^{t_2} \\gamma(s) \\int_{t_1}^{t_2} e^{\\lambda (s-t)} dt ds\\\\\n& \\leq & \\lambda^{-1} \\left( \\int_0^{t_1} e^{\\lambda (s-t_1)}\\gamma(s) ds + \\int_{t_1}^{t_2} \\gamma(s) ds \\right) \n\\end{eqnarray*}\nThe integral of the term $b(0) e^{-\\lambda t}$ is\n$$\\int_{t_1}^{t_2} b(0) e^{-\\lambda t} dt = \\lambda^{-1} b(0) \\left( e^{-\\lambda t_1} - e^{-\\lambda t_2} \\right).$$\nThus\n\\begin{eqnarray*}\n \\int_{t_1}^{t_2} \\beta(t) dt & \\leq & \\lambda^{-1} \\left( b(0) e^{-\\lambda t_1} + \\int_0^{t_1} e^{\\lambda (s-t_1)}\\gamma(s) ds + \\int_{t_1}^{t_2} \\gamma(s) ds \\right) \n\\end{eqnarray*}\nand the claim of the lemma follows.\n\\end{proof}\n\n\\section{Smooth convergence of the flow}\nNow everything is in place to prove stability of the spinor flow. We obtain slightly sharper theorems than in the introduction:\n\\begin{thm}\n\\label{stabP}\nSuppose $\\bar{\\Phi} = (\\bar{g}, \\bar{\\varphi})$ is a critical point of $\\mathcal{E}$, such that $\\bar{g}$ has no Killing fields. Then for any $k > \\frac{n}{2} + 5$ there exists a $H^k$ neighborhood $U$ of $\\bar{\\Phi}$, such that any solution of the negative gradient flow $\\Phi_t$ with initial condition $\\Phi_0 = \\Phi \\in U$ converges in $H^k$ to a critical point. The speed of convergence is exponential.\n\\end{thm}\n\\begin{thm}\n\\label{stabVC}\nSuppose $\\bar{\\Phi} = (\\bar{g}, \\bar{\\varphi})$ is a volume constrained minimizer of $\\mathcal{E}$ and suppose the set of critical points is a manifold near $\\bar{\\Phi}$. Suppose furthermore, that $\\bar{g}$ has no Killing fields and $k > \\frac{n}{2} + 5$. Then there exists a $H^k$ neighborhood $U$ of $\\bar{\\Phi}$, such that a solution of the volume constrained negative gradient flow $\\Phi_t$ with initial condition $\\Phi_0 = \\Phi \\in U$ converges in $H^k$ to a critical point. The speed of convergence is exponential.\n\nIf the critical set is not a manifold, but $\\theta$ in proposition \\ref{LIVC} can be chosen to be larger than $3\/2$, then there exists a $H^k$ neighborhood $U$ of $\\bar{\\Phi}$, such that a solution of the volume constrained negative gradient flow $\\Phi_t$ with initial condition $\\Phi_0 = \\Phi \\in U$ converges in $H^k$ to a critical point. The speed of convergence is $O(T^{-\\kappa})$, $\\kappa = \\frac{2\\theta -3}{2-\\theta} >0$.\n\\end{thm}\n\nWe will reduce the proof of these theorems to the following two lemmas:\n\\begin{lemma}[Existence near critical points]\n\\label{UniformExistence}\nLet $\\bar{\\Phi}$ be a critical point of $\\mathcal{E}$ and let $T, \\epsilon > 0, k > \\frac{n}{2} + 2$.\nThen there exists $\\delta > 0$, such that for any $\\Phi$ with $\\|\\Phi - \\bar{\\Phi}\\|_{H^k} < \\delta$, the flow\n$$\\partial_t \\Phi_t = \\tilde{Q}(\\Phi_t), \\Phi_0 = \\Phi$$\nexists until time $T$ and $\\|\\Phi_T - \\bar{\\Phi}\\|_{H^k} < \\epsilon$. The same result holds for volume constrained critical points and the volume constrained flow. \n\\end{lemma}\nThe proof is analogous to the proof of corollary 8.6 in \\cite{Weiss2012}\n\\begin{lemma}[Decay of the gradient in a Sobolev norm]\n\\label{GradientDecay}\nSuppose $\\bar{\\Phi}$ is a critical point of $\\mathcal{E}$. Then for any $k > \\frac{n}{2} + 5$ there exists a $H^k$ neighborhood $U$ of $\\bar{\\Phi}$, a neighborhood $V$ of $\\operatorname{id}_M$ in $\\operatorname{Diff}(M)$, constants $C, \\alpha > 0$, such that for $\\Phi \\in U$ the gauged spinor flow $\\tilde{\\Phi}_t$ with initial condition $\\Phi$ fulfills the following estimate \n\\begin{equation}\n\\label{GEa}\n\\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^k} \\leq C e^{-\\alpha T}\n\\end{equation}\nas long as $\\Phi_t$ and $f_t$ remain in the neighborhoods $U$ and $V$ respectively.\n\nAnalogously, if $\\bar{\\Phi}$ is a volume constrained critical point of $\\mathcal{E}$ and the critical set near $\\bar{\\Phi}$ is a manifold, then for any $k > \\frac{n}{2} + 5$ there exists a $H^k$ neighborhood $U$ of $\\bar{\\Phi}$, a neighborhood $V$ of $\\operatorname{id}_M$ in $\\operatorname{Diff}(M)$, constants $C, \\alpha > 0$, such that for $\\Phi \\in U$ the volume normalized gauged spinor flow $\\tilde{\\Phi}_t$ with initial condition $\\Phi$ fulfills the following estimate \n\\begin{equation}\n\\label{GEb}\n\\|\\mathring{\\tilde{Q}}(\\tilde{\\Phi}_t)\\|_{H^k} \\leq C e^{-\\alpha T}\n\\end{equation}\nas long as $\\Phi_t$ and $f_t$ remain in the neighborhoods $U$ and $V$ respectively. If the critical set is not a manifold we instead find $C, \\beta > 0$, such that\n\\begin{equation}\n\\label{GEc}\n\\|\\mathring{\\tilde{Q}}(\\tilde{\\Phi}_t)\\|_{H^k} \\leq \\frac{C}{1+ T^\\beta}\n\\end{equation}\n\\end{lemma}\n\\begin{proof}[Proof of the lemma]\nWe start with the first case.\nWe will show this estimate by combining the gradient estimate from the \u0141ojasiewicz inequality and the estimate of the mapping flow. This will give us an estimate of the time integral of $\\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^s}$ for $s=-3$, which we will then improve via parabolic regularity.\nWe consider the spinor flow\n$$\\partial_t \\Phi_t = Q(\\Phi_t), \\Phi_0 = \\Phi,$$\nthe gauged spinor flow\n$$\\partial_t \\tilde{\\Phi}_t = \\tilde{Q}(\\tilde{\\Phi}_t), \\tilde{\\Phi}_0 = \\Phi$$\nand the mapping flow\n$$\\partial_t f_t = P_{g_t, \\bar{g}}(f), f_0 = \\operatorname{id}_M.$$\nThen we have that\n$$\\tilde{\\Phi}_t = F_t^* \\Phi_t$$\nand hence\n\\begin{eqnarray*}\n\\tilde{Q}(\\tilde{\\Phi}_t) & = & \\partial_t (F_t^* \\Phi_t) \\\\\n& = & F_t^* \\tilde{\\mathcal{L}}_{X_t} \\Phi_t + F_t^* \\dot{\\Phi}_t\n\\end{eqnarray*}\nwhere $X_t = \\frac{d}{dt} f_t$ and $\\tilde{\\mathcal{L}}$ is the spinorial Lie derivative.\n\nMultiplication of Sobolev functions $H^k \\times H^s \\to H^s$ for negative $s$ and positive $k$ is continous, if $k > -s$ and $k > n\/2$, where $n$ is the dimension of the manifold, see theorem 2 (i), sect. 4.4.3 in \\cite{Runst96}. In particular, our choice of $k$ allows any $s \\geq -3$.\n\nWe will use this to estimate $\\tilde{\\mathcal{L}}_{X_t} \\Phi_t$ in the $H^s$ norm.\nRecall that \n$$\\tilde{\\mathcal{L}}_X \\Phi = (\\mathcal{L}_X g, \\tilde{\\mathcal{L}}_X \\varphi) = (2 \\delta_g^* X^{\\flat}, \\nabla_X^g \\varphi - \\frac{1}{4} dX^{\\flat} \\cdot \\varphi).$$\nIn local coordinates we have\n$$\\mathcal{L}_X g = p_1(g_{jk}, \\partial_l g_{mn}, X^i) + p_2(g_{ij}, \\partial_k X^l)$$\nfor some polynomials $p_1, p_2$, which are linear in the partial derivative terms and the $X^i$ terms. Likewise we have\n$$\\tilde{\\mathcal{L}}_X \\varphi = q_1(X^i, \\partial_j\\varphi^{\\alpha}) + q_2(g_ij, \\partial_l g_{mn}, X^k, \\varphi^{\\alpha})$$\nfor polynomials $q_1, q_2$, linear in the partial derivative terms and the $X^i$ terms.\nFrom this follows, using the multiplication theorem above and the fact that $H^{k-1}$ is a Banach algebra (since it embeds into $C^2$),\n\\begin{eqnarray*}\n\\|\\tilde{\\mathcal{L}}_X \\Phi\\|_{H^s} & \\leq & C \\left( \\|DX\\|_{H^s} \\sum_{d=1}^r \\|\\Phi\\|_{H^{k-1}}^d + \\|X\\|_{H^s} \\sum_{d=1}^r \\|D\\Phi\\|_{H^{k-1}}^d \\right)\\\\\n& \\leq & C \\left( \\|X\\|_{H^{s+1}} \\sum_{d=1}^r \\|\\Phi\\|_{H^{k-1}}^d + \\|X\\|_{H^s} \\sum_{d=1}^r \\|\\Phi\\|_{H^{k}}^d \\right)\\\\\n& \\leq & C \\left(\\|X\\|_{H^{s+1}} \\sum_{d=1}^r \\|\\Phi\\|_{H^{k}}^d\\right)\n\\end{eqnarray*}\nfor $k > -s + n\/2 + 2$, where $r$ is the maximal degree of the polynomials $p_1, p_2, q_1, q_2$.\nSince we will choose $s=-3$ and $k > n\/2 + 5$, this will be the case.\n\nFurthermore, given a diffeomorphism $f: M \\to M$ and a lift to the topological spin structure $F: \\tilde{P} \\to \\tilde{P}$, we have\n$$F^* \\Phi = \\Phi \\circ F,$$\nwhere we view $\\Phi$ as an equivariant map $\\Phi: \\tilde{P} \\to \\left(\\widetilde{\\operatorname{GL}_n^+} \\times \\Sigma_n\\right)\/\\operatorname{Spin}(n)$.\nUsing the transformation rule, we can derive an estimate\n$$\\|u \\circ f\\|_{W^{k,p}(M)} \\leq \\nu(\\|f\\|_{C^{\\max \\{k, 1\\}}}) \\|u\\|_{W^{k,p}(M)}$$\nfor the integral Sobolev spaces.\nFor real $s$, we conclude the following inequality by interpolation and duality\n$$\\|F^* \\Phi\\|_{H^s} \\leq \\tilde{\\nu} (\\|F\\|_{C^{\\lceil|s|\\rceil}}) \\|\\Phi\\|_{H^s},$$\nwhere $\\nu, \\tilde{\\nu}: [0, \\infty) \\to [0, \\infty)$ are continuous functions.\n \nIn conclusion we obtain\n\\begin{eqnarray*}\n\\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^s} & = & \\|F_t^* \\tilde{\\mathcal{L}}_{X_t} \\Phi_t + F_t^* \\dot{\\Phi}_t\\|_{H^s} \\\\\n& \\leq & C \\nu(\\|F_t\\|_{C^{\\lceil|s|\\rceil}}) ( \\|X_t\\|_{H^{s+1}} \\|\\Phi_t\\|_{H^k} + \\|\\dot{\\Phi}_t\\|_{H^s} )\n\\end{eqnarray*}\nWe will assume both $f_t$ and $\\Phi_t$ to remain in a bounded $H^k$ neighborhood, thus we can estimate their norms by a constant, hence we obtain\n$$\\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^s} \\leq C (\\|\\dot{f}_t\\|_{H^{s+1}} + \\|\\dot{\\Phi}_t\\|_{H^s}).$$\n\nIt remains to choose a neighborhood of $\\bar{\\Phi}$ so that we can also estimate the terms $\\|\\dot{f}_t\\|_{H^{s+1}}$ and $\\|\\dot{\\Phi}_t\\|_{H^s}$.\n\nBy theorem \\ref{L2cv} there exists a $H^k$ neighborhood $U$ of $\\bar{\\Phi}$, such that for any $\\Phi \\in U$ it holds\n$$\\int_T^{T_{\\max}} \\|Q(\\Phi_t)\\|_{L^2} dt \\leq C e^{-\\alpha T}.$$\n\nChoose a neighborhood $U\\times V_m$ of $(\\operatorname{id}_M, \\bar{g})$ such that we have the mapping flow estimate \\ref{MFE}.\nChoose a neighborhood $V_s$ of $\\bar{\\Phi}$, such that we have the $L^2$ estimate of the gradient along the spinor flow as in theorem \\ref{L2cv}. We may assume that $\\pi_{\\Sigma}(V_s) = V_m$.\nFurthermore, we choose the neighborhoods to be bounded in $H^k$.\n\nNow choose $\\Phi \\in V_s$ as initial condition for the spinor and the spinor-DeTurck flow. As above we denote these flows by $\\Phi_t$ and $\\tilde{\\Phi}_t$ respectively and by $f_t$ we mean the associated mapping flow.\nWe will now estimate the integral of the $H^{-3}$ norm of $\\tilde{Q}(\\tilde{\\Phi}_t)$.\nRecall that we have \n$$\\int_{T_1}^{T_2} \\|\\dot{\\Phi}_t\\|_{L^2} dt \\leq C e^{-\\alpha T_1}$$\nfrom theorem \\ref{L2cv}. For $\\dot{f_t}$ we get the estimate\n$$\\int_{T_1}^{T_2} \\|\\dot{f}_t\\|_{H^{-2}} dt \\leq C \\left( \\int_0^{T_1} \\|\\dot{g}_t\\|_{L^2} e^{\\lambda(t-T_1)} dt + \\int_{T_1}^{T_2} \\|\\dot{g}_t\\|_{L^2} dt + e^{-\\lambda T_1} \\right).$$\nThe second term can be bounded by $C e^{-\\alpha T_1}$ by the previous estimate, since $\\|\\dot{g}_t\\|_{L^2} \\leq \\|\\dot{\\Phi}_t\\|_{L^2}$. The first term we decompose into\n$$\\int_0^{T_1\/2} \\|\\dot{g}_t\\|_{L^2} e^{\\lambda(t-T_1)} dt < C e^{-\\lambda T_1\/2}$$\nand\n$$\\int_{T_1\/2}^{T_1} \\|\\dot{g}_t\\|_{L^2} e^{\\lambda(t-T_1)} dt < C e^{-\\alpha T_1\/2}$$\nagain using the estimate for $\\|\\dot{g}_t\\|$.\nThus\n$$\\int_{T_1}^{T_2} \\|\\dot{f}_t\\|_{H^{-2}} dt < C e^{-\\mu T}$$\nfor some $C>0, \\mu >0$. We will use the same constants in the estimate of $\\dot{g}_t$.\nPutting these estimates together we obtain\n\\begin{eqnarray*}\n\\int_{T_1}^{T_2} \\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^{-3}} dt & \\leq & C \\int_{T_1}^{T_2} \\|\\dot{f}_t\\|_{H^{-2}} + \\|\\dot{\\Phi}_t\\|_{H^{-3}} dt\\\\\n& \\leq & C e^{-\\mu T_1}\n\\end{eqnarray*}\nSince $\\tilde{Q}$ is a continuous map from $H^k$ to $H^{k-2}$, because $H^k$ embeds into $C^3$, and $\\tilde{\\Phi}_t$ is in a bounded $H^k$ neighborhood, we obtain that $\\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^{-3}} \\leq \\tilde{C}$.\nHence we may estimate\n$$\\int_{T_1}^{T_2} \\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^{-3}}^2 dt \\leq \\tilde{C} \\int_{T_1}^{T_2} \\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^{-3}} dt \\leq C \\tilde{C} e^{-\\mu T_1}.$$\n\nSince $\\tilde{Q}_t = \\tilde{Q}(\\tilde{\\Phi}_t)$ fulfills the linear strongly parabolic equation\n$$\\partial_t \\tilde{Q}_t = D\\tilde{Q}(\\tilde{\\Phi}_t) \\tilde{Q}_t,$$\nwe may now apply the parabolic estimate \\ref{PE} to obtain\n$$\\|\\tilde{Q}(\\tilde{\\Phi}_{T + \\delta})\\|_{H^k} \\leq C e^{-\\mu T} = \\tilde{C} e^{-\\mu (T+\\delta)}.$$\n(Since $\\tilde{\\Phi}_t$ remains in a bounded neighborhood of $\\bar{\\Phi}$, the parabolic inequality for $D\\tilde{Q}(\\tilde{\\Phi}_t)$ can be chosen independent of $\\tilde{\\Phi}_t$.\nIn particular $\\delta$ can be chosen independently of $T$ and $\\Phi$, hence the estimate gets worse by a constant factor $e^{\\mu \\delta}$.)\n\nThe argument for the estimate (\\ref{GEb}) is identical and for the estimate (\\ref{GEc}) the argument runs in parallel until we apply the gradient estimate. Then we get the following estimate:\n$$\\int_{T_1}^{T_2} \\|\\dot{\\Phi}_t\\|_{L^2} dt \\leq \\frac{C}{1 + T^{\\beta}}.$$\nSimilarly as above, we can estimate\n$$\\int_{T_1}^{T_2} \\|\\dot{f}_t\\|_{H^{-2}} dt \\leq \\frac{C}{1 + T^{\\beta}}.$$\nThus\n$$\\int_{T_1}^{T_2} \\|\\tilde{\\mathring{Q}}(\\tilde{\\Phi}_t)\\|_{H^{-3}} dt \\leq \\frac{C}{1 + T^{\\beta}}$$\nand hence\n$$\\|\\tilde{Q}(\\tilde{\\Phi}_t)\\|_{H^k} \\leq \\frac{C}{1 + T^{\\beta}}$$\nas claimed.\n\\end{proof}\n\n\n\\begin{proof}[Proof of theorem \\ref{stabP}]\nIn the following $B_\\rho$ denotes the ball of radius $\\rho$ around $\\bar{\\Phi}$ with respect to the $H^k$ norm, and in this proof ``flow'' always refers to the gauged spinor flow. \nUsing lemmas \\ref{UniformExistence} and \\ref{GradientDecay}, choose $0 < \\gamma < \\delta < \\epsilon$ and $T$, such that\n\\begin{enumerate}[label=(\\roman*)]\n\\item The estimate from lemma \\ref{GradientDecay} holds on $B_{\\epsilon}$.\n\\item For any $\\Phi \\in B_{\\delta}$ the flow exists until time $1$ and stays in $B_{\\epsilon}$\n\\item $\\int_T^{\\infty} C e^{-\\alpha t} dt < \\frac{\\delta}{3}$, where $C$ and $\\alpha$ as in lemma \\ref{GradientDecay}\n\\item For any $\\Phi \\in B_{\\gamma}$ the flow exists until time $T$ and remains in $B_{\\delta\/3}$.\n\\end{enumerate}\nNow let $\\Phi \\in B_{\\gamma}$.\nThen denote by $\\Phi_t$ the flow\n$$\\partial_t \\Phi_t = \\tilde{Q}(\\Phi_t), \\Phi_0 = \\Phi.$$\nDenote by $\\hat{T} \\in (0, \\infty]$ the maximal time, such that the flow with initial condition $\\Phi$ exists in $B_{\\delta}$. The condition on $B_{\\delta}$ ensures that $\\Phi_{\\hat{T}}$ exists and $\\|\\Phi_{\\hat{T}} - \\bar{\\Phi}\\|_{H^k} = \\delta$.\nOn the other hand,\n\\begin{eqnarray*}\n\\|\\bar{\\Phi} - \\Phi_{\\hat{T}}\\|_{H^k} & \\leq & \\|\\bar{\\Phi} - \\Phi_T\\|_{H^k} + \\|\\Phi_T - \\Phi_{\\hat{T}}\\|_{H^k} \\\\\n& \\leq & \\frac{\\delta}{3} + \\int_T^{\\hat{T}} \\|\\tilde{Q}(\\Phi_t)\\|_{H^k} dt \\\\\n& \\leq & \\frac{\\delta}{3} + \\int_T^{\\hat{T}} C e^{-\\alpha t} dt \\\\\n& \\leq & \\frac{2}{3} \\delta\n\\end{eqnarray*}\nThis is a contradiction and we conclude $\\hat{T} = \\infty$.\nAdditionally, \n$$\\int_T^{\\infty} \\|\\tilde{Q}(\\Phi_t)\\|_{H^k} dt \\leq \\frac{\\delta}{3},$$\nand we conclude that the limit\n$$\\Phi_{\\infty} = \\Phi_T + \\int_T^{\\infty} \\tilde{Q}(\\Phi_t) dt$$\nexists in $H^k$ and\n$$\\|\\Phi_{\\infty} - \\Phi_t\\|_{H^k} \\leq \\int_t^{\\infty} \\|\\tilde{Q}(\\Phi_t)\\|_{H^k} dt \\leq C e^{-\\alpha t}.$$\nSince \n$$\\lim_{t\\to \\infty} \\mathcal{E}(\\Phi_t) = 0,$$\n$\\Phi_{\\infty}$ is a critical point.\nWe have shown that the gauged spinor flow converges for $\\Phi \\in B_{\\gamma}$ to a critical point in $B_{\\delta}$.\nGiven that the mapping flow is a strongly parabolic equation, the velocity along the flow solves a linear strongly parabolic equation and we can apply the parabolic regularity estimate and the mapping flow estimate to obtain that the mapping flow converges exponentially in any $H^k$ norm. Since the spinor flow is given by $(F_t^{-1})^* \\Phi_t$, the spinor flow also converges exponentially.\n\\end{proof}\n\\begin{proof}[Proof of theorem \\ref{stabVC}]\nWhen the critical set is a manifold, the proof is entirely analogous to the previous proof. If the critical set is not a manifold, we have the weaker estimate\n$$\\|\\tilde{Q}(\\Phi_t)\\|_{H^k} \\leq \\frac{C}{1 + T^{\\gamma}}.$$\nThe exponent $\\gamma$ can be computed from $\\theta$ in the \u0141ojasiewicz inequality as $\\gamma = \\frac{\\theta - 1}{2 - \\theta}$. Hence if $\\theta > 3\/2$, $\\gamma > 1$. In that case we find\n$$\\int_T^{\\infty} \\frac{C}{1 + t^{\\gamma}} dt \\leq C\\frac{1}{T^{\\gamma-1}} \\xrightarrow{T \\to \\infty} 0$$\nand we can show existence and convergence of the flow as in the previous proof.\nWe define\n$$\\Phi_{\\infty} = \\Phi_T + \\int_T^{\\infty} \\tilde{Q}(\\Phi_t) dt$$\nand using that\n$$|\\mathcal{E}(\\Phi_t) - \\mathcal{E}(\\bar{\\Phi})| \\leq \\frac{C}{1+ T^{\\beta}}$$\nwe obtain\n$$\\mathcal{E}(\\Phi_{\\infty}) = \\lim_{t\\to \\infty} \\mathcal{E}(\\Phi_t) = \\mathcal{E}(\\bar{\\Phi})$$\nand hence $\\Phi_{\\infty}$ is also a local minimum, and in particular a critical point of $\\mathcal{E}|_{\\mathcal{N}^1}$.\nThe speed of convergence is then given by $\\frac{1}{T^{\\gamma-1}}$.\n\\end{proof}\n\\bibliographystyle{plain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzjuvc b/data_all_eng_slimpj/shuffled/split2/finalzzjuvc new file mode 100644 index 0000000000000000000000000000000000000000..5e32de6311d51326ea9204ae3760d37feaf245bd --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzjuvc @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nIdentifying line outages is crucial to understanding current system conditions and preventing cascading outages.\nHistorically, line outages have been identified based on breaker status indications; however, sometimes the reported line statuses are incorrect. \nFailure to update the system models can cause inaccurate state estimation and threaten system reliability~\\cite{NERC_EMSoutage_2017}, especially for systems that are under stress. \nFor example, in the 2011 San Diego blackout, operators could not detect overloaded lines because of an incorrect network model~\\cite{OutageReport2011,Chen2015Quickest}.\n\nPhasor measurement units (PMUs), because of their unique characteristics (namely, global synchronization and high reporting rate), have been installed on many modern power grids to provide better visibility of grid behavior.\nIn particular, there have been many reported efforts to use PMUs to improve topology models (in particular, to detect one or more line outages) using various approaches (\\cite{Tate_line_2008,Chen2015Quickest,Tate_double_2009,Zhu_sparse_2012,Emami_external_2013}).\nMethods in~\\cite{Tate_line_2008} and~\\cite{Tate_double_2009} are based on hypothesis testing for single-line and double-line outage cases, while reference~\\cite{Zhu_sparse_2012} proposes an overcomplete representation and formulates the problem in terms of sparse vector estimation.\nAlternative approaches include integer programming (\\cite{Emami_external_2013}) and identification based on the small-signal linearized power grid model (\\cite{Chen2015Quickest}).\nMost of the existing works use PMU voltage angle measurements rather than both magnitude and angle data since they rely on dc power flow models, which only considers angles.\nPMU-based disturbance detection methods have been developed and used by utilities and operators.\nLine switching events are identified as one of the applications ~\\cite{NASPI_using}.\nIn fact, several applications are being developed and tested (e.g., by ISO New England for external system transmission element tripping and PJM for detection and triangulation of large disturbances).\nMeanwhile, Operador Nacional do Sistema El\u00e9trico (ONS) in Brazil deployed a WAMS system which identifies transmission line tripping based on angle disturbance of nearby PMUs.\nTo the best of our knowledge, details of these applications are not publicly available for comparison.\nIn this research, we extended the single line outage identification algorithm in~\\cite{Tate_line_2008} by utilizing voltage phasor measurements from PMUs.\nFirst, the algorithm computes expected voltage phasors for all possible outage scenarios by solving ac power flows.\nNext the pre- and post-outage voltage phasor difference can be calculated (hereinafter called expected values).\nBy comparing the expected and the observed values, the hypothetical outage scenario that is closest to the observation will be identified. \nUnlike prior approaches, which have relied on the relatively inaccurate dc power flow model, the proposed method uses the full ac power flow model to identify outages.\nIdeally, system responses after different outages are distinct enough to be correctly identified.\nHowever, due to measurement uncertainty, if two outages lead to similar (but not identical) responses, they may be confused and thus misidentified.\nIn some cases, misidentification may lead to wrong operation that aggravates the situation especially when the system is under stressful conditions.\nFor example, misoperation (tripping heavily loaded but not faulted lines) played a significant role in the 2003 US-Canada blackout and the 2015 Turkish blackout~\\cite{abdullah_distance_2018}.\nIn such cases, a more conservative result (inconclusive) sometimes is better than an incorrect result (i.e., no identification is better than an erroneous identification).\nThis practical problem has not been addressed in previous research.\nIn order to acknowledge measurement uncertainty and improve identification accuracy, a rejection filtering technique is introduced.\nWith the rejection filter in place, instead of definitive identification results, the events are now labeled as conclusive or inconclusive.\nIn summary, a two-stage framework for single line outage identification is proposed.\n\nExtensive tests have been conducted to show the relationships between identification results and different conditions (including filtering methods, threshold values, and PMU placements) on the IEEE 30-bus system.\nAdditionally, results using the Ontario network are presented.\nSection~\\ref{sec: Algorithm} gives the first stage of the algorithm for single line outage identification.\nSection~\\ref{sec:filtering} discusses the rejection filtering algorithm (the second stage). \nCase studies are presented in Section~\\ref{sec:case study}.\nLastly, concluding remarks and future work are presented in Section~\\ref{sec: conclusions and future work}.\n\n\\section{Stage 1: Main Identification Algorithm}\n\\label{sec: Algorithm}\nIn this section, the main identification algorithm will be presented. \nThe assumptions we make include: \\text{(1)} all measurements are taken from a system in quasi-steady state, and \\text{(2)} the ac power flow model is available, since steady-state models are available in modern energy management systems for power flow calculations, such as contingency analysis~\\cite{wu_power_2005}.\n\nInspired by~\\cite{Tate_line_2008}, the algorithm is based on hypothesis testing using voltage phasor measurements.\nBy comparing the simulated voltage changes due to each hypothetical line outage to the observed, the case that is closest to the observations is identified as the outage source.\nOutages are assumed to be equally likely in all lines (that would not lead to islands).\n\\Cref{Eqn: criterion} to \\Cref{Eqn: V obs} describe the identification algorithm. \n\\begin{align}\n\\label{Eqn: criterion}\nl^{\\ast}&=\\text{arg} \\underset{l \\in \\mathcal{L}} {\\: \\min} \\:E(l)\\\\\n\\label{Eqn: E def}\nE(l)&=\\|\\Delta\\boldsymbol{\\bar{V}_{exp,l}}-\\Delta\\boldsymbol{\\bar{V}_{obs}}\\|\\\\\n\\label{Eqn: V exp}\n\\Delta\\boldsymbol{\\bar{V}_{exp,l}}&=(\\boldsymbol{\\bar{V}_{exp,l}}-\\boldsymbol{\\bar{V}^{pre}})\\\\\n\\label{Eqn: V obs}\n\\Delta\\boldsymbol{\\bar{V}_{obs}}&=\\boldsymbol{\\bar{V}_{obs}^{post}}-\\boldsymbol{\\bar{V}_{obs}^{pre}}\n\\end{align}\nAn error measure $E(l)$ given a hypothetical outage in line $l$ (where ${l \\in \\mathcal{L}}$) is defined as (\\ref{Eqn: E def}) to quantify the difference between the expected voltage phasor change and its observed counterpart.\nSimilar to the list commonly used in online contingency analysis~\\cite{savulescu2014real}, the set $\\mathcal{L}$ represents all lines to be checked for outage occurrence, the cardinality of which (denoted by ${L}$) is the number of lines to be checked.\nThe most likely line ${l^{\\ast}}$ that leads to the smallest ${{E}(l)}$ (\\ref{Eqn: criterion}) is identified as the cause of outage.\nThe observed voltage change after an event ($\\Delta\\boldsymbol{\\bar{V}_{obs}}$) is defined as the difference between the observed pre-event voltage ($\\boldsymbol{\\bar{V}_{obs}^{pre}}$) and the post-event voltage ($\\boldsymbol{\\bar{V}_{obs}^{post}}$).\nAnalogously, the expected voltage change (${\\Delta \\boldsymbol{\\bar{V}_{exp,l}}}$) is defined based on the pre-event voltage ($\\boldsymbol{\\bar{V}^{pre}}$, computed from state estimator) and the expected post-event voltage (${\\boldsymbol{\\bar{V}_{exp,l}}}$, computed for all potential line outages ${l \\in \\mathcal{L}}$ through power flow calculations).\n\nNote that $\\boldsymbol{\\bar{V}^{pre}}$ is used instead of the pre-outage voltage measured by PMUs because (\\ref{Eqn: V exp}) can then be updated constantly and does not require detection of an outage.\nIn contrast, (\\ref{Eqn: criterion}), (\\ref{Eqn: E def}), (\\ref{Eqn: V obs}) and (\\ref{Eqn: rank}) are only updated when an outage is detected.\nIn our study, the ac power flow method, in particular, the Newton method, is used to solve for ${\\boldsymbol{\\bar{V}_{exp,l}}}$.\nThis is the most computationally expensive step.\nFor example, one successful ac power flow solution takes around 0.1 s for the Ontario system (with 3488 buses, introduced in Section \\ref{sec: line out Ontario}) using MATPOWER~\\cite{Zimmerman_matpower_2011}.\nBy comparison, the rest of the outage identification only takes $0.0075$ s due to highly efficient sorting algorithms. \nAll identification algorithms were implemented in MATLAB on a computer with an Intel Xeon E5-1607 processor and 8 GB RAM. \nThe results can likely be improved using better computation resources and simple parallelism \\cite{Jun_1995,roberge_parallel_2017}.\nHowever, simulation of system responses due to hypothetical outages is commonly used in contingency analysis, which reduces the additional computation burden associated with the proposed algorithm.\nNote that unlike phase angles that are typically used in previous works, all voltage vectors in our algorithm are phasors (with both magnitude and angle) with a length of $P$, where $P$ is the number of PMUs in a system.\nEmpirically, the algorithm is based on the following ranking of the error measure ${{E}(l)}$.\n\\setlength{\\belowdisplayskip}{3pt}\n\\setlength{\\abovedisplayskip}{3pt}\n\\setlength{\\belowdisplayshortskip}{3pt}\n\\setlength{\\abovedisplayshortskip}{3pt}\n\\begin{align}\n\\label{Eqn: rank}\n0\\leq E(l^{\\ast}=l_{r_1})\\leq E(l_{r_2}) \\leq \\ldots \\leq E(l_{r_{L}})\n\\end{align}\nThe subscript of $l_{r_i}$ means this line occupies the $i^{\\text{th}}$ place in the ranking among $L$ potential candidates.\nThe line outage ($l^{\\ast}$) that leads to the smallest error (i.e., $1^{\\text{st}}$ in the ranking) with respect to the observation is identified as the cause. \nGiven PMU measurements due to the actual outage of line $l_a$, if ${l^{\\ast} = l_a}$, then the outage is successfully identified.\n\n\\section{Stage 2: Rejection Filtering Algorithms}\n\\label{sec:filtering}\nWhen measurements are ideal without uncertainty, almost all outage cases should be identified correctly even with a small number of PMUs installed in the system. \nTwo exceptions would be series lines with zero injection at shared buses and identical parallel lines. \nGiven measurements with uncertainties, more outage cases (not the aforementioned situations) are likely to be misidentified.\nMeasurement uncertainty may stem from measurement errors (e.g., instrument transformers, the A\/D converters, and the communication cables~\\cite{zhu_enhanced_2006}) or random errors (e.g. unknown fluctuations that can not be captured deterministically in principle).\nIn our research, we use independent Gaussian distributions for voltage phasor magnitude and angle to model measurement uncertainty.\n\nWhether a case will be misidentified is determined by the system as well as the measurement uncertainty model.\nA simple example for a 4-bus system (\\cite{Zimmerman_matpower_2011,grainger_power_1994}) is used to demonstrate the impact of uncertainty on identification results.\nThe system consists of 4 buses and 4 branches with a PMU installed on bus 2 and 1 (the reference bus).\nAssuming there is no measurement error on the reference bus, the ranking in (\\ref{Eqn: rank}) solely depends on measurements at bus 2.\n\\begin{figure}[htbp]\n\t\\centering\n\t\\includegraphics[width=\\linewidth]{delta_phasor_misranks.pdf}\n\t\\caption[Impact of measurement uncertainty on line outage identification in a 4-bus system]{Demonstration of measurement uncertainty impact on identification, ${\\Delta\\boldsymbol{\\bar{V}_{exp,l}},l\\in \\{1,2\\}}$ and ${\\Delta\\boldsymbol{\\bar{V}_{obs,1}}}$ in 4-bus system, ${\\sigma_{v}=0.002\/\\sqrt{3}}$ pu and ${\\sigma_{\\theta}=0.01\/\\sqrt{3}}$ degrees}\n\t\\label{fig_4gs}\n\\end{figure}\nFig.~\\ref{fig_4gs} shows simulated responses after the outage of line 1 (pink crosses) considering uncertainty, along with the ideal response due to the outage of line 1 (blue cross) and line 2 (blue star).\nThe pink crosses represents 1000 random realizations corresponding to possible measurements after the line 1 outage, generated based on independent distributions.\nSpecifically, we assume the magnitude and the angle uncertainty follow a zero-mean Gaussian distribution with standard deviation ${\\sigma_{v}}$ and ${\\sigma_{\\theta}}$ respectively.\nThe main identification algorithm is represented by the solid line that partitions the graph into two regions.\nAny point in the left region is closer to ${\\Delta\\boldsymbol{\\bar{V}_{exp,1}}}$ (represented by the blue cross), which means if an observation falls in this region, line 1 will be correctly identified.\nHowever, due to measurement uncertainty, the observations may fall to the right region and be closer to ${\\Delta\\boldsymbol{\\bar{V}_{exp,2}}}$ (represented by the blue star). \nIn such cases, the event will be misidentified.\n\nIf we set up a rejection filter (represented by the band delineated by two dashed lines on each side of the original identification solid line) so that all cases in the right region are labeled as inconclusive, then they will not be misidentified.\nThey are in fact misidentified-filtered cases (those would have been misidentified but filtered out to be inconclusive).\nHowever, depending on how we define the filter threshold (band position and width), it is likely that some cases in the left region also fall within the band and become inconclusive (i.e., correct-filtered cases).\n\nThere may be different ways to set up the filter threshold. \nIn this study, the threshold is determined by $\\Delta E$, where ${\\Delta E = E(l_{r_2}) - E (l_{r_1})}$. \nNamely the difference between the first two most promising candidates is used to set up the threshold.\n\\begin{equation}\n\\Delta E^{(r)}_{l_a} = E^{(r)}_{l_a}(l_{r_2}) - E^{(r)}_{l_a} (l_{r_1}) < \\epsilon,\n\\end{equation}\nwhere ${E^{(r)}_{l_a}(l)}$ denote the error measure in (\\ref{Eqn: E def}) but for the actual outage ${l_a}$ specifically.\nDue to randomness, multiple runs are conducted based on multiple, independent uncertainty distributions.\nThe superscript $r$ indicates the error measure is computed based on the $r^{th}$ random realization.\n\n\\begin{figure}[!ht]\n\t\\centering\n\t\\includegraphics[width=0.9\\linewidth]{four_types_graffle_random.pdf}\n\t\\caption{Categories of line identification results (30-bus system with 20 PMUs)}\n\t\\label{fig_results_illustrate_1}\n\\end{figure}\nA visual representation of this process is in \\cref{fig_results_illustrate_1}.\nThe area of each circle represents the number of cases in each category.\nThe red inner circle represents misidentified cases which correspond to the cases in the right region in Fig.~\\ref{fig_4gs} (i.e., misidentified).\nIf the rejection filter is defined as an ellipse which will not be coincident with the inner circle, then by covering the inner circle (thus eliminating misidentified), some correct cases will become inconclusive (correct filtered) as well. \nThe filter threshold defines the sizes of ellipses.\nAs will be shown in the case studies, as the threshold is increased, the misidentified cases will decrease to zero while the inconclusive cases will go up as a result of increased number of correct-filtered cases.\nThis corresponds the bottom right circle in Fig.~\\ref{fig_results_illustrate_1}. \nIn the course of minimizing the misidentified cases, we probably sacrifice some cases which would have been identified correctly. \nThe goal is to minimize misidentified cases without introducing a significant number of correct-filtered cases.\n\n\n\\section{Case Studies}\n\\label{sec:case study}\nA comprehensive comparison of different techniques and the choices of filter thresholds are presented in this section. \nThe first experiment is the comparison of the ac and the dc approach using the IEEE 30-bus test system. \nThen the results using rejection filtering are presented.\nLastly, the impact of the rejection filter threshold is discussed. \nThe identification algorithm is further tested using a 3488-bus model of the Ontario power system.\nIn all tests, we assume the measurement uncertainty in the magnitude and angle observed follows a zero mean Gaussian distribution~\\cite{chakhchoukh_pmu_2014} with standard deviation ${0.002\/\\sqrt{3}}$ pu and ${0.01\/\\sqrt{3}}$ degrees respectively.\n1000 different placements are generated randomly for each possible ${P\\in\\{2,\\ldots,B\\}}$ where ${B}$ is the total number of buses in a system.\nIf the total number of placements is less then 1000, all placements are considered.\nFor comparison, 100 randomly generated measurements for each PMU are shared among all placements.\n\n\\subsection{IEEE 30-Bus System}\n\\begin{figure}[hbtp]\n\n\t\\centering\n\t\\begin{subfigure}[b]{0.48\\linewidth}\n\t\t\\centering\n\t\t\\includegraphics[trim= 15 0 15 0,width=\\linewidth]{ac_dc_pcorrect.pdf}\n\t\t\\caption{correctly identified, no filter}\n\t\t\\label{fig_ac_dc_pcorrect}\n\t\\end{subfigure}\n\t\\begin{subfigure}[b]{0.48\\linewidth}\n\t\t\\centering\n\t\t\\includegraphics[trim= 15 0 15 0,width=\\linewidth]{ac2_set2.pdf}\n\t\t\\caption{ac, $\\Delta E$}\n\t\t\\label{fig_ac2_set2}\n\t\\end{subfigure}\n\t\\caption[Comparison of dc and ac approaches (${E_{r_2}}$ and $\\Delta E$) for line outage identification]{Comparison of dc and ac approaches (with or without $\\Delta E$) for line outage identification (The threshold $\\epsilon$ is chosen so that the mean misidentification rate is driven under $0.00015\\%$)}\n\t\\label{fig: Comparison of DC and 2 AC}\n\\end{figure}\nFig.~\\ref{fig_ac_dc_pcorrect} shows the correctly identified rate of ac and dc approaches without filtering techniques for the IEEE 30-bus system.\nAs expected, this figure shows that the ac approach is needed to achieve high identification accuracy.\nFor example, even in the best-case scenario of complete PMU coverage, the dc accuracy is only 70.15\\%, whereas the ac accuracy is 97.4\\%. \nFig.~\\ref{fig_ac2_set2} shows\nperformance of the rejection filter based on four types of identification results: correct, misidentified, correct-filtered and misidentified-filtered.\nThe results in Fig. \\ref{fig: Comparison of DC and 2 AC} are generated using thresholds so that the misidentification rate is driven to nearly zero (specifically the mean misidentification rate is driven under $0.00015\\%$ over all random realizations, all placements and all outages).\nIf we compare Fig.~\\ref{fig_ac_dc_pcorrect} to \\ref{fig_ac2_set2}, the drop of correctly identified rate is because of increased inconclusive cases.\nFor example, for the ac approach, with 30 PMUs the accuracy was 97.4\\%.\nIt drops to around $83\\%$ when the $\\Delta E$ filter is used.\n\n\\subsection{Results of Different Rejection Filter Thresholds}\nThis section discusses the impact of the rejection filter $\\epsilon$ on the identification results. \nTo isolate impact of the threshold, the results should be generated using different PMU placements.\nAt the beginning of the section, we mentioned 1000 different placements are generated randomly for each possible ${P\\in\\{2,\\ldots,B\\}}$ where ${B=30}$.\nAdditionally, 100 randomly generated measurements for each PMU are shared among all placements.\nThe number of lines in the 30-bus system to be checked for outages is 38.\nIf we consider all these possible combinations, $1.102\\times10^8$ cases are needed for just one threshold level.\nSince enumeration over all possible scenarios takes significant amount of time, we focus on the behavior with a varying range of $\\epsilon$ (from 0 to 0.0045) and four specific levels of PMU coverage (10\\%, 20\\%, 50\\% and 100\\%).\nThe measurement uncertainty levels remain the same as in the previous section.\n\n\n\\begin{figure}[!ht]\n\n\t\\centering\n\t\\begin{subfigure}[b]{0.47\\linewidth}\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{30bus10per.pdf}\n\t\t\\caption{10\\% coverage}\n\t\n\t\t\\label{fig_diff_threshold_30bus_10per}\n\t\\end{subfigure}\n\t\\begin{subfigure}[b]{0.47\\linewidth}\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{30bus20per.pdf}\n\t\t\\caption{20\\% coverage}\n\t\t\\label{fig_diff_threshold_30bus_20per}\n\t\\end{subfigure}\n\n\t\\centering\n\t\\begin{subfigure}[b]{0.47\\linewidth}\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{30bus50per.pdf}\n\t\t\\caption{50\\% coverage}\n\t\t\\label{fig_diff_threshold_30bus_50per}\n\t\\end{subfigure}\n\t\\begin{subfigure}[b]{0.47\\linewidth}\n\t\t\\centering\n\t\t\\includegraphics[trim= 15 0 0 0, width=0.83\\linewidth]{30bus100per.pdf}\n\t\t\\caption{100\\% coverage}\n\t\t\\label{fig_diff_threshold_30bus_100per}\n\t\\end{subfigure}\n\t\\caption[Identification results vs $\\epsilon$ (30-bus system, $\\Delta E$ filter, four coverage levels)]{Identification results vs $\\epsilon$ (30-bus system, $\\Delta E$ filter with different coverage (legends are the same for 4 figures))}\n\t\\label{fig_ac_thresholds_complete}\n\\end{figure}\nThe trade-off between correct and misidentified cases as a result of different $\\epsilon$ is illustrated by Fig.~\\ref{fig_ac_thresholds_complete}.\nThe horizontal lines in each figure represents the correctly identified\/misidentified level without filtering.\nFor example, without any rejection filter, with $10\\%$ coverage, the misidentified rate is about $45\\%$ while the correctly identified rate is $55\\%$.\nThis corresponds to a red inner circle with an area of $45\\%$ of the entire circle in Fig.~\\ref{fig_results_illustrate_1}.\nBy increasing the threshold, more misidentified cases (in red) will be filtered out and become misidentified-filtered (in pink), whilst correctly identified cases (in green) become correct-filtered (in yellow).\n\nFig.~\\ref{fig_ac_thresholds_complete} indicates the misidentified percentage (in red) is very sensitive to the threshold when $\\epsilon < 1\\times10^{-3} $ (particularly when the coverage is low).\nIn such cases, the misidentified rate drops rapidly as $\\epsilon$ increases from zero.\nIt also decays faster than the correctly identified rate (by comparing red to green). \nFor example, with 10\\% coverage (Fig.~\\ref{fig_diff_threshold_30bus_10per}), by increasing the threshold from 0 to $1\\times10^{-3}$, the misidentified rate drops more than 40\\% while the correctly identified rate only drops about 30\\%.\nHowever, when the misidentified rate is low (around zero), further elimination of misidentified cases leads to significant loss of correctly identified cases. \nFor example, by increasing the threshold from $1\\times10^{-3}$ to $2\\times10^{-3}$ in Fig.~\\ref{fig_diff_threshold_30bus_10per}, the green area decreases significantly (about $10\\%$) compared to the trivial gain in eliminating red cases. \nThe results also indicate that, ultimately, higher coverage is needed to achieve better identification accuracy.\nAs the number of PMUs goes up, the ratio between correctly identified and correct-filtered cases also increases (i.e., green versus yellow).\nThis means fewer correctly identified cases will be sacrificed when the number of PMUs is high. \n\nDepending on the acceptable level of the misidentification rate or the correct-filtered rate, operators can decide on an empirical value for the threshold.\nIf misidentification is considered to be worse than a lower correct rate (e.g. compared to false alarms, the operator would rather accept more inconclusive cases), then the threshold can be set accordingly to eliminate misidentified cases.\nOtherwise, a very small non-zero level (e.g. $0.0015\\%$) can be chosen instead, with minimal impact on the correctly identified cases.\n\n\\subsection{Results of the Ontario Power System}\n\\label{sec: line out Ontario}\nTo evaluate performance on a more realistic system, results were also obtained using a model of the Ontario power system, consisting of 3488 buses, 864 generators, 1290 loads, 2242 branches and 1697 transformers.\nAmong the original branches, 1762 single-line outage cases are to be checked for occurrence without introducing islands.\nPMUs are assumed to be placed on the actual PMU locations and, alternatively, a set of high voltage buses in Ontario.\n\nFirst, tests were conducted using 26 bus voltages based on the current PMU locations in the Ontario network \\cite{curtis_north_2011}.\nTo evaluate the performance of the line outage detection algorithms for realistic, future PMU deployments, we also considered cases where all buses above a certain voltage level are monitored by PMUs.\nIn particular, we consider two cases: monitoring all buses with nominal voltages greater than or equal to 230 kV (an additional 53 buses) or 220 kV (an additional 842 buses).\n\\begin{figure}[hbtp]\n\t\\centering\n\t\\begin{subfigure}[b]{0.5\\linewidth}\n\t\t\\centering\n\t\t\\includegraphics[trim=0 0 20 20, height = 0.15\\textheight,width=\\linewidth]{threshold_hydro_mis.pdf}\n\t\t\\caption{26 PMUs}\n\t\t\\label{fig_ieso_26PMU}\n\t\\end{subfigure}\n\t\\begin{subfigure}[b]{0.5\\linewidth}\n\t\t\\centering\n\t\t\\includegraphics[trim=0 0 20 0, height = 0.15\\textheight,width=\\linewidth]{threshold_hydro_79_mis.pdf}\n\t\t\\caption{79 PMUs (230 kV+)}\n\t\t\\label{fig_ieso_79PMU}\n\t\\end{subfigure}\n\t\\begin{subfigure}[b]{0.48\\linewidth}\n\t\t\\includegraphics[trim=0 0 20 0, height = 0.15\\textheight,width=\\linewidth]{threshold_hydro_868_mis.pdf}\n\t\t\\caption{868 PMUs (220 kV+)}\n\t\t\\label{fig_ieso_868PMU}\n\t\\end{subfigure}\n\t\\caption{Identification results vs $\\epsilon$ (the Ontario system, $\\Delta E$ filter, three coverage levels)}\n\t\\label{fig_ieso_complete}\n\\end{figure}\nFig.~\\ref{fig_ieso_complete} shows the identification results using different thresholds.\nConsistent with the previous results, the observations about impact of filter threshold and PMU coverage are still valid for the Ontario system.\nWhen the coverage goes up, the threshold required to eliminate misidentified cases also decreases, which results in not only a higher correctly identified rate but also higher ratio of correct versus correct-filtered.\nThe horizontal line in each figure represents the separation of correctly identified (above the line) and misidentified (below the line) without rejection filter.\nThe accuracy given by the initial 26 PMUs is low (22.7\\% correct versus 77.3\\% misidentified) due to the very limited number of PMUs and the large number of outage scenarios.\nHowever, as the coverage increases by adding PMUs at high voltage buses, the results have been improved significantly.\nWhen 220 kV+ buses are monitored (868 PMUs), the correctly identified rate without filtering rises up to more than 70\\%.\nThis indicates monitoring high voltage buses is very beneficial and should be considered for future PMU deployments.\n\n\\section{Conclusions and Future Work}\n\\label{sec: conclusions and future work}\nThe current proposed methods have been implemented and tested on several systems of different scales.\nThe results show that the proposed identification algorithm using the ac power flow model achieves better identification accuracy compared to the dc approach. \nAdditionally, relatively high identification accuracy is achieved with a small number of PMUs.\nBy using rejection filtering techniques, the misidentified rate can be further reduced, which is crucial for online utilization of the event detection.\nThe results also demonstrate there are significant benefits of having a higher PMU coverage.\nLow number of PMUs makes it difficult to identify the outage on the Ontario system, but future PMU deployments (e.g., with all high voltage buses monitored) exhibit good performance. \n\n\n\n\n\n\n\n\n\\ifCLASSOPTIONcaptionsoff\n \\newpage\n\\fi\n\n\n\n\n\n\\bibliographystyle{IEEEtran}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nMixed action approaches have been studied by several groups,\nsuch as Domain-wall fermion (DWF) valence on Asqtad fermion sea~\\cite{Edwards:2005ym}, overlap valence on DWF sea~\\cite{Allton:2006nu}, overlap valence on \nclover sea~\\cite{Durr:2007ez}, and overlap valence\non twisted-mass fermion sea~\\cite{Cichy:2009dy}. In view of the fact that it is numerically intensive to simulate chiral fermions\n(DWF or overlap), it is deemed practical to use the cheaper fermion formulation for generating gauge configurations and\nthe more expensive fermion discretization for the valence as an expedient approach toward dynamical QCD simulations with chiral fermions.\nMany current algebra relations depend only on the chiral properties of the\nvalence sector. The mixed action theory with different fermions for the valence and the sea is a generalization of the\npartially quenched theory with different sea and valence quark masses.\nThe mixed action partially quenched chiral perturbation theory (MAPQ$\\chi$PT) has been developed for\nGinsparg-Wilson fermions on Wilson sea~\\cite{Bar:2003mh} and staggered sea~\\cite{Bar:2005tu}, and has been worked out for\nmany hadronic quantities to next-to-leading order (NLO), such as pseudoscalar masses and decay \nconstants~\\cite{Bar:2003mh,Bar:2005tu,Chen:2007ug},\nisovector scalar $a_0$ correlator~\\cite{Golterman:2005xa,Prelovsek:2005rf,Aubin:2008wk,WalkerLoud:2008bp},\nheavy-light decay constants~\\cite{Aubin:2005aq}, and baryon masses~\\cite{Tiburzi:2005is, WalkerLoud:2008bp}.\n\nIn the mixed action chiral perturbation theory with chiral valence fermions, it is shown~\\cite{Bar:2003mh} that to\nNLO there is no $\\mathcal{O}(a^2)$ correction to the valence-valence meson mass due to\nthe chiral symmetry of the valence fermion. Furthermore, both the chiral Lagrangian and the chiral extrapolation\nformulas for hadron properties to the one-loop level (except $\\theta$-dependent quantities) are independent of the\nsea fermion formulation~\\cite{Chen:2006wf}. The LO mixed-action chiral Lagrangian involves only one more term with\n$\\mathcal{O}(a^2)$ discretization dependence which is characterized by a low energy constant $\\Delta_{mix}$.\nThe LO pseudoscalar meson masses for overlap valence and DWF sea are given as\n\\begin{eqnarray} \\label{eq:dd}\nm_{vv'}^2 &=& B_{ov}(m_v +m_{v'}), \\nonumber \\\\\nm_{vs}^2 &=& B_{ov}m_v + B_{dw}(m_s + m_{res}) + a^2\\Delta_{mix}, \\nonumber \\\\\nm_{ss'}^2 &=& B_{dw}(m_s + m_{s'} + 2 m_{res}),\n\\end{eqnarray}\nwhere $m_{vv'}\/m_{ss'}$ is the mass of the pseudoscalar meson made up of valence\/sea quark and antiquark.\n$m_{vs}$ is the mass of the mixed valence and sea pseudoscalar meson. Up to numerical accuracy, there is no\nresidual mass for the valence overlap fermion. The DWF sea has a residual mass $m_{res}$ which vanishes as\n$L_S \\rightarrow \\infty$. $\\Delta_{mix}$ enters in the mixed meson mass $m_{vs}$ as an\n$\\mathcal{O}(a^2)$ error which vanishes in the continuum limit. We should note that, unlike the partially\nquenched case, even when the quark masses in the valence and sea match, the unitarity is still violated due\nto the use of mixed actions. The degree of unitarity violation at finite lattice spacing depends on the size of $\\Delta_{mix}$.\n\n$\\Delta_{mix}$ has been calculated for DWF\nvalence and Asqtad fermion sea which gives\n$\\Delta_{mix} = 0.249(6)\\,{\\rm GeV}^4$ \\cite{Orginos:2007tw} at $a=0.125\\,{\\rm fm}$, $0.211(16)\\,{\\rm GeV}^4$ at $a=0.12\\,{\\rm fm}$ \\cite{Aubin:2008wk}, and $0.173(36)\\,{\\rm GeV}^4$ at $a=0.09\\,{\\rm fm}$ \\cite{Aubin:2008wk}. It is also calculated for overlap valence and clover sea which yields $\\Delta_{mix} = 0.35(14)\/0.55(23)\\,{\\rm GeV}^4$ at $m_{\\pi} = 190\/300\\,{\\rm MeV}$ and $a=0.09\\,{\\rm fm}$~\\cite{Durr:2007ef}.~This means that for a valence pion of $300\\,{\\rm MeV}$, $\\Delta_{mix}$ produces, at $a=0.12\\,{\\rm fm}$, a shift of $\\sim$ $110-240\\,{\\rm MeV}$ and,\nat $a =0.09\\,{\\rm fm}$, a shift of $\\sim$ $55-153\\,{\\rm MeV}$ for these cases, which are substantial portions of\nthe valence pion mass. \n\nWe have used valence overlap fermions on the 2+1-flavor DWF sea to study hadron \nspectroscopy~\\cite{Li:2010pw,Dong:2009wk,Mathur:2010ed,Gong:2011nr}. In this work, we calculate \n$\\Delta_{mix}$ for such a mixed action approach which is needed for chiral extrapolation\nin MAPQ$\\chi$PT.\n\n\n\\section{Calculation Details}\nThe $\\Delta_{mix}$ parameter is calculated on four ensembles of the $2+1$-flavor domain wall fermion gauge configurations \\cite{Allton:2008pn, Mawhinney:2009jy}. Two different lattice spacings were used to study the dependence on the cutoff. In addition, we also used multiple sea masses, for the $32^3 \\times 64$ lattices, to study the sea quark mass dependence of $\\Delta_{mix}$. Details of the ensembles are listed in Table \\ref{tab:ensembles}.\n\\begin{table}[htdp]\n\\begin{center}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nLattice Size& $a^{-1}(\\,{\\rm GeV})$& $a m_l$& $ m_{ss}(\\,{\\rm MeV})$\\\\ \n\\hline\n\\hline\n$24^3 \\times 64$ & 1.73(3) & 0.005 & 329(1)\\\\\n$32^3 \\times 64$ & 2.32(3) & 0.004 & 298(1)\\\\\n$32^3 \\times 64$ & 2.32(3) & 0.006 & 350(2)\\\\\n$32^3 \\times 64$ & 2.32(3) & 0.008 & 399(1)\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\caption{Details of the DWF ensembles used in this work.}\n\\label{tab:ensembles}\n\\end{table}\n\nThere are different parameterization schemes \\cite{Orginos:2007tw, Aubin:2008wk} one can use to relate $\\Delta_{mix}$ to the quark and pseudoscalar masses from Eq.~(\\ref{eq:dd}). In this paper we choose to parameterize $\\Delta_{mix}$ as \n \\begin{equation}\n\\label{eq:dmix}\nm_{vs}^2 -\\frac{1}{2}m_{ss}^2 = B_{ov} m_v + a^2\\Delta_{mix}.\n\\end{equation}\nThis is similar to the parameterization used in \\cite{Aubin:2008wk}. The quantity $\\delta m^2 \\equiv m_{vs}^2 -\\frac{1}{2}m_{ss}^2$, has a linear behavior in $m_v$ and can be calculated directly by computing only the pseudoscalar masses $m_{vs}$, and $m_{ss}$. We see that in the regime where Eq.~(\\ref{eq:dmix}) is valid, $\\Delta_{mix}$ is equivalent to $\\delta m^2$ in the limit $ m_v \\rightarrow 0$.\n\nIn this work we used an overlap operator with a HYP-smeared kernel. This was shown to have better numerical properties~\\cite{Li:2010pw}. We calculated the masses $m_{vs}$ and $m_{ss}$ using 50 configurations for each ensemble. Recall from Section I that $m_{ss}$ requires the propagators for the sea quark mass which were computed with DWF. $m_{vs}$ needs the propagators for both overlap and DWF. The DWF propagators were made available by LHPC. The overlap propagators were computed using a polynomial approximation \\cite{Alexandru:2011sc} to the matrix sign function. They were used to compute $14-16$ values of $m_{vs}$. A multi-shifted version of the conjugate gradient algorithm \\cite{Jegerlehner:1996pm} was implemented for the overlap propagator calculation to compute all masses at once. To accelerate the inversions for the overlap propagators we employed a deflation technique which has been seen to speed up the calculation significantly~\\cite{Li:2010pw}. \n\nWe fitted correlators using single state exponential fits to extract the pion masses. The fitting windows were adjusted to get a reasonable $\\chi^2\/dof$ and are the same for all the masses in each ensemble. For the $24^3 \\times 64$ ensemble the details are presented in Table \\ref{tab:pfit1}. For one of the sea quark mass in the $32^3 \\times 64$ ensemble, the results are presented in Table \\ref{tab:pfit2}. \n\\begin{table}[htdp]\n\\begin{center}\n\\caption{Pion mass fitting details for the $24^3 \\times 64$ lattice. $m_{ss} \\simeq 329\\,{\\rm MeV}$.}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n$a m_{v}$& $m_{vs}(\\,{\\rm MeV})$ & $\\chi^2_{vs}\/dof$ & $m_{vv}(\\,{\\rm MeV})$ &$\\chi^2_{vv}\/dof$ \\\\ \n\\hline\n\\hline\n0.0014& 274(3) & 1.0 &122(4) & 1.8 \\\\ \n0.0027& 278(2) & 1.1 &154(2) & 1.4 \\\\ \n0.0046& 287(2) & 1.2 &188(2) & 1.6 \\\\ \n0.0081& 305(2) & 1.2 &242(2) & 1.6 \\\\ \n0.0102& 315(2) & 1.2 &270(2) & 1.4 \\\\ \n0.0135& 331(2) & 1.1 &308(2) & 1.3 \\\\ \n0.0153& 339(1) & 1.1 &327(2) & 1.3 \\\\ \n0.0160& 342(1) & 1.1 &334(2) & 1.3 \\\\ \n0.0172& 347(1) & 1.1 &346(2) & 1.3 \\\\ \n0.0243& 378(1) & 1.2 &409(1) & 1.6 \\\\ \n0.0290& 397(1) & 1.2 &445(1) & 1.7 \\\\ \n0.0365& 426(1) & 1.2 &498(1) & 1.5 \\\\ \n0.0434& 451(1) & 1.2 &542(1) & 1.3 \\\\ \n0.0489& 471(1) & 1.2 &576(1) & 1.2 \\\\ \n0.0670& 531(1) & 1.0 &677(1) & 1.3 \\\\ \n0.0710& 543(1) & 1.0 &698(1) & 1.4 \\\\ \n\\hline\n\\hline\n\\end{tabular}\n\\label{tab:pfit1}\n\\end{center}\n\\end{table}\n\\begin{table}[htdp]\n\\begin{center}\n\\caption{Pion mass fitting details for the $32^3 \\times 64$ lattice for $a m_l = 0.004$. $m_{ss} \\simeq 298\\,{\\rm MeV}$.}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n$a m_{v}$& $m_{vs}(\\,{\\rm MeV})$ & $ \\chi^2_{vs}\/dof$ & $m_{vv}(\\,{\\rm MeV})$ &$\\chi^2_{vv}\/dof$ \\\\ \n\\hline\n\\hline\n0.0007& 237(3)&1.0& 126(10)&1.5\\\\\n0.0015& 238(2)&1.0& 136(7)& 1.2\\\\\n0.0025& 246(2)&1.0& 160(4)& 1.2\\\\\n0.0035& 254(2)&1.0& 184(3)& 1.2\\\\\n0.0046& 264(2)&1.0& 209(3)& 1.2\\\\\n0.0059& 274(2)&1.0& 235(3)& 1.2\\\\\n0.0068& 282(2)&1.0& 253(3)& 1.3\\\\\n0.0076& 289(2)&1.0& 268(3)& 1.4\\\\\n0.0089& 299(2)&1.0& 288(3)& 1.6\\\\\n0.0112& 317(2)&1.1& 324(4)& 1.0\\\\\n0.0129& 329(2)&1.1& 337(5)& 1.0\\\\\n0.0152& 344(2)&1.2& 365(5)& 1.3\\\\\n0.0180& 362(2)&1.3& 396(6)& 1.5\\\\\n0.0240& 399(3)&1.6& 454(9)& 1.9\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\label{tab:pfit2}\n\\end{center}\n\\end{table}%\n\n\n\\section{Fitting strategies and Results}\n\n\\begin{figure*}[t]\n\\includegraphics[width= 3.4in]{mvvsq24_2.pdf}\n\\includegraphics[width= 3.4in]{mvvsq32_2.pdf}\n\\caption{$(a m_{vv})^2\/2(a m_v)$ as a function of $a m_v$ for\nthe $24^3 \\times 64$ ensemble (left) and \nthe three $32^3 \\times 64$ ensembles (right).}\n\\label{fitregion}\n\\end{figure*}\n\nExtracting $\\Delta_{mix}$ requires fits to squares of multiple pion propagators with different valence quark masses; the resulting \nmasses will be correlated since they all come from a single ensemble. \nTo compute the covariance matrix we would need to perform an augmented $\\chi^2$-fit involving the pion propagators for all valence quark masses simultaneously. This is not numerically stable due to the large number of parameters in the model. We thus have to use an alternative procedure to take into account these correlations. To gauge the systematic errors introduced by our choice of fitting method we will use multiple fitting procedures. In this section we will describe three different fitting strategies. These methods differ in the way we account for correlations among the different valence masses.\n \nIn method I we follow the standard jackknife philosophy by defining a $\\Delta_{mix}$ estimator directly in terms of the raw pion propagators; the error is then determined by the variance over the jackknife ensemble. In method II we use the jackknife procedure to estimate the $\\delta m^2$ covariance matrix and then compute $\\Delta_{mix}$ using a standard correlated fit~\\cite{Luscher:2010ae}. These two procedures are used for both the $24^3 \\times 64$ and $32^3 \\times 64$ ensembles. However, since there are three sea quark masses for the $32^3 \\times 64$ volume, we can perform an {\\em uncorrelated} fit using $\\delta m^2$ computed on the three independent ensembles---this is our method III. From the different methods we can obtain a systematic uncertainty for our results. \n\nIn each method we performed a binned jackknife analysis of $\\delta m^2 =m_{vs}^2 -\\frac{1}{2}m_{ss}^2$. Because we performed four inversions with four different point sources per configuration we binned in units of four and eight. This allowed us to drop one or two whole configurations in each jackknife subsample. We find the uncertainties in both cases to be of comparable size, indicating that autocorrelation is negligible. \n\n\nThe first step in our fitting is to determine a range of quark masses where the tree-level relation between $m^2_{vv}$ and $m_v$ holds so that we can use Eq.~(\\ref{eq:dd}). Below this range, one expects to see chiral logs including partially quenched logs and other non-linear $m_v$ dependence from the NLO in $\\chi$PT. Above this range, tree-level $\\chi$PT is not expected to be valid. We do this by plotting $(a m_{vv})^2 \/2(a m_v)$ as a function of $a m_v$ as shown in Fig. \\ref{fitregion}. We choose the fitting range in the region where the ratio $(a m_{vv})^2\/2 (a m_v)$ is fairly flat; these ranges are tabulated in Table \\ref{dmix:fitrange} and are used in all three fitting methods. We note that in the range we are fitting, $m_{\\pi}L > 4$ for both $m_{vs}$ and $m_{vv}$ so that the volume dependence is expected to be small.\n\n\\begin{table}[b]\n\\begin{center}\n \\begin{tabular}{|c|c|c|}\n \\hline\n Lattice&$a m_l$& $a m_v $ fit range\\\\\n \\hline\n \\hline\n $24^364$&0.005&0.0243 - 0.0489 \\\\\n $32^364$&0.004&0.0112 - 0.0240\\\\\n $32^364$&0.006&0.0112 - 0.0240 \\\\\n $32^364$&0.008&0.0112 - 0.0240\\\\\n \\hline\n \\hline\n\\end{tabular}\n\\end{center}\n\\caption{Range of quark masses, $a m_v$, used in the fitting procedures to extract $\\Delta_{mix}$ via Eq.~(\\ref{eq:dd}).}\n\\label{dmix:fitrange}\n\\end{table}\n\n\\begin{figure*}[t]\n\\includegraphics[width= 3.4in]{dmix_24.pdf}\n\\includegraphics[width= 3.4in]{all3.pdf}\n\\caption{Extracting $\\Delta_{mix}$ from a linear extrapolation of $\\delta m^2$ for the $24^3 \\times 64$ ensemble (left) \nand the three $32^3 \\times 64$ ensembles (right). For the $32^3 \\times 64$ plot the inset shows the intercept of the \nfit and the error bars of the extracted values of $\\Delta_{mix}$.}\n\\label{dmixfit}\n\\end{figure*}\n\n\\subsection{Method I: weighted averaging}\nIn this method we used a weighted linear fit to extract the value of $\\Delta_{mix}$ for each bin. The weights, $\\sigma_{\\delta m^2}^2$, are given by \n\\begin{equation}\n\\sigma_{\\delta m^2}^2 = 4 m_{vs}^2\\sigma_{ m_{vs}}^2 + m_{ss}^2 \\sigma_{m_{ss}}^2,\n\\label{dmix:err1}\n\\end{equation}\nwhere $\\sigma_{m_{vs}}$, and $\\sigma_{m_{ss}}$ are the uncertainties of the mixed and DWF pion masses respectively. Eq.~(\\ref{dmix:err1}) was derived using the standard error propagation formula neglecting correlation between $m_{vs}$ and $m_{ss}$. The cross-correlations will be accounted for by the external jackknife procedure. By using weighted fitting, less importance is given to data points with larger uncertainties. After fitting each bin we then have a jackknife ensemble \\{$\\Delta_{mix}$\\}. We use square brackets, [~], to indicate a particular jackknife sample and angle brackets, $\\langle ~\\rangle$, to denote jackknife averages. For the case of $\\Delta_{mix}$, its jackknife average and the corresponding uncertainty is given by\n\\begin{eqnarray}\n\\langle \\Delta_{mix} \\rangle &=& \\frac{1}{N}\\sum_{k=1}^{N}\\left[ \\Delta_{mix}\\right]_k \\nonumber \\\\\n\\sigma_{\\Delta_{mix}} &=& \\sqrt{(N-1)\\left(\\langle \\Delta_{mix}^2\\rangle-\\langle \\Delta_{mix}\\rangle^2 \\right)}\\nonumber. \\\\\n\\end{eqnarray}\n\nThe extracted values of $\\Delta_{mix}$, using this method, are presented in Table \\ref{dmix:method1}. We also list the corresponding values of $B_{ov}$. Figure \\ref{dmixfit} shows $a^2 \\delta m^2$ as a function of $a m_{v}$ and the corresponding linear fit.\n\n\\begin{table}[b]\n\\begin{center}\n \\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n \\multirow{2}{*}{Lattice}&\\multirow{2}{*}{$a m_l$}& \\multicolumn{2}{|c|}{$\\Delta_{mix}(\\,{\\rm GeV}^4)$}& \\multicolumn{2}{|c|}{$B_{ov}$(\\,{\\rm GeV})}\\\\\n &&\\multicolumn{1}{c}{I}&II&\\multicolumn{1}{c}{I}&II\\\\\n \\hline\n \\hline\n $24^364$&0.005&0.032(6) & 0.028(5) &1.85(2) &1.88(2) \\\\\n $32^364$&0.004&0.040(14)& 0.025(9) &1.88(10)&1.95(6)\\\\\n $32^364$&0.006&0.054(9) & 0.050(8) &1.81(5) &1.81(5)\\\\\n $32^364$&0.008&0.059(13)& 0.063(13)&1.74(6) &1.73(6) \\\\\n \\hline\n \\hline\n\\end{tabular}\n\\end{center}\n\\caption{Extracted values of $\\Delta_{mix}$ and $B_{ov}$ using fitting methods I and II.}\n\\label{dmix:method1}\n\\end{table}\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n\n\\subsection{Method II: covariance matrix}\nIn our second method we perform a correlated valence-quark mass fit to the function $\\delta m^2 = B_{ov} m_v + a^2 \\Delta_{mix}$. The central values are taken to be the jackknife averages,\n\\begin{equation}\n\\langle(\\delta m^2)_i \\rangle = \\frac{1}{N}\\sum_{k=1}^N\\left [(\\delta m^2)_i\\right]_k \\,.\n\\end{equation}\nWe minimize the correlated $\\chi^2$-function,\n\\begin{eqnarray*}\n\\chi^2& =& \\sum_{i,j} \\left(\\langle(\\delta m^2)_i \\rangle - (\\delta m^2)_i\\right)C_{i,j}^{-1} \\nonumber\\\\\n&\\times&\\left(\\ \\langle(\\delta m^2)_j \\rangle -(\\delta m^2)_j\\right) \\,, \\nonumber \\\\\n\\end{eqnarray*}\nwith the covariance matrix, $C_{i,j}$, given by the jackknife estimate\n\\begin{eqnarray}\nC_{ij} &=& \\frac{(N-1) } {N} \\sum_{k=1}^N \\left(\\left[(\\delta m^2)_i\\right]_k - \\langle(\\delta m^2)_i \\rangle \\right) \\nonumber \\\\\n&\\times& \\left(\\left[(\\delta m^2)_j\\right]_k - \\langle (\\delta m^2)_j \\rangle\\right) \\,. \\nonumber \\\\\n\\label{covmat}\n\\end{eqnarray}\nThe subscripts $i$ and $j$ index the valence-quark mass. The factor $(N-1)\/N$ differs from the usual definition of the covariance matrix to account for the correlation of the jackknife samples~\\cite{Luscher:2010ae}. The fit uncertainties are obtained by constructing the standard Hessian matrix. Results of this method are tabulated in Table \\ref{dmix:method1}.\n \n \n \n \n \n \n \n \n \n \n \n \n \n\\subsection{Method III: uncorrelated fitting}\nThe methods described in the two previous sections closely parallel the method performed by \\cite{Aubin:2008wk} in which for each lattice ensemble a string of partially quenched meson masses are calculated; from them we extract $\\Delta_{mix}$. In this section we perform a fit on the three $32^3 \\times 64$ ensembles based on the value of $\\delta m^2$ measured at the point where the valence and sea quark masses match \\footnote{As mentioned in \\cite{Aubin:2008wk} one can never get rid of partial quenching effects even in the case where the pseudoscalar mesons of both the DWF and overlap actions match. This is because discretization errors are different for the two actions.}. There are no cross-correlations among the masses since the ensembles are independent. \n\nThe value of $\\delta m^2$ is computed for the pion mass that most closely satisfies the condition $m_{ss} \\approx m_{vv}$. Because it is not easy to match a priori the pseudoscalar masses and it is expensive to regenerate overlap propagators with different masses, we performed interpolation among the existing data points to obtain a better approximation of where $m_{ss}$ and $m_{vv}$ match.\n\nWe do an uncorrelated $\\chi^2$-fit with the error bars obtained by a jackknife procedure. Figure \\ref{dmix:32method3} shows the data points and the fit results. For this case we find $\\Delta_{mix} = 0.042(24)\\,{\\rm GeV}^4$, and $B_{ov} = 1.82(16)\\,{\\rm GeV}$. \n\n\\begin{figure}[t]\n\\includegraphics[width= 3.4in]{3264_full3.pdf} \n\\caption{Determining $\\Delta_{mix}$ by performing the linear extrapolation as in Fig.~\\ref{dmixfit}. Each point corresponds to one of the $32^3 \\times 64$ lattices where $m_{ss} \\approx m_{vv}$. A magnified view of the extrapolation in the neighborhood of the chiral limit, along with the extrapolated value, is shown in the inset.}\n\\label{dmix:32method3}\n\\end{figure}\n\n\\section{Discussion}\n\n\nAs a first step, we look at the lattice spacing dependence. We compare the two ensembles with $m_{ss}$ close to $300\\,{\\rm MeV}$. \nThese correspond to $a m_l = 0.005$ and $a m_l = 0.004$ with $a = 0.114\\,{\\rm fm}$ and $a=0.085\\,{\\rm fm}$ respectively.\nWe average the central values and the errors from the two fitting methods, separately for each lattice spacing. \nWe find at $a = 0.114\\,{\\rm fm}$ $\\Delta_{mix} = 0.030(6)\\,{\\rm GeV}^4$ and for $a = 0.085\\,{\\rm fm}$ $\\Delta_{mix} = 0.033(12)\\,{\\rm GeV}^4$. \nWe see that the lattice spacing dependence is smaller than our errors; this indicates that $\\Delta_{mix}$ is capturing \nthe dominant lattice artifact for the parameters used in this study. \n\nTo discuss the sea quark dependence of $\\Delta_{mix}$ we plot in Fig.~\\ref{dmix:32method4} the results of \nmethods~I, II, and III for the ensembles with $a = 0.085\\,{\\rm fm}$. We note that the results are consistent within \ntwo sigma. This is consistent with LO MAPQ$\\chi$PT in Eq.~(\\ref{eq:dd}) where $\\Delta_{mix}$ is a low energy \nconstant, independent of the valence and sea masses.\n\nWe now combine the $\\Delta_{mix}$ values extracted from the two lightest sea mass ensembles to produce our final result.\nWe use these ensembles because the LO MAPQ$\\chi$PT is expected to describe the data better at lower sea quark masses.\nFor each of the fitting methods we combine the results of the $a = 0.114\\,{\\rm fm}$ and $a=0.085\\,{\\rm fm}$ ensembles.\nSince the two ensembles are statistically independent, it is straightforward to combine these results: for method~I we get \n$\\Delta_{mix} = 0.033(6)\\,{\\rm GeV}^4$ and for method~II we get $\\Delta_{mix} = 0.027(5)\\,{\\rm GeV}^4$. We can now use the values\ndetermined using these two methods to estimate the systematic fitting errors. \nWe quote the final result with two uncertainties, the first statistical and the second associated with fitting systematics.\nThe central value and the statistical error are taken to be the average of the results from methods~I and II. \nThe systematic error is the standard deviation of the results from these two methods. \nWe get $\\Delta_{mix}=0.030(6)(5)\\,{\\rm GeV}^4$.\n\n\n\\begin{figure}[t]\n\\includegraphics[width= 3.4in]{3264_vs_chiral.pdf} \n\\caption{$\\Delta_{mix}$ for $ a= 0.085\\,{\\rm fm}$. The empty symbols indicate the results of method I and the full symbols are from method II. The continuous line and the shaded region are the results of the chiral extrapolation in method III.}\n\\label{dmix:32method4}\n\\end{figure}\n\n\n\n\n\nTable \\ref{dmix:others} lists calculated values of $\\Delta_{mix}$ for\npion masses close to $300\\,{\\rm MeV}$ using different mixed actions. We\nnotice that our values of $\\Delta_{mix}$ are significantly smaller\nthan overlap on clover or DWF on Asqtad for comparable lattice spacings and pion masses. \nFor both $m_{vv}$ and $m_{ss}$ at $300\\,{\\rm MeV}$, our calculated $\\Delta_{mix}$ will\nshift the pion mass up by $10$ and $16\\,{\\rm MeV}$ for the $32^3 \\times 64$ lattice at\n$a = 0.085\\,{\\rm fm}$ and $24^3 \\times 64$ lattice at $a = 0.114\\,{\\rm fm}$, respectively.\nThese are substantially smaller than the corresponding $55-153\\,{\\rm MeV}$ and\n$110-240\\,{\\rm MeV}$ shifts that we mentioned in Sec. I.\nThe value of $\\Delta_{mix}$ is significantly smaller in our case probably because \nthe sea and valence fermion actions are similar. Both of them are approximations of \nthe matrix sign function, but with different kernels.\n\n\n\\begin{table}[htbp]\n\\begin{center}\n\\begin{tabular}{|l|c|c|c|}\n\\hline\n~~~~Mixed Action& Ref. & $a\\,{\\rm fm}$&$\\Delta_{mix}(\\,{\\rm GeV}^4)$\\\\\n\\hline\n\\hline\nDWF on staggered& \\cite{Orginos:2007tw}&0.125& 0.249(6)\\\\\nDWF on staggered&\\cite{Aubin:2008wk}& 0.12& 0.211(16)\\\\\nDWF on staggered& \\cite{Aubin:2008wk}& 0.09& 0.173(36)\\\\\noverlap on clover& \\cite{Durr:2007ef}&0.09&0.55(23)\\\\\noverlap on DWF & this work & 0.114 & 0.030(6)\\\\\noverlap on DWF & this work & 0.085& 0.033(12)\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\caption{$\\Delta_{mix}$ values for DWF valence quarks on staggered sea quarks for pion mass at $300\\,{\\rm MeV}$.}\n\\label{dmix:others}\n\\end{table}\n\nThe previous calculation \\cite{Li:2010pw} of $\\Delta_{mix}$, for\noverlap on the DWF sea, has roughly the same value but the sign is\nnegative. That calculation measured $\\Delta_{mix}$ by examining the\nstates that wrap around the time boundary. This indirect \nmethod is less reliable and the errors are large. \n\n\\section{Conclusion}\nWe calculated the additive mixed action pseudoscalar meson mass parameter,\n$\\Delta_{mix}$, for the case of valence overlap fermions on a DWF sea.\n$\\Delta_{mix}$ is significant because it enters\ninto mixed action partially quenched chiral perturbation theory (MAPQ$\\chi$PT) for\nchiral extrapolation of many low energy observables.\n\nTwo different lattice spacings were used to examine the cut-off behavior.\nFor a pion mass close to $300\\,{\\rm MeV}$ we find $\\Delta_{mix}=0.030(6)\\,{\\rm GeV}^4$ at $a =0.114\\,{\\rm fm}$ and \n$\\Delta_{mix}=0.033(12)\\,{\\rm GeV}^4$ at $a =0.085\\,{\\rm fm}$. They are the same within errors. \nOur calculated $\\Delta_{mix}$ will\nshift the pion mass up by $10$ and $16\\,{\\rm MeV}$ for the $32^3 \\times 64$ lattice at\n$a = 0.085\\,{\\rm fm}$ and $24^3 \\times 64$ lattice at $a = 0.114\\,{\\rm fm}$, respectively.\nWe studied the sea quark mass dependence of $\\Delta_{mix}$ at $a = 0.085\\,{\\rm fm}$ and\nwe find that they agree within two sigma. Combining the results extracted from\nthe ensembles with the lightest sea quarks, we get $\\Delta_{mix}=0.030(6)(5)\\,{\\rm GeV}^4$,\nwhere the first error is statistical and the second is the systematic error\nassociated with the fitting method.\n \nWhen compared to previous mixed action studies, DWF on staggered or overlap on clover, the values of $\\Delta_{mix}$ of overlap on DWF\nare almost an order of magnitude smaller. This is most likely due to the fact that the sea and valence fermions\nare similar. \n\n\\begin{acknowledgements}\nWe would like to thank J. Negele, A. Pochinsky, M. Engelhardt, and\nLHPC for generously making available the DWF propagators for all the\nensembles used in this paper. This work is supported in part by\nU.S. Department of Energy grants DE-FG05-84ER40154,\nDE-FG02-95ER40907, GW IMPACT collaboration, DE-FG02-05ER41368, and DST-SR\/S2\/RJN-19\/2007, India.\n\\end{acknowledgements}\n\n\\newpage\n\\bibliographystyle{jhep-3}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section{Introduction}\n\\label{sec:intro}\nThe compensation of quantum numbers plays a key role in our understanding of the\nfragmentation process whereby partons transform into observable hadrons. \nConsequently, baryon production in hadronic \\ensuremath{{\\rm e}^+{\\rm e}^-}\\ annihilation final states\nprovides data very well suited to test \nphenomenological fragmentation models. In particular, the study of di-lambda \npairs allows a subtle testing of model predictions because of the relatively \nlarge rates and the necessity to compensate two quantum numbers: \nbaryon number and strangeness. \n\nFragmentation models such as \\jt \\cite{jt} and \\hw \\cite{hw} are based on\na chainlike production of hadrons with local compensation of quantum numbers.\nIn \\jt, particle production is implemented via string fragmentation. Baryons (B)\nare formed when a diquark pair is contained in the string \n(see diagram a below), thus resulting in a strong baryon-antibaryon \ncorrelation. This correlation can be softened by the ``popcorn effect'' when \nan additional meson (M) is produced between the baryon pair as shown in \nthe diagrams b and c below.\nIn contrast, \\hw\\ describes fragmentation via the formation of clusters and \ntheir subsequent decay. Baryons are produced by the isotropic cluster decay \ninto a baryon pair, which can result in stronger correlations than those \npredicted by \\jt .\n\n\\vspace*{-2.5cm}\n\\begin{center}\n \\resizebox{\\textwidth}{!}{\n \\includegraphics{pr251_diagr.eps}\n }\n\\end{center}\n\\vspace{-2.5cm}\n\nDi-lambda production in multihadronic \\ensuremath{{\\rm Z}^0}\\ decays has been studied over the \npast years by experiments at {\\sc Petra}, {\\sc Pep} and \\lep \n\\cite{petra,pep,aleph_corr,delphi_corr,opal_corr}. These experiments \nreport short-range correlations as observed in the distributions of the \nrapidities $y$ or rapidity differences \\ensuremath{|\\Delta y|}\\ of correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs.\nThe rapidity of a particle is defined as\n$y=\\frac{1}{2}\\ln\\left(\\frac{E+p_{\\scriptscriptstyle\\parallel}}{E-\np_{\\scriptscriptstyle\\parallel}}\\right)$, \nwhere $E$ is the energy of the particle and $p_{\\scriptscriptstyle\\parallel}$ \nthe longitudinal momentum with respect to the thrust axis. Rapidity differences \nare Lorentz-invariant under boosts along the event axis.\nThese correlations are compared to predictions of \\jt\\ and \\hw . \nSatisfactory agreement is found with the predictions of \\jt\\ when $\\rho$, the \n``popcorn parameter''\\footnote{The value of $\\rho$ can be set in \\jt\\ with the \nparameter PARJ(5): $\\rho=\\frac{BM\\bar{B}\\rule{0cm}{2.5ex}}{B\\bar{B}+BM\\bar{B}} = \n\\frac{\\rm PARJ(5)}{0.5+{\\rm PARJ(5)}}$.},\nis set to the default value, $\\rho=0.5$. However, the tune of other parameters \nmodeling baryon production significantly influences the predictions \n\\cite{james_early}. \n\\hw\\ on the other hand predicts correlations much larger than those \nexperimentally observed.\n\nThe full data sample of 4.3~million hadronic \\ensuremath{{\\rm Z}^0}\\ decays collected with \nthe {\\sc Opal}\\ detector at \\lep\\ in the region of the \\ensuremath{{\\rm Z}^0}\\ peak is used in this \ninvestigation. It supplements the earlier {\\sc Opal}\\ \nwork\\cite{opal_corr} by increased statistics and a more robust technique \nto remove the background contributions from the \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ and \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}\\ \nsamples in order to obtain a correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ sample which is as clean as \npossible. \nThe \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ correlations are investigated mainly via rapidity differences. \nThey are compared to the earlier \\lep\\ results and to the predictions of \n\\jt\\ and \\hw. The predictions of the recent \\jt\\ modification \n{\\sc Mops}\\ (MOdified Popcorn Scenarium)~\\cite{mops} are also considered. \nCorrelated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs are also studied in 2-jet events in which models can\nbe tested with improved sensitivity (compared to the full data sample)\nwhen rapidity differences are investigated. \nFinally, we study correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs within the same and within different\njets. \n\nSection~\\ref{sec:procedure} gives a short description of the {\\sc Opal}\\ detector \nand\npresents the selection of the \\l\\ events\\footnote \n{For simplicity \\l\\ refers to both \\l\\ and \\ensuremath{\\bar{\\Lambda}}.}\nin the total sample and also in 2-jet and 3-jet events, for both experimental \nand simulated data.\nIn section~\\ref{sec:method} the separation of the \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ and \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}\\ samples \nfrom the background and the determination of the rates of correlated \n\\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs as a function of the rapidity differences \\ensuremath{|\\Delta y|}\\ are discussed. \nSection~\\ref{sec:results} contains the measured rates with their errors and a \ncomparison to earlier results as well as the presentation of the differential \ndistributions as a function of \\ensuremath{|\\Delta y|}\\ and \\ensuremath{\\cos \\theta^*}, where \\ensuremath{\\theta^*}\\ is the angle \nbetween the thrust axis and the \\l\\ momentum calculated in the rest frame of \nthe di-lambda pair.\nIn section~\\ref{sec:comparison} the models are tested using the production \nrates of \\l\\ pairs as well as the \\ensuremath{\\cos \\theta^*}\\ and \\ensuremath{|\\Delta y|}\\ spectra of correlated \n\\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs. \nThe range of di-lambda correlations is investigated in section~\\ref{sec:jets}\nby the assignment of the \\l's to the jets. \nConclusions are drawn in section~\\ref{sec:summary}.\n\\section{Experimental Procedure}\n\\label{sec:procedure}\n\\subsection{The OPAL Detector}\n\\label{subs:detec}\nA detailed description of the {\\sc Opal}\\ detector can be found in \nRef.~\\cite{detector}. \nOf most relevance for the present analysis is the tracking system and \nthe electromagnetic calorimeter. \nThe tracking system consists of a silicon microvertex detector, an inner\nvertex gas chamber, a large-volume jet chamber and specialized chambers at\nthe outer radius of the jet chamber which improve the measurements in\nthe $z$ direction ($z$-chambers)\\footnote\n{The coordinate system is defined so that $z$~is the coordinate parallel \nto the e$^-$ beam axis, $r$~is the coordinate normal to the beam axis, \n$\\phi$~is the azimuthal angle around the beam axis, and $\\theta$~is the \npolar angle \\mbox{with respect to~$z$.}}. The tracking system covers the region\n$|\\cos\\theta|<0.95$ and is located within a solenoidal magnet coil with\nan axial field of~0.435~T. \nThe tracking detectors provide momentum measurements of charged\nparticles, and particle identification from measurements of the\nionization energy loss, d$E$\/d$x$.\nElectromagnetic energy is measured by a lead-glass calorimeter \nlocated outside the magnet coil, which covers $|\\cos\\theta|<0.98$.\n\\subsection{Data Samples}\n\\label{subs:data}\nThe analysis is based on hadronic \\ensuremath{{\\rm Z}^0}\\ decays collected around the \n\\ensuremath{{\\rm Z}^0}\\ peak from 1990 to 1995 (total \\lep\\ 1 statistics). The hadronic events \nwere selected with the standard {\\sc Opal}\\ procedure~\\cite{tkmh} based on the \nnumber and quality of the measured tracks and the electromagnetic clusters and \non the amount of visible energy in the event. In addition, events with the \nthrust axis close to the beam direction were rejected by requiring \n$|\\cos \\theta_{\\rm {thrust}}|< 0.9$, where $\\theta_{\\rm thrust}$ is the polar \nangle of the thrust axis. With the additional requirement that the jet chamber\nand the z-chambers were fully operational, a total of 3.895 million hadronic \nevents remained for further analysis, with an efficiency of ($98.4 \\pm 0.4$)\\%. \nThe remaining background processes, \nsuch as $\\ensuremath{{\\rm e}^+{\\rm e}^-} \\rightarrow \\tau^+ \\tau^-$ and two photon events, were estimated \nto be at negligible level (0.1\\% or less).\n\nAfter the \\l-selection which will be described below, the selection of 2- and \n3-jet events was performed. Charged tracks and electromagnetic \nclusters not associated with any track were grouped into jets using the \n\\ensuremath{{\\rm k}_{\\perp}}\\ recombination algorithm \\cite{durham} with a cut value\n$y_{\\rm cut}=0.005$. In addition to the standard selection criteria, the\nenergy of the clusters and the momenta of the charged tracks had to be less \nthan 60~GeV\/$c$.\nTo improve the quality of the jets it was finally required that there be at \nleast two charged particles per jet (in addition to the possible tracks from \n\\l\\ decays) and that the minimum energy per jet was $5$~GeV.\nThe cuts on the quality of jets were chosen to be this loose to keep the \nkinematic range as large as possible for comparison with fragmentation models. \nIn total, samples of 1.7 million 2-jet events and 1.4 million 3-jet events \nwere available for further analysis corresponding to $45\\%$ and $36\\%$,\nrespectively, of the entire data set.\n\\subsection{Monte Carlo Event Samples} \n\\label{subs:mc} \nMonte Carlo hadronic events with a full simulation of the OPAL detector \n\\cite{gopal} and including initial-state photon radiation were used \n(a) for evaluation of detector acceptance and resolution \nand (b) for studying the efficiency of the di-lambda reconstruction as a \nfunction of the rapidity differences. \nIn total, seven million simulated events were available, of which four million\nwere generated by \\jt~7.4 with fragmentation parameters \ndescribed in~\\cite{jt7.4}, and three million were generated by \\jt~7.3 with \nfragmentation parameters described in~\\cite{jt7.3}.\nThe two \\jt\\ versions differ in the particle decay tables and heavy meson \nresonances. \nThere are also some differences in the simulation of baryon production between \nthe two samples.\nTheir small influence on the efficiency correction to the experimental \ndata is accounted for in the systematic errors (see \nsection~\\ref{subs:syserr}). \n\nFor comparison with the experimental results, the Monte Carlo models \n\\jt~7.4 and \\hw~5.9\\cite{hw5.8}\\footnote \n{The fragmentation parameters of \\hw~5.9 were identical to those used in \n our tuned version of \\hw~5.8\\cite{hw5.8} with the exception of the maximum\n cluster mass ({\\tt CLMAX}) which was set to 3.75 GeV in order to improve the \n description of the mean charged particle multiplicity in inclusive \n hadronic \\ensuremath{{\\rm Z}^0}\\ decays.} \nwere used. Both models give a good description of global \nevent shapes and many inclusive particle production rates, but differ in \ntheir description of the perturbative phase and their implementation of the \nhadronization mechanism. \n\nTracks and clusters are selected in the Monte Carlo events, which \ninclude detector simulation, in the same way as for the data, and the \nresulting four-vectors of particles are referred to as being at the \n`detector level'. Alternatively, for testing the model predictions, \nMonte Carlo samples without \ninitial-state photon radiation nor detector simulation are used, with \nall charged and neutral particles with mean lifetimes greater than \n$3\\times10^{-10}$~s treated as stable. The four-vectors of the \nresulting particles are referred to as being at `generator level'. \n\\subsection{\\l\\ Reconstruction } \n\\label{subs:lambda} \nNeutral strange \\l\\ baryons were reconstructed in their decay \nchannel \\l ~$\\rightarrow \\pi^-$p as described in \\cite{opal_sp}. \nBriefly, tracks of opposite charge were paired and regarded as a secondary \nvertex candidate if the track pair intersection in the plane \nperpendicular to the beam axis satisfied the criteria of a neutral two-body \ndecay with a decay length of at least 1 cm.\n \nEach candidate track pair was refitted with the \nconstraint that the tracks originated from a common vertex, and \nbackground from photon conversions was suppressed. Information from \nd$E$\/d$x$ measurements was used as in \\cite{opal_sp} to help identify the \n$\\pi$ and p for further background suppression, primarily due to \n\\ensuremath{{\\rm K}^{0}_{\\rm S}} $\\rightarrow \\pi^+ \\pi^-$. Two sets of cuts, called `method 1' and \n`method 2' are described in \\cite{opal_sp} for \\l\\ identification. \nFor the present analysis, \\l\\ candidates were reconstructed using method~1, \nwhich is optimized to have good mass and momentum resolution. \n\n\\begin{sloppypar} \nBy these means a narrow \\l\\ mass peak above a small background has been \nobtained. The selection of di-lambda candidates with both invariant masses in \nthe range \\mbox{1.1057~GeV\/$c^2<$ $m_{\\pi p}<$ 1.1257~GeV\/$c^2$} (region A in \nfigure~\\ref{fig:m2dim}) retains most of the \\l\\ signal for further analysis. \n\\end{sloppypar}\n\\section{Selection of Correlated \\l-pairs}\n\\label{sec:method}\n\\subsection{Method}\n\\label{subs:mgeneral}\nEvents with more than one \\l\\ candidate that had passed the above selection \ncriteria were considered and all possible pair combinations of the \\l\\ and \n\\ensuremath{\\bar{\\Lambda}}\\ baryons within an event were formed. This resulted in pairs of \\ensuremath{\\Lambda\\bar{\\Lambda}}, \n\\l\\l\\ and \\ensuremath{\\bar{\\Lambda}}\\lb. Combinations were rejected if the pair had a track in \ncommon. The remaining pairs are henceforth referred to as \\l-pair candidates. \n\nThe three types of baryon pairs can be grouped into two classes: pairs with \ndifferent baryon numbers \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ and pairs with equal baryon numbers \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}. \nOnly in \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs can the baryon and flavor quantum numbers be compensated \nby correlated production. \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}\\ pairs can never be produced in correlation\nand hence they will occur only in events with more than one baryon-antibaryon \npair (B$\\bar{\\rm B}$). In such events uncorrelated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs from \ndifferent (B$\\bar{\\rm B}$) pairs are also possible. The number of uncorrelated \n\\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs corresponds to the number of pairs with same baryon number. \nHence, the number of correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs can be derived via \n\\begin{equation}\nN_{\\ensuremath{\\Lambda\\bar{\\Lambda}}}^{\\rm corr.} = N_{\\ensuremath{\\Lambda\\bar{\\Lambda}}} - (N_{\\ll} + N_{\\ensuremath{\\bar{\\Lambda}\\bar{\\Lambda}}})\\: .\n\\end{equation}\nAt this stage 9479 \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ and 4217 (\\l\\l+\\ensuremath{\\bar{\\Lambda}}\\lb) pair candidates are \nselected.\n\\subsection{Background Subtraction and Efficiency Correction}\n\\label{subs:backgr} \nDue to the small statistical errors it is necessary to keep systematic \nuncertainties as low as possible in this analysis. \nThe correct subtraction of {\\mbox{non-\\l\\ }} background from the pairs is \ntherefore of particular importance. This background consists mainly of other \nlong-lived particles with similar decay topologies (namely \\ensuremath{{\\rm K}^{0}_{\\rm S}} $\\rightarrow\n\\pi^+ \\pi^-$) and random \ntrack combinations. An important contribution to the contamination is the \nso-called correlated background from \\l candidates that have been reconstructed \nwith one false decay track. \nThey are more numerous in pairs with opposite baryon number because the \nnumber of \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs is far higher than the number of \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}\\ pairs. \nFor this reason the background has to be estimated in the two samples \nseparately. \nBackground pairs occur when either one or both \\l-candidates are fake.\nIn the two-dimensional mass plane in figure~\\ref{fig:m2dim}, pairs with one \nfake \\l\\ form horizontal and vertical bands of background, while pairs with \ntwo fake candidates are uniformly distributed in the region above the lower \nmass bounds. \n\nThe background was subtracted using a two-dimensional sideband method. The \nbackground in the signal region A was measured from two mass windows \n(sidebands) of the same size (regions B$_1$ and B$_2$) placed \nin the two bands of background. \nIn this way the background with two fake candidates is counted twice. The latter\nwas determined from region C outside the bands.\nHence, the signal is obtained with the subtraction: \n\\begin{center} \n Signal = $N_{\\rm A} - (N_{\\rm B_1} + N_{\\rm B_2} - N_{\\rm C})$ . \n\\end{center}\nWe optimized the position of the sidebands with a MC test investigating the\ndeviations between the background-corrected sample and the true-\\l\\ sample. \nThe stability of this method was tested in the experimental data by shifting \nthe position of the sidebands by one half of the band size from the optimized \nposition. The fluctuations were of the same size as the deviations found in \nthe MC. \n\nFinally the background-corrected \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ and \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}\\ signal distributions were \ncorrected for detector acceptance and reconstruction efficiency as functions \nof \\ensuremath{|\\Delta y|}\\ and \\ensuremath{\\cos \\theta^*} . The average efficiency in the total hadronic sample \nwas found to be $\\approx 2\\%$, varying between 1.3\\% and 2.5\\% over the \n\\ensuremath{|\\Delta y|}\/\\ensuremath{\\cos \\theta^*}\\ range.\n\\section{Experimental Results}\n\\label{sec:results}\nIn an earlier OPAL paper~\\cite{opal_corr} based on the 1990 and 1991 data \nsamples we already investigated the production dynamics of baryon-antibaryon \npairs. \nIn this section, we present the rates and differential distributions of \n\\l~pairs using the full 1990 to 1995 LEP~1 data in three samples: \nthe entire set of multihadronic events, the 2-jet and the 3-jet events. \n\\subsection{Pair Production Rates}\n\\label{subs:rates}\nThe resulting rates for \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ and \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}\\ pairs in all hadronic events, \ndetermined as sum over all corrected \\ensuremath{|\\Delta y|}\\ bins, are \ngiven in table~\\ref{tab:rates}. The rates for the correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs \nare derived according to equation (1) from the difference of the opposite \nand same baryon number pairs. Compared to the results from other \\lep\\ \nexperiments and to the previous {\\sc Opal}\\ publication, good agreement is found. \n\nThe di-lambda rates in 2- and 3-jet events are listed in \ntable~\\ref{tab:2_3jrates}.\nIn 3-jet events, due to the higher color charge of the gluons, the average \npair multiplicity is higher. \n\\subsection{Differential Distributions}\n\\label{subs:distributions}\nWe studied the correlations in the differential \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ spectra using the \nobservables \\ensuremath{|\\Delta y|}\\ and \\ensuremath{\\cos \\theta^*}\\ as they are particularly sensitive for \ncomparison with Monte Carlo models.\nThe differential distributions are shown in figure~\\ref{fig:distrib}.\nThe short-range correlations show up as a peak in the region \\ensuremath{|\\Delta y|}\\ $\\le$ 2.0.\n\nWhen investigating \\ensuremath{|\\Delta y|}\\ distributions, we will restrict ourselves to \n2-jet events.\nThis is due to the fact that in 3-jet events many particle momenta have large \nangles to the thrust axis, resulting in smaller longitudinal momenta and \nsmaller rapidity differences, independent of correlations. As a result the \\ensuremath{|\\Delta y|}\\ \ndistribution is broader and less steep in 2-jet events than in the 3-jet or the \ntotal sample (see figure~\\ref{fig:distrib}a). Consequently, also the range of \nvariations is larger in 2-jet events and yields a higher sensitivity in the \ncomparison with model predictions.\n\\subsection{Systematic Errors}\n\\label{subs:syserr}\nThe systematic error is found to be largely independent of \\ensuremath{|\\Delta y|}\\ and \\ensuremath{\\cos \\theta^*} , \nand in the subsequent discussion of the differential distributions of \nthe correlated pairs, only normalized distributions are considered. These are \nlargely insensitive to effects of systematic uncertainties. Consequently, the \nsystmatic errors discussed below are mainly relevant for the total rates.\n\nFor the determination of the experimental uncertainties we considered the \nfollowing sources of systematic effects:\n\\begin{itemize}\n\\item Uncertainties due to the subtraction of background via the \n sidebands. These were estimated using simulated events by applying the \n analysis to the fully detector simulated MC and comparing the rate from the \n background corrected sample to the true number.\n\\item Efficiency uncertainties. These were estimated from the difference of \n the results when the efficiency\n correction was done using both \\jt\\ versions 7.3 \n and 7.4 samples in combination and using them separately.\n\\item The statistical error of the efficiency due to the limited sample size\n of the simulated events at detector level.\n\\item Uncertainties in the modelling of the cut variables used for the \\l\\\n selection. This error is taken from a former analysis \n \\cite{opal_sp} where it was determined very precisely for single \\l 's. \n The error given there is doubled for the \\l\\ pairs in the present \n analysis.\n\\end{itemize}\n\nThese effects contribute to the total systematic error as shown in\ntable~\\ref{tab:syserr}, where the relative systematic errors from the \ndifferent sources are compared to the total systematic as well as to the \nstatistical error. Statistical and total systematic errors contribute about \nequally.\n\\section{Comparison with Fragmentation Models}\n\\label{sec:comparison}\n\nWe start the discussion with the numbers and distributions of the models with\nOPAL default tunes that optimize the general performance of the models and the \nagreement with the measured single particle rates. \n\n\\subsection{Pair Production Rates}\n\\label{subs:comp_rates}\n\nWe investigate the di-lambda rates first in the total hadronic data sample\ncomparing the measured rates to the predictions of the models \\jt~7.4, {\\sc Mops}\\ \nand \\hw~5.9 (see table~\\ref{tab:rates}). None of the models gives a perfect \ndescription of the data but \\hw\\ clearly exhibits the largest disagreement. \n\nThe comparison of the di-lambda rates in 2- and 3-jet events is given in \ntable~\\ref{tab:2_3jrates}. The higher multiplicity in 3-jet events compared to \n2-jet events is qualitatively well described by all three models. \nHowever, only \\jt\\ yields a prediction compatible with the measured numbers.\nIn the 2-jet event sample the agreement is excellent.\nIn the 3-jet sample all the measured rates exceed the \\jt\\ predictions. \nThis can be compared to the observation that \\l\\ rates in gluon jets are too low\nin \\jt~\\cite{opal_jets} .\n\n\\subsection{Differential Distributions}\n\\label{subs:comp_distributions}\n\nTo further investigate the nature of the \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ correlations we \ncompare the differential distributions of correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ pairs with the \npredictions of the various models. We use the variables \\ensuremath{\\cos \\theta^*}\\ and \\ensuremath{|\\Delta y|}\\ \nand test their sensitivity to distinguish between the different fragmentation \nmodels and baryon production mechanisms. All distributions are of the type \n$ \\frac{1}{N} \\frac{dN}{d(\\ensuremath{|\\Delta y|})}$, $N$ being the total number of entries. \nThis has the advantage that they are independent of the total rates and \nthat the systematic errors mostly cancel out, since they are nearly independent \nof both \\ensuremath{|\\Delta y|}\\ and \\ensuremath{\\cos \\theta^*} .\n\nThe angle \\ensuremath{\\theta^*}\\ is particularly suited to distinguish between string and \ncluster fragmentation. The mostly isotropic cluster decay (\\hw) results in a \nrelatively flat \\ensuremath{\\cos \\theta^*}\\ distribution whereas string fragmentation produces \nthe correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ system predominantly close to the thrust axis, i.e., with \n$\\ensuremath{\\cos \\theta^*} \\approx 1$. These predictions are compared to the measurement \nin figure~\\ref{fig:comp_theta}. The data show a distribution that is strongly \npeaked towards \\ensuremath{\\cos \\theta^*} = 1 and therefore clearly rule out the \\hw\\ cluster \nmodel. \nThe predictions of {\\sc Mops}\\ agree somewhat better with the experimental \ndistribution but they also fail to model the forward peak correctly. \nOnly \\jt\\ yields a good description of the data.\n\nOn the other hand, especially in 2-jet events, the rapidity difference \\ensuremath{|\\Delta y|}\\ is \nmore sensitive to show differences in the strength of the correlations.\nThe experimental data and model predictions are compared in \nfigure~\\ref{fig:compall_2j}.\nAgain \\jt\\ gives the best, albeit not completely satisfactory, description of \nthe measured distribution. \\hw\\ generates correlations which are far too \nstrong .\nThe {\\sc Mops}\\ model with its built-in facility to allow for several ``popcorn \nmesons'' should yield weaker correlations than \\jt ; however, in contrast to \nthis naive expectation it produces a narrower \\ensuremath{|\\Delta y|}\\ distribution, i.e. \nstronger correlations. \nWe see the following possible reasons for this: first of all, and different \nfrom \\jt , a new kinematic property is built into {\\sc Mops}: the \nlow-$\\Gamma$-suppression\\cite{mops}. This suppresses popcorn fluctuations \nat early times in the color field, resulting in very strong correlations. \nSecondly, it appears that the strength of the correlations is influenced more\nby the rate of baryon production via the popcorn mechanism than by the actual \nnumber of intermediate mesons produced. \nAs \\jt\\ and {\\sc Mops}\\ are tuned to show the same mean number of popcorn mesons \ninstead of popcorn systems, {\\sc Mops}\\ has fewer popcorn systems and therefore \nstronger correlations.\n\n\\subsection{Tuning of Models}\n\\label{subs:tuning}\n\nIn an earlier {\\sc Opal}\\ analysis of strange baryons\\cite{james_early} it was \nobserved that the agreement between experimental data and \\jt\\ predictions\ncan be improved by adjusting some of the diquark parameters: \nimproving the predicted shape of the \\ensuremath{|\\Delta y|}\\ distribution was possible by varying \nthe popcorn parameter, $\\rho$=PARJ(5), that influences the frequency of \npopcorn production and hence the correlation strength. It was found that \n$\\rho$ acts on both the shape of the rapidity spectrum and the production \nrates.\nTwo other parameters were used to correct for this change of predicted \nmultiplicities:\nthe ratio of the strange to non-strange diquarks over strange to \nnon-strange quarks, (us:ud\/s:d) = PARJ(3), and the ratio of spin-1 to spin-0 \ndiquarks, $(1\/3 \\cdot [{\\rm qq}]_1\/[{\\rm qq}]_0)$=PARJ(4). \nThese last parameters affect mainly the rates and leave the spectra nearly\nunmodified.\nWhen attempting to improve the predictions of the {\\sc Mops}\\ model in the same \nmanner, the most direct correspondence to $\\rho$ in \\jt\\ is the \n{\\sc Mops}\\ parameter PARJ(8)=$\\beta({\\rm u})$, the transverse mass of an \nintermediate u-quark. The higher the transverse mass of the intermediate \nsystem (with several quark pairs possible), the lower the probability to \nproduce this popcorn system and the stronger the correlations. \nTherefore, in both \\jt\\ and {\\sc Mops}\\ we tried first to improve the \nagreement with the data distributions by tuning \nthe parameters that influence \nthe correlation strength (figure~\\ref{fig:compjt_tunes} for \\jt .) \nIn \\jt , the popcorn probability $\\rho$ was varied from 0\\%-90\\%, while in \n{\\sc Mops}\\ the transverse mass of a u-quark, $\\beta({\\rm u})$=PARJ(8), was altered \nbetween 0.2 and 1.0~GeV$^{-1}$. \nAll other parameters remained at the {\\sc Opal}\\ default values.\nAs can be seen from table~\\ref{tab:jtrates_tune}, for the $\\rho$ parameter,\nthese variations affect not only the shape of the \\ensuremath{|\\Delta y|}\\ distribution but \nalso the di-lambda rates, as expected.\n\nThe predictions with the different popcorn parameter values in \\jt\\ are \ncompared to the data in figure~\\ref{fig:compjt_tunes}a.\nOnly the results from parameter settings above the default value \nof $\\rho = 0.5$ are shown, since lower values give a poorer agreement with the \ndata. \nThe best agreement \nis found in the range $0.6<\\rho<0.8$.\nPopcorn values within this range also yield good agreement between data and \npredictions for the \\ensuremath{\\cos \\theta^*}\\ distribution.\nHowever, when the influence of the popcorn parameter on the predicted\ndi-lambda rates is also considered (table~\\ref{tab:jtrates_tune}) \nuse has to be made of the other two \\jt\\ parameters that affect the strange \nbaryon production in order to tune the rates back to values corresponding to\nthe measurement. It can be seen from table~\\ref{tab:jtrates_tune} and \nfigure~\\ref{fig:compjt_tunes}b that such\na tune clearly produces a better agreement with the rates (also for single \nparticle production) while it does not change the spectra of rapidity \ndifferences significantly. Using the results of other {\\sc Opal}\\ analyses,\nit can also be seen that the tune does not change the strange meson (\\ensuremath{{\\rm K}^{0}_{\\rm S}})\nrate, nor does it affect the non-strange baryon (p) rate significantly.\nThe known problems~\\cite{james_early} in modeling the decuplet baryon rates \nare also seen here.\nNo further attempt has been made to globally optimize the parameter set, \nhowever. \n\nThe tune of parameter PARJ(8) in {\\sc Mops}\\ did not result in an improvement. \nAlthough the value of the parameter was varied in a comparatively wide \nrange, the effect on the \\ensuremath{|\\Delta y|}\\ distribution was almost imperceptible. \nPARJ(8) clearly is not suited to adjust the {\\sc Mops}\\ model to the data. \nTherefore, we tested another parameter of the model using the relative \ndifference between the fragmentation function $f(z)$ for baryons and mesons, \nthe parameter PARJ(45). Again, the variation did not notably change the shape \nof the distribution. \nThis relatively poor performance of the {\\sc Mops}\\ Monte Carlo in describing \nthe \\ensuremath{|\\Delta y|}\\ dependent \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ correlations seems to be connected to the known \nshortcomings of the model in describing $p_{\\perp}$-related \ndistributions~\\cite{mops}.\n\\section{Di-lambdas in Jets}\n\\label{sec:jets}\nAfter studying the strength of \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ correlations in the \\ensuremath{|\\Delta y|}\\ spectra, we will \nnow present results on the range of the correlations by assigning \nboth partners from a correlated pair to the reconstructed jets in an event. \nFor short-range correlations both partners are expected \nwithin the same jet whereas long-range correlations (which can be obtained by \nthe production of baryons from the primary quarks) should result in an \nassignment to different jets. \nWe use the following two classifications for the assignment study: \nboth partners within the same jet, and each partner in a different jet. \n\nDue to the fact that it is impossible to map 2-jet events at detector level \nto 2-jet events at generator level, we do not attempt to apply efficiency \ncorrections but compare our uncorrected results with the \\jt\\ predictions at \ndetector level.\nWe count the number of \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ and \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}\\ pairs in \neach sample and obtain the number of correlated pairs again from the \nrelation \n\\mbox{$N_{\\ensuremath{\\Lambda\\bar{\\Lambda}}}^{\\rm correlated} = N_{\\ensuremath{\\Lambda\\bar{\\Lambda}}} - (N_{\\ll} + N_{\\ensuremath{\\bar{\\Lambda}\\bar{\\Lambda}}})$}.\nThe amount of background in like- and unlike-sign pairs approximately cancels \nout in this subtraction as long as the contribution from the correlated \nbackground (see section 3.2) can be neglected. \nThe numbers of pairs obtained from the same jet and from different jets are \nlisted in table~\\ref{tab:injets} \nfor both 2- and 3-jet events.\nThe major part of the correlated pairs is reconstructed \nwithin the \nsame jet (about 96\\% in 2-jet events, 81\\% in 3-jet events) whereas only a \nvery small fraction is found in different jets. These experimental numbers are \nin excellent agreement with the \\jt\\ predictions at detector level and support\nthe assumption of short-range compensation of baryon number \nand strangeness in the fragmentation process.\n\\section{Summary}\n\\label{sec:summary}\n\\ensuremath{\\Lambda\\bar{\\Lambda}}\\ correlations have been studied in 4.3 million multihadronic \\ensuremath{{\\rm Z}^0}\\\ndecays, with the correlated sample obtained from the difference: \n\\ensuremath{\\Lambda\\bar{\\Lambda}_{\\rm corr}} =\\ensuremath{\\Lambda\\bar{\\Lambda}}--(\\ll+\\ensuremath{\\bar{\\Lambda}\\bar{\\Lambda}}).\nThe analysis has been performed in terms of \\ensuremath{\\cos \\theta^*}\\ and rapidity differences \n\\ensuremath{|\\Delta y|} . As the rapidity is defined with respect to the event (thrust) \naxis, the sensitivity of the analysis is seen to be higher in 2-jet events.\nTherefore three data samples have been analyzed: the \nentire hadronic event sample, 2-jet events ($45\\%$), and 3-jet events ($36\\%$).\nThe experimental findings have been used to study the baryon production\nmechanism implemented in various phenomenological fragmentation models.\n\nThe following results have been obtained:\n\\begin{itemize}\n\\item In the full data set, the measured production rates of \\ensuremath{\\Lambda\\bar{\\Lambda}}, \\ensuremath{\\Lambda\\Lambda(\\bar{\\Lambda}\\bar{\\Lambda})}\\ \n and, consequently, \\ensuremath{\\Lambda\\bar{\\Lambda}_{\\rm corr}}\\ are in good agreement with a previous {\\sc Opal}\\ \n measurement and results from \\aleph\\ and {\\sc Delphi} , but show significantly \n smaller errors. \n\\item The \\ensuremath{\\cos \\theta^*}\\ distribution of correlated \\l-pairs is well suited to \n distinguish between isotropic cluster and non-isotropic string decay, and \n clearly favours the latter, implemented in \\jt . The predictions of the \n isotropic cluster model \\hw\\ are ruled out by the data: they do not describe \n the features of correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ production. \n\\item The rapidity difference \\ensuremath{|\\Delta y|}\\ is used to study the strength of correlated \n di-lambda production. The measured distribution \n exhibits strong local correlations.\n\\item Satisfactory reproduction of the experimental results is obtained\n with the predictions of the string fragmentation model \\jt . Improved\n agreement can be found by tuning some of the default parameters used \n by {\\sc Opal} . After adjusting the popcorn parameter, to improve the description \n of the \\ensuremath{|\\Delta y|}\\ spectrum, other parameters, fixing the fraction of diquarks with \n strangeness and spin1, have to be modified to readjust the predicted rates \n to the experimental ones. This procedure does not affect the previously \n optimized \\ensuremath{|\\Delta y|}\\ distribution. \n The \\hw\\ model cannot describe the measured \\ensuremath{|\\Delta y|}\\ spectra, and the predictions \n of {\\sc Mops} , a recently published modification of the \\jt\\ model, also fail to \n reproduce the experimental data, even after some parameter tuning. \n\\item In the 2-jet and 3-jet event samples it is found that correlated \\ensuremath{\\Lambda\\bar{\\Lambda}}\\ \n pairs are produced predominantly within the same jet, supporting the \n assumption of a short-range compensation of quantum numbers.\n Again, the \\jt\\ predictions are in good agreement with the experimental \n results. \n\\end{itemize} \nIn conclusion, the analysis of correlated di-lambda pairs proves to be a\nvery effective tool to test fragmentation models. \\jt\\ is the only candidate \nmodel studied, describing the data successfully. \n\n\\bigskip \\bigskip \\bigskip \\bigskip\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{INTRODUCTION}\n\nThe connection between endomorphisms of factors and families of isometric operators has been explored most notably by Arveson \\cite{arveson}, among others \\cite{brenken1,brenken2,laca,longo}. In \\cite{laca}, Laca determines that given a normal $*$-endomorphism $\\alpha$ of $B(H)$ there exists an $n\\in\\mathbb{N}\\cup\\{\\infty\\}$ and $*$-representation $\\pi:\\mathcal{E}_n\\to B(H)$, where $\\mathcal{E}_n$ denotes the Toeplitz algebra for $n$ orthogonal isometries $v_1,...,v_n$, such that\n\\begin{equation*}\\alpha(T)=\\sum_{i=1}^n\\pi(v_i)T\\pi(v_i)^*\\end{equation*}\nfor each $T\\in B(H)$. The $n$ value is unique but the representation $\\pi$ may differ by automorphisms of $\\mathcal{E}_n$ which arise specifically from unitary transformations of the Hilbert space $\\ell^2(\\{v_1,...,v_n\\})\\subseteq\\mathcal{E}_n$ \\cite[Proposition 2.2]{laca}. \n\nIn \\cite{brenken1} and \\cite{brenken2} Brenken extends the connection by determining that, for a von Neumann algebra which can be decomposed into a direct sum of Type I factors, a certain class of $*$-endomorphisms correspond to representations of certain $C^*$-algebras associated with (possibly infinite) matrices which arise as the adjacency matrices for directed graphs. The $*$-endomorphisms studied by Brenken are, however, required to either be unital \\cite{brenken1} or satisfy a number of restrictive conditions \\cite[pp 25]{brenken2}.\n\nOur results extend the work of Brenken by eliminating the conditions imposed on the $*$-endomorphisms. The fundamental difference in our approach is that we will investigate representations of the Toeplitz algebra of a $C^*$-correspondence, whereas Brenken concerned himself with the so-called relative Cuntz-Pimnser algebras. We recover Brenken's results as a special case when the endomorphisms are assumed unital. Our primary result in this line is Theorem \\ref{theorem3}.\n\nOur results also continue the spirit of Laca's investigations which allow for equivalencies between $*$-endomorphisms to be encoded as transformations of an underlying linear object: a Hilbert space in the case of Laca and a $C^*$-correspondence in the present work. Our primary results along these lines are Theorems \\ref{theorem1} and \\ref{theorem2}.\n\n\\section{PRELIMINARIES}\nFirst we will establish our terminology and notation.\n\n\\begin{defi} A \\emph{graph} is a tuple $E=(E^0,E^1,r,s)$ consisting of a \\emph{vertex set} $E^0$, an \\emph{edge set} $E^1$, and \\emph{range} and \\emph{source} maps $r,s:E^1\\to E^0$.\n\\end{defi}\n\nWe will only consider graphs where $E^0$ and $E^1$ are at most countable.\n\n\\begin{defi} Let $A$ be a $C^*$-algebra. A set $X$ is a \\emph{$C^*$-correspondence over $A$} provided that it is a right Hilbert $A$-module and there is a $*$-homomorphism $\\phi:A\\to L(X)$, where $L(X)$ denotes the space of adjointable $A$-module homomorphisms from $X$ to itself. \n\\end{defi} \n\nGiven a $C^*$-correspondence $X$ over $A$, we will denote the $A$-valued inner product of $x,y\\in X$ by ``$\\langle x,y\\rangle_A$\" (perhaps omitting the $A$) and the right action of $a\\in A$ on $x\\in X$ will be written as ``$x\\cdot a$\". The map $\\phi:A\\to L(X)$ may sometimes be written as $\\phi_X$ for clarity.\n\nOur primary objects of study will be certain $C^*$-correspondences which arise from graphs. The following constructions are due originally to Fowler and Raeburn \\cite[Example 1.2]{fowlerraeburn}, although we will adopt the modern convention for the roles of $r$ and $s$. \n\n\\begin{defi} Given a graph $E$, the \\emph{graph correspondence} $X(E)$ is the set of all functions $x:E^1\\to\\mathbb{C}$ for which $\\hat{x}(v):=\\sum_{e\\in s^{-1}(v)}|x(e)|^2$ extends to a function $\\hat{x}\\in C_0(E^0)$. We give $X(E)$ the structure of a $C^*$-correspondence over $C_0(E^0)$ as follows:\n\\begin{align*}x\\cdot a&:e\\mapsto x(e)a(s(e)),\\\\\n\\phi(a)x&:e\\mapsto a(r(e))x(e),\\\\\n\\langle x,y\\rangle&:v\\mapsto\\sum_{e\\in s^{-1}(v)}\\overline{x(e)}y(e).\\end{align*}\nwhich is to say that $a\\in C_0(E^0)$ acts on the right of $X(E)$ as multiplication by $a\\circ s$ and acts on the left as multiplication by $a\\circ r$.\n\\end{defi}\n\nThe sets $\\{\\delta_e:e\\in E^1\\}$ and $\\{\\delta_v:v\\in E^0\\}$ are dense in $X(E)$ and $C_0(E^0)$, respectively, in the appropriate senses. For $e\\in E^1$ and $v\\in E^0$ we have the following useful relations: $\\langle \\delta_e,\\delta_e\\rangle=\\delta_{s(e)}$, $\\delta_e\\cdot\\delta_v=\\delta_e$ if $v=s(e)$ and is $0$ otherwise, and $\\phi(\\delta_v)\\delta_e=\\delta_e$ if $v=r(e)$ and is $0$ otherwise.\n\n\n\\begin{defi}\\label{toeplitzrepdef} Given a $C^*$-correspondence $X$ over $A$ and given another $C^*$-algebra $B$, a \\emph{Toeplitz representation} of $X$ in $B$ is a pair $(\\sigma,\\pi)$ consisting of a linear map $\\sigma:X\\to B$ and a $*$-homomorphism $\\pi:A\\to B$ such that for all $x,y\\in X$ and $a\\in A$\n\t\\begin{enumerate}\n\t\\item $\\sigma(x\\cdot a)=\\sigma(x)\\pi(a)$,\n\t\\item $\\sigma(\\phi(a)x)=\\pi(a)\\sigma(x)$, and\n\t\\item $\\pi(\\langle x,y\\rangle)=\\sigma(x)^*\\sigma(y).$\n\t\\end{enumerate}\n\\end{defi}\n\nFor a graph correspondence $X(E)$, a Toeplitz representation $(\\sigma,\\pi)$ is determined entirely by the values $\\{\\sigma(\\delta_e):e\\in E^1\\}$ and $\\{\\pi(\\delta_v):v\\in E^0\\}$. Property (iii) of a Toeplitz representation guarantees that $\\sigma(\\delta_e)$ is a partial isometry with source projection $\\pi(\\delta_{s(e)})$.\n\n\\begin{defi}\\cite[Proposition 1.3]{fowlerraeburn} Given a $C^*$-correspondence $X$ over $A$, the \\emph{Toeplitz algebra of $X$}, denoted $\\mathcal{T}_X$, is the $C^*$-algebra which is universal in the following sense: there exists a Toeplitz representation $(\\sigma_u,\\pi_u)$ of $X$ in $\\mathcal{T}_X$ such that if $(\\sigma,\\pi)$ is another Toeplitz representation of $X$ in a $C^*$-algebra $B$ then there exists a unique $*$-homomorphism $\\rho_{\\sigma,\\pi}:\\mathcal{T}_X\\to B$ such that $\\sigma=\\rho_{\\sigma,\\pi}\\circ\\sigma_u$ and $\\pi=\\rho_{\\sigma,\\pi}\\circ\\pi_u$.\n\\end{defi}\n\nThat $\\mathcal{T}_X$ exists was proven by Pimnser in \\cite{pimsner}.\n\nGiven a graph $E$ we may consider the Toeplitz algebra of its graph correspondence, cumbersomely denoted $\\mathcal{T}_{X(E)}$. Unless there is danger of confusion, we will abuse notation and make no distinction between elements of $X(E)$ and $C_0(E^0)$ and their images in $\\mathcal{T}_{X(E)}$ under the universal maps $\\sigma_u$ and $\\pi_u$. \n\nIf $\\tau:\\mathcal{T}_{X(E)}\\to B(H)$ is a $*$-representation then, for each $e\\in E^1$, $\\tau(\\delta_e)$ is a partial isometry with source projection $\\tau(\\delta_{s(e)})$ and range projection contained in $\\tau(\\delta_{r(e)})$.\n\nIf $E$ is the graph with but a single vertex and $n$ edges then $X(E)$ is a Hilbert space of dimension $n$ and $\\mathcal{T}_{X(E)}$ is isomorphic to the classical Toeplitz algebra $\\mathcal{E}_n$. In this case the elements $\\{\\delta_e:e\\in E^1\\}$ are precisely the generating isometries of $\\mathcal{E}_n$. The space $X(E)$ plays a significant role in the analysis of endomorphisms of $B(H)$ in \\cite{laca}, and it is for this reason that we are considering the generalized Toeplitz algebras $\\mathcal{T}_{X(E)}$ in our investigations.\n\n\\section{COHERENT UNITARY EQUIVALENCE}\n\nTwo graphs $E$ and $F$ are isomorphic if there are two bijections $\\psi^0:E^0\\to F^0$ and $\\psi^1:E^1\\to F^1$ for which $r_F\\circ\\psi^1=\\psi^0\\circ r_E$ and $s_F\\circ\\psi^1=\\psi^0\\circ s_E$. In order to encode such an isomorphism at the level of the graph correspondences $X(E)$ and $X(F)$, we offer the following novel definition.\n\n\\begin{defi} Let $X$ and $Y$ be $C^*$-correspondences over $A$ and $B$, respectively. A \\emph{coherent unitary equivalence} between $X$ and $Y$ is a pair $(U,\\alpha)$ consisting of a bijective linear map $U:X\\to Y$ and a $*$-isomorphism $\\alpha:A\\to B$ for which \n\t\\begin{enumerate}\n\t\\item $U(x\\cdot a)=(Ux)\\cdot \\alpha(a)$ for all $x\\in X$ and $a\\in A$,\n\t\\item $U(\\phi_X(a)x)=\\phi_Y(\\alpha(a))Ux$ for all $x\\in X$ and $a\\in A$, and\n\t\\item $\\langle Ux,y\\rangle_Y=\\alpha(\\langle x,U^{-1}y\\rangle_X)$ for all $x\\in X$ and $y\\in Y$.\n\t\\end{enumerate}\nRoutine calculations will verify that coherent unitary equivalence is an equivalence relation.\n\\end{defi}\n\n\\begin{proposition} If $E$ and $F$ are isomorphic graphs then $X(E)$ and $X(F)$ are coherently unitarily equivalent.\n\\end{proposition} \n\\begin{proof} We'll assume $(\\psi^0,\\psi^1)$ to be an isomorphism from $F$ to $E$.\n\nFor $a\\in C_0(E^0)$, $\\alpha(a):=a\\circ\\psi^0$ clearly defines a $*$-isomorphism $\\alpha:C_0(E^0)\\to C_0(F^0)$. For $x\\in X(E)$ define $Ux:=x\\circ\\psi^1$.\nFor $v\\in F^1$ we have\n\\begin{equation*}\\sum_{e\\in s_F^{-1}(v)}|Ux(e)|^2=\\sum_{e\\in s_F^{-1}(v)}|x(\\psi^1(e))|^2=\\sum_{f\\in s_E^{-1}(\\psi^0(v))}|x(f)|^2\\end{equation*}\n(using the fact that if $s_E(e)=v$ then $s_F(\\psi^1(e))=\\psi^0(v)$) and so $\\widehat{Ux}(v)=\\hat{x}(\\psi^1(v))$. As $\\hat{x}\\in C_0(E^0)$ it follows immediately that $\\widehat{Ux}\\in C_0(F^0)$, i.e. $Ux\\in X(F)$. Identical arguments show that $U^{-1}y:=y\\circ(\\psi^1)^{-1}$ is a map from $X(F)$ to $X(E)$ which is a two-sided inverse for $U$. Hence $U:X(E)\\to X(F)$ is a bijection which is naturally linear.\n\nGiven $x\\in X(E)$, $a\\in C_0(E^0)$, and $e\\in E^1$ we have\n\\begin{align*}\nU(x\\cdot a)&=(x(a\\circ s_E))\\circ\\psi^1=(x\\circ\\psi^1)(a\\circ s_E\\circ\\psi^1)=(Ux)(a\\circ\\psi^0\\circ s_F)=Ux\\cdot\\alpha(a)\\\\\nU(\\phi_E(a)x)&=((a\\circ r_E)x)\\circ\\psi^1=(a\\circ r_E\\circ\\psi^1)(x\\circ\\psi^1)=(a\\circ \\psi^0\\circ r_F)(Ux)=\\phi_F(\\alpha(a))Ux\n\\end{align*}\nand, given $v\\in F^0$,\n\\begin{align*}\n\\langle Ux,y\\rangle(v)&=\\sum_{e\\in s_F^{-1}(v)}\\overline{Ux(e)}y(e)=\\sum_{e\\in s_F^{-1}(v)}\\!\\!\\overline{x(\\psi^1(e))}y(e)=\\!\\!\\!\\sum_{f\\in s_E^{-1}(\\psi^0(v))}\\hspace{-.5cm}\\overline{x(f)}y((\\psi^1)^{-1}(f))\\\\\n&=\\sum_{f\\in s_E^{-1}(\\psi^0(v))}\\overline{x(f)}U^{-1}y(f)=\\langle x,U^{-1}y\\rangle(\\psi^0(v))= \\alpha(\\langle x,U^{-1}y\\rangle)(v)\n\\end{align*}\n(the first inner product is that of $X(F)$ and the later two are that of $X(E)$). Thus the pair of $U$ and $\\alpha$ satisfies the definition of a coherent unitary equivalence.\n\\end{proof}\n\nNot every coherent unitary equivalence comes from a graph isomorphism in the sense of the preceding Proposition. As a simple example, consider the graph $E$ with but a single vertex and two edges. In this case $C_0(E^0)=\\mathbb{C}$ and $X(E)=\\mathbb{C}^2$. Hence any unitary $U\\in M_2(\\mathbb{C})$ forms (with the identify on $C_0(E^0)$) a coherent unitary equivalence. However, the only such equivalences arising from graph isomorphisms would be those of the two permutation matrices.\n\n\\begin{proposition}\\label{isotoeplitz} If there is a coherent unitary equivalence between $X$ and $Y$ then $\\mathcal{T}_X$ and $\\mathcal{T}_Y$ are $*$-isomorphic. \n\\end{proposition}\n\\begin{proof} Let $A$ and $B$ be the coefficient $C^*$-algebras for $X$ and $Y$, respectively.\nSuppose that $(U,\\alpha)$ is a coherent unitary equivalence between $X$ and $Y$ and let $(\\sigma,\\pi)$ be a Toeplitz representation of $Y$. For $x\\in X$ and $a\\in A$\n\\begin{align*}\n\\sigma(U(x\\cdot a))&=\\sigma(Ux\\alpha(a))=\\sigma(Ux)\\pi(\\alpha(a))\\\\\n\\sigma(U(\\phi_X(a)x))&=\\sigma(\\alpha(a)Ux)=\\pi(\\alpha(a))\\sigma(Ux)\n\\end{align*}\nand for $x_1,x_2\\in X$\n\\begin{equation*}\\pi\\circ\\alpha(\\langle x_1,x_2\\rangle_A)=\\pi(\\langle Ux_1,Ux_2\\rangle_B)=\\sigma(Ux_1)^*\\sigma(Ux_2).\\end{equation*}\nHence $(\\sigma\\circ U,\\pi\\circ \\alpha)$ is a Toeplitz representation of $X$.\n\nIn particular, $(\\sigma_Y\\circ U,\\pi_B\\circ\\alpha)$ is a Toeplitz representation of $X$ where $(\\sigma_Y,\\pi_B)$ is the universal Toeplitz representation of $Y$ in $\\mathcal{T}_Y$. By the universal property of $\\mathcal{T}_X$, there is a $*$-homomorphism $\\theta:\\mathcal{T}_X\\to\\mathcal{T}_Y$ such that $\\theta\\circ\\sigma_X=\\sigma_Y\\circ U$ and $\\theta\\circ\\pi_A=\\pi_B\\circ \\alpha$, where $(\\sigma_X,\\pi_A)$ is the universal representation of $X$ in $\\mathcal{T}_X$.\n\nSimilarly $(\\sigma_X\\circ U^{-1},\\pi_A\\circ \\alpha^{-1})$ is a Toeplitz representation of $Y$ and induces a $*$-homomorphism $\\theta':\\mathcal{T}_Y\\to \\mathcal{T}_X$ for which $\\theta'\\circ\\sigma_Y=\\sigma_X\\circ U^{-1}$ and $\\theta'\\circ\\pi_B=\\pi_A\\circ \\alpha^{-1}$. Thus\n$$\\sigma_Y=\\sigma_Y\\circ U\\circ U^{-1}=\\theta\\circ\\sigma_X\\circ U^{-1}=\\theta\\circ\\theta'\\circ\\sigma_Y$$\nand similarly $\\pi_B=\\theta\\circ\\theta'\\circ\\pi_B$. Since the identity $id$ on $\\mathcal{T}_Y$ also has the property that $\\pi_B=id\\circ\\pi_B$ and $\\sigma_Y=id\\circ\\sigma_Y$, it follows by the universal property of $\\mathcal{T}_Y$ that $\\theta\\circ\\theta'=id$. Identical reasoning verifies that $\\theta'\\circ\\theta$ is the identity on $\\mathcal{T}_X$. Thus $\\theta$ is our desired $*$-isomorphism.\n\\end{proof}\n\nGoing forward we will be exclusively interested in Toeplitz algebras associated to graph correspondences, and so offer the following corollary.\n\\begin{corollary}\\label{autosfromcoherent} Let $E$ and $F$ be graphs. If $(U,\\alpha)$ is a coherent unitary equivalence between $X(E)$ and $X(F)$ then there is a $*$-isomorphism $\\Gamma_{U,\\alpha}:\\mathcal{T}_{X(E)}\\to\\mathcal{T}_{X(F)}$ for which $\\Gamma_{U,\\alpha}(\\delta_e)=U\\delta_{e}$ and $\\Gamma_{U,\\alpha}(\\delta_v)=\\alpha(\\delta_v)$ for all $e\\in E^1$ and $v\\in E^0$.\n\\end{corollary}\nThis is immediately seen from the proof of the previous Proposition if we recall that we identify $X(E)$ and $X(F)$ with their images in $\\mathcal{T}_{X(E)}$ and $\\mathcal{T}_{X(F)}$, respectively, under the appropriate universal maps. This also implies that if $E$ and $F$ are isomorphic graphs then $\\mathcal{T}_{X(E)}$ and $\\mathcal{T}_{X(F)}$ are $*$-isomorphic, which is unsurprising.\n\n\\section{ENDOMORPHISMS FROM GRAPHS}\n\nThroughout this section we will let $E=(E^0,E^1,r,s)$ be a given graph whose vertex and edge sets are at most countable. All $*$-representations will be assumed non-degenerate.\n\n\\begin{proposition} Given a $*$-representation $\\tau:\\mathcal{T}_{X(E)}\\to B(H)$, the assignments\n\\begin{equation*}Ad_\\tau(w):=\\sum_{e\\in E^1}\\tau(\\delta_e)w\\tau(\\delta_e)^*\\end{equation*}\n(the sum is taken as a SOT limit) define a $*$-endomorphism $Ad_\\tau$ of the von Neumann algebra $W=\\{\\tau(\\delta_v):v\\in E^0\\}'$ (this notation will denote the relative commutant in $B(H)$).\n\\end{proposition}\n\\begin{proof} First, notice that for $e\\in E^1$ and $w\\in W$ the term $\\tau(\\delta_e)w\\tau(\\delta_e)^*$ has its support projection contained in $\\tau(\\delta_e^*\\delta_e)$. Since the partial isometries $\\tau(\\delta_e)$ have mutually orthogonal ranges, it follows that for every $h\\in H$, $\\tau(\\delta_e)w\\tau(\\delta_e)^*h$ is nonzero for at most one $e\\in E^1$. Thus the sum converges in the SOT.\n\nCertainly $Ad_\\tau$ is linear and has $Ad_\\tau(w^*)=Ad_\\tau(w)^*$ for each $w\\in W$. Given $w_1,w_2\\in W$ we find that\n\\begin{align*}Ad_\\tau(w_1)Ad_\\tau(w_2)&=\\bigg(\\sum_{e\\in E^1}\\tau(\\delta_e)w_1\\tau(\\delta_e)^*\\bigg)\\bigg(\\sum_{f\\in E^1}\\tau(\\delta_f)w_2\\tau(\\delta_f)^*\\bigg)\\\\\n\t&= \\sum_{e,f\\in E^1}\\tau(\\delta_e)w_1\\tau(\\delta_e)^*\\tau(\\delta_f)w_2\\tau(\\delta_f)^*\\\\\n\t&= \\sum_{e\\in E^1}\\tau(\\delta_e)w_1\\tau(\\delta_{s(e)})w_2\\tau(\\delta_e)^*\\\\\n\t&=\\sum_{e\\in E^1}\\tau(\\delta_e)\\tau(\\delta_{s(e)})w_1w_2\\tau(\\delta_e)^*\\\\\n\t&=\\sum_{e\\in E^1}\\tau(\\delta_e)w_1w_2\\tau(\\delta_e)^*\\\\\n\t&=Ad_\\tau(w_1w_2)\n\t\\end{align*}\nand so $Ad_\\tau$ is multiplicative. Note that any potential issues with SOT-convergence of the product are circumvented by $E^1$ being at most countable. All that remains is to verify that $Ad_\\tau(w)\\in W$ for each $w\\in W$. To that end we first note that $\\delta_e^*\\delta_v=\\delta_e^*$ if $v=r(e)$ and is zero otherwise. By taking adjoints, $\\delta_v\\delta_e=\\delta_e$ if $v=r(e)$ and is zero otherwise. Thus, given $w\\in W$ and $v\\in E^0$ we find\n\\begin{equation*}Ad_\\tau(w)\\tau(\\delta_v)=\\sum_{e\\in r^{-1}(v)}\\tau(\\delta_e)w\\tau(\\delta_e)^*=\\tau(\\delta_v)Ad_\\tau(w)\\end{equation*}\nand so $Ad_\\tau(w)$ commutes with each $\\tau(\\delta_v)$.\n\\end{proof}\n\nWe note that the von Neumann algebra $W=\\{\\tau(\\delta_v):v\\in E^0\\}$, because the $\\tau(\\delta_v)$ are a family of mutually orthogonal projections, is precisely equal to $\\displaystyle{\\bigoplus_{v\\in E^0} \\tau(\\delta_v)B(H)\\tau(\\delta_v)}$ which is a sum of Type I factors. \n\nThe following is a construction which we believe to be folklore, but our use of it is motivated by observations made by Muhly and Solel \\cite{muhlysolel}. Given a $*$-representation $\\tau:\\mathcal{T}_{X(E)}\\to B(H)$ let $W=\\{\\tau(\\delta_v):v\\in E^0\\}'$. The space\n\\begin{equation*}\\mathcal{I}_\\tau:=\\left\\{T\\in B(H):Ad_\\tau(w)T=Tw\\ ,\\ w\\in W \\right\\}\\end{equation*}\nis a $C^*$-correspondence over $W'$. The left and right actions of $W'$ are simply multiplication within $B(H)$ and the $W'$-valued inner product is defined by $\\langle T,S\\rangle_{W'}:=T^*S$.\n\nBecause our endomorphism is of the form $Ad_\\tau$, we can say more: for $w\\in W$ and $e\\in E^1$\n\\begin{equation*}Ad_\\tau(w)\\tau(\\delta_e)=\\sum_{f\\in E^1}\\!\\!\\tau(\\delta_f)w\\tau(\\delta_f)^*\\tau(\\delta_e)=\\tau(\\delta_e)w\\tau(\\delta_{s(e)})=\\tau(\\delta_e)\\tau(\\delta_{s(e)})w=\\tau(\\delta_e)w\\end{equation*}\nand so $\\tau(\\delta_e)\\in\\mathcal{I}_\\tau$ for each $e\\in E^1$. As $\\tau(\\delta_v)\\in W'$ for each $v\\in E^0$ we finally have $\\tau(X(E))\\subseteq \\mathcal{I}_\\tau$.\n\n\\begin{theorem}\\label{theorem1} Suppose that $E$ and $F$ are graphs and $\\tau_1:\\mathcal{T}_{X(E)}\\to B(H)$ and $\\tau_2:\\mathcal{T}_{X(F)}\\to B(H)$ are two faithful $*$-representations. If $Ad_{\\tau_1}=Ad_{\\tau_2}$ on $W=\\{\\tau_1(\\delta_v):v\\in E^0\\}'=\\{\\tau_2(\\delta_v):v\\in F^0\\}'$ then there is a coherent unitary equivalence $(U,\\alpha)$ between $X(E)$ and $X(F)$ such that $\\tau_2=\\tau_1\\circ\\Gamma_{U,\\alpha}$. \n\\end{theorem}\nHere $\\Gamma_{U,\\alpha}$ is the $*$-isomorphism from $\\mathcal{T}_{X(E)}$ to $\\mathcal{T}_{X(F)}$ arising from $(U,\\alpha)$ as given in Corollary \\ref{autosfromcoherent}.\n\\begin{proof} \nSince $\\{\\tau_1(\\delta_v):v\\in E^0\\}$ and $\\{\\tau_2(\\delta_v):v\\in F^0\\}$ are sets of orthogonal projections with the same commutant they are in fact equal, and in particular $E^0$ and $F^0$ are of the same cardinality. To ease notation we'll denote these projections by $P_v$, $v\\in E^0$, (with no assumption that $P_v=\\tau_1(\\delta_v)$ or similar) hence\n\\begin{equation*}\\{P_v:v\\in E^0\\}=\\{\\tau_1(\\delta_v):v\\in E^0\\}=\\{\\tau_2(\\delta_v):v\\in F^0\\}.\\end{equation*}\n\nAs $Ad_{\\tau_1}=Ad_{\\tau_2}$ we have that $\\mathcal{I}_{\\tau_1}=\\mathcal{I}_{\\tau_2}$ and we'll call this module simply $\\mathcal{I}$.\n\nAs $\\tau_1(\\delta_e)\\in\\mathcal{I}$ for each $e\\in E^1$ we have\n\\begin{equation*}\\tau_1(\\delta_e)=\\tau_1(\\delta_e)I=Ad_{\\tau_2}(I)\\tau_1(\\delta_e)=\\sum_{f\\in F^1}\\tau_2(\\delta_f)\\tau_2(\\delta_f)^*\\tau_1(\\delta_e)\\end{equation*}\nhence $\\tau_1(\\delta_e)$ is in the $W'$-submodule of $\\mathcal{I}$ generated by $\\tau_2(X(E))$. Similarly, for each $f\\in F^1$, $\\tau_2(\\delta_f)$ is in the $W'$-submodule generated by $\\tau_1(X(E))$. Thus $\\tau_1(X(E))$ and $\\tau_2(X(E))$ generate the same $W'$-submodule of $\\mathcal{I}$.\n\nGiven $e\\in E^1$ and $f\\in F^1$ we have seen that \n\\begin{equation*}\\tau_2(\\delta_f)^*\\tau_1(\\delta_e)\\in W'=\\{P_v:v\\in E^0\\}''=\\ell^\\infty(\\{P_v:v\\in E^0\\}).\\end{equation*}\nNotice however that $\\tau_2(\\delta_f)^*\\tau_1(\\delta_e)\\tau_1(\\delta_v)=0$ unless $v=s(e)$ and hence $\\tau_2(\\delta_f)^*\\tau_1(\\delta_e)$ is a multiple of $\\tau_1(\\delta_{s(e)})$ only, i.e. is an element of $C_0(\\{P_v:v\\in E^0\\})$. Since before we obtained $\\tau_1(\\delta_e)=\\sum_{f\\in F^1}\\tau_2(\\delta_f)\\tau_2(\\delta_f)^*\\tau_1(\\delta_e)$ for all $e\\in E^1$, it now follows that $\\tau_1(X(E))$ and $\\tau_2(X(E))$ generate the same correspondence over $C_0(\\{P_v:v\\in E^0\\})$. It is important to note that this correspondence has three different actions of $C_0(\\{P_v:v\\in E^0\\})$: the ones inherited through $\\tau_1$ and $\\tau_2$ and simple operator multiplication in $B(H)$.\n \nFinally we have that $\\tau_1(C_0(E^0))=\\tau_2(C_0(F^0))$ and $\\tau_1(X(E))=\\tau_2(X(F))$ as sets and so, because both representations are faithful by hypothesis, $\\tau_2^{-1}\\circ\\tau_1$ is a well-defined bijection between $X(E)$ and $X(F)$ and between $C_0(E^0)$ and $C_0(F^0)$. Denote by $U$ and $\\alpha$ the restrictions of $\\tau_2^{-1}\\circ\\tau_1$ to $X(E)$ and to $C_0(E^0)$, respectively.\n\nGiven $x\\in X(E)$, $y\\in X(F)$, and $a\\in C_0(E^0)$ we have\n\\begin{align*}U(xa)&=\\tau_2^{-1}\\circ\\tau_1(xa)=\\tau_2^{-1}\\circ\\tau_1(x)\\tau_2^{-1}\\circ\\tau_1(a)=(Ux)\\alpha(a),\\\\\nU(\\phi(a)x)&=\\tau_2^{-1}\\circ\\tau_1(\\phi(a)x)=\\tau_2^{-1}\\circ\\tau_1(a)\\tau_2^{-1}\\circ\\tau_1(x)=\\alpha(a)Ux,\\\\\n\\langle Ux,y\\rangle&=[\\tau_2^{-1}\\circ\\tau_1(x)]^*y=\\tau_2^{-1}\\circ\\tau_1(x^*\\tau_1^{-1}\\circ\\tau_2(y))=\\alpha(\\langle x,\\tau_1^{-1}\\circ\\tau_2(y)\\rangle)=\\alpha(\\langle x,U^{-1}y\\rangle).\\end{align*}\nand so $(U,\\alpha)$ is a coherent unitary equivalence between $X(E)$ and itself.\n\nIt follows from Corollary \\ref{autosfromcoherent} that $(U,\\alpha)$ induces a $*$-isomorphism $\\Gamma_{U,\\alpha}:\\mathcal{T}_{X(E)}\\to\\mathcal{T}_{X(F)}$ and, by construction, $\\tau_2\\circ \\Gamma_{U,\\alpha}=\\tau_1$.\n\\end{proof}\n\nNotice we have shown that if $\\tau_1:\\mathcal{T}_{X(E)}\\to B(H)$ and $\\tau_2:\\mathcal{T}_{X(F)}\\to B(H)$ generate identical $*$-endomorphims $Ad_{\\tau_1}$ and $Ad_{\\tau_2}$ then $\\mathcal{T}_{X(E)}$ and $\\mathcal{T}_{X(F)}$ are $*$-isomorphic as $C^*$-algebras. Hence in subsequent results we will concern ourselves only with two representations $\\tau_1$, $\\tau_2$ of the same Toeplitz algebra $\\mathcal{T}_{X(E)}$.\n\n\nOur result is a generalization of Laca's \\cite[Proposition 2.2]{laca}. When $E$ is the graph with a single vertex and $n\\in\\mathbb{N}\\cup\\{ \\infty\\}$ edges we have already seen that $\\mathcal{T}_{X(E)}=\\mathcal{E}_n$. If $\\tau_1$ and $\\tau_2$ are faithful and nondegenerate then $W=B(H)$. The map $\\alpha$ is the identity on $C_0(E^0)=\\mathbb{C}$ and $U$ is a unitary operator on the Hilbert space $X(E)=\\ell^2(\\{v_1,...,v_n\\})$. Hence $\\Gamma_{U,\\alpha}$ is a $*$-automorphism of $\\mathcal{E}_n$ which fixes the Hilbert space $X(E)$ and implements the equivalence between the two representations.\n\nWe will conclude this section with a discussion of conjugacy conditions for endomorphisms of the type we've been examining. Recall that two endomorphisms $\\alpha$ and $\\beta$ are said to be \\emph{conjugate} if there is an automorphism $\\gamma$ such that $\\alpha\\circ\\gamma=\\gamma\\circ\\beta$.\n\n\\begin{lemma}\\label{spatialdiagonal} If $P_1,P_2,...\\in B(H)$ is an at most countable family of orthogonal projections and $\\gamma$ is a $*$-automorphism of $W=\\{P_1,P_2,...\\}'$ then there exists a unitary $U\\in B(H)$ such that $\\gamma(w)=UwU^*$ for all $w\\in W$.\n\\end{lemma}\n\\begin{proof} Note that for each $n$, $\\gamma$ restricts to a $*$-isomorphism $\\gamma_n$ between $P_nB(H)P_n=B(P_nH)$ and $\\gamma(P_n)B(H)\\gamma(P_n)=B(\\gamma(P_n)H)$. Such isomorphisms are always spatial and so there are unitaries $U_n:B(P_nH)\\to B(\\gamma(P_n)H)$ such that $\\gamma_n(w)=U_nwU_n^*$. It is then immediate that $U:=U_1\\oplus U_2\\oplus...$ is a unitary in $B(H)$ and $UwU^*=\\gamma(w)$ for each $w\\in W$.\n\\end{proof}\n\n\\begin{theorem}\\label{theorem2} Suppose that $\\tau_1,\\tau_2:\\mathcal{T}_{X(E)}\\to B(H)$ are two faithful $*$-representations such that $Ad_{\\tau_1}$ and $Ad_{\\tau_2}$ are conjugate $*$-endomorphisms of $W=\\{\\tau_1(\\delta_v):v\\in E^0\\}'=\\{\\tau_2(\\delta_v):v\\in E^0\\}'$. Then there is a coherent unitary equivalence $(U,\\alpha)$ between $X(E)$ and itself such that $\\tau_2$ and $\\tau_1\\circ\\Gamma_{U,\\alpha}$ are unitarily equivalent $*$-representations.\n\\end{theorem}\n\n\\begin{proof} Let $\\gamma$ be an $*$-automorphism of $W$ such that $Ad_{\\tau_1}\\circ\\gamma=\\gamma\\circ Ad_{\\tau_2}$ and let $V\\in B(H)$ be the unitary for which $\\gamma(w)=VwV^*$ according the Lemma \\ref{spatialdiagonal}. Then $Ad_{\\tau_2}(w)=V^*Ad_{\\tau_1}(VwV^*)V$ for all $w\\in W$. Define $\\kappa:\\mathcal{T}_{X(E)}\\to B(H)$ by $\\kappa(t):=V\\tau_1(t)V^*$ and note that $\\kappa$ is a $*$-representation of $\\mathcal{T}_{X(E)}$ such that \n\\begin{equation*}Ad_{\\kappa}(w)=\\sum_{e\\in E^1}\\kappa(\\delta_e)w\\kappa(\\delta_e)^*=\\sum_{e\\in E^1}V\\tau_1(\\delta_e)V^*wV\\tau_1(\\delta_e)^*V^*=VAd_{\\tau_1}(V^*wV)V^*\\end{equation*}\nand so $Ad_{\\kappa}=Ad_{\\tau_2}$ on $W$. Applying Theorem \\ref{theorem1} we obtain a coherent unitary equivalence $(U,\\alpha)$ inducing the $*$-automorphism $\\Gamma_{U,\\alpha}$ of $\\mathcal{T}_{X(E)}$ such that $\\tau_2=\\kappa\\circ\\Gamma_{U,\\alpha}$. As now $\\tau_2(t)=V[\\tau_1\\circ\\Gamma_{U,\\alpha}(t)]V^*$ for each $t\\in \\mathcal{T}_{X(E)}$, we have that $\\tau_2$ and $\\tau_1\\circ\\Gamma_{U,\\alpha}$ are unitarily equivalent, as desired.\n\\end{proof}\n\n\\section{GRAPHS FROM ENDOMORPHISMS}\n\nIn this final section we will demonstrate that all $*$-endomorphisms of von Neumann algebras which are sums of Type I factors are obtained in the natural way from representations of Toeplitz algebras for graph correspondences. Our result is a significant generalization of \\cite[Theorem 3.9]{brenken2} which places technical restrictions on the endomorphisms. In the case of unital endomorphisms our results are comparable.\n\n\\begin{theorem}\\label{theorem3} Let $W=\\bigoplus W_i\\subseteq B(H)$ be a countable sum of Type I factors. Let $P_1,P_2,...\\in B(H)$ be projections such that $W_i=P_iB(H)P_i$. If $\\alpha$ is a normal $*$-endomorphism of $W$ then there exists a graph $E$ and $*$-representation $\\tau:\\mathcal{T}_{X(E)}\\to B(H)$ such that $\\alpha=Ad_\\tau$.\n\\end{theorem}\n\\begin{proof} Without loss of generality we may assume that $\\sum P_i=I$. If this were not the case we may define $P_0=(\\sum P_i)^\\perp$ and $W_0=P_0B(H)P_0$ and extend $\\alpha$ to a normal $*$-endomorphism of $W\\oplus W_0$ with $\\alpha|_{W_0}=id$. \n\nFor $i>0$ define $H_i=P_iH$. For $i,j>0$ and $x\\in W$ define $\\alpha_{ij}(x)=P_j\\alpha(P_ix)$. Then $\\alpha_{ij}$ restricts to a $*$-homomorphism between $B(H_i)=P_iB(H)P_i=W_i$ and $B(H_j)=P_jB(H)P_j=W_j$ as seen by\n\\begin{equation*}P_j\\alpha(P_ix)P_j\\alpha(P_iy)=P_j\\left(P_j\\alpha(P_ix)\\right)\\alpha(P_iy)=P_j\\alpha(P_ixP_iy)=P_j\\alpha(P_ixy).\\end{equation*}\n\nSo $\\alpha_{ij}$ is a $*$-homomorphism between two Type I factors, and thus by \\cite[Proposition 2.1]{arveson} if $\\alpha_{ij}$ is nonzero there exists $n_{ij}\\in\\mathbb{N}\\cup\\{\\infty\\}$ and isometries $V_k^{(ij)}\\in B(H_i,H_j)$, $k=1,...,n_{ij}$ such that $\\alpha_{ij}|_{B(H_i)}(T)=\\sum_{k=1}^{n_{ij}}V_k^{(ij)}TV_k^{(ij)*}$.\nWe will identify the $V_k^{(ij)}$ with their associated partial isometries in $B(H)$, so that $V_k^{(ij)*}V_k^{(ij)}=P_i$ and $V_k^{(ij)}V_k^{(ij)*}\\leq P_j$.\n\nSet $E^0:=\\{P_1,P_2...,\\}$ and $E^1:=\\bigcup_{i,j}\\{V_k^{(ij)}:k=1,...,n_{ij}\\}$. Define maps $r,s:E^1\\to E^0$ by $r(V_k^{(ij)})=P_j$ and $s(V_k^{(ij)})=P_i$. Then $E=(E^0,E^1,r,s)$ is a graph. It is trivial to see that the identity maps on $E^1$ and $E^0$ extend to a Toeplitz representation of $X(E)$ which in turn induces a $*$-representation $\\tau:\\mathcal{T}_{X(E)}\\to B(H)$.\n\nFinally, we have that for each $x\\in W$\n\\begin{equation*}\\alpha(x)=\\sum_{i,j>0} P_j\\alpha(P_ix)=\\sum_{i,j>0}\\alpha_{ij}(x)=\\sum_{i,j>0}\\sum_{k=1}^{n_{ij}} V_k^{(ij)}xV_k^{(ij)*}=\\sum_{f\\in E^1}\\tau(\\delta_f)x\\tau(\\delta_f)^*\\end{equation*}\nas desired.\n\\end{proof}\nAs a consequence, the possible $*$-endomorphisms of a given von Neumann algebra $W=\\{P_1,P_2,...\\}'$ coincide, up to conjugacy, with the coherent unitary equivalence classes of $C^*$-correspondences over $C_0(\\{P_1,P_2,...\\})$. \n\n\\subsection{Cuntz-Pimsner Algebras} To discuss the special case of unital $*$-endomorphisms we will need to briefly sketch the defining features and universal properties of the so-called Cuntz-Pimsner algebras. The relationship of these Cuntz-Pimnser algebras to our Toeplitz algebras is analogous to the relationship between the classical Toeplitz algebras $\\mathcal{E}_n$ and classical Cuntz algebras $\\O_n$. The Cuntz-Pimsner algebras were originally defined in \\cite{pimsner} though our treatment will take its cues from \\cite{fowlerraeburnmuhly} and \\cite{fowlerraeburn}.\n\nRecalling that $X(E)$ is a $C^*$-correspondence over $C_0(E^0)$, notice that $I=C_0(\\{v\\in\\ E^0:r^{-1}(v)\\text{ is finite }\\})$ is a closed, two-sided ideal in $C_0(E^0)$. As noted in \\cite[Proposition 4.4]{fowlerraeburn} (and recalling our modern reversal of $r$ and $s$ from the presentation in that work), elements of $I$ are precisely those elements of $C_0(E^0)$ whose left action on $X(E)$ is compact. Since our graphs have at most countable vertices and edges, $X(E)$ is countably generated as a $C^*$-correspondence over $C_0(E^0)$. It follows, \\cite[Remark 3.9 following Definition 3.8]{pimsner} that the \\emph{Cuntz-Pimnser algebra} of $X(E)$ is the $C^*$-algebra $\\O_{X(E)}$ which is universal for Toeplitz representations $(\\sigma,\\pi)$ (as in Definition \\ref{toeplitzrepdef}) which additionally satisfy:\n\\begin{enumerate}\n\\item[(iv)] $\\displaystyle{\\pi(a)=\\sum_{f\\in E^1}\\sigma(\\phi(a)\\delta_f)\\sigma(\\delta_f)^*}$ for all $a\\in I$.\n\\end{enumerate}\nor, equivalently (see \\cite[Example 1.5]{fowlerraeburnmuhly}),\n\\begin{enumerate}\n\\item[(iv)$'$] $\\displaystyle{\\pi(\\delta_v)=\\sum_{f\\in r^{-1}(v)}\\sigma(\\delta_f)\\sigma(\\delta_f)^*}$ for all $v\\in E^0$ with $|r^{-1}(v)|<\\infty$.\n\\end{enumerate}\nRepresentations satisfying (i)-(iv)$'$ are often termed ``coisometric\" Toeplitz representations. With this characterization of $\\O_{X(E)}$ we may recognize that Theorem \\ref{theorem3} has more to say when the endomorphism is unital.\n\n\\begin{cor} Let $W=\\bigoplus W_i\\subseteq B(H)$ be a countable sum of Type I factors. Let $P_1,P_2,...\\in B(H)$ be projections such that $W_i=P_iB(H)P_i$. If $\\alpha$ is a normal, unital $*$-endomorphism of $W$ then there exists a graph $E$ and $*$-representation $\\tau:\\O_{X(E)}\\to B(H)$ such that $\\alpha=Ad_\\tau$.\n\\end{cor}\n\\begin{proof} We define the partial isometries $V_k^{(ij)}$ and graph $E$ in the same manner as in the proof of Theorem \\ref{theorem3}. Since $\\alpha$ is unital we have\n\n$$P_n=P_n\\alpha(I)=\\sum_{i,j>0}\\sum_{k=1}^{n_{ij}} P_nV_k^{(ij)}V_k^{(ij)*}=\\sum_{i>0}\\sum_{k=1}^{n_{in}} V_k^{(in)}V_k^{(in)*}$$\nfor every $P_n$. Recalling that the representation of $(\\sigma,\\pi)$ $E$ on $B(H)$ is the pair of identity maps, this becomes\n$$\\pi(\\delta_v)=\\sum_{f\\in E^1}\\pi(\\delta_v)\\sigma(\\delta_f)\\sigma(\\delta_f)^*=\\sum_{f\\in r^{-1}(v)}\\sigma(\\delta_f)\\sigma(\\delta_f)^*$$\nfor all $v\\in E^0$. In particular this holds for all edges $v$ with $r^{-1}(v)$ finite (i.e.\\ all $P_j$ such that $\\{V_k^{(ij)}: i>0, k=1,...,n_{ij}\\}$ is finite). Thus condition (iv)$'$ is satisfied and the identity maps on $E^0$ and $E^1$ induce a representation of $\\O_{X(E)}$. The fact that $\\alpha=Ad_\\tau$ follows as before.\n\\end{proof}\n\n\\bibliographystyle{plain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Conclusion}\nSequential execution of multi-robot coordinated behaviors can be employed to solve real-world complex missions. However, sequences of behaviors can be executed only if the robots meet all required communication constraints in finite time. In this paper, we described a distributed framework for the sequential composition of coordinated behaviors designed on finite-time convergence control barrier functions. The resulting composition framework is formulated in the form of a quadratic program, which is solved locally by individual robots. \\mymod{Although the focus of this paper is on coordinated motion, the application of the proposed framework is relevant to other form of autonomous collaborations where the robots need to satisfy prescribed pair-wise proximity requirements that change over time}. Finally, a large-scale multi-task scenario, denoted ``Securing a Building\" mission is proposed as an ideal environment for testing multi-robot techniques.\n\n\\appendices\n\\input{sections\/researchOpportunities}\n\n\\bibliographystyle{IEEEtran}\n\n\\section{Behaviors Dynamic} \\label{sec:appendixB}\n\\paragraph{Rendezvous}\n\\begin{equation}\n\tu_i = \\sum_{j \\in \\mathcal{N}_i} x_j - x_i\n\\end{equation}\n\n\\paragraph{Scatter}\n\\begin{equation}\n\tu_i = \\sum_{j \\in \\mathcal{N}_i} x_i - x_j\n\\end{equation}\n\n\n\\paragraph{Formation control}\n\\begin{equation}\n\tu_i = \\sum_{j \\in \\mathcal{N}_i} (\\|x_i-x_j\\|^2-\\theta_{ij}^2)(x_j - x_i)\n\\end{equation}\n\nwhere $\\theta_{ij}$ must be feasible.\n\n\\paragraph{Leader-follower}\n\\begin{align}\n\tu_f &= \\sum_{j \\in \\mathcal{N}_i} (\\|x_i-x_j\\|^2-\\theta_{ij}^2)(x_j - x_i) \\\\\n\tu_l &= \\sum_{j \\in \\mathcal{N}_i} \\left( (\\|x_i-x_j\\|^2-\\theta_{ij}^2)(x_i - x_i) \\right) + \\gamma_g(x_g-x_i) \n\\end{align}\n\n\\paragraph{Leader formation}\n\\begin{align}\n\tu_f &= \\sum_{j \\in \\mathcal{N}_i} (\\|x_i-x_j\\|^2-\\theta_{ij}^2)(x_j - x_i) \\\\\n\tu_l &= \\sum_{j \\in \\mathcal{N}_i} \\left( (\\|x_i-x_j\\|^2-\\theta_{ij}^2)(x_i - x_i) \\right) + \\gamma_g(x_g-x_i) \n\\end{align}\nwhere $\\theta_{ij}$ must be feasible.\n\n\\paragraph{Cyclic pursuit}\n\\begin{equation}\n\tu_i = \\sum_{j \\in \\mathcal{N}_i} R(\\theta)\\,(x_j - x_i) \n\\end{equation}\nwhere $\\mathcal{G}$ must be $C_N$ (cycle graph).\n\n\\paragraph{Lattice}\n\\begin{equation}\nu_i = \\sum_{j \\in \\mathcal{N}_i} (\\|x_i-x_j\\|^2-\\theta^2)(x_j - x_i)\n\\end{equation}\nwhere $\\theta \\simeq 0.8$ max sensing radius. \n\n\\paragraph{Coverage}\n\\begin{equation}\nu_i = c_i - x_i\n\\end{equation}\nwhere $c_i$ centroid of Voronoi tessellation.\n\n\\paragraph{Swarming}\n\\begin{equation}\nu_i = \\sum_{j \\in \\mathcal{N}_i} \\dots (x_j - x_i)\n\\end{equation}\n\n\\section{Securing a Building as Benchmark Scenario} \\label{sec:appendixA}\nTesting the performance of techniques and algorithms for the control of multi-robot systems in real-world scenarios is a challenging task. \\mymod{This is particularly true when addressing novel approaches, as the focus on specific aspects of the problem might obscure all-around performance assessments.} To this end, thanks to its modularity, the {\\it Securing a Building} mission is an ideal testing framework. In this section, we suggest a number of selected research topics, for which this mission could serve as a testing framework when aiming to evaluate performance of new techniques. \\mymod{This appendix is by no mean proposed as a complete list of subjects relevant to multi-robot systems but rather as a discussion to stimulate application of the {\\it Securing a Building} as a versatile, real-world testing scenario.}\n\n\\paragraph{Team Assembly} Considerable efforts have been devoted to the development of team composition techniques for heterogeneous robots~\\cite{prorok2016formalizing},~\\cite{koes2005heterogeneous}. Based on the skill set required to solve a particular task, e.g., certain actuation, sensing, locomotion, or communication capabilities, the question is to find a recruitment rule that produces a team capable of delivering the best performance. For instance, in the RESCUE phase, robots capable of opening doors may be required for the {\\it maneuvering} agents, while agility and communication capabilities might be preferred during the FIND phase.\n\n\\paragraph{Communication} In the context of autonomous networked systems, central roles are played by the flow of information between agents, and the infrastructure required for it~\\cite{gupta2016survey}. A number of questions can be posed in relation to the distribution of agents over a domain, given the constraints of communication systems, such as limited range, power requirements, and privacy of the information.\n\n\\paragraph{Unknown Environment} The amount of prior knowledge about the environment plays an important role in the definition of both low-level robot controllers and high-level mission plans. The performance of distributed solutions to the localization and mapping problems~\\cite{forster2013collaborative} can be tested on the {\\it Securing a Building}. Aspect of interest include balancing between exploitation and exploration of the environment applied, for instance, to the building exploration planning.\n\n\\paragraph{Resilience} Failure of the mission can be attributed to factors such as damaged components, sensing errors, communication dropouts, delays, control disturbances, reduction of functionalities due to adversarial attacks, etc. A number of different research thrusts focus on the problem of detecting and responding to faults and malicious attacks in multi-agent and cyber-physical systems~\\cite{pasqualetti2011consensus,pierpaoli2018fault,fawzi2014secure}.\n\n\n\\section{\\mymod{Distributed Composition of Behaviors}}\\label{sec:multAgImp}\nThe composition framework discussed in the previous section reduces to the team-wise minimum norm controller~(\\ref{eq:minQP1}), which is not directly solvable by individual robots. \\mymod{In addition to this, a centralized supervisor is needed in order to synchronize behavior transitions.} In this section, we \\mymod{formulate a decentralized solution to problem~\\ref{pr:problem} which can be implemented by the robots using only information from their neighbors. Furthermore, we also include those} additional constraints necessary for the safe operations of the robots, e.g., inter-agent collisions and obstacles avoidance~\\cite{wang2016multi}. The formulation is derived following the approach described in~\\cite{squires2019composition}, which we adapt here to our framework.\n\n\\subsection{Distributed Finite-Time Convergence Control Barrier Functions}\nThe limitation in solving problem~(\\ref{eq:minQP1}) in a distributed fashion is represented by the fact that knowledge of dynamics, input $\\hat{u}$, and state $x$ for the entire team need to be available. In addition, solution of~(\\ref{eq:minQP1}), provides the control inputs for the entire team, which are unnecessary to the individual robots.\n\nIn order to develop the correct decentralized formulation of~(\\ref{eq:minQP1}), we first define a decomposition of the dynamics~(\\ref{eq:ensembleDynamics}). We denote by $\\mathcal{D}_i\\subset$ \\mymod{$\\mathbb{R}^d$} and $U_i \\subset$ \\mymod{$\\mathbb{R}^m$} configuration space and set of feasible controls for agent $i$ respectively. In addition, by denoting with $\\bar{f},\\,\\bar{g}: \\mathcal{D}_i \\mapsto$ \\mymod{$\\mathbb{R}^d$} the node-level terms of the control affine dynamics of agent $i$, the ensemble dynamics can be written as:\n\\begin{equation} \\label{eq:decoup_dyn}\n\t\\dot{x} = \\bar{f}(x_i)\\otimes {\\bf 1}_n + (\\bar{g}(x_i) \\otimes I_n)\\,\\begin{bmatrix} u_1 \\\\ \\vdots \\\\ u_n \\end{bmatrix},\n\\end{equation}\nwhere $u_i \\in U_i$ is the $i^{\\text{th}}$ robot's control input, $\\otimes$ is the Kronecker product, and ${\\bf 1}_n$ and $I_n$ are vector of ones and identity matrix of size $n$ respectively.\n\nLet's consider two sequential behaviors $\\mathcal{B}_{k-1}$ and $\\mathcal{B}_{k}$. Upon completion of $\\mathcal{B}_{k-1}$, for all edges $(i,j)\\in E_k$, robots' configuration should satisfy \n\\begin{equation} \\label{eq:dec_const_edge}\n\t\\dot{h}_{ij}^c(x_i,x_j) + \\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c(x_i,x_j)) \\geq 0.\n\\end{equation}\nFrom the $i^{\\text{th}}$ robot's point of view, the set of constraints that need to be satisfied in order to execute the new behavior are\n\\begin{equation} \\label{eq:dec_const_agenti}\n\t\\dot{h}^c_{ij}(x_i,x_j) + \\bar{\\alpha}_{\\rho,\\gamma}(h^c_{ij}(x_i,x_j)) \\geq 0 \\quad \\forall j\\in \\mathcal{N}_{k}^i,\n\\end{equation}\nwhere we recall that $\\mathcal{N}_{k}^i$ is the set of neighbors to robot $i$ required by behavior $\\mathcal{B}_k$. However, since constraint~(\\ref{eq:dec_const_agenti}) appears exactly twice across the team of robots, it can be relaxed by considering the admissible set of control inputs\n\\begin{equation} \\label{eq:admGraphControl}\nK_{k}^{c,i} = \\bigcap_{j\\in\\mathcal{N}_k^i} K_{k,ij}^{c,i}\n\\end{equation}\nwith\n\\begin{equation}\nK_{k,ij}^{c,i} = \\{u_i\\in U_i \\,| \\,L_{\\bar{f}}h_{ij}^c + L_{\\bar{g}}h_{ij}^cu_i + \\frac{\\bar{\\alpha} _{\\rho,\\gamma}(h_{ij}^c)}{2} \\geq 0 \\},\n\\end{equation}\nwhere dependence from $x_i$ and $x_j$ is omitted for clarity.\n\n\\begin{theorem} \\label{thm:fcbfControl_dist}\n\tDenoting with $x_0 = [x_{0,1}^T,\\dots,x_{0,n}^T]^T$ the initial state of a multi-agent system with dynamics described as in~(\\ref{eq:decoup_dyn}), any controller $\\mathcal{U}_i:\\mathcal{D}_i^{|\\mathcal{N}_k^i|} \\mapsto U_i$ such that $\\mathcal{U}_i(x_{0}) \\in K_{k}^{c,i}$ for all $x_{0} \\in \\mathcal{D}_i^{|\\mathcal{N}_k^i|}$, will drive the ensemble state to $\\mathcal{C}^c_k $ within time\n\\begin{equation}\n\tT_k = \\max_{\\substack{(i,j) \\in E_k \\\\ \\text{s.t.} \\,\\, h^c_{ij}(x_{0,i},x_{0,j})<0}} \\left\\{ \\frac{1}{ \\gamma(1-\\rho)} | h_{ij}^c(x_{0,i},x_{0,j}) |^{1-\\rho} \\right\\}.\n\\end{equation}\n\\end{theorem}\n\n\n\n\\begin{IEEEproof}\nFrom Theorem~\\ref{thm:FCBF}, agents $i$ and $j$, with $(i,j)\\in E_k$, will satisfy $h_{ij}^c \\geq 0$ in finite time if\n\\begin{equation} \\label{eq:connect_const}\n\\dot{h}_{ij}^c + \\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c) \\geq 0. \n\\end{equation}\n\nConsidering the node level dynamics in~(\\ref{eq:decoup_dyn}), the constraint~(\\ref{eq:connect_const}) reduces to\n\n\\begin{equation} \\label{eq:const2agents}\n\\begin{aligned}\n&\\frac{\\partial h_{ij}^c}{\\partial x_i} \\left( \\bar{f} + \\bar{g}u_i \\right) \\, + \\, \\frac{\\partial h_{ij}^c}{\\partial x_j}\\left(\\bar{f} + \\bar{g}u_j\\right) + \\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c) \\geq 0 \\\\\n&2\\,L_{\\bar{f}}h_{ij}^c + L_{\\bar{g}}h_{ij}^c\\,u_i + L_{\\bar{g}}h_{ij}^c u_j + \\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c) \\geq 0\n\\end{aligned}\n\\end{equation}\nwhich will be satisfied if both agents $i$ and $j$ satisfy the constraint \n\\begin{equation} \\label{eq:dec_const}\n\t\\dot{h}_{ij}(x_i,x_j) + \\frac{\\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}(x_i,x_j))}{2} \\geq 0.\n\\end{equation}\nIn addition, as discussed in Theorem~\\ref{thm:fcbfControl}, constraint~(\\ref{eq:const2agents}) will still be satisfied at time\n\n\\begin{equation}\n\tT_{ij} \\leq \\frac{1}{\\gamma(1-\\rho)} | h_{ij}^c(x_{0,i},x_{0,j}) |^{1-\\rho}.\n\\end{equation} \n\nThe same argument can be repeated for all pairs $(i,j) \\in E_k$, and condition $\\mathcal{G}_k \\subseteq \\mathcal{G}(t)$ will be satisfied within time\n\n\\begin{equation}\n\tT_k = \\max_{\\substack{(i,j) \\in E_k \\\\ \\text{s.t.} \\,\\, h^c_{ij}(x_{0,i},x_{0,j})<0}} \\left\\{ T_{ij} \\right\\}.\n\\end{equation}\t\n\\end{IEEEproof}\n\nApplying the same design principle described in Section~\\ref{sec:ftcontrolBF}, the minimally invasive control action can be computed by each robot as\n\\begin{equation}\\label{eq:minQP1_dist}\nu^*_i = \\argmin_{u_i \\in U_i} \\| \\hat{u}_{k-1,i} - u_i \\|^2 \\\\\n\\end{equation}\nsubject to\n\\begin{equation}\\label{eq:constrTransition_dist}\nL_{\\bar{f}}\\,h_{ij}^c + L_{\\bar{g}}\\,h_{ij}^c\\,u_i + \\frac{\\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c)}{2} \\geq 0 , \\quad \\forall j\\in \\mathcal{N}_{k-1}^i \\cup \\mathcal{N}_{k}^i.\n\\end{equation}\nSimilarly to constraint~(\\ref{eq:constrTransition}), once all edges in $E_k$ are available, constraint~(\\ref{eq:constrTransition_dist}) is substituted with\n\\begin{equation}\\label{eq:constrExecution_dist}\nL_{\\bar{f}}\\,h_{ij}^c + L_{\\bar{g}}\\,h_{ij}^c\\,u_i + \\frac{\\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c)}{2} \\geq 0 , \\quad \\forall j\\in\\mathcal{N}_{k}^i,\n\\end{equation}\nwhich remains active until $\\mathcal{B}_k$ is completed.\n\nWe note that, in order for agent $i$ to respect~(\\ref{eq:constrExecution_dist}), the only external information needed is the state of all current neighbors, i.e. $x_j$ for all $j\\in\\mathcal{N}^i_k$. On the other side, in order to respect (\\ref{eq:constrTransition_dist}), robots need to have access to the state of the future neighbors. This requirement can be satisfied through a state estimation scheme (e.g. EKF~\\cite{williams2015observability}), which in turn requires knowledge of robots' dynamics (known for homogeneous teams) or network localization techniques~\\cite{aspnes2006theory}.\n\\mymod{\n\\begin{remark}\nThe ability of each robot to have access to an estimate of their future neighbors' state does not eliminate the necessity of establishing neighborhood relationships. In fact, a certain proximity structure between robots might be required by desired controllers' performance that cannot be met through state estimations, or by collaboration tasks that require physical interaction between the robots, e.g. collaborative manipulation~\\cite{culbertson2018decentralized}, sharing of resources~\\cite{ramachandran2019resilience}.\n\\end{remark}}\n\n\\subsection{Additional Constraints}\nIn addition to the proximity constraints discussed above, additional limitations might be imposed on the robots' configuration by the mission and the environment. For illustrative purposes, we consider inter-robots collisions and obstacle avoidance. Following the approach described in~\\cite{wang2016multi}, we encode each pair-wise separation condition through the following barrier certificate\n\\begin{equation}\n\th_{ij}^a(x) = \\| x_i-x_j \\|^2 - D_a^2 \n\\end{equation}\nand the minimum separation $D_a$ between the robots is satisfied if $h_{ij}^a(x) \\geq 0$, for all physical neighbors $j\\in \\mathcal{N}^i(t)$.\n\nSimilarly, avoidance of fixed obstacles can be introduce by considering $M$ ellipsoidal regions of the domain, described by centers $o=[o_1^T,\\dots,o_M^T]^T$. For every agent-obstacle pair $(i,m)$ we define a pairwise barrier function as\n\\begin{align}\nh_{im}^o(x) &=(x_i-o_m)^T\\,P_m\\,(x_i-o_m) - 1 \\\\\nP_m &= \\begin{bmatrix}\n\ta_m & 0 \\\\ 0 & b_m \n\\end{bmatrix}\n\\quad a_m,b_m > 0.\n\\end{align}\nThe object avoidance constraints are satisfied if $h_{im}^o(x)\\geq 0$, for all $i\\in V$ and $m \\in \\{1,\\dots,M \\}=\\mathcal{I}_M$.\n\nCollecting all the constraints, we expand the problem formulation in~(\\ref{eq:minQP1}) to\n\n\\begin{equation} \\label{eq:minQP2}\n\\begin{aligned}\n&\\qquad u_i^* = \\arg \\min_{u_i \\in U_i} \\| \\hat{u}_{k-1,i} - u_i \\|^2 &\\\\\n&L_f\\,h_{ij}^c + L_g\\,h_{ij}^c\\,u_i + \\frac{\\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c)}{2} \\geq 0, & \\forall j\\in \\mathcal{N}_{k}^i \\\\\n&L_f\\,h_{ij}^s + L_g\\,h_{ij}^s\\,u_i + \\alpha(h_{ij}^{s}) \\geq 0, &\\forall j\\in \\mathcal{N}^i(t)\\\\\n&L_f\\,h_{im}^o + L_g\\,h_{im}^o\\,u_i + \\alpha(h_{ij}^{s}) \\geq 0, &\\forall m \\in \\mathcal{I}_M\n\\end{aligned}\n\\end{equation}\nwhere $\\alpha$ is a locally Lipschitz extended class-$\\mathcal{K}$ function and the first constraint is replaced by~(\\ref{eq:constrTransition_dist}) during transitions. In conclusion, \\mymod{if there exists a set of control inputs $u = [u_1,\\dots,u_N]$ that simultaneously satisfies all constraints in~(\\ref{eq:minQP2}), for all behaviors $k=1,\\dots,M$, Problem~\\ref{pr:problem} will be solved by the robots.}\n\n\\subsection{\\mymod{Decentralized Behaviors Sequencing}}\n\\mymod{For the correct execution of the behaviors sequencing, each robot should start assembling a new graph only after all other robots have completed the current behavior. Similarly, a new behavior should start once all robots satisfy the neighbors' requirements for it. Now, we describe a decentralized strategy that allows execution of these two transitions without the need of a supervisor, nor synchronization between the robots.}\n\n\\mymod{With reference to Fig.~\\ref{fig:automata}, at any given time, each robot's mode of operation is described by a binary variable $\\alpha_i$ that describes whether robot $i$ is assembling the graph for an upcoming behavior ($\\alpha_i=1$) or executing a behavior ($\\alpha_i=0$). Without loss of generality, assume robots' initial configuration satisfies the communication requirements for the first behavior, which is then executed ($\\alpha_i = 0$). Once all robots have completed the first behavior, they start assembling the graph required by the following one ($\\alpha_i = 1$), while minimally perturbing the behavior just concluded. Once the new graph is satisfied $\\mathcal{G}_2 \\subseteq \\mathcal{G}(t)$, robots start behavior $\\mathcal{B}_2$ and exit from assembly mode ($\\alpha_i = 0$). This process repeats, until no successive behavior exists.} \n\n\\mymod{A correct execution of this process requires robot to agree on when to perform transitions $\\alpha_i = 0 \\rightarrow 1$ and $\\alpha_i = 1 \\rightarrow 0$. To this end, we take inspiration from the consensus-based algorithm described in~\\cite{wagenpfeil2009distributed} and we note that this choice is not central to the contribution of this paper. For each robot, we define a binary variable available only to robot $i$, $s_{t,i} \\in \\{0,1\\}$ that denotes whether robot $i$ itself has completed its current task $s_{t,i} = 1$ ($s_{t,i} = 0$ if robot has not completed its current task). In addition, we introduce a variable $\\sigma_i \\in \\mathbb{R}_+$, shared among neighbors, continuously updated through the following consensus-based process\n\\begin{equation} \\label{eq:sync}\n \\sigma_i^+ = s_{t,i} \\frac{1}{|\\mathcal{N}_i(t)|+1}\\left(\n \\sum_{j \\in \\mathcal{N}_i(t)} \\sigma_j + 1 \\right),\n\\end{equation}\nwhere $\\sigma_i^+$ represent the variable's value after the update. Owing to the diffusion of $\\sigma_1,\\dots,\\sigma_N$ throughout the network, we can interpret $\\sigma_i$'s as local measures of the team-wise completion of a task. As proved in~\\cite{wagenpfeil2009distributed}, if $s_{t,i}=1$ for all $i=1,\\dots N$ (i.e., all robots are capable to complete the current behavior), by following~(\\ref{eq:sync}), $\\lim_{t \\rightarrow \\infty} \\sigma_i = 1$, for all $i=1,\\dots N$. Therefore, robot $i$ starts assembling a new communication graph once the value of $\\sigma_i$ is close enough to $1$ (see~\\cite{wagenpfeil2009distributed} for a discussion on how to choose the switching threshold). A similar process is used for the transition $\\alpha_i=1 \\rightarrow 0$, where we replace $s_{t,i}$ and $\\sigma_i$ with $s_{a,i}$ and $\\eta_i$ respectively. The distributed sequencing procedure is summarized in Algorithm~\\ref{alg:dis_sequence}.}\n\n\n\\begin{algorithm}[h]\n\\caption{Distributed composition of behaviors.} \\label{alg:dis_sequence}\n$\\pi \\gets \\{\\mathcal{B}_1,\\dots, \\mathcal{B}_M\\}$ \\tcc*[r]{initialize behaviors}\n$k = 1$\\;\n$\\alpha_i \\gets 0$\\;\n\\While{$k < M+1$}{\n\\tcc*[h]{Aggregate data from neighbors} \\\\\n\\For{$j \\in \\mathcal{N}_i(t)$}\n{\n$\\{X_i, \\Sigma_i, H_i\\} \\gets \\{X_i, \\Sigma_i, H_i\\} \\cup \\{ x_j , \\sigma_j , \\eta_j \\}$ \\;\n}\n\\tcc*[h]{Compute nominal control} \\\\\n$\\hat{u}_i \\gets \\mathcal{U}_k(x_i,X_i)$\\; \n\\tcc*[h]{Compute team-wise completion states} \\\\\n\n\\eIf{$\\alpha_i == 0$}{\n\\lIf{{\\tt task complete}}\n{$s_e \\gets 1$}\n\\lElse{$s_e \\gets 0$} \n$\\sigma_i:=s_e\\,\\frac{1}{|\\mathcal{N}_k^i|+1}(\\sum_{j \\in \\mathcal{N}_i(t)} \\sigma_j + 1)$\\;\n\\If{$\\sigma_i > \\bar{\\sigma}$}{\n$\\alpha_i\\gets 1$\\; $k \\gets k+1$ \\;\n}\n}{\n\\lIf{{\\tt assembly complete}}\n{$s_a \\gets 1$}\n\\lElse{$s_a \\gets 0$} \n$\\eta:=s_a\\,\\frac{1}{|\\mathcal{N}_k^i|+1}(\\sum_{j \\in \\mathcal{N}_i(t)} \\eta_j + 1)$\\;\n\\If{$\\eta>\\bar{\\eta}$}{\n$\\alpha_i\\gets 0$\\; $s_e\\gets 0$ \\;}\n}\n\\tcc*[h]{Solve FCBF QP} \\\\\n$u_i \\gets QP(\\hat{u}_i,X_i,x_i)$\n}\n\\end{algorithm}\n\\begin{figure}\n \\centering\n \\includegraphics[width=0.8\\columnwidth]{graphics\/automata.pdf}\n \\caption{Representation of the distributed sequencing framework and information flow. At all times, each robot's state is in either {\\tt behavior execution} ($\\alpha_i=0$) or {\\tt graph assembly} ($\\alpha_i=0$) modes. Switching between the two modes is triggered by the variables $\\sigma_i$ and $\\eta_i$ whose values is continuously) updated through~(\\ref{eq:sync}). When a switching between {\\tt graph assembly} and {\\tt behavior execution} occurs, a new behavior is started.}\n \\label{fig:automata}\n\\end{figure}\n\n\\subsection{Applications}\n\\begin{figure*}[t]\n\\centerline{ \n\\subcaptionbox{\\label{fig2:a}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/T0_task2_fixed}}~\n\\subcaptionbox{\\label{fig2:b}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/T1_task2_fixed}}~\n\\subcaptionbox{\\label{fig2:c}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/T2_task23_fixed}}\n} \n\\vspace{0.25cm}\n\\centerline{ \n\\subcaptionbox{\\label{fig2:d}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/T3_task3_fixed}}~\n\\subcaptionbox{\\label{fig2:e}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/T4_task3_fixed}}~\n\\subcaptionbox{\\label{fig2:f}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/T5_task3_fixed}}\n}\n\\caption{Overhead screen-shots from experiments on the Robotarium. Five robots execute two behaviors in sequence (cyclic-pursuit and formation). In figure, green patches represent robots that have completed their task, black rings represent robots that have all neighbors needed for the following task, and green lines represent edges that are available in the current communication graph. From (a) to (b) robots complete the first behavior. During second behavior, additional edges $(2,5)$ and $(3,5)$ are required (red dashed line represent missing edges). From (c) to (d), robots $2,3,5$ reduce their distance below the communication threshold. After the new graph is complete (d), robots initiate the second behavior (e) and complete it (f).\n\\label{fig:5RobotsExp}}\n\\end{figure*}\n\n\\mymod{We implemented the distributed sequencing framework on the Robotarium~\\cite{pickem2017robotarium}, on a team of $5$ differential drive robots. For this example, controllers are designed assuming a single integrator model, i.e. $\\bar{f}(x_i)=[0,0]^T$ and $\\bar{g}(x_i)=I_2$. In this example, robots execute a transition between two behaviors, where $\\mathcal{B}_1$ is a cyclic-pursuit behavior and $\\mathcal{B}_2$ is a formation assembly with leader. Cyclic-pursuit behavior is obtained through the following controller:\n\\begin{equation*}\n\t\\hat{u}_i = \\sum_{j \\in \\mathcal{N}_1^i} R(\\phi)\\,(x_j - x_i) \\quad \\forall \\, i=1,\\dots,5,\n\\end{equation*}\nwhere $R(\\phi)\\in SO(2)$ is the rotation matrix of angle $\\phi$, which is related to the desired cycle radius. Importantly, for this behavior to work, the communication graph $\\mathcal{G}_1$ must be a cycle graph. Considering robot $1$ as leader, the formation control behavior can be achieved with\n\\begin{align*}\n\\hat{u}_1 &= \\sum_{j \\in \\mathcal{N}_2^i} \\left( (\\|x_i-x_j\\|^2-\\theta_{ij}^2)(x_i - x_i) \\right) + \\gamma_g(x_g-x_i) \\\\\n\t\\hat{u}_i &= \\sum_{j \\in \\mathcal{N}_2^i} (\\|x_i-x_j\\|^2-\\theta_{ij}^2)(x_j - x_i) \\quad i = 2,\\dots 5\n\\end{align*}\nwhere $\\theta_{ij} \\in \\mathbb{R}_+$ is the desired inter-robot distance, $x_g \\in \\mathcal{D}$ is the leader's goal, and $\\gamma_g \\in \\mathbb{R}_+$ the corresponding proportional gain. In the case of formation control, it is known that the Euclidean embedding of $\\mathcal{G}_2$ must be a rigid framework (see for instance~\\cite{mesbahi2010graph} and references therein). With reference to Fig.~\\ref{fig:5RobotsExp}, robots initially execute $\\mathcal{B}_1$ for a certain amount of time (a). Once completed (b) (green patches represent robots that have completed their current behavior), robots start assembling $\\mathcal{G}_2$ (c), after which $\\mathcal{B}_2$ is executed until $\\| \\hat{u}_i \\|$ is below a pre-defined threshold (d-f).}\n\n\\mymod{In Fig.~\\ref{fig:transition} we can observe the value of the two consensus variables $\\sigma_i$ and $\\eta_i$ for all robots during the behavior transition. Background colors represent the time intervals during which the two behaviors were executed, while the darker region in the middle corresponds to the assembly of the new graph. We observe the assembly and task variables $\\eta_i$ and $\\sigma_i$ approaching the value $1$ simultaneously for all robots, thus triggering a synchronized start of the successive phase.}\n\n\\mymod{The robustness of our technique was tested by simulating uniformly distributed delays between the robots. Results for this case are shown in Fig.~\\ref{fig:transition_delay} where we observe that although convergence of $\\eta_i$ and $\\sigma_i$ is no longer monotonic, robots still reach agreement on when to switch to the successive phase.}\n\n\\mymod{Finally, in order to show the benefits of the minimally invasive approach, we compare it with an alternative technique inspired by~\\cite{twu2010graph}, where, upon collective completion of a behavior, robots execute rendezvous until the communication graph required by the successive behavior is assembled. As shown by the simulation results for a sequence of $7$ behaviors (Fig.~\\ref{fig:energyComp}), the mean of the input's norm when considering our framework (red solid line) is always lower than the one obtained using the rendezvous as {\\it glue} behavior. Importantly, since transitions between behaviors occur faster in the minimally invasive case, the lower control effort cannot be attributed to a more {\\it relaxed} choice of controller gains.}\n\n\\begin{figure}[h]\n \\centering\n \\includegraphics[width = \\columnwidth]{graphics\/transition.png}\n \\caption{Task and assembly consensus variables $\\sigma_i$ and $\\eta_i$ for $i=1,\\dots,N$ during a transition between two behaviors.}\n \\label{fig:transition}\n\\end{figure}\n\\begin{figure}[h]\n \\centering\n \\includegraphics[width = \\columnwidth]{graphics\/transition_delay.png}\n \\caption{Task and assembly consensus variables $\\sigma_i$ and $\\eta_i$ for $i=1,\\dots,N$ during a transition between two behaviors with communication delays.}\n \\label{fig:transition_delay}\n\\end{figure}\n\n\\begin{figure}[h!]\n \\centering\n \\includegraphics[trim ={2cm 0 0 0}, width=1.05\\columnwidth]{graphics\/energyComparison.png}\n \\caption{Control input comparison between the minimally invasive sequencing framework proposed in this paper (red) and a sequencing based on rendezvous as {\\it gluing} behavior (blue). Solid lines represent the mean of the control input across all robots, while shaded regions represent the interval between minimum and maximum control input.}\n \\label{fig:energyComp}\n\\end{figure}\n\\section{Problem Formulation}\n\\label{sec:problem}\nWe denote the state of a team of $n$ homogeneous mobile robots operating in a $d$-dimensional and connected domain $\\mathcal{D}$ as $x(t) = [x_1(t)^T,\\dots,x_n (t)^T]^T \\in \\mathcal{D} \\subset$ $\\mathbb{R}^{dn}$ where $x_i(t)\\in\\mathbb{R}^d$ is the position of robot $i$ at time $t$. As part of the coordinated nature of the behaviors being performed by the robots, each robot executes a control protocol which depends on the state of the subset of robots with which it interacts. We assume robots can communicate if the distance between them is less or equal to a sensing threshold $\\Delta\\in\\mathbb{R}_{>0}$. Thus, the list of possible interactions between agents are described by a time-varying, undirected, proximity graph $\\mathcal{G}(t)=(V,E(t))$, where $V=\\{1,\\dots,n \\}$ is the set of nodes representing the robots and $E(t)$ is the set of interacting pairs at time $t$, where \n\\begin{equation}\nE(t) = \\{ (i,j) \\in V \\times V\\, | \\, \\| x_i(t)-x_j(t)\\| \\leq \\Delta \\}.\t\n\\end{equation}\nFor each robot $i=1,\\dots,n$, we denote the set of available neighbors at time $t$ as $\\mathcal{N}_i(t) = \\{ j \\in V\\, | \\, (i,j) \\in E(t) \\}$, which depends on the position of the robots at time $t$.\n\nThe ensemble dynamics of the multi-agent system is described by\n\\begin{equation}\n\t\\dot{x} = f(x) + g(x)\\,u\n\t\\label{eq:ensembleDynamics}\n\\end{equation}\nwhere $f$ and $g$ are continuous locally Lipschitz continuous functions and $u = [u_1^T,\\dots,u_n^T]^T \\in U \\subset \\mathbb{R}^m$ is the vector of inputs, which depends on the particular behavior being executed. At all times, the control input $u$ in~(\\ref{eq:ensembleDynamics}) is given by a controller $\\mathcal{U}$, which can be defined as a state feedback law $\\mathcal{U}: \\mathcal{D} \\mapsto U$ or by a combination of both external parameters and state feedback law $\\mathcal{U}: \\mathcal{D} \\times \\Theta \\mapsto U$, where $\\Theta$ is a space of parameters appropriate for the behavior. For instance, the controller corresponding to a {\\it weighted consensus} belongs to the first case. On the other side, a leader-follower protocol where followers maintain prescribed inter-agent distances is described by a controller that depends on both state feedback (followers' control) and exogenous parameters (leader's goal) (see Section~\\ref{sec:multAgImp} for examples).\n\nWe represent a {\\it mission} by an ordered sequence of $M$ coordinated behaviors\n\\begin{equation}\n\t\\pi = \\{ \\mathcal{B}_1,\\dots, \\mathcal{B}_M\\}.\n\\end{equation}\nThe $k^{\\text{th}}$ behavior in $\\pi$ is defined by the pair\n\\begin{equation}\\label{eq:tuple}\n\t\\mathcal{B}_k = \\{ \\mathcal{U}_k,\\, \\mathcal{G}_k\\},\n\\end{equation}\nwhere $\\mathcal{U}_k$ represents the coordinated controller described above and $\\mathcal{G}_k$ is the interaction graph required by behavior $\\mathcal{B}_k$ to function properly. We assume the list of behaviors $\\pi$ to be fixed and available to all robots. \\mymod{We will use the term {\\it behavior} to refer to a generalized multi-robot controller in the form~(\\ref{eq:tuple}) and to {\\it task} as the objective of the controller.}\n\nAs discussed in Section~\\ref{sec:intro}, each behavior requires a certain interaction structure between the robots (i.e., pairs of robots that need to be neighbors). With reference to~(\\ref{eq:tuple}), we describe an interaction structure via the graph $\\mathcal{G}_k=(V,E_k)$. Thus, denoting by $t_k^\\vdash$ and $t_k^\\dashv$ the starting and ending times for behavior $k$, the robots' configuration needs to satisfy $\\mathcal{G}_k \\subseteq \\mathcal{G}(t)$ for all $t \\in [t_k^\\vdash, t_k^\\dashv]$. In other words, as shown in Fig.~\\ref{fig:beh_seq}, the interaction structure required by each behavior needs to be a spanning graph of the graph induced by the state of the agents during the interval of time the behavior is executed. \\mymod{At this point, given a list of behaviors constituting the mission $\\pi$ and the corresponding multi-robot controllers, we want to design a procedure that enables robots to assemble and maintain the communication graph required by each behavior.}\n\n\n\\mymod{\n\\begin{problem} \\label{pr:problem}\nGiven an ordered sequence of coordinated behaviors $\\pi= \\{ \\mathcal{B}_1,\\dots, \\mathcal{B}_M\\}$, where each $\\mathcal{B}_k = \\{ \\mathcal{U}_k,\\, \\mathcal{G}_k\\}$ can be completed by the robots in finite-time, design a feedback control policy to compose the behaviors such that\n\\begin{equation}\n\\mathcal{G}(t) \\supseteq\n \\begin{cases}\n \\mathcal{G}_k \\, &t \\in [t_k^\\vdash , t_k^\\dashv] \\\\\n \\mathcal{G}_k \\cup \\mathcal{G}_{k+1} \\, &t \\in (t_k^\\dashv,t_{k+1}^\\vdash)\n\\end{cases} \\quad \\forall \\, k=1,\\dots,M-1.\n\\end{equation}\n\\end{problem}\n}\n\\section{Finite-Time Barrier Functions} \\label{sec:fcbf}\nIn this section we review the general definition of Finite-time Convergence Control Barrier Function (FCBF) which was first introduced in~\\cite{li2018formally} and inspired by the finite-time stability analysis for autonomous system introduced in~\\cite{bhat2000finite}. Given a dynamical system operating in an open set $\\mathcal{D} \\subseteq \\mathbb{R}^n$ and a set $\\mathcal{C}\\subset\\mathcal{D}$, barrier functions~\\cite{xu2015robustness} are Lyapunov-like functions that guarantee forward invariance of $\\mathcal{C}$ with respect to the state of the system. In other words, if an appropriate barrier function exists, it can be used to show that if the state of a system is in $\\mathcal{C}$ at some time, it will be in $\\mathcal{C}$ thereafter. The concept of barrier functions was extended to Zeroing Control Barrier Functions (ZCBF) in~\\cite{xu2015robustness}, where asymptotic convergence of the state to the set $\\mathcal{C}$ was discussed. Thus, provided that an appropriate ZCBF exists, if the state of the system is not in $\\mathcal{C}$ at some initial time, it will asymptotically converge to $\\mathcal{C}$. \n\nAs discussed in the introduction, before execution of a coordinated behavior, robots need to satisfy certain spatial configurations imposed by the behavior itself. Importantly asymptotic convergence to the correct configuration is not sufficient. In fact, if we consider $\\mathcal{C}$ as the joint set of all initial configurations required for a particular behavior, the state must strictly belong to $\\mathcal{C}$ for the behavior to work properly. Following this observation, the need for a finite-time convergence extension of the previous concepts becomes clear. In particular, denoting the state of the system as $x(t)\\in\\mathcal{D}$, we are interested in verifying the following conditions:\n\\begin{itemize}\n\t\\item if $x(t_0)\\in\\mathcal{C}$, then $x(t)\\in\\mathcal{C}$ for all $t > t_0$\n\t\\item if $x(t_0) \\notin \\mathcal{C}$, then $x(t)\\in\\mathcal{C}$ for some $t_00$, which is continuous everywhere and locally Lipschitz everywhere except at the origin~\\cite{bhat2000finite}.\n\\end{definition}\n\n\\begin{definition}~\\cite{li2018formally} For a dynamical system \n\\begin{equation} \\label{eq:affinesystem}\n\t\\dot{x} = f(x)+g(x)u\n\\end{equation} with $x\\in\\mathcal{D}$, $u \\in U \\subset \\mathbb{R}^m$, and for a set $\\mathcal{C}$ induced by $h$, if there exists a function $\\bar{\\alpha}_ {\\rho,\\gamma}(h(x))$ of the form~(\\ref{eq:alpha}) such that\n\t\\begin{equation}\n\t\t\\sup_{u \\in U}\\bigg\\{ L_fh(x) + L_gh(x)u + \\bar{\\alpha} _{\\rho,\\gamma}(h(x)) \\bigg\\} \\geq 0 \\quad \\forall x\\in\\mathcal{D},\n\t\\end{equation}\nthen, the function $h$ is a {\\it Finite-time Convergence Barrier Function }(FCBF) defined on $\\mathcal{D}$.\n\\end{definition}\n\nFollowing from the definition above, we define the set of admissible control inputs as\n\\begin{equation} \\label{eq:admisU}\n\tK(x) = \\{ u \\in U \\, | \\, L_fh(x) + L_gh(x)u + \\bar{\\alpha} _{\\rho,\\gamma}(h(x)) \\geq 0\\}.\n\\end{equation}\n\n\\begin{theorem} \\label{thm:FCBF} \\cite{li2018formally}~Given a set $\\mathcal{C}\\subset \\mathbb{R}^n$, any Lipschitz continuous controller $\\mathcal{U}: \\mathcal{D} \\mapsto U$ such that \n\\begin{equation} \\label{eq:fcbf_controllers}\n\t\\mathcal{U}(x) \\in K(x) \\qquad \\forall x\\in\\mathcal{D},\n\\end{equation}\nrenders $\\mathcal{C}$ forward invariant for the system~(\\ref{eq:affinesystem}). Moreover, given an initial state $x_0 \\in \\mathcal{D} \\backslash \\mathcal{C}$, the same controller $\\mathcal{U}$ results in $x(T)\\in\\mathcal{C}$, where\n\t\\begin{equation}\n\t\tT \\leq \\frac{1}{\\gamma(1-\\rho)}|h(x_0)|^{1-\\rho}.\n\t\\end{equation}\n\\end{theorem}\n\n\\iffalse\n\\begin{IEEEproof}\n\tLet's consider the following Lyapunov function $V(x)=\\max\\{0,-h(x)\\}$. It can be verified that\n\\begin{align}\n\t\tV(x) &> 0 \\qquad x\\in\\mathcal{D}\\backslash \\mathcal{C} \\\\\n\t\tV(x) &= 0 \\qquad x\\in\\mathcal{C}\n\\end{align}\t\n\nIn addition, since\n\\begin{equation}\n\t\\frac{\\partial V(x)}{\\partial h(x)}=\n\t\\begin{cases}\n\t-1 &\\qquad x\\in\\mathcal{D}\\backslash \\mathcal{C} \\\\\n\t 0 &\\qquad x\\in\\mathcal{C}\n\t\\end{cases}\n\\end{equation} \t\nit follows that $\\dot{V}(x(t)) \\leq -\\gamma\\,V^\\rho(x(t))$, for all $t$. \n\nConsider $x_0 = x(t_0) \\in \\mathcal{C}$. Since $V(x_0)=0$ and $\\dot{V}(t)=0$, we have $x(t)\\in\\mathcal{C}$ for all $t>t_0$, from which forward invariance of $\\mathcal{C}$ under $\\mathcal{U}$ follows. Now, consider $x_0 \\in \\mathcal{D}\\backslash\\mathcal{C}$. As shown in~\\cite{bhat2000finite}, the dynamics $\\dot{h} _1(t) = -\\gamma\\,\\text{sign}(h_1(t))\\, |h_1(t)|^\\rho$, with $\\rho \\in [0,1)$ and $\\gamma>0$ drives $h_1$ to the origin, and the minimum time $T_1$ for which $h_1$ reaches $0$ (denoted finite-settling time) is\n\\begin{equation}\n\tT_1 = \\frac{1}{\\gamma(1-\\rho)}|h_1(t_0)|^{1-\\rho}.\n\\end{equation} \nBy applying the comparison lemma~\\cite{pachpatte1997inequalities}, if $h(0) \\geq h_1(0)$ and $\\dot{h}(t) \\geq \\dot{h}_1(t)$, then $h(t) \\geq h_1(t)$ for all $t \\geq 0$ and consequently under the effect of $\\mathcal{U}$ we have\n\\begin{equation}\n\th(x(T)) = 0, \\quad T \\leq T_1.\n\\end{equation}\n\\end{IEEEproof}\n\\fi\n\nIn conclusion, by selecting a controller that verifies condition~(\\ref{eq:fcbf_controllers}), both forward invariance and finite-time convergence to the desired set are guaranteed. \n\n\n\\section{Composition of Coordinated Behaviors}\n\\label{sec:seq_framework}\n\nIn addition to the list $\\pi$, transitions between behaviors need to be synchronized, i.e., for each behavior $\\mathcal{B}_k$, $k=1,\\dots, M$, robots must 1) start assembling $\\mathcal{G}_{k+1}$ only after all robots have completed $\\mathcal{B}_k$ and 2) start executing $\\mathcal{B}_{k+1}$ only after condition $\\mathcal{G}_{k+1} \\subseteq \\mathcal{G}(t)$ is satisfied. We assume the existence of a discrete counter $\\sigma \\in [1,\\dots,M]$ which indicates the active behavior and a binary signal\n\\begin{equation}\n\\eta(\\sigma) = \\begin{cases}\n\t1 \\quad \\text{if} \\quad \\mathcal{G}_k \\subseteq \\mathcal{G}(t) \\\\\n\t0 \\quad \\text{o.w.}\n\\end{cases}\n\\end{equation} \nwhich describes whether the interaction structure required by behavior $\\mathcal{B}_\\sigma$ is available. In this section, we assume both signals to be controlled by a supervisor and made available to the robots at all times, e.g., through a dedicated static communication network. \\mymod{In the next section, we discuss the extension to a fully distributed framework.}\n\n\\begin{figure}[h!]\n\\includegraphics[width=\\columnwidth]{graphics\/figure_Sequence.pdf}\n \\caption{Schematic representation of the behaviors sequencing framework. Behavior $\\mathcal{B}_k$ is executed during the blue portion of the timeline and $\\mathcal{B}_{k+1}$ is executed during the orange portion. Sequential execution of behaviors requires each agent to reach a spatial configuration such that the desired graph is a spanning graph of the communication graph, i.e., $\\mathcal{G}_k \\subseteq \\mathcal{G}(t_k^\\vdash)$ and $\\mathcal{G}_{k+1} \\subseteq \\mathcal{G}(t_{k+1}^\\vdash)$ respectively. \\label{fig:beh_seq}}\n\\end{figure}\n\nFollowing from the communication modality assumed for the robots, communication constraints can be expressed in terms of relative distance between the robots. In other words, behavior $\\mathcal{B}_k$ can be correctly executed if, for all $t\\in[t_k^\\vdash,t_k^\\dashv]$, all the distances between pairs in $E_k$ are below the proximity threshold $\\Delta$. To this end, a convenient pair-wise connectivity FCBF can be defined as\n\\begin{equation}\n\th_{ij}^c(x) = \\Delta^2 - \\| x_i - x_j \\|^2,\n\t\\label{eq:commBarriers}\n\\end{equation}\nand we note that if $\\|x_i-x_j\\| \\leq \\Delta$, then $h_{ij}^c(x)\\geq 0$.\nIn addition, the edge-level and ensemble-level connectivity constraint sets for behavior $\\mathcal{B}_k$ are\n\\begin{align}\n\\mathcal{C}_{ij}^c &= \\{ x \\in \\mathcal{D} \\,|\\, h_{ij}^c(x) \\geq 0 \\} \\\\\n\\mathcal{C}^c_k &= \\{ x \\in \\mathcal{D} \\,|\\, h_{ij}^c(x) \\geq 0, \\, \\forall (i,j)\\in E_k\\}.\n\\end{align}\n\nFollowing the definition given in~(\\ref{eq:admisU}), the admissible set of control inputs that guarantees finite-time convergence to $\\mathcal{C}^c_k$ is:\t\n\\begin{multline} \\label{eq:admGraphinput}\n\tK_k^c (x) = \\{ u \\in U \\, | \\, \\dot{h}_{ij}^c(x) + \\bar{\\alpha} _{\\rho,\\gamma}(h_{ij}^c(x)) \\geq 0 , \\\\ \\forall (i,j)\\in E_k \\}\n\\end{multline}\n\n\\begin{theorem} \\label{thm:fcbfControl}\n\tDenoting with $x_0$ the initial state of the system with dynamics~(\\ref{eq:ensembleDynamics}), any controller $\\mathcal{U}:\\mathcal{D} \\mapsto U$ such that $\\mathcal{U}(x_0) \\in K_k^c(x_0)$ for all $x_o \\in \\mathcal{D}$, will drive the system to $\\mathcal{C}^c_k $ within time\n\\begin{equation} \\label{eq:fcTime}\n\tT_k = \\max_{ (i,j) \\in E_k | h^c_{ij}(x_0)<0} \\left\\{ \\frac{1}{ \\gamma(1-\\rho)} | h_{ij}^c(x_0) |^{1-\\rho} \\right\\}.\\end{equation}\n\\end{theorem}\n\n\\begin{IEEEproof} \n\tConsider all pairs of agents $i$ and $j$, such that $(i,j) \\in E_k$. If $h_{ij}^c(x_0) \\geq 0$, i.e., agents $i$ and $j$ are within communication distance, the forward invariance property of $\\mathcal{U}$, guarantees that $i$ and $j$ will stay connected. In this case, the state will reach $\\mathcal{C}_{ij}^c$, within time $T_{ij}=0$. On the other side, consider $h_{ij}^c(x_0)<0$. Any $\\mathcal{U}(x_0) \\in K_k^c(x_0)$ satisfies the finite-time convergence barrier certificates, and because of Theorem~\\ref{thm:FCBF}, if $x_0 \\notin \\mathcal{C}_{ij}^c$, then $x(T_{ij})\\in \\mathcal{C}_{ij}^c $, with\n\\begin{equation}\nT_{ij} \\leq \\frac{1}{ \\gamma(1-\\rho)} | h_{ij}^c(x_0) |^{1-\\rho}.\n\\end{equation} \nSince every communication constraint $\\mathcal{C}_{ij}^c $ will be reached within time $T_{ij}$, the total time required to drive $x(t)$ to $\\mathcal{C}_k^c$ is upper bounded by\n\\begin{equation}\n\tT_k = \\max_{(i,j) \\in E_k | h^c_{ij}(x_0)<0} T_{ij}.\n\\end{equation}\t\n\\end{IEEEproof} \nWhen selecting control inputs from set~(\\ref{eq:admGraphinput}), the system~(\\ref{eq:ensembleDynamics}) will satisfy requirements for behavior $\\mathcal{B}_k$ in finite time.\n\n\\subsection{Finite-Time Convergence Control Barrier Functions}\n\\label{sec:ftcontrolBF}\nOnce behavior $\\mathcal{B}_{k-1}$ is completed, robots are required to converge to the set $\\mathcal{C}^c_{k}$ before behavior $\\mathcal{B}_{k}$ can start. Under the lead of the external supervisor, the change of behavior is communicated to the robots through the signal $\\sigma$, which transitions from $k-1$ to $k$ once $\\mathcal{B}_{k-1}$ is completed. Now, although finite-time convergence to $\\mathcal{C}^c_{k}$ can be achieved by selecting any control input in $K_{k}^c(x)$, we seek to minimally perturb the execution of the behavior just concluded, namely $\\mathcal{B}_{k-1}$. This can be accomplished by solving a problem similar to the one proposed in~\\cite{ames2014control}, which we adapt to our framework. Denoting with $\\hat{u}_{k}=\\mathcal{U}_{k}(x)$ the nominal control input from behavior $\\mathcal{B}_{k}$, during transition between $\\mathcal{B}_{k-1}$ and $\\mathcal{B}_{k}$ the actual control input to the robots $u^*$ is defined as\n\\begin{equation}\\label{eq:minQP1}\nu^* = \\argmin_{u \\in U} \\| \\hat{u}_{k-1} - u \\|^2 \\\\\n\\end{equation}\nsubject to\n\\begin{equation} \\label{eq:constrTransition}\n L_f\\,h_{ij}^c + L_g\\,h_{ij}^c\\,u + \\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c) \\geq 0, \n\\end{equation}\nfor all $(i,j) \\in E_{k-1} \\cup E_{k}$. Once all required edges $E_{k}$ are established (i.e., $\\eta=1$), edges in $E_{k-1}$ are no longer necessary. At this point, under the effect of the controller $\\mathcal{U}_{k}$, the list of constraints in~(\\ref{eq:constrTransition}) is substituted with\n\\begin{equation} \\label{eq:constrExecution}\n L_f\\,h_{ij}^c + L_g\\,h_{ij}^c\\,u + \\bar{\\alpha}_{\\rho,\\gamma}(h_{ij}^c) \\geq 0,\n\\end{equation}\nfor all $(i,j)\\in E_{k}$. Since the cost function is convex and the inequality constraints~(\\ref{eq:constrTransition}) and~(\\ref{eq:constrExecution}) are control affine, the problem can be solved in real-time. In conclusion, because of the finite-time convergence and forward invariance properties of the above formulation, \\mymod{if $\\mathcal{B}_{k-1}$ can be completed and a solution to~(\\ref{eq:minQP1}-\\ref{eq:constrTransition}) (or (\\ref{eq:minQP1}-\\ref{eq:constrExecution})) exists}, robots will converge to the configuration required by $\\mathcal{B}_{k}$, and maintain it throughout its execution.\n\\mymod{\n\\begin{remark}\nThe solution of~(\\ref{eq:minQP1}-\\ref{eq:constrTransition}) (or (\\ref{eq:minQP1}-\\ref{eq:constrExecution})) is contingent upon the existence of a control input capable to solve all constraints. In other words, $K_k^c(x) \\cap K_{k+1}^c(x)$ (or $K_k^c(x)$) should not be empty for all times. For this, it is necessary that a robot's configuration that satisfies all constraints of the problem exists. However, this is not sufficient as the progress towards the desired configuration might be obstructed by constraints on the actuators or deadlock configurations. Although we do not address this directly, it is possible to mitigate feasibility issues by considering, for example, constraints relaxation, sum of squares barrier functions, or pre-defined back-up controllers (see~\\cite{ames2019control} and references therein). \\label{rmk:feasibility}\n\\end{remark}}\n\n\n\n\\subsection{Initial Constraints}\nIn addition to the communication constraints considered above, certain missions might require additional conditions to be met before each behavior can start. For example, during the exploration tasks it might be desirable for one robot to always stay within range of communication with a human-operator, or to maintain a minimum distance from an unsafe area. Assuming $\\mathcal{B}_k$ requires a number of distinct $s_k$ of such constraints, we encode the entire set of initial conditions through a list of barrier functions $h_\\ell^s(x)$, with $\\ell = 1,\\dots,s_k $:\n\\begin{equation} \\label{eq:initialSet}\n\\mathcal{C}^{s}_k = \\{ x \\in \\mathcal{D} \\,|\\, h_\\ell^s(x) \\geq 0, \\, \\forall \\ell = 1,\\dots,s_k \\}.\n\\end{equation}\nFollowing this definition, we define a set of admissible control inputs similar to the one in~(\\ref{eq:admGraphinput}) that will drive the state of the system to the desired set within finite time:\n\\begin{multline} \\label{eq:admInitCond}\n\tK_k^s (x) = \\{ u\\in U \\, | \\, \\dot{h}_\\ell^s(x) + \\bar{\\alpha} _{\\rho,\\gamma}(h_\\ell^s(x)) \\geq 0, \\\\\n\t \\forall \\ell = 1,\\dots,s_k \\}.\n\\end{multline}\n\nThe set of controls satisfying both communication and initial conditions constraints can thus be obtained by intersection of set~(\\ref{eq:admInitCond}) and~(\\ref{eq:admGraphinput}):\n\\begin{equation}\n\tK_k(x) = K_k^c (x) \\bigcap K_k^s (x).\n\\end{equation}\n\nWe note that the results in Theorem~\\ref{thm:fcbfControl} and the formulation of minimally invasive controller in~(\\ref{eq:minQP1}) still holds valid by considering the set $K_k(x)$ instead of $K_k^c (x)$ as the set of admissible control inputs.\n\\section{Case Study: Securing a Building} \\label{sec:securing}\nThe objective of this section is to describe the {\\it Securing a Building} mission, which will be used as testing scenario for the composition framework. We describe now the main structure and objective of the mission, while we deconstruct it into coordinated behaviors in the next subsection.\n\n\\subsection{Mission Overview}\nIn the Securing a Building mission, a group of robots are deployed in an urban environment to identify an unknown target building and rescue a subject located inside. Based on \\cite{FieldManual}, we\ndecompose this mission into the following 4 phases:\n\nFIND - First, the robots are tasked with identifying the target building by means of surveillance of the perimeters of all the buildings. For efficient exploration, robots can be broken into sub-teams. Each team reports collected information at the base after each building has been investigated. Once the target building has been identified, the robots reunite and prepare for the next phase.\n\nISOLATE - The robots isolate the target building by patrolling its perimeter. To achieve this, the robots are divided into two subgroups - the {\\it security agents} responsible for boundary protection and the {\\it maneuvering agents} tasked with entering the building.\n\nRESCUE - During the rescue phase, the security agents keep patrolling around the building. In the meanwhile, the maneuvering agents enter the building, clear the rooms, and seize positions as they maneuver through the building to find the subject to be rescued. Once the subject has been located, the robots transport it to the safe zone.\n\nFOLLOW-THROUGH - As the interior of the building is being cleared, individual robots are left inside as beacons, while the remaining robots from the maneuvering agents leave the building, gather on the outside with the security agents, and report back to the base station.\n\nA number of arguments support the choice of the Securing a Building mission as an ideal scenario for testing multi-robot techniques and algorithms. First, the requirement of spatially diverse functionalities that cannot be provided by single robots naturally requires the use of multi-robot systems. Second, the final goal of the mission, namely rescuing the subjects of interest, reflect the fact that general real-world missions cannot be accomplished with single controllers. Lastly, thanks to its modularity, techniques focusing on specific aspects of the mission can be integrated and tested without influencing the overall structure of the mission (see the Appendix for details).\n\n\\subsection{Securing a Building Through Composition of Behaviors}\n\n\\begin{figure*}[t]\n\\centerline{ \\includegraphics[width=1.9\\columnwidth]{graphics\/missionChart1.pdf}}\n\\caption{Mission design chart showing how coordinated behaviors are composed together to tackle the Securing a Building mission. The four bold titles are the mission phases and the large boxes below them indicate specific agent roles\nand associated behaviors. The arrows in the chart indicate the transitions between different behaviors. We note that he choice of controllers that produces the behaviors in the chart is not unique.\\label{fig:missionChart}}\n\\end{figure*}\n\nWe deconstruct the Securing a Building mission through ordered sequences of coordinated behaviors. The process is summarized in Fig.~\\ref{fig:missionChart}. We refer to behaviors in terms of their main objectives, acknowledging that different implementations can be used to achieve the same results. We highlight these behaviors in parenthesis.\n\n\\paragraph{FIND} Robots initially coordinate with the operator at the base station ({\\it rendezvous}). After that, robots are divided into different search teams, each assigned with a list of buildings to investigate ({\\it task allocation}). Subsequently, all the teams investigate their own lists of buildings. First team of robots travels to the vicinity of a building ({\\it leader-follower}), then start to survey the exterior of the building ({\\it perimeter patrol}), and return to the base ({\\it leader-follower}). This process repeats until the target building is discovered.\n\n\\paragraph{ISOLATE} Robots gather near the base ({\\it rendezvous}), then are divided into {\\it security} and {\\it maneuvering} agents ({\\it task allocation}). After traveling from the base to the vicinity of the target building ({\\it go-to-goal}), security agents protect the building's perimeter ({\\it cyclic pursuit}), until the end of the RESCUE phase. Meanwhile, the maneuvering agents locate the building's entrance, by following its perimeter ({\\it perimeter patrol}). Once the entrance has been found, the maneuvering agents gather at the entrance ({\\it rendezvous}) and create a formation ({\\it formation control}) before entering.\n\n\\paragraph{RESCUE} The maneuvering agents enter the building in formation ({\\it formation control}) and cover the interior area ({\\it area coverage}). Once the location of the subject to rescue is identified, the robots form a circular closure around the subject ({\\it cyclic pursuit}). Then, the robots transport the subject to the safety zone, while maintaining the circular closure around the subject ({\\it containment control}).\n\n\\paragraph{FOLLOW-THROUGH} Maneuvering agents spread ({\\it scatter}) over the interior of the building. To signify that the area has been cleared, few robots are left inside the building as beacons ({\\it persistent coverage}). The rest of the maneuvering agents and the security agents reunite outside the building ({\\it rendezvous}). At last, they return to the base ({\\it leader-follower}).\n\n\n\\subsection{Results}\nWe tested the behavior composition framework described in Section~\\ref{sec:multAgImp} on the Securing a Building mission, which was executed on the Robotarium~\\cite{pickem2017robotarium}. In Fig.~\\ref{fig:experiment}, we display selected snapshots of the mission obtained by a camera mounted on the ceiling. In the experiment, $8$ differential-drive robots, indexed $1,\\dots,8$ are deployed in a simulated urban environment composed of $6$ buildings, blue polygons indexed $1,\\dots,6$. In this experiment, we simulate a maximum sensor range $\\Delta = 0.5$m. Because of the different spatial scales between FIND\/ISOLATE phases and RESCUE\/FOLLOW-THROUGH phases, the entire mission is divided in two parts. In the first part (Fig.~\\ref{fig2:a} to Fig.~\\ref{fig2:d}) the experiment is performed at a {\\it neighborhood}-level scale. The remaining two phases are executed in a zoomed-in environment, which focuses on the one building of interest (Fig.~\\ref{fig2:d} to Fig.~\\ref{fig2:f}).\n\nDuring FIND phase (Fig.~\\ref{fig2:a} and~\\ref{fig2:b}), two groups of robots $\\text{\\sc team}1:\\{1,2,3,4\\}$ and $\\text{\\sc team}2:\\{5,6,7,8\\}$ investigates preassigned lists of buildings, leaving some agents near the base station (the purple filled dot in the top right corner) if destination building cannot be reached without breaking the connectivity constraints. The red polygon in Fig.~\\ref{fig2:b} and~\\ref{fig2:c} is the target building after being identified by $\\text{\\sc team}1$. During the ISOLATE phase (Fig.~\\ref{fig2:c}), maneuvering agents look for the entrance, while the security agents secure the outer perimeter. \n\nDuring the RESCUE phase (Fig.~\\ref{fig2:d} to~\\ref{fig2:e}), the agents inside the building, i.e. $\\text{\\sc team}1$, localize the target (red dot) using Voronoi coverage (Fig.~\\ref{fig2:d}) and escort it to the safe area (red circle) as shown in (Fig.~\\ref{fig2:e}). Finally, robots $1$ and $2$ are left as beacon inside the building, while remaining robots return to the base (Fig.~\\ref{fig2:f}).\n\n\\begin{figure*}[t]\n\\centerline{ \n\\subcaptionbox{\\label{fig2:a}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/simulation1}}~\n\\subcaptionbox{\\label{fig2:b}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/simulation2}}~\n\\subcaptionbox{\\label{fig2:c}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/simulation4}}\n} \\vspace{0.25cm}\n\\centerline{ \n\\subcaptionbox{\\label{fig2:d}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/simulation7}}~\n\\subcaptionbox{\\label{fig2:e}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/simulation9}}~\n\\subcaptionbox{\\label{fig2:f}}{\\includegraphics[width=0.66\\columnwidth, height=0.39\\columnwidth]{graphics\/simulation11}}\n}\n\\caption{Overhead screen-shots from experiments on the Robotarium. A team of eight robots is divided in $\\text{\\sc team}1:\\{1,2,3,4\\}$ and $\\text{\\sc team}2:\\{5,6,7,8\\}$. Because of the different spatial scales between FIND\/ISOLATE phases and RESCUE\/FOLLOW-THROUGH phases the mission is executed on two different environments. Each team is assigned with a list of three buildings to inspect sequentially. FIND: (a) perimeter patrol of buildings $2$ and $5$; (b) building $4$ is identified as the target building, while $\\text{\\sc team}1$ waits for $\\text{\\sc team}2$ to return to base. ISOLATE: (c) $\\text{\\sc team}2$ secures perimeter of building, while $\\text{\\sc team}1$ inspects exterior of building, searching for the entrance. RESCUE: after entering the building, $\\text{\\sc team}1$ performs domain coverage of the building until target (red dot) is identified (d); after this, (e) robots escort target to safe location (red circle). FOLLOW-THROUGH: finally, two robots are left as beacons inside the building while all remaining robots return to base (f). \n\\label{fig:experiment}}\n\n\\end{figure*}\n\\section{Related Work} \nThe problem of partitioning complex objectives into simpler tasks can be solved by sequentially composing {\\it primitives}, e.g.,~\\cite{cassandras2009introduction}, or by blending them simultaneously in a {\\it hierarchical} fashion. An example of hierarchical composition for single robot motion control is navigation between points while avoiding obstacles, e.g.,~\\cite{arkin1998behavior}. \\mymod{In general, the problem of controlling a system by composing different modes of operation pertains to hybrid systems and {\\it multi-modal} control domains~\\cite{koutsoukos2000supervisory}.}\n\nBecause of the complexity emerging from the composition of distinct controllers, guarantees on the safety and correctness of the final results need to be established~\\cite{kress2018synthesis}. Provable correct composition of control policies is investigated in the formal methods literature. Recently, compositional strategies inspired from formal methods have been used for the development of control strategies for multi-robot systems~\\cite{srinivasan2018control,garg2019control,meyer2019hierarchical,chen2018verifiable}. In particular, the authors of~\\cite{srinivasan2018control} use tools from linear temporal logic (LTL) for the specification of behaviors to be executed by the system. The solution is based on a sequence of constrained reachability problems, each consisting of a target set to be reached in finite time and a safety set within which the system must stay at all times. \\mymod{A related approach is developed in~\\cite{garg2019control}, where the problem of prescribed-time convergence to spatio-temporal specifications is formulated using control barrier functions.} The authors in~\\cite{meyer2019hierarchical} discuss a hierarchical decomposition method for controller synthesis given LTL specifications.\n\nIn the context of controllers composition for multi-robot systems, in~\\cite{belta2007symbolic} the authors use symbolic methods in order to generate high-level instructions from form of {\\it human-like} language. The authors of~\\cite{klavins2000formalism} introduce a framework for the composition of controllers in robotic systems using Petri Nets. In~\\cite{marino2009behavioral}, behaviors from the Null-Space-Behaviors framework are combined in order to solve ad-hoc tasks, such as perimeter patrol. A supervisor, represented as a finite state automata, selects high-level behaviors by assembling low-level behaviors. In~\\cite{nagavalli2017automated}, a revised version of the $A^*$ algorithm is used to generate an optimal path of behaviors, such that the overall cost of the mission is minimized. Similarly, in~\\cite{vukosavljev2019hierarchically} motion planning for a team of quadcopters is solved by defining higher level motion primitives obtained by a spatial partition of the environment. However, none of these approaches specifically address the problem of correct composition between primitives, which is the focus of this paper.\n\nAs discussed in the previous section, coordination between agents is possible only if particular interactions exist between the robots. In multi-robot systems, interaction requirements are commonly investigated in terms of connectivity maintenance, i.e., a certain graph or node-connectivity needs to be guaranteed at all times. Methods employed in the solution to this problem include edge weight functions~\\cite{ji2007distributed}, control rules based on estimate of algebraic connectivity~\\cite{sabattini2013distributed}, hybrid control~\\cite{zavlanos2009hybrid}, passivity~ \\cite{igarashi2009passivity}, and barrier functions~\\cite{wang2016multi}. If connectivity between agents needs to be guaranteed in non-nominal circumstances, resilient solutions must be in place as well, e.g.,\n\\cite{ramachandran2019resilience}, \\cite{panerati2019robust}, and~\\cite{varadharajan2019unbroken}. Notably, a technique based on graph process specifications for the sequential composition of different multi-agent controllers is discussed in~\\cite{twu2010graph}. Similar to our work, the authors in~\\cite{twu2010graph} bridge the gap between composition of controllers and the topology requirements by encoding requisites for each controller in terms of graphs.\nHowever, while in \\cite{twu2010graph} {\\it incompatible} controllers are combined through the introduction of a {\\it bridging} controller, in our approach controllers are minimally modified by the robots in order to satisfy upcoming requirements. Our approach significantly reduces the complexity of the composition process, \\mymod{minimizes the energy spent by the robots to switch between behaviors}, and can accommodate additional constraints, such as inter-robot collisions and obstacles avoidance.\n\n\\section{Introduction} \\label{sec:intro}\n\\IEEEPARstart{A}{s} our understanding of how to structure control and coordination protocols for teams of robots increases, a number of application domains have been identified, such as entertainment~\\cite{ackerman2014flying}\\cite{du2018fast}, surveillance~\\cite{santos2018coverage}~\\cite{shishika2018local}, manipulation~\\cite{han2018hybrid}, and search-and-rescue~\\cite{suarez2011survey}. Along with a decrease in the production and manufacturing costs associated with the platforms themselves, these applications have been enabled by a number of theoretical results that have emerged at the intersection of different disciplines such as robotics, controls, computer science, and graph theory~\\cite{zelazo2018graph}. \n\nFrom a motion controls perspective, one notable requirement is given by the need to define actions capable to solve team-wise objectives on the basis of locally available information. For instance, different extensions of the consensus equation have been used to arrive at locally defined controllers with provable, global convergence properties~\\cite{cortes2017coordinated}. In this way, it is possible to construct coordinated controllers for the solution of motion control problems, such as rendezvous~\\cite{lin2003multi}~\\cite{ren2005coordination}, cyclic pursuit~\\cite{ramirez2009cyclic}, formation control~\\cite{lawton2003decentralized}~\\cite{buckley2017infinitesimally}, coverage~\\cite{cortes2004coverage}~\\cite{santos2018coverage}, leader-based control~\\cite{mesbahi2010graph}, and flocking~\\cite{tanner2007flocking}. Particular instantiations of some of these behaviors are shown in Fig.~\\ref{fig:coordBehExamples} on a group of six simulated differential drive robots.\n\\begin{figure}\n\t\\begin{center}\n\t\t\\includegraphics[trim={2cm 2cm 1cm 2cm},width=0.35\\columnwidth]{graphics\/redezvous.pdf}~\n\t\t\\includegraphics[trim={3cm 2cm 2.5cm 2cm},width=0.32\\columnwidth]{graphics\/cyclicPurs.pdf}~\n\t\t\\includegraphics[trim={2cm 2cm 2cm 2cm},width=0.32\\columnwidth]{graphics\/leadFollow.pdf}\n\t\t\\caption{Simulation of three distributed multi-agent behaviors on a group of differential drive robots. From the left: rendezvous, cyclic-pursuit, and leader-follower. Solid lines indicate the past trajectories of the robots. \\label{fig:coordBehExamples}}\n\t\\end{center}\n\\end{figure}\n\nFor the correct execution of the controllers mentioned, a sufficiently rich set of information needs to be available to the robots. Representing the flow of information between the robots through {\\it graphs}, with vertices and edges being respectively the robots and the pair-wise ability of sharing information, those conditions can be encoded in terms of particular graphs that need to exist between the robots. For example, rendezvous requires a spanning out-branching tree~\\cite{mesbahi2010graph}, cyclic-pursuit requires a cyclic graph~\\cite{ramirez2009cyclic}, formation control a rigid graph~\\cite{mesbahi2010graph}, and a Delaunay graph is required for most of coverage control problems~\\cite{cortes2004coverage}. \n\nEven though the coordinated behaviors mentioned above can address a number of different tasks, they have limited utility in the context of real-world missions, which can rarely be represented as single tasks. However, the utility of these behaviors can be greatly expanded if they are sequenced together, which is the primary consideration in this paper. But, for a construction like this to work, it is necessary that the required information is available to the robots as they transition from one behavior to the next.\n\nAs such, the problem of composing different behaviors, can be recast in terms of the ability of the robots to establish the interactions needed at each stage of a mission. In particular, when the communication between agents depends on their relative configurations (e.g. relative distance or orientation), realizing a certain communication structure directly affects the configuration of the system, which in turn, affects the execution of the mission itself. In order to overcome this coupling, we separate the problem of generating a sequence of behaviors that corresponds to the solution of a mission objective (e.g.~\\cite{nagavalli2017automated}) from their composition. In this work we focus on the problem of designing a composition framework given a sequence of coordinated behaviors. \\mymod{Although the focus of this paper is confined to motion control tasks, our framework is applicable to other forms of autonomous collaboration where desired interaction structures between the robots are required by the mission, e.g., sharing of resources in heterogeneous teams~\\cite{ramachandran2019resilience} or coordinated manipulation~\\cite{culbertson2018decentralized}}.\n\nThe contribution of this paper is twofold. Firstly, extending the results in~\\cite{li2018formally}, we propose a fully decentralized framework for composing a given sequence of multi-robot coordinated behaviors. Secondly, responding to the lack of established large-scale scenarios for the testing of multi-robot techniques, we propose a scenario called {\\it Securing a Building}, which is rich and complex enough to capture many challenges and objectives of real-world implementations. \\mymod{The significance of our framework is demonstrated through implementation of the Securing a Building scenario on a team of mobile robots.}\n\nThe remaining of this paper is organized as follows. In Section~\\ref{sec:fcbf} we review the definition of finite-time convergence barrier functions, while in Section~\\ref{sec:problem} we present a centralized multi-robot composition framework, which is extended to a \\mymod{fully decentralized} formulation in Section~\\ref{sec:multAgImp}. In Section~\\ref{sec:securing}, we describe the {\\it Securing a Building} case study and its implementation. Finally, motivated by the lack of well-established scenarios for testing and comparing multi-agent robotics techniques, \\mymod{in Appendix~\\ref{sec:appendixA} we discuss supportive arguments for considering the Securing a Building as a multi-robot benchmark scenario.}","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzkxso b/data_all_eng_slimpj/shuffled/split2/finalzzkxso new file mode 100644 index 0000000000000000000000000000000000000000..3fa09e03d9dc7ed2800059bbe3b122a0990a6fb9 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzkxso @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction \\label{sec:introduction}}\n\n\nThe bismuth chalcogenides \\ch{Bi2Ch3} (\\ch{Ch} = \\ch{S}, \\ch{Se}, or \\ch{Te}) of the layered tetradymite structure are an interesting class of highly two dimensional narrow bandgap semiconductors.\nStrong spin-orbit coupling inverts the energy ordering of their bands, making them bulk \\glspl{ti} characterized by a single Dirac cone at the Brillouin zone centre and a topologically protected metallic surface state~\\cite{2009-Hsieh-N-460-1101, 2009-Zhang-NP-5-438}.\nThis has augmented longstanding interest in their thermoelectric properties with significant efforts (theoretical and experimental) to understand their electronic properties in detail~\\cite{2013-Cava-JMCC-1-3176, 2017-Heremans-NRM-2-17049}.\nConsisting of weakly interacting \\ch{Ch-Bi-Ch-Bi-Ch} atomic \\glspl{ql} (see \\latin{e.g.}, Figure~1 in Ref.~\\onlinecite{2019-McFadden-PRB-99-125201}), they can also accommodate intercalant species such as \\ch{Li^{+}} in the \\gls{vdw} gap between \\glspl{ql}~\\cite{1988-Paraskevopoulos-MSEB-1-147, 1989-Julien-SSI-36-113, 2010-Bludska-JSSC-183-2813}, similar to the layered \\glspl{tmd}~\\cite{1978-Whittingham-PSSC-12-41, 1987-Friend-AP-36-1}.\nAlthough their bandgaps are \\SI{\\sim 150}{\\milli\\electronvolt}, doping by intrinsic defects, such as \\ch{Ch} vacancies, yields crystals that are far from insulating.\nTo increase the contrast in conductivity between the bulk and the metallic \\gls{tss}, the crystals are often compensated extrinsically.\nFor example, \\ch{Ca} substitution for \\ch{Bi} suppresses the self-doped $n$-type conductivity in \\ch{Bi2Se3}~\\cite{2009-Hor-PRB-79-195208, 2009-Hsieh-N-460-1101}.\nDoping can also be used to modulate their magnetic properties, yielding magnetic \\glspl{ti} where the \\gls{tss} is gapped~\\cite{2010-Chen-S-329-659}, for example, by substitution of \\ch{Bi} with a paramagnetic transition metal~\\cite{2010-Hor-PRB-81-195203, 2019-Tokura-NRP-1-126}.\n\n\nThe intriguing electronic properties of the tetradymite \\glspl{ti} have predominantly been investigated using surface sensitive probes in real and reciprocal space (\\latin{e.g.}, \\gls{sts} and \\gls{arpes}), as well as other bulk methods.\nIn complement to these studies, \\gls{nmr} offers the ability to probe their electronic ground state and low-energy excitations through the hyperfine coupling of the nuclear spin probe to the surrounding electrons.\nSuch a local probe is especially useful when disorder masks sharp reciprocal space features, as is the case in \\ch{Bi2Ch3}.\nThe availability of a useful \\gls{nmr} nucleus, however, is usually determined by elemental composition and natural (or enriched) isotopic abundance, as well as the specific nuclear properties such as the gyromagnetic ratio $\\gamma$ and, for spin $> 1\/2$, the nuclear electric quadrupole moment $Q$.\nWhile the \\ch{Bi2Ch3} family naturally contain several \\gls{nmr} nuclei~\\cite{2013-Nisson-PRB-87-195202, 2014-Koumoulis-AFM-24-1519, 2016-Levin-JPCC-120-25196}, they are either low-abundance or have a large $Q$.\nAs an alternative, here we use an ion-implanted \\gls{nmr} probe at ultratrace concentrations, with detection based on the asymmetric property of radioactive $\\beta$-decay, known as \\gls{bnmr}~\\cite{2015-MacFarlane-SSNMR-68-1}.\n\n\nA key feature of ion-implanted \\gls{bnmr} is the depth resolution\nafforded by control of the incident beam energy~\\cite{2014-Morris-HI-225-173, 2015-MacFarlane-SSNMR-68-1},\nwhich dictates the stopping distribution of the implanted \\gls{nmr} probes.\nAt \\si{\\kilo\\electronvolt} energies,\nthe depth can be varied on the nanometer length-scale and,\nat the lowest accessible energies,\nit may be able to sense the \\gls{tss} in the tetradymite \\glspl{ti},\nwhich is likely confined to depths \\SI{< 1}{\\nano\\meter} below the surface.\nHere one expects Korringa relaxation and Knight shifts from the \\gls{tss} electrons,\nmodified by the phase space restrictions imposed by their chirality.\nThe magnitude of each will depend on both the \\gls{tss} carrier density and\nthe strength of the coupling to the implanted nuclei.\nWhile the motivation to study the \\gls{tss} is strong,\nhere we report the ``bulk'' response of an implanted \\ch{^{8}Li} probe\nin two doped \\ch{Bi2Ch3} \\glspl{ti}.\nThis is an essential step toward detecting the\n\\gls{tss}~\\cite{2014-MacFarlane-PRB-90-214422, 2019-McFadden-PRB-99-125201},\nbut it also demonstrates the sensitivity of the implanted \\ch{^{8}Li} to\nthe carriers in such heavily compensated narrow gap semiconductors.\n\n\nUsing \\gls{bnmr}, we study two single crystals of doped \\ch{Bi2Ch3} --- compensated \\gls{bsc} and magnetic \\gls{btm} --- each with a beam of highly polarized \\ch{^{8}Li^{+}}.\nIn many respects, \\gls{bnmr} is closely related to \\gls{musr}, but the radioactive lifetime is much longer, making the frequency range of dynamics it is sensitive to more comparable to conventional \\gls{nmr}.\nIn addition to purely electronic phenomena, in solids containing mobile species, \\gls{nmr} is also well known for its sensitivity to low frequency diffusive fluctuations~\\cite{1948-Bloembergen-PR-73-679, 1982-Kanert-PR-91-183, 1994-MullerWarmuth-PIR-17-339}, as are often encountered in intercalation compounds.\nAt ion-implantation energies sufficient to probe the bulk of \\gls{bsc} and \\gls{btm}, we find evidence for ionic mobility of \\ch{^{8}Li^{+}} above \\SI{\\sim 200}{\\kelvin}, likely due to \\gls{2d} diffusion in the \\gls{vdw} gap.\nAt low temperature, we find Korringa relaxation and a small temperature dependent negative Knight shift in \\gls{bsc}, allowing a detailed comparison with \\ch{^{8}Li} in the structurally similar \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}.\nIn \\gls{btm}, the effects of the \\ch{Mn} moments predominate, but remarkably the signal can be followed through the magnetic transition.\nAt low temperature, we find a prominent critical peak in the relaxation that is suppressed in a high applied field, and a broad, intense resonance that is strongly shifted.\nThis detailed characterization of the \\ch{^{8}Li} \\gls{nmr} response is an important step towards using depth-resolved \\gls{bnmr} to study the low-energy properties of the chiral \\gls{tss}.\n\n\n\\section{Experiment \\label{sec:experiment}}\n\n\nDoped \\gls{ti} single crystals \\gls{bsc} and \\gls{btm} with nominal stoichiometries \\ch{Bi_{1.99}Ca_{0.01}Se_{3}} and \\ch{Bi_{1.9}Mn_{0.1}Te_{3}} were grown as described in Refs.~\\onlinecite{2009-Hor-PRB-79-195208, 2010-Hor-PRB-81-195203} and magnetically characterized using a Quantum Design \\gls{mpms}.\nIn the \\gls{btm}, a ferromagnetic transition was identified at $T_{C} \\approx \\SI{13}{\\kelvin}$, consistent with similar \\ch{Mn} concentrations~\\cite{2010-Hor-PRB-81-195203, 2013-Watson-NJP-15-103016, 2016-Zimmermann-PRB-94-125205, 2019-Vaknin-PRB-99-220404}.\nIn contrast, the susceptibility of the \\gls{bsc} crystal was too weak to measure accurately,\nbut the data show no evidence for a Curie tail at low-$T$ that could originate from dilute paramagnetic defects.\n\n\n\\gls{bnmr} experiments were performed at TRIUMF's \\gls{isac} facility in Vancouver, Canada.\nDetailed accounts of the technique can be found in Refs.~\\onlinecite{2015-MacFarlane-SSNMR-68-1, 2019-McFadden-PRB-99-125201}.\nA low-energy highly polarized beam of \\ch{^{8}Li^{+}} was implanted into the samples mounted in one of two dedicated spectrometers~\\cite{2014-Morris-HI-225-173, 2015-MacFarlane-SSNMR-68-1}.\nPrior to mounting, the crystals were cleaved in air and affixed to sapphire plates using \\ch{Ag} paint (SPI Supplies, West Chester, PA).\nThe approximate crystal dimensions were \\SI{7.8 x 2.5 x 0.5}{\\milli\\meter} (\\gls{bsc}) and \\SI{5.3 x 4.8 x 0.5}{\\milli\\meter} (\\gls{btm}).\nWith the crystals attached, the plates were then clamped to an aluminum holder threaded into an \\gls{uhv} helium coldfinger cryostat.\nThe incident \\ch{^{8}Li^{+}} ion beam had a typical flux of \\SI{\\sim e6}{ions\\per\\second} over a beam spot \\SI{\\sim 2}{\\milli\\metre} in diameter.\nAt the implantation energies $E$ used here (between \\SIrange{1}{25}{\\kilo\\electronvolt}), \\ch{^{8}Li^{+}} stopping profiles were simulated for \\num{e5} ions using the \\gls{srim} Monte Carlo code (see \\Cref{sec:implantation})~\\cite{srim}.\nFor $E > \\SI{1}{\\kilo\\electronvolt}$, a negligible fraction of the \\ch{^{8}Li^{+}} stop near enough to the\ncrystal surface to sense the \\gls{tss}.\nMost of the data is taken at \\SI{20}{\\kilo\\electronvolt}, where the implantation depth is \\SI{\\sim 100}{\\nano\\meter}, and the results thus reflect the bulk behavior.\n\n\nThe probe nucleus \\ch{^{8}Li} has nuclear spin $I=2$, gyromagnetic ratio $\\gamma \/ 2 \\pi = \\SI{6.3016}{\\mega\\hertz\\per\\tesla}$, nuclear electric quadrupole moment $Q = \\SI[retain-explicit-plus]{+32.6}{\\milli\\barn}$, and radioactive lifetime $\\tau_{\\beta} = \\SI{1.21}{\\second}$.\nThe nuclear spin is polarized in-flight by collinear optical pumping with circularly polarized light~\\cite{2014-Levy-HI-225-165}, yielding a polarization of \\SI{\\sim 70}{\\percent}~\\cite{2014-MacFarlane-JPCS-551-012059}.\nIn each measurement, we alternate the sense of circular polarization\n(left and right) of the pumping light,\nproducing either ``positive'' or ``negative'' helicity in the \\ch{^{8}Li} beam\n(i.e., its nuclear spin polarization is aligned or counter-aligned with the beam).\nData were collected separately for each helicity, which are usually combined,\nbut in some cases, are considered independently to reveal ``helicity-resolved'' properties.\nWhile helicity-resolved spectra are useful to elucidate details of resonance lines\n(see \\Cref{sec:helicities}),\ncombined spectra are helpful to remove detection systematics\n(see \\latin{e.g.},~\\cite{1983-Ackermann-TCP-31-291, 2015-MacFarlane-SSNMR-68-1}).\nThe \\ch{^{8}Li} polarization was monitored after implantation through the anisotropic radioactive $\\beta$-decay, similar to \\gls{musr}.\nSpecifically, the experimental asymmetry $A$ (proportional to the average longitudinal spin-polarization) was measured by combining the rates in two opposed scintillation counters~\\cite{1983-Ackermann-TCP-31-291, 2015-MacFarlane-SSNMR-68-1}.\nThe proportionality constant depends on the experimental geometry and the details of the $\\beta$-decay (here, on the order of \\num{\\sim 0.1}).\n\n\n\\Gls{slr} measurements were performed by monitoring the transient decay of spin-polarization both during and following a pulse of beam lasting several seconds.\nDuring the pulse, the polarization approaches a steady-state value, while after the pulse, it relaxes to essentially zero.\nAt the edge of the pulse, there is a discontinuity in the slope, characteristic of \\gls{bnmr} \\gls{slr} spectra (see \\latin{e.g.}, \\Cref{fig:bsc-slr-spectra}).\nNote that unlike conventional \\gls{nmr}, no \\gls{rf} field is required for the \\gls{slr} measurements.\nAs a result, it is generally more expedient to measure \\gls{slr} than the resonance;\nhowever, there is no spectral resolution of the relaxation, which represents the \\gls{slr} of all the \\ch{^{8}Li}.\nThe temperature dependence of the \\gls{slr} rate was measured at several applied magnetic fields $B_{0}$:\n\\SI{6.55}{\\tesla} parallel to the \\ch{Bi2Ch3} trigonal $c$-axis;\nand at lower fields \\SI{\\leq 20}{\\milli\\tesla} perpendicular to the $c$-axis.\nA typical \\gls{slr} measurement took \\SI{\\sim 20}{\\minute}.\n\n\nResonances were acquired using a \\gls{dc} \\ch{^{8}Li^{+}} beam and a \\gls{cw} transverse \\gls{rf} magnetic field $B_{1}$.\nIn this measurement mode, the \\gls{rf} frequency is stepped slowly through the \\ch{^{8}Li} Larmor frequency\n\\begin{equation*} \\label{eq:larmor}\n \\omega_{0} = 2 \\pi \\nu_{0} = \\gamma B_{0}\n\\end{equation*}\nand the spin of any on-resonance \\ch{^{8}Li} is rapidly precessed, resulting in a loss in the average time-integrated $\\beta$-decay asymmetry.\nThe resonance amplitudes are determined by several factors:\nthe baseline asymmetry (\\latin{i.e.}, the time integral of the \\gls{slr});\nthe \\gls{rf} amplitude $B_{1}$;\nthe presence of slow, spectral \\ch{^{8}Li} dynamics occurring up to the second timescale (see \\latin{e.g.}, Ref.~\\onlinecite{2017-McFadden-CM-29-10187});\nand, for quadrupole satellite transitions, the relative populations of the magnetic sublevels are somewhat different than conventional pulsed \\gls{nmr}~\\cite{1990-Slichter-PMR, 2015-MacFarlane-SSNMR-68-1, 2019-McFadden-PRB-99-125201}.\nResonances were recorded over a temperature range of \\SIrange{4}{315}{\\kelvin} at both high and low magnetic fields.\nAt high field, the resonance frequency was calibrated against its position in a single crystal \\ch{MgO} at \\SI{300}{\\kelvin}, with the superconducting solenoid persistent.\nA single spectrum typically took \\SI{\\sim 30}{\\minute} to acquire.\n\n\n\\section{Results and analysis \\label{sec:results}}\n\n\n\\subsection{\\ch{Bi2Se3:Ca} \\label{sec:results:bsc}}\n\n\nTypical \\ch{^{8}Li} \\gls{slr} spectra in \\gls{bsc}, at both high and low magnetic field, are shown in \\Cref{fig:bsc-slr-spectra}.\nTo aid comparison, $A(t)$ has been normalized by its initial value $A_{0}$ determined from fits described below.\nClearly, the \\gls{slr} is strongly temperature and field dependent.\nAt low field, the \\gls{slr} is very much faster, due to additional relaxation from fluctuations of the host lattice nuclear spins~\\cite{2009-Hossain-PRB-79-144518}.\nThe temperature dependence of the relaxation is non-monotonic, indicating that some of the low frequency fluctuations at $\\omega_{0}$ are frozen out at low temperature.\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{bsc-slr-spectra.pdf}\n\\caption[\nTypical \\ch{^{8}Li} \\acrlong{slr} spectra in \\ch{Ca} doped \\ch{Bi2Se3} at high and low magnetic field.\n]{ \\label{fig:bsc-slr-spectra}\nTypical \\ch{^{8}Li} \\gls{slr} spectra in \\ch{Ca} doped \\ch{Bi2Se3} at high (left) and low (right) magnetic field with \\ch{^{8}Li^{+}} implanted at \\SI{20}{\\kilo\\electronvolt}.\nThe shaded region indicates the duration of the \\ch{^{8}Li^{+}} beam pulse.\nThe relaxation is strongly field dependent, increasing at lower fields, and it increases non-monotonically with increasing temperature.\nThe solid black lines show fits to a stretched exponential described in the text.\nThe initial asymmetry $A_{0}$ from the fits is used to normalize the data which are binned by a factor of \\num{20} for clarity.\n}\n\\end{figure}\n\n\nThe relaxation is non-exponential at \\emph{all} temperatures \\emph{and} fields, so the data were fit with the phenomenological stretched exponential.\nThis approach was also used for \\ch{^{8}Li} in \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201} and in conventional \\gls{nmr} of related materials~\\cite{2013-Nisson-PRB-87-195202, 2014-Koumoulis-AFM-24-1519, 2016-Levin-JPCC-120-25196}.\nExplicitly, for a \\ch{^{8}Li^{+}} implanted at time $t^{\\prime}$, the spin polarization at time $t > t^{\\prime}$ follows:\n\\begin{equation} \\label{eq:strexp}\n R \\left ( t, t^{\\prime} \\right ) = \\exp \\left \\{ - \\left [ \\lambda \\left ( t-t^{\\prime} \\right ) \\right ]^{\\beta} \\right \\},\n\\end{equation}\nwhere $\\lambda \\equiv 1\/T_{1}$ is the \\gls{slr} rate and $0 < \\beta \\leq 1$ is the stretching exponent.\nThis is the simplest model that fits the data well with the minimal number of free parameters, for the entire \\ch{Bi2Ch3} tetradymite family of \\glspl{ti}.\n\n\nUsing \\Cref{eq:strexp} convoluted with the beam pulse, \\gls{slr} spectra in \\gls{bsc}, grouped by magnetic field $B_{0}$ and implantation energy $E$, were fit simultaneously with a shared common initial asymmetry $A_{0}(B_{0}, E)$.\nNote that the statistical uncertainties in the data are strongly time-dependent (see \\latin{e.g.}, \\Cref{fig:bsc-slr-spectra}), which must be accounted for in the analysis.\nUsing custom C++ code incorporating the MINUIT minimization routines~\\cite{1975-James-CPC-10-343} implemented within the ROOT data analysis framework~\\cite{1997-Brun-NIMA-389-81}, we find the global least-squares fit for each dataset.\nThe fit quality is good ($\\tilde{\\chi}_{\\mathrm{global}}^{2} \\approx 1.02$) and a subset of the results are shown in \\Cref{fig:bsc-slr-spectra} as solid black lines.\nThe large values of $A_{0}$ extracted from the fits (\\SI{\\sim 10}{\\percent} for $B_{0} = \\SI{6.55}{\\tesla}$ and \\SI{\\sim 15}{\\percent} for $B_{0} = \\SI{15}{\\milli\\tesla}$) are consistent with the full beam polarization, with no missing fraction.\nThe fit parameters are plotted in \\Cref{fig:bsc-slr-fits}, showing agreement with the qualitative observations above.\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{bsc-slr-fits.pdf}\n\\caption[\nTemperature and field dependence of the \\ch{^{8}Li} \\acrlong{slr} rate $1\/T_{1}$ and stretching exponent $\\beta$ in \\ch{Ca} doped \\ch{Bi2Se3}.\n]{ \\label{fig:bsc-slr-fits}\nTemperature and field dependence of the \\ch{^{8}Li} \\gls{slr} rate $1\/T_{1}$ and stretching exponent $\\beta$ in \\ch{Ca} doped \\ch{Bi2Se3}.\n$\\beta$ is nearly independent of temperature and field at \\num{\\sim 0.6} (dotted line), except at low field around the large $1\/T_{1}$ peak seen in the bottom panel.\nThe solid and dashed black lines are global fits to \\Cref{eq:rlx}, consisting of a linear $T$-dependence with a non-zero intercept and two \\gls{slr} rate peaks, labelled with index $i$.\nIndependent of the choice of $J_{n}$ used in the analysis, the model captures all the main features of the data.\nThe $T$-linear contribution to $1\/T_{1}$ is shown as the dotted black line.\n}\n\\end{figure}\n\n\nWe now consider a model for the temperature and field dependence of $1\/T_{1}$.\nWe interpret the local maxima in $1\/T_{1}$ in \\Cref{fig:bsc-slr-fits} as \\gls{bpp} peaks~\\cite{1948-Bloembergen-PR-73-679}, caused by a fluctuating field coupled to the \\ch{^{8}Li} nuclear spin with a characteristic rate that sweeps through $\\omega_{0}$ at the peak temperature~\\cite{1948-Bloembergen-PR-73-679, 1979-Richards-TCP-15-141, 1988-Beckmann-PR-171-85}.\nPotential sources of the fluctuations are discussed below.\nThe rate peaks are superposed on a smooth background that is approximately linear, reminiscent of Korringa relaxation in metals~\\cite{1950-Korringa-P-16-601, 1990-Slichter-PMR}.\nThis is surprising, since \\gls{bsc} is a semiconductor, but it is similar to \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}.\nWe discuss this point further in \\Cref{sec:discussion:electronic}.\n\n\nFrom this, we adopt the following model for the total \\gls{slr} rate:\n\\begin{equation} \\label{eq:rlx}\n 1\/T_{1} = a + b T + \\sum_{i} c_{i} \\left ( J_{1,i} + 4J_{2,i} \\right ) .\n\\end{equation}\nIn \\Cref{eq:rlx}, the first two terms account for the $T$-linear contribution with a finite intercept $a$, while the remaining terms describe the $i^{\\mathrm{th}}$ $1\/T_{1}$ peak in terms of a coupling constant $c_{i}$ (proportional to the mean-squared transverse fluctuating field) and the $n$-quantum \\gls{nmr} spectral density functions $J_{n,i}$~\\cite{1988-Beckmann-PR-171-85}.\nIn general, $J_{n,i}$ is frequency dependent and peaked at a temperature where the fluctuation rate matches $\\sim n \\omega_{0}$.\nWhile the precise form of $J_{n,i}$ is not known \\latin{a priori}, the simplest expression, obtained for isotropic \\gls{3d} fluctuations, has a Debye (Lorentzian) form~\\cite{1948-Bloembergen-PR-73-679, 1988-Beckmann-PR-171-85}:\n\\begin{equation} \\label{eq:j3d}\n J_{n}^{\\mathrm{3D}} = \\frac{\\tau_{c}}{1 + \\left (n \\omega_{0} \\tau_{c} \\right )^{2}} ,\n\\end{equation}\nwhere $\\tau_{c}$ is the (exponential) correlation time of the fluctuations.\nAlternatively, when the fluctuations are \\gls{2d} in character, as might be anticipated for such a layered crystal,\n$J_{n}$ may be described by the empirical expression~\\cite{1979-Richards-TCP-15-141, 1994-Kuchler-SSI-70-434}:\n\\begin{equation} \\label{eq:j2d}\n J_{n}^{\\mathrm{2D}} = \\tau_{c} \\ln \\left ( 1 + \\left ( n \\omega_{0} \\tau_{c} \\right )^{-2} \\right ).\n\\end{equation}\nFor both \\Cref{eq:j3d,eq:j2d}, we assume that $\\tau_{c}$ is thermally activated, following an Arrhenius dependence:\n\\begin{equation} \\label{eq:arrhenius}\n \\tau_{c}^{-1} = \\tau_{0}^{-1} \\exp \\left ( - \\frac{ E_{A} }{ k_{B} T } \\right ),\n\\end{equation}\nwhere $E_{A}$ is the activation energy, $\\tau_{0}$ is a prefactor, $k_{B}$ is the Boltzmann constant.\nIf the fluctuations are due to \\ch{^{8}Li^{+}} hopping, $\\tau_{c}^{-1}$ is the site-to-site hop rate.\n\n\nUsing the above expressions, we fit the $1\/T_{1}$ data using a global procedure wherein the kinetic parameters (\\latin{i.e.}, $E_{A, i}$ and $\\tau_{0, i}^{-1}$) are shared at all the different $\\omega_{0}$.\nThis was necessary to fit the data at \\SI{6.55}{\\tesla} where the relaxation is very slow.\nFor comparison, we applied this procedure using both $J_{n}^{\\mathrm{3D}}$ and $J_{n}^{\\mathrm{2D}}$ and the fit results are shown in \\Cref{fig:bsc-slr-fits} as solid ($J_{n}^{\\mathrm{3D}}$) and dashed ($J_{n}^{\\mathrm{2D}}$) lines, clearly capturing the main features of the data.\nThe analysis distinguishes two processes, $i = 1, 2$ in \\Cref{eq:rlx}:\none $(i = 1)$ that onsets at lower temperature with a shallow Arrhenius slope of \\SI{\\sim 0.1}{\\electronvolt}\nthat yields the weaker peaks in $1\/T_1$ at both fields;\nand a higher barrier process $(i = 2)$ with an $E_{A}$ of \\SI{\\sim 0.4}{\\electronvolt} that\nyields the more prominent peak in the low field relaxation,\nwhile the corresponding high field peak must lie above the accessible temperature range.\nThe resulting fit parameters are given in \\Cref{tab:bsc-slr-fits}.\nWe discuss the results in \\Cref{sec:discussion:dynamics}.\n\n\n\\begin{table}\n\\centering\n\\caption[\nArrhenius parameters obtained from the analysis of the temperature dependence of $1\/T_{1}$ in \\ch{Ca} doped \\ch{Bi2Se3}.\n]{ \\label{tab:bsc-slr-fits}\nArrhenius parameters in \\Cref{eq:arrhenius} obtained from the analysis of the temperature dependence of $1\/T_{1}$ in \\ch{Ca} doped \\ch{Bi2Se3} shown in \\Cref{fig:bsc-slr-fits}.\nThe two processes giving rise to the rate peaks are labelled with index $i$.\nGood agreement is found between the $E_{A}$s determined using the spectral density functions $J_{n}$ for \\gls{2d} and \\gls{3d} fluctuations [\\Cref{eq:j2d,eq:j3d}].\n}\n\\begin{tabular}{c S S S S}\n\\toprule\n& \\multicolumn{2}{c}{$i = 1$} & \\multicolumn{2}{c}{$i = 2$} \\\\\n$J_{n}$ & {$\\tau_{0}^{-1}$ (\\SI{e10}{\\per\\second})} & {$E_{A}$ (\\si{\\electronvolt})} & {$\\tau_{0}^{-1}$ (\\SI{e14}{\\per\\second})} & {$E_{A}$ (\\si{\\electronvolt})} \\\\\n\\midrule\n3D & 8.4 \\pm 2.7 & 0.113 \\pm 0.005 & 7 \\pm 5 & 0.395 \\pm 0.015 \\\\\n2D & 9 \\pm 3 & 0.106 \\pm 0.005 & 110 \\pm 90 & 0.430 \\pm 0.016 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\n\nWe now turn to the \\ch{^{8}Li} resonances, with typical spectra shown in \\Cref{fig:bsc-1f-spectra-lf}.\nAs anticipated for a non-cubic crystal, the spectrum is quadrupole split, confirmed unambiguously by the helicity-resolved spectra (see \\Cref{fig:bsc-1f-spectra-helicities} in \\Cref{sec:helicities}).\nThis splitting, on the order of a few \\si{\\kilo\\hertz}~\\footnote{Note that the large $A_{0}$ down to low field precludes \\ch{^{8}Li^{+}} sites with very large \\glspl{efg}.},\nis determined by the \\gls{efg} and is a signature of the crystallographic \\ch{^{8}Li} site.\nBesides this, an unsplit component is also apparent, very close to (within \\SI{\\sim 100}{\\hertz}) the centre-of-mass of the four satellites.\nAt low temperature, the ``central'' and split components are nearly equal, but as the temperature is raised, the unsplit line grows to dominate the spectrum, accompanied by a slight narrowing.\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{bsc-1f-spectra-lf.pdf}\n\\caption[\n\\ch{^{8}Li} resonance spectra in \\acrlong{bsc} at low magnetic field.\n]{ \\label{fig:bsc-1f-spectra-lf}\n\\ch{^{8}Li} resonance spectra in \\gls{bsc} at low magnetic field.\nThe vertical scale is the same for all spectra;\nthey have been normalized to account for changes in intensity due to \\gls{slr}~\\cite{2009-Hossain-PB-404-914}, with their baselines (shown as dashed grey lines) shifted to match the temperature.\nThe spectra consist of a small and nearly temperature-independent quadrupole split pattern, centred about an unsplit Lorentzian line, whose amplitude grows above \\SI{\\sim 150}{\\kelvin}.\nNote the quadrupole pattern of an integer spin nucleus like \\ch{^{8}Li}, has no central satellite (main line).\nThe solid black lines are fits to a sum of this Lorentzian plus and $2I = 4$ quadrupole satellites (see text).\n}\n\\end{figure}\n\n\nThe scale of the quadrupole splitting is determined by the product of the principal component of the \\gls{efg} tensor $eq$ with the nuclear electric quadrupole moment $eQ$.\nWe quantify this with a conventional definition of the quadrupole frequency (for $I=2$)~\\cite{1957-Cohen-SSP-5-321}:\n\\begin{equation*}\n \\nu_{q} = \\frac{e^{2} q Q}{8 h}.\n\\end{equation*}\nIn high field, a first order perturbation treatment of the quadrupole interaction is sufficient to obtain accurate satellite positions.\nHowever, at low field, where $\\nu_{q} \/ \\nu_{0} \\approx \\SI{6}{\\percent}$, second order terms are required~\\cite{1957-Cohen-SSP-5-321, 1990-Taulelle-NASC-393}.\nBased on the change in satellite splittings by a factor \\num{2} in going from $B_{0} \\parallel c$ to $B_{0} \\perp c$, we assume the asymmetry parameter of the \\gls{efg} $\\eta = \\num{0}$ (\\latin{i.e.}, the \\gls{efg} is axially symmetric).\nThis is reasonable based on likely interstitial sites for \\ch{^{8}Li^{+}}~\\cite{2019-McFadden-PRB-99-125201}.\nPairs of helicity-resolved spectra were fit with $\\nu_{0}$ and $\\nu_{q}$ as shared free parameters, in addition to linewidths and amplitudes.\nAs the difference between the frequency of the unsplit line and the center of the quadrupole split pattern was too small to measure accurately,\nthe fits were additionally constrained to have the same central frequency $\\nu_{0}$.\nThis is identical to the approach used for \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}.\nA subset of the results (after recombining the two helicities) are shown in \\Cref{fig:bsc-1f-spectra-lf} as solid black lines.\n\n\nThe main result is the strong temperature dependence of the resonance amplitude shown in \\Cref{fig:bsc-1f-fits-amp}.\nWhile the satellite amplitudes are nearly temperature independent, the central component increases substantially above \\SI{150}{\\kelvin}, tending to plateau above the $1\/T_{1}$ peak.\nThe other parameters are quite insensitive to temperature.\nTypical linewidths (\\latin{i.e.}, \\acrlong{fwhm}) are \\SI{\\sim 2.2}{\\kilo\\hertz} for the satellites and \\SI{\\sim 3.8}{\\kilo\\hertz} for the central component.\nThe quadrupole frequency $\\nu_{q} \\approx \\SI{5.5}{\\kilo\\hertz}$ varies weakly, increasing slightly as temperature is lowered.\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{bsc-1f-fits-amp.pdf}\n\\caption[\nThe resonance amplitude as a function of temperature in \\ch{Ca} doped \\ch{Bi2Se3} at \\SI{15}{\\milli\\tesla}.\n]{ \\label{fig:bsc-1f-fits-amp}\nThe resonance amplitude as a function of temperature in \\ch{Ca} doped \\ch{Bi2Se3} at \\SI{15}{\\milli\\tesla}.\nWhile the amplitude of the satellite lines are nearly temperature independent, the central component increases substantially above \\SI{150}{\\kelvin}, plateauing on the high-$T$ side of the $1\/T_{1}$ maximum (grey band).\nThe solid line is a guide, while the dashed line indicates the estimated saturation value for the Lorentizan component.\n}\n\\end{figure}\n\n\nWe also measured resonances at room and base temperature in high field (\\SI{6.55}{\\tesla}) where \\ch{^{8}Li} is sensitive to small magnetic shifts.\nFrom the fits, we use $\\nu_{0}$ to calculate the raw relative frequency shift $\\delta$ in parts per million (\\si{\\ppm}) using:\n\\begin{equation} \\label{eq:shift}\n \\delta = 10^{6} \\left ( \\frac{\\nu_{0} - \\nu_{\\ch{MgO}} }{ \\nu_{\\ch{MgO}} } \\right ) ,\n\\end{equation}\nwhere $\\nu_{\\ch{MgO}}$ is the reference frequency position in \\ch{MgO} at \\SI{300}{\\kelvin}.\nThe shifts are small:\n\\SI[retain-explicit-plus]{+12 \\pm 2}{\\ppm} at room temperature and \\SI{-17 \\pm 3}{\\ppm} at \\SI{5}{\\kelvin}, the latter considerably smaller in magnitude than in \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}.\nBecause \\ch{^{8}Li} \\gls{nmr} shifts are generally so small, it is essential to account for the demagnetization field of the sample itself.\nFrom $\\delta$, the corrected shift $K$ is obtained by the \\gls{cgs} expression~\\cite{2008-Xu-JMR-191-47}:\n\\begin{equation}\n K = \\delta + 4 \\pi \\left ( N - \\frac{1}{3} \\right ) \\chi_{v}\n\\end{equation}\nwhere $N$ is the dimensionless demagnetization factor that depends only on the shape of the sample and $\\chi_{v}$ is the dimensionless (volume) susceptibility.\nFor a thin film, $N$ is \\num{1}~\\cite{2008-Xu-JMR-191-47}, but for the thin platelet crystals used here, we estimate $N$ is on the order of \\num{\\sim 0.8}, treating them as oblate ellipsoids~\\cite{1945-Osborn-PR-67-351}.\nFor the susceptibility, we take the average of literature values reported for pure \\ch{Bi2Se3}~\\cite{1958-Matyas-CJP-8-309, 2003-Kulbachinskii-PB-329-1251, 2015-Pafinlov-JPCM-27-456002, 2016-Chong-JAC-686-245}, giving $\\chi_{v}^{\\mathrm{CGS}} \\approx \\SI{-2.4e-6}{\\emu\\per\\centi\\meter\\cubed}$.\nNote that we have excluded several reports~\\cite{2008-Janicek-PB-403-3553, 2012-Young-PRB-86-075137, 2014-Zhao-NM-13-580} whose results disagree by an order of magnitude from those predicted by Pascal's constants~\\cite{2008-Bain-JCE-85-532}.\nApplying the correction for \\gls{bsc} yields $K$s of \\SI[retain-explicit-plus]{-2 \\pm 2}{\\ppm} and \\SI{-31 \\pm 3}{\\ppm} at room and base temperature, respectively.\nWe discuss this below in \\Cref{sec:discussion:electronic}.\n\n\n\\subsection{\\ch{Bi2Te3:Mn} \\label{sec:results:btm}}\n\n\nTypical \\ch{^{8}Li} \\gls{slr} spectra at high and low field in the magnetically doped \\gls{btm} are shown in \\Cref{fig:btm-slr-spectra}.\nIn contrast to nonmagnetic \\gls{bsc}, the relaxation at high field is fast, typical of paramagnets with unpaired electron spins~\\cite{2015-MacFarlane-PRB-92-064409, 2016-Cortie-PRL-116-106103,2011-Song-PRB-84-054414}.\nThe fast high field rate produces a much less pronounced field dependence.\nAt low field, the \\gls{slr} rate is peaked at low temperature.\nThe relaxation is also non-exponential and fits well using \\Cref{eq:strexp}, with a stretching exponent systematically smaller than in the nonmagnetic \\gls{bsc} or \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}.\nWe analyzed the data with the same global approach, obtaining good quality fits ($\\tilde{\\chi}_{\\mathrm{global}}^{2} \\approx 1.01$) demonstrated by the solid black lines in \\Cref{fig:btm-slr-spectra}.\nThe shared values of $A_{0}$ from the fits are large (\\SI{\\sim 10}{\\percent} for $B_{0} = \\SI{6.55}{\\tesla}$ and \\SI{\\sim 15}{\\percent} for $B_{0} = \\SI{20}{\\milli\\tesla}$), consistent with the full beam polarization, implying that there is remarkably no magnetic wipeout from very fast relaxation~\\cite{2015-MacFarlane-PRB-92-064409}, even at low field.\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{btm-slr-spectra.pdf}\n\\caption[\nTypical \\ch{^{8}Li} \\acrlong{slr} spectra in \\ch{Mn} doped \\ch{Bi2Te3} at high and low magnetic field.\n]{ \\label{fig:btm-slr-spectra}\nTypical \\ch{^{8}Li} \\gls{slr} spectra in \\ch{Mn} doped \\ch{Bi2Te3} at high (left) and low (right) magnetic field for \\ch{^{8}Li^{+}} implantation energies of \\SI{20}{\\kilo\\electronvolt} and \\SI{8}{\\kilo\\electronvolt}, respectively.\nThe shaded region denotes the duration of the \\ch{^{8}Li^{+}} beam pulse.\nThe \\gls{slr} is substantial and orders of magnitude faster than in \\gls{bsc} (see \\Cref{fig:bsc-slr-spectra}) at high field.\nThe field dependence to the \\gls{slr} is much weaker than in the nonmagnetic tetradymites.\nThe solid black lines are fits to a stretched exponential convoluted with the \\ch{^{8}Li^{+}} beam pulse as described in the text.\nThe initial asymmetry $A_{0}$ from the fit is used to normalize the spectra.\nThe high and low field spectra have been binned for by factors of \\num{20} and \\num{5}, respectively.\n}\n\\end{figure}\n\n\nThe fit parameters are shown in \\Cref{fig:btm-slr-fits}.\nAt all temperatures, especially at high field, the \\gls{slr} rate $1\/T_{1}$ is orders of magnitude larger than in the nonmagnetic analogs.\nNo clear $1\/T_{1}$ \\gls{bpp} peaks can be identified between \\SIrange[range-units=single,range-phrase=--]{100}{300}{\\kelvin};\nhowever, in the low field data, a critical divergence is evident at the magnetometric transition at about \\SI{13}{\\kelvin}.\nIn high field, this feature is largely washed out, with a remnant peak near \\SI{50}{\\kelvin}.\nAbove \\SI{200}{\\kelvin}, the \\gls{slr} rate increases very rapidly and is well-described by $1\/T_{1} \\propto \\exp [ - E_{A} \/ ( k_{B} T )]$,\nwith $E_{A} \\approx \\SI{0.2}{\\electronvolt}$ at both fields.\nWe discuss this below in \\Cref{sec:discussion:dynamics}.\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{btm-slr-fits.pdf}\n\\caption[\nTemperature dependence of the \\ch{^{8}Li} \\acrlong{slr} rate $1\/T_{1}$ in \\ch{Mn} doped \\ch{Bi2Te3} at high and low field.\n]{ \\label{fig:btm-slr-fits}\nTemperature dependence of the \\ch{^{8}Li} \\gls{slr} rate $1\/T_{1}$ in \\ch{Mn} doped \\ch{Bi2Te3} at high and low field.\nAt low field, $1\/T_{1}$ shows a critical peak at the ferromagnetic transition at $T_{C} \\approx \\SI{13}{\\kelvin}$, as the \\ch{Mn} spin fluctuations freeze out.\nAbove \\SI{200}{\\kelvin}, the \\gls{slr} rate increases exponentially in manner nearly independent of applied field.\nThe solid grey lines are drawn to guide the eye.\n}\n\\end{figure}\n\n\nIn contrast to \\gls{bsc} and \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}, the resonance in \\gls{btm} consists of a single broad Lorentzian with none of the resolved fine structure (see \\Cref{fig:btm-1f-spectra}).\nSurprisingly, the very broad line has significant intensity, dwarfing the quadrupole pattern in \\gls{bsc} in both width and amplitude.\nIn addition, there is a large negative shift at \\SI{10}{\\kelvin}.\nAt room temperature, the line is somewhat narrower, and the shift is reduced in magnitude.\nQuantitative results from Lorentzian fits are summarized in \\Cref{tab:btm-1f-fits}.\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{btm-1f-spectra.pdf}\n\\caption[\nTypical \\ch{^{8}Li} resonances in \\ch{Mn} doped \\ch{Bi2Te3} and \\ch{Ca} doped \\ch{Bi2Se3} at high magnetic field.\n]{ \\label{fig:btm-1f-spectra}\nTypical \\ch{^{8}Li} resonances in \\ch{Mn} doped \\ch{Bi2Te3} and \\ch{Ca} doped \\ch{Bi2Se3} at high magnetic field.\nThe vertical scale is the same for both spectra;\nthey have been normalized to account for changes in intensity and baseline~\\cite{2009-Hossain-PB-404-914}.\nThe lineshape in the magnetic \\gls{btm} is well-described by a broad Lorentzian (solid black line) with no quadrupolar splitting.\nA large negative shift is also apparent for \\gls{btm} with respect to the reference frequency in \\ch{MgO} (vertical dashed line).\nThe dotted vertical line indicates the expected resonance position due to demagnetization, revealing a large positive hyperfine field (\\SI{\\sim 38}{\\gauss}) at \\SI{5}{\\kelvin} in the magnetic state.\n}\n\\end{figure}\n\n\n\\begin{table}\n\\centering\n\\caption[\nResults from the analysis of the \\ch{^{8}Li} resonance in \\acrlong{btm} at high and low temperature with $B_{0} = \\SI{6.55}{\\tesla} \\parallel (001)$.\n]{ \\label{tab:btm-1f-fits}\nResults from the analysis of the \\ch{^{8}Li} resonance in \\gls{btm} at high and low temperature with $B_{0} = \\SI{6.55}{\\tesla} \\parallel (001)$.\nThe (bulk) magnetization $M$ measured with $\\SI{1.0}{\\tesla} \\parallel (001)$ is included for comparison.\n}\n\\begin{tabular}{S S S S[retain-explicit-plus] S}\n\\toprule\n{$T$ (\\si{\\kelvin})} & {$\\tilde{A}$ (\\si{\\percent})} & {\\acrshort{fwhm} (\\si{\\kilo\\hertz})} & {$\\delta$ (\\si{\\ppm})} & {$M$ (\\si{\\emu\\per\\centi\\meter\\cubed})} \\\\\n\\midrule\n294 & 33 \\pm 5 & 16.2 \\pm 1.2 & +10 \\pm 9 & 0.076 \\\\\n 10 & 14.4 \\pm 0.8 & 41.7 \\pm 1.6 & -206 \\pm 12 & 7.698 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\n\n\\section{Discussion \\label{sec:discussion}}\n\n\nThe \\ch{^{8}Li} \\gls{nmr} properties of nonmagnetic \\gls{bsc} are quite similar to previous measurements in \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}.\nThe resonance spectra show a similar splitting ($\\nu_q$ is about \\SI{25}{\\percent} smaller in \\gls{bsc}), indicating a similar site for \\ch{^{8}Li}.\nThe resemblance of the spectra extends to the detailed temperature dependence, including the growth of the unsplit line approaching room temperature.\nSurprisingly, the \\gls{bsc} spectra are better resolved than \\gls{bts}, implying a higher degree of order, despite the \\ch{Ca} doping.\nThis is also evident in the \\gls{slr}, with a stretching exponent $\\beta$ closer to unity in \\gls{bsc} than in \\gls{bts}.\nThis likely reflects additional disorder in \\gls{bts} from \\ch{Bi\/Te} anti-site defects~\\cite{2012-Scanlon-AM-24-2154} that are much more prevalent than for \\ch{Bi\/Se}, due to the difference in radii and electronegativity.\nThe sharp quadrupolar pattern indicates a well-defined crystallographic \\ch{^{8}Li^{+}} site, and the corresponding small \\gls{efg} suggests it is in the \\gls{vdw} gap.\n\\Gls{dft} calculations of the \\gls{efg} may enable a precise site assignment.\nThe high field \\gls{slr} is also similar to \\gls{bts}:\nit is slow and near the lower limit measurable due to the finite \\ch{^{8}Li} lifetime $\\tau_\\beta$ and comparable to the \\gls{vdw} metal \\ch{NbSe2}~\\cite{2006-Wang-PB-374-239}, where the carrier concentration is much higher, but significantly slower than the \\gls{ti} alloy \\ch{Bi_{1-x}Sb_{x}}~\\cite{2014-MacFarlane-PRB-90-214422}.\nThe low field enhancement of $1\/T_1$ is also similar, so it cannot be essentially related to the dilute \\ch{^{125}Te} moments that are absent in \\gls{bsc}, but probably determined primarily by the \\SI{100}{\\percent} abundant \\ch{^{209}Bi}.\nFrom such a detailed similarity, it is clear that a quantitative comparison with \\gls{bts} and other \\gls{vdw} materials will be useful.\n\n\nWith these similarities in mind, the rest of the discussion is organized as follows: in \\Cref{sec:discussion:dynamics},\nwe consider evidence of mobility of the \\ch{^{8}Li^{+}} ion;\nin \\Cref{sec:discussion:electronic}, electronic effects at low temperature in \\gls{bsc};\nand the magnetic properties of \\gls{btm} in \\Cref{sec:discussion:magnetic}.\n\n\n\\subsection{Dynamics of the \\ch{Li^{+}} ion \\label{sec:discussion:dynamics}}\n\nIn \\gls{bts}, we considered if the evolution of the spectrum with temperature (similar to \\Cref{fig:bsc-1f-spectra-lf}) was the result of a site change transition from a meta-stable quadrupolar \\ch{^{8}Li^{+}} site to a lower energy site with very small \\gls{efg} at higher temperature~\\cite{2019-McFadden-PRB-99-125201}, similar to elemental \\ch{Nb}~\\cite{2009-Parolin-PRB-80-174109}.\nWe now consider an alternative explanation; namely, dynamic averaging of the quadrupolar interaction due to \\ch{^{8}Li^{+}} motion.\nExamples of this are found in conventional \\ch{^{7}Li} \\gls{nmr}, where, unlike \\ch{^{8}Li}, the $I = 3\/2$ quadrupole spectrum has a main line (the $m = \\pm 1\/2$ satellite) overlapping the averaged resonance~\\cite{2012-Galven-CM-24-3335, 2012-Indris-JPCC-116-14243}.\nDynamic averaging is suggested by the onset near, but below, the \\gls{slr} rate peak (see \\Cref{fig:bsc-1f-fits-amp}).\nHowever, for hopping between equivalent interstitial sites (probably the quasi-octahedral Wyckoff $3b$ site in the \\gls{vdw} gap --- see Figure~12 in Ref.~\\onlinecite{2019-McFadden-PRB-99-125201}),\none does not expect that the \\gls{efg} will average to a value near zero (required to explain the unsplit line).\nA point charge estimate reveals the quasi-tetrahedral (Wyckoff $6c$) site, thought to be the saddle point in the potential for \\ch{Li^{+}} between adjacent $3b$ sites~\\cite{2016-Gosalvez-PRB-93-075429}, has an \\gls{efg} of opposite sign to the $3b$ site.\nIf $6c$ is instead a shallow minimum, the \\ch{^{8}Li^{+}} residence time there may be long enough that the \\gls{efg} averages to near zero.\nIn the fast motion limit at higher temperatures, one would then expect the quadrupole splitting to re-emerge when the residence time in the $6c$ ``transition'' site becomes much shorter~\\cite{2012-Indris-JPCC-116-14243}.\n\n\nWe now examine the two kinetic processes causing the $1\/T_{1}$ peaks in \\gls{bsc}.\nIt is surprising to find two distinct thermally activated processes sweeping through the \\gls{nmr} frequency, especially since only a single process was found in \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}.\nFirst, we consider the weaker feature, the low temperature $(i=1)$ peaks.\nIn layered materials, small intercalates can undergo highly localized motion at relatively low temperatures below the onset of free diffusion~\\cite{1982-Kanert-PR-91-183, 1994-MullerWarmuth-PIR-17-339}.\nSuch local motion may be the source of the \\gls{slr} rate peak, but it is quite ineffective at narrowing the resonance, consistent with the absence of any lineshape changes in the vicinity of the $i=1$ peaks.\nCaged local motion is usually characterized by a small activation barrier, comparable to the \\SI{\\sim 0.1}{\\electronvolt} observed here.\nSimilar phenomena have been observed at low temperature, for example, in neutron activated \\ch{^{8}Li} \\gls{bnmr} of \\ch{Li} intercalated graphite, \\ch{LiC12}~\\cite{1989-Schirmer-SM-34-589, 1995-Schirmer-ZNA-50-643}.\nIt is not clear why such motion would be absent in \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}, which has a larger \\gls{vdw} gap than \\gls{bsc} (\\SI{2.698}{\\angstrom} vs.\\ \\SI{2.568}{\\angstrom}).\nAlternatively, this feature in the relaxation may have an electronic origin,\nperhaps related to the emergent low-$T$ magnetism in \\ch{MoTe2} observed\nby \\gls{musr}~\\cite{2018-Guguchia-SA-4-eaat3672} and \\ch{^{8}Li} \\gls{bnmr}~\\cite{Krieger-tbp}.\n\n\nIn contrast, the \\gls{slr} rate peak above \\SI{200}{\\kelvin} $(i = 2)$ is almost certainly due to \\gls{efg} fluctuations caused by stochastic \\ch{^{8}Li^{+}} motion.\nFrom the data, we cannot conclude that this is long-range diffusion, but the room temperature \\ch{Li^{+}} intercalability of \\ch{Bi2Se3}~\\cite{1988-Paraskevopoulos-MSEB-1-147, 1989-Julien-SSI-36-113, 2010-Bludska-JSSC-183-2813} suggests it is.\nIts barrier, on the order of \\SI{\\sim 0.4}{\\electronvolt}, is comparable to other \\gls{vdw} gap layered ion conductors, but it is about twice as high as in \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}, possibly a result of the \\ch{Se} (rather than \\ch{Te}) bounded \\gls{vdw} gap, which provides less space between neighbouring \\glspl{ql}.\n\n\nWe now consider the Arrhenius law prefactors $\\tau_{0}^{-1}$, that, for ionic diffusion, are typically in the range \\SIrange[range-phrase=--,range-units=single]{e12}{e14}{\\per\\second}.\nFor the low-$T$ process ($i = 1$), independent of the form of $J_{n}$ (see \\Cref{tab:bsc-slr-fits}), $\\tau_{0}^{-1} \\approx \\SI{9e10}{\\per\\second}$ is unusually low.\nIn contrast, for the high-$T$ ($i = 2$) process, it is much larger and depends strongly on $J_{n}$.\nFor \\gls{3d} diffusion, it is in the expected range, while the \\gls{2d} model yields an extremely large value, \\SI{\\sim e16}{\\per\\second},\nin the realm of prefactor anomalies~\\cite{1983-Villa-SSI-9-1421} and opposite to the small value expected for\nlow dimensional diffusion~\\cite{1978-Richards-SSC-25-1019}.\nSimilar behaviour was observed recently in \\ch{^{7}Li} \\gls{nmr} of \\ch{LiC6}~\\cite{2013-Langer-PRB-88-094304},\nwhere surprisingly, $J_{n}^{\\mathrm{2D}}$ was concluded to be less appropriate than $J_{n}^{\\mathrm{3D}}$,\nsuggesting that \\ch{Li} motion in the \\gls{vdw} gap is not as ideally \\gls{2d} as might be expected.\nIn \\gls{bsc}, the anomaly may be related to local dynamics that onset at lower $T$, imparting some \\gls{3d} character to the motion.\n\n\nGiven the evidence for long-range \\ch{Li^{+}} motion in \\gls{bsc} and \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}, the absence of a relaxation peak in \\gls{btm} may seem unexpected.\nBoth \\ch{Ca^{2+}} and \\ch{Mn^{2+}} dopants (substitutional for \\ch{Bi^{3+}}) have an effective $-1$ charge yielding an attractive trapping potential for the positive interstitial \\ch{^{8}Li^{+}}, but the \\ch{Mn} concentration is an order of magnitude larger.\nThe high trap density in \\gls{btm} will suppress \\ch{Li^{+}} mobility.\nThe exponential increase in $1\/T_{1}$ above \\SI{200}{\\kelvin} may be the onset of a diffusive \\gls{bpp} peak, but, in this case, one does not expect it to be so similar between the two very different magnetic fields.\nThis may reflect a trade-off between the increase in $\\omega_{0}$ that shifts the peak to higher temperature, slowing the relaxation on its low-$T$ flank, and the increased polarization of the \\ch{Mn} moments by the field that amplifies local magnetic inhomogeneities.\nA motional origin for this increase is consistent with the apparent $E_{A} \\sim \\SI{0.2}{\\electronvolt}$, similar to \\ch{^{8}Li^{+}} in \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201}, which also has a \\ch{Te} bounded \\gls{vdw} gap of similar size to \\gls{btm} (\\SI{2.620}{\\angstrom}).\nHowever, it may have a different explanation, see below in \\Cref{sec:discussion:magnetic}.\n\n\n\\subsection{Electronic effects at low temperature \\label{sec:discussion:electronic}}\n\n\nBismuth chalcogenide (\\ch{Bi2Ch3}) crystals exhibit substantial bulk conductivity, despite a narrow gap in the \\gls{3d} band structure, making it difficult to distinguish effects of the metallic \\gls{tss}.\nThis is due to native defects (\\latin{e.g.}, \\ch{Ch} vacancies) that are difficult or impossible to avoid~\\cite{2013-Cava-JMCC-1-3176}.\nExtrinsic dopants, such as substitutional \\ch{Ca\/Bi}, can be used to compensate the spontaneous $n$-type doping.\nBrahlek \\latin{et al.}\\ have argued~\\cite{2015-Brahlek-SSC-215-54} that, even for the most insulating compensated samples, the carrier densities far exceed the Mott criterion, making them heavily doped semiconductors in the metallic regime.\nIn this case, we expect metallic \\gls{nmr} characteristics~\\cite{1978-Holcomb-SUSSP-19-251}, namely a magnetic Knight shift $K$, proportional to the carrier spin susceptibility $\\chi_{s}$~\\footnote{%\nThe \\gls{nmr} shift $K$ is comprised of several terms including both the spin and orbital response of all the surrounding electrons. In metals, where the local carrier density is substantial,\nthe Fermi contact coupling with the electron spins often dominates, as we have assumed here.%\n}.\nIn the simplest (isotropic) case,\n\\begin{equation} \\label{eq:knight-shift}\n K = A \\chi_{s},\n\\end{equation}\nwhere $A$ is the hyperfine coupling constant, which is accompanied by a \\gls{slr} rate following the Korringa law~\\cite{1950-Korringa-P-16-601, 1990-Slichter-PMR},\n\\begin{equation} \\label{eq:korringa-rate}\n \\frac{1}{T_{1}} = 4 \\pi \\hbar A^{2} \\gamma_{n}^{2} \\left ( \\frac{\\chi_{s}}{g^{*}\\mu_{B}} \\right )^{2} k_{B} T.\n\\end{equation}\nHere, $\\gamma_{n}$ is the nuclear gyromagnetic ratio, $g^{*}$ is the carrier $g$-factor, and $\\mu_{B}$ is the Bohr magneton.\nCombining \\Cref{eq:knight-shift,eq:korringa-rate}, we obtain the Korringa product, which is independent of the value of $A$,\n\\begin{equation} \\label{eq:korringa-product}\n T_{1}TK^{2} = \\frac{\\hbar (g^{*} \\mu_B)^{2}}{4 \\pi k_{B} \\gamma_{n}} = S(g^{*}).\n\\end{equation}\nFor \\ch{^{8}Li},\n\\begin{equation*}\n S(g^{*}) \\approx 1.20 \\times 10^{-5} \\left ( \\frac{ g^{*} }{g_0} \\right )^{2}~\\si{\\second\\kelvin},\n\\end{equation*}\nwhere, unlike in metals, we have allowed for an effective $g$-factor that may be far from its free electron value\n$g_0 \\approx 2$~\\cite{1972-Look-PRB-5-3406}.\nIndeed, recent \\gls{epr} measurements in \\ch{Bi2Se3} find $g^{*} \\approx 30$~\\cite{2016-Wolos-PRB-93-155114}.\n\n\nAccording to Ref.~\\onlinecite{2015-Brahlek-SSC-215-54}, \\gls{bts} and \\gls{bsc} lie on opposite sides of the Ioffe-Regel limit, where the carrier mean free path is equal to its Fermi wavelength, with \\gls{bsc} having a higher carrier density and mobility.\nA comparative Korringa analysis could test this assertion and, to this end, using \\Cref{eq:korringa-product} we define the dimensionless Korringa ratio as\n\\begin{equation} \\label{eq:korringa-constant}\n \\mathscr{K} = \\frac{T_{1}TK^2}{S(g^{*})}.\n\\end{equation}\nBelow the Ioffe-Regel limit, the autocorrelation function of the local hyperfine field at the nucleus, due to the carriers (that determines $T_{1}$) becomes limited by the diffusive transport correlation time.\nThis has been shown to enhance the Korringa rate (\\latin{i.e.}, shortening $T_{1}$)~\\cite{1971-Warren-PRB-3-3708, 1983-Gotze-ZPB-54-49}.\nFrom this, one expects $\\mathscr{K}$ would be smaller in \\gls{bts} than in \\gls{bsc}.\n\n\nThere are, however, significant difficulties in determining the experimental $\\mathscr{K}$.\nFirst, the Korringa slope depends on magnetic field.\nAt low fields, this is due to coupling with the host nuclear spins, a phenomenon that is quenched in high fields where the \\ch{^{8}Li} \\gls{nmr} has no spectral overlap with the \\gls{nmr} of host nuclei.\nFor example, in simple metals, we find the expected field-independent Korringa slope at high fields in the Tesla range~\\cite{2015-MacFarlane-SSNMR-68-1}.\nIn contrast, in \\gls{bts}, the slope decreases substantially with increasing field, even at high fields~\\cite{2019-McFadden-PRB-99-125201}.\nWe suggested this could be the result of magnetic carrier freeze-out.\nWhile we do not have comparably extensive data in \\gls{bsc}, we can compare the slope at the same field, \\SI{6.55}{\\tesla} (see \\Cref{tab:korringa}).\nHere, in both materials, the relaxation is extremely slow, exhibiting no curvature in the \\gls{slr} during the \\ch{^{8}Li} lifetime\n(\\Cref{fig:bsc-slr-spectra}), so the uncertainties in $1\/T_{1}T$ are likely underestimates.\nThe larger slope is, however, consistent with a higher carrier density $n$ in \\gls{bsc}.\nThe Korringa slopes should scale~\\cite{1972-Look-PRB-5-3406} as $n^{2\/3}$.\nUsing $n \\sim \\SI{1e19}{\\per\\centi\\meter\\cubed}$ in \\gls{bsc}~\\cite{2009-Hor-PRB-79-195208} and \\SI{\\sim 2e17}{\\per\\centi\\meter\\cubed} in \\gls{bts}~\\cite{2011-Jia-PRB-84-235206}, the slopes should differ by a factor of \\num{\\sim 14}, while experimentally the ratio is \\num{\\sim 5}.\n\n\nThe next difficulty is accurately quantifying the shift $K$, which is quite small with a relatively large demagnetization correction.\nExperimentally, the zero of shift, defined by the calibration in \\ch{MgO}, differs from the true zero (where $\\chi_{s} = 0$) by the difference in chemical (orbital) shifts between \\ch{MgO} and the chalcogenide.\nHowever, because \\ch{Li} chemical shifts are universally very small, this should not be a large difference, perhaps a few \\si{\\ppm}.\nThe negative low temperature shift is also somewhat surprising.\nThe hyperfine coupling $A$ for \\ch{Li} is usually determined by a slight hybridization of the vacant $2s$ orbital with the host conduction band.\nAs the $s$ orbital has density at the nucleus, the resulting coupling is usually positive, with the $d$ band metals \\ch{Pd} and \\ch{Pt} being exceptional~\\cite{2015-MacFarlane-SSNMR-68-1}.\nFor a positive $A$, the sign of $K$ is determined by the sign of $g^{*}$, which has not yet been\nconclusively measured in either \\gls{bsc} or \\gls{bts}.\nA more serious concern is that $K$ depends on temperature (in contrast to simple metals) and, at least in \\gls{bts}, also on applied field~\\cite{2019-McFadden-PRB-99-125201}.\nTo avoid ambiguity from the field dependence, we similarly restrict comparison to the same field, \\SI{6.55}{\\tesla}.\nA similarly temperature dependent shift (for the \\ch{^{207}Pb} \\gls{nmr}) was found in the narrow band semiconductor \\ch{PbTe}~\\cite{1973-Hewes-PRB-7-5195}, where it was explained by the temperature dependence of the Fermi level $E_{F}$~\\cite{1970-Senturia-PRB-1-4045}.\nAt low-$T$ in the heavily $p$-type \\ch{PbTe}, $E_{F}$ occurs in the valence (or a nearby impurity) band, but with increasing temperature, moves upward into the gap, causing a reduction in $|K|$.\nWith this in mind, we assume the low temperature shift is the most relevant for a Korringa comparison.\nWithout a measured $g^{*}$ in \\gls{bts}, we simply assume it is the same as \\gls{bsc}, and use the $g^{*}_{\\parallel}$ from \\gls{epr}~\\cite{2016-Wolos-PRB-93-155114} to calculate the values of $\\mathscr{K}$ in \\Cref{tab:korringa}.\n\n\n\\begin{table}\n\\centering\n\\caption[\nKorringa analysis of \\acrlong{bsc} and \\acrlong{bts} at \\SI{6.55}{\\tesla} and low temperature.\n]{ \\label{tab:korringa}\nKorringa analysis of \\gls{bsc} and \\gls{bts}~\\cite{2019-McFadden-PRB-99-125201} at \\SI{6.55}{\\tesla} and low temperature.\nTo calculate $\\mathscr{K}$, we take $S(g^{*})$ in \\Cref{eq:korringa-constant} to be \\SI{2.69e-3}{\\second\\kelvin}, using the $g_{\\parallel}^{*}$ from \\gls{epr}~\\cite{2016-Wolos-PRB-93-155114}.\n}\n\\begin{tabular}{l S S S}\n\\toprule\n & {$1\/(T_{1}T)$ (\\SI{e-6}{\\per\\second\\per\\kelvin})} & {$K$ (\\si{\\ppm})} & {$\\mathscr{K}$} \\\\\n\\midrule\n\\acrlong{bsc} & 9.5 \\pm 0.8 & -31 \\pm 3 & 0.038 \\pm 0.008 \\\\\n\\acrlong{bts} & 1.79 \\pm 0.07 & -115 \\pm 3 & 2.78 \\pm 0.18 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\n\nThe values of $\\mathscr{K}$ are just opposite to the expectation of faster relaxation for diffusive \\gls{bts} compared to metallic\n\\gls{bsc}~\\cite{2015-Brahlek-SSC-215-54}.\nElectronic correlations can, however, significantly alter the Korringa ratio to an extent that depends on disorder~\\cite{1994-Shastry-PRL-72-1933}.\nThere should be no significant correlations in the broad bulk bands of the chalcogenides, but in narrow impurity bands, this is certainly a possibility.\nWe note that $\\mathscr{K}$ is also less than \\num{1} in \\ch{PbTe}~\\cite{1973-Alexander-JN-1-251}, similar to \\gls{bsc}.\nAt this stage, without more data and a better understanding of the considerations mentioned above, it is premature to draw further conclusions.\n\n\n\\subsection{Magnetism in \\acrlong{btm} \\label{sec:discussion:magnetic}}\n\n\nIn the \\ch{Mn} doped \\ch{Bi2Te3} at low field, the relaxation from magnetic \\ch{Mn^{2+}} becomes faster as the spin fluctuations slow down on cooling towards $T_{C}$.\nIn particular, the low-$T$ increase in $1\/T_{1}$ occurs near where correlations among the \\ch{Mn} spins become evident in \\gls{epr}~\\cite{2016-Zimmermann-PRB-94-125205, 2017-Talanov-AMR-48-143}.\nIn remarkable contrast to ferromagnetic \\ch{EuO}~\\cite{2015-MacFarlane-PRB-92-064409}, the signal is not wiped out in the vicinity of $T_{C}$, but $1\/T_{1}$ does become very fast.\nThis is likely a consequence of a relatively small hyperfine coupling consistent with a \\ch{Li} site in the \\gls{vdw} gap.\n\n\nHigh applied field slows the \\ch{Mn} spins more continuously starting from a higher temperature, suppressing the critical peak and reducing $1\/T_{1}$ significantly, a well-known phenomenon in \\gls{nmr} and \\gls{musr} at magnetic transitions (see \\latin{e.g.}, Ref.~\\onlinecite{2001-Heffner-PRB-63-094408}).\nThis also explains the small critical peak in the \\ch{^{8}Li} \\gls{slr} in the dilute magnetic semiconductor, \\ch{Ga_{1-x}Mn_{x}As}~\\cite{2011-Song-PRB-84-054414}.\nAs in \\ch{GaAs}, \\ch{Mn} in \\ch{Bi2Te3} is both a magnetic and electronic dopant.\nAt this concentration, \\gls{btm} is $p$-type with a metallic carrier density of \\SI{\\sim 7e19}{\\per\\centi\\meter\\cubed}~\\cite{2010-Hor-PRB-81-195203, 2016-Zimmermann-PRB-94-125205}.\nHowever, the difference in scale of $1\/T_{1}$ at high field between \\Cref{fig:btm-slr-fits,fig:bsc-slr-fits} shows that the \\ch{Mn} spins completely dominate the carrier relaxation.\n\n\nIt is also remarkable that the resonance is so clear in the magnetic state, in contrast to \\ch{Ga_{1-x}Mn_{x}As}~\\cite{2011-Song-PRB-84-054414}.\nThe difference is not the linewidth, but rather the enhanced amplitude.\nThis may be due to slow \\ch{^{8}Li} spectral dynamics occurring on the timescale of $\\tau_{\\beta}$ that effectively enhance the amplitude, \nfor example, slow fluctuations of the ordered \\ch{Mn} moments, not far below $T_{C}$.\nSimilar behavior was found in rutile \\ch{TiO2} at low temperature, where it was attributed to field fluctuations due to a nearby electron polaron~\\cite{2017-McFadden-CM-29-10187}.\nEnhancement of the \\gls{rf} field at nuclei in a ferromagnet~\\cite{1959-Gossard-PRL-3-164} may also play a role.\n\n\nAbove \\SI{200}{\\kelvin}, the activated increase in $1\/T_{1}$ may indicate the onset of diffusive \\ch{^{8}Li^{+}} motion, similar to the nonmagnetic analogs, with the additional effect that the local magnetic field from the \\ch{Mn} spins is also modulated by \\ch{^{8}Li} hopping (not just the \\gls{efg}), similar to the ordered magnetic \\gls{vdw} layered \\ch{CrSe2}~\\cite{2020-Ticknor-RSCA-10-8190}.\nHowever, it may instead mark the onset of thermal excitation of electrons across the bandgap that is narrowed by \\ch{Mn} doping\\cite{2010-Hor-PRB-81-195203}.\nThermally excited carriers may also explain the increase in $1\/T_1$ at comparable temperatures in \\ch{Ga_{1-x}Mn_{x}As}~\\cite{2011-Song-PRB-84-054414}, a very different medium for \\ch{Li^+} diffusion.\nThermally increased carrier density would strengthen interaction between the \\ch{Mn} moments, extend their effects via \\gls{rkky} polarization, and increase $1\/T_{1}$.\nMeasurements at higher temperatures may be able to discriminate these possibilities, but it is likely that both processes will contribute.\n\n\nThe stretched \\gls{slr} and field-dependent $1\/T_{1}$\nare characteristic of the \\gls{nmr} response in a disordered glassy magnetic state, consistent with\nthe random Mn\/Bi site disorder. The magnetic properties of \\gls{btm}~\\cite{2010-Hor-PRB-81-195203, 2013-Watson-NJP-15-103016, 2016-Zimmermann-PRB-94-125205, 2019-Vaknin-PRB-99-220404} are similar to the dilute (ferro)magnetic semiconductors that include a uniform magnetization of the carriers. In this context, our data provides a \\emph{local} characterization of the inhomogeneous magnetic state of \\gls{btm} that will be useful in developing a detailed microscopic understanding of its magnetism.\nHaving established the effects of \\ch{Mn} magnetism in the bulk, it would be interesting to use lower implantation energies to study how they may be altered in the surface region by coupling to the \\gls{tss}.\n\n\n\\section{Conclusion \\label{sec:conclusion}}\n\n\nUsing implanted \\ch{^{8}Li} \\gls{bnmr}, we have studied the electronic and magnetic properties of the doped \\glspl{ti} \\gls{bsc} and \\gls{btm}.\nFrom \\gls{slr} measurements, we find evidence at temperatures above \\SI{\\sim 200}{\\kelvin} for site-to-site hopping of isolated \\ch{Li^{+}} with an Arrhenius activation energy of \\SI{\\sim 0.4}{\\electronvolt} in \\gls{bsc}.\nAt lower temperature the electronic properties dominate, giving rise to Korringa-like relaxation and negative Knight shifts, similar to isostructural \\gls{bts}.\nA quantitative comparison reveals Korringa ratios opposite to expectations across the Ioffe-Regel limit.\nIn \\gls{btm}, the magnetism from dilute \\ch{Mn} moments dominates all other spin interactions, but the \\gls{bnmr} signal remains measurable through the magnetic transition at $T_{C}$, where a critical peak in the \\gls{slr} rate is observed.\nThe \\SI{\\sim 0.2}{\\electronvolt} activation energy from the high temperature increase in the \\gls{slr} may be related to \\ch{Li} mobility or to thermal carrier excitations.\n\n\nWith these new results, a more complete picture of the implanted \\ch{^{8}Li} \\gls{nmr} probe of the tetradymite \\ch{Bi} chalcogenides (and other \\gls{vdw} chalcogenides) is beginning to emerge.\nAt high temperatures, isolated \\ch{^{8}Li^{+}} has a tendency to mobilize, providing unique access to the kinetic parameters governing \\ch{Li^{+}} diffusion in the ultra-dilute limit.\nAt low temperature, \\ch{^{8}Li} is sensitive to the local metallic and magnetic properties of the host.\nWith the bulk \\gls{nmr} response now established in \\ch{Bi2Ch3} \\glspl{ti}, the prospect of directly probing the chiral \\gls{tss} with the depth resolution provided by \\gls{bnmr} remains promising.\n\n\n\\begin{acknowledgments}\nWe thank:\nR.\\ Abasalti, D.\\ J.\\ Arseneau, S.\\ Daviel, B.\\ Hitti, K.\\ Olchanski, and D.\\ Vyas for their excellent technical support;\nM.\\ H.\\ Dehn, T.\\ J.\\ Parolin, O.\\ Ofer, Z.\\ Salman, Q.\\ Song, and D.\\ Wang for assistance with early measurements;\nas well as D.\\ E.\\ MacLaughlin, S.\\ D.\\ Senturia, and A.\\ Wolos for useful discussions.\nThis work was supported by NSERC Discovery grants to R.F.K.\\ and W.A.M.\nAdditionally, R.M.L.M.\\ and A.C.\\ acknowledge support from their NSERC CREATE IsoSiM Fellowships.\nThe crystal growth at Princeton University was supported by the ARO-sponsored MURI on topological insulators, grant number W911NF1210461.\n\\end{acknowledgments}\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\begin{table*}\n\\begin{minipage}[htbp]{\\textwidth}\n\\caption{Parameters of the electron number density\nprofiles.}\\label{table_neCC} \\centering\n\\renewcommand{\\footnoterule}{} \n \\begin{tabular}{lcccccc}\n \\hline\n \n Cluster & $n_{e0}$ & $\\beta$ & $\\theta_{c1}$ & $\\theta_{c2}$ & $f$ \\\\\n & (10$^{-2}$cm$^{-3}$) & & (arcsec) & (arcsec) & \\\\\n \\hline\n \\hline\n A1413 & $3.89\\pm0.54$ & $0.535\\pm0.016$ & $6.7\\pm1.4$ & $40.1\\pm4.1$ & $0.760\\pm0.020$ \\\\\n A1689 & $4.15\\pm0.31$ & $0.871\\pm0.040$ & $21.6\\pm1.0$ & $104.5\\pm5.3$ & $0.870\\pm0.010$ \\\\\n A1835 & $11.3\\pm0.4$ & $0.802\\pm0.015$ & $9.3\\pm0.2$ & $63.8\\pm1.6$ & $0.940\\pm0.001$ \\\\\n A2204 & $20.4\\pm1.1$ & $0.716\\pm0.028$ & $7.5\\pm0.3$ & $67.6\\pm1.9$ & $0.959\\pm0.004$ \\\\\n A2261 & $4.07\\pm0.59$ & $0.631\\pm0.024$ & $10.2\\pm1.8$ & $39.1\\pm5.9$ & $0.760\\pm0.050$ \\\\\n MS1358.4+6245 & $9.63\\pm0.79$ & $0.676\\pm0.017$ & $3.3\\pm0.2$ & $37.0\\pm1.8$ & $0.934\\pm0.003$ \\\\\n RXJ1347.5-1145 & $28.5\\pm1.4$ & $0.632\\pm0.009$ & $4.0\\pm0.2$ & $23.3\\pm1.6$ & $0.942\\pm0.004$ \\\\\n ZW3146 & $16.9\\pm0.3$ & $0.669\\pm0.005$ & $4.4\\pm0.1$ & $25.8\\pm0.6$ & $0.882\\pm0.004$ \\\\\n \\hline\n \\end{tabular}\n\\end{minipage}\n\\end{table*}\n\nClusters of galaxies are the largest gravitationally-bound objects\narising thus far from the process of hierarchical structure\nformation (\\cite{Voit05}). As the most recent and most massive\nobjects of the Universe, clusters are excellent probes for studying\nits formation and evolution. The observed state of gas within a\ncluster is determined by a combination of shock heating during\naccretion, radiative cooling, and thermal feedback produced by the\ncooling itself, so the density and temperature of the ICM represent\nthe full thermal history of clusters' formation. To better\nunderstand the physics of ICM, it is necessary to have sufficient\nknowledge of the gas density and temperature distributions. Though\nclusters are the ideal target objects for X-ray observations of the\nhot ICM, millimeter and sub-millimeter measurements provide\nindependent and complementary tools for studying the same ICM by\nexploiting the Sunyaev Zel'dovich (SZ) effect (\\cite{Sunyaev72}).\n\nThe SZ effect is the Comptonization of the cosmic microwave\nbackground (CMB) photons, coming from the last scattering surface,\nby the hot electrons population of the ICM. The photon energy\nvariation, which is caused by the scattering process, can be\nexpressed as CMB temperature variations\n\n\\begin{equation}\\label{eq_SZ}\n\\Delta T_{SZ}=yT_{CMB}f(x)(1+\\delta_n(x,\\theta_e))+\\Delta T_{kin}\n\\end{equation}\nwhere\n\n\\begin{equation}\\label{eq_y}\ny=\\int\\theta_ed\\tau_e=\\int\\left(\\frac{k_BT_e}{m_ec^2}\\right)\\sigma_Tn_edl\\propto\\int\nP_edl\n\\end{equation}\nrepresents the comptonization parameter, $x=(h\\nu)\/(k_BT_{CMB})$ the\ndimensionless frequency, $h$ and $k_B$ are respectively the Planck\nand Boltzmann constants, $T_{CMB}$, $m_e$ and $\\sigma_T$, the CMB\ntemperature at $z=0$, the electron mass at rest and the Thomson\ncross section, $\\theta_e$ represents the dimensionless thermal\nenergy of the ICM, $\\tau_e$ is the electron optical depth. The\nparameters $n_e$, $T_e$, and $P_e$ are the electron number density,\ntemperature, and pressure of the ICM,\n$\\delta_n(x,\\theta_e)=f_n(x)\\theta_e^n\/f(x)$ is the relativistic\ncorrection term that accounts for the thermal energy of the\nelectrons involved in the scattering processes, where\n$f(x)=x[(e^x+1)\/(e^x-1)-4]$ is a dimensionless quantity that\ndescribes the spectral signature of the effect, and the subscript\n$n$ indicates the maximum order of the relativistic correction\n($n=4$ in this work, \\cite{Nozawa95}). The last term of Eq.\n\\ref{eq_SZ} is the kinematic component of the SZ effect, which\ncontains the contribution from the bulk motion of the electron\npopulation with respect to the last scattering surface reference\nframe. For the purpose of this paper, this term is omitted, assuming\nthat it is disentangled from the thermal component by\nmulti-frequency observations, together with the signal from the\nprimary CMB emission.\n\nThe SZ effect is redshift independent and, for this reason, it is\npossible to detect distant clusters without any existing X-ray or\noptical observations. This is the case of the ongoing ground-based\nexperiments such as SPT (\\cite{Ruhl04}), ACT (\\cite{Kosowsky03}),\nand the all sky survey like Planck (\\cite{Planck11a}) or the\nupgraded MITO (\\cite{DePetris07}) and OLIMPO (\\cite{Masi08}) with\nnew spectroscopic capabilities and the proposed 30-m diameter C-CAT\n(\\cite{Sebring06}). However, some assumptions on cluster physics\nstill have to be made in order to directly extract cluster\nobservables.\n\nEstimates of cluster's total mass can be derived by SZ observations\nwhen X-ray or lensing measurements are available or by empirically\ncalibrated scaling relations linking the SZ flux to the total mass\n(e.g. \\cite{Vikhlinin09, Arnaud10, Planck11b, Comis11}). Total mass\ncan also be determined by SZ observations alone when applying\nthermal energy constraints (\\cite{Mroczkowski11}).\n\nTo accurately reproduce the gas inside the cluster, an ICM universal\nmodel is mandatory (e.g. \\cite{Nagai07, Arnaud10}). In this paper we\nconfirm that the simple isothermal \\textit{beta}-model is clearly an\ninappropriate cluster representation for total mass recovery by SZ\nobservations, particularly in the presence of relaxed cool core (CC)\nclusters. These objects show a well studied peaked density profile\nwith a temperature decrement in the core region (\\cite{Jones84}). In\nthe local universe this class of clusters is observationally a\nsignificant percentage of the total cluster population\n(\\cite{Eckert11}). Even if the X-ray estimated CC fraction is biased\nby selection effects in flux-limited samples, recently a 35\\% of\nclusters have picked up in the SZ high signal-to-noise ratio Planck\nearly cluster data-set (\\cite{Planck11c}) are CC clusters. Large\nscatter in mass estimates of CC clusters has been highlighted\npreviously using numerical simulations by Hallman et al. (2006) and\nHallman et al. (2007).\n\nWe investigate the bias on the mass in a limited sample of eight\nnearby ($0.110^{14}$ $M_{\\odot}$) CC\nclusters observed by Chandra. The SZ maps of these clusters, which\nare expressed in thermodynamic temperature units and convolved with\nseveral instrumental beams, are dealt with by applying the\nisothermal \\textit{beta}-model. The total mass is derived in three\ndifferent ways: by assuming hydrostatic equilibrium and a fixed gas\nfraction and by applying a self similar scaling relation. To focus\nonly on the consequences of the employed ICM model, in our analysis\nwe neglect all the contaminants present in the sky by assuming in\nthis way the best situation to recover cluster total mass.\n\nIn Sect. \\ref{sec_ne_Te}, we discuss the electron number density\nradial profile and follow self-similar studies to characterize a\nuniversal electron temperature radial profile of a limited sample of\neight CC clusters observed by Chandra. In Sect. \\ref{sec_MCMC} we\ngenerate maps of the SZ effect in thermodynamic temperature. In\nSect. \\ref{sec_total_mass} we evaluate cluster total mass under\ndifferent sets of assumptions. The bias on the recovered mass is\ndescribed in Sect. \\ref{sec_MB}, which discusses the main\ncontributions. Conclusions are summarized in Sect.\n\\ref{sec_conclusions}.\n\n\\section{Electron number density and temperature\nprofiles}\\label{sec_ne_Te}\n\n\\begin{table*}\n\\begin{minipage}[hbpt]{\\textwidth}\n\\caption{CC galaxy clusters properties used in the analysis to\ngenerate a universal $T_e$ profile for this class of clusters.}\n\\label{table_te} \\centering\n\\renewcommand{\\footnoterule}{} \n \\begin{tabular}{l c c c c c c}\n \\hline\n \n Cluster & z & $D_A$ & $r_{500}$\\footnote{\\cite{Morandi07}} & $\\theta_{500}$ & $T_X$\\footnote{Temperature scale calculated by a weighted mean of Bonamente et al. (2006) data.}& $T_{e0}$\\footnote{Electron temperature of the cluster obtained by fitting the isothermal \\textit{beta}-model to X-ray data (\\cite{Bonamente06}).}\\\\\n name & & (Gpc) & (kpc) & (arcsec) & (keV)& (keV)\\\\\n \\hline\n \\hline\n $A1413$ & 0.142 & 0.52 & $1195\\pm232$ & $321\\pm62$ & $6.6\\pm0.6$& $7.3\\pm0.2$\\\\\n $A1689$ & 0.183 & 0.63 & $1402\\pm260$ & $377\\pm70$ & $8.7\\pm0.9$& $10.0\\pm0.3$\\\\\n $A1835$ & 0.252 & 0.81 & $1439\\pm414$ & $387\\pm111$ & $10.5\\pm1.0$& $8.4\\pm0.2$\\\\\n $A2204$ & 0.152 & 0.55 & $1796\\pm320$ & $483\\pm86$ & $11.3\\pm1.8$& $6.5\\pm0.2$\\\\\n $A2261$ & 0.224 & 0.74 & $1201\\pm168$ & $323\\pm45$ & $7.0\\pm1.0$& $7.2\\pm0.4$\\\\\n $MS1358.4+6245$ & 0.327 & 0.97 & $1633\\pm885$ & $439\\pm238$ & $8.4\\pm1.1$& $8.3\\pm0.6$\\\\\n $RXJ1347.5-1145$ & 0.451 & 1.19 & $1734\\pm170$ & $466\\pm46$ & $14.8\\pm1.5$& $13.5\\pm0.5$\\\\\n $ZW3146$ & 0.291 & 0.90 & $1804\\pm344$ & $485\\pm92$ & $8.7\\pm0.4$& $6.6\\pm0.1$\\\\\n \\hline\n \\end{tabular}\n\\end{minipage}\n\\end{table*}\n\nA general parametric model for the cluster atmosphere must be\ndefined to forecast the shape of cluster SZ signals in matched\nfilter techniques for detecting clusters in blind surveys. ACT has\ndetected new clusters assuming a two-dimensional Gaussian profile as\nfilter (\\cite{Sehgal10}), while SPT has detected a projected\nspherical \\textit{beta} profile (\\cite{Vanderlinde10}) and Planck a\nuniversal pressure profile (\\cite{Melin11}).\n\nThe approach for determining the total mass cluster can be\ndifferent. High-quality X-ray data allows an accurate modeling of\ncluster morphology, but in the case of low angular resolution and\/or\nlow signal-to-noise ratio a simple isothermal \\textit{beta}-model is\nstill applied (e.g. \\cite{Marriage10, Sayers11}).\n\nIn this work we analyze this model (\\cite{Cavaliere78}), which is\nbased on the very general assumption that the electron temperature\n$T_e$ is constant along the whole considered cluster radial\nextension and that the electron number density follows a spherical\ndistribution as\n\n\\begin{equation}\\label{eq_beta}\nn_{e,ISO}(r)=n_{e0}{\\left(1+\\frac{r^2}{r_c^2}\\right)}^{-\\frac{3}{2}\\beta},\n\\end{equation}\nwhere $n_{e0}$ is the central electron number density, $r_c$ the\ncore radius, and $\\beta$ the power law index. The subscript ISO\nindicates, hereafter, the isothermal \\textit{beta}-model. The proved\ninadequacy of this model is compensated for by the advantage of\nextracting a simple analytic expression for the $y$ parameter along\nthe off-axis angular separation, $\\theta$,\n\n\\begin{equation}\\label{eq_ybeta}\ny_{ISO}(\\theta)=y_0{\\left(1+\\frac{\\theta^2}{\\theta_c^2}\\right)}^{\\frac{1}{2}-\\frac{3}{2}\\beta}\n\\end{equation}\nwhere\n\n\\begin{equation}\\label{eq_y0}\ny_0=n_{e0}\\frac{k_BT_e}{m_ec^2}\\sigma_T\nr_c\\sqrt{\\pi}\\frac{\\Gamma\\left(\\frac{3}{2}\\beta-\\frac{1}{2}\\right)}{\\Gamma\\left(\\frac{3}{2}\\beta\\right)},\n\\end{equation}\nand $\\theta_c=r_c\/D_A$, with $D_A$ the angular diameter distance,\nwhich has been calculated for each cluster by using\n\n\\begin{equation}\nD_A=\\frac{c}{H_0(1+z)}\\int^z_0\\frac{dz'}{E(z')},\n\\end{equation}\nwhere $H_0$ is the Hubble constant and\n$E(z)={\\left[\\Omega_{M}(1+z)^3+\\Omega_{\\Lambda}\\right]}^{1\/2}$. We\nadopt a $\\Lambda CDM$ cosmology with $H_0=70$ km\/s\/Mpc,\n$\\Omega_M=0.3$, and $\\Omega_{\\Lambda}=0.7$.\n\nIt is easy to find clusters that are not relaxed or that display\nstructures that are very difficult to describe with this model.\nTherefore, it is realistic to assume that many newly discovered\nclusters in blind SZ surveys exhibit such significant deviations as\nwell. To explore a particular ICM gas morphology, we focus on CC\nclusters. We started analyzing a small sample of eight objects\nextracted from the Chandra dataset investigated in Bonamente et al.\n(2006).\n\nThe study of a central region, commonly known as the core region,\nhas been challenged by high-resolution numerical simulations\n(\\cite{Navarro95, Borgani04, Kay04, Nagai07, Henning09}). These\nworks lead to an agreement on whether there is a cooling core in the\nvery central denser gas region ($r<0.1r_{500}$) of some clusters, as\nwell as a slower decline in the temperature at large radii\n($r>0.2r_{500}$). As usual we refer to $r_{500}$ as the radius of\nthe cluster that defines a volume with mean density $500$ times the\ncritical density $\\rho_{crit}$ at cluster redshift. The choice of\n$r_{500}$ is motivated by simulation results from Evrard et al.\n(1996) showing the gas within this radius relaxed and in hydrostatic\nequilibrium.\n\nMoreover, many observational studies in X-rays have shown that the\nX-ray surface brightness, hence the underlying density, cannot be\nrepresented correctly by a \\textit{beta} profile. A second component\nshould be added or a peaked central part introduced in order to\nproperly fit the observation. The observed deprojected density\nprofiles are peaked for CC systems and flatter for morphologically\ndisturbed clusters.\n\nThe $n_e$ cluster profile can be described by a double\n\\textit{beta}-model profile\n\n\\begin{eqnarray}\\label{eq_2beta}\nn_{e,CC}(r)&=&n_{e0}\\left[f{\\left(1+\\frac{r^2}{r_{c1}^2}\\right)}^{-\\frac{3}{2}\\beta}+(1-f)\n{\\left(1+\\frac{r^2}{r_{c2}^2}\\right)}^{-\\frac{3}{2}\\beta}\\right],\\nonumber\\\\\n\\end{eqnarray}\nwhere the parameters' data have been taken from Bonamente et al.\n(2006) and adapted to this work to have a symmetric standard\ndeviation (D'Agostini, 2003).\n\nThis distribution is a generalization of a double\n\\textit{beta}-model profile of the electron number density,\ndeveloped by La Roque et al. (2005), but instead using the same\n$\\beta$ parameter for both the central region and the outskirts, as\nin Bonamente et al. (2006). The $r_{c1}=\\theta_{c1}\/D_A$ and\n$r_{c2}=\\theta_{c2}\/D_A$ are the core radii of the inner and outer\ndistributions, and $f$ is a parameter defined between 0 and 1 that\nrepresents how the core region dominates the outer region. These\nparameters, together with $n_{e0}$, are taken from Bonamente et al.\n(2006) and summarized in Table \\ref{table_neCC} for the selected\nclusters.\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\columnwidth]{Contefg1.eps}\\\\\n \\caption{\\itshape Radial electron temperature profiles of a Chandra-selected sample of CC galaxy clusters (\\cite{Bonamente06}) and the best\n fit (solid line) of the electron temperature profile with the 1$\\sigma$ error\n (dotted lines) proposed in this work. Temperatures and radii\n are expressed in terms of $T_X$ and $r_{500}$, respectively, which\n are used as scale quantities throughout this work.}\\label{fig_fitTe}\n\\end{figure}\n\nWe describe the temperature profile as\n\n\\begin{eqnarray}\\label{eq_Te_general}\n\\frac{T_{e,CC}(r)}{T_X}=\\left\\{\n\\begin{array}{rl}\nA_1{\\left(\\frac{r}{r_{500}}\\right)}^{m_1} & \\mbox{ for } \\frac{r}{r_{500}}<0.1 \\\\\nA_2{\\left(\\frac{r}{r_{500}}\\right)}^{m_2} & \\mbox{ for }\n\\frac{r}{r_{500}}>0.2\n\\end{array}\n\\right.\n\\end{eqnarray}\nwhere $A_{1,2}$ are determined by fixing the position at which the\ntwo power laws, described by the $m_1$ and $m_2$ parameters,\nintersect each other, as explained in Appendix \\ref{sec_appendix}.\n\nThe search for a universal temperature profile in the cluster halo\nregion has been a target of several works based on observations\n(e.g. \\cite{Markevitch98, DeGrandi02, Zhang04, Vikhlinin05,\nVikhlinin06, Sanderson06, Zhang07, Pratt07, Zhang08}) and\nhydrodynamical simulations (e.g., \\cite{Loken02, Borgani04, Kay04,\nPiffaretti08}).\n\n\\begin{table*}\n\\begin{minipage}[htbp]{\\textwidth}\n\\caption{Parameters of the single \\textit{beta}-model, estimated\nusing the MCMC procedure described in the text, as best fit of the\nCC cluster maps.}\\label{table_neISO} \\centering\n\\renewcommand{\\footnoterule}{} \n \\begin{tabular}{llccccc}\n \\hline\n \n Cluster & FWHM fov & $\\Delta T_{SZ0}$ & $\\beta$ & $\\theta_c$ \\\\\n & (arcmin) & ($\\mu$K) & & (arcsec) \\\\\n \\hline\n \\hline\n $Cl_{A1413}$ & 1& $-273\\pm9$ & $0.731\\pm0.006$ & $66.1\\pm3.0$ \\\\\n & 4.5& $-224\\pm14$ & $0.743\\pm0.009$ & $81.7\\pm7.3$ \\\\\n & 7& $-195\\pm11$ & $0.758\\pm0.011$ & $96.4\\pm8.0$ \\\\\n \\hline\n $Cl_{A1689}$& 1& $-554\\pm8$ & $0.979\\pm0.006$ & $80.2\\pm1.5$ \\\\\n & 4.5& $-430\\pm12$ & $0.994\\pm0.004$ & $93.7\\pm2.2$ \\\\\n & 7& $-592\\pm84$ & $0.909\\pm0.018$ & $61.4\\pm8.3$ \\\\\n \\hline\n $Cl_{A1835}$& 1& $-709\\pm10$ & $0.951\\pm0.006$ & $56.8\\pm1.1$ \\\\\n & 4.5& $-468\\pm29$ & $0.980\\pm0.013$ & $76.0\\pm5.0$ \\\\\n & 7& $-818\\pm88$ & $0.870\\pm0.010$ & $36.0\\pm3.6$ \\\\\n \\hline\n $Cl_{A2204}$& 1& $-727\\pm8$ & $0.848\\pm0.003$ & $68.4\\pm0.9$ \\\\\n & 4.5& $-551\\pm18$ & $0.865\\pm0.007$ & $86.0\\pm3.6$ \\\\\n & 7& $-514\\pm39$ & $0.859\\pm0.017$ & $86.9\\pm9.0$ \\\\\n \\hline\n $Cl_{A2261}$ & 1& $-399\\pm10$ & $0.830\\pm0.006$ & $52.3\\pm1.7$ \\\\\n & 4.5& $-273\\pm17$ & $0.849\\pm0.008$ & $71.6\\pm4.6$ \\\\\n & 7& $-323\\pm32$ & $0.816\\pm0.013$ & $55.5\\pm6.4$ \\\\\n \\hline\n $Cl_{MS1358.4+6245}$& 1& $-304\\pm12$ & $0.857\\pm0.011$ & $49.6\\pm2.7$ \\\\\n & 4.5& $-245\\pm28$ & $0.856\\pm0.013$ & $56.0\\pm6.1$ \\\\\n & 7& $-1246\\pm116$ & $0.753\\pm0.009$ & $8.7\\pm1.2$ \\\\\n \\hline\n $Cl_{RXJ1347.5-1145}$& 1& $-1683\\pm15$ & $0.835\\pm0.002$ & $37.7\\pm0.5$ \\\\\n & 4.5& $-893\\pm30$ & $0.860\\pm0.005$ & $62.7\\pm2.5$ \\\\\n & 7& $-739\\pm41$ & $0.873\\pm0.011$ & $73.1\\pm5.1$ \\\\\n \\hline\n $Cl_{ZW3146}$& 1& $-645\\pm13$ & $0.839\\pm0.005$ & $42.4\\pm1.1$ \\\\\n & 4.5& $-404\\pm38$ & $0.857\\pm0.013$ & $61.3\\pm6.7$ \\\\\n & 7 & $-585\\pm77$ & $0.801\\pm0.011$ & $36.5\\pm5.2$ \\\\\n \\hline\n \\end{tabular}\n\\end{minipage}\n\\end{table*}\n\nThe profile, proposed in this paper specifically for CC clusters,\nfollows both the central drop and the outer decline of the gas\ntemperature. The function is formalized in the\n$\\log\\left(r\/r_{500}\\right)-\\log\\left(T_e(r)\/T_X\\right)$ plane on\nwhich the power laws of Eq. \\ref{eq_Te_general} become linear\nfunctions (see Appendix \\ref{sec_appendix} for a complete\ntreatment). To fix the parameters $A_1$ and $A_2$ of the radial\nelectron temperature profile and to confirm the power laws indices\n$m_1$ and $m_2$, we fit a co-adding of Chandra electron temperature\nnormalized to $T_X$ data, of the CC clusters selection with the\nproposed function. $T_X$ represents the average temperature value in\nthe range $(0.1\\div1.0)r_{500}$, and it is used to scale each\ncluster, in order to fit the universal temperature function to the\nmeasured profiles. In Table \\ref{table_te} we report the cluster\nredshift, $z$, and the angular diameter distance, $D_A$. The scale\nradius $r_{500}$ and temperature $T_X$ are also collected. The\nchosen radial range, which is used to calculate the scale\ntemperature $T_X$, corresponds to a cut in the central region\n($r<0.1r_{500}$). Obviously this value cannot be compared easily\nwith results of other works because it strictly depends on the data\nradial extension. In fact, an important source of bias is the\ntemperature definition (\\cite{Vikhlinin06}). Here, we use the\nspectroscopic temperature $T_X$. In Figure \\ref{fig_fitTe} the\ntemperature data of our cluster sample with the best fit are\nplotted. By following Appendix \\ref{sec_appendix}, the resulting\n$T_e$ profile parameters are $A_1=2.41\\pm0.14$, $A_2=0.55\\pm0.10$,\n$m_1=0.38\\pm0.02$ and $m_2=-0.29\\pm0.11$, which univocally define\nthe $T_e(r)$ function. We note that, even if the power law that\ndescribes the outskirts of the cluster temperature distribution\nsuffers larger uncertainties, $m_1$ and $m_2$ are both compatible,\nwithin one standard deviation, with estimates available in the\nliterature. For example, Zhang et al. (2008) find $m_1=0.38\\pm0.04$,\nfor $r<0.2r_{500}$, in agreement with Sanderson et al. (2006), who\nproposes $m_1=0.4$, for $r<0.1r_{500}$. For radii larger then\n$0.2r_{500}$, Zhang et al. (2008) fitted a selected sample of data\nfrom XMM-Newton finding structurally similar behavior to ours with\n$m_2=-0.28\\pm0.19$.\n\n\\section{Pipeline of cluster simulation}\\label{sec_MCMC}\n\nThe analysis reported in this paper can be summarized in the\nfollowing steps:\n\\begin{itemize}\n \\item construction of an SZ signal distortion profile $\\Delta T_{SZ}\n (\\theta)$, using the ICM information coming from existing X-ray\n observations;\n \\item convolution of the cluster SZ map with several instrumental beam profiles;\n \\item extraction of the ICM parameters as in the assumptions of the isothermal \\textit{beta}-model;\n \\item estimation of the cluster total mass $M_{tot}$;\n \\item calculation of the bias on cluster total mass due to\n the incorrect description of the ICM.\n\\end{itemize}\n\nIn the first step, we generate angular profiles of the SZ signal\ndistortion $\\Delta T_{SZ} (\\theta)$, assuming an observing frequency\nof 150 GHz. The electron number density $n_e$ and temperature $T_e$\nprofiles are constructed using the equations presented in Sect.\n\\ref{sec_ne_Te}, which we assume to be a good representation of a\ncool core ICM. The angular profile of the comptonization parameter\nis then evaluated by projecting the electron pressure profile on the\nplane orthogonal to the cluster line of sight. The SZ signal is\nobtained using Eq. \\ref{eq_SZ}.\n\nThe $\\Delta T_{SZ} (\\theta)$ profiles are then convolved by\nconsidering three different instrumental beam profiles modeled as a\nGaussian, corresponding to large single dishes (SPT or ACT) with\n$FWHM=1$ arcmin, medium size telescopes (MITO or OLIMPO) with\n$FWHM=4.5$ arcmin, and small apertures (Planck) with $FWHM=7$\narcmin.\n\nThe errors associated to the convolved $\\Delta T_{SZ}(\\theta)$\nprofiles are treated as only due to instrumental noise. An\noptimistic choice of the sensitivity, for all the observatories, is\n6 $\\mu$K\/beam, corresponding to the Planck channel at 143 GHz\n(\\cite{Planck11c}) assuming the necessary integration time on source\nfor the other experiments. Contaminants are not included in the\nstudy since we wish to assess our ability to extract the mass of the\nclusters under ideal conditions.\n\nTo simulate the missing knowledge of X-ray observational results, we\nignore cluster morphology and assume the most general model for it:\nan isothermal \\textit{beta}-model, that expressed in temperature is\n\n\\begin{equation}\\label{eq_DeltaT_beta}\n\\Delta T_{SZ}=\\Delta\nT_{SZ0}{\\left(1+\\frac{\\theta^2}{\\theta_c^2}\\right)}^{\\frac{1}{2}-\\frac{3}{2}\\beta}.\n\\end{equation}\n\nWe apply the Metropolis Hastings (M-H) algorithm Monte Carlo Markov\nChain (MCMC) procedure to fit this equation (after a convolution\nwith the corresponding instrumental beam) on the simulated profiles\nto extract the parameters $\\Delta T_{SZ0}$, $\\beta$ and $\\theta_c$.\nFor each cluster, at a fixed field of view (\\textit{fov}), we\nanalyze the accepted set of parameters derived by the MCMC\nprocedure. The degeneracy among the extracted \\emph{beta}-model\nparameters affects their uncertainties.\n\n\\begin{figure}\n \n \\includegraphics[width=\\columnwidth]{Contefg2.eps}\\\\\n \\caption{\\itshape Radial profiles of the electron temperature, $T_e$ (top),\n number density, $n_e$ (middle) and pressure, $P_e$ (bottom) for\n the cluster ZW3146. The red dashed curve describes the CC template\n while the black solid curve represents the ISO model (the shadowed regions define\n 1$\\sigma$ uncertainties), with parameters extracted by MCMC analysis\n considering $\\Delta T_{SZ}(\\theta)$ profiles convolved with a beam\n of 7 arcmin (FWHM).}\\label{fig_ne_Te_pe_profiles1}\n\\end{figure}\n\nWe obtain the \\textit{beta}-model parameter set that is most\nconsistent with the $\\Delta T_{SZ}(\\theta)$ profile, given the\nassumed instrumental noise and beam sizes. All the parameters\nresulting from the MCMC analysis are collected in Table\n\\ref{table_neISO} for each cluster and for each \\textit{fov}. Figure\n\\ref{fig_ne_Te_pe_profiles1} shows the electron temperature (top),\nnumber density (middle), and pressure (bottom) profiles for only the\ncluster ZW3146, as an example, of both the original CC ICM and the\nrecovered ISO model. The errors associated to the curves account for\nthe 1$\\sigma$ uncertainties on the parameters.\n\n\\section{Cluster total mass estimation}\\label{sec_total_mass}\n\nWe want to stress the consequences of the assumptions on the ICM\nphysics when we miss X-ray information. A quantity, such as the\ntotal mass $M_{tot}$, can be biased by a different physical state of\nthe ICM (i.e. mergers or cooling flow mechanisms). In particular we\nestimate the mass for both the ICM discussed templates ($CC$ and\n$ISO$), by using the following different approaches:\n\n\\begin{itemize}\n \\item hydrostatic equilibrium assumption for the cluster\n gas (hydrostatic equilibrium, HE);\n \\item gas fraction independence of cluster physical state (fixed gas fraction, FGF);\n \\item $M_{tot}-Y$ scaling relation (scaling law,\n SL), as derived in the standard self-similar collapse scenario.\n\\end{itemize}\n\nThe masses are calculated, in particular, within a fixed integration\nradius $r_{int}$ (aperture radius), which we arbitrarily choose\nequal to the $r_{500}$ values as reported in Morandi et al. (2007,\nsee Table \\ref{table_te}). This choice is motivated by the need to\nfix an aperture radius within which to estimate integrated\nquantities. We point out that $r_{int}$ does not always correspond\nto the same overdensity, due to the different assumed ICM templates.\nIt is clear that, hereafter, results associated to the clusters\nsimulated in this work cannot be considered as describing the true\nICM physics of the observed objects. We choose, however, to maintain\nthe link with the ``native'' cluster in the name ($NAME \\rightarrow\nCl_{NAME}$).\n\n\\subsection{Hydrostatic equilibrium}\n\n\\begin{table*}\n\\begin{minipage}[htbp]{\\textwidth}\n\\caption{Total cluster masses calculated considering the HE and FGF\napproaches.} \\label{table_mass HE_FGF} \\centering\n\\renewcommand{\\footnoterule}{} \n \\begin{tabular}{llcc}\n \\hline\n \n Cluster & ICM template & $M_{tot,HE}$ & $M_{tot,FGF}$\\footnote{derived by $M_{gas}$ assuming\n$f_{gas}=0.1$} \\\\\n & (FWHM fov) & (10$^{14}$ $M_{\\odot}$)& (10$^{14}$ $M_{\\odot}$) \\\\\n \\hline\n \\hline\n A1413 & CC &$3.85\\pm0.77$& $8.73\\pm2.03$\\\\\n $Cl_{A1413}$& ISO (1') &$6.59\\pm0.60$&$6.21\\pm0.35$\\\\\n & ISO (4.5') &$6.67\\pm0.59$&$6.02\\pm0.59$\\\\\n & ISO (7') &$6.66\\pm0.58$&$5.93\\pm0.89$\\\\\n \\hline\n A1689 & CC & $8.96\\pm1.97$&$12.72\\pm2.33$\\\\\n $Cl_{A1689}$& ISO (1') &$13.56\\pm1.41$& $9.88\\pm3.12$\\\\\n & ISO (4.5') &$13.61\\pm1.33$&$9.14\\pm4.08$\\\\\n & ISO (7') &$12.66\\pm1.33$&$8.86\\pm2.01$\\\\\n \\hline\n A1835 & CC & $10.23\\pm2.30$&$12.72\\pm1.18$\\\\\n $Cl_{A1835}$& ISO (1') &$16.21\\pm1.61$& $10.08\\pm0.33$\\\\\n & ISO (4.5') &$16.32\\pm1.55$&$9.13\\pm0.94$\\\\\n & ISO (7') &$15.08\\pm1.40$&$7.99\\pm1.44$\\\\\n \\hline\n A2204 & CC & $12.91\\pm3.30$&$14.84\\pm2.96$\\\\\n $Cl_{A2204}$& ISO (1') &$19.88\\pm3.27$&$10.13\\pm0.24$\\\\\n & ISO (4.5') &$20.33\\pm3.05$&$10.87\\pm0.67$\\\\\n & ISO (7') &$19.89\\pm2.86$&$10.32\\pm1.54$\\\\\n \\hline\n A2261 & CC & $4.79\\pm1.11$&$10.55\\pm3.48$\\\\\n $Cl_{A2261}$& ISO (1') &$7.88\\pm1.09$&$7.80\\pm3.50$\\\\\n & ISO (4.5') &$7.84\\pm1.18$&$7.24\\pm1.22$\\\\\n & ISO (7') &$7.81\\pm1.08$&$6.94\\pm1.09$\\\\\n \\hline\n MS1358.4+6245 & CC & $8.09\\pm1.75$&$10.23\\pm1.65$\\\\\n $Cl_{MS1358.4+6245}$& ISO (1') &$13.27\\pm1.77$&$7.74\\pm0.63$\\\\\n & ISO (4.5') &$13.17\\pm1.88$&$7.27\\pm1.22$\\\\\n & ISO (7') &$11.90\\pm1.51$&$6.12\\pm1.14$\\\\\n \\hline\n RXJ1347.5-1145 & CC & $14.66\\pm3.02$&$32.34\\pm4.05$\\\\\n $Cl_{RXJ1347.5-1145}$& ISO (1') &$24.45\\pm2.49$& $25.04\\pm0.48$\\\\\n & ISO (4.5') &$24.47\\pm2.54$&$22.08\\pm1.30$\\\\\n & ISO (7') &$24.66\\pm2.49$&$21.07\\pm1.84$\\\\\n \\hline\n ZW3146 & CC & $9.06\\pm1.64$&$17.88\\pm1.05$\\\\\n $Cl_{ZW3146}$& ISO (1') &$15.05\\pm0.60$& $13.77\\pm0.58$\\\\\n & ISO (4.5') &$15.14\\pm0.67$&$12.72\\pm1.97$\\\\\n & ISO (7') &$14.44\\pm0.64$&$12.03\\pm2.61$\\\\\n \\hline\n \\end{tabular}\n\\end{minipage}\n\\end{table*}\n\nThe first approach, HE, assumes a spherical symmetry for the\ncluster, so that the hydrostatic equilibrium equation can be written\n(\\cite{Sarazin88}) as\n\n\\begin{equation}\\label{eq_hyd_eq}\n\\frac{dP_{gas}(r)}{dr}=-\\rho_{gas}(r)G\\frac{M_{tot}(10^{14}$ $M_{\\odot}$). Under the assumption of an\nisothermal \\textit{beta}-model, the cluster total mass was derived\napplying three different approaches: the hydrostatic equilibrium\nequation, a fixed gas fraction, and a self-similar $M_{tot}-Y$\nrelation.\n\nAssuming we had no information from X-ray observations, we reported\nthe bias on the derived total mass as dependent on electron gas\ntemperature. Only in the case of hydrostatic equilibrium does this\nbias appear almost constant for the considered clusters in the range\nof 50-80 \\%. Incidentally, we notice that an electron temperature\nvalue exists for which the FGF and SL mass biases vanish. This could\nbe the only case in which a simple isothermal \\textit{beta}-model\naccurately reproduces the mass of CC clusters.\n\nThe large biases on total cluster mass recovery in CC clusters\nrepresent another reason to definitely discard the isothermal\n\\textit{beta}-model for this purpose and to firmly support more\nsophisticated models, with universal pressure profiles (e.g.\n\\cite{Arnaud10}). This is already employed for modeling cluster\natmospheres in almost all the present blind-survey data reduction\n(SPT and Planck), and it is planned in the next future for ACT\nobservations.\n\n\\begin{acknowledgements}\nPart of this work has been supported by funding from Ateneo\n2009-C26A09FTJ7. We thank S. Borgani and the anonymous referee for\ntheir useful comments and suggestions.\n\\end{acknowledgements}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nThe {\\it mapping class group} of a compact connected, possibly with boundary, surface \n$S$, $\\mod_S$, is defined as \nthe group of isotopy\nclasses of homeomorphisms $S\\rightarrow S$. The algebraic investigation\nof that group goes as far back as Dehn and Nielsen, who first showed that the mapping class\ngroup of an orientable surface is embedded into the group of outer automorphisms of the\nfundamental group of $S$, ${\\rm Out}(\\pi_1(S))$. This embedding was later extended to a wider \nclass of 2-orbifolds. Maclachlan and Harvey \\cite{mh} proved that $\\mod_S\\le {\\rm Out}(\\pi_1(S))$ \nfor a surface $S$ obtained from a compact surface of genus $k$ by deleting $s$ points,\n$t$ discs and $r$ marked points. Recently, Fujiwara \\cite{fujiwara} extended the above \nembedding to the case of hyperbolic 2-orbifolds (orientable or non-orientable) with finite volume. \n\nGrossman \\cite{Gr} was the first to show that if $S_k$ is a compact\norientable surface of genus $k$, then $\\mod_{S_k}$\nis residually finite. Recently, Allenby, Kim and Tang \\cite{akt} extended the result\nof Grossman for non-orientable closed surfaces. Ivanov \\cite{ivanov} gave a geometric proof\nof the result of Grossman which seems that can easily be extended to the non-orientable \ncase. The proofs of both results in \\cite{Gr} and \\cite{akt} are using\ncombinatorial group theory arguments. One of the key ingredients of both results\nis the fact that the group of conjugating automorphisms of the fundamental group of the surface\ncoincides with the group of its inner automorphisms. If $G$ is a group, then an automorphism \n$f$ of $G$ is called a {\\it conjugating automorphism} if $f(g)$ is a conjugate of $g$ for \nevery $g\\in G$. The conjugating automorphisms of a group $G$ form a subgroup of the group \nof automorphisms of $G$, ${\\rm Aut}(G)$, which\nwe denote by ${\\rm Conj}(G)$. Moreover, ${\\rm Conj}(G)$ is normal in ${\\rm Aut}(G)$ and \ncontains the subgroup of inner automorphisms of $G$, ${\\rm Inn}(G)$. \n\n\nIn the present note we investigate the residual finiteness of ${\\rm Out}(G)$ for hyperbolic\ngroups $G$. Our study is, as well, based on the investigation of the group of conjugating \nautomorphisms of $G$.\nIn fact, using the powerful geometric ideas developed by Paulin \\cite{Pau} and \nBestvina \\cite{bestvina} in the context of ultralimits of metric spaces, \nwe show that in a hyperbolic group $G$, the quotient group ${\\rm Conj}(G)\/{\\rm Inn}(G)$ is\nalways finite. The main theorem shows that every virtually torsion-free subgroup of the\nouter automorphism group of a conjugacy separable, hyperbolic group is residually\nfinite.\n\nAs a result, we obtain the residual finiteness of the outer automorphism group \nof finitely generated Fuchsian groups and of free-by-finite groups. Consequently the \nmapping class groups of hyperbolic 2-orbifolds with finite volume are \nresidually finite. Hence, we retrieve the results of Grossman and of\nAllenby, Kim and Tang as special cases of our corollaries.\n\nIn an addendum at the end of the paper we show how to generalize the main\nresults for relatively hyperbolic groups. \n\n\n\\section{Main Results}\n\nThe next lemma is what really proved in ~\\cite[Theorem 1]{Gr}. We\nreproduce here the proof for the reader's convenience.\n\n\\begin{lem}\\label{lem:Gr} Let $G$ be a finitely generated, conjugacy separable\ngroup. Then the quotient group ${\\rm Aut}(G)\/{\\rm Conj}(G)$ is residually\nfinite.\n\\end{lem}\n\n\\begin{proof}\nSince $G$ is finitely generated, we can find a family\n$(K_{i})_{i\\in I}$ of characteristics subgroups of finite index in\n$G$ such that for each normal subgroup $N$ of finite index in $G$\nthere is a subgroup $K_{i}$ in the above family, contained in $N$. \nEvery automorphism $f$ of $G$ induces an\nautomorphism $f_{i}$ of $G\/K_{i}$ which acts as a permutation on the\nconjugacy classes of $G\/K_{i}$. Therefore, for each $i\\in I$ we have a\nhomomorphism $\\pi_{i}:{\\rm Aut}(G)\\rightarrow S_{i}$, where $S_{i}$ denotes the\npermutation group on the set of conjugacy classes of the finite group\n$G\/K_{i}$. Since each conjugating automorphism preserves conjugacy\nclasses, the group ${\\rm Conj}(G)$ is contained in the intersection\n$\\bigcap _{i\\in I}$ Ker$(\\pi_{i})$ of the kernels of the $\\pi_{i}$'s.\nNow let $f\\in \\bigcap _{i\\in I}$ Ker$(\\pi_{i})$. Then for every $g\\in G$, \nthe elements $f(g)$ and $g$ are conjugate in each quotient\n$G\/K_{i}$. In particular, the elements $f(g)$ and $g$ are\nconjugate in each finite quotient of $G$ and thus they are\nconjugate in $G$, by the conjugacy separability of $G$. This means\nthat $f$ is a conjugating automorphism of $G$ and hence\n${\\rm Conj}(G)=\\bigcap_{i\\in I}$ Ker$(\\pi_{i})$.\n\\end{proof}\n\nThe following material on ultralimits of metric spaces can be\nfound in detail in the paper of Kapovich and Leeb \\cite{kl}.\n\nA $\\textsl{filter}$ on the set of natural numbers $\\mathbb{N}$ is\na non-empty set $\\omega$ of subsets of $\\mathbb{N}$ with the\nfollowing properties:\n\n\\begin{enumerate}\n\\item The empty set is not contained in $\\omega$.\n\\item $\\omega$ is closed under finite intersections.\n\\item If $A\\in \\omega$ and $A\\subseteq B$, then $B\\in \\omega$.\n\\end{enumerate}\n\nA filter $\\omega$ is called $\\textsl{ultrafilter}$ if it is\nmaximal. An ultrafilter is called \\textsl{non-principal} if it\ncontains the complements of the finite subsets of $\\mathbb{N}$.\nLet $\\omega$ be an ultrafilter on $\\mathbb{N}$ and\n$a:\\mathbb{N}\\rightarrow [0,\\infty)$ a sequence of non-negative real\nnumbers. Then there is a unique point $l$, which we denote by\n$\\textrm{lim}_{\\omega}a_{i}$, in the one-point compactification\n$[0,\\infty]$ of $[0,\\infty)$ such that for each neighborhood $U$\nof $l$, the inverse image $a^{-1}(U)$ of $U$ under $a$ is\ncontained in $\\omega$.\n\nLet $(X_{i},d_{i},x_{i}^{0})_{i\\in \\mathbb{N}}$ be a sequence of\nbased metric spaces and let $\\omega$ be an ultrafilter on\n$\\mathbb{N}$. On the subspace $Y$ of $\\prod_{i\\in\n\\mathbb{N}}X_{i}$ consisting of all sequences $(x_{i})$ for which\n$\\textrm{lim}_{\\omega}d_{i}(x_{i},x_{i}^{0})<\\infty$, we define a\npseudo-metric $d_{\\omega}$ by\n$d_{\\omega}\\big((x_{i}),(y_{i})\\big)=\\textrm{lim}_{\\omega}\\big(d_{i}(x_{i},y_{i})\\big)$.\nThe \\textsl{based ultralimit}\n$(X_{\\omega},d_{\\omega},x_{\\omega})$, where\n$x_{\\omega}=(x_{i}^{0})_{i\\in \\mathbb{N}}$, is the associated\nmetric space.\n\n\\bigskip\n\nLet $X$ be a geodesic metric space and $\\delta$ a non-negative real number.\nThe space $X$ is called {\\it $\\delta$-hyperbolic\\\/} if for every triangle $\\Delta\\subset X$\nwith geodesic sides, each side is contained in the $\\delta$-neighbourhood of the\nunion of the two other sides.\n\nLet $G$ be a finitely generated group and let $X=X(G,S)$ be the Cayley graph of\n$G$ with respect to a finite generating set $S$ for $G$, closed under inverses.\nThe group $G$ is called {\\it $\\delta$-hyperbolic\\\/} if $X$ is a $\\delta$-hyperbolic\nspace with respect to the word metric. \n\n\nIn the next lemma we use various results concerning actions of groups on $\\mathbb R$-trees.\nFor details, we refer the reader \nto the paper of Morgan and Shalen \\cite{ms}.\n\nThe key point of Lemma \\ref{lem:finite} is the following statement.\nAssume that an action of a hyperbolic group $G$ on a real tree $Y$ is obtained \nas a limit of a sequence of actions of $G$ on its Cayley graph $X$, where each \naction in the sequence is the natural action of $G$ on $X$ twisted by an \nautomorphism of $G$. If the conjugacy class of an element $g$ in $G$ is periodic \nunder these automorphisms, then $g$ is elliptic when acting on the limit tree $Y$. \n\n\nAs it was pointed out by the referee, the above statement seems\nto be well known to the experts (see \\cite{bestvina2}). Nonetheless, the\nauthors failed to track down a reference for its proof. So, a proof of it,\nis included in the lemma for the reader's convenience and completeness. \n\n\\begin{lem}\\label{lem:finite} Let $G$ be a hyperbolic group. Then the group \n${\\rm Inn}(G)$ of inner automorphisms of $G$ is of\nfinite index in ${\\rm Conj}(G)$.\n\\end{lem}\n\n\\begin{proof} Suppose to the contrary that ${\\rm Inn}(G)$ is of infinite\nindex in ${\\rm Conj}(G)$. Fix an infinite sequence\n$f_{1},f_{2},\\dots,f_{n},\\dots$ of conjugating automorphisms of\n$G$ representing pairwise distinct cosets of ${\\rm Inn}(G)$ in ${\\rm Conj}(G)$. We\napply the method of Bestvina and Paulin, using ultrafilters, to\nconstruct an $\\mathbb{R}$-tree on which $G$ acts by isometries.\n\nLet $X=X(G,S)$ be the Cayley graph of $G$ with respect to a finite\ngenerating set $S$ closed under inverses. Then $X$ with the associated\nword metric $d$ is a $\\delta$-hyperbolic metric space for some $\\delta\\ge 0$. \nEach $f_{i}$ gives an\naction $\\rho_{i}$ by isometries of $G$ on $X$ by\n$\\rho_i(g,x)=f_{i}(g)x$. The outer automorphism group of a\nvirtually cyclic group is finite. Thus, we may assume that $G$ is\nnon-elementary. In that case the action of $G$ on the boundary\n$\\partial X$ of $X$ is non-trivial and Lemma 2.1 in ~\\cite{Pau}\napplies to any action $\\rho_{i}$, yielding a sequence $x_{i}^{0}$\nof elements of $X$ such that\n\\begin{equation} \\label{eq:min} \\underset{g\\in\nS}{\\textrm{max}}\\,d\\big(x_{i}^{0},f_{i}(g)x_{i}^{0}\\big)\\leq\n\\underset{g\\in S}{\\textrm{max}}\\,d\\big(x,f_{i}(g)x\\big)\n\\end{equation}\nfor all $x$ in $X$. Let $\\lambda_{i}=\\underset{g\\in\nS}{\\textrm{max}}\\,d\\big(x_{i}^{0},f_{i}(g)x_{i}^{0}\\big)$. Then\n\\begin{equation} \\label{eq:trineq}\nd\\big(x_{i}^{0},f_{i}(g)x_{i}^{0}\\big)\\leq \\lambda_{i} \\|g\\|\n\\end{equation}\nfor all $g$ in $G$, where $\\|g\\|$ denotes the word-length of $g$\nwith respect to $S$. If the sequence $(\\lambda_{i})$ contains a\nbounded subsequence, then the argument in ~\\cite[Case 1, p.\n338]{Pau} shows that there are indices $i$ and $j$ with $i\\neq j$\nsuch that the automorphisms $f_{i}$ and $f_{j}$ differ by an inner automorphism\nof $G$ and consequently they give rise to the same coset of ${\\rm Inn}(G)$, \ncontradicting the choice of the $f_{i}$. It\nfollows that\n$\\underset{i\\rightarrow\\infty}{\\textrm{lim}}\\lambda_{i}=\\infty$.\nWe consider the sequence $(X_{i},d_{i},x_{i}^{0})$ of based metric\nspaces, where $X_{i}=X$ and $d_{i}=\\frac{d}{\\lambda_{i}}$. Note\nthat the space $(X_{i},d_{i},x_{i}^{0})$ is\n$\\frac{\\delta}{\\lambda_{i}}$-hyperbolic for all $i$.\nFor any non-principal\nultrafilter $\\omega$ on $\\mathbb{N}$, let\n$(X_{\\omega},d_{\\omega},x_{\\omega})$ be the corresponding based\nultralimit. The fact that the distance $d_{\\omega}(x,y)$ of two\npoints $x=(x_{i})$ and $y=(y_{i})$ of $X_{\\omega}$ is approximated\nby the distances $d_{i}(x_{i},y_{i})$ for infinitely many $i$,\nimplies that $X_{\\omega}$ is a $0$-hyperbolic space (i.e., an\n$\\mathbb{R}$-tree), since $\\underset{i\\rightarrow\n\\infty}{\\textrm{lim}}\\frac{\\delta}{\\lambda_{i}}=0$. The action of\n$G$ on $X_{\\omega}$ is given by\n$g\\cdot(x_{i})=\\big(f_{i}(g)x_{i}\\big)$. Inequality\n~(\\ref{eq:trineq}) ensures that the action is well-defined. We will\nshow that the action of $G$ on $X_{\\omega}$ is trivial (i.e. there\nis a global fixed point).\n\nLet $g$ be an element of $G$ which acts as a hyperbolic isometry\non $X_{\\omega}$, and let $\\t_{\\omega}(g)$ denote its translation\nlength. Fix $x=(x_{i})\\in X_{\\omega}$ such that $x$ lies on the axis of $g$. Then\n$$\\t_{\\omega}(g)=d_{\\omega}(gx,x)=\\left(\\textrm{lim}_{\\omega}\nd_i(f_i(g)x_i,x_i)=\\right)\n\\displaystyle\\frac{d_{\\omega}(g^{n}x,x)}{n}$$ for all positive integers $n$.\nIn particular,\n$d_{\\omega}(gx,x)=\\displaystyle\\frac{d_{\\omega}(g^{2}x,x)}{2}$ and thus\n\\begin{equation}\\label{eq:2}\n\\textrm{lim}_{\\omega}\\Big(2d_{i}\\big(f_{i}(g)x_{i},x_{i}\\big)-d_{i}\n\\big(f_{i}(g)^{2}x_{i},x_{i}\\big)\\Big)\n=0.\n\\end{equation}\nFor each $i$, we fix an element $y_{i}$ of $X$ on which the\ndisplacement function of $f_{i}(g)$ attains its minimum\n$\\t(f_{i}(g))$, i.e.\n$\\t(f_{i}(g))=d\\big(f_{i}(g)y_{i},y_{i}\\big)=\\textrm{inf\\,}\\{d\\big(f_{i}(g)y,y\\big)|\\,y\\in\nX\\}$.\\footnote{The reader should not confuse this with the algebraic translation length of \nthe elements of a group $G$.} \nSince $X$ is a $\\delta$-hyperbolic space, there is a non-negative\nconstant $K(\\delta)$ depending only on $\\delta$ such that\n\\begin{equation}\\label{ineq:a}\nd\\big(f_{i}(g)x_{i},x_{i}\\big)\\geq\n2d(x_{i},y_{i})+\\t(f_{i}(g))-K(\\delta).\n\\end{equation}\nThen,\n\\begin{equation} \\label{ineq:b}\n\\begin{array}{ccl} A & = &\n2d\\big(f_{i}(g)x_{i},x_{i}\\big)-d\\big(f_{i}(g)^{2}x_{i},x_{i}\\big)\\\\\n & \\geq &\n 4d(x_{i},y_{i})+2\\t(f_{i}(g))-2K(\\delta)-d\\big(f_{i}(g)^{2}x_{i},x_{i}\\big)\\\\\n & \\geq & 4d(x_{i},y_{i})+2\\t(f_{i}(g))-2K(\\delta)-2d(x_{i},y_{i})-2\\t(f_{i}(g))\\\\\n & = & 2d(x_{i},y_{i})-2K(\\delta)\\,,\n \\end{array}\\end{equation}\nwhere the second inequality follows from the triangle one. Working\nin a similar way, we see that\n\\begin{equation}\n2d(x_{i},y_{i})-K(\\delta)\\leq\nd\\big(f_{i}(g)x_{i},x_{i}\\big)-\\t(f_{i}(g))\\leq 2d(x_{i},y_{i})\n\\end{equation}\nand hence we can say that \n\\begin{equation}\\label{ineq:d}\n\\big|d\\big(f_{i}(g)x_{i},x_{i}\\big)-\\t(f_{i}(g))\\big|\\leq\n2d(x_{i},y_{i})+K(\\delta).\n\\end{equation}\nConsequently,\n\\[\\begin{array}{ccl}\n\\Big|\\t_{\\omega}(g)-\\frac{\\t(f_{i}(g))}{\\lambda_{i}}\\Big| & \\leq &\n\\Big|\\t_{\\omega}(g)-d_{i}\\big(f_{i}(g)x_{i},x_{i}\\big)\\Big|+\n\\Big|d_{i}\\big(f_{i}(g)x_{i},x_{i}\\big)-\n\\frac{\\t(f_{i}(g))}{\\lambda_{i}}\\Big| \\\\\n & \\leq &\n \\Big|\\t_{\\omega}(g)-d_{i}\\big(f_{i}(g)x_{i},x_{i}\\big)\\Big|+2\\displaystyle\\frac{d(x_{i},y_{i})}\n{\\lambda_{i}}+\\frac{K(\\delta)}{\\lambda_{i}}\\\\\n & \\leq &\n \\Big|\\t_{\\omega}(g)-d_{i}\\big(f_{i}(g)x_{i},x_{i}\\big)\\Big|+\\displaystyle\\frac{|A|}{\\lambda_{i}}+\n 3\\frac{K(\\delta)}{\\lambda_{i}}\\,,\\\\\n\\end{array}\\]\nwhere the second inequality follows from ~(\\ref{ineq:d}) and the third\none from ~(\\ref{ineq:b}). The $\\omega$-limit of each term in\nthe right-hand side of the above inequality is $0$ (for the second term\nsee ~(\\ref{eq:2})). Therefore\n\\[\\t_{\\omega}(g)=\\textrm{lim}_{\\omega}\\frac{\\t(f_{i}(g))}{\\lambda_{i}}=0\\,,\\]\nsince $\\t(f_{i}(g))=\\t(g)$ for all $i$ ($f_{i}$ being a conjugating\nautomorphism). Hence, each element $g$ of the finitely generated\ngroup $G$ acts as an elliptic isometry on $X_{\\omega}$. This means\nthat the action of $G$ on $X_{\\omega}$ has a global fixed point, say $z=(z_i)$. \nIt follows that for every $\\varepsilon>0$ and every\nfinite subset $F$ of $G$, there is an $\\Omega\\in \\omega$ such that\n\\[d_{i}\\big(z_{i},f_{i}(g)z_{i}\\big)=\\frac{d(z_{i},f_{i}(g)z_{i})}{\\lambda_{i}}<\n\\varepsilon,\\] for all $i\\in \\Omega$ and $g\\in F$. This\ncontradicts to the minimality of $\\lambda_{i}$ (see ~(\\ref{eq:min})).\n\\end{proof}\n\nWe should mention here that this is the best possible result in that generality.\nIndeed, as shown by Burnside \\cite{burnside} and subsequently by several other\nauthors (see also Sah \\cite{sah}), there are finite groups that posses non-trivial \nouter conjugating automorphisms\n(known also as outer, class preserving automorphisms). Therefore one can \neasily construct free-by-finite groups with outer conjugating automorphisms by\nconsidering the direct product of the above mentioned finite groups by free groups. \n\nWe are now able to show our main theorem.\n\n\\begin{thm}\\label{theorem}\nLet $G$ be a conjugacy separable, hyperbolic group. Then each\nvirtually torsion-free subgroup of the outer automorphism group\n${\\rm Out}(G)$ of $G$ is residually finite.\n\\end{thm}\n\\begin{proof} It suffices to show that each torsion-free subgroup $H$\nof ${\\rm Out}(G)$ is residually finite. We consider the following short\nexact sequence\n\\[1 \\rightarrow {\\rm Conj}(G)\\big\/{\\rm Inn}(G)\\stackrel{i}{\\rightarrow} {\\rm Aut}(G)\\big\/{\\rm Inn}(G)\\stackrel{\\pi}{\\rightarrow} \n{\\rm Aut}(G)\\big\/{\\rm Conj}(G) \\rightarrow 1\\,.\\]\n By Lemma ~\\ref{lem:finite} the first term is finite. This implies that the \nrestriction of $\\pi$ on $H$\nis a monomorphism. It follows that $H$ is residually finite being\nisomorphic to a subgroup of ${\\rm Aut}(G)\\big\/{\\rm Conj}(G)$, which is\nresidually finite by Lemma ~\\ref{lem:Gr}.\n\\end{proof}\n\n\nIn view of the above theorem, given a group $G$ it is natural to\nseek conditions under which the outer automorphism group ${\\rm Out}(G)$\nof $G$ is virtually torsion-free. Recently, Guirardel and Levitt ~\\cite{gl} have shown that the\nouter automorphism group of a hyperbolic group $G$ is virtually\ntorsion-free, provided that $G$ is virtually torsion-free. Thus, by\nTheorem ~\\ref{theorem}, the outer automorphism group of a\nconjugacy separable hyperbolic group is residually finite, since \na residually finite hyperbolic group is virtually torsion-free. \n\nThe next lemma is more or less\nknown (see ~\\cite{gl,Mc}).\n\n\n\\begin{lem}\\label{lem:vir} Let $G$ be a finitely generated group containing a\nnormal subgroup $N$ of finite index whose center is trivial. If\n${\\rm Out}(N)$ is virtually torsion-free, then so is ${\\rm Out}(G)$.\n\\end{lem}\n\n\\begin{proof} Let ${\\rm Aut}_N(G)$ denote the subgroup of ${\\rm Aut}(G)$\nconsisting of those automorphisms of $G$ which fix $N$ and induce\nthe identity on $G\/N$. The restriction map $\\phi: {\\rm Aut}_N(G) \\rightarrow {\\rm Aut}(N)$ is an\ninjection. Indeed, let $g\\in G$ and $f\\in {\\rm Aut}_N(G)$. Then\n$f(g)=gh_{g}$ for some $h_{g}\\in N$. Suppose now that $f$ is in\nthe kernel of the restriction map. Then for each $h$ in $N$ we\nhave $ghg^{-1}=f(ghg^{-1})=gh_{g}hh_{g}^{-1}g^{-1}$. This implies\nthat $h_{g}$ is in the center of $N$, which is trivial, and\ntherefore $f$ is the identity. From the injectivity of $\\phi$ we\nget $\\phi\\big({\\rm Inn}_N(G)\\big)={\\rm Inn}(N)$, where ${\\rm Inn}_N(G)$ denotes\nthe (normal) subgroup of ${\\rm Inn}(G)$ consisting of all inner\nautomorphisms of $G$ induced by elements of $N$. We conclude that\nthe quotient group ${\\rm Aut}_N(G)\/{\\rm Inn}_N(G)$ embeds into ${\\rm Out}(N)$. In\nparticular, ${\\rm Aut}_N(G)\/{\\rm Inn}_N(G)$ is virtually torsion-free.\n\nNow the restriction of the natural projection $\\pi:{\\rm Aut}(G) \\rightarrow\n{\\rm Out}(G)$ to ${\\rm Aut}_N(G)$ induces a map $\\tilde{\\pi}:{\\rm Aut}_N(G)\/{\\rm Inn}_N(G) \n\\rightarrow {\\rm Out}(G)$. The kernel of $\\tilde{\\pi}$ (which is equal to\n${\\rm Inn}(G)\/{\\rm Inn}_N(G)$) is finite, since $N$ is of finite index in\n$G$, while its image has finite index in ${\\rm Out}(G)$, since ${\\rm Aut}_N(G)$ \nis of finite index in ${\\rm Aut}(G)$, by ~\\cite[Lemma 1]{Mc}. It\nfollows that each finite-index, torsion-free subgroup of ${\\rm Aut}_N(G)\/{\\rm Inn}_N(G)$ \nembeds as a subgroup of finite index in ${\\rm Out}(G)$,\nwhich proves the lemma.\n\\end{proof}\n\n\\begin{cor}\\label{cor:free-by-finite}\nThe outer automorphism group of a finitely generated,\nfree-by-finite group is residually finite.\n\\end{cor}\n\\begin{proof} It is well-known that every finitely generated,\nfree-by-finite group $G$ is hyperbolic. Furthermore, $G$ is\nconjugacy separable by ~\\cite{Dy}. Therefore Theorem\n~\\ref{theorem} applies to $G$. On the other hand, it is also known\nthat the outer automorphism group of a free group is virtually\ntorsion-free. If $G$ is virtually infinite cyclic, then ${\\rm Out}(G)$\nis finite. In the case where $G$ is not virtually cyclic, the\ncenter of a free subgroup of finite index in $G$ is trivial, and\nthe result follows from Lemma ~\\ref{lem:vir}.\n\\end{proof}\n\nRecall that a Fuchsian group is a discrete subgroup of the group of \nisometries of the hyperbolic plane $\\mathbb{H}^{2}$.\n\n\\begin{cor} The outer automorphism group of a finitely generated,\nFuchsian group is residually finite.\n\\end{cor}\n\n\\begin{proof} Every finitely generated Fuchsian group $G$ contains \na normal torsion-free subgroup of finite \nindex, say $N$, which is either a free group or the fundamental group of an\norientable surface group of genus $g\\ge 2$. So $N$ is hyperbolic since it is either free or \nquasi-isometric to $\\H^2$. Moreover $N$ is conjugacy separable \\cite[Theorem 3.3]{stebe} and \nits outer automorphism group ${\\rm Out}(N)$ is virtually torsion-free and so $N$ \nsatisfies the hypotheses of\nTheorem \\ref{theorem}. Hence, ${\\rm Out}(N)$ is residually finite.\n\nIf $N$ is cyclic then $G$ is virtually cyclic and so ${\\rm Out}(G)$ is finite. In all other\ncases, $N$ is a normal subgroup of finite index in $G$ with trivial centre (since it is\na non-cyclic torsion-free hyperbolic group) and so from the proof of \nLemma \\ref{lem:vir} we have that ${\\rm Aut}_N(G)\/{\\rm Inn}_N(G)$ is a subgroup of ${\\rm Out}(N)$. \nHence, every subgroup of ${\\rm Aut}_N(G)\/{\\rm Inn}_N(G)$ is residually finite. But, again from the proof of \nLemma \\ref{lem:vir}, there is a torsion-free subgroup of ${\\rm Aut}_N(G)\/{\\rm Inn}_N(G)$ \nthat embeds as a finite index subgroup \nin ${\\rm Out}(G)$. Therefore, ${\\rm Out}(G)$ is residually finite.\n\\end{proof}\n\n\nThe corollary below generalizes the results of Grossman \\cite{Gr} and of \nAllenby, Kim and Tang \\cite{akt}. Notice that for the exceptional cases of the Torus and\nthe Klein bottle it is easily verified that the result still holds.\n\\begin{cor}\nThe mapping class group $\\mod_S$ of a hyperbolic 2-orbifold $S$ with finite volume\nis residually finite.\n\\end{cor}\n\n\\begin{proof}\nThe fundamental group of a hyperbolic 2-orbifold with finite volume is a\nfinitely generated Fuchsian group. Hence, the proof is an immediate consequence \nof the results of Fujiwara \\cite{fujiwara}, the\nabove corollaries and the fact that subgroups of residually finite groups\nare residually finite. \n\\end{proof}\n\n\n\\subsubsection*{Addendum. Added, October 8, 2005.} Only recently the authors found out that their \nmain result can be\ngeneralized to relatively hyperbolic groups by using \\cite[Theorem\n1.1]{bs}.\n\nRelatively hyperbolic groups were introduced by\nGromov in \\cite{gromov}, in order to generalize notions such as\nthe fundamental group of a complete, non-compact, finite volume \nhyperbolic manifold and to give a hyperbolic version of small \ncancellation theory over free groups by adopting the geometric language \nof manifolds with cusps. \n\nThis notion has been \ndeveloped by several authors and, in particular, various \ncharacterizations of relatively hyperbolic groups\nhave been given (see \\cite{bow,osin} and \\cite{ds} \nand references therein).\n\nWe recall here one of Bowditch's equivalent definitions. A finitely\ngenerated group $G$ is {\\it hyperbolic relative to a\nfamily of finitely generated subgroups\\\/} $\\mathcal{G}$ if $G$ admits a \nproper, discontinuous and isometric action on a proper, hyperbolic \npath metric space $X$ such that $G$ acts on the ideal boundary of \n$X$ as a geometrically\nfinite convergence group and the elements of $\\mathcal G$\nare the maximal parabolic subgroups of $G$.\n\n We should mention here that Farb\n\\cite{farb} introduced a weaker notion of relative hyperbolicity \nfor groups using constructions on the Cayley graph of the groups.\n\n\n\n\n\nExcept of the fundamental groups of hyperbolic manifolds\nof finite volume, another interesting family of relatively \nhyperbolic groups are the fundamental groups of graphs of finitely generated\ngroups with finite edge groups which are hyperbolic relative to the \nfamily of vertex groups (since their action on the Bass-Serre tree \nsatisfies definition 2 in \\cite{bow}).\n\nThe key lemma of the paper, Lemma 2.2, generalizes to\nrelatively hyperbolic groups.\n\n\\setcounter{section}{2} \\setcounter{thm}{1}\n\n\\begin{lem}\\hskip -.2cm{$\\mathbf '$}\nLet $G$ be a relatively hyperbolic group. Then the group\nInn$(G)$ of inner automorphisms of $G$ is of finite\nindex in Conj$(G)$.\n\\end{lem}\n\n\\noindent {\\it Sketch of proof.} In this case the Cayley graph\nof $G$ is replaced by the $\\d$-hyperbolic metric space $X$ on\nwhich $G$ acts by isometries.\n\nLet $\\l_i=\\inf\\limits_{x\\in X}\\max\\limits_{s\\in S} d(x,f_i(s)x)$\nwhere $S$ is a fixed finite generating set $G$ and let $x_i^0\\in X$\nsuch that $\\max\\limits_{s\\in S}d(x_i^0,f_i(s)x_i^0)\\le \\l_i+\\frac 1i$.\n\nBy the proof of Theorem 1.1 in \\cite{bs}, the sequence $\\l_i$\nconverges to infinity. Hence for every non-primitive ultrafilter\n$\\omega$ on $\\mathbb N$ the based ultralimit $(X_{\\omega},d_{\\omega},x_{w}^0)$ of the\nsequence of based metric spaces $(X,\\frac{d}{\\l_i},x_i^0)$ is an\n$\\mathbb R$-tree. Moreover, there is an induced non-trivial isometric\n$G$-action on $(X_{\\omega},d_{\\omega},x_{w}^0)$, given by $g\\cdot\nx_i=f_i(g)x_i$.\n\nFollowing step-by-step the proof of Lemma 2.2 we arrive again at the\ncontradiction that the action has a global fixed point.\n\nThe reader should be careful in the following.\n\\begin{enumerate}\n\\item The elements $y_i$ of $X$ are chosen such that\n$$\\t(f_i(g))\\le d(f_i(g)y_i,y_i) \\le \\t(f_i(g))+\\frac 1i.$$\n\\item The inequalities (5), (6) and (7) become\n$$ A\\ge 2d(x_i,y_i)-2K(\\d) -\\frac 2i \\eqno (5') $$\n$$ 2d(x_i,y_i)-K(\\d) \\le d(f_i(g)x_i,x_i)-\\t(f_i(g))\\le 2d(x_i,y_i)+\\frac 1i \\eqno (6')$$\n$$ \\textrm{and} \\quad |d(f_i(g)x_i,x_i)-\\t(f_i(g))| \\le 2d(x_i,y_i)+K(\\d)+\\frac 1i, \\eqno (7')$$\n\\noindent respectively.\n\n\\item Finally, the last inequality becomes\n$$\\left|\\t_{\\omega}(g) - \\frac{\\t(f_i(g))}{\\l_i}\\right|\\le\n\\left| \\t_{\\omega}(g)- d_i(f_i(g)x_i,x_i)\\right|+\\frac{|A|}{\\l_i}+3\\frac{K(\\d)}{\\l_i}+\\frac{3}{i\\l_i}.$$\n\\end{enumerate}\n\\hfill$\\Box$\n\nConsequently, Theorem 2.3 generalizes as follows.\n\n\\setcounter{thm}{2}\n\\begin{thm}\\hskip -.2cm{$\\mathbf '$}\nLet $G$ be a conjugacy separable, relatively hyperbolic group.\nThen each virtually torsion-free subgroup of the outer\nautomorphism group {\\rm Out}$(G)$ of $G$ is residually finite.\n\\end{thm}\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction} \n\nThe Thomas-Ehrman Level Displacement formalism (TELD)~\\cite{bib:ehr51,bib:tho52} is an established technique for calculating the level displacement between mirror pairs. It \nis found to be particularly useful in situations where a reaction proceeds via a proton resonant state in a proton-rich nucleus.\nLargely, this usefulness derives from the fact that such states are above the particle decay threshold, usually resulting in proton partial\nwidths too narrow to be measured experimentally. Thus, by appealling to the charge symmetry of the nuclear force, one may make use of\nrelatively abundant spectroscopic data of analogue states in the mirror nucleus to determine the properties of the astrophysically interesting states. \nExamples in the literature are the $^{21}$Ne-$^{21}$Na~\\cite{bib:mar57}, $^{20}$F-$^{20}$Na~\\cite{bib:lan86}, \n$^{18}$O-$^{18}$Ne~\\cite{bib:wie88}, $^{22}$Ne-$^{22}$Mg~\\cite{bib:rui03} and $^{46}$Ti-$^{46}$Cr~\\cite{bib:hor02,bib:he07} mirror nuclear pairs.\nHowever, a survey of the literature finds inconsistency in the definition of critical parameters, leading to errors in the calculations. \nIn the present work a complete and consistent TELD formalism is presented and made available for wider use. \n\n\\section{The Wave Function}\nHere we reproduce and expand upon the original work of Thomas~\\cite{bib:tho52}, using, for consistency, exactly the same terminology. The channel radius\nof two interacting bodies is defined as $a_c=1.44\\times(A_{1c}^{1\/3}+A_{2c}^{1\/3})$ fm, with $A_{1c}$ and $A_{2c}$ being the mass numbers of the bodies\nof the pair; the reduced mass is $M_c= A_{1c} A_{2c}\/(A_{1c}+A_{2c})$; the energy of relative motion is $\\epsilon_c$, which may be positive or negative.\nThe subscript $c$ is used to describe all of the features of the channel, unless it is necessary to distinguish the positive-energy ($\\epsilon_{c+}>0$)\nfrom the negative-energy ($\\epsilon_{c-}<0$) channels in which case the symbols $c+$ and $c-$ are used, respectively.\n\nFor external wave functions, a radial factor (Equ. 1 of ref~\\cite{bib:tho52}) may be written that satisfies the wave equation\n\\begin{equation}\n\\overline{F}_c^{\\prime\\prime} + (2M_c\/\\hbar^2)(\\epsilon_c - \\mho_c)\\overline{F}_c = 0,\n\\label{eq:1}\n\\end{equation}\nwhere a prime signifies differentiation with respect to $r$ (in the following descriptions, all the derivatives are with respect to $r$ unless stated\notherwise). The interaction potential may be written\n\\begin{equation}\n\\mho_c = Z_{1c}Z_{2c}e^2r_c^{-1} + (\\hbar^2\/2M_c)\\ell(\\ell+1)r_c^{-2},\n\\label{eq:2}\n\\end{equation}\nwhere the nuclear potential term disappears in the external region. In the notation of Yost, Wheeler, and Breit~\\cite{bib:yos36}, the positive-energy\nsolution, which is regular at the origin, is designated by ${F(kr)}$ and has the asymptotic form for large $r$,\n\\begin{equation}\nF_{c+} \\thicksim \\sin(x-\\frac{1}{2}\\ell \\pi -\\eta \\mathrm{ln}2x+ \\sigma).\n\\label{eq:3}\n\\end{equation}\nLikewise, there is a solution which is linearly independent of $F$ and irregular at the origin which is conveniently taken with the asymptotic form\nfor large $r$,\n\\begin{equation}\nG_{c+} \\thicksim \\cos(x-\\frac{1}{2}\\ell \\pi -\\eta \\mathrm{ln}2x+ \\sigma).\n\\label{eq:4}\n\\end{equation}\nThe quantities entering Equ.~\\ref{eq:3} and~\\ref{eq:4} are\n\\begin{eqnarray}\nx_{c\\pm} & = & kr \\nonumber \\\\\nk_{c\\pm} & = & p\/\\hbar =(2M_c|\\epsilon|\/\\hbar^2)^{1\/2} \\nonumber,\n\\end{eqnarray}\nwith Sommerfeld parameter\n\\begin{eqnarray}\n\\eta_{c\\pm}=M_cZ_{1c}Z_{2c}e^2\/\\hbar^2k =Z_{1c}Z_{2c}e^2\/\\hbar v \\nonumber,\n\\end{eqnarray}\nand\n\\begin{eqnarray}\n\\sigma_{c+}=\\mathrm{arg}\\Gamma(1+\\ell+i\\eta) \\nonumber.\n\\end{eqnarray}\nIt is worth noting that $x$ is replaced with $\\rho$ in some formulations.\n\nThe general solution of this equation, $\\overline{F}(r)$, is a linear combination of $F$ and $G$. The Wronskian relation for these two particular\nsolutions, which directly follows from Equ.~\\ref{eq:1}-~\\ref{eq:4} is\n\\begin{equation}\nF^\\prime G-G^\\prime F=k_{c+}.\n\\label{eq:5}\n\\end{equation}\nExtensive tables~\\cite{bib:blo50} and several computer codes~\\cite{bib:bar74,bib:bar82,bib:sea02} have been developed for evaluating $F$ and $G$ and\ntheir derivatives when $\\eta>0$.\n\nFor the $c-$ channels, only the solution to Equ.~\\ref{eq:1}, vanishing at large distances from the origin, can occur; it is the\nWhittaker function~\\cite{bib:whi35,bib:mag48},\n\\begin{widetext}\n\\begin{eqnarray}\nW_{-\\eta,\\ell +\\frac{1}{2}}(2x_{c-})=\n\\frac{e^{-x-\\eta \\mathrm{ln}2x}}{\\Gamma (1+\\ell+\\eta)}\\int_0^\\infty t^{\\ell+\\eta}e^{-t}\\left (1+\\frac{t}{2x} \\right) ^{\\ell-\\eta}dt.\n\\label{eq:8}\n\\end{eqnarray}\n\\end{widetext}\nWhittaker function and its derivative may be accurately calculated using the {\\tt whittaker\\_w}~\\cite{bib:nob04} computer code.\nHowever, it is useful to note that if there is no Coulomb interaction in a $c-$ channel, one has from Equ.~\\ref{eq:8} for $s$, $p$, $d$, and $f$\norbitals the simpler relations\n\\begin{eqnarray}\n& & W_{0,\\frac{1}{2}}(2x)=e^{-x} \\label{eq:9} \\\\\n& & W_{0,\\frac{3}{2}}(2x)=(1+x^{-1})e^{-x} \\label{eq:10} \\\\\n& & W_{0,\\frac{5}{2}}(2x)=(1+3x^{-1}+3x^{-2})e^{-x} \\label{eq:11} \\\\\n& & W_{0,\\frac{7}{2}}(2x)=(1+6x^{-1}+15x^{-2}+15x^{-3})e^{-x} \\label{eq:12}\n\\end{eqnarray}\nwhich can be used for checking the results from a more complicated code.\n\nIn discussing conditions at the nuclear surface, one needs to evaluate the real and imaginary parts of the logarithmic derivatives,\n$g_c=E^\\prime\/E$, and these are \\cite{bib:tho52},\n\\begin{eqnarray}\n& & g_{c+}^{Re}=(FF^\\prime+GG^\\prime)(F^2+G^2)^{-1} \\label{eq:13} \\\\ \n& & g_{c+}^{Im}=k(F^2+G^2)^{-1} \\label{eq:14} \\\\\n& & g_{c-}^{Re}=W^\\prime W^{-1} \\label{eq:15} \\\\\n& & g_{c-}^{Im}=0 \\label{eq:16} \n\\end{eqnarray}\nwhere $g_c=g^{Re}+\\mathrm{i}g^{Im}$ and $r_c=a_c$. Although the simple WKB approximation \\cite{bib:tho52,bib:mar57} can perform well in calculating the\nlogarithmic derivatives of the Coulomb and Whittaker functions in specified regions, modern computer codes perform essentially exact calculations and are preferred.\nFor example, the difference between the WKB approximation and the exact evaluation of $g_{c-}$, performed using the code {\\tt whittaker\\_w}~\\cite{bib:nob04},\nin the region 0.1$ \\overline{n} - n$, which bounds the extra\nnumber of intervals needed in the scheme caused by missing Hadamard matrices.\nFor $n \\leq 10000$, $\\delta_n\/n \\ll 1$ except for the few exceptional values\nof $n$ as a numerical fact.\nFor completeness, we present arguments for $c \\approx 1$ for {\\em arbitrarily\nlarge} $n$ in Appendix~\\ref{sec:largen}. \nThis is based on Paley's construction and the prime number theorem. \nFinally, if Hadamard's conjecture is proven, $\\delta_n \\leq 3$ $\\forall n$.\n\n\\begin{figure}[ht]\n\\begin{center}\n\\mbox{\\psfig{file=cvsnsmall.eps,width=1.7in}\\psfig{file=cvsn.eps,width=1.74in}}\n\\vspace*{2ex}\n\\caption{Plots of $c$ vs $n$, where $cn = \\overline{n} = m_n$ is the minimun\nnumber of time intervals required to perform decoupling or selective\nrecoupling for an $n$-spin system. $c$ for $n \\leq 100$ and $101 \\geq n \\leq\n10000$ are plotted separately.}\n\\label{fig:c}\n\\end{center}\n\\end{figure}\n\n\\section{Conclusion} \n\nWe reduce the problem of decoupling and selective recoupling in heteronuclear\nspin systems to finding sign matrices which is further reduced to finding\nHadamard matrices.\nWhile the most difficult task of constructing Hadamard matrices is not\ndiscussed in this paper, solutions already exist in the literature. \nEven more important is that the connection to Hadamard matrices results in\nvery efficient schemes.\n\nSome properties of the scheme are as follows. \nFirst of all, the scheme is optimal in the following sense. \nThe rows of Hadamard matrices and their negations form the codewords of first\norder Reed-Muller codes, which are {\\em perfect\ncodes}~\\cite{vanLint92,MacWilliams77}.\nIt follows that, for each Hadamard matrix, it is impossible to add an extra\nrow which is orthogonal to all the existing ones.\nTherefore, for a given $n$, $m_n = \\overline{n}$ is in fact the minimum number\nof time intervals necessary for decoupling or recoupling, if one restricts \nto the class of pulse sequences considered.\nSecond, the scheme applies for arbitrary duration of the time intervals. \nThis is a consequence of the commutivity of all the terms in the hamiltonian,\nwhich in turn comes from the large separations of the Zeeman frequencies\ncompared to the coupling constants. \nSpin systems can be chosen to satisfy this condition.\nFinally, disjoint pairs of spins can couple in parallel. \n\nWe outline possible simplifications of the scheme for systems with restricted\nrange of coupling.\nFor example, a linear spin system with $n$ spins but only $k$-nearest\nneighbor coupling can be decoupled by a scheme for $k$ spins only. \nThe $i$-th row of the $n \\times \\overline{k}$ sign matrix can be chosen to be\nthe $r$-th row of $H(\\overline{k})$, where $i \\equiv r \\bmod k$.\nSelective recoupling can be implemented using a decoupling scheme for $k+1$\nspins. The sign matrix is constructed as in decoupling using\n$H(\\overline{k+1})$ but the rows for the spins to be coupled are chosen to be\nthe $k+1$-th row different from all existing rows~\\cite{kplus1}.\nThis method involving periodic boundary conditions generalizes to other\nspatial structures. The size of the scheme depends on $k$ and the exact \nspatial structure but not on $n$.\n\nThe scheme has several limitations. \nFirst of all, it only applies to systems in which spins can be individually\naddressed by short pulses and coupling has the simplified form given by\nEq.(\\ref{eq:dipolar}).\nThese conditions are essential to the simplicity of the scheme. They can all\nbe satisfied if the Zeeman frequencies have large separations.\nSecond, generalizations to include couplings of higher order than bilinear\nremain to be developed.\nFurthermore, in practice, RF pulses are inexact and have finite\ndurations, leading to imperfect transformations and residual errors.\n\nThe present discussion is only one example of a more general issue, that the\nnaturally occuring hamiltonian in a system does not directly give rise to\nconvenient quantum logic gates or other computations such as simulation of\nquantum systems~\\cite{Terhal98}.\nEfficient conversion of the given system hamiltonian to a useful form is\nnecessary and is an important challenge for future research.\n\n\\section{Acknowledgments}\n\nThis work was supported by DARPA under contract DAAG55-97-1-0341 and Nippon\nTelegraph and Telephone Corporation (NTT). D.L. acknowledges support of an\nIBM Co-operative Fellowship. We thank Hoi-Fung Chau, Kai-Man Tsang, Hoi-Kwong\nLo, Alex Pines, Xinlan Zhou and Lieven Vandersypen for helpful comments.\n\n \n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzleov b/data_all_eng_slimpj/shuffled/split2/finalzzleov new file mode 100644 index 0000000000000000000000000000000000000000..bbdcba9ff71f35c685a80ec5bd7d8c2a1597d1b8 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzleov @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\label{sec:introduction}\n\nA key element in the development of machine learning methods is the exploitation of the underlying structure of the data through appropriate architectures.\nFor example, convolutional neural networks make use of local, translation-invariant correlations in image-like data and compile them into characteristic features \\cite{Hinton, Ciresan, ILSVRC2012, He2015, deOliveira:2015xxd, Aurisano:2016jvx, Komiske:2016rsd, Kasieczka:2017nvn, Erdmann:2017str, Shilon:2018xlp, Delaquis:2018zqi, Adams:2018bvi}.\n\nIn particle physics, characteristic features are used to identify particles, to separate signal from background processes, or to perform calibrations.\nThese features are usually expressed in manually engineered variables based on physics reasoning.\nAs input for machine learning methods in particle physics, these so-called high-level variables were in many cases superior to the direct use of particle four momenta, often referred to as low-level variables.\nRecently, several working groups have shown that deep neural networks can perform better if they are trained with both specifically constructed variables and low-level variables \\cite{Baldi2014:exotics, Baldi2015:higgs, Adam2015:kaggle, Guest2016:jetflavor, Baldi:2016fql, Shimmin:2017PhRvD, Louppe:2017ipp, Datta:2017rhs, Erdmann:ttH2017, Butter:2017cot, deOliveira:2017pjk, Stoye:2017, Sirunyan:2018mvw}.\nThis observation suggests that the networks extract additional useful information from the training data.\n\nIn this paper, we attempt to autonomize the process of finding suitable variables that describe the main characteristics of a particle physics task.\nThrough a training process, a neural network should learn to construct these variables from raw particle information.\nThis requires an architecture tailored towards the structure of particle collision events.\nWe will present such an architecture here and investigate its impact on the separation power for signal and background processes in comparison to domain-unspecific deep neural networks.\nFurthermore, we will uncover characteristic features which are identified by the network as particularly suitable.\n\nParticle collisions at high energies produce many particles which are often short-lived.\nThese short-lived particles decay into particles of the final state, sometimes through a cascade of multiple decays.\nIntermediate particles can be reconstructed by using energy-momentum conservation when a parent particle decays into its daughter particles.\nBy assigning to each particle a four-vector defined by the particle energy and its momentum vector, the sum of the four-vectors of the daughter particles gives the four-vector of the parent particle.\nFor low-energy particle collisions, bubble chamber images of parents and their daughter particles were recorded in textbook quality.\nEvidently, here the decay angular distributions of the daughter particles are distorted by the movement of the parent particle, but can be recovered in the rest frame of the parent particle after Lorentz transformation.\n\nThe same principles of particle cascades apply to high-energy particle collisions at colliders.\nHere, the particles of interest are, for example, top quarks or Higgs bosons, which remain invisible in the detector due to their short lifetimes, but can be reconstructed from their decay products.\nExploiting the properties of such parent particles and their daughter particles in appropriate rest frames is a key to the search for high-level variables characterizing a physics process.\n\nFor the autonomous search for sensitive variables, we propose a two-stage network, composed of a so-called Lorentz Boost Network (LBN) followed by an application-specific deep neural network (NN).\nThe LBN takes only the four-vectors of the final-state particles as input.\nIn the LBN there are two ways of combining particles, one to create composite particles, the other to form appropriate rest frames.\nUsing Lorentz transformations, the composite particles are then boosted into the rest frames.\nThus, the decay characteristics of a parent particle can be exploited directly.\n\nFinally, characteristic features are derived from the boosted composite particles: masses, angles, etc.\nThe second network stage (NN) then takes these variables as input to solve a specific problem, e.g. the separation of signal and background processes.\nWhile the first stage constitutes a novel network architecture, the latter network is interchangeable and can be adapted depending on the analysis task.\n\nThis paper is structured as follows.\nFirst, we explain the network architecture in detail.\nSecond, we present the simulated dataset we use to investigate the performance of our architecture in comparison to typical deep neural networks.\nThereafter, we review the particles, rest frames, and characteristic variables created by the network to gain insight into what is learned in the training process, before finally presenting our conclusions.\n\n\n\\section{Network architecture}\n\\label{sec:architecture}\n\nIn this section we explain the structural concept on which the Lorentz Boost Network (LBN) is based and introduce the network architecture.\n\nThe measured final state of a collision event is typically rather complex owing to the high energies of the particles involved.\nIn principle, all available information is encoded in the particles' four-vectors, but the comprehensive extraction of relevant properties poses a significant challenge.\nTo this end, physicists engineer high-level variables to decipher and factorize the probability distributions inherent to the underlying physics processes.\nThese high-level variables are often fed into machine learning algorithms to efficiently combine their descriptive power in the context of a specific research question.\nHowever, the consistent result of \\cite{Baldi2014:exotics, Baldi2015:higgs, Adam2015:kaggle, Guest2016:jetflavor, Baldi:2016fql, Shimmin:2017PhRvD, Louppe:2017ipp, Datta:2017rhs, Erdmann:ttH2017, Butter:2017cot, deOliveira:2017pjk, Stoye:2017, Sirunyan:2018mvw} is that the combination of both low- and high-level variables tends to provide superior performance.\nThis observation suggests that low-level variables potentially contain additional, useful information that is absent in hand-crafted high-level variables.\n\nThe aim of the LBN is, given only low-level variables as input, to autonomously determine a comprehensive set of physics-motivated variables that maximizes the relevant information for solving the physics task in the subsequent neural network application.\n\\Fig{fig:lbn_arch} shows the proposed two-stage network architecture (LBN+NN) in detail.\nThe first stage is the LBN and constitutes the novel contribution.\nIt consists of several parts, namely the combination of input four-vectors to particles and rest frames, subsequent Lorentz transformations, and the extraction of suitable high-level variables.\nThe second stage can be some form of deep neural network (NN) with an objective function depending on the specific research question.\n\\begin{figure}[h!tbp]\n \\centering\n \\includegraphics[width=1.0\\textwidth]{lbn_architecture.pdf}\n \\caption{\n The two-stage deep neural network architecture consists of the Lorentz Boost Network (LBN) and a subsequent deep neural network (NN).\n In the LBN, the input four-vectors ($E, p_x, p_y, p_z$) are combined in two independent ways before each of the combined particles is boosted into its particular rest frame, which is formed from a different particle combination.\n The boosted particles are characterized by variables which can be, e.g., invariant masses, transverse momenta, pseudorapidities, and angular distances between them.\n These features serve as input to the second network designed to accomplish a particular analysis task.\n }\n \\label{fig:lbn_arch}\n\\end{figure}\n\nThe LBN combines $N$ input four-vectors, consisting of energies $E$ and momentum components $p_x, p_y, p_z$, to create $M$ particles and $M$ corresponding rest frames according to weighted sums using trainable weights.\nThrough Lorentz transformation, each combined particle is boosted into its dedicated rest frame.\nAfter that, a generic set of features is extracted from the properties of these $M$ boosted particles.\n\nExamples of variables that can be reconstructed with this structure include spin-dependent angular distributions, such as those observed during the decay of a top quark with subsequent leptonic decay of the W boson.\nBy boosting the charged lepton into the rest frame of the W boson, its decay angular distribution can be investigated.\nIn more sophisticated scenarios, the LBN is also capable of accessing further properties that rely on the characteristics of two different rest frames.\nAn example is a variable usually referred to as $\\cos(\\theta^*)$, which is defined by the angular difference between the directions of the charged lepton in the W boson's rest frame, and the W boson in the rest frame of the top quark.\nThe procedure of how this variable can be reconstructed in the LBN is depicted in \\Fig{fig:lbn_example}.\n\\begin{figure}[h!tbp]\n \\centering\n \\includegraphics[width=0.7\\textwidth]{lbn_theta_star.pdf}\n \\caption{\n Example of a possible feature engineering in top quark decays addressing the angular distance of the direction of the W boson in the top rest system and the direction of the lepton in the W boson rest system, commonly referred to as $\\cos(\\theta^*)$.\n }\n \\label{fig:lbn_example}\n\\end{figure}\n\nThe number $N$ of incoming particles is to be chosen according to the research question.\nThe number of matching combinations $M$ is a hyperparameter to be adjusted.\nIn this paper we introduce a specific version of the LBN which constructs $M$ particle combinations, and each combination will have its own suitable rest system.\nOther variants are conceivable and will be mentioned below.\n\nIn the following paragraphs we describe the network architecture in detail.\n\n\\subsection*{Combinations}\n\\label{sec:architecture:combinations}\n\nThe purpose of the first LBN layer is to construct arbitrary particles and suitable rest frames for subsequent boosting.\nThe construction is realized via linear combinations of $N$ input four-vectors,\n\\begin{equation}\n X =\n \\begin{bmatrix}\n E_1 & p_{x,1} & p_{y,1} & p_{z,1}\\\\\n E_2 & p_{x,2} & p_{y,2} & p_{z,2}\\\\\n \\vdots & \\vdots & \\vdots & \\vdots\\\\\n E_N & p_{x,N} & p_{y,N} & p_{z,N}\n \\end{bmatrix},\n\\end{equation}\nto a number of $M$ particles and rest frames, which are represented by four-vectors accordingly.\nHere, $M$ is a hyperparameter of the LBN and its choice is related to the respective physics application.\nThe coefficients $W$ of all linear combinations $C$,\n\\begin{equation}\n C_{m} = \\sum_{n=1}^N W_{mn} \\cdot X_{n}\n\\end{equation}\nwith $m \\in \\left[1, M\\right]$, are free parameters and subject to optimization within the scope of the training process.\nIn the following, combined particles and rest frames are referred to as $C^P$ and $C^R$, respectively.\nTaking both into consideration, this amounts to a total of $2 \\cdot N \\cdot M$ degrees of freedom in the LBN.\nIn order to prevent the construction of objects which would lead to unphysical implications when applying Lorentz transformations, i.e., four-vectors not fulfilling $E > m > 0$, all parameters $W_{mn}$ are restricted to positive values.\nWe initialize the weights randomly according to a half-normal distribution with mean $0$ and standard deviation $1 \/ M$.\nIt should be noted that, in order to maintain essential physical properties and relations between input four-vectors, feature normalization is not applied at this point.\n\n\n\\subsection*{Lorentz transformation}\n\\label{sec:architecture:boost}\n\nThe boosting layer performs a Lorentz transformation of the combined particles into their associated rest frames.\nThe generic transformation of a four-vector $q$ is defined as $q^* = \\Lambda \\cdot q$ with the boost matrix\n\\begin{align}\n \\Lambda &=\n \\begin{bmatrix}\n \\gamma & -\\gamma\\beta n_x & -\\gamma\\beta n_y & -\\gamma\\beta n_z\\\\\n -\\gamma\\beta n_x & 1 + (\\gamma - 1) n_x^2 & (\\gamma - 1) n_x n_y & (\\gamma - 1) n_x n_z\\\\\n -\\gamma\\beta n_y & (\\gamma - 1) n_y n_x & 1 + (\\gamma - 1) n_y^2 & (\\gamma - 1) n_y n_z\\\\\n -\\gamma\\beta n_z & (\\gamma - 1) n_z n_x & (\\gamma - 1) n_z n_y & 1 + (\\gamma - 1) n_z^2\n \\end{bmatrix}.\n \\label{eqn:lambda}\n\\end{align}\nThe relativistic parameters $\\gamma = E \/ m$, $\\vec{\\beta} = \\vec{p} \/ E$, and $\\vec{n} = \\vec{\\beta} \/ \\beta$ are to be derived per rest-frame four-vector $C^R_m$.\n\nThe technical implementation within deep learning algorithms requires a vectorized representation of the Lorentz transformation.\nTo this end, we rewrite the boost matrix in \\Eqn{eqn:lambda} as\n\\begin{align}\n \\Lambda &= I \\, + \\, (U \\oplus \\gamma) \\, \\odot \\, ((U \\oplus 1) \\cdot \\beta \\, - \\, U) \\, \\odot \\, (e \\cdot e^T)\\\\[2mm]\n \\text{with} \\quad U &=\n \\begin{bmatrix}\n -1^{1 \\times 1} & 0^{1 \\times 3}\\\\\n 0^{3 \\times 1} & -1^{3 \\times 3}\n \\end{bmatrix},\n \\quad e =\n \\begin{bmatrix}\n 1^{1 \\times 1}\\\\\n -\\vec{n}^{3 \\times 1}\n \\end{bmatrix},\n\\end{align}\nand the $4 \\times 4$ unit matrix $I$.\nThe operators $\\oplus$ and $\\odot$ denote elementwise addition and multiplication, respectively.\nThis notation also allows for the extension by additional dimensions to account for the number of combinations $M$ and an arbitrary batch size.\nAs a result, all boosted four-vectors $B$ are efficiently computed by a single, broadcasted matrix multiplication, $B = \\Lambda^R \\cdot C^P$.\n\nWhile the description above focuses on a pairwise mapping approach, i.e., particle $C^P_m$ is boosted into rest frame $C^R_m$, other configurations are conceivable:\n\\begin{itemize}\n \\item\n The input four-vectors are combined to build $M$ particles and $K$ rest frames.\n Each combined particle is transformed into all rest frames, resulting in $K \\cdot M$ boosted four-vectors.\n\n \\item\n The input four-vectors are combined only to build $M$ particles, which simultaneously serve as rest frames.\n Each combined particle is transformed into all rest frames derived from the other particles, resulting in $M^2 - M$ boosted four-vectors.\n\\end{itemize}\nThe specific advantages of these configurations can, in general, depend on aspects of the respective physics application.\nResults of these variants will be the target of future investigations.\n\n\n\\subsection*{Feature extraction}\n\\label{sec:architecture:features}\n\nFollowing the boosting layer, features are extracted from the Lorentz transformed four-vectors, which can then be utilized in a subsequent neural network.\nFor the projection of $M \\times 4$ particle properties into $F \\times 1$ features, we employ a distinct yet customizable set of generic mappings.\nThe autonomy of the network is not about finding entirely new features, but rather about factorizing probability densities in the most effective way possible to answer a scientific question.\nTherefore, we let the LBN network work autonomously to find suitable particle combinations and rest frames which enable this factorization, but then use established particle characterizations.\n\nWe differentiate between two types of generic mappings:\n\\begin{enumerate}\n \\item\n Single features are extracted per boosted four-vector.\n Besides the vector elements ($E$, $p_x$, $p_y$, $p_z$) themselves, derived features such as mass, transverse and longitudinal momentum, pseudorapidity, and azimuth can be derived.\n\n \\item\n Pairwise features are extracted for all pairs of boosted four-vectors.\n Examples are the cosine of their spatial angular difference, their distance in the $\\eta-\\phi$ plane, or their distance in Minkowski space.\n In contrast to single features, pairwise features introduce close connections among boosted four-vectors and, by means of backpropagation, between trainable combinations of particles and rest frames.\n\\end{enumerate}\nThe set of extracted features is concatenated to a single output vector.\nProvided that the employed batch size is sufficiently large, batch normalization with floating averages adjusted during training can be performed after this layer \\cite{batch_norm}.\n\n\n\\subsection*{Subsequent problem-specific application}\n\\label{sec:architecture:application}\n\nIn the previous sections, we described the LBN as shown in the left box in \\Fig{fig:lbn_arch}, namely how input vectors are combined and boosted into dedicated rest frames, followed by how features of these transformed four-vectors are compiled.\nThese features are intended to maximize the information content to be exploited in a subsequent, problem-specific deep neural network NN as shown in the right box in \\Fig{fig:lbn_arch}.\n\nThe objective function of the NN defines the type of learning process.\nWeight updates are performed as usual through backpropagation.\nThese updates apply to the weights of the subsequent network as well as to the trainable weights of the combination layer in the LBN.\n\nIn the following, we evaluate how well the autonomous feature engineering of the compound LBN+NN network operates.\nWe compare its performance with only low-level information to that of typical, fully-connected deep neural networks (DNNs).\nWe alternatively supply the DNNs with low-level information, sophisticated high-level variables (see \\Apx{sec:appendix}), and the combination of both.\n\n\n\\section{Simulated datasets}\n\\label{sec:simulations}\n\nThe Pythia $8.2.26$ program package \\cite{Pythia} was used to simulate $\\ttH$ and $\\ttbb$ events.\nExamples of corresponding Feynman diagrams are shown in \\Fig{fig:feynman}.\nThe matrix elements contain angular correlations of the decay products from heavy resonances.\nBeam conditions correspond to LHC proton-proton collisions at $\\sqrt{s} = 13$\\,TeV.\nOnly the dominant gluon-gluon process is enabled and Higgs boson decays into bottom quark pairs are favored.\nHadronization is performed with the Lund string fragmentation model.\n\\begin{figure}[h!tbp]\n \\centering\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.7\\textwidth]{feynman_ttH.pdf}\n \\caption{}\n \\label{fig:feynman_ttH}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.7\\textwidth]{feynman_ttbb.pdf}\n \\caption{}\n \\label{fig:feynman_ttbb}\n \\end{subfigure}\n \\caption{Example Feynman diagrams of a) $\\ttH$ and b) $\\ttbb$ processes.}\n \\label{fig:feynman}\n\\end{figure}\n\nTo analyze a typical final state as observed in an LHC detector, we use the DELPHES package \\cite{deFavereau:2013fsa}.\nDELPHES provides a modular framework designed to parameterize the simulation of a multi-purpose detector.\nIn our study, we chose to simulate the CMS detector response.\nAll important effects such as pile-up, deflection of charged particles in magnetic fields, electromagnetic and hadronic calorimetry, and muon detection systems are covered.\nThe simulated output consists of muons, electrons, tau leptons, photons, jets, and missing transverse momentum from a particle flow algorithm.\nAs the neutrino leaves the detector without interacting, we identify its transverse momentum components from the measurement of missing transverse energy, and we reconstruct its longitudinal momentum by constraining the mass of the leptonically decaying W boson to $80.4$\\,GeV.\n\nFollowing the detector simulation, an event selection with the following criteria was carried out:\nEvents must have at least six jets clustered with the anti-$k_T$ algorithm, implemented in the FastJet package \\cite{Cacciari:2011ma}, with a radius parameter of $\\Delta R = 0.4$.\nA jet is accepted if its transverse momentum fulfills $p_t > 25$\\,GeV and its absolute pseudorapidity is within $|\\eta| < 2.4$.\nFurthermore, exactly one electron or muon with $p_t > 20$\\,GeV and $|\\eta| < 2.1$ is required.\nEvents with further leptons are rejected to focus solely on semi-leptonic decays of the $\\ttbar$ system.\nFinally, a matching is performed between the final-state partons of the generator and the jets of the detector simulation with the maximum distance $\\Delta R = 0.3$, whereby the matching must be unambiguously successful for all partons.\nFor $\\ttH$ events, the combined selection efficiency was measured as $2.3$\\,\\%.\n\nFor $\\ttbb$ processes, further measures are taken to identify the two additional bottom quark jets.\nThe definition is an adaption of \\cite{Sirunyan:2018mvw}.\nAt generator level, the anti-$k_T$ algorithm with a radius parameter of $\\Delta R = 0.4$ is used to cluster all stable final-state particles.\nIf a generator jet is found to originate from one of the top quarks or W boson decays, or if its transverse momentum is below a threshold of $20$\\,GeV, it is excluded from further considerations.\nFor each remaining jet, we then count the number of contained bottom hadrons using the available hadronization and decay history.\nAn event is a $\\ttbb$ candidate if at least two generator jets contain one or more distinct bottom hadrons.\nFinally, a matching is performed between the four quarks resulting from the $\\ttbar$ decay and the two identified generator jets on the one hand, and the selected, reconstructed jets on the other hand.\nSimilar to $\\ttH$, a $\\ttbb$ event is accepted if all six generator-level objects are unambiguously matched to a selected jet.\nThis leaves a fraction of $0.02$\\,\\% of the generated $\\ttbb$ events.\n\nA total of $10^6$ events remain, consisting of an evenly distributed number of $\\ttH$ and $\\ttbb$ events, which are then divided into training and validation datasets at a ratio of $80:20$.\n\nFor further analysis, a distinct naming scheme for reconstructed jets is introduced that is inspired by the semi-leptonic decay characteristics of the $\\ttbar$ system.\nThe two light quark jets of the hadronically decaying W boson are named $\\qi$ and $\\qii$, where the index $1$ refers to the jet with the greater transverse momentum.\nThe bottom jet that is associated to this W boson within a top quark decay is referred to as $\\bhad$.\nAccordingly, the bottom quark jet related to the leptonically decaying W boson is referred to as $\\blep$.\nThe remaining jets are named $\\bi$ and $\\bii$, and transparently refer to the decay products of the Higgs boson for $\\ttH$, or to the additional b quark jets for $\\ttbb$ events.\n\nIn \\Fig{fig:dataset} we show, by way of example, distributions of the generated $\\ttH$ and $\\ttbb$ events.\nIn \\Fig{fig:dataset-a} we compare the transverse momentum of the jet $\\bi$, and in \\Fig{fig:dataset-b} the largest difference in pseudorapidity between a jet and the charged lepton.\nIn \\Fig{fig:dataset-c} we show the invariant mass of the closest jet pair, and in \\Fig{fig:dataset-d} the event sphericity.\n\\begin{figure}[h!tbp]\n \\centering\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{dataset_bj1_pt.pdf}\n \\caption{}\n \\label{fig:dataset-a}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{dataset_jet_lep_max_abs_deta.pdf}\n \\caption{}\n \\label{fig:dataset-b}\n \\end{subfigure}\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{dataset_jet_closest_pair_mass.pdf}\n \\caption{}\n \\label{fig:dataset-c}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{dataset_sphericity.pdf}\n \\caption{}\n \\label{fig:dataset-d}\n \\end{subfigure}\n \\caption{\n Exemplary comparisons of kinematic distributions of $\\ttH$ and $\\ttbb$ events; a)~transverse momentum of the jet $\\bi$, b)~largest difference in pseudorapidity of a jet to the charged lepton, c)~invariant mass of the closest jet pair, d)~event sphericity.\n }\n \\label{fig:dataset}\n\\end{figure}\n\n\n\\section{Benchmarks of network performance}\n\\label{sec:performance}\n\nAs a benchmark, the LBN is utilized in the classification task of distinguishing a signal process from a background process.\nIn this example, we use the production of a top quark pair in association with a Higgs boson decaying into bottom quarks ($\\ttH$) as a signal process, and top quark pairs produced with $2$ additional bottom jets ($\\ttbb$) as a background process.\nIn both processes, the final state consists of eight four-vectors, four of which represent bottom quark jets, two describe light quark jets, one denotes a charged lepton, and one a neutrino.\n\nWithin the benchmark, the LBN competes against other frequently used deep neural network setups, which we refer to as DNN here.\nFor a meaningful comparison, we perform extensive searches for the optimal hyperparameters in each setup.\nAs explained above, the LBN consists of only very few parameters to be trained in addition to those of the following neural network (LBN+NN).\nStill, by varying the number $M$ of particle combinations to be constructed from the eight input four-vectors, and by varying the number $F$ and types of generated features, we performed a total of $346$ training runs of the LBN+NN setup, and a similar amount for the competing DNN.\nThe best performing architectures are listed in \\Apx{sec:appendix}.\n\nThe LBN network exclusively receives the four-vectors of the eight particles introduced above, which shall be denoted by 'LBN low' in the following.\nFor the networks marked with DNN we use three variants of inputs.\nIn the first variant, the DNN receives only the four-vectors of the eight particles ('DNN-low').\nFor the second variant, physics knowledge is applied in the form of $26$ sophisticated high-level variables which are inspired by \\cite{Sirunyan:2018mvw} and incorporate comprehensive event information ('DNN-high').\nA list of these variables, such as the event sphericity \\cite{event_shape_variables} and Fox-Wolfram moments \\cite{fox_wolfram_moments}, can be found in the \\Apx{sec:appendix}.\nIn the third variant, we provide the DNNs with both low-level and high-level variables as input ('DNN-combined').\n\nIn the first set of our benchmark tests, the task of input particle identification is bypassed by using the generator information in order to exclusively measure the performance of the LBN.\nJets associated to certain quarks through matching are consistently placed at the same position of the $N$ input four-vectors.\nWe quantify how well the event classification of the networks performs with the integral of the receiver operating characteristic curve (ROC AUC).\n\n\\Fig{fig:perf_comparison_a} shows the performance of all training runs involved in the hyperparameter search.\nThe best training is marked with a horizontal line, while the distribution of results is denoted by the orange and blue shapes which manifestly depend on the structure of the scanned hyperparameter space.\nFor the LBN, the training runs achieve stable results since there are only minor variations in the resulting ROC AUC.\n\\begin{figure}[h!tbp]\n \\centering\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{training_distributions_best.pdf}\n \\caption{}\n \\label{fig:perf_comparison_a}\n \\end{subfigure}%\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{training_distributions_reco.pdf}\n \\caption{}\n \\label{fig:perf_comparison_b}\n \\end{subfigure}\n \\caption{\n Benchmarks of the LBN performance in comparison to typical deep neural network architectures (DNNs) with three variants of input variables;\n low=four-vectors, high=physics-motivated variables, combined=low+high.\n Ordering of the input four-vectors is done by a) generator information and b) jet transverse momenta.\n }\n \\label{fig:perf_comparison}\n\\end{figure}\n\nAlso shown are the training runs of the three input variants for the DNNs.\nIf the eight input four-vectors are ordered according to the generator information, the 'DNN low' setup already achieves results equivalent to the combination of low-level and high-level variables ('DNN combined').\nHowever, both setups are unable to match the 'LBN low' result.\nNote that high-level variables are mostly independent of the input ordering by construction, and hence contain reduced information compared to the ordered four-vectors.\nConsequently, the weaker performance of the 'DNN high' is well understood.\n\nIn the second set of our benchmark test, we disregard generator information and instead sort the six jets according to their transverse momenta, followed by the charged lepton and the neutrino.\nThis order can be easily established in measured data.\n\\Fig{fig:perf_comparison_b} shows that this alternative order consistently reduces the ROC AUC score of all networks.\n\n\nThe training runs of the LBN again show stable results with few exceptions and achieve the best performance in separating events from signal and background processes.\nFor the DNNs, the above-mentioned hierarchy emerges, i.e., the best results are achieved using the combination of high- and low-level variables, followed by only high-level variables with reasonably good results,\nwhereas the DNN exhibits the weakest performance with only low-level information.\n\nOverall, the performance of the compound LBN+NN structure based on the four-vector inputs is quite convincing in this demanding benchmark test.\n\n\n\\section{Visualizations of network predictions}\n\\label{sec:insights}\n\nSeveral components of the LBN architecture have a direct physics interpretation.\nTheir investigation can provide insights into what the network is learning.\nIn the following, we investigate only the best network trained on the generator-ordered input in order to establish which of the quark and lepton four-vectors are combined.\n\nThe number of particle combinations to create is a hyperparameter of the architecture.\nFor the generator ordering, we obtain the best results for $13$ particles.\nThis matches well with our intuition since the Feynman diagram also contains $13$ particles in the cascade.\nIn total we extract $F = 143$ features in the LBN that serve as input to the subsequent deep neural network NN (\\Fig{fig:lbn_arch}).\nFor details refer to the \\Apx{sec:appendix}.\n\nIn our particular setup, each combined particle has a separate corresponding rest frame.\nAs a consequence of demanding the second network to solve a research question, the decisions about which four-vectors are combined for the composite particle and which for its rest frame are strongly correlated.\nA correlation also exists between all $13$ systems of combined particles and their rest frames from the pairwise angular distances exploited by the LBN as additional properties.\n\nWe start by looking at the weights used to combine the input four-vectors to form the particle combinations and rest frames.\n\n\n\\subsection*{Weight matrices of particle and rest-frame combinations}\n\n\\Fig{fig:weight_gen_particles_normed} shows the weight matrices of the LBN.\nEach column of the two matrices for combined particles (red) and rest frames (green) is normalized so that the weight of each input four-vector (bottom quark jet $\\bi$ of the Higgs, etc.) is shown as a percentage.\nThe color code reflects the sum of the particle weights for the Higgs boson (or $b\\bar{b}$ system for $\\ttbb$) and the two top quarks, respectively.\n\\begin{figure}[h!tbp]\n \\centering\n \\begin{subfigure}{1\\textwidth}\n \\centering\n \\includegraphics[width=0.65\\textwidth]{weight_particles.pdf}\n \\caption{}\n \\label{fig:weight_gen_particles_normed-a}\n \\end{subfigure}\n \\begin{subfigure}{1\\textwidth}\n \\centering\n \\includegraphics[width=0.65\\textwidth]{weight_restframes.pdf}\n \\caption{}\n \\label{fig:weight_gen_particles_normed-b}\n \\end{subfigure}\n \\caption{\n Particle combinations (red) and their rest frames (green).\n The numbers represent the relative weights of the input particles for each combination $i$.\n The color code reflects the sum of the particle weights for the Higgs boson ($\\ttH$), the $b\\bar{b}$ system ($\\ttbb$), or the two top quarks.\n For better clarity of the presentation, the zero weights are kept white.\n }\n \\label{fig:weight_gen_particles_normed}\n\\end{figure}\n\nNote that combined particles and rest frames are complementary.\nExtreme examples of this can be seen in columns $2$ and $6$, in which one of the two bottom quark jets from the Higgs boson decay was selected as the rest frame and a broad combination of all other four-vector vectors was formed whose properties are exploited for the event classification.\n\nConversely, in column $0$ a Higgs-like combination is transformed into the rest frame of all particles, to which the hadronic top quark makes the main contribution.\nSimilarly, in columns $1$ and $7$, top quark-like combinations are formed and also transformed into rest frames formed by many four-vectors.\nThese types of combinations allow for the Lorentz boost of the center-of-mass system of the scattering process to be nearly compensated.\n\nIt is striking that four-vector combinations forming a top quark are often selected.\nIn \\Fig{fig:correlations}, we quantitatively assess the $13$ possible combinations of the combined particles (red) and the $13$ rest frames (green) in order to determine which combinations are typically formed between the eight input four-vectors.\n\\begin{figure}[htbp]\n \\centering\n \\begin{subfigure}{.49\\textwidth}\n \\centering\n \\includegraphics[width=1\\textwidth]{correlations_particles.pdf}\n \\caption{}\n \\label{fig:correlations-a}\n \\end{subfigure}\n \\begin{subfigure}{.49\\textwidth}\n \\centering\n \\includegraphics[width=1\\textwidth]{correlations_restframes.pdf}\n \\caption{}\n \\label{fig:correlations-b}\n \\end{subfigure}\n \\caption{\n Correlations of quarks and leptons for particle combinations (red) and for rest frames (green).\n Both the numbers and the color code represent the correlation strength.\n }\n \\label{fig:correlations}\n\\end{figure}\n\nIn order to build rest frames (green), the LBN network recognizes combinations leading to the top quarks and treats the bottom quark jets from the Higgs individually.\nThis appears to be an appropriate choice as the top quark pair is the same in $\\ttbb$ and $\\ttH$ events while the two bottom quarks typically originate from gluon splitting or the Higgs decay, respectively.\nFor the hadronic top quark, the W boson ($75$\\,\\%) is often accounted for as well.\nWith the leptonic top, the LBN network considers bottom quark jet and lepton ($91$\\,\\%).\nThe lepton and neutrino are related to form the W ($68$\\,\\%).\n\nTo form the particle combinations (red), the LBN network builds the light quark jets to the W boson ($78$\\,\\%) and the hadronic top quark.\nIn the leptonic top quark, the LBN combines bottom quark jets and leptons ($61$\\,\\%).\nSometimes the lepton and the neutrino are also correlated to build the W boson ($22$\\,\\%).\nThe LBN rarely combines the Higgs boson ($-4$\\,\\%).\n\nThe positive and negative correlations show that the LBN forms physics-motivated particle combinations from the training data.\nIn the next step, we investigate the usefulness of these combinations for the separation of $\\ttH$ and $\\ttbb$ events.\n\n\\subsection*{Distributions resulting from combined particle properties}\n\nThe performance of particle combinations and their transformations into reference frames in the LBN is evaluated through the extracted features which support the classification of signal and background events.\nHere, we present the distributions of different variables calculated from the combined particles.\n\n\\Fig{fig:m_gen_ttH_a} shows the invariant mass $m$ distributions of all $13$ combined particles for the $\\ttH$ signal dataset.\n\\Fig{fig:m_gen_ttH_b} shows the mass distributions for the $\\ttbb$ background dataset.\nThe difference between the two histograms is shown in Figure~\\ref{fig:m_gen_ttH_c}, where combined particle $0$ of the first column shows the largest difference between signal and background.\n\\Fig{fig:weight_gen_particles_normed} shows that this combination $0$ approximates a Higgs-like configuration (red).\n\\begin{figure}[h!tbp]\n \\centering\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{feature_m_gen_ttH.pdf}\n \\caption{}\n \\label{fig:m_gen_ttH_a}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{feature_m_gen_ttbb.pdf}\n \\caption{}\n \\label{fig:m_gen_ttH_b}\n \\end{subfigure}\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{feature_m_gen_diff.pdf}\n \\caption{}\n \\label{fig:m_gen_ttH_c}\n \\end{subfigure}%\n \n \n \n \\caption{\n Masses of combined particles for a) $\\ttH$, b) $\\ttbb$, and c) the differences between $\\ttH$ and $\\ttbb$.\n }\n \\label{fig:m_gen_ttH}\n\\end{figure}\n\nFor this Higgs-like configuration (combined particle $0$), we show mass distributions in \\Fig{fig:slices_a}.\nFor $\\ttH$, the invariant mass exhibits a narrow distribution around $m = 22$ a.u. reflecting the Higgs-like combined particle, while the $\\ttbb$ background is widely distributed.\nNote that because of the weighted four-vector combinations, arbitrary units are used, so that the Higgs boson mass is not stated in GeV.\n\\begin{figure}[h!tbp]\n \\centering\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{sliced_m_gen_0.pdf}\n \\caption{}\n \\label{fig:slices_a}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{sliced_m_gen_5.pdf}\n \\caption{}\n \\label{fig:slices_b}\n \\end{subfigure}\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{sliced_pt_gen_0.pdf}\n \\caption{}\n \\label{fig:slices_c}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{sliced_pt_gen_5.pdf}\n \\caption{}\n \\label{fig:slices_d}\n \\end{subfigure}\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{sliced_eta_gen_0.pdf}\n \\caption{}\n \\label{fig:slices_e}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{sliced_eta_gen_5.pdf}\n \\caption{}\n \\label{fig:slices_f}\n \\end{subfigure}\n \\caption{\n Example comparisons of kinematic distributions for $\\ttH$ and $\\ttbb$ processes.\n Figures a) and b) show the masses of the combined particles $0$ and $5$.\n Figures c)-f) also present their transverse momenta and pseudorapidities in the rest frames.\n }\n \\label{fig:slices}\n\\end{figure}\n\nAnalogously, we determine the transverse momentum $p_t$ and pseudorapidity $\\eta$ distributions of the $13$ particles for signal and background events, and then compute their difference to visualize distinctions between $\\ttH$ and $\\ttbb$.\nWhile the invariant mass distribution can also be formed without Lorentz transformation, both the $p_t$ and $\\eta$ distributions are determined in the respective rest frames.\nClear differences in the distributions are apparent in \\Fig{fig:gen_ttH_ttbb_a} for the transverse momenta of the combined particles $11, 8, 5, 10$ (in descending order of importance), while the combined particles $5, 9, 12, 10$ are most prominent for the pseudorapidities (\\Fig{fig:gen_ttH_ttbb_b}).\n\\begin{figure}[h!tbp]\n \\centering\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{feature_pt_gen_diff.pdf}\n \\caption{}\n \\label{fig:gen_ttH_ttbb_a}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.9\\textwidth]{feature_eta_gen_diff.pdf}\n \\caption{}\n \\label{fig:gen_ttH_ttbb_b}\n \\end{subfigure}\n \\caption{\n Differences between $t\\ttH$ and $\\ttbb$ processes for combined particles in the rest frames of particle combinations, a) transverse momenta and b) pseudorapidities.\n }\n \\label{fig:gen_ttH_ttbb}\n\\end{figure}\n\nAs a selection of the many possible distributions, in \\Fig{fig:slices} we show the invariant mass $m$, the transverse momentum $p_t$, and the pseudorapidity $\\eta$ for two combined particles.\n\\Figs{fig:slices_a}, \\ref{fig:slices_c}, and \\ref{fig:slices_e} present the distributions of the combined Higgs-like particle $0$.\nIn addition to the prominent difference in the mass distributions, for $\\ttH$ events its $p_t$ is smaller while its direction is more central in $\\eta$ compared to $\\ttbb$ events.\n\nFurthermore, in \\Figs{fig:slices_b}, \\ref{fig:slices_d}, and \\ref{fig:slices_f} we show combined particle $5$ because of its separation power in pseudorapidity and in transverse momentum (\\Fig{fig:gen_ttH_ttbb}).\nCombination $5$ is essentially determined by the bottom quark jet $\\bi$ boosted into a rest frame of all other four-vectors (\\Fig{fig:weight_gen_particles_normed}).\nHere, the distribution of mass $m$ for $\\ttH$ events is smaller, $p_t$ is larger and more defined, and $\\eta$ is more central than for $\\ttbb$ events.\n\nIn the example of the characteristic angular distribution of the charged lepton in top quark decays (\\Fig{fig:lbn_example}, $\\cos{\\theta^*}$), two different reference systems are combined to determine the opening angle $\\theta^*$ between the W boson in the top quark rest frame and the lepton in the W boson rest frame.\nAs mentioned above, the LBN is capable of calculating these complex angular correlations involving four-vectors evaluated in different rest frames.\n\nAs an example, we select distributions for angular distances of combined particles $2$ and $5$ (\\Fig{fig:weight_gen_particles_normed}).\nParticle $2$ is a combination of all four-vectors boosted into the rest frame of the bottom quark jet $\\bii$, whereas particle $5$ corresponds to the bottom quark jet $\\bi$ boosted into a combination of all four-vectors.\n\\Fig{fig:cosangle_a} shows that, for $\\ttbb$ background events, combined particles $2$ and $5$ predominantly propagate back-to-back, while the $\\ttH$ signal distribution scatters broadly around $90$ deg.\n\\begin{figure}[h!tbp]\n \\centering\n \\begin{subfigure}{0.5\\textwidth}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{sliced_pair_cos_gen_2_5.pdf}\n \\caption{}\n \\label{fig:cosangle_a}\n \\end{subfigure}%\n \\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{sliced_pair_cos_gen_2_6.pdf}\n \\caption{}\n \\label{fig:cosangle_b}\n \\end{subfigure}\n \\caption{\n Cosine of the opening angles between combined particle $2$ ($\\ttbar + \\bi$) in its rest frame $\\bii$ and a) combined particle $5$ ($\\bi$) in its rest frame ($\\ttbar + \\bi + \\bii$), and b) combined particle $6$ ($\\ttbar + \\bii$) in its rest frame ($\\bi$).\n }\n \\label{fig:cosangle}\n\\end{figure}\n\nThe opposite holds true for the angles between combined particles $2$ and $6$ (\\Fig{fig:weight_gen_particles_normed}).\nHere, the bottom quark jet $\\bi$, or alternatively $\\bii$, serves as a rest frame for characterizing combinations of both top quarks together with the other bottom quark jet.\n\\Fig{fig:cosangle_b} shows that, for $\\ttbb$, combined particles $2$ and $6$ preferably propagate in the same direction, whereas for $\\ttH$ signal events this is less pronounced.\n\nOverall, it can be concluded that the LBN network, when given only low-level variables as input, seems to autonomously build and transform particle combinations leading to extracted features that are suitable for the task of separating signal and background events.\n\n\n\\section{Conclusions}\n\\label{sec:conclusions}\n\nThe various physics processes from which the events of high-energy particle collisions originate lead to complex particle final states.\nOne reason for the complexity is the high energy that leads to Lorentz boosts of the particles and their decay products.\nFor an analysis task such as the identification of processes, we reconstruct aspects of the probability distributions from which the particles were generated.\nHow well probability distributions of individual physical processes are reflected is compiled in a series of characteristic variables.\nTo accomplish this, both the Minkowski metric for the correct calculation of energy-momentum conservation or invariant masses and the Lorentz transformation into the rest frames of parent particles are necessary.\nSo far, such characteristic variables have been engineered by physicists.\n\nIn this paper, we presented a general two-stage neural network architecture.\nThe first stage, the novel Lorentz Boost Network, contains at its core an efficient and fully vectorized implementation for performing Lorentz transformations.\nThe aim of this stage is to autonomously generate variables suitable for the characterization of collision events when using only particle four-vectors.\nFor this purpose, the input four-vectors are combined separately into composite particles and rest frames.\nThe composite particles are boosted into corresponding rest frames and a generic set of variables is extracted from the boosted particles.\nThe second stage of the network then uses these autonomously generated variables to solve a particular physics question.\nBoth the weights used to create the combinations of input four-vectors and the parameters of the second stage are learned together in a supervised training process.\n\nTo assess the performance of the LBN, we investigated a benchmark task of distinguishing $\\ttH$ and $\\ttbb$ processes.\nWe demonstrated the improved separation power of our model compared to domain-unspecific deep neural networks where the latter even used sophisticated high-level variables as input in addition to the four-vectors.\nFurthermore, we developed visualizations to gain insight into the training process and discovered that the LBN learns to identify physics-motivated particle combinations from the data used for training.\nExamples are top-quark-like combinations or the approximate compensation of the Lorentz boost of the center-of-mass system of the scattering process.\n\nThe LBN is a multipurpose method that uses Lorentz transformations to exploit and uncover structures in particle collision events.\nIt is part of an ongoing comparison of methods to identify boosted top quark jets and has already been successfully applied at the IML workshop challenge at CERN \\cite{iml2018}.\nThe source code of our implementation in TensorFlow \\cite{tensorflow} is publically available under BSD license \\cite{lbncode}.\n\n\\begin{appendix}\n\n\\section*{Appendix}\n\\label{sec:appendix}\n\n\\subsection*{Network parameters}\n\nThe network parameters of the best performing networks are illustrated in Table~\\ref{tab:network-parameters}.\n\\begin{table}[h!tbp]\n \\centering\n \\caption{\n All deep neural networks use a fully connected architecture with $n_\\text{layers}$ and $n_\\text{nodes}$.\n ELU is used as the activation function.\n The Adam optimizer \\cite{kingma2014} is employed with decay parameters $\\beta_1 = 0.9$, $\\beta_2 = 0.999$, and a learning rate of $10^{-4}$.\n $L_2$ normalization is applied with a factor of $10^{-4}$.\n Batch normalization between layers was utilized in every configuration.\n The generic mappings in LBN feature extraction layer create $E, p_T, \\eta, \\phi, m$, and pairwise $\\cos(\\varphi)$.\n }\n \\label{tab:network-parameters}\n \\begin{tabular}{c|cc|cc|cc|cc}\n \\toprule\n Network & \\multicolumn{2}{c|}{\\textbf{LBN+NN}} & \\multicolumn{6}{c}{\\textbf{DNN}}\\\\\n Variables & \\multicolumn{2}{c|}{\\textbf{low-level}} & \\multicolumn{2}{c|}{\\textbf{low-level}} & \\multicolumn{2}{c|}{\\textbf{high-level}} & \\multicolumn{2}{c}{\\textbf{combined}} \\\\\n Input Ordering & \\textbf{gen.} & $\\mathbf{p_T}$ & \\textbf{gen.} & $\\mathbf{p_T}$ & \\textbf{gen.} & $\\mathbf{p_T}$ & \\textbf{gen.} & $\\mathbf{p_T}$\\\\\n \\midrule\n $M_\\text{part.,rest fr.}$ & $13$ & $16$ & \\verb|-| & \\verb|-| & \\verb|-| & \\verb|-| & \\verb|-| & \\verb|-|\\\\\n $n_\\text{layers}$ & $8$ & $8$ & $8$ & $8$ & $4$ & $4$ & $8$ & $6$\\\\\n $n_\\text{nodes}$ & $1024$ & $1024$ & $1024$ & $1024$ & $512$ & $512$ & $1024$ & $1024$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\n\n\\subsection*{High-level variables}\n\nThe high-level variables employed in the training of the DNN benchmark comparison are inspired by \\cite{Sirunyan:2018mvw}:\n\\begin{itemize}\n \\item\n The event shape variables sphericity, transverse sphericity, aplanarity, centrality \\cite{event_shape_variables}.\n\n \\item\n The first five Fox-Wolfram moments \\cite{fox_wolfram_moments}.\n\n \\item\n The cosine of spatial angular difference $\\theta^*$ between the charged lepton in the W boson rest frame and the W boson direction when boosted into the rest frame of its corresponding top quark.\n In the hadronic branch, the down-type quark is used owing to its increased spin analyzing power \\cite{spin_analyzing_power}.\n\n \\item\n The minimum, maximum and average of the distance in pseudorapidity $\\eta$ and $\\phi$ phase space $\\Delta R$ of jet pairs.\n\n \\item\n The minimum, maximum and average $|\\Delta\\eta|$ of jet pairs.\n\n \\item\n The minimum and maximum of the distance in $\\Delta R$ of jet-lepton pairs.\n\n \\item\n The minimum, maximum and average $|\\Delta\\eta|$ of jet-lepton pairs.\n\n \\item\n The sum of the transverse momenta of all jets.\n\n \\item\n The transverse momentum and the mass of the jet pair with the smallest $\\Delta R$.\n\n \\item\n The transverse momentum and the mass of the jet pair whose combined mass is closest to the Higgs boson mass m$_H = 125$\\,GeV \\cite{CMS2012:higgs}.\n\\end{itemize}\n\n\\end{appendix}\n\n\n\\acknowledgments\nWe wish to thank Jonas Glombitza for his valuable comments on the manuscript.\nWe would also like to thank Jean-Roch Vlimant, Benjamin Fischer, Dennis Noll, and David Schmidt for a variety of fruitful discussions.\nThis work is supported by the Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia, and by the Federal Ministry of Education and Research (BMBF).\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec:intro}\n\nScattering amplitudes and cross sections simplify in infrared kinematic limits enabling insight into their all orders perturbative structure.\nOne of the most powerful approaches to studying the all orders structure is the use of renormalization group (RG) techniques. Depending on the particular nature of the kinematic limit considered, these RG equations often describe evolution not just in the standard virtuality scale, $\\mu$, but also in other additional physical scales. A common example in gauge theories are rapidity evolution equations, which allow for the resummation of infrared logarithms associated with hierarchical scales in rapidity. Classic examples include the Sudakov form factor \\cite{Collins:1989bt}, the Collins-Soper equation \\cite{Collins:1981uk,Collins:1981va,Collins:1984kg} describing the $p_T$ spectrum for color singlet boson production in hadron colliders, the BFKL evolution equations describing the Regge limit \\cite{Kuraev:1977fs,Balitsky:1978ic,Lipatov:1985uk}, and the rapidity renormalization group \\cite{Chiu:2011qc,Chiu:2012ir} describing more general event shape observables.\n\nThere has recently been significant effort towards understanding subleading power corrections to infrared limits \\cite{Manohar:2002fd,Beneke:2002ph,Pirjol:2002km,Beneke:2002ni,Bauer:2003mga,Hill:2004if,Lee:2004ja,Dokshitzer:2005bf,Trott:2005vw,Laenen:2008ux,Laenen:2008gt,Paz:2009ut,Benzke:2010js,Laenen:2010uz,Freedman:2013vya,Freedman:2014uta,Bonocore:2014wua,Larkoski:2014bxa,Bonocore:2015esa,Bonocore:2016awd,Kolodrubetz:2016uim,Moult:2016fqy,Boughezal:2016zws,DelDuca:2017twk,Balitsky:2017flc,Moult:2017jsg,Goerke:2017lei,Balitsky:2017gis,Beneke:2017ztn,Feige:2017zci,Moult:2017rpl,Chang:2017atu,Beneke:2018gvs,Beneke:2018rbh,Moult:2018jjd,Ebert:2018lzn,Ebert:2018gsn,Bhattacharya:2018vph,Boughezal:2018mvf,vanBeekveld:2019prq,vanBeekveld:2019cks,Bahjat-Abbas:2019fqa,Beneke:2019kgv,Boughezal:2019ggi,Moult:2019mog,Beneke:2019mua,Cieri:2019tfv,Moult:2019uhz,Beneke:2019oqx} with the ultimate goal of achieving a systematic expansion, much like for problems where their exists a local operator product expansion (OPE). Using Soft Collinear Effective Theory (SCET) \\cite{Bauer:2000ew, Bauer:2000yr, Bauer:2001ct, Bauer:2001yt} subleading power infrared logarithms were resummed to all orders using RG techniques for a particular class of event shape observables where only virtuality evolution is required. This was achieved both in pure Yang-Mills theory \\cite{Moult:2018jjd}, and including quarks in $\\mathcal{N}=1$ QCD \\cite{Moult:2019uhz}, and a conjecture for the result including quarks in QCD was presented in \\cite{Moult:2019uhz}. Subleading power infrared logarithms have also been resummed for color singlet production at kinematic threshold, when only soft real radiation is present \\cite{Beneke:2018gvs,Beneke:2019mua,Bahjat-Abbas:2019fqa}.\n\nIn this paper we build on the recent advances in understanding the structure of subleading power renormalization group equations in SCET, and consider for the first time the resummation of subleading power rapidity logarithms. Using renormalization group consistency arguments, we derive a class of subleading power rapidity evolution equations. These equations involve mixing into new class of operators, which play a crucial role in the renormalization group equations. We call these operators ``rapidity identity operators\", and we derive their renormalization group properties, and solve the associated RG equations. \n\nWe apply our evolution equations to derive the all orders structure of the power suppressed leading logarithms for the energy-energy correlator (EEC) event shape in $\\mathcal{N}=4$ super-Yang-Mills (SYM) theory in the back-to-back (double light cone) limit. Denoting these power suppressed contributions by $\\text{EEC}^{(2)}$, we find the remarkably simple formula\n\\begin{align}\n\\boxed{\\text{EEC}^{(2)}=-\\sqrt{2a_s}~D\\left[ \\sqrt{\\frac{\\Gamma^{\\mathrm{cusp}}}{2}} \\log(1-z) \\right]}\\,,\n\\end{align}\nwhere $D(x)=1\/2\\sqrt{\\pi}e^{-x^2}\\mathrm{erfi}(x)$ is Dawson's integral, $a_s=\\alpha_s\/(4\\pi)C_A$, and $\\Gamma^{\\mathrm{cusp}}$ is the cusp anomalous dimension \\cite{Korchemsky:1987wg}. This result provides insight into new all orders structures appearing in subleading power infrared limits. Since this extends the classic Sudakov exponential \\cite{Sudakov:1954sw}, we will refer to this functional form as ``Dawson's Sudakov\". The particular example of the EEC observable was chosen in this paper, since its exact structure for generic angles is known to $\\mathcal{O}(\\alpha_s^3)$ due to the remarkable calculation of \\cite{Henn:2019gkr}, and we find perfect agreement with the expansion of their results in the back-to-back limit to this order, providing a strong check of our techniques. While we focus on the EEC in $\\mathcal{N}=4$, this observable has an identical resummation structure to $p_T$ resummation, and therefore the techniques we have developed apply more generally, both to the EEC in QCD, and to the $p_T$ distribution of color singlet bosons at hadron colliders.\n\nAn outline of this paper is as follows. In \\Sec{sec:EEC_b2b} we review the known structure of the EEC observable in the back-to-back limit, and relate it to the case of the $p_T$ spectrum of color singlet bosons, which is perhaps more familiar to the resummation community. In \\Sec{sec:FO} we perform a fixed order calculation of the EEC at subleading power in SCET, which allows us to understand the structure of the subleading power rapidity divergences, and provides the boundary data for our RG approach. In \\Sec{sec:RRG_NLP} we study the structure of subleading power rapidity evolution equations, introduce the rapidity identity operators, and analytically solve their associated evolution equations. In \\Sec{sec:N4} we apply these evolution equations to the particular case of the EEC in $\\mathcal{N}=4$ SYM to derive the subleading power leading logarithmic series, and we comment on some of the interesting features of the result. We also compare our result with the fixed order calculation of \\cite{Henn:2019gkr} expanded in the back-to-back limit, finding perfect agreement. We conclude in \\Sec{sec:conc}, and discuss many directions for improving our understanding of the infrared properties of gauge theories at subleading powers. \n\n\\section{The Energy-Energy Correlator in the Back-to-Back Limit}\\label{sec:EEC_b2b}\n\nIn this section we introduce the EEC observable, and review its structure in the back-to-back limit at leading power. We then discuss the resummation of the associated infrared logarithms using the rapidity renormalization group approach. This will allow us to introduce our notation, before proceeding to subleading power. \n\nAn additional goal of this section is to make clear the relation between the EEC in the back-to-back limit and more standard $p_T$ resummation, which may be more familiar to the resummation community. This should also make clear that the techniques we develop are directly applicable to the case of $p_T$ resummation, although we leave a complete treatment of $p_T$ resummation to a future publication due to complexities related to the treatment of the initial state hadrons. Some other recent work towards understanding subleading power factorization for $p_T$ can be found in \\cite{Balitsky:2017flc,Balitsky:2017gis}.\n\n\nFor a color singlet source, the EEC is defined as \\cite{Basham:1978bw}\n\\begin{align}\\label{eq:EEC_intro}\n\\text{EEC}(\\chi)=\\sum\\limits_{a,b} \\int d\\sigma_{V\\to a+b+X} \\frac{2 E_a E_b}{Q^2 \\sigma_{\\text{tot}}} \\delta(\\cos(\\theta_{ab}) - \\cos(\\chi))\\,,\n\\end{align}\nwhere the sum is over all pairs of final state particles, $E_a$ are the energies of the particles, and $\\theta_{ab}$ are the angles between pairs of particles. Energy correlators are a theoretically nice class of event shape observable, since they can be directly expressed in terms of energy flow operators \\cite{Hofman:2008ar,Belitsky:2013xxa,Belitsky:2013bja,Belitsky:2013ofa}\n\\begin{align}\n\\mathcal{E}(\\vec n) =\\int\\limits_0^\\infty dt \\lim_{r\\to \\infty} r^2 n^i T_{0i}(t,r \\vec n)\\,.\n\\end{align}\nIn particular, the EEC is given by the four-point Wightman correlator\n\\begin{align}\n\\frac{1}{\\sigma_{\\rm tot}} \\frac{d\\sigma}{dz}=\\frac{\\langle \\mathcal{O} \\mathcal{E}(\\vec n_1) \\mathcal{E}(\\vec n_2) \\mathcal{O}^\\dagger \\rangle }{\\langle \\mathcal{O} \\mathcal{O}^\\dagger \\rangle}\\,,\n\\end{align}\nwhere we have introduced the convenient variable\n\\begin{align}\nz=\\frac{1-\\cos {\\chi}}{2}\\,,\n\\end{align}\nand $\\mathcal{O}$ is a source operator that creates the excitation.\n\nThere has been significant recent progress in understanding the EEC, both in QCD, as well as in conformal field theories. In QCD, the EEC has been computed for arbitrary angles at next-to-leading order (NLO) analytically \\cite{Dixon:2018qgp,Luo:2019nig} and at NNLO numerically \\cite{DelDuca:2016csb,DelDuca:2016ily}. In $\\mathcal{N}=4$ it has been computed for arbitrary angles to NNLO \\cite{Belitsky:2013ofa,Henn:2019gkr}. There has also been significant progress in understanding the limits of the EEC, namely the collinear ($z\\to 0$) limit \\cite{Dixon:2019uzg,Kravchuk:2018htv,Kologlu:2019bco,Kologlu:2019mfz,Korchemsky:2019nzm}, and the back-to-back ($z\\to 1$) limit \\cite{deFlorian:2004mp,Moult:2018jzp,Korchemsky:2019nzm,Gao:2019ojf}. Here we will focus on the EEC in the back-to-back limit, where it exhibits Sudakov double logarithms. As we will explain shortly, these double logarithms are directly related to those appearing for transverse momentum resummation. In this section we follow closely the factorization derived in \\cite{Moult:2018jzp} using the rapidity renormalization group \\cite{Chiu:2012ir,Chiu:2011qc}. An alternative approach to studying this limit directly from the four point correlator was given in \\cite{Korchemsky:2019nzm}.\n\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=0.75\\columnwidth]{figures\/back_to_back}\n\\end{center}\n\\caption{The kinematics of the EEC in the back-to-back limit. Wide angle soft radiation recoils the two jets in a manner identical to the case of $p_T$ for color singlet boson production in hadronic collisions. This provides a precise relation between the factorization formulas in the two cases. (Figure from \\cite{Moult:2018jzp}.) }\n\\label{fig:schematic_factorization}\n\\end{figure}\n\nThe back-to-back limit corresponds to the region of phase space where there are two collimated jets accompanied by low energy soft radiation, which recoils the directions of the jets so that they are not exactly back-to-back. This configuration is illustrated in \\Fig{fig:schematic_factorization}. A simple exercise shows that the angle between two partons correlated by the EEC is related to the transverse momentum of these particles within the jets and the transverse momentum of the soft radiation that recoils these jets, by\n\\begin{align}\n\\label{eq:z_in_back_to_back}\n1-z=\\frac{1}{Q^2} \\left| \\frac{\\vec k_{\\perp,i}^h}{x_i}+\\frac{\\vec k_{\\perp,j}^h}{x_j}-\\vec k_{\\perp,s}^h \\right|^2 +\\mathcal{O}(1-z)\\,.\n\\end{align}\nHere $x_i=2E_i\/Q$, where $Q$ is the center of mass energy.\nThis relation makes clear the connection between the EEC in the back-to-back limit and transverse momentum resummation, which we will shortly extend to subleading powers.\n\nTo describe the EEC in this limit, we use an effective field theory description of the soft and collinear dynamics, where the relevant modes have the scalings\n\\begin{align}\\label{eq:pc_modes}\np_s\\sim Q(\\lambda, \\lambda, \\lambda)\\,, \\qquad p_c \\sim Q(\\lambda^2, 1, \\lambda) \\,, \\qquad p_{\\bar c} \\sim Q(1,\\lambda^2, \\lambda)\\,.\n\\end{align}\nHere $\\lambda$ is a power counting parameter and is defined as\n\\begin{align}\n\\lambda\\sim \\sqrt{1-z}\\,.\n\\end{align}\nThis scaling defines what is referred to as an SCET$_{\\rm II}$~ theory \\cite{Bauer:2002aj}. Crucially, unlike in SCET$_{\\rm I}$, the soft and collinear modes in SCET$_{\\rm II}$~ have the same virtualities, but different rapidities. This factorization into soft and collinear modes therefore introduces divergences as $k^+\/k^-\\to \\infty$ or $k^+\/k^-\\to 0$ \\cite{Collins:1992tv,Manohar:2006nz,Collins:2008ht,Chiu:2012ir,Vladimirov:2017ksc}, which are referred to as rapidity divergences. To regulate these divergences, one must introduce a regulator that breaks boost invariance, allowing the soft and collinear modes to be distinguished. Once such a regulator is introduced, renormalization group evolution equations can be derived to resum the associated rapidity logarithms \\cite{Chiu:2012ir,Chiu:2011qc}.\n\nUsing the effective field theory, one can systematically expand the cross section for either transverse momentum, or for the EEC in powers of the observable. For the EEC, we write\n\\begin{align}\\label{eq:xsec_expand}\n\\frac{\\mathrm{d} \\sigma}{\\mathrm{d} z}\n&= \\frac{\\mathrm{d}\\sigma^{(0)}}{\\mathrm{d} z} + \\frac{\\mathrm{d}\\sigma^{(2)}}{\\mathrm{d} z}+ \\frac{\\mathrm{d}\\sigma^{(4)}}{\\mathrm{d} z} + \\dotsb{\\nonumber} \\\\\n&=\\text{EEC}^{(0)}+\\text{EEC}^{(2)}+\\text{EEC}^{(4)}\\,,\n\\end{align}\nwhere we will occasionally use the second notation, since it is more compact.\nHere\n\\begin{align}\n\\frac{\\mathrm{d}\\sigma^{(0)}}{\\mathrm{d} z}\n&\\sim \\delta(1-z)+ \\biggl[\\frac{ \\ord{1} }{1-z}\\biggr]_+\n\\,, \n\\end{align}\nis referred to as the leading power cross section, and\ndescribes all terms scaling like $\\mathcal{O}((1-z)^{-1})$ modulo logarithms. All the other terms in the expansion of the cross section are suppressed by explicit powers of $(1-z)$\n\\begin{align}\\label{eq:scaling_lam2}\n\\frac{\\mathrm{d}\\sigma^{(2k)}}{\\mathrm{d} z} &\\sim \\ord{(1-z)^{k-1}}\n\\,.\\end{align}\nThe focus of this paper will be on deriving the structure of the leading logarithms in $\\mathrm{d}\\sigma^{(2)}\/\\mathrm{d} z$, which is also referred to as the next-to-leading power (NLP) cross section. \n\n\nFor the leading power cross section, $d\\sigma^{(0)}\/dz$, one can derive a factorization formula describing in a factorized manner the contributions of the soft and collinear modes to the EEC in the $z\\to 1$ limit \\cite{Moult:2018jzp}\n\\begin{equation}\\label{eq:fact_final}\n\\hspace{-0.35cm}\\frac{d\\sigma^{(0)}}{dz}= \\frac{1}{2} \\int d^2 \\vec k_\\perp \\int \\frac{d^2 \\vec b_\\perp}{(2 \\pi)^2} e^{-i \\vec b_\\perp \\cdot \\vec k_\\perp} H(Q,\\mu) J^q_{\\text{EEC}} (\\vec b_\\perp,\\mu,\\nu) J^{\\bar q}_{\\text{EEC}} (\\vec b_\\perp,\\mu,\\nu) S_{\\text{EEC}}(\\vec b_\\perp,\\mu,\\nu) \\delta \\left( 1-z- \\frac{\\vec k_\\perp^2}{Q^2} \\right )\\,,\n\\end{equation}\nin terms of a hard function, $H$, jet functions, $J$, and a soft function, $S$. This factorization is nearly equivalent to the factorization for the $p_T$ for color singlet boson production (This factorization formula was originally derived in \\cite{Collins:1981uk,Collins:1981va,Collins:1984kg}, and was derived in terms of the rapidity renormalization group used here in \\cite{Chiu:2012ir,Chiu:2011qc}), \n\\begin{align}\\label{eq:pt_fact}\n\\frac{1}{\\sigma} \\frac{d^3 \\sigma^{(0)}}{d^2 \\vec p_T dY dQ^2} = H(Q,\\mu) \\int \\frac{d^2 \\vec b_\\perp}{(2\\pi)^2} e^{i\\vec b_\\perp \\cdot \\vec p_T} \\left[ B \\times B \\right] (\\vec b_\\perp, \\mu, \\nu) S_\\perp(\\vec b_\\perp, \\mu, \\nu)\\,,\n\\end{align}\nup to the fact that the jet functions are moved to the initial state, where they are referred to as beam functions \\cite{Stewart:2009yx}. Apart from our intrinsic interest in understanding the kinematic limits of the EEC, this relation between the EEC and $p_T$ is one of our primary motivations for studying the EEC. Lessons derived from the EEC can be directly applied to understanding the structure of subleading power logarithms for $p_T$, which is a phenomenologically important observable at the LHC, for example, for precision studies of the Higgs boson. Here we briefly discuss the objects appearing in the factorization formula, both to emphasize the close connections between the EEC and $p_T$, as well as to introduce the general structure of the $\\mu$ and $\\nu$ rapidity evolution equations. \n\n\nThe hard functions, $H(Q,\\mu)$, appearing in the factorization formulas for the EEC and $p_T$ are identical. They describe hard virtual corrections, and satisfy a multiplicative renormalization group equation (RGE) in $\\mu$\n\\begin{align}\n\\mu \\frac{d}{d\\mu} H(Q,\\mu) =2 \\left[\\Gamma^{\\mathrm{cusp}}(\\alpha_s) \\ln\\frac{Q^2}{\\mu^2} + \\gamma^H(\\alpha_s) \\right] H(Q,\\mu)\\,.\n\\end{align}\nThey are independent of rapidity. The soft functions appearing in both $p_T$ and the EEC can be proven to be identical \\cite{Moult:2018jzp}. They are matrix elements of Wilson lines, which for quarks and gluons are defined as\n\\begin{align}\n S_q(\\vec p_T) &= \\frac{1}{N_c} \\big\\langle 0 \\big| \\mathrm{Tr} \\bigl\\{\n \\mathrm{T} \\big[S^\\dagger_{\\bn} S_n\\big]\n \\delta^{(2)}(\\vec p_T-\\mathcal{P}_\\perp)\\overline{\\mathrm{T}} \\big[S^\\dagger_{n} S_{\\bn} \\big]\n \\bigr\\{ \\big| 0 \\big\\rangle\n\\,,{\\nonumber}\\\\\n S(\\vec p_T) &= \\frac{1}{N_c^2 - 1} \\big\\langle 0 \\big| \\mathrm{Tr} \\bigl\\{\n \\mathrm{T} \\big[\\mathcal{S}^\\dagger_{\\bn} \\mathcal{S}_n\\big]\n \\delta^{(2)}(\\vec p_T-\\mathcal{P}_\\perp) \\overline{\\mathrm{T}} \\big[\\mathcal{S}^\\dagger_{n} \\mathcal{S}_{\\bn} \\big]\n \\bigr\\} \\big| 0 \\big\\rangle\n\\,.\\end{align}\nHere $\\mathrm{T}$ and $\\overline{\\mathrm{T}}$ denote time and anti-time ordering, and $S_n$ and $\\mathcal{S}_n$ denote Wilson lines in the fundamental and adjoint representations, respectively. Explicitly, \n\\begin{align}\n S_n(x) &= \\mathrm{P} \\exp\\biggl[ \\mathrm{i} g \\int_{-\\infty}^0 \\mathrm{d} s\\, n \\cdot A(x+sn)\\biggr]\\,,\n\\end{align}\nand similarly for the adjoint Wilson lines. These soft functions satisfy the $\\mu$ and $\\nu$ RGEs\n\\begin{align}\n\\nu \\frac{d}{d\\nu}S(\\vec p_T)&=\\int d\\vec q_T \\gamma^S_\\nu(p_T-q_T) S(\\vec q_T)\\,, {\\nonumber} \\\\\n\\mu \\frac{d}{d\\mu}S(\\vec p_T)&=\\gamma_\\mu^S S(\\vec p_T)\\,,\n\\end{align}\nwith the anomalous dimensions\n\\begin{align}\n\\gamma_\\mu^S &=4\\Gamma^{\\mathrm{cusp}}(\\alpha_s)\\log \\left( \\frac{\\mu}{\\nu}\\right)\\,,{\\nonumber} \\\\\n\\gamma_\\nu^S &=2\\Gamma^{\\mathrm{cusp}} (\\alpha_s)\\mathcal{L}_0\\left( \\vec p_T,\\mu \\right)\\,.\n\\end{align}\nHere the color representation is implicit in the cusp anomalous dimension, and $\\mathcal{L}_0$ is a standard plus function (see e.g. \\cite{Ebert:2016gcn} for a detailed discussion of two-dimensional plus distributions, and for a definition of the conventions that are used here). Since we will ultimately be interested in $\\mathcal{N}=4$ where all particles are in the same representation, we will drop the quark and gluon labels on the soft functions.\n\nThe only difference between $p_T$ and the EEC lies in the collinear sector, namely whether one uses beam functions or jet functions. The jet functions for the EEC were recently computed to NNLO \\cite{Luo:2019bmw,Luo:2019hmp}. Since in this paper we will be focused on resummation at LL, we can always choose to run all functions to the jet scale, and thereby avoid a discussion of the collinear sector for simplicity. The structure of the power corrections to the beam (jet) functions, and their matching to the parton distributions (fragmentation functions) is interesting, and will be presented in future work, since it is important for a complete understanding of $p_T$ at subleading powers.\n\nThese renormalization group evolution equations in both $\\mu$ and $\\nu$ allow for a derivation of the all orders structure of logarithms in the $z\\to 1$ limit, at leading order in the $(1-z)$ expansion. Performing the renormalization group evolution, one finds for a non-conformal field theory \\cite{Moult:2018jzp} (i.e. allowing for a running coupling)\n\\begin{align}\n \\label{eq:resformula}\n\\frac{d\\sigma^{(0)}}{dz} = &\\; \\frac{1}{4} \\int\\limits_0^\\infty db\\, b\n J_0(bQ\\sqrt{1-z})H(Q,\\mu_h) j^q_{\\text{EEC}}(b,b_0\/b,Q) j^{\\bar\n q}_{\\text{EEC}}(b,b_0\/b,Q) S_{\\text{EEC}}( b,\\mu_s, \\nu_s) \n{\\nonumber}\\\\\n&\\; \\cdot\n \\left(\\frac{Q^2}{\\nu_s^2}\\right)^{\\gamma^r_{\\text{EEC}}(\\alpha_s(b_0\/b))}\n \\exp \\left[ \\int\\limits_{\\mu_s^2}^{\\mu_h^2}\n \\frac{d\\bar{\\mu}^2}{\\bar{\\mu}^2} \\Gamma^{\\mathrm{cusp}}(\\alpha_s(\\bar \\mu)) \\ln\n \\frac{b^2\\bar{\\mu}^2}{b_0^2} \\right.\n{\\nonumber}\\\\\n&\\;\n\\left. +\n \\int\\limits_{\\mu_h^2}^{b_0^2\/b^2}\\frac{d\\bar{\\mu}^2}{\\bar{\\mu}^2}\n \\left(\\Gamma^{\\mathrm{cusp}}(\\alpha_s(\\bar \\mu)) \\ln\\frac{b^2 Q^2}{b_0^2} +\n \\gamma^H (\\alpha_s(\\bar \\mu)) \\right) -\n \\int\\limits_{\\mu_s^2}^{b_0^2\/b^2}\\frac{d\\bar{\\mu}^2}{\\bar{\\mu}^2}\n \\gamma^s_{\\text{EEC}} (\\alpha_s(\\bar \\mu)) \\right]\\,.\n\\end{align}\nFor the particular case of a conformal theory, this expression simplifies considerably, both due to the fact that the coupling doesn't run, and also since in a conformal field theory there is an equivalence between the rapidity anomalous dimension and the soft anomalous dimension \\cite{Vladimirov:2016dll,Vladimirov:2017ksc}. Combining this equivalence with the relations for the soft anomalous dimension derived in \\cite{Dixon:2008gr} (see also \\cite{Falcioni:2019nxk} for recent work on relations between different soft functions), we have\n\\begin{align}\n\\gamma^r= -\\mathcal{G}_0+2B\\,,\n\\end{align}\nwhere $B$ is the virtual anomalous dimension (the coefficient of $\\delta(1-x)$ in the DGLAP kernel), and $\\mathcal{G}_0$ is the collinear anomalous dimension. We then find\n\\begin{align}\n \\label{eq:resformula_N4}\n\\frac{d\\sigma^{(0)}}{dz} = &\\; \\frac{1}{4} \\int\\limits_0^\\infty db\\, b\n J_0(bQ\\sqrt{1-z})H(Q,\\mu_h) j^q_{\\text{EEC}}(b,b_0\/b,Q) j^{\\bar\n q}_{\\text{EEC}}(b,b_0\/b,Q) S_{\\text{EEC}}( b,\\mu_s, \\nu_s) \n{\\nonumber}\\\\\n&\\exp \\left[ \\Gamma^{\\mathrm{cusp}} \\log^2\\left( \\frac{b^2 Q^2}{b_0^2} \\right) +2B\\log\\left( \\frac{b^2 Q^2}{b_0^2} \\right) \\right]\\,.\n\\end{align}\nBoth the cusp anomalous dimension, as well as the $B$ anomalous dimension are known from integrability \\cite{Eden:2006rx,Beisert:2006ez,Freyhult:2007pz,Freyhult:2009my,Fioravanti:2009xt}. It is interesting that only the two anomalous dimensions that are known from integrability appear in the final result. The collinear anomalous dimension, which drops out of the final result in a conformal theory, is known to four loops in $\\mathcal{N}=4$ \\cite{Dixon:2017nat}.\n\n\n\nThis formula describes the leading power asymptotics of the EEC in the $z\\to 1$ limit to all orders in the coupling (Indeed, in $\\mathcal{N}=4$, it should also apply at finite coupling). The goal of this paper will be to start to understand the all orders structure of the subleading power corrections to this formula in $(1-z)$. While we do not have a complete factorization formula or all orders understanding, we will be able to deduce much of the structure from general consistency and symmetry arguments. Ultimately, we would like to be able to classify the operators that appear in the description of the subleading powers in this limit, and understand their renormalization group evolution. This paper represents a first step in this direction. \n\n\n\nWe conclude this section by noting that the result of \\Eq{eq:resformula_N4} can also be derived in a conformal field theory by directly considering the structure of the four point correlator in the double light cone limit \\cite{Korchemsky:2019nzm} (see also \\cite{Alday:2013cwa}), and using the duality between the correlator and a Wilson loop \\cite{Alday:2010zy}, as well as known results for the structure constants \\cite{Eden:2012rr}. It would be interesting to understand systematically the OPE of the correlator in this limit and the operators that appear from this perspective. There has been some study of the double light cone limit in \\cite{Alday:2015ota}. It would be interesting to understand it in more detail and develop a systematic OPE, much like exists in the collinear limit \\cite{Alday:2010ku,Basso:2014jfa,Basso:2007wd,Basso:2010in,Basso:2015uxa,Basso:2013vsa,Basso:2013aha,Basso:2014koa,Basso:2014nra}. It would also be interesting if the recently introduced light ray OPE \\cite{Kravchuk:2018htv,Kologlu:2019bco,Kologlu:2019mfz} can provide insight into this limit. However, we leave these directions to future work.\n\n\n\\section{Fixed Order Calculation of the EEC at Subleading Power}\\label{sec:FO}\n\nHaving discussed the structure of the EEC in the back-to-back limit, as well as the factorization theorem describing its leading power asymptotics, in this section we begin our study of the subleading corrections in powers of $(1-z)$ by performing a fixed order calculation. This is important both for understanding the structure of the subleading power rapidity divergences for the EEC, and for providing the boundary conditions for the renormalization group studied later in the paper. We will perform this calculation both in QCD, as well as in $\\mathcal{N}=4$. We follow closely the calculation for the power corrections for $p_T$ presented in \\cite{Ebert:2018gsn}. In \\Sec{sec:intuition} we summarize some of the intuition derived from this calculation, which provides significant insight into the structure of the subleading power renormalization group evolution, which we will then study in more detail in \\Sec{sec:RRG_NLP}.\n\n\n\\subsection{Leading Order Calculation in $\\mathcal{N}=4$ SYM and QCD}\\label{sec:FO2}\n\n\nIn this section, we perform the leading order (LO) calculation of the EEC at NLP in both QCD and $\\mathcal{N}=4$. This section is rather technical, and assumes some familiarity with fixed order calculations at subleading power in SCET (see e.g. \\cite{Moult:2016fqy,Moult:2017jsg,Ebert:2018lzn,Ebert:2018gsn} for more detailed discussions).\n\nThe EEC observable can be written as\n\\begin{align}\n\t\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}}{\\mathrm{d} y} &= \\sum_{a,b} \\int \\mathrm{d} \\Phi_{V \\to a+b+X} \\left|\\mathcal{M}_{V \\to a+b+X}\\right|^2 \\frac{E_a E_b}{q_V^2}\\delta\\left(y-\\cos^2 \\frac{\\theta_{ab}}{2}\\right) \\,,\n\\end{align}\nwhere we have used $y \\equiv 1-z$, so that $y \\to 0$ in the back-to-back limit. To perform the calculation it is convenient to write the observable definition in a boost invariant manner\n\\begin{align}\n\t\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}}{\\mathrm{d} y} &= \\frac{1}{2 (1-y) q_V^2}\\sum_{a,b} \\int \\mathrm{d} \\Phi_{V \\to a+b+X} \\left|\\mathcal{M}_{V \\to a+b+X}\\right|^2\\, p_a\\cdot p_b\\,\\delta\\left[y - \\left(1 -\\frac{q_V^2p_a\\cdot p_b}{2 p_a\\cdot q_V p_b \\cdot q_V}\\right)\\right] \\,,\n\\end{align}\nwhere $q_V^\\mu$ is the momentum of the vector boson. This definition is convenient since if we are correlating a particular pair of particles $\\{a,b\\}$, we can boost the system such that the particles being correlated are back-to-back and the vector boson (or source) recoils against the unmeasured radiation in the perpendicular direction.\n\n\nGiven this setup, we begin by considering a single perturbative emission, which is sufficient for the LO calculation. We will denote by $p_a^\\mu$ and $p^\\mu_b$ the momenta of the particles we are correlating, and $k^\\mu$ the momentum of the unmeasured radiation. This translates to the following choice of kinematics\\footnote{Note that here $p_{a,b}^\\mu$ and $k^\\mu$ are outgoing while $q_V^\\mu$ is incoming.}\n\\begin{align}\\label{eq:kinematic}\n\tp_a^\\mu &= (q^-_V - k^-) \\frac{n^\\mu}{2}\\,,&& k^\\mu = k^- \\frac{n^\\mu}{2} + k^+ \\frac{\\bn^\\mu}{2} + k_\\perp^\\mu\\,,{\\nonumber}\\\\\n\tp_b^\\mu &= (q^+_V - k^+) \\frac{\\bn^\\mu}{2}\\,,&& q_V^\\mu = q_V^- \\frac{n^\\mu}{2} + q_V^+ \\frac{\\bn^\\mu}{2} + k_\\perp^\\mu\\,.\n\\end{align}\nThe measurement of the EEC observable then takes the form of the following constraint\n\\begin{align}\n\ty = 1 - \\frac{(q^-_V - k^-)(q^+_V - k^+) q_V^2}{q_V^+ (q^-_V - k^-) q_V^- (q^+_V - k^+)} = \\frac{q^+_V q^-_V - q_V^2}{q^+_V q^-_V} = \\frac{{\\vec{k}_\\perp^{\\,2}}}{q_V^2 - {\\vec{k}_\\perp^{\\,2}}}\\,,\n\\end{align}\nwhich we can rewrite as a constraint on ${\\vec{k}_\\perp^{\\,2}}$ \n\\begin{equation}\n\t\\delta\\left(y - \\frac{{\\vec{k}_\\perp^{\\,2}}}{q_V^2 - {\\vec{k}_\\perp^{\\,2}}} \\right) = \\delta\\left({\\vec{k}_\\perp^{\\,2}} - q_V^2\\frac{y}{1-y}\\right) \\frac{q_V^2}{(1-y)^2}\\,.\n\\end{equation}\nThis extends the relation between the EEC and $p_T$ to subleading powers.\n\n\\subsection*{Master Formula}\n\nUsing this result for the measurement function, the cross section for the EEC with one emission reads\n\\begin{align}\n\t\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}}{\\mathrm{d} y} &= \\frac{1}{2 (1-y)^3}\\sum_{a,b}\\int \\mathrm{d} \\Phi |\\mathcal{M}_{V \\to q\\bar{q} g}|^2\\, p_a \\cdot p_b\\,\\delta\\left({\\vec{k}_\\perp^{\\,2}} - q_V^2\\frac{y}{1-y}\\right){\\nonumber}\\,.\n\\end{align}\nFor our particular choice of frame where $p_a^\\mu$ has no $\\perp$ component, we have \\cite{Fleming:2007qr}\n\\begin{align}\n\t \\int \\frac{\\mathrm{d}^{d-2} p_{a\\perp}}{(2\\pi)^{d-2}} &= \\sum_n d^2 p_{\\perp,n} \\int \\frac{\\mathrm{d}^{d-2} p_{a\\perp}}{(2\\pi)^{d-2}}\\delta^{(d-2)}(p^\\mu_{a\\perp}) = \\frac{|\\vec{p}_a|^{d-2}}{(2\\pi)^{d-2}} \\int \\mathrm{d} \\Omega_{d-2}\\,.\n\\end{align}\nUsing\n\\begin{align}\\label{eq:paPS}\n\t\\int {\\mathrm{d}\\!\\!\\!{}^-}^d p_a \\delta_+(p_a^2) &= \\frac{1}{2} \\int \\frac{\\mathrm{d} p_a^-}{(2\\pi)} \\frac{\\mathrm{d} p_a^+}{(2\\pi)} (2\\pi) \\theta(p_a^+ + p_a^-)\\delta(p_a^+ p_a^- ) |\\vec{p}_a|^2 \\int \\frac{ \\mathrm{d}\\Omega_{d-2}}{(2\\pi)^{d-2}} {\\nonumber}\\\\\n\t&= \\frac{1}{2(2\\pi)^{d-1}} \\int \\frac{\\mathrm{d} p_a^-}{p_a^-} \\theta(p_a^-) |p_a^-\/2|^2\\int \\mathrm{d} \\Omega_{d-2} = C \\times \\int \\mathrm{d} p_a^- \\,p_a^-\\,, \n\\end{align}\nwe can write the cross section with a single emission as\n\\begin{align}\n\t\\frac{\\mathrm{d} \\sigma_{\\text{\\text{EEC}}}}{\\mathrm{d} y} &\\sim \\frac{1}{2 (1-y)^3}\\sum_{a,b} \\int {\\mathrm{d}\\!\\!\\!{}^-}^d k \\delta_+(k^2) |\\mathcal{M}_{V \\to q\\bar{q} g}|^2\\, (p_a \\cdot p_b)^2\\,\\delta\\left({\\vec{k}_\\perp^{\\,2}} - q_V^2\\frac{y}{1-y}\\right){\\nonumber}\\,\\\\\n\t&\\sim \\frac{1}{2 (1-y)^3}\\sum_{a,b} \\int_0^{q^-} \\frac{\\mathrm{d} k^-}{k^-} \\int \\mathrm{d} {\\vec{k}_\\perp^{\\,2}} \\frac{\\mu^{2\\epsilon}}{{\\vec{k}_\\perp^{\\,2\\epsilon}}} |\\mathcal{M}_{V \\to q\\bar{q} g}|^2\\, (p_a \\cdot p_b)^2\\,\\delta\\left({\\vec{k}_\\perp^{\\,2}} - q_V^2\\frac{y}{1-y}\\right){\\nonumber}\\,\\\\\n\t&\\sim \\frac{1}{2 (1-y)^3}\\left(\\frac{\\mu^{2}}{q_V^2}\\frac{1-y}{y}\\right)^\\epsilon \\sum_{a,b} \\int_0^{q^-} \\frac{\\mathrm{d} k^-}{k^-} |\\mathcal{M}_{V \\to q\\bar{q} g}|^2(k^-,y)\\, (p_a \\cdot p_b)^2 (k^-,y)\\,,\n\\end{align}\nwhere everything is a function of $k^-$, $y$ and $q_V^2$. Up to this point, we have not expanded anything, but we have enforced the measurement, momentum conservation and the choice of frame. We can now express $k^-$ as a dimensionless fraction $x$ via\n\\begin{equation}\\label{eq:xdefinition}\n\tx = \\frac{k^-}{q^-}\\,,\n\\end{equation} \nand we can write all Mandelstam invariants in terms of $x$ and $y$\n\\begin{align}\n\tk^-&= x q^- \\,,\\qquad {\\vec{k}_\\perp^{\\,2}} = q_V^2\\frac{y}{1-y}\\,,\\qquad k^+ = \\frac{{\\vec{k}_\\perp^{\\,2}}}{k^-} = \\frac{q_V^2}{q^-}\\frac{y}{1-y} \\frac{1}{x}\\,,{\\nonumber} \\\\\n\tq^2_V &\\equiv q^+ q^- - {\\vec{k}_\\perp^{\\,2}} = q_V^+ q_V^- - q_V^2\\frac{y}{1-y} \\quad\\implies\\quad q_V^2 = q^+_V q^-_V(1-y)\\,, {\\nonumber} \\\\\n\ts_{ab}(x,y) &= 2p_a \\cdot p_b = (q_V^- - k^-)(q_V^+ - k^+) = q_V^+q_V^- (1-x)\\left(1 - \\frac{y}{x}\\right) {\\nonumber} \\\\\n\t&= q_V^2 \\frac{(1-x)}{(1-y)}\\left(1 - \\frac{y}{x}\\right) \\,,{\\nonumber}\\\\\n\ts_{ak}(x,y) &= 2p_a \\cdot k = (q_V^- - k^-)k^+ = q_V^2\\frac{y}{(1-y)}\\frac{(1-x)}{x}\\,,{\\nonumber}\\\\\n\ts_{bk}(x,y) &= 2p_b \\cdot k = (q_V^+ - k^+)k^- = q_V^2 \\frac{x}{(1-y)}\\left(1 - \\frac{y}{x}\\right)\\,.\n\\end{align}\nWith these expressions one can easily check that\n\\begin{equation}\n\ts_{ab}+ s_{ak}+ s_{kb} = q_V^2\\,,\n\\end{equation}\nwith no power corrections.\nWe can now use the expressions for $s_{ab}$ to arrive at\n\\begin{align}\n\t\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}}{\\mathrm{d} y} &= \\frac{1}{(1-y)^5}\\left(\\frac{\\mu^{2}}{q_V^2}\\frac{1-y}{y}\\right)^\\epsilon \\sum_{a,b} \\int_0^{1} \\frac{\\mathrm{d} x}{x} (1-x)^2 \\left(1 - \\frac{y}{x}\\right)^2|\\mathcal{M}_{V \\to q\\bar{q} g}|^2(x,y)\\,.\n\\end{align}\nExpanding in the collinear limit is now the same as expanding in $y$, and we find up to NLP\n\\begin{align}\\label{eq:collinearMasterUnreg}\n\t\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}}{\\mathrm{d} y} &=\\left(\\frac{\\mu^{2}}{q_V^2 y}\\right)^\\epsilon \\sum_{a,b} \\int_0^{1} \\frac{\\mathrm{d} x}{x} (1-x)^2 \\left[A^{(0)}(x)+A^{(2)}(x) + y A^{(0)}(x)\\left( 5-\\frac{2}{x} -\\epsilon \\right) \\right]\\,.\n\\end{align}\nHere $A^{(0)}(x)$ and $A^{(2)}(x)$ are the expansion of the squared matrix elements in the collinear limits,\n\\begin{align}\n|\\mathcal{M}_{V \\to q\\bar{q} g}|^2(x,y)=A^{(0)}(x)+A^{(2)}(x) + y A^{(0)}(x)+\\cdots.\n\\end{align}\n\n\\subsection*{Rapidity Regulator}\n\nThe expression for the EEC in \\eq{collinearMasterUnreg} is divergent as $x \\to 0$. This is a rapidity divergence and must be regulated with a rapidity regulator. Here we present the result using pure rapidity regularization \\cite{Ebert:2018gsn}, which greatly simplifies the calculation, particularly for the constant (the non-logarithmically enhanced term). We have also computed the logarithm using the more standard $\\eta$-regulator \\cite{Chiu:2012ir,Chiu:2011qc}, and find an identical result.\n\n\nWe take as a regulator the rapidity in the $n$-collinear sector, normalized by the rapidity of the color singlet\\footnote{In the rest frame of the decaying boson this would be 1. Here we use a slightly boosted frame, so adding this factor is necessary to guarantee that the result is independent of the frame.}\n\\begin{equation}\n\te^{-Y_V}e^Y_n = \\frac{q_V^+}{q_V^-}\\frac{p_1^- + k^-}{p_1^+ + k^+} = \\frac{q_V^+}{k^+} = \\frac{q_V^+ q_V^- x}{{\\vec{k}_\\perp^{\\,2}}} = \\frac{q_V^+ q_V^- x (1-y)}{q_V^2 y } = \\frac{x}{y}\\,.\n\\end{equation}\nThe rapidity regulated result is then given by\n\\begin{align}\\label{eq:collinearMasterReg}\n\t\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}}{\\mathrm{d} y} &=\\left(\\frac{\\mu^{2}}{q_V^2 y}\\right)^\\epsilon \\frac{y^\\eta}{\\upsilon^\\eta} \\sum_{a,b} \\int_0^{1} \\frac{\\mathrm{d} x}{x^{1+\\eta}} (1-x)^2 \\left[A^{(2)}(x) + y A^{(0)}(x)\\left( 5-\\frac{2}{x} -\\epsilon \\right) \\right]\\,,\n\\end{align}\nwhich can be straightforwardly integrated.\n\n\\subsection*{Results}\n\nPlugging in the expression for $A^{(0)}$ and $A^{(2)}$ in QCD and $\\mathcal{N}=4$ gives the result for the EEC up to NLP\n\\begin{align}\\label{eq:results}\n\t\\frac{1}{\\sigma_0}\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}^\\text{QCD}}{\\mathrm{d} y} &= -\\frac{1}{y}\\left(\\log y + \\frac{3}{4}\\right) - 5\\log y-\\frac{9}{4} +\\mathcal{O}(y)\\,, {\\nonumber} \\\\\n\t\\frac{1}{\\sigma_0}\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}^{\\mathcal{N}=4}}{\\mathrm{d} y} &=- \\frac{\\log y}{y}- 2\\log y +\\mathcal{O}(y)\\,.\n\\end{align}\nThis agrees with the expansion of the full angle result for $\\mathcal{N}=4$ in \\cite{Belitsky:2013ofa,Belitsky:2013xxa,Belitsky:2013bja}, as well as the classic QCD result \\cite{Basham:1978bw} for both the logarithm and the constant. This agreement (in particular for the constant) illustrates that we have the correct effective field theory setup, and that we understand the regularization of rapidity divergences at subleading power. This is further supported by our calculation of the subleading power corrections for the case of $p_T$ in \\cite{Ebert:2018gsn}, which was performed using the same formalism. While this expansion is an inefficient way to compute these subleading terms, which can much more easily be obtained by performing the full calculation and expanding the result, the ability to systematically compute the terms at each order in the power expansion will allow us to perform an all orders resummation by deriving renormalization group evolution equations in rapidity.\n\n\n\\subsection{Physical Intuition for Subleading Power Rapidity Divergences}\\label{sec:intuition}\n\nIn this section we wish to summarize some of the general lessons learned from the above fixed order calculation (as well as from the calculation of the fixed order subleading rapidity logarithms for $p_T$ in \\cite{Ebert:2018gsn}), which provides significant clues into the structure of the subleading power rapidity renormalization group. Since we have shown above that the description of the EEC in the back-to-back limit is ultimately formulated in the EFT in terms of transverse momentum, here we will phrase all the functions in terms of transverse momentum, however, these can straightforwardly be converted back to derive results for the EEC.\nWe will also phrase this discussion in terms of the $\\eta$ regulator \\cite{Chiu:2012ir,Chiu:2011qc}, due to the fact that it is more familiar for most readers.\n\nThe first important observation from the fixed order calculation is that at NLP there are no purely virtual corrections at lowest order in $\\alpha_s$ (such a correction would appear as $y\\delta(y)=0$). The lowest order result for the EEC in $\\mathcal{N}=4$, which we use as an example due to its simpler structure, is given by\n\\begin{align}\\label{eq:N4_NLP_fixedorder}\n\t\\frac{1}{\\sigma_0}\\frac{\\mathrm{d} \\sigma_{\\text{EEC}}^{\\mathcal{N}=4}}{\\mathrm{d} (1-z)} &= - 2\\log(1-z) +\\mathcal{O}(1-z)\\,.\n\\end{align}\nHere the single logarithm comes from real soft or collinear emissions. Since the soft and collinear sectors lie at the same virtuality, this guarantees that at subleading power the lowest order logarithm must be a rapidity logarithm. This can be made explicit by writing down a general ansatz for the one-loop result. This approach was first introduced in the SCET$_{\\rm I}$~ case in \\cite{Moult:2016fqy}. Here we will phrase it in terms of the underlying $p_T$ dependence, since this is perhaps more familiar to the resummation community. The general form of the one-loop result for the NLP corrections to $p_T$ can be written as\n\\begin{align}\n\\frac{d\\sigma^{(2)}}{dp_T^2}=\\left( \\frac{\\mu^2}{p_T^2} \\right)^\\epsilon \\left( \\frac{\\nu}{p_T} \\right)^\\eta \\left[ \\frac{s_\\epsilon}{\\epsilon}+\\frac{s_\\eta}{\\eta} \\right] +\\left( \\frac{\\mu^2}{p_T^2} \\right)^\\epsilon \\left( \\frac{\\nu}{Q} \\right)^\\eta \\left[ \\frac{c_\\epsilon}{\\epsilon}+\\frac{c_\\eta}{\\eta} \\right] \\,.\n\\end{align}\nExpanding this, we find\n\\begin{align}\n\\frac{d\\sigma^{(2)}}{dp_T^2}=\\left( \\frac{c_\\epsilon+s_\\epsilon}{\\epsilon} \\right) + \\left( \\frac{c_\\eta+s_\\eta}{\\eta} \\right) +2(c_\\epsilon+s_\\epsilon) \\log\\frac{\\mu}{p_T} +s_\\eta \\log \\frac{\\nu}{p_T} +c_\\eta \\log\\frac{\\nu}{Q}\\,.\n\\end{align}\nDemanding that there are no $1\/\\epsilon$ or $1\/\\eta$ poles in the final answer imposes the conditions \n\\begin{align}\nc_\\epsilon=-s_\\epsilon\\,, \\qquad c_\\eta=-s_\\eta\\,,\n\\end{align}\nand shows that the lowest order logarithm appearing in the cross section is a rapidity logarithm.\n\nThis simple observation provides considerable insight into the structure of the subleading power renormalization group evolution equations. In \\cite{Moult:2018jjd} it was shown that the way that a single logarithm at the first non-vanishing order can be generated is through renormalization group mixing. In the particular case studied in \\cite{Moult:2018jjd} there was only a virtuality renormalization group, and therefore the single logarithm was generated by the $\\mu$-RGE. However, here we see that for observables that have both a $\\mu$ and $\\nu$ RGE, this mixing will always occur in the $\\nu$ RGE, since the lowest order logarithm is always a rapidity logarithm. \n\nOur fixed order calculation also provides insight into the structure of the cancellation of rapidity divergences at subleading power. Although we have not written down a complete set of SCET$_{\\rm II}$~operators, we briefly comment on the physical intuition for the cancellation of rapidity anomalous dimensions, which will then determine how renormalization group consistency appears in the effective theory. We can consider the case of the NLP correction from a soft quark, since this will provide the clearest picture of the differences between rapidity divergences and virtuality divergences. At lowest order, the soft function for the emission of a soft quark is given by\n\\begin{align}\\label{eq:quark_soft}\nS^{(2)}_q&= \\fd{3cm}{figures\/soft_quark_scetii_low}{\\nonumber} \\\\\n&\\propto \\mu^{2\\epsilon} \\nu^\\eta \\int d\\hspace*{-0.08em}\\bar{}\\hspace*{0.1em}^d k |k^+-k^-|^{-\\eta} \\delta^+ (k^2) \\frac{(p_\\perp^2)^{-\\epsilon}}{\\Gamma(1-\\epsilon) \\pi^\\epsilon} \\delta^{d-2} (p_\\perp-\\hat \\mathcal{P}_\\perp) \\\\\n&=\\frac{1}{\\eta} + \\log{\\frac{\\nu}{p_\\perp}} +\\cdots\\,.\n\\end{align}\nThis soft function is $\\epsilon$ finite, but exhibits the expected $\\eta$ divergence. This divergence cannot be absorbed into the renormalization of this soft function, since it starts at $\\alpha_s$, and therefore we must find the class of operators that this soft function mixes into. We will derive the structure of these operators in \\Sec{sec:RRG_NLP}.\n\nWhile the fact that this operator must mix into a new operator is familiar from other studies at NLP, what is different is that the integrand for the soft function is ``1\". This implies that an equal part of the divergence comes from when the quark goes to infinite rapidity in either direction. This has an interesting interpretation, which can guide the physical intuition for how the cancellation of rapidity divergences occurs. As the soft quark goes collinear in the direction of the collinear quark, the rapidity divergence must be cancelled by a collinear rapidity divergence from a subleading power jet function describing two collinear quarks. One the other hand, as the soft quark goes collinear with the gluon, it must be cancelled by a subleading power jet function involving a collinear quark and gluon field. In pictures, the two limits are shown as\n\\begin{align}\n\\fd{3cm}{figures\/qq_op_low.pdf}\\xleftarrow[]{\\eta\\to -\\infty} \\fd{3cm}{figures\/soft_quark_low.pdf} \\xrightarrow[]{\\eta\\to \\infty} \\fd{3cm}{figures\/qg_collinear_low.pdf}\\,.\n\\end{align}\nThis is of course exactly what happens at leading power, however, at leading power the jet functions are identical in all limits, giving a much simpler structure. The structure for the cancellation of the rapidity divergences at subleading power implies that renormalization group consistent subsets of operators will appear in triplets, as opposed to doublets, as was observed in \\cite{Moult:2018jjd} for the $\\mu$-RGE. The factorization formula for a single pair of triplets will take the (extremely) schematic form\n\\begin{align}\n\\frac{d\\sigma^{(2)}}{dz}&=H_1 J_{\\bar n}^{(2)} J_n^{(0)} S^{(0)} \n+H_2 J_{\\bar n}^{(0)} J_n^{(2)} S^{(0)}\n+H_3 J_{\\bar n}^{(0)} J_n^{(0)} S^{(2)}\\,,\n\\end{align}\nwhere the jet and soft functions with superscript $(2)$ denote power suppressed functions. In terms of pictures, we have\n\\begin{align}\\label{eq:schematic_factorization}\n\\frac{d\\sigma^{(2)}}{dz}&={\\nonumber} \\\\\n&\\underbrace{ \\left| \\fd{1.5cm}{figures\/LP_hardfunc_low.pdf}~ \\right|^2 \\cdot \\int dr_2^+ dr_3^+ \\fd{3cm}{figures\/soft_quark_collinear_piece_purjet_low.pdf}\\otimes \\fd{3cm}{figures\/1quark_jetfunction_low} \\otimes \\fd{3cm}{figures\/soft_quark_diagram_wilsonframe_low.pdf} }_{\\text{Soft Quark Correction}}{\\nonumber} \\\\\n +&\\underbrace{ \\int d \\omega_1 d \\omega_2 \\left| \\fd{1.5cm}{figures\/NLP_hard_2quark_low}~ \\right|^2\\otimes\\fd{3cm}{figures\/2quark_jetfunc_low}\\otimes \\fd{3cm}{figures\/1gluon_jetfunc_low}\\otimes \\fd{3cm}{figures\/eikonal_factor_gluon_low.pdf} }_{\\text{Collinear Quark Correction 1}} {\\nonumber} \\\\\n +&\\underbrace{ \\int d \\omega_1 d \\omega_2 \\left| \\fd{1.5cm}{figures\/NLP_1quark1gluon_hard_low}~\\right|^2 \\otimes~\\fd{3cm}{figures\/1quark_jetfunction_low}\\otimes \\fd{3cm}{figures\/1quark1gluon_jetfunc_low}\\otimes \\fd{3cm}{figures\/eikonal_factor_gluon_low.pdf}}_{\\text{Collinear Quark Correction 2}}\\,,\n\\end{align}\nwhich perhaps makes more clear schematically how the cancellation between $\\nu$ anomalous dimension occurs between the soft and collinear sectors of the theory. A similar triplet exists from the corrections associated with soft gluon emission. Each of the power suppressed functions in \\Eq{eq:schematic_factorization} will mix in rapidity, as was illustrated for the soft function in \\Eq{eq:quark_soft}. To perform the renormalizaton, we must therefore identify which operators are being mixed into in each case.\n\nIt is interesting to compare this to the structure of the subleading power factorization for SCET$_{\\rm I}$~ event shapes, which is described in \\cite{Moult:2018jjd,Moult:2019uhz}. There subleading power jet functions involving two quark fields, and those involving a quark and gluon field are in separately renormalization group consistent pairings. Therefore, we find that the SCET$_{\\rm II}$~ case gives rise to a much tighter structure in the EFT, since it links multiple hard scattering operators. Note that this also suggests that the issue of endpoint divergences is harder to avoid, although this is a topic that we leave for future study.\n\nIn conclusion, we have learned a number of general lessons about the subleading power $\\nu$-RG from our perturbative calculation. In particular, we have seen that subleading power corrections to the EEC (and more generally any observable that involves both rapidity and virtuality renormalization group flows) involve a mixing in rapidity at the first non-trivial order into a new class of operators. In \\Sec{sec:RRG_NLP} we will derive the structure of these new operators by studying the consistency of the structure of the renormalization group.\n\n\\section{Rapidity Renormalization Group at Subleading Power}\\label{sec:RRG_NLP}\n\nIn the previous section we have shown that for the EEC, and more generally for subleading corrections to observables with both rapidity and virtuality scales, the subleading power rapidity RGE will always involve mixing into additional operators. The goal of this section will be to derive the structure of the operators that arise from this mixing, as well as their renormalization group properties. As in our study of thrust at subleading powers \\cite{Moult:2018jjd}, our approach to gain a handle on the structure of the RG equations at subleading power will be to use an illustrative example of subleading power jet and soft functions whose renormalization group structure can be obtained from the known leading power RG equations. Once the particular form of the operators is derived, they can then be applied in other situations. \n\nWe will find that for the case of rapidity divergences considered here, the use of an illustrative example is more subtle than for the $\\mu$ RGE due to the appearance of additional divergences that appear. We will also find that due to this, there are multiple (two distinct) operators that can be mixed into. Therefore, our analysis in \\Sec{sec:example} should be viewed as providing motivation for the type of operators that appear, although in the initial form that they arise, they will involve unregularized integrals. With this motivation for their structure, we are then able to use the commutativity of the $\\mu$ and $\\nu$ RGEs in \\Sec{sec:consistency} to fix the RG properties of these operators and provide regularized definitions. We then solve the associated evolution equations for the newly introduced operators in \\Sec{sec:solution}.\n\n\\subsection{An Illustrative Example}\\label{sec:example}\n\nWe will begin by considering the LP soft function for $p_T$, defined as\n\\begin{align}\n S(\\vec p_T) &= \\frac{1}{N_c^2 - 1} \\big\\langle 0 \\big| \\mathrm{Tr} \\bigl\\{\n \\mathrm{T} \\big[\\mathcal{S}^\\dagger_{\\bn} \\mathcal{S}_n\\big]\n \\delta^{(2)}(\\vec p_T-\\mathcal{P}_\\perp) \\overline{\\mathrm{T}} \\big[\\mathcal{S}^\\dagger_{n} \\mathcal{S}_{\\bn} \\big]\n \\bigr\\} \\big| 0 \\big\\rangle\n\\,.\\end{align}\nThis soft function satisfies the $\\mu$ and $\\nu$ RGEs\n\\begin{align}\n\\nu \\frac{d}{d\\nu}S(\\vec p_T)&=\\int d\\vec q_T \\gamma^S_\\nu(p_T-q_T) S(\\vec q_T)\\,, {\\nonumber} \\\\\n\\mu \\frac{d}{d\\mu}S(\\vec p_T)&=\\gamma_\\mu^S S(\\vec p_T)\\,,\n\\end{align}\nwith the anomalous dimensions\n\\begin{align}\n\\gamma_\\mu^S &=4\\Gamma^{\\mathrm{cusp}}(\\alpha_s)\\log \\left( \\frac{\\mu}{\\nu}\\right)\\,,{\\nonumber} \\\\\n\\gamma_\\nu^S &=2\\Gamma^{\\mathrm{cusp}} (\\alpha_s)\\mathcal{L}_0\\left( \\vec p_T,\\mu \\right)\\,.\n\\end{align}\nCrucially, the $\\mu$ anomalous dimension is multiplicative, while the $\\nu$ anomalous dimension is a convolution in $p_T$ space. It is this fact that will ultimately lead to the subleading power rapidity renormalization group having a more interesting structure. \n\nA simple trick to understand the structure of subleading power RG equations that was first used in \\cite{Moult:2018jjd} is to consider jet and soft functions obtained by multiplying the leading power jet and soft functions by a kinematic invariant. In the present case, we \ncan consider the subleading power soft function defined by\n\\begin{align}\nS_{p_T^2}^{(2)}(\\vec p_T)=\\vec p_T^2 S(\\vec p_T)\\,.\n\\end{align}\nThe superscript $(2)$ indicates that this function is power suppressed due to the explicit factor of $p_T^2$, and the subscript $p_T^2$ is meant to identify the nature of this power suppression. The structure of this function is known to all orders, since it is inherited from the known structure of the leading power soft function. However, understanding how this structure is manifested in the renormalization group structure of $S_{p_T^2}^{(2)}$ is non-trivial, and will reveal new operators.\n\n\n\nFirst, we note that the $\\mu$ RGE for this subleading power soft function is identical to the RGE of the leading power soft function, since the $\\mu$ RGE is multiplicative. We therefore have\n\\begin{align}\n\\mu \\frac{d}{d\\mu}S_{p_T^2}^{(2)}(\\vec p_T)=\\gamma^\\mu_S S_{p_T^2}^{(2)}(\\vec p_T)\\,.\n\\end{align}\nHowever, we find a more interesting behavior for the $\\nu$ RGE, due to the fact that it is a convolution in $p_T$. Multiplying both sides of the LP RGE by $\\vec p_T^2$, and using the identity\n\\begin{align}\\label{eq:pt_expand}\n\\vec p_T^2 =(\\vec p_T-\\vec q_T)^2 +\\vec q_T^2 +2(\\vec p_T- \\vec q_T) \\cdot \\vec q_T\\,,\n\\end{align}\nwe arrive at the equation\n\\begin{align}\n\\nu \\frac{d}{d\\nu}S_{p_T^2}^{(2)}(\\vec p_T)&=\\int d\\vec q_T (\\vec p_T-\\vec q_T)^2 \\gamma_S(p_T-q_T) S(q_T) \\\\\n&+\\int d\\vec q_T \\gamma_S(p_T-q_T) \\left[ 2(\\vec p_T- \\vec q_T) \\cdot \\vec q_T S(q_T)\\right] + \\int d\\vec q_T \\gamma_S(p_T-q_T) \\left[ \\vec q_T^2 S(\\vec q_T)\\right]\\,. {\\nonumber}\n\\end{align}\nNote that we must arrange \\Eq{eq:pt_expand} in this form, so that it is a kernel in $\\vec p_T- \\vec q_T$ multiplying a function of $q_T$.\nSimplifying this result, we find that we can write it as\n\\begin{align}\n\\nu \\frac{d}{d\\nu}S_{p_T^2}^{(2)}(\\vec p_T)&=2\\Gamma^{\\mathrm{cusp}} {\\mathbb{I}}_S \\\\\n&+\\int d\\vec q_T \\gamma_S(p_T-q_T) 2(\\vec p_T- \\vec q_T) \\cdot \\vec S^{(1)}(q_T) +\\int d\\vec q_T \\gamma_S(p_T-q_T) S_{p_T^2}^{(2)}(\\vec q_T)\\,.{\\nonumber}\n\\end{align}\nHere we see a renormalization group mixing with two power suppressed functions. The first is \n\\begin{align}\n{\\mathbb{I}}_S\\propto\\int d^2\\vec q_T~ S(\\vec q_T)\\,,\n\\end{align}\nwhich we will refer to as the ``rapidity identity operator\", and will play a crucial role in our subsequent analysis. As written, the integral over $q_T$ goes to infinity, and therefore this expression is ill-defined, and will require regularization. For this reason we have also been glib about what this operator depends on. In the next section we will present a way of deriving its renormalization group properties, as well as a regularized definition. The goal of this section is merely to illustrate that the subleading power soft function mixes into a new operator which is loosely related to a moment of the leading power soft function. The second operator arising in the mixing is \n\\begin{align}\n\\vec S^{(1)}(\\vec p_T)=\\vec p_T S^{(0)}(\\vec p_T)\\,,\n\\end{align}\nwhich is a vector soft function which scales like $\\mathcal{O}(\\lambda)$. This function can only appear in a factorization formula if it is dotted into a vector jet function, or some other vector quantity. \n\n\nWhile we believe that it would be extremely interesting to study in more detail the complete structure of this illustrative example, and we will return to this in future work, here we focus only on the elements of these equations that are required at leading logarithmic accuracy. We note that\n\\begin{align}\n{\\mathbb{I}}_S=1+\\mathcal{O}(\\alpha_s)\\,, \\qquad S^{(1)}(\\vec p_T)=0+\\mathcal{O}(\\alpha_s)\\,.\n\\end{align}\nTherefore, in the leading logarithmic series, $S_{p_T^2}^{(2)}$ mixes into ${\\mathbb{I}}_S$, and we can ignore $S^{(1)}$. We can therefore simplify our equation to\n\\begin{align}\n\\nu \\frac{d}{d\\nu}S_{p_T^2}^{(2)}(\\vec p_T,\\mu,\\nu)&=2\\Gamma^{\\mathrm{cusp}} {\\mathbb{I}}_S +\\int d\\vec q_T \\gamma_S(p_T-q_T) S_{p_T^2}^{(2)}(\\vec q_T,\\mu,\\nu)\\,.\n\\end{align}\nWe see that what is occurring is that the power suppressed soft function is mixing with the rapidity identity operator. This provides a renormalization group derivation of the perturbative calculations in \\Sec{sec:FO}, where one generates a rapidity divergence and associated logarithm at the lowest non-trivial order. This is simply a perturbative description of the mixing into ${\\mathbb{I}}_S$. Now, with this general structure in mind, we would like to understand the properties of this rapidity identity operator.\n\n\n\nWe emphasize again that we have performed only a cursory study of this illustrative example so as to be able to illustrate the renormalization group mixing in rapidity, and to identify the structure of the rapidity identity operator. It would be particularly interesting to study the complete structure of this illustrative example to all logarithmic orders, and in particular to better understand the structure of the convolutions in $p_T$. However, since the focus of this paper is on deriving the leading logarithmic series for the EEC, we will leave this to future work.\n\n\n\n\n\\subsection{Rapidity Identity Operators}\\label{sec:consistency}\n\n\n\nIn the previous section, we found that the subleading power rapidity renormalization group involves a mixing into the rapidity identity operator, which is loosely related to the first moment of the leading power soft function\n\\begin{align}\\label{eq:identity_unregularized}\n{\\mathbb{I}}_S\\propto\\int d^2\\vec q_T~ S(\\vec q_T)\\,.\n\\end{align}\nThe goal of this section will be to understand how to make sense of this operator, since it is ill-defined as currently written. This is a crucial difference as compared with the case of thrust considered in \\cite{Moult:2018jjd}. There a similar first moment operator appears, defined as \\cite{Moult:2018jjd}\n\\begin{align}\\label{eq:theta_soft_first}\nS^{(2)}_{g,\\theta}(k,\\mu)&= \\frac{1}{(N_c^2-1)} {\\rm tr} \\langle 0 | \\mathcal{Y}^T_{\\bar n} (0)\\mathcal{Y}_n(0) \\theta(k-\\hat \\mathcal{T}) \\mathcal{Y}_n^T(0) \\mathcal{Y}_{\\bar n}(0) |0\\rangle\\,.\n\\end{align}\nHowever, there the first moment is a finite integral, and does not introduce new divergences, allowing all the properties of this operator to be immediately deduced from those of the leading power operator. For the case of $p_T$ considered here, additional arguments must be used to fully fix the structure of the rapidity identity operator.\n\n\nThe $\\mu^2$ dependence of the rapidity identity operator can be derived using the commutativity of the RG \\cite{Chiu:2012ir,Chiu:2011qc}, namely that\n\\begin{align}\n\\left[ \\frac{d}{d\\mu}, \\frac{d}{d\\nu} \\right]=0\\,,\n\\end{align}\nwhich ensures the path independence of the $\\mu$ and $\\nu$ rapidity evolution. Here we consider a general subleading power soft operator $S^{(2)}$ (not necessarily $S_{p_T^2}^{(2)}$). If we assume that this operator mixes into a single identity type operator, then we obtain\n\\begin{align}\n\\mu \\frac{d}{d\\mu} \\left[ \\nu \\frac{d}{d\\nu} S^{(2)} \\right]&=\\mu \\frac{d}{d\\mu} \\left[ \\gamma_{\\delta{\\mathbb{I}} } {\\mathbb{I}}_S+\\gamma_\\nu S^{(2)} \\right]\\,,{\\nonumber} \\\\\n\\nu \\frac{d}{d\\nu} \\left[ \\mu \\frac{d}{d\\mu} S^{(2)} \\right]&= \\nu\\frac{d}{d\\nu} \\left[ \\gamma^S_\\mu S^{(2)} \\right]\\,.\n\\end{align}\nHere, we have used $\\gamma_{\\delta{\\mathbb{I}} }$ to denote the mixing anomalous dimension.\nPerforming the next differentiation, we then obtain the equality\n\\begin{align}\n\\gamma_{\\delta{\\mathbb{I}} } \\mu \\frac{d}{d\\mu} {\\mathbb{I}}_S +\\left[ \\mu \\frac{d}{d\\mu} \\gamma_\\nu \\right]S^{(2)} +\\gamma_\\nu \\gamma_\\mu^S S^{(2)} =\\gamma_\\mu^S \\gamma_{\\delta{\\mathbb{I}} } {\\mathbb{I}}_S +\\left[ \\nu \\frac{d}{d\\nu} \\right]S^{(2)} +\\gamma_\\nu \\gamma_\\mu^S S^{(2)}\\,.\n\\end{align}\nUsing the fact that commutativity is satisfied for the leading power anomalous dimensions, we arrive at\n\\begin{align}\n\\mu \\frac{d}{d\\mu}{\\mathbb{I}}_S = \\gamma_\\mu^S {\\mathbb{I}}_S\\,.\n\\end{align}\nThis fixes the $\\mu$ anomalous dimension of the rapidity identity operator. \n\n\nTo fix the $\\nu$ anomalous dimension, we now apply commutativity to ${\\mathbb{I}}_S$ itself, and use the fact that we know the $\\mu$ anomalous dimension. We then have\n\\begin{align}\n\\left[ \\frac{d}{d\\mu}, \\frac{d}{d\\nu} \\right] {\\mathbb{I}}_S =0\\,,\n\\end{align}\nwhich gives the equation (at lowest order in $\\alpha_s$, which is sufficient for LL)\n\\begin{align}\n\\mu \\frac{d}{d\\mu} \\left( \\nu \\frac{d}{d\\nu} \\right) {\\mathbb{I}}_S=-4 \\Gamma^{\\mathrm{cusp}}\\,,\n\\end{align}\nwhich can then be solved for\n\\begin{align}\n\\nu \\frac{d}{d\\nu} {\\mathbb{I}}_S= -4 \\Gamma^{\\mathrm{cusp}} \\log \\left( \\frac{\\mu^2}{\\Lambda^2} \\right)\\,,\n\\end{align}\nwhere $\\Lambda^2$ is an as yet to be determined scale. The only available scales at leading logarithmic accuracy are $\\Lambda^2=p_T^2, \\mu^2, \\nu^2$. The cases $\\Lambda^2=p_T^2, \\nu^2$ both give the same behavior for the $\\nu$ RGE on the hyperbola $\\mu=p_T$, and therefore we will not treat them separately. It would be interesting to explore in more detail the differences between these RGEs.\n\n\nWe therefore find two distinct identity operators with two different rapidity anomalous dimensions\n\\begin{align}\n\\nu \\frac{d}{d\\nu}{\\mathbb{I}}^\\nu_S(\\mu, \\nu) &= -\\gamma_\\mu^S {\\mathbb{I}}^\\nu_S(\\mu, \\nu)\\,, {\\nonumber} \\\\\n\\nu \\frac{d}{d\\nu}{\\mathbb{I}}^{p_T^2}_S(p_T^2,\\mu, \\nu) &= 2 \\Gamma^{\\mathrm{cusp}} \\log \\left( \\frac{p_T^2}{\\mu^2} \\right) {\\mathbb{I}}^{p_T^2}_S(p_T^2,\\mu, \\nu)\\,.\n\\end{align}\nHere we have again used the superscript $p_T^2$ to indicate the identity function that the $S_{p_T^2}^{(2)}$ function mixes into, as we will argue shortly, and the superscript $\\nu$ to indicate the identity function that has a non-trivial $\\nu$ RGE on the $\\mu=p_T$ hyperbola.\n\nWhile this argument allows us to derive the renormalization group properties of these operators, which is sufficient for the purposes of this paper, it is also interesting to give explicit example of functions that realize this behavior. This is easy to do by defining regularizations of the integral in \\Eq{eq:identity_unregularized}. \nFunctions which give the behavior of the different identity operators at LL are\n\\begin{align}\n{\\mathbb{I}}^\\nu_S(\\mu, \\nu)&=\\int_0^{\\nu^2} d^2\\vec q_T~ S(\\vec q_T)\\,, \\\\\n{\\mathbb{I}}_S^{p_T^2}(p_T^2,\\mu, \\nu)&=\\int_0^{p_T^2} d^2\\vec q_T~ S(\\vec q_T)\\,.\n\\end{align}\nThe first function is a function of only $\\mu^2\/\\nu^2$, while the second also depends on $p_T^2$. These two functions have different properties under boosts, and therefore do not themselves mix. This provides leading logarithmic definitions of the rapidity identity operators. It would be extremely interesting to understand how to extend these definitions beyond leading logarithm, however, we leave this to future work.\n\n\nFor the particular soft function considered in our illustrative example, $S_{p_T^2}^{(2)}=p_T^2 S^{(0)}$, one can use the knowledge of the two loop soft function to show that it is the operator ${\\mathbb{I}}^{p_T^2}_S(p_T^2,\\mu, \\nu)$ that is being mixed into. This can also be argued directly by symmetry grounds: at the scale $\\mu=p_T$, the leading power soft function does not flow in $\\nu$ at leading logarithmic accuracy. This is due to its boost invariance. This property is not broken by multiplying by $p_T^2$, and therefore must also be a property of the counterterm operator that is being mixed with. This identifies the operator ${\\mathbb{I}}^{p_T^2}_S(p_T^2,\\mu, \\nu)$. We can therefore make more precise the equation earlier for the RG of this function\n\\begin{align}\n\\nu \\frac{d}{d\\nu}S_{p_T^2}^{(2)}(\\vec p_T,\\mu,\\nu)&=2\\Gamma^{\\mathrm{cusp}} {\\mathbb{I}}^{p_T^2}_S(\\vec p_T,\\mu,\\nu) +\\int d\\vec q_T \\gamma_S(p_T-q_T) S_{p_T^2}^{(2)}(\\vec q_T,\\mu,\\nu)\\,.\n\\end{align}\nFor subleading power soft functions with explicit fields inserted, this argument no longer holds, and one can mix into the other operator.\n\nIt is also interesting to arrive at these conclusions for the structure of the renormalization group by manipulation of the renormalization group equations. This approach is ultimately ill-defined due to the lack of convergence of the integrals, but it gives the same results as derived from the commutativity of the RG, and provides additional insight into the origin of this behavior.\nRecall that the leading power renormalization group evolution equations are\n\\begin{align}\n\\nu \\frac{d}{d\\nu}S(\\vec p_T)&=\\int d\\vec q_T \\gamma^S_\\nu(p_T-q_T) S(q_T)\\,, {\\nonumber} \\\\\n\\mu \\frac{d}{d\\mu}S(\\vec p_T)&=\\gamma_\\mu^S S(\\vec p_T)\\,,\n\\end{align}\nwith the anomalous dimensions\n\\begin{align}\n\\gamma_\\mu^S &=4\\Gamma^{\\mathrm{cusp}}(\\alpha_s)\\log \\left( \\frac{\\mu}{\\nu}\\right)\\,,{\\nonumber} \\\\\n\\gamma_\\nu^S &=2\\Gamma^{\\mathrm{cusp}} (\\alpha_s)\\mathcal{L}_0\\left( \\vec p_T,\\mu \\right)\\,.\n\\end{align}\nSince the $\\mu$ anomalous dimension is multiplicative, this should not be changed if we integrate over $q_T$. In other words, we have\n\\begin{align}\n\\int d^2 p_T \\left[ \\mu \\frac{d}{d\\mu}S(\\vec p_T)=\\gamma^\\mu_S S(\\vec p_T) \\right] \\implies \\mu \\frac{d}{d\\mu} {\\mathbb{I}}_S= \\gamma^\\mu_S {\\mathbb{I}}_S\\,,\n\\end{align}\nwhich immediately leads to the fact that ${\\mathbb{I}}_S$ is multiplicatively renormalized in $\\mu$ with the same anomalous dimension as the leading power soft function. For the $\\nu$ renormalization group equation, we have\n\\begin{align}\n\\nu \\frac{d}{d\\nu} {\\mathbb{I}}_S &=\\int d^2 \\vec q_T \\nu \\frac{d}{d\\nu} S(\\vec q_T){\\nonumber} \\\\\n&=\\int d^2 \\vec q_T \\left[ \\int d^2 \\vec p_T \\gamma_S(\\vec q_T-\\vec p_T) S(\\vec p_T) \\right]\\,.\n\\end{align}\nNow, performing the shift $\\vec q_T\\to \\vec q_T +\\vec p_T$, we obtain\n\\begin{align}\n\\nu \\frac{d}{d\\nu} {\\mathbb{I}}_S &=\\left[ \\int d^2 \\vec q_T \\gamma_S(\\vec q_T) \\right]{\\mathbb{I}}_S\\,,\n\\end{align}\nwhich is again multiplicative. The expression in square brackets is not well defined, and must be fixed by some regularization, as was shown above. In this case, this shift argument may no longer be valid. However, this exercise is merely meant to illustrate another perspective on why the rapidity identity operator should satisfy a multiplicative $\\nu$ RGE, and the argument presented earlier in this section should be taken as primary.\n\n\nTherefore, in summary, we have shown that there are non-trivial rapidity identity operators that arise at subleading power, and we have identified the renormalization group properties of these operators at leading logarithmic accuracy. The first rapidity identity operator does not depend on the observable, and its anomalous dimensions are given by\n\\begin{align}\\label{eq:rap_identity_summary}\n\\mu \\frac{d}{d\\mu} {\\mathbb{I}}^\\nu_S \\left( \\frac{\\mu^2}{\\nu^2} \\right) &= -2\\Gamma^{\\mathrm{cusp}}\\log\\left( \\frac{\\nu^2}{\\mu^2} \\right)~ {\\mathbb{I}}^\\nu_S\\left( \\frac{\\mu^2}{\\nu^2} \\right)\\,, {\\nonumber} \\\\\n\\nu \\frac{d}{d\\nu} {\\mathbb{I}}^\\nu_S\\left( \\frac{\\mu^2}{\\nu^2} \\right) &= 2\\Gamma^{\\mathrm{cusp}} \\log\\left( \\frac{\\nu^2}{\\mu^2} \\right)~ {\\mathbb{I}}^\\nu_S\\left( \\frac{\\mu^2}{\\nu^2} \\right)\\,.\n\\end{align}\nThe second rapidity identity operator depends on the observable, and its anomalous dimensions are given by\n\\begin{align}\\label{eq:rap_identity2_summary}\n\\mu \\frac{d}{d\\mu} {\\mathbb{I}}^{p_T^2}_S \\left( p_T^2,\\mu, \\nu \\right) &= -2\\Gamma^{\\mathrm{cusp}}\\log\\left( \\frac{\\nu^2}{\\mu^2} \\right)~ {\\mathbb{I}}^{p_T^2}_S\\left( p_T^2,\\mu, \\nu \\right)\\,, {\\nonumber} \\\\\n\\nu \\frac{d}{d\\nu} {\\mathbb{I}}^{p_T^2}_S\\left( p_T^2,\\mu, \\nu \\right) &= 2\\Gamma^{\\mathrm{cusp}}\\log\\left( \\frac{p_T^2}{\\mu^2} \\right)~ {\\mathbb{I}}^{p_T^2}_S\\left( p_T^2,\\mu, \\nu \\right)\\,.\n\\end{align}\nHere we have written the anomalous dimension in terms of the cusp anomalous dimension \\cite{Korchemsky:1987wg}, which is given by $\\Gamma^{\\mathrm{cusp}}=4(\\alpha_s\/4\\pi)C_A+\\mathcal{O}(\\alpha_s^2)$.\nWe expect these functions to appear ubiquitously in subleading power rapidity renormalization, and we therefore believe their identification is an important first step towards an understanding of the structure of subleading power rapidity logarithms.\n\nOne also has rapidity identity operators in the jet\/beam sectors, that are defined analogously to the soft operators. Their anomalous dimensions are fixed to be identical to the soft rapidity identity operators (up to a sign) by RG consistency. For the particular case of the EEC, we can avoid them by always running to the jet scale. They are interesting for the case of $p_T$ resummation where one must consider their matching onto the parton distribution functions (PDFs). We will present a more detail discussion of these structures in future work.\n\nIt is also interesting to note that the subleading power Regge limit for massive scattering amplitudes has recently been studied in $\\mathcal{N}=4$ SYM in \\cite{Bruser:2018jnc}. Their solution also involves an interesting operator mixing. Since there are connections between the Regge limit and the EEC at leading power due to conformal transformations, it would be interesting to understand if these persist at subleading power.\n\n\\subsection{Analytic Solution of Renormalization Group Evolution Equations}\\label{sec:solution}\n\nIn this section we provide an analytic solution to the renormalization group evolution equations of the subleading power soft functions when mixing with either type of identity operator. Here we will consider only the case of fixed coupling, since our current application will be to $\\mathcal{N}=4$ (which is conformal), and the reader who is interested in extending these results to running coupling can consult \\cite{Moult:2018jjd}. We will also only study the renormalization group flow in $\\nu$ at the scale $\\mu=p_T$ to simplify the analysis. This will be sufficient for our applications, since we can always run the hard function down to the scale $\\mu=p_T$. We will consider separately the two different types of identity operators, since we know that due to their different properties under boosts, they themselves cannot mix to generate a more complicated RG structure.\n\n\\subsection*{Identity Operator ${\\mathbb{I}}^{p_T^2}_S \\left( p_T^2,\\mu, \\nu \\right)$:}\n\nWe first consider the case of mixing with the operator ${\\mathbb{I}}_S \\left( p_T^2,\\mu, \\nu \\right)$, which gives rise to a simple $\\nu$ RGE at the scale $\\mu=p_T$. In this case, we get the RGE\n\\begin{align}\n\\nu \\frac{d}{d\\nu}\\left(\\begin{array}{c} S^{(2)} \\\\ {\\mathbb{I}}^{p_T^2}_S \\end{array} \\right) &= \\left( \\begin{array}{cc}0&\\gamma_{\\delta{\\mathbb{I}} }\\, \\\\ 0 & 0 \\end{array} \\right) \\left(\\begin{array}{c} S^{(2)} \\\\ {\\mathbb{I}}^{p_T^2}_S \\end{array} \\right) \\,, \n\\end{align}\nwith the boundary conditions\n\\begin{align}\n{\\mathbb{I}}^{p_T^2}_S(\\mu=p_T, \\nu=p_T)=1\\,, \\qquad S^{(2)}(\\mu=p_T,\\nu=p_T)=0\\,.\n\\end{align}\nNote that the specific choice of $\\mu=p_T$ is important for achieving this simple form of the RG since it eliminates the need to consider the diagonal terms in the mixing matrix. More generally, these would be required, but for LL resummation as considered in this paper, the particular path considered here suffices, as explained in more detail in \\Sec{sec:N4_LL}.\n\nThis RGE is trivial to solve, and generates a single logarithm from the mixing\n\\begin{align}\nS^{(2)}(\\mu=p_T, \\nu=Q)=\n \\gamma_{\\delta{\\mathbb{I}} } \\log(p_T\/Q) {\\mathbb{I}}^{p_T^2}_S(\\mu=p_T, \\nu=p_T) \\,.\n\\end{align}\nNo additional logarithms are generated. Since this is the case that applies to the soft function $S_{p_T^2}^{(2)}=p_T^2 S^{(0)}$, one can easily check using the known form of the two-loop soft function that there is indeed only a single logarithm at any $\\nu$ scale for $\\mu=p_T$. \n\nIn an actual factorization formula, this single rapidity logarithm is then dressed by a Sudakov coming from running the hard function to the scale $\\mu=p_T$, giving rise to a result of the form\n\\begin{align}\n\\gamma_{\\delta{\\mathbb{I}} } \\log(p_T\/Q) \\exp \\left[ -2 a_s \\log^2\\left( \\frac{ p_T^2}{Q^2} \\right) \\right] H(Q) {\\mathbb{I}}^{p_T^2}_S(\\mu=p_T, \\nu=p_T)\\,.\n\\end{align}\n This provides quite an interesting structure, namely a single logarithm arising from $\\nu$ evolution, which is then dressed by double logarithms from $\\mu$ evolution. The $\\nu$ and $\\mu$ RGEs therefore completely factorize at leading logarithmic order. An identical structure was observed for the case of thrust in \\cite{Moult:2018jjd}, however, there both the single logarithm and the tower of double logarithms arise from the $\\mu$ RGE. \n\n\\subsection*{Identity Operator ${\\mathbb{I}}^\\nu_S \\left(\\mu, \\nu \\right)$:}\n\nMixing with the rapidity identity operator ${\\mathbb{I}}^\\nu_S \\left(\\mu, \\nu \\right)$ gives rise to a more non-trivial rapidity flow at the scale $\\mu=p_T$.\nIn this case, we get the RGE\n\\begin{align}\n\\nu \\frac{d}{d\\nu}\\left(\\begin{array}{c} S^{(2)} \\\\ {\\mathbb{I}}^\\nu_S \\end{array} \\right) &= \\left( \\begin{array}{cc}0&\\gamma_{\\delta{\\mathbb{I}} }\\, \\\\ 0 & \\gamma^\\mu_S \\end{array} \\right) \\left(\\begin{array}{c} S^{(2)} \\\\ {\\mathbb{I}}^\\nu_S \\end{array} \\right) \\,, \n\\end{align}\nwith the boundary conditions\n\\begin{align}\n{\\mathbb{I}}^\\nu_S(\\mu=p_T, \\nu=p_T)=1\\,, \\qquad S^{(2)}(\\mu=p_T,\\nu=p_T)=0\\,.\n\\end{align}\nThis RGE is is a specific case of the general RGE solved in \\cite{Moult:2018jjd}. However, here we can solve it in two steps by first solving for the identity operator, and then plugging it in to the solution for the soft function. This will provide some insight into the structure of the final solution.\n\nThe solution of\n\\begin{align}\n\\nu \\frac{d}{d\\nu} {\\mathbb{I}}^\\nu_S =\\gamma^\\mu_S {\\mathbb{I}}^\\nu_S \\equiv \\tilde \\gamma \\log\\left( \\frac{\\nu^2}{p_T^2} \\right) {\\mathbb{I}}^\\nu_S\\,,\n\\end{align}\nis easily found to be\n\\begin{align}\n {\\mathbb{I}}^\\nu_S=\\exp\\left( \\frac{\\tilde \\gamma}{4} \\log^2\\left( \\frac{\\nu^2}{p_T^2} \\right) \\right){\\mathbb{I}}^\\nu_S(\\mu=p_T, \\nu=p_T)\\,.\n\\end{align}\nThe original soft function then satisfies the inhomogeneous equation\n\\begin{align}\n\\nu \\frac{d}{d\\nu} S^{(2)}=\\gamma_{\\delta{\\mathbb{I}} } \\exp\\left( \\frac{\\tilde \\gamma}{4} \\log^2\\left( \\frac{\\nu^2}{p_T^2} \\right) \\right){\\mathbb{I}}_S^\\nu(\\mu=p_T, \\nu=p_T)\\,.\n\\end{align}\nWe can integrate this equation up to the scale $\\nu=Q$ to find\n\\begin{align}\nS^{(2)}(\\mu=p_T, \\nu=Q)=\n - \\frac{\\sqrt{\\pi} \\gamma_{\\delta{\\mathbb{I}} }}{ \\sqrt{\\tilde \\gamma}} \\mathrm{erfi}\\left[ \\sqrt{\\tilde \\gamma} \\log(p_T\/Q) \\right] {\\mathbb{I}}^\\nu_S(\\mu=p_T, \\nu=p_T) \\,, \n\\end{align}\nwhere $\\mathrm{erfi}$ is the imaginary error function, defined as \n\\begin{align}\n\\mathrm{erfi}(z)=-i \\mathrm{erf}(iz)\\,.\n\\end{align}\nThe fact that the solution is not simply a Sudakov is quite interesting, and shows that the subleading power logarithms have a more interesting structure than at leading power. We expect that this structure will be quite common in the study of subleading power corrections to observables with rapidity evolution.\nThe $\\mathrm{erfi}$ function satisfies the identify\n\\begin{align}\n\\frac{d}{dz}\\mathrm{erfi}(z) = \\frac{2}{\\sqrt{\\pi}} e^{z^2}\\,.\n\\end{align}\nIt is perhaps quite intuitive that an integral of a Sudakov appears, since the rapidity identity operator is the integral of the leading power soft function. However, this is a new structure that has not previously appeared in subleading power calculations. It is amusing to note that a similar structure also appears in the calculation of Sudakov safe observables \\cite{Larkoski:2015lea} due to the integration over a resummed result.\n\n\\section{The Energy-Energy Correlator in $\\mathcal{N}=4$ SYM}\\label{sec:N4}\n\nIn this section we use our subleading power rapidity renormalization group to derive the leading logarithmic series at NLP for a physical observable, namely the EEC in $\\mathcal{N}=4$ SYM. We then compare our predictions with the recent calculation of \\cite{Henn:2019gkr} to $\\mathcal{O}(\\alpha_s^3)$ finding perfect agreement.\n\n\\subsection{Leading Logarithmic Resummation at Subleading Power}\\label{sec:N4_LL}\n\nIn performing the resummation of subleading power logarithms for the EEC, we must clearly state several assumptions that are made, which we hope can be better understood in future work. Nevertheless, we believe that the fact that our result agrees with the calculation of \\cite{Henn:2019gkr} provides strong support for these assumptions. The goal of this paper has been to understand how far one can get in understanding the subleading power rapidity renormalization group using only symmetries and consistency arguments. Using this approach, we found that at leading logarithmic order, there are two distinct identity operators with different renormalization group properties. To derive the structure of the resummed result for the EEC in the back-to-back limit, our approach will therefore be to match a linear combination of these two solutions to the know expansion of the EEC. To fix both coefficients requires two inputs, which we take to be the $\\alpha_s$ and $\\alpha_s^2$ leading logarithms. This then completely fixes our result, which can then be used to predict the coefficient of the $\\alpha_s^3$ leading logarithm, for which we will find agreement with the calculation of \\cite{Henn:2019gkr}. This approach should simple be viewed as a shortcut to a complete operator based analysis, which enables us to explore the general structure of the rapidity evolution equations at NLP, and show that they predict non-trivial behavior of the NLP series for a physical observable. A more complete operator based analysis will be presented in a future paper.\n\nSecondly, we must also assume that there exists a consistent factorization at subleading powers that does not have endpoint divergences. The presence of endpoint divergences in the factorization formula would violate our derivations based on the consistency of the RG. At this stage, both for the standard $\\mu$ renormalization group at subleading power, and for the subleading power rapidity renormalization group considered here, this is still ultimately an assumption. In general, endpoint divergences are known to appear generically at next-to-leading logarithm, but have also been shown to appear even at LL in certain cases when fields with different color representations are involved (e.g. both quarks and gluons) \\cite{Moult:2019uhz}. However, since in $\\mathcal{N}=4$ all fields are in the same representation, we work under the assumption that there are no endpoint divergences at leading logarithmic accuracy. We will see that this assumption is strongly supported by the fact that we are able to exactly reproduce to $\\mathcal{O}(\\alpha_s^3)$ the highly non-trivial series that arises from the exact calculation of \\cite{Henn:2019gkr}. However, we acknowledge that before our techniques can be more widely applied, it will be important to understand when endpoint divergences do occur in the rapidity renormalization group, and how they can be resolved. \n\nTherefore, working under the assumption of convergent convolutions for the subleading power factorization, all anomalous dimensions are fixed by symmetries, as described above, and the resummation of the subleading power logarithms is now a simple application of the renormalization group evolution equations derived in \\Sec{sec:RRG_NLP}. To perform the resummation, one must resum logarithms in both $\\mu$ and $\\nu$. We choose to perform this resummation using the following evolution path\n\\begin{itemize}\n\\item Run the hard functions from $\\mu=Q$ to $\\mu=p_T$.\n\\item Run the soft functions in rapidity from $\\nu=p_T$ to $\\nu=Q$.\n\\end{itemize}\nThis path is the most convenient, since it avoids the need to perform any resummation of the rapidity anomalous dimensions \\cite{Chiu:2012ir,Chiu:2011qc}. To run the hard function from $\\mu=Q$ down to the soft scale, we use the evolution equations\n\\begin{align}\n\\mu \\frac{d}{d\\mu} H = \\gamma_H H\\,, \\qquad \\gamma_H =-8 a_s \\log \\left( \\frac{ \\mu^2}{Q^2} \\right)\\,,\n\\end{align}\nHere and throughout this section, we will use $a_s=\\alpha_s\/(4\\pi)C_A$ to simplify the notation.\nThe renormalization group equation for the hard function has the simple solution\n\\begin{align}\nH(p_T)=H(Q) \\exp \\left[ -2 a_s \\log^2\\left( \\frac{ p_T^2}{Q^2} \\right) \\right]\\,.\n\\end{align}\nFor the soft function evolution, we use the results derived in \\Sec{sec:RRG_NLP} for the evolution of the two different types of rapidity identity operators. Since these rapidity identity functions cannot themselves mix due to different boost properties, the result at LL order is necessarily a linear combination of the two. For the first type of mixing, we have\n\\begin{align}\nS_1^{(2)}(\\mu=p_T, \\nu=Q)=\n \\gamma_{\\delta{\\mathbb{I}},1 } \\log(p_T\/Q) {\\mathbb{I}}^{p_T^2}_S(\\mu=p_T, \\nu=p_T) \\,,\n\\end{align}\nwhere $ \\gamma_{\\delta{\\mathbb{I}},1 }$ is the anomalous dimension for mixing into the $ {\\mathbb{I}}^{p_T^2}_S$ soft function, and for the second type, we have\n\\begin{align}\nS_2^{(2)}(\\mu=p_T, \\nu=Q)=\n - \\frac{\\sqrt{\\pi} \\gamma_{\\delta{\\mathbb{I}},2 }}{ \\sqrt{\\tilde \\gamma}} \\mathrm{erfi}\\left[ \\sqrt{\\tilde \\gamma} \\log(p_T\/Q) \\right] {\\mathbb{I}}_S(\\mu=p_T, \\nu=p_T)\\,,\n\\end{align}\nwith $\\tilde \\gamma =8 a_s$, and where $ \\gamma_{\\delta{\\mathbb{I}},2 }$ is the anomalous dimension for mixing into the $ {\\mathbb{I}}^{\\nu}_S$ soft function.\nOur general prediction is then a linear combination of these two \n\\begin{align}\n\\text{EEC}^{(2)}=\\gamma_{\\delta{\\mathbb{I}},1 } \\log(p_T\/Q) \\exp \\left[ -2 a_s \\log^2\\left( \\frac{ p_T^2}{Q^2} \\right) \\right] - \\frac{\\sqrt{\\pi} \\gamma_{\\delta{\\mathbb{I}},2 }}{ \\sqrt{\\tilde \\gamma}} \\mathrm{erfi}\\left[ \\sqrt{\\tilde \\gamma} \\log(p_T\/Q) \\right]\\,.\n\\end{align}\nMatching to the $a_s$ and $a_s^2$ coefficients from expanding the result of \\cite{Belitsky:2013ofa,Belitsky:2013xxa,Belitsky:2013bja}(these coefficients are given in \\Sec{sec:N4_expand}), we find that $\\gamma_{\\delta{\\mathbb{I}},1 }=0$. We therefore find the simple result for the NLP leading logarithmic series to all orders in $a_s$ in $\\mathcal{N}=4$ SYM theory\n\\begin{align}\n\\text{EEC}^{(2)}=-\\frac{\\sqrt{\\pi}a_s}{ \\sqrt{2a_s}} \\mathrm{erfi}\\left[ \\sqrt{2 a_s} \\log(1-z) \\right] \\exp\\left[ -2 a_s \\log(1-z)^2 \\right]\\,.\n\\end{align}\nInterestingly, for the particular case of $\\mathcal{N}=4$, we find that the result only involves the operator ${\\mathbb{I}}^\\nu_S \\left(\\mu, \\nu \\right)$.\nThis result takes an interesting form, going beyond the simple Sudakov exponential \\cite{Sudakov:1954sw} for the leading logarithms at leading power. We believe that this structure will appear somewhat generically in subleading power rapidity resummation. It is interesting to note that up to the prefactor this particular structure is in fact a well known special function, called Dawson's integral, which is defined as\n\\begin{align}\nD(x)=\\frac{1}{2}\\sqrt{\\pi}e^{-x^2}\\mathrm{erfi}(x)\\,.\n\\end{align}\nWe can therefore write our answer for the NLP leading logarithmic series in the simple form\n\\begin{align}\n\\text{EEC}^{(2)}=-\\sqrt{2a_s}~D\\left[ \\sqrt{2 a_s} \\log(1-z) \\right]\\,.\n\\end{align}\nSince the anomalous dimension is fixed by renormalization group consistency to be the cusp anomalous dimension (see \\Eq{eq:rap_identity_summary}), we can rewrite this as\n\\begin{align}\n\\boxed{\\text{EEC}^{(2)}=-\\sqrt{2a_s}~D\\left[ \\sqrt{\\frac{\\Gamma^{\\mathrm{cusp}}}{2}} \\log(1-z) \\right]\\,.}\n\\end{align}\nThis expression is a primary result of this paper. We will refer to this functional form as ``Dawson's Sudakov\". We find it pleasing that despite the somewhat non-trivial functional structure, the double logarithmic asymptotics of the EEC in the back-to-back limit are still driven by the cusp anomalous dimension \\cite{Korchemsky:1987wg} much like at leading power (see \\Eq{eq:resformula_N4}), and as is expected physically. It would be extremely interesting to understand if the subleading power logarithms at next-to-leading power are driven by the collinear anomalous dimension, as is the case at leading power. We note that from \\Eq{eq:N4_NLP_fixedorder} there is no constant term at NLP (it can easily be checked that this is true at any power), and therefore it is plausible that there is a simple generalization of this formula that also incorporates the next-to-leading logarithms.\n\nIn \\Fig{fig:plot}, we compare the standard Sudakov with Dawson's Sudakov. Note that while $\\mathrm{erfi}\\left[ \\sqrt{2 a_s} \\log(1-z) \\right]$ diverges as $z\\to 1$, this is overcome by the suppression from the Sudakov exponential. However, the behavior of the $\\mathrm{erfi}$ leads to a more enhanced behavior of the distribution as $z\\to 1$, as compared to a standard Sudakov.\n\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=0.75\\columnwidth]{figures\/sudakovs}\n\\end{center}\n\\caption{A comparison of the standard Sudakov exponential which describes the all orders exponentiation of the leading power logarithms for the EEC with the subleading power Sudakov involving the imaginary error function ($\\mathrm{erfi}$) derived in this section, which we refer to as Dawson's Sudakov. Dawson's Sudakov exhibits a more peaked structure as $z\\to 1$. A value of $\\alpha_s=0.12$ was chosen to plot the numerical results.}\n\\label{fig:plot}\n\\end{figure}\n\nIt is also interesting to consider the Taylor expansion of our result in $a_s$. We find that it can be written as a remarkably simple series in terms of the double factorial\n\\begin{align}\n\\text{EEC}^{(2)}\n&=\\sum\\limits_{n=0}^{\\infty} \\frac{(-1)^{n+1}}{(2n+1)!!} a_s^{n+1} \\log((1-z)^2)^{2n+1}\\,,\n\\end{align}\n(here we have chosen to move factors of $2$ into the definition of the logarithm, but they can equally well be moved into the definition of $a_s$)\nwhere we recall that the double factorial is defined as\n\\begin{align}\nn!!=n(n-2)(n-4)\\cdots .\n\\end{align}\nExplicitly, the first few terms in the expansion are\n\\begin{align}\\label{eq:EEC_RG_expand}\n\\text{EEC}^{(2)}&=-2 a_s \\log(1-z) +\\frac{8}{3}a_s^2 \\log^3(1-z)-\\frac{32}{15}a_s^3 \\log^5(1-z) +\\frac{128}{105}a_s^4 \\log^7(1-z) {\\nonumber} \\\\\n&-\\frac{512}{945}a_s^5 \\log^9(1-z)+\\frac{2048}{10395}a_s^6 \\log^{11}(1-z)-\\frac{8192}{135135}a_s^7 \\log^{13}(1-z)+\\cdots\\,.\n\\end{align}\nThe presence of the double factorial as compared with the single factorial at leading power generates a more interesting series of rational coefficients. In \\cite{Dixon:2019uzg} the logarithms in the collinear limit of the EEC at each logarithmic order were written as simple infinite sums of factorials multiplied by polynomials in logarithms. It would be very interesting to understand if the subleading logarithms at NLP in the back-to-back limit can also be written as generalized double factorial sums.\n\nIt will be important to understand if this structure persists in QCD. The results in QCD are only known to $a_s^2$ \\cite{Dixon:2018qgp,Luo:2019nig}, therefore, without performing a more complete operator analysis, these results can be used to fix our prediction as a linear combination of our two RG solutions, but do not enable a non-trivial check, which is particularly important due to the possible presence of endpoint divergences. While we will perform an operator based analysis in a future publication, we find that it interesting to conjecture a result. Based on our intuition from the case of the thrust observable \\cite{Moult:2019uhz}, we expect that we are most likely to avoid endpoint divergences for the case of the EEC in Higgs decays to gluons in pure Yang-Mills. Under the assumption that there are no endpoint divergences, we can fix an ansatz in terms of our two RG solutions to be\n\\begin{align}\n\\left.\\text{EEC}^{(2)}\\right|_{\\text{Yang-Mills}}=2 a_s \\log(1-z) \\exp \\left[ -2 a_s \\log^2\\left(1-z \\right) \\right] -4\\sqrt{2a_s}~D\\left[ \\sqrt{\\frac{\\Gamma^{\\mathrm{cusp}}}{2}} \\log(1-z) \\right]\\,,\n\\end{align}\nwhich takes a slightly more complicated form than its $\\mathcal{N}=4$ counterpart, since it involves both types of rapidity identity operators. Unfortunately, unlike for the case of the EEC in $\\mathcal{N}=4$, there is no $a_s^3$ result to which we are able to compare this result. \nIt will interesting to verify or disprove this conjecture using a complete operator analysis, which we leave to future work. This will provide insight into the presence (or lack of) endpoint divergences in this particular case.\n\nIt would also be extremely interesting to understand how to derive this result directly from the four point correlator, or from the light ray OPE \\cite{Kravchuk:2018htv,Kologlu:2019bco,Kologlu:2019mfz}. From the point of view of the four point correlator, the back-to-back limit corresponds to the so called double light cone limit. This has been used to study the EEC in \\cite{Korchemsky:2019nzm}, has been studied in gauge theories in \\cite{Alday:2010zy,Alday:2013cwa}, and has been studied in more general conformal field theories in \\cite{Alday:2015ota}. \n\n\\subsection{Comparison with Direct Fixed Order Calculation to $\\mathcal{O}(\\alpha_s^3)$}\\label{sec:N4_expand}\n\nAs mentioned earlier, we have chosen to perform the resummation for the EEC in $\\mathcal{N}=4$ since we can directly compare with the remarkable calculation of the EEC for arbitrary angles using the triple discontinuity of the four point correlator \\cite{Henn:2019gkr} (The result to $\\mathcal{O}(\\alpha_s^2)$ in $\\mathcal{N}=4$ was calculated in \\cite{Belitsky:2013ofa,Belitsky:2013xxa,Belitsky:2013bja}), and exploiting the large amount of information known about its structure, see e.g. \\cite{Eden:2011we,Eden:2012tu,Drummond:2013nda,Korchemsky:2015ssa,Bourjaily:2016evz}. This calculation of the EEC provides extremely valuable data for understanding the structure of kinematic limits at subleading power, both for the particular case considered here, and beyond.\n\nAlthough we will be interested in the expansion in the $z\\to 1$ limit, it is interesting to understand what parts of the full angle result the back-to-back limit is sensitive to at subleading powers, so we briefly review the structure of the result of \\cite{Henn:2019gkr}.\nThe result of \\cite{Henn:2019gkr} is written as\n \\begin{align}\\label{defF}\n F(\\zeta) \\equiv 4 \\zeta^2 (1-\\zeta) {\\text{EEC}} (\\zeta) \\,,\n \\end{align}\nwhere at NNLO, \n \\begin{align} \\label{resultFatNNLO}\nF_{\\rm NNLO} (\\zeta) &= f_{\\rm HPL} (\\zeta) + \\int_{0}^1 d \\bar z \\int_{0}^{\\bar z } dt \\, \\frac{\\zeta-1}{t(\\zeta-\\bar z)+(1-\\zeta)\\bar z} \\nt \n& \\times \\left[ R_1 ( z, \\bar z ) P_1 (z, \\bar z ) + \n R_2 ( z, \\bar z ) P_2 (z, \\bar z ) \\right] \\,,\n\\end{align} \nwith\n \\begin{align} \n R_1 = \\frac{z \\bar z }{ 1 - z - \\bar z}\\, , \\quad R_2 = \\frac{z^2 \\bar z }{ (1-z)^2 ( 1 - z \\bar z)}\\,.\n \\end{align}\n Here $P_1$ and $P_2$ are weight three HPLS in $z$ and $\\bar z$. These two fold integrals are believed to be elliptic, and so we will refer to them as elliptic contributions. The term $f_{\\rm HPL} (\\zeta)$ is expressed in terms of harmonic polylogarithms. The leading power asymptotics in the back-to-back limit are described entirely by $ f_{\\rm HPL}$. At NLP, we require also the elliptic contributions, showing that subleading powers probe more of the structure of the result. This in turn makes the agreement with our result derived from the RG more non-trivial.\n \n\nThe expansion of $f_{\\rm HPL} (\\zeta)$ can be performed straightforwardly, and produces only $\\zeta$ values, $\\log^n(2)$, and $\\text{Li}_n(1\/2)$. On the other hand, the expansion of the elliptic contribution is more non-trivial, and leads to a more complicated set of constants. To compute the result, we expanded under the integral sign, and integrated using HyperInt \\cite{Panzer:2014caa}. This produced polylogarithms of sixth roots of unity up to weight 5. These were reduced using results from \\cite{Henn:2015sem} to a basis of constants. The final result involves several non-zeta valued constants which were guessed using hints for the classes of numbers that should appear in the answer from \\cite{Broadhurst:1998rz,Fleischer:1999mp,Davydychev:2000na} and reconstructed using the PSLQ algorithm. We found that the elliptic piece could be expressed as \n\\begin{align}\n\\frac{\\text{Elliptic}_{\\text{NLP}}}{2}&=2\\frac{L^5}{5!}+\\frac{1}{2}\\zeta_2 \\frac{L^3}{3!} +\\frac{3}{4}\\zeta_3 \\frac{L^2}{2!}+ \\left(-\\frac{67}{32}+\\frac{3}{4}\\zeta_2+\\frac{9}{4}\\zeta_3 -\\frac{7}{4} \\zeta_4\\right) L{\\nonumber} \\\\\n&+\\frac{85}{32}-\\frac{49}{16}\\zeta_2+\\frac{87}{8}\\zeta_3 +\\frac{37}{4}\\zeta_4 +\\frac{3}{4}\\zeta_2\\zeta_3+\\frac{5}{2}\\zeta_5-\\frac{611}{108}\\zeta_4 \\sqrt{3} \\pi+6\\sqrt{3}I_{2,3}\\,.\n\\end{align}\nHere $I_{2,3}$ is a higher weight Clausen function \\cite{Broadhurst:1998rz} \n\\begin{align}\nI_{2,3}=\\sum\\limits_{m>n>0}\\frac{\\sin\\left(\\frac{\\pi(m-n)}{3}\\right)}{m^{b-a}n^{2a}}\\,.\n\\end{align}\nWhile the leading power result has uniform trascendental weight when expanded in the back-to-back region, this is no longer true at subleading power. \n\n\nCollecting all the terms from both the elliptic and HPL contributions, we find (in our normalization) the following expression for the leading power suppressed logarithms up to $\\mathcal{O}(a_s)^3$\n\\begin{align}\n &\\text{EEC}^{(2)}=-2a_s \\log(1-z) {\\nonumber} \\\\\n &+a_s^2 \\left[\\frac{8}{3} \\log^3(1-z) +3 \\log^2(1-z) +(4+16\\zeta_2)\\log(1-z)+(-12-2\\zeta_2+36 \\zeta_2\\log(2)+5\\zeta_3) \\right]{\\nonumber} \\\\\n &+a_s^3 \\left[-\\frac{32}{15} \\log^5(1-z)-\\frac{16}{3}\\log^4(1-z)-\\left( \\frac{8}{3}+24 \\zeta_2 \\right)\\log^3(1-z) +(4-36\\zeta_2-50\\zeta_3)\\log^2(1-z) \\right. {\\nonumber} \\\\\n &\\left. -\\left(\\frac{131}{2}+4\\zeta_2+372 \\zeta_4+12\\zeta_3 \\right) \\log(1-z) + \\text{const} \\right]\\,,\n\\end{align}\nwhere\n\\begin{align}\n\\text{const}&=-\\frac{3061}{2}\\zeta_5 -96\\zeta_2 \\zeta_3 -\\frac{4888}{27}\\zeta_4 \\pi \\sqrt{3} +192 \\sqrt{3} I_{2,3}+1482 \\zeta_4 \\log(2) -256 \\zeta_2 \\log^3(2) {\\nonumber} \\\\\n&-\\frac{64}{5}\\log^5(2)\n+1536 \\text{Li}_5\\left( \\frac{1}{2} \\right) -544 \\zeta_4 +192 \\zeta_2 \\log^2(2)+16 \\log^4(2)+384 \\text{Li}_4\\left( \\frac{1}{2} \\right) {\\nonumber} \\\\\n& -288 \\zeta_2 \\log(2)+158 \\zeta_3 +55 \\zeta_2 +\\frac{533}{2}\\,.\n\\end{align}\nExtracting out the leading logarithmic series\n\\begin{align}\n\\left. \\text{EEC}^{(2)}\\right|_{\\text{LL}}=-2a_s \\log(1-z) +\\frac{8}{3}a_s^2 \\log^3(1-z) -\\frac{32}{15}a_s^3 \\log^5(1-z)\\,,\n\\end{align}\nwe find that this result agrees exactly with the result derived from the subleading power renormalization group given in \\Eq{eq:EEC_RG_expand}! Note that due to the matching of our RG predictions to the fixed order results, as was explained above, it is really only the $a_s^3$ coefficient that is a prediction. However, this agreement is highly non-trivial, since it probes both the elliptic and polylogarithmic sectors of the full result, and therefore we believe that it provides strong support that our subleading power renormalization group evolution equation is correct. The next-to-leading logarithms also have relatively simple rational coefficients, \n\\begin{align}\n\\left. \\text{EEC}^{(2)}\\right|_{\\text{NLL}}=3a_s^2 \\log^2(1-z) -\\frac{16}{3}a_s^3 \\log^4(1-z)\\,,\n\\end{align}\nand provide data for understanding the structure of subleading power resummation beyond the leading logarithm. It would be interesting to derive them directly from the renormalization group approach.\n\nIt would be interesting to better understand the structure of the numbers appearing in the expansion of the EEC both in the collinear and back-to-back limits, and the functions appearing in the full angle result. This could ultimately allow the result to be bootstrapped from an understanding of these limits, in a similar manner to the hexagon bootstrap for $N=4$ SYM amplitudes \\cite{Dixon:2011pw,Dixon:2014iba,Caron-Huot:2016owq,Dixon:2016nkn,Caron-Huot:2019vjl}. However, the presence of elliptic functions makes this seem like a daunting task, unless more information is known about their structure.\n\n\n\\section{Conclusions}\\label{sec:conc}\n\nIn this paper we have shown how to resum subleading power rapidity logarithms using the rapidity renormalization group, and have taken a first step towards a systematic understanding of subleading power corrections to observables exhibiting hierarchies in rapidity scales. Much like for the virtuality renormalization group at subleading power, the rapidity renormalization group at subleading power involves a non-trivial mixing structure. Using the consistency of the RG equations combined with symmetry arguments, we were able to identify the operators that arise in the mixing, which we termed ``rapidity identity operators\", and we derived their anomalous dimensions. We believe that these operators will play an important role in any future studies of the rapidity renormalization group at subleading power, and are the key to understanding its structure. \n\nTo illustrate our formalism, we resummed the subleading power logarithms appearing in the back-to-back limit of the EEC in $\\mathcal{N}=4$ SYM. This particular observable was chosen since the full analytic result is known to $\\mathcal{O}(\\alpha_s^3)$ from the remarkable calculation of \\cite{Henn:2019gkr}. We found perfect agreement between our result derived using the renormalization group, and the expansion in the back-to-back limit of the calculation of \\cite{Henn:2019gkr}, which provides an extremely strong check on our results. The analytic form of the resummed subleading power logarithms takes an interesting, but extremely simple form, being expressed in terms of Dawson's integral with argument related to the cusp anomalous dimension. We called this structure ``Dawson's Sudakov\". We expect this structure to be generic at subleading power for rapidity dependent observables, much like the Sudakov exponential is at leading power. \n\nSince this represents the first resummation of subleading power rapidity logarithms, there are many directions to extend our results, as well as to better understand the structures that we have introduced in this paper. First, although we have arrived at the structure of the leading logarithms using symmetry and consistency arguments, it would be interesting to use a complete basis of SCET$_{\\rm II}$ ~operators to derive the operator structure of all the subleading power jet and soft functions in SCET$_{\\rm II}$, and perturbatively compute their anomalous dimensions. Second, to go beyond LL, it will be necessary to better understand the structure of the momentum convolutions appearing in the subleading power factorization. We expect that at subleading power this is best done in momentum space using the formalism of \\cite{Ebert:2016gcn}. Finally, we also expect that away from $\\mathcal{N}=4$ SYM theory, divergent convolutions will appear even at LL order, as occurs in SCET$_{\\rm I}$, and so it will be important to understand when these occur, and how they can be overcome.\n\nOn the more formal side, it will be interesting to understand how to extract the subleading power logarithms directly from the four point correlation function, following the approach of \\cite{Korchemsky:2019nzm}, or using the light ray OPE \\cite{Kravchuk:2018htv,Kologlu:2019bco,Kologlu:2019mfz}. While there have been some studies of the double light cone limit in conformal field theories \\cite{Alday:2010zy,Alday:2015ota}, further studies of this limit in conformal field theories could provide insight into behavior of phenomenological interest in QCD, and the EEC provides an example that is of both formal and phenomenological interest.\n\nIt will also be important to apply our formalism to observables of direct phenomenological interest, such as the EEC in QCD, the color singlet $p_T$ distribution in hadron colliders, or to the study of power suppressed logarithms appearing in the Regge limit, which can also be formulated in terms of the rapidity renormalization group \\cite{Rothstein:2016bsq,Moult:2017xpp}. Our work represents the first step in extending recent successes in understanding the structure of subleading power infrared logarithms to subleading power rapidity logarithms, and we hope that this will allow for a much wider set of phenomenologically important applications.\n\n\\begin{acknowledgments}\nWe thank Martin Beneke, Sebastian Jaskiewicz, Robert Szafron, Iain Stewart, Frank Tackmann, Johannes Michel, Markus Ebert, David Simmons-Duffin, Cyuan-Han Chang, Lance Dixon, Johannes Henn, and HuaXing Zhu for useful discussions. We thank Vladimir Smirnov for providing us with reduction tables for polylogarithms of roots of unity. This work was supported in part by the Office of Nuclear Physics of the U.S.\nDepartment of Energy under Contract No. DE-SC0011090, and by the Office of High Energy Physics of the U.S. Department of Energy under Contract No. DE-AC02-76SF00515. This research received\nfunding from the European Research Council (ERC) under the European\nUnion's Horizon 2020 research and innovation programme\n(grant agreement No. 725110), ``Novel structures in scattering amplitudes\".\n\\end{acknowledgments}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction.}\n\nIn \\cite{C}, Cohn introduced the notion of Schreier domain. A domain $D$ is said to be a {\\em Schreier domain} if $(1)$ $D$ is integrally closed and $(2)$ whenever $I, J_1, J_2$ are principal ideals of $D$ and $I\\supseteq J_1J_2$, then $I = I_1I_2$ for some principal ideals $I_1,I_2$ of $D$ with $I_i \\supseteq J_i$ for $i = 1,2$. The study of Schreier domains was continued in \\cite{MR} and \\cite{Zps}. In \\cite{Zps}, a domain was called a {\\em pre-Schreier domain} if it satisfies condition $(2)$ above.\nSubsequently, extensions of the ``(pre)-Schreier domain'' concept were studied in \\cite{DM}, \\cite{ADZ}, \\cite{DK}, \\cite{AD} and \\cite{ADE}.\n\nIn \\cite{ADE}, we studied a class of domains that satisfies a Schreier-like condition for all ideals. More precisely, a domain $D$ was called a {\\em sharp domain } if whenever $I \\supseteq AB$ with $I$, $A$, $B$ nonzero ideals of $D$, there exist ideals $A'\\supseteq A$ and $B'\\supseteq B$ such that $I=A'B'$. We recall several results from \\cite{ADE}.\nIf the domain $D$ is Noetherian or Krull, then $D$ is sharp if and only if $D$ is a Dedekind domain \\cite[Corollaries 2 and 12]{ADE}. A sharp domain is pseudo-Dedekind; in particular, a sharp domain is a completely integrally closed GGCD domain \\cite[Proposition 4]{ADE}.\nRecall (cf. \\cite{Z} and \\cite{AK}) that a domain $D$ is called a {\\em pseudo-Dedekind domain} (the name used in \\cite{Z} was {\\em generalized Dedekind domain}) if the $v$-closure of each nonzero ideal of $D$ is invertible. Also, recall from \\cite{AA} that a domain $D$ is called a {\\em generalized GCD domain (GGCD domain)} if the $v$-closure of each nonzero finitely generated ideal of $D$ is invertible. The definition of the $v$-closure is recalled below.\nA valuation domain is sharp if and only if the value group of $D$ is a complete subgroup of the reals \\cite[Proposition 6]{ADE}.\nThe localizations of a sharp domain at the maximal ideals are valuation domains with value group a complete subgroup of the reals; in par\\-ti\\-cu\\-lar, a sharp domain is a Pr\\\"ufer domain of dimension at most one \\cite[Theorem 11]{ADE}.\nThe converse is true for the domains of finite character \\cite[Theorem 15]{ADE}, but not true in general \\cite[Example 13]{ADE} (recall that a {\\em domain of finite character} is a domain whose nonzero elements are contained in only finitely many maximal ideals). A countable sharp domain is a Dedekind domain \\cite[Corollary 17]{ADE}.\n\n\n\n\n\n\n\n\n\n\n\n\nThe purpose of this paper is to study the ``sharp domain'' concept in the star operation setting. To facilitate the reading of the paper, we first review some basic facts about $*$-operations. Let $D$ be a domain with quotient field $K$ and let $F(D)$ denote the set of nonzero fractional ideals of $D$.\nA function $A\\mapsto A^*: F(D) \\rightarrow F(D)$ is called a {\\em star operation} on\n$D$ if $*$ satisfies the following three conditions for all $0\n\\neq a \\in K$ and all $I,J \\in F(D)$:\n\n$(1)$ $D^{*} = D$ and\n$(aI)^{*}=aI^{*}$,\n\n$(2)$ $I \\subseteq I^{*}$ and if $ I\n\\subseteq J$, then $I^{*} \\subseteq J^{*}$,\n\n$(3)$\n$(I^{*})^{*} = I^{*}.$\n\n\\noindent An ideal $I \\in F(D)$ is called a $*$-ideal if $I = I^{*}.$\nFor all $I,J\\in F(D)$, we have\n$(IJ)^{*}=(I^{*}J)^{*}=(I^{*}J^{*})^{*}$.\nThese equations define the so-called {\\em $*$-multiplication.}\nIf $\\{I_\\alpha\\}$ is a subset of $F(D)$ such that $\\cap I_\\alpha\\neq 0$, then\n$\\cap I_\\alpha^*$ is a $*$-ideal.\nAlso, if $\\{I_\\alpha\\}$ is a subset of $F(D)$ such that $\\sum I_\\alpha$ is a fractional ideal, then\n$(\\sum I_\\alpha)^*=(\\sum I_\\alpha^*)^*$.\nThe star operation $*$ is said to be {\\em stable} if $(I \\cap J)^{*} = I^{*}\\cap J^{*}$ for all $I,J\\in F(D)$.\nIf $*$ is a star operation, the function $*_f: F(D) \\rightarrow F(D)$ given by $I^{*_f} = \\cup_H H^{*}$, where $H$ ranges over all\nnonzero finitely generated subideals of $I$, is also a star operation. The star operation $*$ is said to be of\n{\\em finite character} if $*=*_f$. Clearly $(*_f)_f=*_f$.\nDenote by $Max_*(D)$ the set of maximal $*$-ideals, that is, ideals maximal among proper integral\n$*$-ideals of $D$. Every maximal $*$-ideal is a prime ideal.\nThe {\\em $*$-dimension} of $D$ is\n$sup \\{ n\\mid 0\\subset P_1\\subset \\cdots \\subset P_n,$ $P_i$ prime $*$-ideal of $D\\}$.\nAssume that $*$ is a star operation of finite character. Then every proper $*$-ideal is contained in some maximal $*$-ideal, and\nthe map $I\\mapsto I^{\\tilde{*}} = \\cap _{P \\in Max_*(D)}ID_P$ for all $I\\in F(D)$ is a stable star operation of finite character, cf. \\cite[Theorems 2.4, 2.5 and 2.9]{AC}.\nMoreover, $*$ is stable if and only if $*=\\tilde{*}$, cf. \\cite[Corollary 4.2]{A}.\nA $*$-ideal $I$ is of {\\em finite type} if\n$I=(a_1,...,a_n)^{*}$ for some $a_1,...,a_n\\in I.$\nA {\\em Mori domain} is a domain whose $t$-ideals are of finite type (see \\cite{B}). By \\cite{HZ}, an integral domain is said to be a {\\em TV domain} if every $t$-ideal is a $v$-ideal. A Mori domain is a TV domain.\n\nA fractional ideal $I \\in F(D)$ is said to be {\\em $*$-invertible} if\n$(II^{-1})^{*} = D$, where $I^{-1}=(D:I)=\\{ x \\in K \\mid xI \\subseteq D\\}$.\nIf $*$ is of finite character, then $I$ is $*$-invertible if and only if\n$II^{-1}$ is not contained in any maximal $*$-ideal of $D$; in this case $I^*=(a_1,...,a_n)^*$ for some $a_1,...,a_n\\in I$.\nLet $*_1,*_2$ be star operations on $D$. We write $*_1\\leq *_2$, if $I^{*_1}\\subseteq I^{*_2}$ for all\n$I\\in F(D)$. In this case we get $(I^{*_1})^{*_2}=I^{*_2}=(I^{*_2})^{*_1}$ and every\n$*_1$-invertible ideal is $*_2$-invertible.\nSome well-known star operations are: the {\\em $d$-operation} (given by $I\\mapsto I$),\nthe {\\em $v$-operation} (given by $I\\mapsto I_v = (I^{-1})^{-1}$) and the {\\em $t$-operation} (defined by $t=v_f$).\nThe {\\em $w$-operation} is the star operation given by $I \\mapsto I_w= \\{x\\in K \\mid xH \\subseteq\nI$ for some finitely\ngenerated ideal $H$ of $D$ with $H^{-1} =D\\}$. The $w$-operation is a stable star operation of finite character.\nFor an integrally closed domain $D$, the {\\em $b$-operation} on $D$ is the star operation defined by $I\\mapsto I_b=\\cap _{V} IV$ where $V$ runs in the set of all valuation overrings of $D$ (see \\cite[Page 398]{G}). For every $I\\in F(D)$, we have\n$I\\subseteq I_w \\subseteq I_t \\subseteq I_v$. It is known that $Max_w(D)=Max_t(D)$, cf.\n\\cite[Corollaries 2.13 and 2.17]{AC} and $I_w = \\cap _{M \\in Max_t(D)}ID_M$, cf. \\cite[Corollary 2.10]{AC}.\nConsequently, a nonzero fractional ideal is $w$-invertible if and only if it is $t$-invertible. Recall \\cite{EFP} that an integral domain $D$ is said to be {\\em $*$-Dedekind} if every nonzero fractional ideal of $D$ is $*$-invertible. A domain $D$ is called a Prufer {\\em $*$-multiplication domain (P$*$MD)} if every nonzero finitely generated ideal of $D$ is $*_f$-invertible (see \\cite{FJS}).\nFor the general theory of star operations we refer the reader to \\cite[Sections 32 and 34]{G}.\\\\\n\n\nWe introduce the key concept of this paper.\n\n\\begin{definition}\\label{1}\nLet $*$ be a star operation on $D$. We say that a domain $D$ is a {\\em $*$-sharp domain} if whenever $I$, $A$, $B$ are nonzero ideals of $D$ with $I \\supseteq AB$, there exist nonzero ideals $H$ and $J$ such that $I^{*}=(HJ)^{*}$, $H^{*}\\supseteq A$ and $J^{*}\\supseteq B$.\n\\end{definition}\n\n\nThe $d$-sharp domains are just the sharp domains studied in \\cite{ADE}. If $*_1 \\leq *_2$ are star operations and $D$ is $*_1$-sharp, then $D$ is $*_2$-sharp (Proposition \\ref{81}). In particular, if $*$ is a star operation, then every sharp domain is $*$-sharp and every $*$-sharp domain is $v$-sharp. A $t$-sharp domain is $v$-sharp but the converse is not true in general (Remark \\ref{121}).\n\nIn Section 2, we study the $*$-sharp domains in general.\nIn this new context, we generalize most of the results obtained in \\cite{ADE}.\nFor $*\\in \\{d,b,w,t\\}$, every fraction ring of a $*$-sharp domain is $*$-sharp (Proposition \\ref{81}).\nEvery $*$-Dedekind domain is $*$-sharp. In particular, every Krull domain is $t$-sharp (Proposition \\ref{3}).\nLet $D$ be a domain and $*$ a finite character stable star operation such that $D$ is $*$-sharp. Then $D$ is a P$*$MD of $*$-dimension $\\leq 1$; moreover $D_M$ is a valuation domain with value group a complete subgroup of the reals, for each $M\\in Max_*(D)$ (Proposition \\ref{77}).\nThe converse is true for domains whose nonzero elements are contained in only finitely many $*$-maximal ideals (Proposition \\ref{200}).\nIf $*$ is a star operation on $D$ such that $D$ is a $*$-sharp domain, then $I_v$ is $*$-invertible for each nonzero ideal $I$\n(Proposition \\ref{3a}).\nIf $*$ is a finite character stable star operation on $D$ such that $D$ is a $*$-sharp TV domain, then $D$ is $*$-Dedekind (Corollary \\ref{111}).\nA domain $D$ is $v$-sharp if and only if $D$ is completely integrally closed (Corollary \\ref{100}). In particular, every $*$-sharp domain is completely integrally closed.\nIf $*$ is a stable star operation on $D$ such that $D$ is a $*$-sharp domain, then every finitely generated nonzero ideal of $D$ is $*$-invertible (Proposition \\ref{102}).\nIf $D$ is a countable domain and $*$ a finite character stable star operation on $D$ such that $D$ is $*$-sharp, then $D$ is a $*$-Dedekind domain (Corollary \\ref{845}).\n\nIn Section 3, we study the $t$-sharp domains. We obtain the following results.\nEvery $t$-sharp domain $D$ is a PVMD with $t$-dimension $\\leq 1$ and $D_M$ is a valuation domain with value group a complete subgroup of the reals, for each maximal $t$-ideal $M$ of $D$ (Proposition \\ref{11}). A domain is $t$-sharp if and only if it is $w$-sharp (Proposition \\ref{103}).\nA domain $D$ is a Krull domain if and only if $D$ is a $t$-sharp TV domain (Corollary \\ref{400}).\nIf $D$ is a countable $t$-sharp domain, then $D$ is a Krull domain\n(Corollary \\ref{300}).\nA domain $D$ is $t$-sharp if and only if $D[X]$ is $t$-sharp\n(Proposition \\ref{331}) if and only if $D[X]_{N_v}$ is sharp (Proposition \\ref{133}). Here $N_v$ denotes the multiplicative subset of $D[X]$ consisting of all $f\\in D[X]-\\{0\\}$ with $c(f)_v=D$, where $c(f)$ is the ideal generated by the coefficients of $f$.\nLet $D$ be a $t$-sharp domain. Then $N'_v=\\{ f\\in D[[X]]-\\{0\\}\\mid c(f)_v=D\\}$ is a multiplicative set,\n$D[[X]]_{N'_v}$ is a sharp domain and every ideal of $D[[X]]_{N'_v}$ is extended from $D$ (Proposition \\ref{1024}). Moreover, $D[[X]]_{N'_v}$ is a faithfully flat $D[X]_{N_v}$-module\nand there is a one-to-one correspondence between the ideals of $D[X]_{N_v}$ and the ideals of $D[[X]]_{N'_v}$\n(Corollary \\ref{307}).\n\n\nThroughout this paper all rings are (commutative unitary) integral domains. Any unexplained material is standard, as in \\cite{G}, \\cite{H}.\n\n\\section{$*$-sharp domains.}\n\n\n\n\nIn this section we study the $*$-sharp domains for an arbitrary star operation $*$ (see Definition \\ref{1}). We obtain $*$-operation analogues for most of the results in \\cite{ADE}.\n\n\n\\begin{proposition} \\label{4}\nLet $D$ be a domain, $S\\subseteq D$ a multiplicative set and $*$ (resp. $\\sharp$) star operations on $D$ (resp. $D_S$) such that\n$I^*\\subseteq (ID_S)^\\sharp$ for each nonzero ideal $I$ of $D$.\nIf $D$ is $*$-sharp, then the fraction ring $D_S$ is $\\sharp$-sharp.\n\\end{proposition}\n\\begin{proof}\nNote that the condition $I^*\\subseteq (ID_S)^\\sharp$ in the hypothesis is equivalent to $(I^*D_S)^\\sharp=(ID_S)^\\sharp$.\nLet $I,A,B$ be nonzero ideals of $D$ such that $ID_S\\supseteq ABD_S$. Then $C=ID_S \\cap D \\supseteq AB$. As $D$ is $*$-sharp, we have $C^*=(HJ)^*$ with $H,J$ ideals of $D$ such that $H^*\\supseteq A$ and $J^*\\supseteq B$. Since $(WD_S)^\\sharp=(W^*D_S)^\\sharp$ for every nonzero ideal $W$,\nwe get $(ID_S)^\\sharp=(C^*D_S)^\\sharp=((HJ)^*D_S)^\\sharp=(HJD_S)^\\sharp$, $(HD_S)^\\sharp=(H^*D_S)^\\sharp\\supseteq AD_S$ and $(JD_S)^\\sharp\\supseteq BD_S$.\n\\end{proof}\n\n\n\n\n\\begin{proposition} \\label{81}\nLet $D$ be a domain, $*_1 \\leq *_2$ star operations and $D$ and $S\\subseteq D$ a multiplicative set.\n\n$(a)$ If $D$ is $*_1$-sharp, then D is $*_2$-sharp.\n\n$(b)$ If $*\\in \\{d,t,w,b\\}$ and $D$ is $*$-sharp (with $D$ integrally closed if $*=b$), then $D_S$ is $*$-sharp.\n\\end{proposition}\n\\begin{proof}\n$(a)$. Apply Proposition \\ref{4} for $S=\\{1\\}$, $*=*_1$ and $\\sharp=*_2$.\n$(b)$. By Proposition \\ref{4}, it suffices to show that $I^*\\subseteq (ID_S)^*$ for each nonzero ideal $I$ of $D$. This is clear for $*=d$ and true for $*=t$, cf. \\cite[Lemma 3.4]{Kg}. Assume that $x\\in I_w$. Then $xH\\subseteq I$ for some finitely generated nonzero ideal $H$ of $D$ such that $H_v=D$. Hence $(HD_S)_v=D_S$ (cf. \\cite[Lemma 3.4]{Kg}) and $xHD_S\\subseteq ID_S$, thus $x\\in (ID_S)_w$. Assume that $D$ integrally closed. If $V$ is a valuation overring of $D_S$, then $V$ is an overring of $D$, so $I_b\\subseteq IV$. Thus $I_b\\subseteq (ID_S)_b$.\n\\end{proof}\n\nIn \\cite[Theorem 11]{ADE}, it was shown that a sharp domain is a Prufer domain of dimension at most $1$. We extend this result.\n\n\\begin{proposition} \\label{77}\nLet $D$ be a domain and $*$ a finite character stable star operation on $D$ such that $D$ is $*$-sharp. Then $D_M$ is a valuation domain with value group a complete subgroup of the reals, for each $M\\in Max_*(D)$. In particular, $D$ is a P$*$MD of $*$-dimension $\\leq 1$.\n\\end{proposition}\n\\begin{proof}\nLet $M$ be a maximal $*$-ideal. If $I$ is a nonzero ideal of $D$, then $I^*D_M=ID_M$, cf. \\cite[Corollary 4.2]{A}. By Proposition \\ref{4}, applied for $S=D-M$, $*=*$ and $\\sharp=d$, we get that $D_M$ is a sharp domain. Apply \\cite[Theorem 11]{ADE}. The ``in particular'' assertion is clear.\n\\end{proof}\n\n\n\n\\begin{proposition}\\label{3}\nLet $D$ be a domain and $*$ a star operation on $D$. If $D$ is $*$-Dedekind, then $D$ is $*$-sharp. In particular, every Krull domain is $t$-sharp.\n\\end{proposition}\n\\begin{proof}\nLet $I,A,B$ be nonzero ideals of $D$ such that $I \\supseteq AB$. Set $H=I+A$ and $J=IH^{-1}$. Note that $J\\subseteq D$ and $A\\subseteq H$. Since $(HH^{-1})^*=D$, we get $I^*=(HJ)^*$. From $BH=B(A+I) \\subseteq I$, we get $B \\subseteq (BHH^{-1})^* \\subseteq (IH^{-1})^*=J^*$. For the ``in particular statement'', recall that the $t$-Dedekind domains are the Krull domains, cf. \\cite[Theorem 3.6]{Kg1}.\n\\end{proof}\n\n\n\n\n\\begin{proposition}\\label{3a}\nLet $D$ be a domain and $*$ a star operation on $D$ such that $D$ is $*$-sharp. Then $I_v$ is $*$-invertible for each nonzero ideal $I$.\n\\end{proposition}\n\\begin{proof}\nLet $I$ be a nonzero ideal of $D$ and $x \\in I-\\{0\\}$. Then $I(xI^{-1}) \\subseteq xD$. Since $D$ is $*$-sharp, there exist $H,J$ ideals of $D$ such that $H^* \\supseteq I$, $J^* \\supseteq xI^{-1}$ and $xD=(HJ)^*$. Hence $H$ is $*$-invertible and we get\n$H^{-1}=(x^{-1}J)^*\\supseteq (xx^{-1}I^{-1})^*=I^{-1}$, so $H_v\\subseteq I_v$. The opposite inclusion follows from $H^* \\supseteq I$. Thus $I_v=H_v$ is $*$-invertible, because $H^*=H_v$ since $H$ is $*$-invertible.\n\\end{proof}\n\nNext, we extend \\cite[Corollary 12]{ADE} to the star operation setting.\n\n\n\\begin{corollary}\\label{111}\nLet $D$ be a domain and $*$ a finite character stable star operation on $D$ such that $D$ is $*$-sharp. If $D$ is a TV domain (e.g. a Mori domain), then $D$ is $*$-Dedekind.\n\\end{corollary}\n\\begin{proof}\nBy Proposition \\ref{77}, $D$ is a P$*$MD , so $*=t$, cf. \\cite[Proposition 3.15]{FJS}. As $D$ is a TV domain, we get $*=t=v$. By Proposition \\ref{3a}, $D$ is $*$-Dedekind.\n\\end{proof}\n\n\n\n\\begin{corollary}\\label{100}\nA domain $D$ is $v$-sharp if and only if $D$ is completely integrally closed. In particular, any $*$-sharp domain is completely integrally closed.\n\\end{corollary}\n\\begin{proof}\nBy Propositions \\ref{3} and \\ref{3a} (for $*=v$), $D$ is $v$-sharp if and only if $D$ is $v$-Dedekind. By \\cite[Theorem 34.3]{G} or \\cite[Proposition 3.4]{F}, a domain is $v$-Dedekind if and only if it is completely integrally closed. For the ``in particular'' assertion, apply Proposition \\ref{81} taking into account that $*\\leq v$ for each star operation $*$.\n\\end{proof}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\begin{remark}\\label{121}\n$(a)$ There exist a completely integrally closed domain $A$ having some fraction ring which is not completely integrally closed (for instance the ring of entire functions, cf. \\cite[Exercises 16 and 21, page 147]{G}). Thus the $v$-sharp property does not localize, cf. Corollary \\ref{100}. Note that $A$ cannot be $t$-sharp because the $t$-sharp property localizes, cf.\nProposition \\ref{81}.\n$(b)$ Let $D$ be a completely integrally closed domain which is not a PVMD (such a domain is constructed in \\cite{D}). By Corollary \\ref{100} and Proposition \\ref{11}, such a domain is $v$-sharp but not $t$-sharp.\n$(c)$ Let $D$ be a Krul domain of dimension $\\geq 2$ (e.g. $\\mathbb{Z}[X]$). By Proposition \\ref{3} and \\cite[Theorem 11]{ADE}, $D$ is $t$-sharp but not sharp.\n\n\n\\end{remark}\n\nIn the next lemma we recall two well-known facts.\n\n\n\n\\begin{lemma} \\label{71}\nLet $D$ be a domain, $*$ a star operation on $D$ and $I,J,H\\in F(D)$.\n\n$(a)$ If $(I+J)^*=D$, then $(I\\cap J)^*=(IJ)^*$.\n\n$(b)$ If $I$ is $*$-invertible, then $(I(J\\cap H))^*=(IJ\\cap IH)^*$.\n\\end{lemma}\n\\begin{proof}\n$(a)$ Clearly, $(IJ)^*\\subseteq (I\\cap J)^*$. Conversely, since $(I+J)^*=D$, we have $(I\\cap J)^*=((I\\cap J)(I+J))^*\\subseteq (IJ)^*$, thus $(I\\cap J)^*=(IJ)^*$.\n$(b)$ Clearly, $(I(J\\cap H))^*\\subseteq (IJ\\cap IH)^*$. Conversely, because $I$ is $*$-invertible, we have $(IJ\\cap IH)^*=(II^{-1}(IJ\\cap IH))^*\\subseteq (I(I^{-1}IJ\\cap I^{-1}IH))^*\\subseteq (I(J\\cap H))^*$.\n\\end{proof}\n\nThe next result generalizes \\cite[Proposition 10]{ADE}.\n\n\\begin{proposition} \\label{211p}\nLet $D$ be a domain and $*$ a stable star operation on $D$ such that $D$ is $*$-sharp. If $I,J$ are nonzero ideals of $D$ such that $(I+J)_v=D$, then $(I_v+J_v)^*=D$.\n\\end{proposition}\n\\begin{proof}\nLet $K$ be the quotient field of $D$. Changing $I$ by $I_v$ and $J$ by $J_v$, we may assume that $I,J$ are $*$-invertible $v$-ideals, cf. Proposition \\ref{3a}.\nSince $(I+J)^2 \\subseteq I^2+J$ and $D$ is $*$-sharp, there exist two nonzero ideals $A$, $B$ such that $(I^2+J)^*=(AB)^*$ and $I+J\\subseteq A^* \\cap B^*$. We {\\em claim} that $(I^2+J)^*:I=(I+J)^*$. To prove the claim, we perform the following step-by-step computation. First,\n$(I^2+J)^*:I=((I^2+J)^*:_KI)\\cap D=((I^2+J)I^{-1})^*\\cap D=(I+JI^{-1})^*\\cap D$ because $I$ is $*$-invertible. As $*$ is stable, we get $(I+JI^{-1})^*\\cap D=((I+JI^{-1})\\cap D)^*=(I+(JI^{-1}\\cap D))^*$ by modular distributivity.\nSince $I$ is $*$-invertible, we get\n$(I+(JI^{-1}\\cap D))^*=(I+I^{-1}(J\\cap I))^*$, cf. Lemma \\ref{71}.\nUsing the fact that $I$ is $*$-invertible (hence $v$-invertible) and Lemma \\ref{71}, we derive that $(I+I^{-1}(J\\cap I))^* \\subseteq (I+I^{-1}(IJ)_v)^*\\subseteq (I+(II^{-1}J)_v)^*=(I+J_v)^*=(I+J)^*$. Putting all these facts together, we get $(I^2+J)^*:I\\subseteq (I+J)^*$ and the other inclusion is clear. So the claim is proved.\nFrom $(I^2+J)^*=(AB)^*$, we get $A^*\\subseteq (I^2+J)^*:B^*\\subseteq (I^2+J)^*:I=(I+J)^*$, so $A^*=(I+J)^*$. Similarly, we get $B^*=(I+J)^*$, hence $(I^2+J)^*=((I+J)^2)^*$.\nIt follows that $J^*\\subseteq (I^2+J)^*=((I+J)^2)^*\\subseteq (J^2+I)^*$. So $J^*=J^*\\cap (J^2+I)^*=(J\\cap (J^2+I))^*=(J^2+(J\\cap I))^*$ where we have used the fact that $*$ is stable and the modular distributivity. By Lemma \\ref{71}, we have $I\\cap J\\subseteq (IJ)_v$, so we get $J^*=(J^2+(J\\cap I))^*\\subseteq (J^2+(IJ)_v)^*$. Since $J$ is $*$-invertible, we have $D=(JJ^{-1})^*\\subseteq ((J^2+(IJ)_v)J^{-1})^* \\subseteq (J+I_v)^*=(J+I)^*$.\nThus $(I+J)^*=D$.\n\\end{proof}\n\nNote that from Proposition \\ref{211p} we can recover easily \\cite[Proposition 10]{ADE}. Next, we give another extension of \\cite[Theorem 11]{ADE} (besides Proposition \\ref{77}).\n\n\n\\begin{proposition} \\label{102}\nLet $D$ be a domain and $*$ a stable star operation on $D$ such that $D$ is $*$-sharp. Then every finitely generated nonzero ideal of $D$ is $*$-invertible.\n\\end{proposition}\n\\begin{proof}\nLet $x,y\\in D-\\{0\\}$. By Proposition \\ref{3a}, the ideal $I=(xD+yD)_v$ is $*$-invertible (hence $v$-invertible), so $(xI^{-1}+yI^{-1})_v=D$. By Proposition \\ref{211p} we get $(xI^{-1}+yI^{-1})^*=D$, hence $I=((xI^{-1}+yI^{-1})I)^*=(xD+yD)^*$ because $I$ is $*$-invertible. Thus every two-generated nonzero ideal of $D$ is $*$-invertible. Now the proof of \\cite[Proposition 22.2]{G} can be easily adapted to show that every finitely generated nonzero ideal of $D$ is $*$-invertible.\n\\end{proof}\n\n\\begin{remark}\nUnder the assumptions of Proposition \\ref{102}, it does not follow that $D$ is a P$*$MD. Indeed, let $D$ be a completely integrally closed domain which is not a PVMD (such a domain is constructed in \\cite{D}). The $v$-operation on $D$ is stable (cf. \\cite[Theorem 2.8]{ACl}) and $D$ is $v$-sharp (cf. Corollary \\ref{100}).\n\\end{remark}\n\n\nLet $D$ be a domain with quotient field $K$.\nAccording to \\cite{AZ}, a family $\\mathcal{F}$ of nonzero prime ideals of $D$ is called {\\em independent of finite character family (IFC family)},\nif $(1)$ $D=\\cap_{P\\in \\mathcal{F}}D_P$, $(2)$ every nonzero $x\\in D$ belongs to only finitely many members of\n$\\mathcal{F}$ and $(3)$ every nonzero prime ideal of $D$ is contained in at most one member of $\\mathcal{F}$. The follwing result extends \\cite[Theorem 15]{ADE}.\n\n\n\n\n\n\n\\begin{proposition}\\label{200}\nLet $D$ be a domain and $*$ a finite character star operation on $D$. Assume that\n\n$(a)$ every $x\\in D-\\{0\\}$ is contained in only finitely many maximal $*$-ideals, and\n\n$(b)$ for every $M\\in Max_*(D)$, $D_M$ is a valuation domain with value group a complete subgroup of the reals.\n\\\\ Then $D$ is a $\\tilde{*}$-sharp domain and hence $*$-sharp.\n\\end{proposition}\n\\begin{proof}\nBy \\cite{Gr}, $D=\\cap_M D_M$ where $M$ runs in the set of maximal $*$-ideals. Since each $D_M$ is a valuation domain with value group a complete subgroup of the reals, every $M$ has height one.\nIt follows that $Max_*(D)$ is an IFC family. Consider the $\\tilde{*}$ operation, i.e. $I\\mapsto I^{\\tilde{*}}=\\cap_M ID_M$. We show that $D$ is $\\tilde{*}$-sharp.\nLet $I,A,B$ be nonzero ideals of $D$ such that $I\\supseteq AB$.\nLet $P_1$,...,$P_n$ the maximal $*$-ideals of $D$ containing $AB$.\nSince $D_{P_i}$ is sharp, there exist $H_i$, $J_i$ ideals of $D_{P_i}$ such that $ID_{P_i}=H_iJ_i$, $H_i\\supseteq AD_{P_i}$ and\n$J_i\\supseteq BD_{P_i}$ for all $i$ between $1$ and $n$.\nSet $H'_i=H_i\\cap D$, $J'_i=J_i\\cap D$, $i=1,...,n$,\n$H=H'_1\\cdots H'_n$ and $J=J'_1\\cdots J'_n$.\nBy \\cite[Lemma 2.3]{AZ}, $P_i$ is the only element of $Max_*(D)$ containing $H'_i$ (resp. $J'_i$), thus it can be checked that $ID_P=(HJ)D_P$, $HD_P\\supseteq AD_P$ and $JD_P\\supseteq BD_P$ for each $P\\in Max_*(D)$. So, we have $I^{\\tilde{*}}=(HJ)^{\\tilde{*}}$, $H^{\\tilde{*}}\\supseteq A$ and $J^{\\tilde{*}}\\supseteq B$. Consequently, $D$ is $\\tilde{*}$-sharp. By Proposition \\ref{81}, $D$ is $*$-sharp because $\\tilde{*}\\leq *$, cf. \\cite[Theorem 2.4]{AC}.\n\\end{proof}\n\n\\begin{proposition}\\label{822}\nLet $D$ be a contable domain and $*$ a finite character star operation on $D$ such that $D$ is a P$*$MD and $I_v$ is $*$-invertible for each nonzero ideal $I$ of $D$. Then every nonzero element of $D$ is contained in only finitely many maximal $*$-ideals.\n\\end{proposition}\n\\begin{proof}\nDeny. By \\cite[Corollary 5]{DZ}, there exists a nonzero element $z$ and an infinite family $(I_n)_{n\\geq 1}$ of $*$-invertible proper ideals containing $z$ which are mutually $*$-comaximal (that is, $(I_m+I_n)^*=D$ for every $m\\neq n$). For each nonempty set $\\Lambda$ of natural numbers, consider the $v$-ideal $I_\\Lambda=\\cap_{n\\in \\Lambda} I_n$ (note that $z\\in I_\\Lambda$). By hypothesis, $I_\\Lambda$ is $*$-invertible. We claim that $I_\\Lambda\\neq I_{\\Lambda'}$ whenever $\\Lambda$, $\\Lambda'$ are distinct nonempty sets of natural numbers.\nDeny. Then there exists a nonempty set of natural numbers $\\Gamma$ and some $k\\notin \\Gamma$ such that $I_k\\supseteq I_\\Gamma$. Consider the ideal $H=(I_k^{-1} I_\\Gamma)^*\\supseteq I_\\Gamma$. If $n\\in \\Gamma$, then $I_n \\supseteq I_\\Gamma =(I_kH)^*$, so $I_n \\supseteq H$, because $(I_n+I_k)^*=D$. It follows that $I_\\Gamma\\supseteq H$, so $I_\\Gamma = H=(I_k^{-1} I_\\Gamma)^*$. Since $I_\\Gamma$ is $*$-invertible, we get $I_k=D$, a contradiction. Thus the claim is proved. But then it follows that $\\{I_\\Lambda\\mid \\emptyset\\neq \\Lambda\\subseteq \\mathbb{N}\\}$ is an uncountable set of $*$-invertible ideals. This leads to a contradiction, because $D$ being countable, it has countably many $*$-ideals of finite type.\n\\end{proof}\n\n\n\\begin{corollary}\\label{845}\nLet $D$ be a countable domain and $*$ a finite character stable star operation on $D$ such that $D$ is $*$-sharp.\nThen $D$ is a $*$-Dedekind domain.\n\\end{corollary}\n\\begin{proof}\nWe may assume that $D$ is not a field.\nBy Proposition \\ref{77}, $D$ is a P$*$MD. Now Propositions \\ref{3a} and \\ref{822} show that every nonzero element of $D$ is contained in only finitely many maximal $*$-ideals. Let $M$ be a maximal $*$-ideal of $D$. By Proposition \\ref{77}, $D_M$ is a countable valuation domain with value group $\\mathbb{Z}$ or $\\mathbb{R}$, so $D_M$ is a DVR. Thus $D$ is a $*$-Dedekind domain, cf. \\cite[Theorem 4.11]{EFP}.\n\\end{proof}\n\n\n\\section{$t$-sharp domains.}\n\n\nThe $t$-operation is a very useful tool in multiplicative ideal theory. In this section we give some results which are specific for the $t$-sharp domains.\n\n\n\n\\begin{proposition}\\label{11}\nLet $D$ be a $t$-sharp domain. Then $D$ is a PVMD of $t$-dimension $\\leq 1$ and $D_M$ is a valuation domain with value group a complete subgroup of the reals for each maximal $t$-ideal $M$ of $D$.\n\\end{proposition}\n\\begin{proof}\nLet $I$ be finitely generated nonzero ideal of $D$. Then $I_v=I_t$, so Proposition \\ref{3a} shows that $I_t$ is $t$-invertible. Thus $D$ is a PVMD.\nLet $M$ be a maximal $t$-ideal of $D$.\nBy part $(b)$ of Proposition \\ref{81}, $D_M$ is a $t$-sharp valuation domain, so $D_M$ is sharp since for valuation domains $t=d$. Now apply \\cite[Proposition 6]{ADE}.\n\\end{proof}\n\n\n\n\n\n\\begin{proposition}\\label{103}\nLet $D$ be a domain. Then $D$ is $t$-sharp if and only if $D$ is $w$-sharp.\n\\end{proposition}\n\\begin{proof}\nIf $D$ is $w$-sharp, then $D$ is $t$-sharp (cf. Proposition \\ref{81}) because $w\\leq t$. Conversely, assume that $D$ is $t$-sharp. By Proposition \\ref{11}, $D$ is a PVMD. But in a PVMD the $w$-operation coincides with the $t$-operation (cf. \\cite[Theorem 3.5]{Kg}), so $D$ is also $w$-sharp.\n\\end{proof}\n\nCombining Corollary \\ref{111} and Proposition \\ref{103}, we get\n\n\\begin{corollary}\\label{400}\nA domain $D$ is a Krull domain if and only if $D$ is a $t$-sharp TV domain.\n\\end{corollary}\n\n\n\n\\begin{corollary}\\label{300}\nIf $D$ is a countable $t$-sharp domain, then $D$ is a Krull domain.\n\\end{corollary}\n\\begin{proof}\nAssume that $D$ is a countable $t$-sharp domain.\nBy Proposition \\ref{103}, $D$ is $w$-sharp. Moreover the $w$-operation is stable and of finite character, cf. \\cite[Corollary 2.11]{AC}. By Corollary \\ref{845}, $D$ is a $t$-Dedekind domain, that is a Krull domain.\n\\end{proof}\n\n\n\n\n\n\nBy \\cite{Zac}, a domain $D$ is called a {\\em pre-Krull domain} if $I_v$ is $t$-invertible for each nonzero ideal $I$ of $D$ (see also \\cite{L} where a pre-Krull domain is called a $(t,v)$-Dedekind domain).\n\n\n\n\n\n\\begin{proposition} \\label{3x}\nA domain $D$ is $t$-sharp if and only if\n\n$(a)$ $D$ is pre-Krull, and\n\n$(b)$ for all nonzero ideals $I$,$A$,$B$ of $D$ such that $I_v=D$ and $I\\supseteq AB$, there exist nonzero ideals $H$ and $J$ such that $I_t=(HJ)_t$, $H_t\\supseteq A$ and $J_t\\supseteq B$.\n\\end{proposition}\n\\begin{proof}\nThe implication $(\\Rightarrow)$ follows from Proposition \\ref{3a}. Conversely, assume that $(a)$ and $b$ hold.\nLet $I$, $A$ and $B$ be nonzero ideals of $D$ such that $I\\supseteq AB$.\nThen $I_v \\supseteq A_vB_v$ and $I_v$, $A_v$, $B_v$ are $t$-invertible ideals, cf. $(a)$.\nSince $D$ is a pre-Krull domain, $D$ is a PVMD and hence a $t$-Schreier domain cf. \\cite[Corollary 6]{DZ1}.\nSo there exist $t$-invertible ideals $H$ and $J$ such that $I_v=(HJ)_t$, $H_t\\supseteq A_v$ and $J_t\\supseteq B_v$.\nWe have $(II^{-1})_t = (IH^{-1}J^{-1})_t \\supseteq (AH^{-1})(BJ^{-1})$. Set $M=(II^{-1})_t$ and note that $AH^{-1}$ and $BJ^{-1}$ are integral ideals.\nSince $I_v$ is $t$-invertible, $M_v=(I_vI^{-1})_v=D$.\nBy $(b)$, there exist nonzero ideals $N$ and $P$ such that $M_t=(NP)_t$, $N_t\\supseteq AH^{-1}$ and $P_t\\supseteq BJ^{-1}$.\nSumming up, we get\n$I_t=(I_vM)_t=(HJNP)_t=((HN)(JP))_t$, $(HN)_t\\supseteq (AHH^{-1})_t=A_t$ and $(JP)_t\\supseteq (BJJ^{-1})_t=B_t$.\n\\end{proof}\n\n\n\n\\begin{lemma}\\label{332} If D is an integrally closed domain and $I$ a nonzero ideal of $D[X]$ such that $I_v=D[X]$, then $I\\cap D \\neq 0$.\n\\end{lemma}\n\\begin{proof} Assume that $I \\cap D = 0$. By \\cite[Theorem 2.1]{AKZ}, there exist $f\\in D[X]-\\{0\\}$ and $a\\in D-\\{0\\}$ such that $J:=(a\/f)I \\subseteq D[X]$ and $J\\cap D \\neq 0$. We get $(a\/f)D[X]=(a\/f)I_v \\subseteq D[X]$, hence $a\/f \\in D[X]$ and thus $a\/f\\in D$, because $a\\in D-\\{0\\}$. We get $J\\subseteq I$ which is a contradiction because $J\\cap D \\neq 0$ and $I \\cap D = 0$.\n\\end{proof}\n\n\\begin{proposition} \\label{331}\nA domain $D$ is $t$-sharp if and only if $D[X]$ $t$-sharp.\n\\end{proposition}\n\\begin{proof} $(\\Rightarrow).$ Set $\\Omega=D[X]$. Let $I$,$A$,$B$ be nonzero ideals of $\\Omega$ such that $I\\supseteq AB$.\nBy Proposition \\ref{3x}, $D$ is pre-Krull, so $D[X]$ is pre-Krull, cf. \\cite[Theorem 3.3]{L}. Applying Proposition \\ref{3x} for $\\Omega$, we can assume that $I_v=\\Omega$. Changing $A$ by $I+A$ and $B$ by $I+B$, we may assume that $A,B \\supseteq I$. By Lemma \\ref{332} we get $I\\cap D\\neq 0$, so $A\\cap D\\neq 0$ and $B\\cap D\\neq 0$. By \\cite[Theorem 3.2]{AKZ}, we have $I_t=I'_t\\Omega$, $A_t=A'_t\\Omega$ and $B_t=B'_t\\Omega$ for some nonzero ideals $I',A'$ and $B'$ of D. From $I\\supseteq AB$, we get $I'_t\\Omega=I_t\\supseteq A_tB_t=(A'_tB'_t)\\Omega$, hence $I'_t\\supseteq A'B'$.\nAs $D$ is $t$-sharp, there exist nonzero ideals $H$ and $J$ of $D$ such that $I'_t=(HJ)_t$, $H_t\\supseteq A'$ and $J_t\\supseteq B'$.\nHence $I_t=(HJ\\Omega)_t$, $(H\\Omega)_t\\supseteq A$ and $(J\\Omega)_t\\supseteq B$. $(\\Leftarrow).$ Let $I$,$A$,$B$ be nonzero ideals of $D$ such that $I\\supseteq AB$. As $D[X]$ $t$-sharp, there exist nonzero ideals $H$ and $J$ of $\\Omega$ such that $(I\\Omega)_t=(HJ)_t$, $H_t\\supseteq A$ and $J_t\\supseteq B$.\nSince $H_t\\supseteq A$ and $A\\neq 0$, we derive that $H_t\\cap D\\neq 0$, hence $H_t=(M\\Omega)_t$ for some nonzero ideal $M$ of $D$, cf. \\cite[Theorem 3.2]{AKZ}. Similarly, $J_t=(N\\Omega)_t$ for some nonzero ideal $N$ of $D$. Combining the relations above, we get $I_t=(MN)_t$, $M_t\\supseteq A$ and $N_t\\supseteq B$.\n\\end{proof}\n\n\\begin{remark}\nNotice that we do not have a ``$d$-analogue'' of Proposition \\ref{331} because a sharp domain has dimension $\\leq 1$ (see \\cite[Theorem 11]{ADE}). But remark that we do have a ``$v$-analogue'' of Proposition \\ref{331}. Indeed, a domain $D$ is $v$-sharp if and only if $D$ is completely integrally closed\n(cf. Corollary \\ref{100}) and $D$ is completely integrally closed if and only if so is $D[X]$. Similarly, $D$ is $v$-sharp if and only if the power series ring $D[[X]]$ is $v$-sharp.\n\\end{remark}\n\nDenote by $N_v$ the multiplicative set of $D[X]$ consisting of all nonzero polynomials $a_0+a_1X+\\cdots +a_nX^n$ such that $(a_0,a_1,...,a_n)_v=D$. The ring $D[X]_{N_v}$ was studied in \\cite{Kg}.\n\n\n\n\n\n\\begin{proposition}\\label{133}\nA domain $D$ is $t$-sharp if and only if $D[X]_{N_v}$ is sharp.\n\\end{proposition}\n\\begin{proof}\nIf $D$ is $t$-sharp, then $D$ is a PVMD, cf. Proposition \\ref{11}.\nIf $D[X]_{N_v}$ is sharp, then $D[X]_{N_v}$ is a Prufer domain (cf. \\cite[Theorem 11]{ADE}) hence $D$ is a PVMD, cf. \\cite[Theorem 3.7]{Kg}. So we may assume from the beginning that $D$ is a PVMD. Note that the $t$-sharp property of $D$ is in fact a property of the ordered monoid of all integral $t$-ideals of $D$ under the $t$-multiplication. Similarly, the sharp property of $D[X]_{N_v}$ is a property of the ordered monoid of all integral ideals of $D$ under the usual multiplication. Since $D$ is a PVMD, these two monoids are isomorphic (cf. \\cite[Theorem 3.14]{Kg}), so the proof is complete.\n\\end{proof}\n\nWe end our paper with a (partial) power series analogue of Proposition \\ref{133}. A lemma is in order.\n\n\n\\begin{lemma}\\label{101}\nLet $D\\subseteq E$ be a domain extension and every ideal of $E$ is extended from $D$. If $D$ is sharp then $E$ is also sharp.\n\\end{lemma}\n\\begin{proof}\nLet $I,A,B$ be nonzero ideals of $D$ such that $IE\\supseteq ABE$. Then $C=IE \\cap D \\supseteq AB$. As $D$ is sharp, we have $C=HJ$ with $H,J$ ideals of $D$ such that $H\\supseteq A$ and $J\\supseteq B$. We get $IE=CE=HJE$, $HE\\supseteq AE$ and $J\\supseteq BE$.\n\\end{proof}\n\nLet $D$ be a $t$-sharp domain which is not a field. \nBy Proposition \\ref{11}, $D$ is PVMD with $t$-dimension one. Hence \\cite[Proposition 3.3]{AK2} shows that $c(fg)_t=(c(f)c(g))_t$ (thus $c(fg)_v=(c(f)c(g))_v$)\nfor every $f,g\\in D[[X]]-\\{0\\}$, where $c(f)$ is the ideal generated by the coefficients of $f$.\nThen $N'_v=\\{ f\\in D[[X]]-\\{0\\}\\mid c(f)_v=D\\}$\nis a multiplicative subset of the power series ring $D[[X]]$.\n The fraction ring $D[[X]]_{N'_v}$ was studied in \\cite{AK2} and \\cite{L}. Note that $D\\subseteq D[X]_{N_v}\\subseteq D[[X]]_{N'_v}$, where $N_v=\\{ f\\in D[X]-\\{0\\}\\mid c(f)_v=D\\}$.\n\n\n\n\\begin{proposition}\\label{1024}\nIf $D$ is a $t$-sharp domain, then $D[[X]]_{N'_v}$ is sharp and every ideal of $D[[X]]_{N'_v}$ is extended from $D$.\n\\end{proposition}\n\\begin{proof} \nWe may assume that $D$ is not a field. \nBy Proposition \\ref{3x}, $D$ is a pre-Krull domain (alias $(t,v)$-Dedekind domain). As seen in the paragraph preceding this proposition, $c(fg)_v=(c(f)c(g))_v$\nfor every $f,g\\in D[[X]]-\\{0\\}$. By \\cite[Theorem 4.3]{L}\n it follows that every ideal of $D[[X]]_{N'_v}$ is extended from $D$, then, a fortiori, from $D[X]_{N_v}$.\n By Proposition \\ref{133} it follows that $D[X]_{N_v}$ is a sharp domain, hence so is $D[[X]]_{N'_v}$, cf. Lemma \\ref{101}. \n\\end{proof}\n\n\\begin{corollary}\\label{307}\nLet $D$ be a $t$-sharp domain. Then $D[[X]]_{N'_v}$ is a faithfully flat $D[X]_{N_v}$-module and the extension map \n$I\\mapsto ID[[X]]_{N'_v}$ \n is a bijection from the set of ideals of $D[X]_{N_v}$ to the set of ideals of $D[[X]]_{N'_v}$.\n\\end{corollary}\n\\begin{proof}\nSet $E=D[X]_{N_v}$ and $F=D[[X]]_{N'_v}$.\nBy the proof of Proposition \\ref{133} it follows that $E$ is a Prufer domain. Hence \n$F$ is a flat $E$-module because over a Prufer domain every torsion-free module is flat. \nWe show that every proper ideal of $E$ extends to a proper ideal of $F$. \nLet $J$ be a proper nonzero ideal of $E$. By \\cite[Theorem 3.14]{Kg}, $J=IE$ for some ideal $I$ of $D$ such that $I_t\\neq D$. Assume that $JF=F$. Then $IF=JF=F$, so $ID[[X]]$ contains some power series $f$ with $c(f)_v=D$. Write $f=a_1f_1+...+a_nf_n$ with $a_i \\in I$ and $f_i \\in D[[X]]$. Then $D=c(f)_v \\subseteq (a_1,...,a_n)_v\\subseteq I_t$, so $I_t=D$, a contradiction. As $F$ is a flat $E$-module and every proper ideal of $E$ extends to a proper ideal of $F$, it follows that $F$ is a faithfully flat $E$-module, cf. \\cite[Exercise 16, page 45]{AM}. In particular, $HF\\cap E=H$ for each ideal $H$ of $E$. \nBy Proposition \\ref{1024}, every ideal of $F$ is extended from $D$ and hence from $E$ because $D\\subseteq E\\subseteq F$.\nCombining these two facts, it follows that the extension map $I\\mapsto IF$ is a bijection from the set of ideals of $E$ to the set of ideals of $F$.\n\\end{proof}\n \n\n\n\n\n\n\n{\\bf Acknowledgements.} The first author was partially supported by an HEC (Higher Education Commission, Pakistan) grant. The second author gratefully acknowledges the warm\nhospitality of the Abdus Salam School of Mathematical Sciences GCU Lahore during his visits in 2006-2011. The third author was supported by UEFISCDI, project number 83\/2010, PNII-RU code TE\\_46\/2010, program Human Resources.\n\\\\[7mm]\n\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction\\label{sec:1}}\n\nIn this note, we describe a~generalization of the Carath\\'eodory\nform of the calculus of variations in \\emph{second-order }and, for\nspecific Lagrangians, in\\emph{ higher-order} field theory. Our approach\nis based on a geometric relationship between the Poincar\\'e\\textendash Cartan\nand Carath\\'eodory forms,\\emph{ }and analysis of the corresponding\nglobal properties. In \\cite{CramSaun2}, Crampin and Saunders obtained\nthe Carath\\'eodory form for second-order Lagrangians as a~certain\nprojection onto a~sphere bundle. Here, we confirm this result by\nmeans of a different, straightforward method which furthermore allows\nhigher-order generalization. It is a~standard fact in the global\nvariational field theory that the local expressions,\n\\begin{equation}\n\\Theta_{\\lambda}=\\mathscr{L}\\omega_{0}+\\sum_{k=0}^{r-1}\\left(\\sum_{l=0}^{r-1-k}(-1)^{l}d_{p_{1}}\\ldots d_{p_{l}}\\frac{\\partial\\mathscr{L}}{\\partial y_{j_{1}\\ldots j_{k}p_{1}\\ldots p_{l}i}^{\\sigma}}\\right)\\omega_{j_{1}\\ldots j_{k}}^{\\sigma}\\wedge\\omega_{i},\\label{eq:PoiCar}\n\\end{equation}\nwhich generalize the well-known Poincar\\'e\\textendash Cartan form\nof the calculus of variations, define, in general, differential form\n$\\Theta_{\\lambda}$ globally for Lagrangians $\\lambda=\\mathscr{L}\\omega_{0}$\nof order $r=1$ and $r=2$ only; see Krupka \\cite{Krupka-Lepage}\n($\\Theta_{\\lambda}$ is known as the principal component of a~Lepage\nequivalent of Lagrangian $\\lambda$), and Hor\\'ak and Kol\\'a\\v{r}\n\\cite{HorakKolar} (for higher-order Poincar\\'e\\textendash Cartan\nmorphisms). We show that if $\\Theta_{\\lambda}$ is globally defined\ndifferential form for a~\\emph{class} of Lagrangians of order $r\\geq3$,\nthen a~higher-order Carath\\'eodory equivalent for Lagrangians belonging\nto this class naturally arises by means of geometric operations acting\non $\\Theta_{\\lambda}$. To this purpose, for order $r=3$ we analyze\nconditions, which describe the obstructions for globally defined principal\ncomponents of Lepage equivalents \\eqref{eq:PoiCar} (or, higher\\textendash order\nPoincar\\'e\\textendash Cartan forms). \n\nThe above-mentioned differential forms are examples of \\emph{Lepage\nforms}; for a~comprehensive exposition and original references see\nKrupka \\cite{Handbook,Krupka-Book}\\emph{. }Similarly as the well-known\nCartan form describes analytical mechanics in a~coordinate-independent\nway, in variational field theory (or, calculus of variations for multiple-integral\nproblems) this role is played by Lepage forms, in general. These objects\ndefine the same variational functional as it is prescribed by a~given\nLagrangian and, moreover, variational properties (as variations, extremals,\nor Noether's type invariance) of the corresponding functional are\nglobally characterized in terms of geometric operations (such as the\nexterior derivative and the Lie derivative) acting on integrands -\nthe Lepage equivalents of a~Lagrangian.\n\nA concrete application of our result in second-order field theory\nincludes the Carath\\'eodory equivalent of the Hilbert Lagrangian\nin general relativity, which we determine and it will be further studied\nin future works.\n\nBasic underlying structures, well adapted to this paper, can be found\nin Voln\\'a and Urban \\cite{Volna}. If $(U,\\varphi)$, $\\varphi=(x^{i})$,\nis a chart on smooth manifold $X$, we set\n\\begin{equation*}\n{\\color{black}{\\color{black}\\omega_{0}}=dx^{1}\\wedge\\ldots\\wedge dx^{n},}\\qquad{\\color{black}\\omega_{j}}{\\color{black}=i_{\\partial\/\\partial x^{j}}\\omega_{0}=\\frac{1}{(n-1)!}\\varepsilon_{ji_{2}\\ldots i_{n}}dx^{i_{2}}\\wedge\\ldots\\wedge dx^{i_{n}},\n\\end{equation*}\nwhere $\\varepsilon_{i_{1}i_{2}\\ldots i_{n}}$ is the Levi-Civita permutation\nsymbol. If $\\pi:Y\\rightarrow X$ is a~fibered manifold and $W$ an\nopen subset of $Y$, then there exists a~unique morphism $h:\\Omega^{r}W\\rightarrow\\Omega^{r+1}W$\nof exterior algebras of differential forms such that for any fibered\nchart $(V,\\psi)$, $\\psi=(x^{i},y^{\\sigma})$, where $V\\subset W$,\nand any differentiable function $f:W^{r}\\rightarrow\\mathbb{R}$, where\n$W^{r}=(\\pi^{r,0})^{-1}(W)$ and $\\pi^{r,s}:J^{r}Y\\rightarrow J^{s}Y$\nthe jet bundle projection, \n\\[\nhf=f\\circ\\pi^{r+1,r},\\quad\\quad hdf=(d_{i}f)dx^{i},\n\\]\nwhere\n\\begin{equation}\nd_{i}=\\frac{\\partial}{\\partial x^{i}}+\\sum_{j_{1}\\leq\\ldots\\leq j_{k}}\\frac{\\partial}{\\partial y_{j_{1}\\ldots j_{k}}^{\\sigma}}y_{j_{1}\\ldots j_{k}i}^{\\sigma}\\label{eq:FormalDerivative}\n\\end{equation}\nis the $i$-th formal derivative operator associated with $(V,\\psi)$.\nA~differential form $q$-form $\\rho\\in\\Omega_{q}^{r}W$ satisfying\n$h\\rho=0$ is called \\emph{contact}, and $\\rho$ is generated by contact\n$1$-forms\n\\begin{equation*}\n\\omega_{j_{1}\\ldots j_{k}}^{\\sigma}=dy_{j_{1}\\ldots j_{k}}^{\\sigma}-y_{j_{1}\\ldots j_{k}s}^{\\sigma}dx^{s},\\qquad0\\leq k\\leq r-1\n\\end{equation*}\nThroughout, we use the standard geometric concepts: the exterior derivative\n$d$, the contraction $i_{\\Xi}\\rho$ and the Lie derivative $\\partial_{\\Xi}\\rho$\nof a differential form $\\rho$ with respect to a vector field $\\Xi$,\nand the pull-back operation $*$ acting on differential forms.\n\n\\section{Lepage equivalents in first- and second-order field theory}\n\nBy a~\\textit{Lagrangian} $\\lambda$ for a~fibered manifold $\\pi:Y\\rightarrow X$\nof order $r$ we mean an element of the submodule $\\Omega_{n,X}^{r}W$\nof $\\pi^{r}$-horizontal $n$-forms in the module of $n$-forms $\\Omega_{n}^{r}W$,\ndefined on an open subset $W^{r}$ of the $r$-th jet prolongation\n$J^{r}Y$. In a~fibered chart $(V,\\psi)$, $\\psi=(x^{i},y^{\\sigma})$,\nwhere $V\\subset W$, Lagrangian $\\lambda\\in\\Omega_{n,X}^{r}W$ has\nan expression\n\\begin{equation}\n\\lambda=\\mathscr{L}\\omega_{0},\\label{eq:Lagrangian}\n\\end{equation}\nwhere $\\omega_{0}=dx^{1}\\wedge dx^{2}\\wedge\\ldots\\wedge dx^{n}$ is\nthe (local) volume element, and $\\mathscr{L}:V^{r}\\rightarrow\\mathbb{R}$\nis the \\textit{Lagrange function} associated to $\\lambda$ and $(V,\\psi)$.\n\nAn $n$-form $\\rho\\in\\Omega_{n}^{s}W$ is called a\\textit{~Lepage\nequivalent} of $\\lambda\\in\\Omega_{n,X}^{r}W$, if the following two\nconditions are satisfied: \n\n(i) $(\\pi^{q,s+1})^{*}h\\rho=(\\pi^{q,r})^{*}\\lambda$ (i.e. $\\rho$\nis \\textit{equivalent }with $\\lambda$), and \n\n(ii) $hi_{\\xi}d\\rho=0$ for arbitrary $\\pi^{s,0}$-vertical vector\nfield $\\xi$ on $W^{s}$ (i.e. $\\rho$ is a\\textit{~Lepage form}). \n\nThe following theorem describe the structure of the Lepage equivalent\nof a~Lagrangian (see \\cite{Krupka-Lepage,Handbook}).\n\\begin{thm}\n\\label{Thm:LepageEquiv}Let $\\lambda\\in\\Omega_{n,X}^{r}W$ be a~Lagrangian\nof order $r$ for $Y$, locally expressed by \\eqref{eq:Lagrangian}\nwith respect to a~fibered chart $(V,\\psi)$, $\\psi=(x^{i},y^{\\sigma})$.\nAn $n$-form $\\rho\\in\\Omega_{n}^{s}W$ is a~Lepage equivalent of\n$\\lambda$ if and only if\n\\begin{equation}\n(\\pi^{s+1,s})^{*}\\rho=\\Theta_{\\lambda}+d\\mu+\\eta,\\label{eq:LepDecomp}\n\\end{equation}\nwhere $n$-form $\\Theta_{\\lambda}$ is defined on $V^{2r-1}$ by \\emph{\\eqref{eq:PoiCar},}\n$\\mu$ is a~contact $(n-1)$-form, and an $n$-form $\\eta$ has the\norder of contactness $\\geq2$.\n\\end{thm}\n\n$\\Theta_{\\lambda}$ is called the \\emph{principal component} of the\nLepage form $\\rho$ with respect to fibered chart $(V,\\psi)$. In\ngeneral, decomposition \\eqref{eq:LepDecomp} is \\emph{not} uniquely\ndetermined with respect to contact forms $\\mu$, $\\eta$, and the\nprincipal component $\\Theta_{\\lambda}$ need \\emph{not} define a~global\nform on $W^{2r-1}$. Nevertheless, the Lepage equivalent $\\rho$ satisfying\n\\eqref{eq:LepDecomp} is globally defined on $W^{s}$; moreover $E_{\\lambda}=p_{1}d\\rho$\nis a~globally defined $(n+1)$-form on $W^{2r}$, called the \\emph{Euler\\textendash Lagrange\nform} associated to $\\lambda$.\n\nWe recall the known examples of Lepage equivalents of first- and second-order\nLagrangians, determined by means of additional requirements.\n\\begin{lem}\n\\textbf{\\textup{\\label{Lem:PrincipalLepEq}(Principal Lepage form)}}\n\\emph{(a)} For every Lagrangian $\\lambda$ of order $r=1$, there\nexists a unique Lepage equivalent $\\Theta_{\\lambda}$ of $\\lambda$\non $W^{1}$, which is $\\pi^{1,0}$-horizontal and has the order of\ncontactness $\\leq1$. In a fibered chart $(V,\\psi)$, $\\Theta{}_{\\lambda}$\nhas an expression \n\\begin{equation}\n\\Theta_{\\lambda}=\\mathscr{L}\\omega_{0}+\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}\\omega^{\\sigma}\\wedge\\omega_{j}.\\label{eq:Poincare-Cartan}\n\\end{equation}\n\n\\emph{(b)} For every Lagrangian $\\lambda$ of order $r=2$, there\nexists a unique Lepage equivalent $\\Theta_{\\lambda}$ of $\\lambda$\non $W^{3}$, which is $\\pi^{3,1}$-horizontal and has the order of\ncontactness $\\leq1$. In a fibered chart $(V,\\psi)$, $\\Theta{}_{\\lambda}$\nhas an expression \n\n\\begin{equation}\n\\Theta_{\\lambda}=\\mathscr{L}\\omega_{0}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pj}^{\\sigma}}\\right)\\omega^{\\sigma}\\wedge\\omega_{j}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\wedge\\omega_{j}.\\label{eq:Poincare-Cartan-2ndOrder}\n\\end{equation}\n\\end{lem}\n\nFor $r=1$ and $r=2$, the principal component $\\Theta_{\\lambda}$\n\\eqref{eq:PoiCar} is a~\\emph{globally defined} Lepage equivalent\nof $\\lambda$. We point out that for $r\\geq3$ this is \\emph{not}\ntrue (see \\cite{HorakKolar,Krupka-Lepage}). \\eqref{eq:Poincare-Cartan}\nis the well-known \\emph{Poincar\\'e-Cartan form} (cf. Garc\\'ia \\cite{Garcia}),\nand it is generealized for second-order Lagrangians by globally defined\n\\emph{principal Lepage equivalent} \\eqref{eq:Poincare-Cartan-2ndOrder}\non $W^{3}\\subset J^{3}Y$.\n\\begin{lem}\n\\textbf{\\textup{\\label{Lem:Fundamental}(Fundamental Lepage form)}}\nLet $\\lambda\\in\\Omega_{n,X}^{1}W$ be a~Lagrangian of order 1 for\n$Y$, locally expressed by \\eqref{eq:Lagrangian}. There exists a~unique\nLepage equivalent $Z_{\\lambda}\\in\\Omega_{n}^{1}W$ of $\\lambda$,\nwhich satisfies $Z_{h\\rho}=(\\pi^{1,0})^{*}\\rho$ for any $n$-form\n$\\rho\\in\\Omega_{n}^{0}W$ on $W$ such that $h\\rho=\\lambda$. With\nrespect to a fibered chart $(V,\\psi)$, $Z_{\\lambda}$ has an expression\n\\begin{align}\nZ_{\\lambda} & =\\mathscr{L}\\omega_{0}+\\sum_{k=1}^{n}\\frac{1}{(n-k)!}\\frac{1}{(k!)^{2}}\\frac{\\partial^{k}\\mathscr{L}}{\\partial y_{j_{1}}^{\\sigma_{1}}\\ldots\\partial y_{j_{k}}^{\\sigma_{k}}}\\varepsilon_{j_{1}\\ldots j_{k}i_{k+1}\\ldots i_{n}}\\label{eq:Fundamental}\\\\\n & \\quad\\cdot\\omega^{\\sigma_{1}}\\land\\ldots\\wedge\\omega^{\\sigma_{k}}\\wedge dx^{i_{k+1}}\\wedge\\ldots\\wedge dx^{i_{n}}.\\nonumber \n\\end{align}\n\\end{lem}\n\n$Z_{\\lambda}$ \\eqref{eq:Fundamental} is known as the \\emph{fundamental\nLepage form} \\cite{Krupka-Fund.Lep.eq.}, \\cite{Betounes}), and it\nis characterized by the equivalence: $Z_{\\lambda}$ is closed if and\nonly if $\\lambda$ is trivial (i.e. the Euler\\textendash Lagrange\nexpressions associated with $\\lambda$ vanish identically). Recently,\nthe form \\eqref{eq:Fundamental} was studied for variational problems\nfor submanifolds in \\cite{UrbBra}, and applied for studying symmetries\nand conservation laws in \\cite{Javier}.\n\\begin{lem}\n\\textbf{\\textup{\\label{Lem:Caratheodory}(Carath\\'eodory form) }}Let\n$\\lambda\\in\\Omega_{n,X}^{1}W$ be a~non-vanishing Lagrangian of order\n1 for $Y$ \\eqref{eq:Lagrangian}. Then a~differential $n$-form\n$\\Lambda_{\\lambda}\\in\\Omega_{n}^{1}W$, locally expressed as\n\\begin{align}\n\\Lambda_{\\lambda} & =\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}\\left(\\mathscr{L}dx^{j}+\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}\\omega^{\\sigma}\\right)\\label{eq:CaratheodoryForm}\\\\\n & =\\frac{1}{\\mathscr{L}^{n-1}}\\left(\\mathscr{L}dx^{1}+\\frac{\\partial\\mathscr{L}}{\\partial y_{1}^{\\sigma_{1}}}\\omega^{\\sigma_{1}}\\right)\\wedge\\ldots\\wedge\\left(\\mathscr{L}dx^{n}+\\frac{\\partial\\mathscr{L}}{\\partial y_{n}^{\\sigma_{n}}}\\omega^{\\sigma_{n}}\\right),\\nonumber \n\\end{align}\nis a Lepage equivalent of $\\lambda$.\n\\end{lem}\n\n$\\Lambda_{\\lambda}$ \\eqref{eq:Fundamental} is the well-known \\emph{Carath\\'eodory\nform} (cf. \\cite{Caratheodory}), associated to Lagrangian $\\lambda\\in\\Omega_{n,X}^{1}W$,\nwhich is nowhere zero. $\\Lambda_{\\lambda}$ is uniquely characterized\nby the following properties: $\\Lambda_{\\lambda}$ is (i) a~Lepage\nequivalent of $\\lambda$, (ii) decomposable, (iii) $\\pi^{1,0}$-horizontal\n(i.e. semi-basic with respect to projection $\\pi^{1,0}$).\n\n\\section{The Carath\\'eodory form: second-order generalization}\n\nLet $\\lambda\\in\\Omega_{n,X}^{1}W$ be a~\\emph{non-vanishing,} \\emph{first-order}\nLagrangian on $W^{1}\\subset J^{1}Y$. In the next lemma, we describe\na~new observation, showing that the Carath\\'eodory form $\\Lambda_{\\lambda}$\n\\eqref{eq:CaratheodoryForm} arises from the Poincar\\'e-Cartan form\n$\\Theta_{\\lambda}$ \\eqref{eq:Poincare-Cartan} by means of contraction\noperations on differential forms with respect to the formal derivative\nvector fields $d_{i}$ \\eqref{eq:FormalDerivative}.\n\\begin{lem}\n\\label{lem:Car-PC}The Carath\\'eodory form $\\Lambda_{\\lambda}$ \\eqref{eq:CaratheodoryForm}\nand the Poincar\\'e-Cartan form $\\Theta_{\\lambda}$ \\eqref{eq:Poincare-Cartan}\nsatisfy\n\\begin{align*}\n\\Lambda_{\\lambda} & =\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}(-1)^{n-j}i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda}\n\\end{align*}\n\\end{lem}\n\n\\begin{proof}\nFrom the decomposable structure of $\\Lambda_{\\lambda}$, we see that\nwhat is needed to show is the formula\n\\begin{equation*}\ni_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda}=(-1)^{n-j}\\left(\\mathscr{L}dx^{j}+\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}\\omega^{\\sigma}\\right\n\\end{equation*}\nfor every $j$, $1\\leq j\\leq n$. Since $dx^{k}\\wedge\\omega_{j}=\\delta_{j}^{k}\\omega_{0}$,\nthe Poincar\\'e-Cartan form is expressible as\n\\begin{align*}\n\\Theta_{\\lambda} & =\\mathscr{L}\\omega_{0}+\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}\\omega^{\\sigma}\\wedge\\omega_{j}=\\mathscr{L}\\omega_{0}+\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}dy^{\\sigma}\\wedge\\omega_{j}-\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}y_{k}^{\\sigma}dx^{k}\\wedge\\omega_{j}\\\\\n & =\\left(\\mathscr{L}-\\frac{\\partial\\mathscr{L}}{\\partial y_{1}^{\\sigma}}y_{1}^{\\sigma}-\\frac{\\partial\\mathscr{L}}{\\partial y_{2}^{\\sigma}}y_{2}^{\\sigma}-\\ldots-\\frac{\\partial\\mathscr{L}}{\\partial y_{n}^{\\sigma}}y_{n}^{\\sigma}\\right)\\omega_{0}+\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}dy^{\\sigma}\\wedge\\omega_{j}.\n\\end{align*}\nApplying the contraction operations to $\\Theta_{\\lambda}$, we obtain\nby means of a straightforward computation for every $j$,\n\\begin{align*}\n & i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda}=\\left(\\mathscr{L}-\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}y_{j}^{\\sigma}-\\ldots-\\frac{\\partial\\mathscr{L}}{\\partial y_{n}^{\\sigma}}y_{n}^{\\sigma}\\right)dx^{j}\\wedge\\ldots\\wedge dx^{n}\\\\\n & \\quad\\quad+(-1)^{j-1}\\sum_{k=j}^{n}\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}dy^{\\sigma}\\wedge i_{d_{j-1}}\\ldots i_{d_{1}}\\omega_{k}\\\\\n & \\quad\\quad+\\sum_{l=1}^{j-1}(-1)^{l-1}y_{l}^{\\sigma}i_{d_{j-1}}\\ldots i_{d_{l+1}}i_{d_{l-1}}\\ldots i_{d_{1}}\\left(\\sum_{k=j}^{n}\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}\\omega_{k}\\right),\n\\end{align*}\nand\n\\begin{align*}\n & i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda}\\\\\n & \\quad=-\\left(\\mathscr{L}-\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}y_{j}^{\\sigma}-\\sum_{k=j+2}^{n}\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}y_{k}^{\\sigma}\\right)dx^{j}\\wedge dx^{j+2}\\wedge\\ldots\\wedge dx^{n}\\\\\n & \\quad+(-1)^{j}\\sum_{k=j}^{n}\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}dy^{\\sigma}\\wedge i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\omega_{k}\\\\\n & \\quad+\\sum_{l=1}^{j-1}(-1)^{l-1}y_{l}^{\\sigma}i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{l+1}}i_{d_{l-1}}\\ldots i_{d_{1}}\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}\\omega_{j}+\\sum_{k=j+2}^{n}\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}\\omega_{k}\\right)\\\\\n & \\quad+(-1)^{j-1}y_{j+1}^{\\sigma}i_{d_{j-1}}\\ldots i_{d_{1}}\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}\\omega_{j}+\\sum_{k=j+2}^{n}\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}\\omega_{k}\\right).\n\\end{align*}\nFollowing the inductive structure of the preceding expressions, we\nget after the next $n-j-1$ steps,\n\\begin{align*}\n & i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda}\\\\\n & \\quad=(-1)^{n-j}\\left(\\mathscr{L}-\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}y_{j}^{\\sigma}\\right)dx^{j}+(-1)^{n-j}\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}dy^{\\sigma}-(-1)^{n-j}\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}\\sum_{k\\neq j}y_{k}^{\\sigma}dx^{k}\\\\\n & \\quad=(-1)^{n-j}\\left(\\mathscr{L}dx^{j}+\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}\\omega^{\\sigma}\\right),\n\\end{align*}\nas required.\n\\end{proof}\nAn intrinsic nature of Lemma \\ref{lem:Car-PC} indicates a~possible\nextension of the Carath\\'eodory form \\eqref{eq:CaratheodoryForm}\nfor higher-order variational problems. We put\n\\begin{equation}\n\\Lambda_{\\lambda}=\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}(-1)^{n-j}i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda},\\label{eq:2nd-Caratheodory}\n\\end{equation}\nwhere $\\Theta_{\\lambda}$ in \\eqref{eq:2nd-Caratheodory} denotes\nthe principal Lepage equivalent \\eqref{eq:Poincare-Cartan-2ndOrder}\nof a~\\emph{second-order} Lagrangian $\\lambda$, and verify that formula\n\\eqref{eq:2nd-Caratheodory} defines a~global form.\n\\begin{thm}\n\\label{thm:Main}Let $\\lambda\\in\\Omega_{n,X}^{2}W$ be a~non-vanishing\nsecond-order Lagrangian on $W^{2}\\subset J^{2}Y$. Then $\\Lambda_{\\lambda}$\nsatisfies:\n\n\\emph{(a)} Formula \\eqref{eq:2nd-Caratheodory} defines an $n$-form\non $W^{3}\\subset J^{3}Y$. \n\n\\emph{(b)} If $\\lambda\\in\\Omega_{n,X}^{2}W$ has an expression \\eqref{eq:Lagrangian}\nwith respect to a~fibered chart $(V,\\psi)$, $\\psi=(x^{i},y^{\\sigma})$,\nsuch that $V\\subset W$, then $\\Lambda_{\\lambda}$ \\eqref{eq:2nd-Caratheodory}\nis expressed by\n\\begin{align}\n\\Lambda_{\\lambda} & =\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right).\\label{eq:2ndCaratheodoryExpression}\n\\end{align}\n\n\\emph{(c)} $\\Lambda_{\\lambda}\\in\\Omega_{n}^{3}W$ \\eqref{eq:2nd-Caratheodory}\nassociated to a~second-order Lagrangian $\\lambda\\in\\Omega_{n,X}^{2}W$\nis a~Lepage equivalent of $\\lambda$, which is decomposable and $\\pi^{3,1}$-horizontal.\n\\end{thm}\n\n\\begin{proof}\n1. Suppose $(V,\\psi)$, $\\psi=(x^{i},y^{\\sigma})$, and $(\\bar{V},\\bar{\\psi})$,\n$\\bar{\\psi}=(\\bar{x}^{i},\\bar{y}^{\\sigma})$, are two overlapping\nfibered charts on $W$. For $\\lambda\\in\\Omega_{n,X}^{2}W$, the corresponding\nchart expressions $\\lambda=\\mathscr{L}\\omega_{0}$ and $\\lambda=\\bar{\\mathscr{L}}\\bar{\\omega}_{0}$\nsatisfy\n\\begin{equation}\n\\mathscr{L}=\\left(\\bar{\\mathscr{L}}\\circ\\bar{\\psi}^{-1}\\circ\\psi\\right)\\det\\frac{\\partial\\bar{x}^{i}}{\\partial x^{j}}.\\label{eq:LagrangianTransform}\n\\end{equation}\nSince the push-forward vector field $\\bar{d}_{k}$,\n\\[\n\\bar{d}_{k}=\\frac{\\partial}{\\partial\\bar{x}^{k}}+\\bar{y}_{k}^{\\sigma}\\frac{\\partial}{\\partial\\bar{y}^{\\sigma}}+\\bar{y}_{kl}^{\\sigma}\\frac{\\partial}{\\partial\\bar{y}_{l}^{\\sigma}},\n\\]\nof vector field $(\\partial x^{i}\/\\partial\\bar{x}^{k})d_{i}$ with\nrespect to the chart transformation $\\bar{\\psi}^{-1}\\circ\\psi$ satisfies\n\\[\n(\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\left(i_{\\bar{d}_{k}}\\Theta_{\\lambda}\\right)=i_{\\frac{\\partial x^{i}}{\\partial\\bar{x}^{k}}d_{i}}\\left((\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\Theta_{\\lambda}\\right)=\\frac{\\partial x^{i}}{\\partial\\bar{x}^{k}}i_{d_{i}}\\left((\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\Theta_{\\lambda}\\right),\n\\]\nand $\\Theta_{\\lambda}$ \\eqref{eq:Poincare-Cartan-2ndOrder} is globally\ndefined, we get\n\\begin{align*}\n & (\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\frac{1}{\\mathscr{\\bar{L}}^{n-1}}\\bigwedge_{j=1}^{n}(-1)^{n-j}i_{\\bar{d}_{n}}\\ldots i_{\\bar{d}_{j+1}}i_{\\bar{d}_{j-1}}\\ldots i_{\\bar{d}_{1}}\\Theta_{\\lambda}\\\\\n & \\quad=\\frac{1}{\\left(\\bar{\\mathscr{L}}\\circ\\bar{\\psi}^{-1}\\circ\\psi\\right)^{n-1}}\\bigwedge_{j=1}^{n}(-1)^{n-j}\\frac{\\partial x^{i_{1}}}{\\partial\\bar{x}^{1}}\\ldots\\frac{\\partial x^{i_{j-1}}}{\\partial\\bar{x}^{j-1}}\\frac{\\partial x^{i_{j+1}}}{\\partial\\bar{x}^{j+1}}\\ldots\\frac{\\partial x^{i_{n}}}{\\partial\\bar{x}^{n}}\\\\\n & \\quad\\quad\\cdot i_{d_{i_{n}}}\\ldots i_{d_{i_{j+1}}}i_{d_{i_{j-1}}}\\ldots i_{d_{i_{1}}}\\left((\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\Theta_{\\lambda}\\right)\\\\\n & \\quad=\\frac{1}{\\left(\\bar{\\mathscr{L}}\\circ\\bar{\\psi}^{-1}\\circ\\psi\\right)^{n-1}}\\left(\\frac{\\partial x^{i_{1}}}{\\partial\\bar{x}^{1}}\\ldots\\frac{\\partial x^{i_{n}}}{\\partial\\bar{x}^{n}}\\varepsilon_{i_{1}\\ldots i_{n}}\\right)^{n}\\\\\n & \\quad\\quad\\cdot\\bigwedge_{j=1}^{n}(-1)^{n-j}i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}(\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\Theta_{\\lambda}\\\\\n & \\quad=\\frac{1}{\\left((\\bar{\\mathscr{L}}\\circ\\bar{\\psi}^{-1}\\circ\\psi)\\det\\frac{\\partial\\bar{x}^{i}}{\\partial x^{j}}\\right)^{n-1}}\\bigwedge_{j=1}^{n}(-1)^{n-j}i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}(\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\Theta_{\\lambda}\\\\\n & \\quad=\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}(-1)^{n-j}i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}(\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\Theta_{\\lambda}\\\\\n & \\quad=\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}(-1)^{n-j}i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda},\n\\end{align*}\nas required.\n\n2. Analogously to the proof of Lemma \\ref{lem:Car-PC}, we find a~chart\nexpression of $1$-form\n\\[\ni_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda},\n\\]\nwhere $\\Theta_{\\lambda}$ is the principal Lepage equivalent \\eqref{eq:Poincare-Cartan-2ndOrder}.\nUsing $dx^{k}\\wedge\\omega_{j}=\\delta_{j}^{k}\\omega_{0}$, we have\n\\begin{align*}\n\\Theta_{\\lambda} & =\\left(\\mathscr{L}-\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pj}^{\\sigma}}\\right)y_{j}^{\\sigma}-\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}y_{ij}^{\\sigma}\\right)\\omega_{0}\\\\\n & +\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pj}^{\\sigma}}\\right)dy^{\\sigma}\\wedge\\omega_{j}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}dy_{i}^{\\sigma}\\wedge\\omega_{j}.\n\\end{align*}\nThen\n\\begin{align*}\n & i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda}\\\\\n & \\quad=\\left(\\mathscr{L}-\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)y_{k}^{\\sigma}-\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}y_{ik}^{\\sigma}\\right)dx^{j}\\wedge\\ldots\\wedge dx^{n}\\\\\n & \\quad+\\sum_{l=1}^{j-1}(-1)^{l-1}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)y_{l}^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}y_{il}^{\\sigma}\\right)i_{d_{j-1}}\\ldots i_{d_{l+1}}i_{d_{l-1}}\\ldots i_{d_{1}}\\omega_{k}\\\\\n & \\quad+(-1)^{j-1}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)dy^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}dy_{i}^{\\sigma}\\right)\\wedge\\left(i_{d_{j-1}}\\ldots i_{d_{1}}\\omega_{k}\\right)\\\\\n & \\quad=\\left(\\mathscr{L}-\\sum_{k=j}^{n}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)y_{k}^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}y_{ik}^{\\sigma}\\right)\\right)dx^{j}\\wedge\\ldots\\wedge dx^{n}\\\\\n & \\quad+\\sum_{l=1}^{j-1}(-1)^{l-1}\\sum_{k=j}^{n}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)y_{l}^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}y_{il}^{\\sigma}\\right)i_{d_{j-1}}\\ldots i_{d_{l+1}}i_{d_{l-1}}\\ldots i_{d_{1}}\\omega_{k}\\\\\n & \\quad+(-1)^{j-1}\\sum_{k=j}^{n}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)dy^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}dy_{i}^{\\sigma}\\right)\\wedge\\left(i_{d_{j-1}}\\ldots i_{d_{1}}\\omega_{k}\\right),\n\\end{align*}\nand\n\\begin{align*}\n & i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda}\\\\\n & \\quad=\\Biggl(-\\mathscr{L}+\\sum_{\\begin{array}{c}\nk=j\\\\\nk\\neq j+1\n\\end{array}}^{n}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)y_{k}^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}y_{ik}^{\\sigma}\\right)\\Biggr)dx^{j}\\wedge\\bigwedge_{l=j+2}^{n}dx^{l}\\\\\n & \\quad+\\sum_{l=1}^{j-1}(-1)^{l-1}\\sum_{\\begin{array}{c}\nk=j\\\\\nk\\neq j+1\n\\end{array}}^{n}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)y_{l}^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}y_{il}^{\\sigma}\\right)\\\\\n & \\quad\\quad i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{l+1}}i_{d_{l-1}}\\ldots i_{d_{1}}\\omega_{k}\\\\\n & \\quad+(-1)^{j-1}\\sum_{\\begin{array}{c}\nk=j\\\\\nk\\neq j+1\n\\end{array}}^{n}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)y_{j+1}^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}y_{i,j+1}^{\\sigma}\\right)i_{d_{j-1}}\\ldots i_{d_{1}}\\omega_{k}\\\\\n & \\quad+(-1)^{j}\\sum_{k=j}^{n}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pk}^{\\sigma}}\\right)dy^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}dy_{i}^{\\sigma}\\right)\\wedge\\left(i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\omega_{k}\\right).\n\\end{align*}\nAfter another $n-j-1$ steps we obtain\n\\begin{align*}\n & i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda}\\\\\n & =(-1)^{n-j}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pj}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right).\n\\end{align*}\n\n3. From \\eqref{eq:2ndCaratheodoryExpression} it is evident that $\\Lambda_{\\lambda}$\n\\eqref{eq:2nd-Caratheodory} is decomposable, $\\pi^{3,1}$-horizontal,\nand obeys $h\\Lambda_{\\lambda}=\\lambda$. It is sufficient to verify\nthat $\\Lambda_{\\lambda}$ is a~Lepage form, that is $hi_{\\xi}d\\Lambda_{\\lambda}=0$\nfor arbitrary $\\pi^{3,0}$-vertical vector field $\\xi$ on $W^{3}\\subset J^{3}Y$.\nThis follows, however, by means of a~straightforward computation\nusing chart expression \\eqref{eq:2ndCaratheodoryExpression}. Indeed,\nwe have\n\\begin{align*}\n & d\\Lambda_{\\lambda}=(1-n)\\frac{1}{\\mathscr{L}^{n}}d\\mathscr{L}\\wedge\\bigwedge_{j=1}^{n}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right)\\\\\n & \\quad+\\frac{1}{\\mathscr{L}^{n-1}}\\sum_{k=1}^{n}(-1)^{k-1}d\\left(\\mathscr{L}dx^{k}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\omega_{i}^{\\sigma}\\right)\\\\\n & \\quad\\wedge\\bigwedge_{j\\neq k}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right),\n\\end{align*}\nand the contraction of $d\\Lambda_{\\lambda}$ with respect to $\\pi^{3,0}$-vertical\nvector field $\\xi$ reads\n\\begin{align*}\n & i_{\\xi}d\\Lambda_{\\lambda}=(1-n)\\frac{1}{\\mathscr{L}^{n}}i_{\\xi}d\\mathscr{L}\\bigwedge_{j=1}^{n}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right)\\\\\n & \\quad-(1-n)\\frac{1}{\\mathscr{L}^{n}}d\\mathscr{L}\\wedge\\sum_{l=1}^{n}(-1)^{l-1}\\frac{\\partial\\mathscr{L}}{\\partial y_{jl}^{\\sigma}}\\xi_{j}^{\\sigma}\\\\\n & \\quad\\quad\\bigwedge_{j\\neq l}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right)\\\\\n & \\quad+\\frac{1}{\\mathscr{L}^{n-1}}\\sum_{k=1}^{n}(-1)^{k-1}\\left(i_{\\xi}d\\mathscr{L}dx^{k}+i_{\\xi}d\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\right)\\omega^{\\sigma}\\right.\\\\\n & \\quad\\left.-\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\right)\\xi_{j}^{\\sigma}dx^{j}+i_{\\xi}d\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\omega_{i}^{\\sigma}-\\xi_{i}^{\\sigma}d\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}-\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\xi_{is}^{\\sigma}dx^{s}\\right)\\\\\n & \\quad\\quad\\wedge\\bigwedge_{j\\neq k}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right)\\\\\n & \\quad+\\frac{1}{\\mathscr{L}^{n-1}}\\sum_{k=1}^{n}(-1)^{k-1}d\\left(\\mathscr{L}dx^{k}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\omega_{i}^{\\sigma}\\right)\\\\\n & \\quad\\quad\\wedge\\sum_{lk}(-1)^{l}\\frac{\\partial\\mathscr{L}}{\\partial y_{il}^{\\sigma}}\\xi_{i}^{\\sigma}\\bigwedge_{j\\neq k,l}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right).\n\\end{align*}\nHence the horizontal part of $i_{\\xi}d\\Lambda_{\\lambda}$ satisfies\n\\begin{align*}\nhi_{\\xi}d\\Lambda_{\\lambda} & =(1-n)i_{\\xi}d\\mathscr{L}\\omega_{0}-(1-n)\\frac{1}{\\mathscr{L}}\\sum_{l=1}^{n}\\frac{\\partial\\mathscr{L}}{\\partial y_{jl}^{\\sigma}}\\xi_{j}^{\\sigma}d_{l}\\mathscr{L}\\omega_{0}+ni_{\\xi}d\\mathscr{L}\\omega_{0}\\\\\n & -\\sum_{k=1}^{n}\\left(\\xi_{i}^{\\sigma}d_{k}\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\xi_{ik}^{\\sigma}\\right)\\omega_{0}-\\sum_{k=1}^{n}\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{k}^{\\sigma}}-d_{i}\\frac{\\partial\\mathscr{L}}{\\partial y_{ik}^{\\sigma}}\\right)\\xi_{k}^{\\sigma}\\omega_{0}\\\\\n & +\\frac{1}{\\mathscr{L}}\\sum_{k=1}^{n}(-1)^{k-1}d_{s}\\mathscr{L}dx^{s}\\wedge dx^{k}\\wedge\\sum_{lk}(-1)^{l}\\frac{\\partial\\mathscr{L}}{\\partial y_{il}^{\\sigma}}\\xi_{i}^{\\sigma}\\bigwedge_{j\\neq k,l}dx^{j}\\\\\n & =-(1-n)\\frac{1}{\\mathscr{L}}\\sum_{l=1}^{n}\\frac{\\partial\\mathscr{L}}{\\partial y_{jl}^{\\sigma}}\\xi_{j}^{\\sigma}d_{l}\\mathscr{L}\\omega_{0}-\\frac{1}{\\mathscr{L}}\\sum_{k=1}^{n}\\sum_{l\\neq k}\\frac{\\partial\\mathscr{L}}{\\partial y_{il}^{\\sigma}}\\xi_{i}^{\\sigma}d_{s}\\mathscr{L}dx^{s}\\wedge\\omega_{l}\\\\\n & =0,\n\\end{align*}\nwhere the identity $dx^{k}\\wedge\\omega_{l}=\\delta_{l}^{k}\\omega_{0}$\nis applied.\n\\end{proof}\nLepage equivalent $\\Lambda_{\\lambda}$ \\eqref{eq:2nd-Caratheodory}\nis said to be the \\emph{Carath\\'eodory form} associated to $\\lambda\\in\\Omega_{n,X}^{2}W$.\n\n\\section{The Carath\\'{e}odory form and principal Lepage equivalents in higher-order\ntheory}\n\nWe point out that in the proof of Theorem \\ref{thm:Main}, (a), the\nchart independence of formula \\eqref{eq:2nd-Caratheodory} is based\non principal Lepage equivalent $\\Theta_{\\lambda}$ \\eqref{eq:Poincare-Cartan-2ndOrder}\nof a~second-order Lagrangian, which is defined \\emph{globally}. Since\nfor a Lagrangian of order $r\\geq3$, principal components of Lepage\nequivalents are, in general, \\emph{local} expressions (see the Introduction),\nwe are allowed to apply the definition \\eqref{eq:2nd-Caratheodory}\nfor such class of Lagrangians of order $r$ over a~fibered manifold\nwhich assure invariance of local expressions $\\Theta_{\\lambda}$ \\eqref{eq:PoiCar}.\n\nConsider now a~\\emph{third-order} Lagrangian $\\lambda\\in\\Omega_{n,X}^{3}W$.\nThen the principal component $\\Theta_{\\lambda}$ of a~Lepage equivalent\nof $\\lambda$ reads\n\\begin{align}\n\\Theta_{\\lambda} & =\\mathscr{L}\\omega_{0}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pj}^{\\sigma}}+d_{p}d_{q}\\frac{\\partial\\mathscr{L}}{\\partial y_{pqj}^{\\sigma}}\\right)\\omega^{\\sigma}\\wedge\\omega_{j}\\label{eq:PrinComp3}\\\\\n & +\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{kj}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{kpj}^{\\sigma}}\\right)\\omega_{k}^{\\sigma}\\wedge\\omega_{j}+\\frac{\\partial\\mathscr{L}}{\\partial y_{klj}^{\\sigma}}\\omega_{kl}^{\\sigma}\\wedge\\omega_{j}.\\nonumber \n\\end{align}\nIn the following lemma we describe conditions for invariance of \\eqref{eq:PrinComp3}.\n\\begin{lem}\n\\label{lem:3rdCond}The following two conditions are equivalent:\n\n\\emph{(a)} $\\Theta_{\\lambda}$ satisfies\n\\[\n(\\bar{\\psi}^{-1}\\circ\\psi)^{*}\\bar{\\Theta}_{\\lambda}=\\Theta_{\\lambda}.\n\\]\n\\emph{(b)} For arbitrary two overlapping fibered charts on $Y$, $(V,\\psi)$,\n$\\psi=(x^{i},y^{\\sigma})$, and $(\\bar{V},\\bar{\\psi})$, $\\bar{\\psi}=(\\bar{x}^{i},\\bar{y}^{\\sigma})$,\n\\begin{equation}\nd_{k}\\left(\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{l_{1}l_{2}k}^{\\tau}}\\frac{\\partial x^{s}}{\\partial\\bar{x}^{p}}-\\frac{\\partial\\mathscr{L}}{\\partial y_{l_{1}l_{2}s}^{\\tau}}\\frac{\\partial x^{k}}{\\partial\\bar{x}^{p}}\\right)\\frac{\\partial^{2}\\bar{x}^{p}}{\\partial x^{l_{1}}\\partial x^{l_{2}}}\\frac{\\partial y^{\\tau}}{\\partial\\bar{y}^{\\sigma}}\\right)=0.\\label{eq:Obstruction-3rd}\n\\end{equation}\n\\end{lem}\n\n\\begin{proof}\nEquivalence conditions (a) and (b) follows from the chart transformation\n\\begin{align*}\n & \\bar{x}^{k}=\\bar{x}^{k}(x^{s}),\\quad\\bar{y}^{\\sigma}=\\bar{y}^{\\sigma}(x^{s},y^{\\nu}),\\\\\n & \\bar{y}_{k}^{\\sigma}=\\frac{d}{d\\bar{x}^{k}}(\\bar{y}^{\\sigma})=\\frac{\\partial x^{s}}{\\partial\\bar{x}^{k}}\\frac{\\partial\\bar{y}^{\\sigma}}{\\partial x^{s}}+\\frac{\\partial x^{s}}{\\partial\\bar{x}^{k}}\\frac{\\partial\\bar{y}^{\\sigma}}{\\partial y^{\\nu}}y_{s}^{\\nu},\\\\\n & \\bar{y}_{ij}^{\\sigma}=\\frac{d}{d\\bar{x}^{j}}(\\bar{y}_{i}^{\\sigma})=\\frac{\\partial x^{t}}{\\partial\\bar{x}^{j}}\\frac{d}{dx^{t}}\\left(\\frac{\\partial x^{s}}{\\partial\\bar{x}^{i}}\\frac{\\partial\\bar{y}^{\\sigma}}{\\partial x^{s}}+\\frac{\\partial x^{s}}{\\partial\\bar{x}^{i}}\\frac{\\partial\\bar{y}^{\\sigma}}{\\partial y^{\\nu}}y_{s}^{\\nu}\\right),\\\\\n & \\bar{y}_{ijk}^{\\sigma}=\\frac{d}{d\\bar{x}^{k}}(\\bar{y}_{ij}^{\\sigma})=\\frac{\\partial x^{r}}{\\partial\\bar{x}^{k}}\\frac{d}{dx^{r}}\\left(\\frac{\\partial x^{t}}{\\partial\\bar{x}^{j}}\\frac{d}{dx^{t}}\\left(\\frac{\\partial x^{s}}{\\partial\\bar{x}^{i}}\\frac{\\partial\\bar{y}^{\\sigma}}{\\partial x^{s}}+\\frac{\\partial x^{s}}{\\partial\\bar{x}^{i}}\\frac{\\partial\\bar{y}^{\\sigma}}{\\partial y^{\\nu}}y_{s}^{\\nu}\\right)\\right),\n\\end{align*}\napplied to $\\Theta_{\\lambda}$, where Lagrange function $\\mathscr{L}$\nis transformed by \\eqref{eq:LagrangianTransform} and the following\nidentities are employed,\n\\begin{align*}\n & \\bar{\\omega}_{j}=\\frac{\\partial x^{s}}{\\partial\\bar{x}^{j}}\\det\\left(\\frac{\\partial\\bar{x}^{p}}{\\partial x^{q}}\\right)\\omega_{s},\\\\\n & \\bar{\\omega}^{\\sigma}=\\frac{\\partial\\bar{y}^{\\sigma}}{\\partial y^{\\nu}}\\omega^{\\nu},\\quad\\bar{\\omega}_{i}^{\\sigma}=\\frac{\\partial\\bar{y}_{i}^{\\sigma}}{\\partial y^{\\nu}}\\omega^{\\nu}+\\frac{\\partial\\bar{y}_{i}^{\\sigma}}{\\partial y_{p}^{\\nu}}\\omega_{p}^{\\nu},\\quad\\bar{\\omega}_{ij}^{\\sigma}=\\frac{\\partial\\bar{y}_{ij}^{\\sigma}}{\\partial y^{\\nu}}\\omega^{\\nu}+\\frac{\\partial\\bar{y}_{ij}^{\\sigma}}{\\partial y_{p}^{\\nu}}\\omega_{p}^{\\nu}+\\frac{\\partial\\bar{y}_{ij}^{\\sigma}}{\\partial y_{pq}^{\\nu}}\\omega_{pq}^{\\nu}.\n\\end{align*}\n\\end{proof}\n\\begin{thm}\n\\label{thm:Main-3rd}Suppose that a~fibered manifold $\\pi:Y\\rightarrow X$\nand a~non-vanishing third-order Lagrangian $\\lambda\\in\\Omega_{n,X}^{3}W$\nsatisfy condition \\emph{\\eqref{eq:Obstruction-3rd}}. Then $\\Lambda_{\\lambda}$\n\\emph{\\eqref{eq:2nd-Caratheodory}}, where $\\Theta_{\\lambda}$ is\ngiven by \\emph{\\eqref{eq:PrinComp3},} defines a~differential $n$-form\non $W^{5}\\subset J^{5}Y$, which is a~Lepage equivalent of $\\lambda$,\ndecomposable and $\\pi^{5,2}$-horizontal. In a~fibered chart $(V,\\psi)$,\n$\\psi=(x^{i},y^{\\sigma})$, on W, $\\Lambda_{\\lambda}$ has an expression\n\n\\begin{align}\n\\Lambda_{\\lambda} & =\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}\\left(\\mathscr{L}dx^{j}+\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{j}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{pj}^{\\sigma}}+d_{p}d_{q}\\frac{\\partial\\mathscr{L}}{\\partial y_{pqj}^{\\sigma}}\\right)\\omega^{\\sigma}\\right.\\label{eq:3rdCaratheodoryExpression}\\\\\n & \\qquad\\qquad\\qquad+\\left.\\left(\\frac{\\partial\\mathscr{L}}{\\partial y_{ij}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{L}}{\\partial y_{ipj}^{\\sigma}}\\right)\\omega_{i}^{\\sigma}+\\frac{\\partial\\mathscr{L}}{\\partial y_{ikj}^{\\sigma}}\\omega_{ik}^{\\sigma}\\right)\\nonumber \n\\end{align}\n\\end{thm}\n\n\\begin{proof}\nThis is an immediate consequence of Lemma \\ref{lem:3rdCond} and the\nprocedure given by Lemma \\ref{lem:Car-PC} and Theorem \\ref{thm:Main}.\n\\end{proof}\n\\begin{rem}\nNote that according to Lemma \\ref{lem:3rdCond}, characterizing obstructions\nfor the principal component $\\Theta_{\\lambda}$ \\eqref{eq:PrinComp3}\nto be a~global form, the Carath\\'eodory form \\eqref{eq:3rdCaratheodoryExpression}\nis well-defined for third-order Lagrangians on fibered manifolds,\nwhich satisfy condition \\eqref{eq:Obstruction-3rd}. Trivial cases\nwhen \\eqref{eq:Obstruction-3rd} holds identically include namely\n(i) Lagrangians \\emph{independent} of variables $y_{ijk}^{\\sigma}$,\nand (ii) fibered manifolds with bases endowed by smooth structure\nwith \\emph{linear} chart transformations. An example of (ii) are fibered\nmanifolds over two-dimensional open \\emph{M\\\"{o}bius strip} (for\ndetails see \\cite{UV}).\n\\end{rem}\n\nSuppose that a~pair $(\\lambda,\\pi)$, where $\\pi:Y\\rightarrow X$\nis a~fibered manifold and $\\lambda\\in\\Omega_{n,X}^{r}W$ is a Lagrangian\non $W^{r}\\subset J^{r}Y$, induces \\emph{invariant} principal component\n$\\Theta_{\\lambda}$ \\eqref{eq:PoiCar} of a~Lepage equivalent of\n$\\lambda$, with respect to fibered chart transformations on $W$.\nWe call $n$-form $\\Lambda_{\\lambda}$ on $W^{2r-1}$,\n\\begin{equation}\n\\Lambda_{\\lambda}=\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}(-1)^{n-j}i_{d_{n}}\\ldots i_{d_{j+1}}i_{d_{j-1}}\\ldots i_{d_{1}}\\Theta_{\\lambda},\\label{eq:Caratheodory-rth}\n\\end{equation}\nwhere $\\Theta_{\\lambda}$ is given by \\eqref{eq:PoiCar}, the \\emph{Carath\\'eodory\nform} associated to Lagrangian $\\lambda\\in\\Omega_{n,X}^{r}W$.\n\nIn a~fibered chart $(V,\\psi)$, $\\psi=(x^{i},y^{\\sigma})$, on W,\n$\\Lambda_{\\lambda}$ has an expression\n\n\\begin{align}\n\\Lambda_{\\lambda} & =\\frac{1}{\\mathscr{L}^{n-1}}\\bigwedge_{j=1}^{n}\\left(\\mathscr{L}dx^{j}+\\sum_{s=0}^{r-1}\\left(\\sum_{k=0}^{r-1-s}(-1)^{k}d_{p_{1}}\\ldots d_{p_{k}}\\frac{\\partial\\mathscr{L}}{\\partial y_{i_{1}\\ldots i_{s}p_{1}\\ldots p_{k}j}^{\\sigma}}\\right)\\omega_{i_{1}\\ldots i_{s}}^{\\sigma}\\right).\\label{eq:HOCaratheodoryExpression}\n\\end{align}\n\n\n\\section{Example: The Carath\\'eodory equivalent of the Hilbert Lagrangian}\n\nConsider a~fibered manifold $\\mathrm{Met}X$ of metric fields over\n$n$-dimensional manifold $X$ (see \\cite{Volna} for geometry of\n$\\mathrm{Met}X$). In a~chart $(U,\\varphi)$, $\\varphi=(x^{i})$,\non $X$, section $g:X\\supset U\\rightarrow\\mathrm{Met}X$ is expressed\nby $g=g_{ij}dx^{i}\\otimes dx^{j}$, where $g_{ij}$ is symmetric and\nregular at every point $x\\in U$. An induced fibered chart on second\njet prolongation $J^{2}\\mathrm{Met}X$ reads $(V,\\psi)$, $\\psi=(x^{i},g_{jk},g_{jk,l},g_{jk,lm})$.\n\nThe \\emph{Hilbert Lagrangian }is an odd-base $n$-form defined on\n$J^{2}\\mathrm{Met}X$ by \n\\begin{equation}\n\\lambda=\\mathscr{R}\\omega_{0},\\label{eq:Hilbert}\n\\end{equation}\nwhere $\\mathscr{R}=R\\sqrt{|\\det(g_{ij})|}$, $R=R(g_{ij},g_{ij,k},g_{ij,kl})$\nis the \\emph{scalar curvature} on $J^{2}\\mathrm{Met}X$, and $\\mu=\\sqrt{|\\det(g_{ij})|}\\omega_{0}$\nis the \\emph{Riemann volume element}.\n\nThe principal Lepage equivalent of $\\lambda$ \\eqref{eq:Hilbert}\n(cf. formula \\eqref{eq:Poincare-Cartan-2ndOrder}), reads\n\\begin{equation}\n\\Theta_{\\lambda}=\\mathscr{R}\\omega_{0}+\\left(\\left(\\frac{\\partial\\mathscr{R}}{\\partial y_{j}^{\\sigma}}-d_{p}\\frac{\\partial\\mathscr{R}}{\\partial y_{pj}^{\\sigma}}\\right)\\omega^{\\sigma}+\\frac{\\partial\\mathscr{R}}{\\partial y_{ij}^{\\sigma}}\\omega_{i}^{\\sigma}\\right)\\wedge\\omega_{j},\\label{eq:FLE-Hilbert}\n\\end{equation}\nand it is a~globally defined $n$-form on $J^{1}\\mathrm{Met}X$.\n\\eqref{eq:FLE-Hilbert} was used for analysis of structure of Einstein\nequations as a~system of \\emph{first-order} partial differential\nequations (see \\cite{KruStep}). Another Lepage equivalent for a~second-order\nLagrangian in field theory which could be studied in this context\nis given by \\eqref{eq:2ndCaratheodoryExpression},\n\\begin{align*}\n & \\Lambda_{\\lambda}=\\frac{1}{\\mathscr{R}^{n-1}}\\bigwedge_{k=1}^{n}\\left(\\mathscr{R}dx^{k}+\\left(\\frac{\\partial\\mathscr{\\mathscr{R}}}{\\partial g_{ij,k}}-d_{l}\\frac{\\partial\\mathscr{\\mathscr{R}}}{\\partial g_{ij,kl}}\\right)\\omega_{ij}+\\frac{\\partial\\mathscr{\\mathscr{R}}}{\\partial g_{ij,kl}}\\omega_{ij,l}\\right),\n\\end{align*}\nwhere $\\omega_{ij}=dg_{ij}-g_{ij,s}dx^{s}$, $\\omega_{ij,l}=dg_{ij,l}-g_{ij,ls}dx^{s}$.\nUsing a~chart expression of the scalar curvature, we obtain \n\\begin{align*}\n & \\Lambda_{\\lambda}=\\frac{1}{\\mathscr{R}^{n-1}}\\bigwedge_{k=1}^{n}\\left(\\mathscr{R}dx^{k}+\\frac{1}{2}\\sqrt{g}\\left(g^{qp}g^{si}g^{jk}-2g^{sq}g^{pi}g^{jk}+g^{pi}g^{qj}g^{sk}\\right)g_{pq,s}\\omega_{ij}\\right.\\\\\n & \\qquad\\qquad\\qquad\\qquad+\\sqrt{g}\\left(g^{il}g^{kj}-g^{kl}g^{ji}\\right)\\omega_{ij,l}\\biggr).\n\\end{align*}\nThis is the Carath\\'eodory equivalent of the Hilbert Lagrangian.\n\n\\section*{Acknowledgements}\n\nThis work has been completed thanks to the financial support provided\nto the VSB-Technical University of Ostrava by the Czech Ministry of\nEducation, Youth and Sports from the budget for the conceptual development\nof science, research, and innovations for the year 2020, Project No.\nIP2300031.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section{Electronic Submission}\n\\label{submission}\n\nSubmission to ICML 2020 will be entirely electronic, via a web site\n(not email). Information about the submission process and \\LaTeX\\ templates\nare available on the conference web site at:\n\\begin{center}\n\\textbf{\\texttt{http:\/\/icml.cc\/}}\n\\end{center}\n\nThe guidelines below will be enforced for initial submissions and\ncamera-ready copies. Here is a brief summary:\n\\begin{itemize}\n\\item Submissions must be in PDF\\@.\n\\item Submitted papers can be up to eight pages long, not including references, plus unlimited space for references. Accepted papers can be up to nine pages long, not including references, to allow authors to address reviewer comments. Any paper exceeding this length will automatically be rejected. \n\\item \\textbf{Do not include author information or acknowledgements} in your\n initial submission.\n\\item Your paper should be in \\textbf{10 point Times font}.\n\\item Make sure your PDF file only uses Type-1 fonts.\n\\item Place figure captions \\emph{under} the figure (and omit titles from inside\n the graphic file itself). 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For those using the\n\\textbf{\\LaTeX} style file, the original title is automatically set as running\nhead using the \\texttt{fancyhdr} package which is included in the ICML\n2020 style file package. In case that the original title exceeds the\nsize restrictions, a shorter form can be supplied by using\n\n\\verb|\\icmltitlerunning{...}|\n\njust before $\\mathtt{\\backslash begin\\{document\\}}$.\nAuthors using \\textbf{Word} must edit the header of the document themselves.\n\n\\section{Format of the Paper}\n\nAll submissions must follow the specified format.\n\n\\subsection{Dimensions}\n\n\n\n\nThe text of the paper should be formatted in two columns, with an\noverall width of 6.75~inches, height of 9.0~inches, and 0.25~inches\nbetween the columns. The left margin should be 0.75~inches and the top\nmargin 1.0~inch (2.54~cm). The right and bottom margins will depend on\nwhether you print on US letter or A4 paper, but all final versions\nmust be produced for US letter size.\n\nThe paper body should be set in 10~point type with a vertical spacing\nof 11~points. Please use Times typeface throughout the text.\n\n\\subsection{Title}\n\nThe paper title should be set in 14~point bold type and centered\nbetween two horizontal rules that are 1~point thick, with 1.0~inch\nbetween the top rule and the top edge of the page. Capitalize the\nfirst letter of content words and put the rest of the title in lower\ncase.\n\n\\subsection{Author Information for Submission}\n\\label{author info}\n\nICML uses double-blind review, so author information must not appear. If\nyou are using \\LaTeX\\\/ and the \\texttt{icml2020.sty} file, use\n\\verb+\\icmlauthor{...}+ to specify authors and \\verb+\\icmlaffiliation{...}+ to specify affiliations. (Read the TeX code used to produce this document for an example usage.) 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In particular, reviewers are\nnot required to look at the Supplementary Material when writing their\nreview.\n\n\\subsubsection{Camera-Ready Author Information}\n\\label{final author}\n\nIf a paper is accepted, a final camera-ready copy must be prepared.\nFor camera-ready papers, author information should start 0.3~inches below the\nbottom rule surrounding the title. The authors' names should appear in 10~point\nbold type, in a row, separated by white space, and centered. Author names should\nnot be broken across lines. Unbolded superscripted numbers, starting 1, should\nbe used to refer to affiliations.\n\nAffiliations should be numbered in the order of appearance. A single footnote\nblock of text should be used to list all the affiliations. (Academic\naffiliations should list Department, University, City, State\/Region, Country.\nSimilarly for industrial affiliations.)\n\nEach distinct affiliations should be listed once. If an author has multiple\naffiliations, multiple superscripts should be placed after the name, separated\nby thin spaces. If the authors would like to highlight equal contribution by\nmultiple first authors, those authors should have an asterisk placed after their\nname in superscript, and the term ``\\textsuperscript{*}Equal contribution\"\nshould be placed in the footnote block ahead of the list of affiliations. A\nlist of corresponding authors and their emails (in the format Full Name\n\\textless{}email@domain.com\\textgreater{}) can follow the list of affiliations.\nIdeally only one or two names should be listed.\n\nA sample file with author names is included in the ICML2020 style file\npackage. Turn on the \\texttt{[accepted]} option to the stylefile to\nsee the names rendered. All of the guidelines above are implemented\nby the \\LaTeX\\ style file.\n\n\\subsection{Abstract}\n\nThe paper abstract should begin in the left column, 0.4~inches below the final\naddress. The heading `Abstract' should be centered, bold, and in 11~point type.\nThe abstract body should use 10~point type, with a vertical spacing of\n11~points, and should be indented 0.25~inches more than normal on left-hand and\nright-hand margins. Insert 0.4~inches of blank space after the body. Keep your\nabstract brief and self-contained, limiting it to one paragraph and roughly 4--6\nsentences. Gross violations will require correction at the camera-ready phase.\n\n\\subsection{Partitioning the Text}\n\nYou should organize your paper into sections and paragraphs to help\nreaders place a structure on the material and understand its\ncontributions.\n\n\\subsubsection{Sections and Subsections}\n\nSection headings should be numbered, flush left, and set in 11~pt bold\ntype with the content words capitalized. Leave 0.25~inches of space\nbefore the heading and 0.15~inches after the heading.\n\nSimilarly, subsection headings should be numbered, flush left, and set\nin 10~pt bold type with the content words capitalized. Leave\n0.2~inches of space before the heading and 0.13~inches afterward.\n\nFinally, subsubsection headings should be numbered, flush left, and\nset in 10~pt small caps with the content words capitalized. Leave\n0.18~inches of space before the heading and 0.1~inches after the\nheading.\n\nPlease use no more than three levels of headings.\n\n\\subsubsection{Paragraphs and Footnotes}\n\nWithin each section or subsection, you should further partition the\npaper into paragraphs. Do not indent the first line of a given\nparagraph, but insert a blank line between succeeding ones.\n\nYou can use footnotes\\footnote{Footnotes\nshould be complete sentences.} to provide readers with additional\ninformation about a topic without interrupting the flow of the paper.\nIndicate footnotes with a number in the text where the point is most\nrelevant. Place the footnote in 9~point type at the bottom of the\ncolumn in which it appears. Precede the first footnote in a column\nwith a horizontal rule of 0.8~inches.\\footnote{Multiple footnotes can\nappear in each column, in the same order as they appear in the text,\nbut spread them across columns and pages if possible.}\n\n\\begin{figure}[ht]\n\\vskip 0.2in\n\\begin{center}\n\\centerline{\\includegraphics[width=\\columnwidth]{icml_numpapers}}\n\\caption{Historical locations and number of accepted papers for International\nMachine Learning Conferences (ICML 1993 -- ICML 2008) and International\nWorkshops on Machine Learning (ML 1988 -- ML 1992). At the time this figure was\nproduced, the number of accepted papers for ICML 2008 was unknown and instead\nestimated.}\n\\label{icml-historical}\n\\end{center}\n\\vskip -0.2in\n\\end{figure}\n\n\\subsection{Figures}\n\nYou may want to include figures in the paper to illustrate\nyour approach and results. Such artwork should be centered,\nlegible, and separated from the text. Lines should be dark and at\nleast 0.5~points thick for purposes of reproduction, and text should\nnot appear on a gray background.\n\nLabel all distinct components of each figure. If the figure takes the\nform of a graph, then give a name for each axis and include a legend\nthat briefly describes each curve. Do not include a title inside the\nfigure; instead, the caption should serve this function.\n\nNumber figures sequentially, placing the figure number and caption\n\\emph{after} the graphics, with at least 0.1~inches of space before\nthe caption and 0.1~inches after it, as in\nFigure~\\ref{icml-historical}. The figure caption should be set in\n9~point type and centered unless it runs two or more lines, in which\ncase it should be flush left. You may float figures to the top or\nbottom of a column, and you may set wide figures across both columns\n(use the environment \\texttt{figure*} in \\LaTeX). Always place\ntwo-column figures at the top or bottom of the page.\n\n\\subsection{Algorithms}\n\nIf you are using \\LaTeX, please use the ``algorithm'' and ``algorithmic''\nenvironments to format pseudocode. These require\nthe corresponding stylefiles, algorithm.sty and\nalgorithmic.sty, which are supplied with this package.\nAlgorithm~\\ref{alg:example} shows an example.\n\n\\begin{algorithm}[tb]\n \\caption{Bubble Sort}\n \\label{alg:example}\n\\begin{algorithmic}\n \\STATE {\\bfseries Input:} data $x_i$, size $m$\n \\REPEAT\n \\STATE Initialize $noChange = true$.\n \\FOR{$i=1$ {\\bfseries to} $m-1$}\n \\IF{$x_i > x_{i+1}$}\n \\STATE Swap $x_i$ and $x_{i+1}$\n \\STATE $noChange = false$\n \\ENDIF\n \\ENDFOR\n \\UNTIL{$noChange$ is $true$}\n\\end{algorithmic}\n\\end{algorithm}\n\n\\subsection{Tables}\n\nYou may also want to include tables that summarize material. Like\nfigures, these should be centered, legible, and numbered consecutively.\nHowever, place the title \\emph{above} the table with at least\n0.1~inches of space before the title and the same after it, as in\nTable~\\ref{sample-table}. The table title should be set in 9~point\ntype and centered unless it runs two or more lines, in which case it\nshould be flush left.\n\n\n\\begin{table}[t]\n\\caption{Classification accuracies for naive Bayes and flexible\nBayes on various data sets.}\n\\label{sample-table}\n\\vskip 0.15in\n\\begin{center}\n\\begin{small}\n\\begin{sc}\n\\begin{tabular}{lcccr}\n\\toprule\nData set & Naive & Flexible & Better? \\\\\n\\midrule\nBreast & 95.9$\\pm$ 0.2& 96.7$\\pm$ 0.2& $\\surd$ \\\\\nCleveland & 83.3$\\pm$ 0.6& 80.0$\\pm$ 0.6& $\\times$\\\\\nGlass2 & 61.9$\\pm$ 1.4& 83.8$\\pm$ 0.7& $\\surd$ \\\\\nCredit & 74.8$\\pm$ 0.5& 78.3$\\pm$ 0.6& \\\\\nHorse & 73.3$\\pm$ 0.9& 69.7$\\pm$ 1.0& $\\times$\\\\\nMeta & 67.1$\\pm$ 0.6& 76.5$\\pm$ 0.5& $\\surd$ \\\\\nPima & 75.1$\\pm$ 0.6& 73.9$\\pm$ 0.5& \\\\\nVehicle & 44.9$\\pm$ 0.6& 61.5$\\pm$ 0.4& $\\surd$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{sc}\n\\end{small}\n\\end{center}\n\\vskip -0.1in\n\\end{table}\n\nTables contain textual material, whereas figures contain graphical material.\nSpecify the contents of each row and column in the table's topmost\nrow. Again, you may float tables to a column's top or bottom, and set\nwide tables across both columns. Place two-column tables at the\ntop or bottom of the page.\n\n\\subsection{Citations and References}\n\nPlease use APA reference format regardless of your formatter\nor word processor. If you rely on the \\LaTeX\\\/ bibliographic\nfacility, use \\texttt{natbib.sty} and \\texttt{icml2020.bst}\nincluded in the style-file package to obtain this format.\n\nCitations within the text should include the authors' last names and\nyear. If the authors' names are included in the sentence, place only\nthe year in parentheses, for example when referencing Arthur Samuel's\npioneering work \\yrcite{Samuel59}. Otherwise place the entire\nreference in parentheses with the authors and year separated by a\ncomma \\cite{Samuel59}. List multiple references separated by\nsemicolons \\cite{kearns89,Samuel59,mitchell80}. Use the `et~al.'\nconstruct only for citations with three or more authors or after\nlisting all authors to a publication in an earlier reference \\cite{MachineLearningI}.\n\nAuthors should cite their own work in the third person\nin the initial version of their paper submitted for blind review.\nPlease refer to Section~\\ref{author info} for detailed instructions on how to\ncite your own papers.\n\nUse an unnumbered first-level section heading for the references, and use a\nhanging indent style, with the first line of the reference flush against the\nleft margin and subsequent lines indented by 10 points. The references at the\nend of this document give examples for journal articles \\cite{Samuel59},\nconference publications \\cite{langley00}, book chapters \\cite{Newell81}, books\n\\cite{DudaHart2nd}, edited volumes \\cite{MachineLearningI}, technical reports\n\\cite{mitchell80}, and dissertations \\cite{kearns89}.\n\nAlphabetize references by the surnames of the first authors, with\nsingle author entries preceding multiple author entries. Order\nreferences for the same authors by year of publication, with the\nearliest first. Make sure that each reference includes all relevant\ninformation (e.g., page numbers).\n\nPlease put some effort into making references complete, presentable, and\nconsistent. If using bibtex, please protect capital letters of names and\nabbreviations in titles, for example, use \\{B\\}ayesian or \\{L\\}ipschitz\nin your .bib file.\n\n\\section*{Software and Data}\n\nIf a paper is accepted, we strongly encourage the publication of software and data with the\ncamera-ready version of the paper whenever appropriate. This can be\ndone by including a URL in the camera-ready copy. However, \\textbf{do not}\ninclude URLs that reveal your institution or identity in your\nsubmission for review. Instead, provide an anonymous URL or upload\nthe material as ``Supplementary Material'' into the CMT reviewing\nsystem. Note that reviewers are not required to look at this material\nwhen writing their review.\n\n\\section*{Acknowledgements}\n\n\\textbf{Do not} include acknowledgements in the initial version of\nthe paper submitted for blind review.\n\nIf a paper is accepted, the final camera-ready version can (and\nprobably should) include acknowledgements. In this case, please\nplace such acknowledgements in an unnumbered section at the\nend of the paper. Typically, this will include thanks to reviewers\nwho gave useful comments, to colleagues who contributed to the ideas,\nand to funding agencies and corporate sponsors that provided financial\nsupport.\n\n\n\\nocite{langley00}\n\n\n\\section{Introduction}\n With the availability of large collections of unlabeled data, recent work has led to significant advances in self-supervised learning. In particular, contrastive methods have been tremendously successful in learning representations for visual and sequential data \\citep{logeswaran2018efficient,wu2018unsupervised,oord2018representation,henaff2020data,tian2019contrastive,hjelm2018learning,bachman2019learning,he2019momentum,chen2020simple,schneider2019wav2vec,Baevski2020vqwav2vec,baevski2020wav2vec,ravanelli2020multi}. %\n While a number of explanations have been provided as to why contrastive learning leads to such informative representations, existing theoretical predictions and empirical observations appear to be at odds with each other~\\citep{tian2019contrastive,bachman2019learning,wu2020importance,saunshi2019theoretical}. \n \n In a nutshell, contrastive methods aim to learn representations where related samples are aligned (positive pairs, e.g. augmentations of the same image), while unrelated samples are separated (negative pairs)~\\citep{chen2020simple}.\n Intuitively, this leads to invariance to irrelevant details or transformations (by decreasing the distance between positive pairs), while preserving a sufficient amount of information about the input for solving downstream tasks (by increasing the distance between negative pairs)~\\citep{tian2020makes}.\n This intuition has recently been made more precise by \\cite{wang2020understanding}, showing that a commonly used contrastive loss from the InfoNCE family~\\citep{Gutmann12JMLR, oord2018representation, chen2020simple} asymptotically converges to a sum of two losses: an \\emph{alignment} loss that pulls together the representations of positive pairs, and a \\emph{uniformity} loss that maximizes the entropy of the learned latent distribution.\n \n \\begin{figure*}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{figures\/figure_1_reduced.pdf}\n \\caption{We analyze the setup of contrastive learning, in which a feature encoder $f$ is trained with the InfoNCE objective \\citep{Gutmann12JMLR, oord2018representation, chen2020simple} using positive samples (green) and negative samples (orange). We assume the observations are generated by an (unknown) injective generative model $g$ that maps unobservable latent variables from a hypersphere to observations in another manifold. Under these assumptions, the feature encoder $f$ implictly learns to invert the ground-truth generative process $g$ up to linear transformations, i.e., $f = \\mathbf{A} g^{-1}$ with an orthogonal matrix $\\mathbf{A}$, if $f$ minimizes the InfoNCE objective.}\n \\label{fig:header_figure}\n \\end{figure*}\n \n We show that an encoder learned with a contrastive loss from the InfoNCE family can recover the true generative factors of variation (up to rotations) if the process that generated the data fulfills a few weak statistical assumptions. This theory bridges the gap between contrastive learning, nonlinear independent component analysis (ICA) and generative modeling (see Fig.~\\ref{fig:header_figure}).\n Our theory reveals implicit assumptions encoded in the InfoNCE objective about the generative process underlying the data. If these assumptions are violated, we show a principled way of deriving alternative contrastive objectives based on assumptions regarding the positive pair distribution.\n We verify our theoretical findings with controlled experiments, providing evidence that our theory holds true in practice, even if the assumptions on the ground-truth generative model are partially violated. %\n \n To the best of our knowledge, our work is the first to analyze under what circumstances representation learning methods used in practice provably represent the data in terms of its underlying factors of variation. Our theoretical and empirical results suggest that the success of contrastive learning in many practical applications is due to an implicit and approximate inversion of the data generating process, which explains why the learned representations are useful in a wide range of downstream tasks.\n \n In summary, our contributions are:\n \\begin{itemize}\n \\item We establish a theoretical connection between the InfoNCE family of objectives, which is commonly used in self-supervised learning, and nonlinear ICA. We show that training with InfoNCE inverts the data generating process if certain statistical assumptions on the data generating process hold.\n \\item We empirically verify our predictions when the assumed theoretical conditions are fulfilled. In addition, we show successful inversion of the data generating process even if these theoretical assumptions are partially violated. %\n \\item We build on top of the CLEVR rendering pipeline~\\citep{johnson2017clevr} to generate a more visually complex disentanglement benchmark, called \\emph{3DIdent}, that contains hallmarks of natural environments (shadows, different lighting conditions, a 3D object, etc.). We demonstrate that a contrastive loss derived from our theoretical framework can identify the ground-truth factors of such complex, high-resolution images.\n \\end{itemize}\n \n\\section{Related Work}\n\\paragraph{Contrastive Learning}\n Despite the success of contrastive learning (CL), our understanding of the learned representations remains limited, as existing theoretical explanations yield partially contradictory predictions. One way to theoretically motivate CL is to refer to the InfoMax principle \\citep{linsker1988self}, which corresponds to maximizing the mutual information (MI) between different views \\citep{oord2018representation, bachman2019learning, hjelm2018learning, chen2020simple, tian2020makes}. However, as optimizing a tighter bound on the MI can produce worse representations \\citep{tschannen2019mutual}, it is not clear how accurate this motivation describes the behavior of CL.\n \n Another approach aims to explain the success by introducing latent classes \\citep{saunshi2019theoretical}. While this theory has some appeal, there exists a gap between empirical observations and its predictions, e.g. the prediction that an excessive number of negative samples decreases performance does not corroborate with empirical results~\\citep{wu2018unsupervised,tian2019contrastive,he2019momentum,chen2020simple}. However, recent work has suggested some empirical evidence for said theoretical prediction, namely, issues with the commonly used sampling strategy for negative samples, and have proposed ways to mitigate said issues as well~\\citep{robinson2020contrastive, chuang2020debiased}. \n \n \n More recently, the behavior of CL has been analyzed from the perspective of \\emph{alignment} and \\emph{uniformity} properties of representations, demonstrating that these two properties are correlated with downstream performance~\\citep{wang2020understanding}.\n We build on these results to make a connection to cross-entropy minimization from which we can derive identifiability results.%\n\n\\paragraph{Nonlinear ICA}\n Independent Components Analysis (ICA) attempts to find the underlying sources for multidimensional data. In the nonlinear case, said sources correspond to a well-defined nonlinear generative model $g$, which is assumed to be invertible (i.e., injective)~\\citep{Hyvabook,Jutten10}. In other words, nonlinear ICA solves a demixing problem:\n Given observed data $\\mathbf{x} = g(\\mathbf{z})$, it aims to find a model $f$ that equals the inverse generative model $g^{-1}$, which allows for the original sources $\\mathbf{z}$ to be recovered.\n \n \\citet{hyvarinen2018nonlinear} show that the nonlinear demixing problem can be solved as long as the independent components are conditionally mutually independent with respect to some auxiliary variable. The authors further provide practical estimation methods for solving the nonlinear ICA problem~\\citep{hyvarinen2016unsupervised,hyvarinen2017nonlinear}, similar in spirit to noise contrastive estimation (NCE; \\citealp{Gutmann12JMLR}). Recent work has generalized this contribution to VAEs~\\citep{khemakhem2020variational,locatello2020weakly,klindt2020slowvae}, as well as (invertible-by-construction) energy-based models~\\citep{khemakhem2020ice}. We here extend this line of work to more general feed-forward networks trained using InfoNCE~\\citep{oord2018representation}.\n \n In a similar vein, \\citet{roeder2020linear} build on the work of \\citet{hyvarinen2018nonlinear} to show that for a model family which includes InfoNCE, distribution matching implies parameter matching. In contrast, we associate the learned latent representation with the ground-truth generative factors, showing under what conditions the data generating process is inverted, and thus, the true latent factors are recovered.\n \n\\section{Theory}\n \n We will show a connection between contrastive learning and identifiability in the form of nonlinear ICA. For this, we introduce a feature encoder $f$ that maps observations $\\mathbf{x}$ to representations.\n We consider the widely used \\emph{InfoNCE} loss, which often assumes $L^2$ normalized representations \\citep{wu2018unsupervised, he2020momentum, tian2019contrastive, bachman2019learning,chen2020simple},\n \\begin{align} \\label{eq:contrastive_loss}\n &\\mathcal{L}_\\mathsf{contr}(f; \\tau, M) \\quad := \\\\ \n &\\underset{\\substack{\n ({\\bf{x}}, {\\bf{\\tilde x}}) \\sim p_\\mathsf{pos} \\\\\n \\{\\mathbf{x}^-_i\\}_{i=1}^M \\overset{\\text{i.i.d.}}{\\sim} p_\\mathsf{data}\n }}{\\mathbb{E}} \\left[\\, {- \\log \\frac{e^{f(\\mathbf{x})^{\\mathsf{T}} f({\\bf{\\tilde x}}) \/ \\tau }}{e^{f(\\mathbf{x})^{\\mathsf{T}} f({\\bf{\\tilde x}}) \/ \\tau } + \\sum\\limits_{i=1}^M e^{f(\\mathbf{x}^-_i)^{\\mathsf{T}} f({\\bf{\\tilde x}}) \/ \\tau }}}\\,\\right]. \\nonumber\n \\end{align}\n Here $M\\in\\mathbb{Z}_+$ is a fixed number of negative samples, $p_{\\rm{data}}$ is the distribution of all observations and $p_{\\rm{pos}}$ is the distribution of positive pairs.\n This loss was motivated by the InfoMax principle \\citep{linsker1988self}, and has been shown\n to be effective by many recent representation learning methods \\citep{logeswaran2018efficient,wu2018unsupervised,tian2019contrastive,he2019momentum,hjelm2018learning,bachman2019learning,chen2020simple,baevski2020wav2vec}. Our theoretical results also hold for a loss function whose denominator only consists of the second summand across the negative samples (e.g., the SimCLR loss \\citep{chen2020simple}). %\n \n In the spirit of existing literature on nonlinear ICA \\cite{hyvarinen1999nonlinear, harmeling2003kernel,sprekeler2014extension,hyvarinen2016unsupervised,hyvarinen2017nonlinear, Gutmann12JMLR, hyvarinen2018nonlinear, khemakhem2020variational}, we assume that the observations $\\mathbf{x} \\in \\mathcal{X}$ are generated by an invertible (i.e., injective) generative process $g: \\mathcal{Z} \\to \\mathcal{X}$, where $\\mathcal{X} \\subseteq \\mathbb{R}^K$ is the space of observations and $\\mathcal{Z} \\subseteq \\mathbb{R}^N$ with $N\\leq K$ denotes the space of latent factors. Influenced by the commonly used feature normalization in InfoNCE, we further assume that $\\mathcal{Z}$ is the unit hypersphere $\\mathbb{S}^{N-1}$ (see Appx.~\\ref{apx:gt_assumptions}).\n Additionally, we assume that the ground-truth marginal distribution of the latents of the generative process is uniform and that the conditional distribution (under which positive pairs have high density) is a von Mises-Fisher (vMF) distribution:\n \\begin{align} \\label{eq:vmf_conditional}\n p({\\bf{z}}) &= |\\mathcal{Z}|^{-1}, \\quad\\quad p({\\bf{z}}|{\\tilde{\\mathbf{z}}}) = C_p^{-1} e^{\\kappa {\\bf{z}}^\\top {\\tilde{\\mathbf{z}}}} \\quad \\text{with} \\\\ C_p :&= \\int e^{\\kappa {\\bf{z}}^\\top {\\tilde{\\mathbf{z}}}} \\,\\d{\\tilde{\\mathbf{z}}} = \\text{const.}, \\quad \\mathbf{x} = g({\\bf{z}}), \\quad {\\tilde{\\mathbf{x}}} = g({\\tilde{\\mathbf{z}}}). \\nonumber\n \\end{align}\n \n Given these assumptions, we will show that if $f$ minimizes the contrastive loss $\\mathcal{L}_\\mathsf{contr}$, then $f$ solves the demixing problem, i.e., inverts $g$ up to orthogonal linear transformations. %\n \n Our theoretical approach consists of three steps:\n (1) We demonstrate that $\\mathcal{L}_\\mathsf{contr}$ can be interpreted as the cross-entropy between the (conditional) ground-truth and inferred latent distribution. %\n (2) Next, we show that encoders minimizing $\\mathcal{L}_\\mathsf{contr}$ maintain distance, i.e., two latent vectors with distance $\\alpha$ in the ground-truth generative model are mapped to points with the same distance $\\alpha$ in the inferred representation. %\n (3) Finally, we leverage distance preservation to show that minimizers of $\\mathcal{L}_\\mathsf{contr}$ invert the generative process up to orthogonal transformations.\n Detailed proofs are given in Appx.~\\ref{apx:proofs}.\n \n Additionally, we will present similar results for general convex bodies in $\\mathbb{R^N}$ and more general similarity measures, see Sec.~\\ref{sec:extension_rn}. For this, the detailed proofs are given in Appx.~\\ref{apx:rn_extension}. %\n \n\\subsection{Contrastive learning is related to cross-entropy minimization}\n From the perspective of nonlinear ICA, we are interested in understanding how the representations $f({\\bf{x}})$ which minimize the contrastive loss $\\mathcal{L}_\\mathsf{contr}$ (defined in Eq.~\\eqref{eq:contrastive_loss}) are related to the ground-truth source signals ${\\bf{z}}$. To study this relationship, we focus on the map $h = f\\circ g$ between the recovered source signals $h({\\bf{z}})$ and the true source signals ${\\bf{z}}$. Note that this is merely for mathematical convenience; it does not necessitate knowledge regarding neither $g$ nor the ground-truth factors during learning (beyond the assumptions stated in the theorems).\n \n A core insight is a connection between the contrastive loss and the cross-entropy between the ground-truth latent distribution and a certain model distribution. For this, we expand the theoretical results obtained by \\citet{wang2020understanding}:\n \\vspace{\\topsep}\n \\begin{customtheorem}{\\ref*{thm:extended_asym_inf_negatives_CE}}[$\\mathcal{L}_\\mathsf{contr}$ converges to the cross-entropy between latent distributions] \\label{thm:asym_inf_negatives_CE}\n If the ground-truth marginal distribution $p$ is uniform, then for fixed $\\tau > 0$, as the number of negative samples $M \\rightarrow \\infty$, the (normalized) contrastive loss converges to\n \\begin{equations}\n \\lim_{M \\rightarrow \\infty} \\mathcal{L}_\\mathsf{contr}(f; \\tau, M) - \\log M + \\log |\\mathcal{Z}| = \\\\ \\expectunder{{\\bf{z}} \\sim p({\\bf{z}})}{H(p(\\cdot | {\\bf{z}}), q_h(\\cdot | {\\bf{z}}))}\n \\label{eq:contrastive_loss_CE_limit}\n \\end{equations}\n where $H$ is the cross-entropy between the ground-truth conditional distribution $p$ over positive pairs and a conditional distribution $q_{\\rm{h}}$ parameterized by the model $f$,\n \\begin{equations} \\label{eq:qhjoint}\n q_{\\rm{h}}({\\tilde{\\mathbf{z}}}|{\\bf{z}}) &= C_h(\\mathbf{z})^{-1} e^{h({\\tilde{\\mathbf{z}}})\\T h(\\mathbf{z}) \/\\tau} \\\\ \\text{with} \\quad C_h(\\mathbf{z}) :&= \\int e^{h({\\tilde{\\mathbf{z}}})\\T h(\\mathbf{z}) \/\\tau} \\,\\d{\\tilde{\\mathbf{z}}},\n \\end{equations}\n where $C_h({\\bf{z}})\\in\\mathbb{R}^{+}$ is the partition function of $q_{\\rm{h}}$ (see Appx.~\\ref{apx:model_assumptions}).\n \\end{customtheorem}\n \n Next, we show that the minimizers $h^{*}$ of the cross-entropy~(\\ref{eq:qhjoint}) are isometries in the sense that $\\kappa {\\bf{z}}^\\top{\\tilde{\\mathbf{z}}} = h^{*}({\\bf{z}})^\\top h^{*}({\\tilde{\\mathbf{z}}})$ for all ${\\bf{z}}$ and ${\\tilde{\\mathbf{z}}}$. In other words, they preserve the dot product between ${\\bf{z}}$ and ${\\tilde{\\mathbf{z}}}$. %\n \\vspace{\\topsep}\n \\begin{customproposition}{\\ref*{prop:extended_correct_model_ce_isometry}}[Minimizers of the cross-entropy maintain the dot product] \\label{prop:correct_model_ce_isometry}\n Let $\\mathcal{Z} = \\mathbb{S}^{N-1}$, $\\tau > 0$ and consider the ground-truth conditional distribution of the form $p({\\tilde{\\mathbf{z}}} | {\\bf{z}}) = C_p^{-1} \\exp(\\kappa {\\tilde{\\mathbf{z}}}^\\top \\mathbf{z})$. Let $h$ map onto a hypersphere with radius $\\sqrt{\\tau \\kappa}$.\\footnote{Note that in practice this can be implemented as a learnable rescaling operation as the last operation of the network $f$.} Consider the conditional distribution $q_h$ parameterized by the model, as defined above in Theorem~\\ref{thm:asym_inf_negatives_CE}, where the hypothesis class for $h$ (and thus $f$) is assumed to be sufficiently flexible such that $p({\\tilde{\\mathbf{z}}} | \\mathbf{z})$ and $q_{\\rm{h}}({\\tilde{\\mathbf{z}}}|\\mathbf{z})$ can match.\n If $h$ is a minimizer of the cross-entropy $\\mathbb{E}_{p({\\tilde{\\mathbf{z}}} | \\mathbf{z})}[- \\log q_{\\rm{h}}({\\tilde{\\mathbf{z}}} | \\mathbf{z})]$, then $p({\\tilde{\\mathbf{z}}}|\\mathbf{z}) = q_{\\rm{h}}({\\tilde{\\mathbf{z}}} | \\mathbf{z})$ and $\\forall {\\bf{z}}, {\\tilde{\\mathbf{z}}}: \\kappa {\\bf{z}}^\\top{\\tilde{\\mathbf{z}}} = h({\\bf{z}})^\\top h({\\tilde{\\mathbf{z}}})$.\n \\end{customproposition}\n\n\n\\subsection{Contrastive learning identifies ground-truth factors on the hypersphere}\n From the strong geometric property of isometry, we can now deduce a key property of the minimizers $h^*$: %\n \\vspace{\\topsep}\n \\begin{customproposition}{\\ref*{prop:extended_mazurulamspheres}}[Extension of the Mazur-Ulam theorem to hyperspheres and the dot product]\n \\label{prop:mazurulamspheres}\n Let $\\mathcal{Z} = \\mathbb{S}^{N-1}$. If $h: \\mathcal{Z} \\to \\mathcal{Z}$ maintains the dot product up to a constant factor, i.e., $\\forall {\\bf{z}}, {\\tilde{\\mathbf{z}}} \\in \\mathcal{Z}: \\kappa {\\bf{z}}^\\top {\\tilde{\\mathbf{z}}} = h({\\bf{z}})^\\top h({\\tilde{\\mathbf{z}}})$, then $h$ is an orthogonal linear transformation.\n \\end{customproposition}\n\n In the last step, we combine the previous propositions to derive our main result: the minimizers of the contrastive loss $\\mathcal{L}_\\mathsf{contr}$ solve the demixing problem of nonlinear ICA up to linear transformations, i.e., they identify the original sources ${\\bf{z}}$ for observations $g({\\bf{z}})$ up to orthogonal linear transformations. For a hyperspherical space $\\mathcal{Z}$ these correspond to combinations of permutations, rotations and sign flips.\n \\vspace{\\topsep}\n \\begin{customtheorem}{\\ref*{thm:extended_ident_matching}}\\label{thm:ident_matching}\n Let $\\mathcal{Z} = \\mathbb{S}^{N-1}$, the ground-truth marginal be uniform, and the conditional a vMF distribution (cf. Eq.~\\ref{eq:vmf_conditional}). Let the mixing function $g$ be differentiable and injective. If the assumed form of $q_{\\rm{h}}$, as defined above, matches that of $p$, i.e., both are based on the same metric, and if $f$ is differentiable and minimizes the CL loss as defined in Eq.~\\eqref{eq:contrastive_loss}, then for fixed $\\tau > 0$ and $M\\to\\infty$, $h = f \\circ g$ is linear, i.e., $f$ recovers the latent sources up to orthogonal linear transformations.\n \\end{customtheorem}\n Note that we do not assume knowledge of the ground-truth generative model $g$; we only make assumptions about the conditional and marginal distribution of the latents.\n On real data, it is unlikely that the assumed model distribution $q_{\\rm{h}}$ can exactly match the ground-truth conditional. We do, however, \n provide empirical evidence that $h$ is still an affine transformation even if there is a severe mismatch, see Sec.~\\ref{sec:experiments}.\n \n \n\\subsection{Contrastive learning identifies ground-truth factors on convex bodies in \\texorpdfstring{$\\mathbb{R}^N$}{RN}} \\label{sec:extension_rn}\n While the previous theoretical results require $\\mathcal{Z}$ to be a hypersphere, we will now show a similar theorem for the more general case of $\\mathcal{Z}$ being a convex body in $\\mathbb{R}^N$. Note that the hyperrectangle $[a_1, b_1] \\times \\ldots \\times [a_N, b_N]$ is an example of such a convex body.\n \n We follow a similar three step proof strategy as for the hyperspherical case before:\n (1) We begin again by showing that a properly chosen contrastive loss on convex bodies corresponds to the cross-entropy between the ground-truth conditional and a distribution parametrized by the encoder. For this step, we additionally extend the results of \\citet{wang2020understanding} to this latent space and loss function.\n (2) Next, we derive that minimizers of the loss function are isometries of the latent space. Importantly, we do not limit ourselves to a specific metric, thus the result is applicable to a family of contrastive objectives.\n (3) Finally, we show that these minimizers must be affine transformations.\n For a special family of conditional distributions (rotationally asymmetric generalized normal distributions~\\citep{subbotin1923law}), we can further narrow the class of solutions to permutations and sign-flips. %\n For the detailed proofs, see Appx.~\\ref{apx:rn_extension}. \n \n As earlier, we assume that the ground-truth marginal distribution of the latents is uniform. However, we now assume that the conditional distribution is exponential:\n \\begin{equations} \\label{eq:rn_conditional}\n p({\\bf{z}}) &= |\\mathcal{Z}|^{-1}, \\quad\\quad p({\\bf{z}}|{\\tilde{\\mathbf{z}}}) = C_p^{-1} e^{- \\delta({\\bf{z}}, {\\tilde{\\mathbf{z}}})} \\quad \\text{with} \\\\ C_p({\\bf{z}}) :&= \\int e^{-\\delta({\\bf{z}}, {\\tilde{\\mathbf{z}}})} \\,\\d{\\tilde{\\mathbf{z}}}, \\quad \\mathbf{x} = g({\\bf{z}}), \\quad {\\tilde{\\mathbf{x}}} = g({\\tilde{\\mathbf{z}}}),\n \\end{equations}\n where $\\delta$ is a metric induced by a norm (see Appx.~\\ref{apx:rn_gt_assumptions}).\n \n To reflect the differences between this conditional distribution and the one assumed for the hyperspherical case, we need to introduce an adjusted version of the contrastive loss in \\eqref{eq:contrastive_loss}: \n \\begin{definition}[$\\mathcal{L}_{\\delta\\text{-}\\mathsf{contr}}$ objective] \\label{def:delta_contrastive_loss}\n Let $\\delta: \\mathcal{Z} \\times \\mathcal{Z} \\to \\mathbb{R}$ be a metric on $\\mathcal{Z}$. We define the general InfoNCE loss, which uses $\\delta$ as a similarity measure, as\n \n \\begin{align} \\label{eq:delta_contrastive_loss}\n &\\mathcal{L}_{\\delta\\text{-}\\mathsf{contr}}(f; \\tau, M) \\quad :=\\\\\n &\\underset{\\substack{\n ({\\bf{x}}, {\\bf{\\tilde x}}) \\sim p_\\mathsf{pos} \\\\\n \\{\\mathbf{x}^-_i\\}_{i=1}^M \\overset{\\text{i.i.d.}}{\\sim} p_\\mathsf{data}\n }}{\\mathbb{E}} \\hspace{-1em}\\Bigg[\n {- \\log \\frac{e^{-\\delta(f(\\mathbf{x}), f({\\bf{\\tilde x}})) \/ \\tau }}{e^{\\text{--}\\delta(f(\\mathbf{x}), f({\\bf{\\tilde x}})) \/ \\tau } \\hspace{-.3em}+\\hspace{-.3em} \\sum\\limits_{i=1}^M e^{\\text{--}\\delta(f(\\mathbf{x}^\\text{--}_i), f({\\bf{\\tilde x}})) \/ \\tau }}}\\,\\Bigg]. \\nonumber\n \\end{align}\n \\end{definition}\n Note that this is a generalization of the InfoNCE criterion in Eq.~(\\ref{eq:contrastive_loss}). In contrast to the objective above, the representations are no longer assumed to be $L^2$ normalized, and the dot-product is replaced with a more general similarity measure $\\delta$.\n \n Analogous to the previously demonstrated case for the hypersphere, for convex bodies $\\mathcal{Z}$, minimizers of the adjusted $\\mathcal{L}_{\\delta\\text{-}\\mathsf{contr}}$ objective solve the demixing problem of nonlinear ICA up to invertible linear transformations:\n \\begin{customtheorem}{\\ref*{thm:extended_rn_linear_identifiable}} \\label{thm:rn_linear_identifiable}\n Let $\\mathcal{Z}$ be a convex body in $\\mathbb{R}^N$, $h = f\\circ g:\\mathcal{Z}\\to\\mathcal{Z}$, and $\\delta$ be a metric or a semi-metric (cf. Lemma~\\ref{lem:semimetric} in Appx.~\\ref{apx:rn_ce_min_identifiability}), induced by a norm. Further, let the ground-truth marginal distribution be uniform and the conditional distribution be as Eq.~\\eqref{eq:rn_conditional}. Let the mixing function $g$ be differentiable and injective. If the assumed form of $q_{\\rm{h}}$ matches that of $p$, i.e., \n \\begin{equations}\n q_{\\rm{h}}({\\tilde{\\mathbf{z}}}|{\\bf{z}}) &= C_q^{-1}(\\mathbf{z})e^{-\\delta(h({\\tilde{\\mathbf{z}}}), h(\\mathbf{z}))\/\\tau}\\quad \\\\ \\text{with} \\quad C_q(\\mathbf{z}) :&= \\int e^{-\\delta(h({\\tilde{\\mathbf{z}}}), h(\\mathbf{z}))\/\\tau} \\,\\d{\\tilde{\\mathbf{z}}},\n \\end{equations}\n and if $f$ is differentiable and minimizes the $\\mathcal{L}_{\\delta\\text{-}\\mathsf{contr}}$ objective in Eq.~\\eqref{eq:delta_contrastive_loss} for $M \\to \\infty$, we find that $h = f \\circ g$ is invertible and affine, i.e., we recover the latent sources up to affine transformations.\n \\end{customtheorem}\n Note that the model distribution $q_{\\rm{h}}$, which is implicitly described by the choice of the objective, must be of the same form as the ground-truth distribution $p$, i.e., both must be based on the same metric. Thus, identifying different ground-truth conditional distributions requires different contrastive $\\mathcal{L}_{\\delta\\text{-}\\mathsf{contr}}$ objectives.\n This result can be seen as a generalized version of Theorem~\\ref{thm:ident_matching}, as it is valid for any convex body $\\mathcal{Z} \\subseteq \\mathbb{R}^N$, allowing for a larger variety of conditional distributions.\n \n Finally, under the mild restriction that the ground-truth conditional distribution is based on an $L^p$ similarity measure for $p \\geq1, p \\neq 2$, $h$ identifies the ground-truth generative factors up to generalized permutations. A generalized permutation matrix $\\bf{A}$ is a combination of a permutation and element-wise sign-flips, i.e., $\\forall {\\bf{z}}: (\\bf{A}{\\bf{z}})_i = \\alpha_i {\\bf{z}}_{\\sigma(i)}$ with $\\alpha_i = \\pm 1$ and $\\sigma$ being a permutation.\n \\begin{customtheorem}{\\ref*{thm:extended_rn_permutation_identifiable}} \\label{thm:rn_permutation_identifiable}\n Let $\\mathcal{Z}$ be a convex body in $\\mathbb{R}^N$, $h: \\mathcal{Z} \\to \\mathcal{Z}$, and $\\delta$ be an $L^\\alpha$ metric or semi-metric (cf. Lemma~\\ref{lem:semimetric} in Appx.~\\ref{apx:rn_ce_min_identifiability}) for $\\alpha \\geq 1, \\alpha \\neq 2$. Further, let the ground-truth marginal distribution be uniform and the conditional distribution be as Eq.~\\eqref{eq:rn_conditional}, and let the mixing function $g$ be differentiable and invertible. If the assumed form of $q_{\\rm{h}}(\\cdot|{\\bf{z}})$ matches that of $p(\\cdot|{\\bf{z}})$, i.e., both use the same metric $\\delta$ up to a constant scaling factor, and if $f$ is differentiable and minimizes the $\\mathcal{L}_{\\delta\\text{-}\\mathsf{contr}}$ objective in Eq.~\\eqref{eq:delta_contrastive_loss} for $M \\to \\infty$, we find that $h = f \\circ g$ is a composition of input independent permutations, sign flips and rescaling.\n \\end{customtheorem}\n\n\n\\section{Experiments} \\label{sec:experiments}\n\n\\subsection{Validation of theoretical claim} \\label{sec:toy_experiments}\n We validate our theoretical claims under both perfectly matching and violated conditions regarding the ground-truth marginal and conditional distributions. We consider source signals of dimensionality $N=10$, and sample pairs of source signals in two steps: First, we sample from the marginal $p({\\bf{z}})$. For this, we consider both uniform distributions which match our assumptions and non-uniform distributions (e.g., a normal distribution) which violate them. Second, we generate the positive pair by sampling from a conditional distribution $p({\\tilde{\\mathbf{z}}} | {\\bf{z}})$.\n Here, we consider matches with our assumptions on the conditional distribution (von Mises-Fisher for $\\mathcal{Z} = \\mathbb{S}^{N-1}$) as well as violations (e.g. normal, Laplace or generalized normal distribution for $\\mathcal{Z} = \\mathbb{S}^{N-1}$). Further, we consider spaces beyond the hypersphere, such as the bounded box (which is a convex body) and the unbounded $\\mathbb{R}^N$.\n \n We generate the observations with a multi-layer perceptron (MLP), following previous work~\\citep{hyvarinen2016unsupervised,hyvarinen2017nonlinear}.\n Specifically, we use three hidden layers with leaky ReLU units and random weights; to ensure that the MLP $g$ is invertible, we control the condition number of the weight matrices.\n For our feature encoder $f$, we also use an MLP with leaky ReLU units, where the assumed space is denoted by the normalization, or lack thereof, of the encoding. Namely, for the hypersphere (denoted as \\emph{Sphere}) and the hyperrectangle (denoted as \\emph{Box}) we apply an $L^2$ and $L^\\infty$ normalization, respectively. For flexibility in practice, we parameterize the normalization magnitude of the \\emph{Box}, including it as part of the encoder's learnable parameters. On the hypersphere we optimize $\\mathcal{L}_\\mathsf{contr}$ and on the hyperrectangle as well as the unbounded space we optimize $\\mathcal{L}_{\\delta\\text{-}\\mathsf{contr}}$. For further details, see Appx.~\\ref{apx:experiment_details}. %\n \n To test for identifiability up to affine transformations, we fit a linear regression between the ground-truth and recovered sources and report the coefficient of determination ($R^2$). To test for identifiability up to generalized permutations, we leverage the mean correlation coefficient (MCC), as used in previous work~\\citep{hyvarinen2016unsupervised,hyvarinen2017nonlinear}. For further details, see Appx.~\\ref{apx:experiment_details}.\n \n \\begin{table*}[t]\n \\centering\n \\caption{Identifiability up to affine transformations. Mean $\\pm$ standard deviation over $5$ random seeds. Note that only the first row corresponds to a setting that matches (\\cmark) our theoretical assumptions, while the others show results for violated assumptions (\\xmark; see column \\emph{M.}). Note that the identity score only depends on the ground-truth space and the marginal distribution defined for the generative process, while the supervised score additionally depends on the space assumed by the model. \n }\n \\resizebox{\\textwidth}{!}{%\n \\begin{tabular}{ccccccccc}\n \\toprule\n \\multicolumn{3}{c}{Generative process $g$} & \\multicolumn{3}{c}{Model $f$} & \\multicolumn{3}{c}{$R^2$ Score [\\%]} \\\\\n Space & $p(\\cdot)$ & $p(\\cdot|\\cdot)$ & Space & $q_{\\rm{h}}(\\cdot|\\cdot)$ & M. & Identity & Supervised & Unsupervised \\\\\n \\midrule\n Sphere & Uniform & vMF($\\kappa{=}1$) & Sphere & vMF($\\kappa{=}1$) & \\cmark & $66.98 \\pm 2.79$ & $99.71 \\pm 0.05$ & $99.42 \\pm 0.05$ \\\\\n Sphere & Uniform & vMF($\\kappa{=}10$) & Sphere & vMF($\\kappa{=}1$) & \\xmark & \\dittotikz & \\dittotikz & $99.86 \\pm 0.01$ \\\\\n Sphere & Uniform & Laplace($\\lambda{=}0.05$) & Sphere & vMF($\\kappa{=}1$) & \\xmark & \\dittotikz & \\dittotikz & $99.91 \\pm 0.01$ \\\\\n Sphere & Uniform & Normal($\\sigma{=}0.05$) & Sphere & vMF($\\kappa{=}1$) & \\xmark & \\dittotikz & \\dittotikz & $99.86 \\pm 0.00$\\\\\n \\midrule\n Box & Uniform & Normal($\\sigma{=}0.05$) & Unbounded & Normal & \\xmark & $67.93 \\pm 7.40$ & $99.78 \\pm 0.06$ & $99.60 \\pm 0.02$ \\\\\n Box & Uniform & Laplace($\\lambda{=}0.05$) & Unbounded & Normal & \\xmark & \\dittotikz & \\dittotikz & $99.64 \\pm 0.02$ \\\\\n Box & Uniform & Laplace($\\lambda{=}0.05$) & Unbounded & GenNorm($\\beta{=}3$) & \\xmark & \\dittotikz & \\dittotikz & $99.70 \\pm 0.02$\\\\\n Box & Uniform & Normal($\\sigma{=}0.05$) & Unbounded & GenNorm($\\beta{=}3$) & \\xmark & \\dittotikz & \\dittotikz & $99.69 \\pm 0.02$\\\\\n \\midrule\n Sphere & Normal($\\sigma{=}1$) & Laplace($\\lambda{=}0.05$) & Sphere & vMF($\\kappa{=}1$) & \\xmark & $63.37 \\pm 2.41$ & $99.70 \\pm 0.07$ & $99.02 \\pm 0.01$ \\\\\n Sphere & Normal($\\sigma{=}1$) & Normal($\\sigma{=}0.05$) & Sphere & vMF($\\kappa{=}1$) & \\xmark & \\dittotikz & \\dittotikz & $99.02 \\pm 0.02$ \\\\\n \\midrule Unbounded & Laplace($\\lambda{=}1$) & Normal($\\sigma{=}1$) & Unbounded & Normal & \\xmark & $62.49 \\pm 1.65$ & $99.65 \\pm 0.04$ & $98.13 \\pm 0.14$ \\\\ Unbounded & Normal($\\sigma{=}1$) & Normal($\\sigma{=}1$) & Unbounded & Normal & \\xmark & $63.57 \\pm 2.30$ & $99.61 \\pm 0.17$ & $98.76 \\pm 0.03$ \\\\\n \\bottomrule\n \\end{tabular}}\n \\label{tab:results_linear}\n \\end{table*}\n \n \\begin{table*}[t]\n \\centering\n \\caption{Identifiability up to generalized permutations, averaged over $5$ runs. \n Note that while Theorem~\\ref{thm:extended_rn_permutation_identifiable} requires the model latent space to be a convex body and $p(\\cdot|\\cdot)=q_{\\rm{h}}(\\cdot|\\cdot)$, we find that empirically either is sufficient.\n The results are grouped in four blocks corresponding to different types and degrees of violation of assumptions of our theory showing identifiability up to permutations: (1) no violation, violation of the assumptions on either the (2) space or (3) the conditional distribution, or (4) both.\n }\n \\resizebox{\\textwidth}{!}{%\n \\begin{tabular}{ccccccccc}\n \\toprule \\multicolumn{3}{c}{Generative process $g$} & \\multicolumn{3}{c}{Model $f$} & \\multicolumn{3}{c}{MCC Score [\\%]} \\\\\n Space & $p(\\cdot)$ & $p(\\cdot|\\cdot)$ & Space & $q_{\\rm{h}}(\\cdot|\\cdot)$ & M. & Identity & Supervised & Unsupervised \\\\\n \\midrule\n Box & Uniform & Laplace($\\lambda{=}0.05$) & Box & Laplace & \\cmark & $46.55 \\pm 1.34$ & $99.93 \\pm 0.03$ & $98.62 \\pm 0.05$ \\\\\n Box & Uniform & GenNorm($\\beta{=}3$; $\\lambda{=}0.05$) & Box & GenNorm($\\beta{=}3$) & \\cmark & \\dittotikz & \\dittotikz & $99.90 \\pm 0.06$ \\\\\n \\midrule\n Box & Uniform & Normal($\\sigma{=}0.05$) & Box & Normal & \\xmark & \\dittotikz & \\dittotikz & $99.77 \\pm 0.01$ \\\\\n Box & Uniform & Laplace($\\lambda{=}0.05$) & Box & Normal & \\xmark & \\dittotikz & \\dittotikz & $99.76 \\pm 0.02$ \\\\\n Box & Uniform & GenNorm($\\beta{=}3$; $\\lambda{=}0.05$) & Box & Laplace & \\xmark & \\dittotikz & \\dittotikz & $98.80 \\pm 0.02$ \\\\\n \\midrule\n Box & Uniform & Laplace($\\lambda{=}0.05$) & Unbounded & Laplace & \\xmark & \\dittotikz & $99.97 \\pm 0.03$ & $98.57 \\pm 0.02$ \\\\\n Box & Uniform & GenNorm($\\beta{=}3$; $\\lambda{=}0.05$) & Unbounded & GenNorm($\\beta{=}3$) & \\xmark & \\dittotikz & \\dittotikz & $99.85 \\pm 0.01$ \\\\\n \\midrule\n Box & Uniform & Normal($\\sigma{=}0.05$) & Unbounded & Normal & \\xmark & \\dittotikz & \\dittotikz & $58.26 \\pm 3.00$ \\\\\n Box & Uniform & Laplace($\\lambda{=}0.05$) & Unbounded & Normal & \\xmark & \\dittotikz & \\dittotikz & $59.67 \\pm 2.33$ \\\\\n Box & Uniform & Normal($\\sigma{=}0.05$) & Unbounded & GenNorm($\\beta{=}3$) & \\xmark & \\dittotikz & \\dittotikz & $43.80 \\pm 2.15$ \\\\\n \\bottomrule\n \\end{tabular}}\n \\label{tab:perm_results}\n \\end{table*}\n \n We evaluate both identifiability metrics for three different model types.\n First, we ensure that the problem requires nonlinear demixing by considering the identity function for model $f$, which amounts to scoring the observations against the sources (\\textbf{Identity Model}).\n Second, we ensure that the problem is solvable within our model class by training our model $f$ with supervision, minimizing the mean-squared error between $f(g({\\bf{z}}))$ and ${\\bf{z}}$ (\\textbf{Supervised Model}). Third, we fit our model without supervision using a contrastive loss (\\textbf{Unsupervised Model}).\n \n Tables~\\ref{tab:results_linear} and~\\ref{tab:perm_results} show results evaluating identifiability up to affine transformations and generalized permutations, respectively. When assumptions match (see column M.), CL recovers a score close to the empirical upper bound.\n Mismatches in assumptions on the marginal and conditional do not lead to a significant drop in performance with respect to affine identifiability, but do for permutation identifiability compared to the empirical upper bound.\n In many practical scenarios, we use the learned representations to solve a downstream task, thus, identifiability up to affine transformations is often sufficient.\n However, for applications where identification of the individual generative factors is desirable, some knowledge of the underlying generative process is required to choose an appropriate loss function and feature normalization.\n Interestingly, we find that for convex bodies, we obtain identifiability up to permutation even in the case of a normal conditional, which likely is due to the axis-aligned box geometry of the latent domain.\n Finally, note that the drop in performance for identifiability up to permutations in the last group of Tab.~\\ref{tab:perm_results} is a natural consequence of either the ground-truth or the assumed conditional being rotationally symmetric, e.g., a normal distribution, in an unbounded space. Here, rotated versions of the latent space are indistinguishable and, thus, the model cannot align the axes of the reconstruction with that of the ground-truth latent space, resulting in a lower score.\n \n To zoom in on how violations of the uniform marginal assumption influence the identifiability achieved by a model in practice, we perform an ablation on the marginal distribution by interpolating between the theoretically assumed uniform distribution and highly locally concentrated distributions.\n In particular, we consider two cases: (1) a sphere ($\\mathcal{S}^9$) with a vMF marginal around its north pole for different concentration parameters $\\kappa$; (2) a box ($[0,1]^{10}$) with a normal marginal around the box's center for different standard deviations $\\sigma$.\n For both cases, Fig.~\\ref{fig:uniformity_violation} shows the $R^2$ score as a function of the concentration $\\kappa$ and $1\/\\sigma^2$ respectively (black). As a reference, the concentration of the used conditional distribution is highlighted as a dashed line.\n In addition, we also display the probability mass (0--100\\%) that needs to be moved for converting the used marginal distribution (i.e., vMF or normal) into the assumed uniform marginal distribution (blue) as an intuitive measure of the mismatch (i.e., $\\frac{1}{2}\\int |p(\\mathbf{z})\\mathrm{-}p_{\\mathrm{uni}}|\\, \\mathrm{d}\\mathbf{z}$).\n While, we observe significant robustness to mismatch, in both cases, we see performance drop drastically once the marginal distribution is more concentrated than the conditional distribution of positive pairs. In such scenarios, positive pairs are indistinguishable from negative pairs. \n\n \\begin{figure}\n \\centering\n \\includegraphics[width=.9\\linewidth]{figures\/uniformity_violation_sigma_kappa_ablation.pdf}\n \\vspace*{-0.5cm}\n \\caption{Varying degrees of violation of the uniformity assumption for the marginal distribution. The figure shows the $R^2$ score measuring identifiability up to linear transformations (black) as well as the difference between the used marginal and assumed uniform distribution in terms of probability mass (blue) as a function of the marginal's concentration. The black dotted line indicates the concentration of the used conditional distribution.}\n \\label{fig:uniformity_violation}\n \\end{figure}\n \n\\subsection{Extensions to image data}\n Previous studies have demonstrated that representation learning using constrastive learning scales well to complex natural image data \\citep{chen2020simple, chen2020big, henaff2020data}.\n Unfortunately, the true generative factors of natural images are inaccessible, thus we cannot evaluate identifiability scores.\n\n We consider two alternatives.\n First, we evaluate on the recently proposed benchmark \\textit{KITTI Masks}~\\citep{klindt2020slowvae}, which is composed of segmentation masks of natural videos.\n Second, we contribute a novel benchmark (\\textit{3DIdent}; cf. Fig.~\\ref{fig:3dident_examples}) which features aspects of natural scenes, e.g. a complex 3D object and different lighting conditions, while still providing access to the continuous ground-truth factors. For further details, see Appx.~\\ref{apx:3dident_comparison}. \\textit{3DIdent} is available at \\href{https:\/\/zenodo.org\/record\/4502485\/}{zenodo.org\/record\/4502485}.\n\n\\subsubsection{KITTI Masks} \\label{sec:kitti_experiments}\n KITTI Masks~\\citep{klindt2020slowvae} is composed of pedestrian segmentation masks extracted from an autonomous driving vision benchmark KITTI-MOTS~\\citep{geiger2012are}, with natural shapes and continuous natural transitions. We compare to SlowVAE~\\citep{klindt2020slowvae}, the state-of-the-art on the considered dataset. In our experiments, we use the same training hyperparameters (for details see Appx.~\\ref{apx:experiment_details}) and (encoder) architecture as \\citet{klindt2020slowvae}. The positive pairs consist of nearby frames with a time separation $\\overline{\\Delta t}$.\n \n \\begin{table}\n \\centering\n \\caption{\\textbf{KITTI Masks}. Mean $\\pm$ standard deviation over 10 random seeds. $\\overline{\\Delta t}$ indicates the average temporal distance of frames used.}\n \\label{table:MOTSComp}\n \\small\n \\begin{tabular}{clll}\n \\toprule\n & Model & Model Space & MCC [\\%] \\\\\n \\midrule\n \\parbox[t]{16mm}{\\multirow{5}{*}{$\\overline{\\Delta t}=0.05s$}} & SlowVAE & Unbounded & 66.1 $\\pm$ 4.5 \\\\\n & Laplace & Unbounded & 77.1 $\\pm$ 1.0 \\\\\n & Laplace & Box & 74.1 $\\pm$ 4.4 \\\\\n & Normal & Unbounded & 58.3 $\\pm$ 5.4 \\\\\n & Normal & Box & 59.9 $\\pm$ 5.5 \\\\\n \\midrule\n \\parbox[t]{16mm}{\\multirow{5}{*}{$\\overline{\\Delta t}=0.15s$}} & SlowVAE & Unbounded & 79.6 $\\pm$ 5.8 \\\\\n & Laplace & Unbounded & 79.4 $\\pm$ 1.9 \\\\\n & Laplace & Box & 80.9 $\\pm$ 3.8 \\\\\n & Normal & Unbounded & 60.2 $\\pm$ 8.7 \\\\\n & Normal & Box & 68.4 $\\pm$ 6.7 \\\\\n \\bottomrule\n \\end{tabular}\n \\end{table}\n \n As argued and shown in \\citet{klindt2020slowvae}, the transitions in the ground-truth latents between nearby frames is sparse. Unsurprisingly then, Table~\\ref{table:MOTSComp} shows that assuming a Laplace conditional as opposed to a normal conditional in the contrastive loss leads to better identification of the underlying factors of variation. %\n SlowVAE also assumes a Laplace conditional~\\citep{klindt2020slowvae} but appears to struggle if the frames of a positive pair are too similar ($\\overline{\\Delta t}=0.05s$).\n This degradation in performance is likely due to the limited expressiveness of the decoder deployed in SlowVAE.\n \n \n \n \n\\subsubsection{3DIdent} \\label{sec:3dident_experiments}\n \n\\paragraph{Dataset description}\n \\begin{figure*}[htb]\n \\centering\n \\includegraphics[width=\\textwidth]{figures\/3ddis_samples_reduced.pdf}\n \\caption{\\textbf{3DIdent}. Influence of the latent factors ${\\bf{z}}$ on the renderings ${\\bf{x}}$. Each column corresponds to a traversal in one of the ten latent dimensions while the other dimensions are kept fixed. %\n }\n \\label{fig:3dident_examples}\n \\end{figure*}\n \n We build on \\citep{johnson2017clevr} and use the Blender rendering engine \\citep{blender} to create visually complex 3D images (see Fig.~\\ref{fig:3dident_examples}). Each image in the dataset shows a colored 3D object which is located and rotated above a colored ground in a 3D space. Additionally, each scene contains a colored spotlight focused on the object and located on a half-circle around the scene. The observations are encoded with an RGB color space, and the spatial resolution is $224\\times224$ pixels.\n\n The images are rendered based on a $10$-dimensional latent, where: (1) three dimensions describe the XYZ position, (2) three dimensions describe the rotation of the object in Euler angles, (3) two dimensions describe the color of the object and the ground of the scene, respectively, and (4) two dimensions describe the position and color of the spotlight. We use the HSV color space to describe the color of the object and the ground with only one latent each by having the latent factor control the hue value. For more details on the dataset see Sec.~\\ref{apx:3dident_details}.\n \n The dataset contains $250\\,000$ observation-latent pairs where the latents are uniformly sampled from the hyperrectangle $\\mathcal{Z}$. To sample positive pairs $({\\bf{z}}, {\\tilde{\\mathbf{z}}})$ we first sample a value ${\\tilde{\\mathbf{z}}}'$ from the data conditional $p({\\tilde{\\mathbf{z}}}'|{\\bf{z}})$, and then use nearest-neighbor matching\\footnote{We used an Inverted File Index (IVF) with Hierarchical Navigable Small World (HNSW) graph exploration for fast indexing.} implemented by FAISS \\citep{JDH17} to find the latent ${\\tilde{\\mathbf{z}}}$ closest to ${\\tilde{\\mathbf{z}}}'$ (in $L^2$ distance) for which there exists an image rendering. In addition, unlike previous work~\\citep{locatello2018challenging}, we create a hold-out test set with $25\\,000$ distinct observation-latent pairs. \n\n\\paragraph{Experiments and Results}\n \\begin{table*}[htb]\n \\vspace{-0.05cm}\n \\centering\n \\caption{Identifiability up to affine transformations on the test set of 3DIdent. Mean $\\pm$ standard deviation over 3 random seeds. As earlier, only the first row corresponds to a setting that matches the theoretical assumptions for linear identifiability; the others show distinct violations. Supervised training with unbounded space achieves scores of $R^2=(98.67 \\pm 0.03)$\\% and $\\text{MCC}=(99.33 \\pm 0.01)$\\%. The last row refers to using the image augmentations suggested by \\citet{chen2020simple} to generate positive image pairs.\n For performance on the training set, see Appx.~Table~\\ref{tab:3dident_results_train}.\n }\n \\small\n \\begin{tabular}{ccccccc}\n \\toprule\n Dataset & \\multicolumn{3}{c}{Model $f$} & Identity [\\%] & \\multicolumn{2}{c}{Unsupervised [\\%]} \\\\\n $p(\\cdot|\\cdot)$ & Space & $q_{\\rm{h}}(\\cdot|\\cdot)$ & M. & $R^2$ & $R^2$ & MCC \\\\\n \\midrule\n \n Normal & Box & Normal & \\cmark & $5.25 \\pm 1.20$ & $96.73 \\pm 0.10$ & $98.31 \\pm 0.04$\\\\ \n\n Normal & Unbounded & Normal & \\xmark & \\dittotikz & $96.43 \\pm 0.03$ & $54.94 \\pm 0.02$\\\\\n\n Laplace & Box & Normal & \\xmark & \\dittotikz & $96.87 \\pm 0.08$ & $98.38 \\pm 0.03$\\\\\n \n Normal & Sphere & vMF & \\xmark & \\dittotikz & $65.74 \\pm 0.01$ & $42.44 \\pm 3.27$\\\\\n \n Augm. & Sphere & vMF & \\xmark & \\dittotikz & $45.51 \\pm 1.43$ & $46.34 \\pm 1.59$\\\\\n\n \\bottomrule\n \\end{tabular}\n \\label{tab:3dident_results_test}\n \\end{table*}\n \n We train a convolutional feature encoder $f$ composed of a ResNet18 architecture~\\citep{he2015deep} and an additional fully-connected layer, with a LeakyReLU nonlinearity as the hidden activation. For more details, see Appx.~\\ref{apx:experiment_details}. Following the same methodology as in Sec.~\\ref{sec:toy_experiments}, i) depending on the assumed space, the output of the feature encoder is normalized accordingly and ii) in addition to the CL models, we also train a supervised model to serve as an upper bound on performance. We consider normal and Laplace distributions for positive pairs. Note, that due to the finite dataset size we only sample from an approximation of these distributions.\n \n As in Tables~\\ref{tab:results_linear} and~\\ref{tab:perm_results}, the results in Table~\\ref{tab:3dident_results_test} demonstrate that CL reaches scores close to the topline (supervised) performance, and mismatches between the assumed and ground-truth conditional distribution do not harm the performance significantly. However, if the hypothesis class of the encoder is too restrictive to model the ground-truth conditional distribution, we observe a clear drop in performance, i.e., mapping a box onto a sphere. Note, that this corresponds to the InfoNCE objective for $L^2$-normalized representations, commonly used for self-supervised representation learning~\\citep{wu2018unsupervised, he2020momentum, tian2019contrastive, bachman2019learning,chen2020simple}.\n Finally, the last result shows that leveraging image augmentations~\\citep{chen2020simple} as opposed to sampling from a specified conditional distribution of positive pairs $p(\\cdot|\\cdot)$ results in a performance drop. For details on the experiment, see Appx. Sec.~\\ref{apx:experiment_details}. We explain this with the greater mismatch between the conditional distribution assumed by the model and the conditional distribution induced by the augmentations. \n In all, we demonstrate validation of our theoretical claims even for generative processes with higher visual complexity than those considered in Sec.~ \\ref{sec:toy_experiments}.\n \n \n\\section{Conclusion}\n We showed that objectives belonging to the InfoNCE family, the basis for a number of state-of-the-art techniques in self-supervised representation learning, can uncover the true generative factors of variation underlying the observational data. To succeed, these objectives implicitly encode a few weak assumptions about the statistical nature of the underlying generative factors. While these assumptions will likely not be exactly matched in practice, we showed empirically that the underlying factors of variation are identified even if theoretical assumptions are severely violated.\n \n Our theoretical and empirical results suggest that the representations found with contrastive learning implicitly (and approximately) invert the generative process of the data. This could explain why the learned representations are so useful in many downstream tasks. It is known that a decisive aspect of contrastive learning is the right choice of augmentations that form a positive pair. We hope that our framework might prove useful for clarifying the ways in which certain augmentations affect the learned representations, and for finding improved augmentation schemes.\n \n Furthermore, our work opens avenues for constructing more effective contrastive losses. As we demonstrate, imposing a contrastive loss informed by characteristics of the latent space can considerably facilitate inferring the correct semantic descriptors, and thus boost performance in downstream tasks. \n While our framework already allows for a variety of \\emph{conditional} distributions, it is an interesting open question how to adapt it to \\emph{marginal} distributions beyond the uniform implicitly encoded in InfoNCE. Also, future work may extend our theoretical framework by incorporating additional assumptions about our visual world, such as compositionality, hierarchy or objectness. \n Accounting for such inductive biases holds enormous promise in forming the basis for the next generation of self-supervised learning algorithms. \n \n Taken together, we lay a strong theoretical foundation for not only understanding but extending the success of state-of-the-art self-supervised learning techniques.\n\n\\subsection*{Author contributions}\n The project was initiated by WB.\n RSZ, SS and WB jointly derived the theory. RSZ and YS implemented and executed the experiments. The 3DIdent dataset was created by RSZ with feedback from SS, YS, WB and MB.\n RSZ, YS, SS and WB contributed to the final version of the manuscript.\n\n\\subsection*{Acknowledgements}\n We thank\n Muhammad Waleed Gondal,\n Ivan Ustyuzhaninov,\n David Klindt,\n Lukas Schott\n and Luisa Eck\n for helpful discussions.\n We thank Bozidar Antic, Shubham Krishna and Jugoslav Stojcheski for ideas regarding the design of 3DIdent.\n We thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting RSZ, YS and StS.\n StS acknowledges his membership in the European Laboratory for Learning and Intelligent Systems (ELLIS) PhD program.\n We acknowledge support from the German Federal Ministry of Education and Research (BMBF) through the Competence Center for Machine Learning (TUE.AI, FKZ 01IS18039A) and the Bernstein Computational Neuroscience Program T\\\"ubingen (FKZ: 01GQ1002). WB acknowledges support via his Emmy Noether Research Group funded by the German Science Foundation (DFG) under grant no. BR 6382\/1-1 as well as support by Open Philantropy and the Good Ventures Foundation. MB and WB acknowledge funding from the MICrONS program of the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior\/Interior Business Center (DoI\/IBC) contract number D16PC00003.\n\n\\clearpage\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzliuy b/data_all_eng_slimpj/shuffled/split2/finalzzliuy new file mode 100644 index 0000000000000000000000000000000000000000..a1129ca7de2abcf74d64c0ee517115cee0acb15b --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzliuy @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nIn the survey of the Galactic plane carried out by the \\h experiment, many sources emitting at TeV energies remain unidentified to date \\citep[e.g.][]{ah08}.\nMost of the sources are extended beyond the point spread function (PSF) of the \\h experiment ($\\sim 0.06^{\\circ}$ for the analysis presented in this paper). The largest number of conclusive \nidentifications so far can be attributed to pulsar wind nebulae (PWNe) as presented in e.g. \\citet{gallant08}. \nRecently, a new radio SNR, catalogued as \\radio, was discovered by \\citet{tl08} to be in spatial coincidence with \\src, one of the\n unidentified sources presented in \\citet{ah08}. The diameter of the radio shell is nearly 0.5$\\degr$ \nwhich allows, given the brightness of the source and the \\h angular resolution of $\\sim$0.06$\\degr$, for a morphological comparison of the \\g-ray source with the\n shell observed in radio. \nMoreover, at least up to the current date, no radio pulsar or X-ray PWN candidate was found that might alternatively explain the TeV emission. \nThis situation should be compared to other \\g-ray sources like HESS\\,J1813$-$178 or HESS\\,J1640$-$465 \\citep{aharonian06}, which are also in spatial coincidence with radio SNR shells. However, for these latter sources a morphological identification with the radio shells is not possible, \nand the emission can plausibly originate from a PWN seen in X-rays as discussed in\n\\citet{funk07b} for HESS\\,J1640$-$465 and in \\citet{funk07,gotthelf09} for HESS\\,J1813$-$178.\n\n\nObservations of the north-eastern part of \\src with the X-ray satellites \\x, Chandra, and Suzaku have confirmed an X-ray counterpart\n found in archival ROSAT data \\citep[presented in ][]{ah08,tl08}.\nAn X-ray shell partly matching\nthe radio morphology was found and the spectral analysis has revealed that the X-ray emission is of \nsynchrotron origin, indicating that the shock\nwave of the SNR has accelerated electrons up to TeV energies \\citep{ap09,tl10}.\nA compact (unresolved) X-ray source XMMU J173203.3$-$344518 \\citep{halpern10} was observed towards the geometrical center of the remnant\nand has spectral properties reminiscent of central compact objects (CCOs) found in several other supernova shells \\citep[e.g.][]{ps04}.\nA search for pulsations using the EPIC PN cameras onboard \\x shows only marginal evidence of a 1\\,s period \\citep{halpern10}.\n\nGiven the recent discovery of \\radio, little is known about its age and distance. \\cite{tl08} suggested a distance of 3.2 $\\pm$ 0.8 kpc \nassuming that the SNR is at the same distance as the H{\\sc ii} region G353.42-0.37.\n\nAdditional \\h observations, carried out since the discovery paper of \\src \\citep{ah08}, allow to investigate the compatibility of the\nTeV source with the radio shell SNR \\radio. The observations and the data analysis are described\nin Sect. \\ref{sect:data} and the morphological and spectral results in Sect. \\ref{sect:results}. \nThe multi-wavelength counterparts of \\src are described in Sect. \\ref{sect:multi} and a general discussion is presented in Sect. \\ref{sect:discussion}.\n\n\n\n\n\\section{\\h observations and analysis methods }\n\\label{sect:data}\n\n\n\\h is an array of four identical imaging atmospheric Cherenkov telescopes (IACTs)\nlocated in the Khomas Highland of Namibia 1800 m above sea level \\citep{bernlohr03}.\nThe survey of the Galactic plane by the \\h collaboration has led to the discovery of \nthe \\g-ray source HESS J1731-347, presented as an unidentified extended source in \\citet{ah08}.\nIn this first data set, 14 hours of observation time were available. \nAdditional dedicated observations were carried out in July 2007 and \nin July and August 2009 with zenith angles ranging from 9$\\degr$ to 42$\\degr$, the mean angle being 16.5$\\degr$. \nThe total \\h observation time for this target is 59 hours after data quality cuts.\n\nThe data set\nwas analyzed using the \\textit{Model analysis} \\citep{dr09} which exploits the\nfull pixel information by comparing the recorded shower images with a pre-calculated shower model\nusing log-likelihood minimization. In comparison with conventional analysis techniques, no cleaning or parametrization of the image shape is required and the full camera information is used.\nThis method leads to a more accurate reconstruction and better background suppression\nthan more conventional techniques and thus to an improved sensitivity.\n\nSpectral and spatial analyses were carried out using a minimum image intensity of 60 photoelectrons (p.e.) resulting in an energy threshold of 240 GeV and an angular resolution of 0.06$^{\\circ}$ (68\\% containment radius).\n All results presented were cross-checked with a multivariate analysis \\citep{ohm09} using an independent calibration and gamma\/hadron separation,\n which yielded consistent results. Unless otherwise quoted, the error bars in the following section are given at 1$\\sigma$. \\\\\n \n\\section{TeV \\g-rays analysis results}\n\\label{sect:results}\n\n\n\nThe \\h excess map of the region of \\src is shown in Fig.~\\ref{fig:excess} smoothed with a Gaussian of $\\sigma$=0.04$^{\\circ}$. \nFor the background estimation in the image and in the morphology studies, the \\textit{ring background} method presented in \\citet{berge07} was used.\nBecause of the larger data set and the more sensitive reconstruction technique, \nthe presented image is much more detailed than the one shown in the\ndiscovery paper \\citep{ah08}. \nThis reveals a complex region composed of a large and bright structure (\\srca), detected\nat 22$\\sigma$, with a suggestive shell-like morphology.\n\nA smaller and fainter structure named \\object{\\srcb} (detected at 8$\\sigma$) is also observed, the \nproperties of which are presented separately in Sect. \\ref{sect:srcb}.\n\n\n\\subsection{TeV energy morphology}\n\n\\begin{figure}\n \\centering\n\n \\includegraphics[bb= 89 200 465 565, clip,width=7.7cm]{16425f1.ps} \n \\includegraphics[bb= 465 114 516 581, clip,width=0.812cm]{16425f1.ps}\n \n \\vspace{-0.6cm}\n\n \n \\caption{TeV \\g-ray excess map ($1.5\\degr \\times1.5\\degr$) of the \\src region smoothed with a Gaussian width $\\sigma$=0.04$^{\\circ}$.\n The average \\h PSF for the dataset is shown in the inset. The regions used for the spectral analysis of \\srca and \\srcb are respectively represented by the large and small dashed circles.\n The position of the central compact object detected in X-rays is shown with a white cross. The linear scale is in units of excess counts per smoothing Gaussian width. The transition between blue and red in the color scale is at the level of 4$\\sigma$.}\n \\label{fig:excess}\n\\end{figure}\n\nTo further test the hypothesis of a shell morphology for \\srca and its association with the radio SNR, radial and azimuthal profiles in radio and \\g-rays were extracted centered on the\nposition of the CCO ($\\alpha_{\\mathrm{J2000}}=$17h32m03s, $\\delta_{\\mathrm{J2000}}=-34\\degr45'18''$), also coincident\nwith the geometrical center of the radio SNR. The profiles were derived from the uncorrelated \\g-ray excess map\nand corrected for the field of view (FoV) acceptance.\n \nFor the \\g-ray radial profile, the position angles\\footnote{Position angle 0$\\degr$ corresponds to North and 90$\\degr$ to East.} from 270$\\degr$ to 310$\\degr$ were excluded, to avoid contamination from \\srcb, and the resulting radial profile was compared with a sphere and a shell model. \nThe first model is a uniformly emitting sphere of adjustable radius, projected on the sky and then folded with the PSF derived\nfor this analysis ($r_{\\rm 68\\%}=0.06\\degr$).\nThe shell model consists of a uniformly emitting shell of variable outer radius and thickness (defined as $r_{\\rm outer} - r_{\\rm inner}$) projected on the sky and then folded with the same PSF. \n\nIn the morphological test, the best fit statistically favors the shell model and the sphere model is \nruled out at 3.9 $\\sigma$ ($\\chi^{2}$\/dof = \\chishell and \\chisphere \nfor the shell and sphere models respectively). \nIn the case of a shell model, the best fit radius is $0.27^{\\circ} \\pm \\,0.02^{\\circ}$ and the emission is compatible with a thin, spatially\nunresolved, shell with an upper limit thickness of 0.12$^{\\circ}$ (90\\% confidence level). \n \nTo compare the TeV morphology with the shell seen in radio, \n the radio continuum map from the ATCA southern Galactic plane survey (SGPS) \\citep{hg06} was \nsmoothed to match the \\h spatial resolution and a radial profile was extracted (excluding point sources).\nThe radio profile was then scaled by a normalization factor\ncalculated as the ratio of the total number of excess $\\gamma$-rays over the total radio flux on the whole remnant. \nThe resulting profiles, presented in Fig.~\\ref{fig:radprof}, show an extended emission in \n\\g-rays similar to that seen in radio. \n\n\nIn contrast with \\rxj which is brighter in the North-West and SN 1006 that exhibits a bipolar morphology, \nthe azimuthal profile of \\srca (see Fig.~\\ref{fig:azprof}) integrated for $r \\leqslant 0.3 \\degr$ shows no significant deviation from a flat profile ($\\chi^{2}$\/dof = 8.8 \/ 9).\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{16425f2.ps}\n \\vspace{-0.6cm}\n \\caption{The \\g-ray excess and radio radial profiles are shown with green crosses and red squares respectively. \nThe best fits to the \\g-ray data of a sphere and a shell model are overlaid. Both radial profiles \nare centered on the compact central object ($\\alpha_{\\mathrm{J2000}}=$17h32m03s, $\\delta_{\\mathrm{J2000}}=-34\\degr45'18''$). }\n \\label{fig:radprof}\n\\end{figure}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{16425f3.ps}\n \\vspace{-0.6cm}\n \\caption{Normalized azimuthal \\g-ray excess profile restricted to radius r $\\leq0.3\\degr$ and using the same center as in Fig.~\\ref{fig:radprof}. The brightness distribution is compatible with a flat profile.}\n \\label{fig:azprof}\n\\end{figure}\n\n\n\\subsection{Spectral results}\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\columnwidth]{16425f4.ps}\n \\caption{Differential energy spectra of \\srca (filled circles) and \\srcb (open circles). The normalization for the second source has been divided by \n 10 for graphical purposes. Events were binned to reach a significance of at least 2$\\sigma$. \n The best fit power-law models along with the residuals for \\srca are also shown.\n The grey bands correspond to the range of the power-law fit, taking into account statistical errors.}\n \\label{fig:spec}\n\\end{figure}\n\n\n\nThe energy spectrum of the SNR was obtained by means of a forward-folding maximum likelihood fit \\citep{piron01}\nfrom a circular region of 0.3$\\degr$ centered \non the CCO, illustrated by the large dashed circle ($r=0.3^{\\circ}$) in Fig.~\\ref{fig:excess}, chosen to fully enclose\nthe emission of the remnant. \nThe background is estimated using the \\textit{multiple reflected-regions}\ntechnique where background events are selected from regions of the\nsame size and shape as the source region and at equal angular distance from the\nobservation position \\citep{berge07}. \nThe resulting spectrum, shown in Fig.~\\ref{fig:spec}, is well described by a power-law model (equivalent $\\chi^{2}$\/dof = 27.7 \/ 35) defined as\n${\\rm d}N\/{\\rm d}E = N_{\\rm 0} (E\/E_{\\rm 0})^{-\\Gamma} $ where $E_{\\rm 0}$ is the decorrelation energy (energy at which the correlation between the slope and the normalization vanishes).\n The best fit parameters, listed in Table \\ref{tab:param}, \nresult in an integrated 1-10 TeV energy flux of (\\fluxa $\\pm \\, 1.38_{\\rm syst}) \\times 10^{-12}$ erg cm$^{-2}$s$^{-1}$. The flux measured here is lower than what has been derived initially in\n \\citet{ah08} : $(16.2 \\pm 3.6_{\\rm stat} \\pm \\, 3.2_{\\rm syst}) \\times 10^{-12}$ erg cm$^{-2}$s$^{-1}$ in the same energy band.\n However, the region of extraction in the discovery paper was much larger ($r=0.6^{\\circ}$ versus $r=0.3^{\\circ}$\n in this paper), including \\srcb and possibly some surrounding diffuse emission. \n A cross-check to derive the flux from the SNR only using the same data set as used in \\citet{ah08} and following the original analysis method \n gave results consistent with the complete data set presented here thus confirming that the flux difference was mainly due to the choice of the integration region.\n A power-law model with an exponential cutoff was also tested which did not improve the quality of the fit (equivalent $\\chi^{2}$\/dof = 24.0 \/ 34). \n\n\n\n\\begin{figure*}[!t]\n \\centering\n \\begin{tabular}{ccc}\n\\hspace{-0.6cm}\n { \\includegraphics[bb= 43 137 570 650 , clip,width=6.23cm]{16425f5.ps} } & \\hspace{-6mm}\n { \\includegraphics[bb= 40 137 565 641 , clip,width=6.20cm]{16425f6.ps} } & \\hspace{-6mm}\n { \\includegraphics[bb= 43 137 570 648 , clip,width=6.23cm]{16425f7.ps} } \n \n \\end{tabular}\n\n\\vspace{-3mm}\n\n \\caption{Multi-wavelength view of the \\src region. The radio and the \\g-ray image show the same field\n of view while the X-ray image is zoomed in order to show the details of the shell structure. The significance contours at 4, 6 and 8 $\\sigma$ obtained with an\n integration radius of 0.06$\\degr$ and the Galactic plane (white dashed line) are overlaid in the three panels. \n \\textit{Left} : ATCA radio map at 1.4 GHz from the south Galactic plane survey (SGPS)\n in units of Jy\/beam with a beam of 100''. \n The HII regions G353.42-0.37 (left) and G353.381-0.114 (right) are marked with arrows. \\textit{Middle} : \\x observation of a sub-region of the SNR, in the 0.5-4.5 keV energy band, using MOS instruments with units in ph\/cm$^{2}$\/s\/arcmin$^{2}$. The position of the source XMMU J173203.3$-$344518, \n which is likely to be the CCO of the SNR is shown by the red arrow. \\textit{Right} : \n TeV \\g-ray excess map of \\src smoothed with a Gaussian with $\\sigma$=0.04$^{\\circ}$.\n The region used to derive the radio flux and the spectral parameters in X- and \\g-rays for the SED is also shown. }\n\n \\label{fig:4contrib}\n\\end{figure*}\n\n\n\n\\subsection{\\srcb}\n\\label{sect:srcb}\n\n\nA \\g-ray excess of TeV emission was found at the best fit position \n$\\alpha_{\\mathrm{J2000}}=$17h29m35s, $\\delta_{\\mathrm{J2000}}=-34\\degr32'22''$ with a statistical error of 0.035\\degr and the source was therefore\nlabeled \\srcb.\n\nThe source is extended beyond the size of the PSF (Gaussian width $\\sigma=0.12\\degr \\pm 0.03\\degr$)\n and the region used to derive \nthe spectral parameters is shown by the small dashed circle ($r=0.14\\degr$) in Fig.~\\ref{fig:excess}. The spectrum obtained is well modeled by a power-law model (see Fig.~\\ref{fig:spec}) and the best fit parameters are listed in Table \\ref{tab:param}. The integrated flux in the 1-10 TeV energy band is (\\fluxb $\\pm \\, 0.18_{\\rm syst}) \\times 10^{-12}$ erg cm$^{-2}$s$^{-1}$.\n\n\n\n\\begin{table*}\n\\begin{minipage}[t]{\\textwidth}\n\\centering\n\\caption{Best fit spectral parameters obtained for different extraction regions in \\src. The model used is a power-law of the form ${\\rm d}N\/{\\rm d}E = N_{\\rm 0} (E\/E_{\\rm 0})^{-\\Gamma} $. The systematic errors are conservatively estimated to be $\\pm$ 0.2 on the photon index and 20\\% on the flux. }\n\\label{tab:param}\n\n\\renewcommand{\\footnoterule}{} \n\\begin{tabular}{l c c c c } \n\\hline\n\nRegion & Photon index $\\Gamma$ & Decorrelation energy $E_{\\rm 0}$& Normalization $N_{\\rm 0}$ & 1-10 TeV integrated flux \\\\\n & & TeV & $10^{-12}$ cm$^{-2}$s$^{-1}$ TeV$^{-1}$ & $10^{-12}$ erg cm$^{-2}$s$^{-1}$ \\\\\n\\hline\n\n \\srca & \\slopea & 0.783 & \\norma & \\fluxa \\\\\n sub-region of \\srca \\footnote{A spectral analysis corresponding to the FoV of the \\x data (see Fig.~\\ref{fig:4contrib}, center) has been carried out in order to build a SED.} & \\slopesub & 0.780 &\\normsub & \\fluxsub \\\\\n \\srcb & \\slopeb & 0.861 &\\normb & \\fluxb \\\\\n\n\\hline\n\\end{tabular}\n\\end{minipage}\n\\end{table*}\n\n\n\\section{Multi-wavelength counterparts}\n\\label{sect:multi}\n\n\n\n\nOne of the interesting characteristics of \\srca is that non-thermal emission is clearly identified in \nradio, X-rays and at TeV energies.\nIn X-rays however, the access to the spectral properties is limited to a subregion of the SNR as \nthe coverage with the \\x, Chandra and Suzaku satellites is only partial, and the statistics in the ROSAT All Sky Survey data are too low.\nIn order to study the spectral energy distribution (SED) of the source, \n the radio flux and the TeV spectral properties were extracted only from the region observed in X-rays (see region definition in Fig.~\\ref{fig:4contrib}, right). \nThe multi-wavelength counterparts of \\srcb are discussed later in Sect. \\ref{sect:robin}. \n\n\n\\subsection{Radio Continuum}\n\\label{sect:radio}\n\n\nThe shell observed in radio is spatially coincident with the \\g-ray shell and has a similar extent (see radial profile in Fig.~\\ref{fig:radprof}).\nThe flux obtained (excluding point sources) from the SGPS ATCA data in the region observed by the \\x pointing is 0.8 $\\pm$ 0.3 Jy at 1420 MHz.\nThe total radio flux for the SNR measured by \\citet{tl08} is of 2.2 $\\pm$ 0.9 Jy.\nThe compact HII region (G353.42-0.37) located to the West of the remnant at a distance of 3.2 $\\pm$ 0.8 kpc \\citep{tl08} is indicated \nin Fig.~\\ref{fig:4contrib} (left).\n\n \n\n\\subsection{X-rays}\n\\label{sect:xray}\n\n\nIn order to derive spectral information from the X-ray emission from the remnant, the \\x pointing obtained as a follow up of the HESS source \n(ObsId: 0405680201 ; PI: G. P\\\"uhlhofer) was analyzed. \nTo clean the proton flare contamination during the observation, a histogram of the 10-12 keV count rates of each \ncamera was built. A Gaussian fit was then performed in order to remove time intervals where the count rates were beyond 3 $\\sigma$ from the mean value \\citep{pa02}. The remaining exposure time after flare screening is 22 ks out of the 25 ks of observation for MOS and 15 ks for PN. For the image generation, the instrumental background was derived from the compilation of blank sky observations by \\citet{cr07}\n and renormalized in the 10-12 keV band over the whole FoV. The image resulting from the combination of the\n two MOS instruments is presented in Fig.~\\ref{fig:4contrib} (middle). \nFor this mosaic, the data from the PN instrument were not used because of straylight contamination to the\nNorth-East (photons singly reflected by the mirrors) from a bright X-ray source located outside the FoV. \nThis results in some spurious arc features near the border of the FoV in the North-East.\n\n\nThe X-ray emission is characterized by extended emission which is concentrated in arc-like features, similar to broken shell seen from many shell-type SNRs. \nSome of the arcs partly coincide with the radio and \\g-ray shell (see Fig.~\\ref{fig:4contrib}). Some of the structures could hint at an additional, smaller shell, \nbut might also come from irregular SNR expansion in an inhomogeneous and\/or dense medium \\citep{blondin01}. \nA double-shell structure is also observed in \\rxj in X-rays \\citep{lazendic04,cd04,acero09}. \n\nThe spectral analysis of the diffuse X-ray emission was carried out using the Extended Source Analysis Software\n(ESAS\\footnote{http:\/\/xmm2.esac.esa.int\/external\/xmm\\_sw\\_cal\/background\/\nepic\\_esas.shtml})\n provided in the \\x Science Analysis System (SAS v9.0) to model the particle and instrumental backgrounds. \nThe error bars in this section are quoted at 90\\% level confidence.\nFor this analysis, the three instruments PN+MOS1+MOS2 were used and the regions were selected to avoid the straylight features.\n\nThe spectrum derived from the region covered by the FoV of \\x that is used for the SED is shown in Fig.~\\ref{fig:xrayspec}. \nThe emission is well represented by an absorbed power-law model and no emission lines were found \\citep[see also][]{tl10}. \nThe best fit parameters obtained from a joint fit of MOS1, MOS2 and PN spectra are \n$N_{\\rm H}=(1.08 \\pm 0.02) \\times 10^{22}$ cm$^{-2}$, a spectral index $\\Gamma= 2.28 \\pm 0.03$ and a normalization at 1 keV \n$N_{\\rm 0}= (1.37 \\pm 0.05) \\times 10^{-2}$ cm$^{-2}$ s$^{-1}$ keV$^{-1}$.\nA search for spatial spectral variations of the diffuse emission revealed that the power-law index is in most locations in the \nrange $\\Gamma = 2.1-2.5$. Under the assumption of a pure power-law hypothesis, the absorption column significantly increases towards the Galactic plane from \n$N_{\\mathrm{H}} = (0.93 \\pm 0.05) \\times 10^{22}$ cm$^{-2}$ in the South-East region to \n$N_{\\mathrm{H}} = (2.23 \\pm 0.21) \\times 10^{22}$ cm$^{-2}$ in the North-West region (see Fig.~\\ref{fig:xraynh} ; left).\nThe errors on the absorption column shown in Fig.~\\ref{fig:xraynh} (left) are ranging from 5\\% to 12\\%.\n\n \n The bright point source XMMU J173203.3$-$344518 lies at the geometrical center of the radio and \\g-ray shell. \nMarginal evidence for a pulsation at a period of 1 s for the pulsar candidate and a faint nebula (radius of 30'') \nwhose spectral properties are compatible with a dust-scattered halo have been reported by \\citet{halpern10}. \nAs no optical or \nIR counterpart of the point source have been detected and as the X-ray spectrum is well described by a blackbody emission model \nwith $kT\\sim0.5$ keV, the object is\na good candidate to be the CCO of the SNR \\citep{ap09,halpern10,tl10}. \n \n\n\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[bb= 70 25 590 705,clip,width=6.4cm,angle=-90]{16425f8.ps}\n \\vspace{-0.2cm}\n\n \\caption{X-ray spectrum, using the PN camera, extracted from the sub-region of \\srca shown in Fig.~\\ref{fig:4contrib} (right). \n The non-thermal emission from the SNR is described by an absorbed power-law model (dashed line). \n The local astrophysical background, fitted to an off region outside the SNR, is modeled by two components (dotted lines).\n The low energy component is an APEC model (astrophysical plasma emission code, see http:\/\/hea-www.harvard.edu\/APEC)\n representing the background from the Local Bubble and\n the high-energy component is an absorbed power-law representing the hard X-ray background (unresolved AGNs, cataclysmic variables, etc). \n The residuals of the total model (SNR+local astrophysical background) are shown in the lower panel and the $\\chi^{2}$\/ndof is 1921\/1569.}\n \\label{fig:xrayspec}\n\\end{figure}\n\n\n\n\\begin{figure*}\n \\centering\n \\begin{tabular}{cc}\n \n {\\includegraphics[bb=-80 28 504 519,clip,width=7.8cm]{16425f9.ps} } & \\hspace{1.cm}\n {\\includegraphics[bb=45 156 570 620,clip,width=7.6cm]{16425f10.ps} } \\\\ \\vspace{0.5cm}\n \n \\hspace{-2.1cm} {\\includegraphics[bb=-120 0 440 27,clip,width=7.5cm]{16425f9.ps}} & \n \\hspace{-1.5cm} {\\includegraphics[bb=-120 0 440 27,clip,width=7.5cm]{16425f9.ps}} \n \n \\vspace{-0.7cm}\n\n \\end{tabular}\n\n\n\n \\caption{ \\textit{Left } : X-ray absorption map derived from a spectral fit to \\x data assuming an absorbed power-law model. A significant increase of $N_{\\rm H}$ towards the Galactic plane is observed. \\textit{ Right } : Absorption column map derived from atomic and molecular hydrogen when integrating over radial velocities from 0 km\/s to $-$25 km\/s (see Sect. \\ref{sect:CO} for more details). The Galactic plane is represented by the white dashed line. In both panels, the \\x field of view is represented by a dashed circle and the X-ray contours\n obtained from Fig.~\\ref{fig:4contrib} (center) are overlaid. }\n \\label{fig:xraynh}\n\\end{figure*}\n\n\n\\subsection{$^{12}$CO (J=1-0) and HI}\n\\label{sect:CO}\n\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[bb=70 360 585 1040,clip,width=\\columnwidth]{16425f11.ps}\n \\vspace{-0.5cm}\n \\caption{ \n \\textit{Top} : Cumulative absorbing column density (solid line) as a function of radial velocity at the position of highest X-ray absorption (see Sect. \\ref{sect:CO}).\n The relative contributions from the atomic and molecular hydrogen are represented by the dashed and dash-dotted lines respectively.\n \\textit{Middle} : Rotation curve towards the same direction as derived from the model of Galactic rotation of \\citet{hou09}. \n \\textit{Bottom} : $^{12}$CO (dashed line) and HI (dash-dotted line) spectra obtained the region highest X-ray absorption. \n}\n \\label{fig:COspec}\n\\end{figure}\n\nThe comparison of the absorption along the line of sight derived from X-ray data, $^{12}$CO and HI observations can be used to constrain the distance to the SNR.\nThe velocity spectra of the $^{12}$CO emission \\citep[using data from the CfA survey, ][]{dame01} and the HI emission \n\\citep[using data from the SGPS survey, ][]{hg06} derived from the region of highest X-ray \nabsorption ($\\alpha_{\\mathrm{J2000}}=$17h31m43s, $\\delta_{\\mathrm{J2000}}=-34\\degr34'58''$) are shown in Fig.~\\ref{fig:COspec} (bottom).\n\nIn order to derive a lower limit on the integration distance required to match the $N_{\\rm H}$ derived from X-rays, all the material is\nassumed to be at the near distance allowed by the Galactic rotation curve. \nUnder this hypothesis, the cumulative absorption column derived from the atomic and molecular hydrogen shown in Fig.~\\ref{fig:COspec} (top) \nis similar to the one observed in X-rays, $N_{\\mathrm{H}} = (2.23 \\pm 0.21) \\times 10^{22}$ cm$^{-2}$, when integrating\n up to a radial velocity relative to the \\textit{local standard of rest} (LSR) of $-$25 km\/s.\n The CO-to-H$_{2}$ mass conversion factor and the HI brightness temperature to column density used are respectively of\n$1.8\\times10^{20}$ cm$^{-2}$ K$^{-1}$ km$^{-1}$ s \\citep{dame01} and $1.82\\times10^{18}$ cm$^{-2}$ K$^{-1}$ km$^{-1}$ s \\citep{dickey90}.\n \n When integrating up to the same velocity, the map of $N_{\\rm H}$ derived from the atomic and molecular hydrogen shown in Fig.~\\ref{fig:xraynh} \n(right) exhibits an increase of absorption towards the Galactic plane similar to that in the X-ray absorption map in Fig.~\\ref{fig:xraynh} (left).\nThe peak of $^{12}$CO emission at a LSR velocity of $-$18 km\/s is thus in the foreground of the SNR and is likely to be the cause of the gradient of absorption seen in X-rays.\nUsing the circular Galactic rotation model of \\cite{hou09} with a distance to the Galactic center of 8.0 kpc, the \nnearest distance corresponding to the LSR velocity of $-$18 km\/s is \\dist kpc thus setting a lower limit for the distance of the remnant.\n\n\n\\begin{figure}\n \\centering\n\\hspace{-0.3cm} \\includegraphics[bb=56 210 525 585,clip,width=9.3cm]{16425f12.ps}\n\\hspace{-0.43cm} \\includegraphics[bb=56 210 525 585,clip,width=9.3cm]{16425f13.ps}\n \\caption{$^{12}$CO map of the vicinity of \\src integrated from LSR velocity $-$13 km\/s to $-$25 km\/s (top) and from $-$75 km\/s to $-$87 km\/s (bottom)\n respectively corresponding to the intervals of the second and third $^{12}$CO peak shown in Fig.~\\ref{fig:COspec}.\n The HESS significance contours from Fig.~\\ref{fig:4contrib} together with the Galactic plane and the \n Fermi 95\\% position confidence level contours presented in the 1 year catalog are overlaid.\n The linear scale is in units of K km\/s. }\n \\label{fig:COslice}\n\\end{figure}\n\n\n\n\\subsection{GeV \\g-rays}\n\\label{sect:fermi}\n\n\n\nIn the Fermi-LAT first year catalog \\citep{abdo10cat} the source 1FGL J1729.1$-$3452c is found in the neighborhood of \\src as shown in Fig.~\\ref{fig:COslice}. \nThe Fermi source has an analysis flag that indicates that the source position moved beyond its 95\\% error ellipse when changing the model \nof diffuse emission.\nThe $^{12}$CO map in Fig.~\\ref{fig:COslice} shows that the Fermi source is located near a small scale gas clump that could be not well represented in the \ndiffuse emission model. The position of the source presented in the catalog is to be used with caution and is therefore possibly not incompatible with \\srcb.\n\nThe Fermi source has a photon spectral slope of 2.26$\\pm$0.08 and shows neither indication for spectral curvature nor time variability on a time scale of months \n(the catalog does not address shorter or longer time variations). This source is the closest Fermi detection\nnear the newly discovered SNR and the flux derived in the Fermi catalog is used as an upper limit in the SED of the SNR in Fig.~\\ref{fig:SED}.\n\n\n\n\\subsection{Multi-wavelength counterparts for \\srcb}\n\\label{sect:robin}\n\n\nAt radio wavelengths, the \\g-ray contours of \\srcb lie near the HII region G353.381-0.114. Using HI radio recombination line data, the LSR velocity\ncorresponding to this source is either $-$54 km\/s or $-$82 km\/s \\citep{caswell87}.\n In the latter case this HII region could be associated with the molecular cloud observed around\nvelocities of $\\sim$ $-$80 km\/s (see Fig.~\\ref{fig:COslice}). \nAt X-ray energies, no archival dedicated observations were found, and no emission is detected in the ROSAT all sky survey, probably due to the high \nabsorption in the line of sight. As discussed in the previous section, a Fermi source is found to lie close to \\srcb. \n\n\n\n\\section{Discussion}\n\\label{sect:discussion}\n\n\nThe newly discovered SNR \\srca is in several ways comparable to \\rxj and \\vela. Those objects are X-ray synchrotron emitters and exhibit\n no thermal emission lines. A CCO is also found within those three SNRs indicating a core collapse SN.\nMoreover at a distance of \\dist kpc (see Sect. \\ref{sect:CO}),\nthe TeV luminosity of \\srca in the 1-30 TeV energy band is 1.07$\\times (d\/\\dist \\, \\rm{kpc})^{2} \\, 10^{34}$ erg s$^{-1}$ which is similar to the luminosity of \\rxj \n(the brightest TeV shell SNR detected until now), of 0.81$\\times 10^{34}$ erg s$^{-1}$ using a distance of 1 kpc \\citep{fm03,cd04,mt05} \nand slightly higher than that of \\vela with 0.65$\\times 10^{34}$ erg s$^{-1}$ at a distance of 0.75 kpc \\citep{katsuda08}.\nA difference with \\rxj is that the flat \\g-ray azimuthal profile of \\srca (see Fig.~\\ref{fig:azprof}) suggests that the remnant is evolving in a relatively uniform ambient medium and \nthat it is not in interaction with the cloud (shown in Fig.~\\ref{fig:COslice}, top) used to derive a lower limit to the distance of the SNR. \nThis significantly differs from the case of \\rxj which exhibits much brighter \\g-ray emission in the North-West where the shock is thought to interact with denser material.\n\nThe distance used for the luminosity is derived from the absorption in the foreground and provides only a lower limit of \\dist kpc.\nHowever, as it is believed that supernova explosions are more likely to occur in the spiral arms of the Galaxy where the density of massive stars \n(i.e. SNR progenitors) is higher \\citep{russeil03,hou09}, it is likely that \\srca could be located within\nthe Scutum-Crux or Norma arms, which cross the line of sight at $l=353.5^{\\circ}$ at $\\sim$3.0 and $\\sim$4.5 kpc respectively \\citep{hou09}.\nThe next arm in the same line of sight is the Sagittarius arm lying at a distance of 12 kpc. This latter possibility for the location\nof the SNR would lead to a much higher \\g-ray luminosity, an order of magnitude higher than \\rxj. Also at such a distance, the physical size of the \nremnant would exceed 50 pc, substantially larger than other TeV shell SNRs whose physical size is $\\lesssim$ 15 pc. \nAs a result it is reasonable to believe that the real distance to the SNR should not be much larger than the derived lower limit of \\dist kpc. \n\nThe radio flux and the X- and \\g-ray spectra derived in Sect. \\ref{sect:multi} from the sub-region of \\srca that is covered by the FoV of \\x were combined\nin the SED presented in Fig.~\\ref{fig:SED}. The X-ray data were corrected for the interstellar absorption with \n$N_{\\rm H}=1.08 \\times 10^{22}$ cm$^{-2}$.\n To model the SED, a simple one-zone stationary model \\citep[presented in ][]{acero10} was used. \nIn this model, the spectrum of electrons and protons is represented by a power-law of slope $s$ with exponential cutoffs at energies\n$E_{\\rm c,e}$ and $E_{\\rm c,p}$ for the electrons and protons respectively. \nFor the modeling of the object, it is assumed that the measured multi-wavelength emission from the sub-region of \\srca is \nentirely coming from the SNR located at a distance of \\dist kpc. \nAs this distance is only a lower limit, the total energy of accelerated particles ($W_{\\rm e}$ and $W_{\\rm p}$) \nin the SNR should also be viewed as lower limits.\n\nIn the pure leptonic scenario, the slope of the electrons is constrained by the radio and the X-ray synchrotron emission between 1.9 and 2.1 and\nthe strength of the magnetic field required to reproduce the ratio of observed synchrotron and IC emission \nlies between 20 and 30 $\\mu$G for $15 \\leq E_{\\rm c,e} \\leq 25$ TeV. Although the relative ratio of radio, X- and TeV fluxes can be fairly well\nreproduced by this leptonic scenario, the model is inadequate to account for the X-ray and the \\g-ray spectral slope \nas illustrated in Fig.~\\ref{fig:SED} (top). The corresponding parameters for the latter model are summarized in Table \\ref{tab:SED}.\n\nThis limitation no longer occurs in a scenario where the TeV emission is dominated by hadronic\nprocesses as the X- and \\g-ray emission are now independent and both spectral slopes can be reproduced as shown in Fig.~\\ref{fig:SED} (bottom).\nMoreover, the strength of the magnetic field can be increased as it is no longer fixed by the X\/\\g ratio. \nIn order to reproduce the observed TeV flux, the total energy in high-energy protons ($E \\, \\geq$ 1 GeV) assuming a spectral slope of 2.0 \nis $W_{\\rm p} = 2 \\times 10^{50}$ ($n$\/1 cm$^{-3})^{-1}$ ($d$\/\\dist kpc)$^{2}$ erg. \nIt should be noted that this energy content only represents a sub-region of the SNR accounting for $\\sim$ 1\/3 of the total TeV flux\n(see Table \\ref{tab:param}) implying that the total energy transferred to accelerated protons in the whole SNR is \n a substantial fraction of the energy available in the remnant for $n \\sim$ 1 cm$^{-3}$.\nFor this energetic reason, gas densities much below this value appear incompatible with the hadronic emission scenario.\n\n\n\nAlthough it is not possible to measure the density of the ambient medium surrounding the SNR as no X-ray thermal emission is detected,\nan upper limit on the density can be derived. \nIn order to do so, a thermal component, whose normalization is fixed for a given density using the method presented in \\citet{ab07} (Sect. 3.1), is\n added to the X-ray spectrum. \n The shocked ambient medium is assumed to be in a non equilibrium ionization state with an ionization timescale parameter\n$\\tau=10^{9}$ cm$^{-3}$ s and an electron plasma\ntemperature $kT_{\\rm e}$=1 keV. Such values are commonly observed in other young SNRs \nfor which the X-ray emission of the shocked ambient medium has been studied as in e.g. RCW86 \\citep[see][]{vink06}.\nFor the given parameters, the derived upper limit (90\\% confidence level) on the ambient medium density is $10^{-2}$ cm$^{-3}$.\nIn the case of a lower temperature ($kT_{\\rm e}$=0.15 keV), an upper limit of 1 cm$^{-3}$ is reached.\n\nFor a density of 1 cm$^{-3}$ the corresponding shock speed and age of the SNR\nwould be $\\sim$410 km\/s and 14000 yrs in order to match a physical radius of \n$R_{\\rm shock}$=15 pc (0.27$^{\\circ}$ at \\dist kpc), for a SN explosion of $E_{\\rm SN}=1 \\times 10^{51}$ erg with a mass of ejecta of 5 $M_\\odot$ \nusing equations from \\citet{tm99}.\nHowever, this shock speed is an order of magnitude lower than what has been measured in other bright synchrotron emitting SNRs like SN 1006 \n\\citep[$V_{\\rm sh}$=5000 $\\pm$ 400 km\/s at a distance of 2.2 kpc ; ][]{katsuda09}, RCW 86 \\citep[$V_{\\rm sh}$=6000 $\\pm$ 3000 km\/s ; ][]{helder09}, \nCasA \\citep[$V_{\\rm sh}$=4900 km\/s ; ][]{patnaude09} or Tycho \\citep[$V_{\\rm sh} = 3000 \\pm 1000 $ km\/s at a distance of 2.3 kpc ; ][]{katsuda10}. \nAs a rough estimate, the required density to reproduce a canonical shock speed of 3000 km\/s using the aforementioned SN parameters\nis of the order of 0.01 cm$^{-3}$ (compatible with the upper limit derived from the lack of thermal X-ray emission in the previous paragraph) for a corresponding age of $\\sim$ 2500 yrs. \n\nTo summarize, the presented static one-zone model suffers from limitations in both scenarios. In the leptonic case,\nthe model allows to estimate the average B-field ($\\sim 25 \\, \\mu$G) and the total energy in accelerated electrons present in the shell of the SNR but\n fails to reproduce the observed X-ray and \\g-ray spectral slope. In the hadronic model, the high medium density required to \nreproduce the observed TeV flux is hardly compatible with the hydrodynamics of the SNR.\nMore detailed models using non-linear diffuse shock acceleration theory have been developed \n\\citep[e.g.][]{zirakashvili10,ellison10} and would provide more accurate predictions than the simple model presented here.\nIt should be noted that the considered model does not take into account evolution related to radiative cooling\nwhich could yield a steeper gamma-ray spectrum, in better agreement with the data. \nAlso, the presented scenarios do not cover possible non-homogeneous surroundings such as wind bubble blown by the progenitor.\nSuch detailed spectral and evolutionary modeling depends on many poorly known parameters and is therefore beyond the scope of the\npresent discussion.\n\n\nConcerning the source \\srcb, detected in the vicinity of the SNR, the presented multi-wavelength data\ndo not provide a clear understanding of the nature of the object. \nThe closest structures located near the \\g-ray emission are \nthe HII region G353.381-0.114 (seen in the radio in Fig.~\\ref{fig:4contrib}, left) and\na molecular gas clump observed in $^{12}$CO (see Fig.~\\ref{fig:COslice}, bottom) when integrating around a LSR velocity\n of $-$80 km\/s (corresponding to near and far kinematic distances of $\\sim$6 and $\\sim$10 kpc respectively). \nIf the \\g-ray source \\srcb is associated with those gas structures, it would therefore not be associated with the SNR HESS J1731-347 thought to lie at a closer distance.\n \n\n\n\n\\begin{table}\n\\centering\n\\caption{List of the parameters used for the spectral energy distribution modeling presented in Fig. \\ref{fig:SED}. \nThe spectral slope are fixed at 2.0 for the electron and the proton distribution. The density of the ambient medium was set to 1 cm$^{-3}$ in the case of the \nhadronic model.}\n\\label{tab:SED}\n\n\\renewcommand{\\footnoterule}{} \n\\begin{tabular}{l c c c c c } \n\\hline\n\nModel & $E_{\\rm c,e}$ & $E_{\\rm c,p}$ & $W_{\\rm e}$ & $W_{\\rm p}$ & $B$ \\\\\n & TeV & TeV & $10^{47}$ erg & $10^{50}$ erg & $\\mu$G \\\\\n\\hline\n\nleptonic & 18 & $-$ & 1.1 & $-$ & 25 \\\\\nhadronic & 16 & 100 & 0.25 & 2.0 & 50 \\\\\n\n\n\\hline\n\\end{tabular}\n\\end{table}\n\n\n\n\n\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[bb= 142 55 575 707, clip,width=6cm,angle=-90]{16425f14.ps} \\\\\n \\includegraphics[bb= 142 55 575 707 , clip,width=6cm,angle=-90]{16425f15.ps} \n \\vspace{-0.2cm}\n\n \\caption{Broadband SED for the sub-region of \\srca that is observed in X-rays (see Fig.~\\ref{fig:4contrib}, right, for region definition).\n A purely leptonic (top) and a hadronic (bottom) scenario are shown and the corresponding parameters for both models are\n presented in Table \\ref{tab:SED}. The infrared (IR) seed photons energy density and temperature were \n respectively set to 1 eV cm$^{-3}$ and 40 K following the model from \\citet{porter08} for a galactocentric radius\n of 4 kpc. \n The flux from the nearby Fermi source 1FGL J1729.1$-$3452c is represented and used as an upper limit. }\n \\label{fig:SED}\n\\end{figure}\n\n\n\\section{Conclusion}\n\n\nThe newly discovered SNR \\srca exhibits a significant shell morphology spatially resolved by \\h, similar to the one observed in radio.\nTogether with \\rxj, \\vela and SN 1006, \\srca is now the fourth\\footnote{With the current statistics, the shell morphology of RCW86 is not statistically significant \\citep{aharonian09}.} TeV \\g-ray source to join this small but growing class. \nA lower limit to the distance of the SNR of \\dist kpc was obtained by comparing the absorption derived from the X-rays and from HI and $^{12}$CO observations.\n\nThe multi-wavelength emission from \nthe SNR, detected in radio, X-rays and \\g-rays, was combined in an SED to investigate the origin of the \\g-ray emission assuming that the broadband emission\nstems from the same region (one-zone model). While the measured fluxes can\nbe accounted for in a purely leptonic model with a magnetic field of the order of 25 $\\mu$G, this simple model fails to reproduce the spectral shape of\n the X- and \\g-ray emission.\nA second model that assumes that the TeV emission is produced by hadronic processes is able to reproduce the spectral slopes in X- and \\g-rays at the cost\nof requiring that a large fraction of the kinetic energy of the explosion must be transferred to the accelerated protons and a high ambient medium density \nof $n\\sim$1 cm$^{-3}$ for $d \\geq$ \\dist kpc.\nMoreover for such a density, the corresponding shock speed of the SNR would be an order of magnitude lower than in other SNRs exhibiting bright \nsynchrotron emission.\n\n\n\n\n\n\n\\begin{acknowledgements}\nThe support of the Namibian authorities and of the University of Namibia\nin facilitating the construction and operation of HESS is gratefully\nacknowledged, as is the support by the German Ministry for Education and\nResearch (BMBF), the Max Planck Society, the French Ministry for Research,\nthe CNRS-IN2P3 and the Astroparticle Interdisciplinary Programme of the\nCNRS, the U.K. Science and Technology Facilities Council (STFC),\nthe IPNP of the Charles University, the Polish Ministry of Science and \nHigher Education, the South African Department of\nScience and Technology and National Research Foundation, and by the\nUniversity of Namibia. We appreciate the excellent work of the technical\nsupport staff in Berlin, Durham, Hamburg, Heidelberg, Palaiseau, Paris,\nSaclay, and in Namibia in the construction and operation of the\nequipment. Article based on observations obtained with XMM-Newton, \nan ESA science mission with instruments and contributions directly \nfunded by ESA Member States and NASA. \n\\end{acknowledgements}\n\\vspace{-0.4cm}\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{Supplemental Material}\nSec.~I provides additional information on the experimental setup, on the reliability of the measurements yielding negative values for $Q_D$ and on the absence of ions within the laser excitation volume. Within the theoretical description based on the DCS, after a brief recall of the mean-field Sec.~II describes the construction of a basis of\nnon-symmetric states the construction of the bath of the non-symmetric states and their excitation by the laser. Sec.~III discusses the vdW parameters determining the Rydberg interactions.\n\n\\section{Set-up}\n\\indent Experiments are performed using a two-photon excitation scheme for Rydberg excitations in a small cloud of around $10^5$ $^{87}$Rb atoms trapped in a MOT of radius around 30~$\\mu{}$m and densities between $1\\times10^{10}$ and $5\\times10^{10}$ atoms\/cm$^{3}$ determined to within a factor of $2$ from fluorescence images (for details see \\cite{Viteau2010}). The sizes of the excitation beams (around 200~$\\mu{}$m) are chosen such as to obtain intense beams that are approximately uniform across the atomic sample. Single atom two-photon Rabi frequencies of around 100 kHz are achieved. The excitation lasers are pulsed for up to $20\\,\\mathrm{\\mu s}$, after which the Rydberg atoms are field ionized and the ions are detected by a channeltron. For the two-photon excitation, a laser beam at 421~nm is detuned around 0.5--1~GHz from the $\\mathrm{5S_{1\/2}(F=2)}\\rightarrow\\mathrm{6P_{3\/2}(F'=3)}$ transition, and an infra-red beam of wavelength 1013--1015~nm provides the second step to the Rybderg state $\\mathrm{6P_{3\/2}(F'=3)}\\rightarrow\\mathrm{nS\/nD}$. Both of these beams are controlled using acousto-optic modulators (AOMs) with a rise-time of around 80~ns. Both lasers are stabilised to a scanning Fabry-Perot cavity to prevent long-term frequency drift. We estimate that the two-photon linewidth on the timescale of the excitation pulses is around 300~kHz. In order to achieve reliable statistics, the excitation sequence is repeated up to a few hundred times with the repetition rate limited to $10$ Hz in order to avoid a buildup of charge on the quartz cell surrounded by the external field plates \\cite{Viteau2011}. The complete acquisition procedure takes around 30~s.\nThe detection efficiency for ions in our experiment is around $40\\%$.\\\\\n\\indent In order to verify that the observed sub-Poissonian statistics for resonant excitation is actually due to collective Rydberg excitations and not an artefact of the limitations of the experimental apparatus, we performed a series of additional experiments. Most importantly, we performed an experiment that interpolated between Poissonian and sub-Poissonian statistics for a fixed average number of Rydberg excitations, thus ruling out a spurious effect due to a saturation of the channeltron. To that end, the volume of the MOT was varied using a combination of magnetic field gradients, trapping beam sizes and laser powers chosen such as to keep the number of excited Rydberg atoms constant. As expected, for a small cloud the Rydberg dynamics was in the highly collective regime and a negative $Q_D$ was measured. As the volume increased with the number of Rydberg excitations kept fixed, the excitation dynamics tended to a Poissonian distribution with $Q\\approx 0$. Moreover, we performed experiments in which the first step laser was tuned to resonance with the $\\mathrm{5S_{1\/2}(F=2)}\\rightarrow\\mathrm{6P_{3\/2}(F'=3)}$ transition so that atoms were directly ionized through the absorption of two photons at 421 nm. Up until counting saturation, this process was found to obey Poissonian statistics with $Q_{D}=0$.\\\\\n\\indent The creation of spurious ions, that is, those not created by field ionization of Rydberg atoms, is a delicate issue in studies of the dipole blockade. The Stark shift due to a single ion is larger than the van-der-Waals interaction between two atoms in the investigated $D_{5\/2}$ state. Thus the creation of one ion in the sample can lead to a blockade due to Coulomb interactions which suppresses the excitation of other Rydberg atoms and thus mimicks the dipole blockade. A possible cause for the presence of spurious ions is the absorption of two 421~nm photons leading to direct ionization from the intermediate 6$^2$P$_{3\/2}$ state \\cite{Viteau2011}. By detuning the blue laser 1~GHz from this state excitation the probability of creating an ion is $3\\times 10^{-7}$ per $2\\,\\mathrm{\\mu s}$ pulse. For the low atoms numbers in our experiment this equates to around $3\\times 10^{-2}$ per shot and thus is not a problem. Also, black-body radiation limits the lifetime of Rydberg atoms, for the 71$D_{5\/2}$ state the effective lifetime is $\\sim$150$~\\mu{}$s~\\cite{Beterov2009}; however, the excitation and ionization is performed within $<10~\\mu$s and thus should have negligible impact on the counting statistics. Finally, at the typical atomic densities used in the experiment the decay process is not affected by super-radiance. Any loss from the initially prepared state will, therefore, follow Poissonian statistics.\n\n\\section{Theory}\n\\label{theory}\n\\subsection{Mean field}\nIn a many-body system with interactions, the mean-field theory replaces all interatomic interactions with an average or effective interaction, reducing the many-body problem to an effective one-body problem. Within this approach the Rydberg state energy is shifted by the vdW interaction characterized by the parameter $W_{0}$ given by a sum over all the Rydberg pairs inside the atomic cloud, see \\cite{Tong2004}. For instance, in a medium with uniform atomic density $n$ and for a $C_6$ interaction depending as $r^{-6}$ on the interatomic distance $r$, $W_0$ is approximatively $\\frac{C_{6}}{^6}\\approx C_{6}\\left( \\frac{4\\pi }{3}\\right)^{2}n^2$.\n\n\n\\subsection{vdW basis in the cooperative approach}\nThe vdW interaction couples the fully symmetrical DCSs to the remaining\nDCSs within the fixed $N$ subspace having\nthe large $C_{N_0}^{N}$ degeneracy. The most appropriate orthonormal basis within that subspace is composed of the symmetrical DCSs $ \\left\\vert N\\right\\rangle_s$ and the superposition of non-symmetrical states $\\left\\{ \\left\\vert N,q \\right\\rangle_{ns} \\right\\}$, with $1\\le q \\le C_{N_0}^{N}-1$, for which the vdW interaction matrix is diagonal. To obtain the correct number of states within the $C_{N_0}^{N}$ ensemble, we first remove from the $\\left\\{ \\left\\vert N,q\\right\\rangle \\right\\}$ set one state labelled $\\left\\vert N,p\\right\\rangle $, assuming $W_{pp}^{N}=W_{ss}^{N}$. Considering\nthe state\n\\begin{equation}\n\\left\\vert N,{\\widetilde q}\\right\\rangle =\\left\\vert N,\n\\right\\rangle +\\frac{1}{\\sqrt{C_{N_0}^{N}}-1} \\left\\vert N,p\\right\\rangle,\n\\end{equation}\nfor $1\\le q \\le C_{N_0}^{N}-1$. Then the basis is constructed by subtracting for each $\\vert N,{\\widetilde q}\\rangle $ state its projection on the symmetrical\n$\\left\\vert N\\right\\rangle _{s}$ state. We obtain the $\\left\\{ \\left\\vert N,q \\right\\rangle_{ns} \\right\\} $ states\ndefined a\n\\begin{equation}\n\\left\\vert N,q\\right\\rangle _{ns}=\\left[ \\left\\vert N,{\\widetilde q}\n\\right\\rangle -_{s}\\left\\langle N\\mid N,{\\widetilde q}\\right\\rangle \\left\\vert\nN\\right\\rangle _{s}\\right] .\n\\end{equation}\nIt is easy to verify that such a basis $\\left\\{ \\left\\vert N\\right\\rangle\n_{s},\\left\\{ \\left\\vert N,q\\right\\rangle _{ns}\\right\\} \\right\\} $ is\northonormal. The van der Waals coupling is diagonal with eigenvalue $W_{qq}^{N}$ for the restricted basis $\\left\\{ \\left\\vert N,q\\right\\rangle _{ns}\\right\\} $; these eigenvalues are not modified by the above basis changes. The coupling between the states $\\left\\vert N\\right\\rangle\n_{s}$\\ and $\\left\\vert N,q\\right\\rangle _{ns}$ i\n\\begin{equation}\nW_{sq}^{N}=_{s}\\left\\langle N\\right\\vert W\\left\\vert N,q\\right\\rangle _{ns}\n\\frac{1}{\\sqrt{C_{N}^{N}}}\\left[ W_{qq}^{N}-W_{ss}^{N}\\right] .\n\\label{Wenergies}\n\\end{equation}\n\\indent The above construction of the basis is very general, provided we know\nthe basis which diagonalizes the coupling. The only approximation is the\nexistence of the state $\\left\\vert N,p\\right\\rangle $. For\na large number of atoms, such an approximation is reasonable because it is\npossible to identify a state $\\left\\vert N,\\widetilde{p}\\right\\rangle $ such that $W_{{\\widetilde p}{\\widetilde p}}^{N}\\approx W_{ss}^{N}$.\n\n\\subsection{Enlarged wavefunction evolution}\nBy writing the enlarged atomic wave-function as\n\\begin{equation}\n\\left\\vert \\Psi \\left( t \\right) \\right\\rangle =\\sum_{N=0}^{N_0} a_{N}\\left( t\\right) \\left\\vert N\\right\\rangle_s + \\sum_{q} b^q_{N }\\left( t\\right) \\left\\vert\nN,q \\right\\rangle_{ns},\n\\end{equation} the $a_N$ time evolution of\n Eqs. (2) and (3) in the main text is completed by terms describing the ($b^q_N, a_N$) vdW coupling. As a first approximation, we neglect the laser excitation of the amplitudes $b^q_N$ because of their large degeneracy and the resulting\nweak occupation for each of them. Thus eliminating the equations for $b^q_N$, we obtain the following integro-differential equations:\n\\begin{eqnarray}\n{\\dot a}_{N} &=&-i(W_{ss}^N-N\\delta)a_{N}-i\\frac{\\Omega }{2}\\left[\\sqrt{\\left( N_0-N\\right) \\left(\nN+1\\right) }a_{N+1}+\\sqrt{N\\left( N_0-N+1\\right)}a_{N-1}\\right] \\nonumber \\\\\n&+&\\sum_q\\int_{0}^{t} \\left[ -\\frac{d^{2\n}{d\\tau ^{2}}-2i\\frac{d}{d\\tau }W_{ss}^N+\\left(W_{ss}^N\\right)^{2}\\right] \\frac{e^ { i\\left(W_{qq}^N-N\\delta\\right)\\tau}}{\\sqrt{C_{N_0}^N}} a_{N}\\left(\nt-\\tau \\right) d\\tau.\n\\label{differointegral}\n\\end{eqnarray}\n\n\\begin{figure}[htp]\n\\includegraphics[width=0.75\\linewidth]{rydberg_Fig5.eps}\n\\caption{Real and imaginary part of $f^s_{N}\\left( t \\right) $ vs the delay time $\\tau$ for typical parameters investigated in our experiment. }\n\\label{correlationfunction}\n\\end{figure}\n\nThe coupling $b_N \\to a_N$ appearing in Eq. \\eqref{differointegral} is described through the correlation function $f_N(\\tau)$ as \\begin{equation}\n{\\dot a_{N}}=-\\int_{0}^{t}f_N(\\tau) e^{iN\\delta \\tau} a_N\\left(t-\\tau \\right) d\\tau,\n\\end{equation} with the correlation function given by\n\\begin{equation}\nf_N\\left( \\tau \\right) =\\sum_q\\frac{1}{C^N_{N_0}}\\left[ \\left[ \n\\frac{d^{2}}{d\\tau ^{2}}-2i\\frac{d}{d\\tau }W^N_{ss}+(W^N_{ss})^{2}\\right]e^\n{ -iW^N_{qq}\\tau} \\right],\n\\end{equation\nwhere $W^N_{sq}$ was eliminated using Eq. \\eqref{Wenergies}. With only two Rydberg excited atoms within the volume $V$, $f_N$ becomes\n\\begin{equation}\nf_N\\left( \\tau \\right) =\\frac{W_0^{2}}{4V}\\int_{2V\/N}^{V}\\left[\n\\frac{1}{v^{4}}-\\frac{N}{v^{2}V^{2}}+\\frac{N^{2}}{4V^{4}}\\right]\ne^{ -i\\frac{2W_0}{v^{2}}\\tau} dv.\n\\end{equation}\nThe above integral has an analytical solution. The solution for the correlation function is composed of a long-memory component\n$f^l_{N}\\left( \\tau \\right) $ and a short-memory one, $f^s_{N}\\left( \\tau \\right) $. The real and imaginary parts of $f^s_{N}\\left( \\tau \\right) $ are represented in Fig.~\\ref{correlationfunction}. The $f^s_{N}\\left( \\tau \\right) $ contribution treated in the Markov approximation leads to the following imaginary term:\n\\begin{equation}\n-\\int_{0}^{t}f^s_{N}\\left( \\tau \\right) \\exp{\\left(i\\delta j \\tau\\right)}a_{N}\\left( t-\\tau \\right) d\\tau\n=iW^N_{ss}a_{N}\\left( t\\right).\n\\end{equation\nThis term exactly compensates exactly the blockade energy term introduced in Eq.~\\eqref{differointegral} above for the symmetric DCS theory and, as a consequence, produces the symmetrical resonance excitation profiles reported in the Fig. 1 of the main text.\\\\\n\\indent The long-memory part can be treated exactly, but a good approximation, up to a few tens of Rydberg excitations, is\n\\begin{equation}\nf^l_{N}\\left( \\tau \\right) =(W^N_{ss})^{2}.\n\\end{equation}\nUsing the above short-memory and long-memory parts,\nthe vdW coupling can be described by representing the\nensemble of the non-symmetrical levels with $j$ Rydberg excitation in terms of a single\nlevel $\\ c_j$ defined as the superposition all asymmetric DCS's of the fixed $N$ subspace. Within this simplified scheme for each number $N$ of Rydberg excitations the evolution is described by the following system of two equations, one for $a_N$ and one for a single level $\\ c_N$, reported also in the text:\n\\begin{eqnarray}\n{\\dot a_{N}}&=&-iN\\delta a_{N}\n-i\\frac{\\Omega }{2}\\left[\\sqrt{\\left( N_0-N\\right) \\left( N+1\\right)}\na_{j+1}+\\sqrt{N\\left( N_0-N+1\\right)}a_{N-1}\\right] -iW_{ss}^Nc_{N}, \\label{equationA} \\\\\n{\\dot c}_{N} &=&-iN\\delta c_{N}-iW_{ss}^Na_{N}.\n\\label{equationC}\n\\end{eqnarray}\n\\subsection{Laser excitation of the non-symmetric bath}\nThe last step of this theoretical analysis is the introduction of the laser\nexcitation into evolution of $c_N$.\nWe treat it perturbatively, with a limitation on the temporal evolution imposed by the spectral linewidth $\\Delta \\omega$ of the excitation laser. The final result is a modification of Eq. \\eqref{equationC} into \\begin{equation}\n{\\dot c}_{N} =-\\left(\\frac{\\Gamma_N}{2}+iN\\delta\\right) c_{N}-iW_{ss}^Na_{N} +\\frac{\\Gamma_N}{2} \\left|c_{N-1}\\right|,\n\\label{FinalEquation}\n\\end{equation}\nwith all coherence terms $c_Nc^*_{N-1}$ set to zero. $\\Gamma_N$ depends on the laser linewidth $\\Delta \\omega$ as\n\\begin{equation}\n\\Gamma_N=\\frac{\\Omega^2(N_0-2N)}{4}\\left[\\frac{\\Delta \\omega\/2}{\\left(\\Delta \\omega\/2\\right)^2+\\left(\\delta-NW_{ss}\\right)^2}+\\frac{\\Delta \\omega\/2}{\\left(\\Delta \\omega\/2\\right)^2+\\left(\\delta+NW_{ss}\\right)^2}\\right].\n\\end{equation}\n\n\\section{Rydberg interaction parameters}\n\\label{parameters}\nThe numerical results rely on the precise values for the vdW coupling strengths. In a first approximation, we can consider that, in zero field, two Rydberg atoms experience a ${C_6}\/{r^6}$ interaction, $C_6$ being calculated on the basis of perturbation theory of \\cite{SInger2005}. This model fails to describe the interaction accurately, first at small interatomic distances and secondly because of the Zeeman degeneracy of the two-atom Rydberg state. While at large interatomic distances, the two Rydberg states are dipole coupled via one additional state, at very short interatomic distances a large number of states is coupled, and the overall coupling is reduced. In our calculation of the interaction energy $W_0$, we introduce an appropriate cut-off radius (roughly twice the atomic size) because the excitation of two Rydberg atoms having a distance smaller than the cut-off radius is very unlikely. At intermediate interatomic distances the perturbative treatment of \\cite{SInger2005} is not fully valid because of crossing in the molecular levels. Then a full diagonalization of the Hamiltonian leads to a more complex dependence of the interatomic coupling on the interatomic distance, ${C_6}\/{r^6}$ at large distance and ${C_3}\/{r^3}$, i.e. a dipole-dipole interaction, at smaller ones. Finally, Walker and Saffman~\\cite{WalkerSaffman2008} have shown the Rydberg interaction is strongly dependent of the relative orientation of magnetic momentum of the colliding atoms of the pair. For the of $D_{\\frac{5}{2}}$ states this leads to a variety of 21 possible interaction curves whose strengths vary by more than two orders of magnitude. The average presented in~\\cite{WalkerSaffman2008} allowed us to obtain an effective dipole-dipole interaction, i.e., a single interaction curve for the calculation of the parameter $W_0$. The variation in the interaction curves is reflected in the uncertainty in our determination of the coupling strength for the theoretical curves in Fig. 1 of the main text.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe developing field of superconducting spintronics\ncomprises a plenty of fascinating phenomena that may complement\nnonsuperconducting spintronics devices~\\cite{rf:Zutic}.\nMesoscopic hybrid structures consisting of superconducting and magnetic materials have attracted\nconsiderable attention as devices with novel functionalities~\\cite{rf:Buzdin1}.\nOne of most interesting effects is the formation of $\\pi$-states in\nsuperconductor\/ferromagnetic-metal\/superconductor (S\/FM\/S) Josephson junctions~\\cite{rf:Bulaevskii}.\nUnder appropriate conditions a ferromagnet can become a $\\pi$-phase shifter, providing \nthe phase difference $\\phi=\\pi$ between two superconductors in the ground state\nin contrast to $\\phi=0$ in ordinary Josephson junctions.\nRecently a quiet qubit based on S\/FM\/S $\\pi$-junction~\\cite{rf:Ioffe} has been\nsuggested as a promising device to realize quantum computation because\nthe spontaneously generated two-level system in this structure is robust against\ndecoherence due to external fluctuations.\nHowever, low energy quasiparticle excitations in a FM provide strong dissipation~\\cite{rf:Schon}.\nTherefore Josephson $\\pi$ junctions with a nonmetallic interlayers are highly\ndesired for qubit applications~\\cite{rf:Kawabata1}.\nMoreover, from the fundamental view point, the Josephson transport through a $ferromagnetic$ $insulator$ (FI) has been studied based on phenomenological models~\\cite{rf:SFIS} and not yet been explored explicitly.\n\nIn this Letter, we study theoretically the Josephson effect\nin superconductor\/ferromagnetic-insulator\/superconductor (S\/FI\/S)\njunctions using the tight-binding model.\nWe show that the ground state in such structures alternates between the 0- and $\\pi$-states\nwhen the thickness of a FI ($L_F$) is increasing by a single atomic layer.\nThis remarkable effect originates from the characteristic band\nstructure of a FI. Quasiparticles in the electron and hole branches\nacquire different phase shifts while propagating across a FI.\nWe will show that the phase difference is exactly $\\pi L_F$ due to the band structure of a FI,\nthus providing the atomic-scale 0-$\\pi$ transition.\nThis mechanism is in striking contrast to the proximity induced $0$-$\\pi$ transition in conventional S\/FM\/S junctions.\nOn the basis of the obtained results, we predict a stable $\\pi$-state in a\nJosephson junction based on high-$T_c$ superconductors with a La$_2$BaCuO$_5$ barrier, \nwhere electric current flows along the $c$ axis\nof cuprates. \n\\begin{figure}[b]\n\\begin{center}\n\\includegraphics[width=8.5cm]{fig1.eps}\n\\end{center}\n\\caption{(color online) (a)\nThe Josephson junction with a ferromagnetic-insulator (FI) barrier\non the tight-binding lattice.\nThe magnetic moment in FI is chosen along the $z$ axis in spin space.\nThe band structure of a FI (b) and of standard band insulator (c) in the\nBogoliubov-de Gennes picture. The dispersion for a hole with spin $\\sigma$ is\nobtained as a mirror image of the dispersion for an electron with spin\n$\\sigma$ with respect to Fermi energy.\n }\n\\label{fig1}\n\\end{figure}\n\n\n\nLet us first consider an S\/FI\/S junction in the two-dimensional tight-binding model as shown\nin Fig.~1(a). The vector $\\boldsymbol{r}=j{\\boldsymbol{x}}+m{\\boldsymbol{y}}$ points\nto a lattice site, where ${\\boldsymbol{x}}$ and ${\\boldsymbol{y}}$ are unit vectors\nin the $x$ and $y$ directions, respectively.\nThe thickness of a FI layer is $L_F$.\nIn the $y$ direction, we apply the hard wall boundary condition for the number of\nlattice sites being $W$.\nElectronic states in a superconductor are described by the\nmean-field Hamiltonian,\n$\n{\\cal H} =(1\/2)\n\\sum_{\\boldsymbol{r},\\boldsymbol{r}^{\\prime } \\in \\text{S} }%\n( \\tilde{c}_{\\boldsymbol{r}}^{\\dagger }\\;\\hat{h}_{\\boldsymbol{r},%\n\\boldsymbol{r}^{\\prime }}\\;\\tilde{c}_{\\boldsymbol{r}^{\\prime }}^{{}}-%\n\\overline{\\tilde{c}_{\\boldsymbol{r}}}\\;\\hat{h}_{\\boldsymbol{r},\\boldsymbol{r}%\n^{\\prime }}^{\\ast }\\;\\overline{\\tilde{c}_{\\boldsymbol{r}^{\\prime }}^{\\dagger\n}})\n+(1\/2)\n\\sum_{\\boldsymbol{r}\\in \\text{S}} ( \\tilde{c}_{%\n\\boldsymbol{r}}^{\\dagger }\\;\\hat{\\Delta}\\;\\overline{\\tilde{c}_{\\boldsymbol{r}%\n}^{\\dagger }}-\\overline{\\tilde{c}_{\\boldsymbol{r}}}\\;\\hat{\\Delta}^{\\ast }\\;%\n\\tilde{c}_{\\boldsymbol{r}} )\n$\nwith\n$\\overline{\\tilde{c}}_{\\boldsymbol{r}}=\\left( c_{\\boldsymbol{r}%\n,\\uparrow },c_{\\boldsymbol{r},\\downarrow }\\right) $,\n where\n$ c_{\\boldsymbol{r} ,\\sigma }^{\\dagger }$ ($c_{\\boldsymbol{r},\\sigma }^{{}}$)\n is the creation (annihilation) operator of an electron at $\\boldsymbol{r}$ with spin $\\sigma\n(= \\uparrow $ or $\\downarrow $ ), $\\overline{\\tilde{c}}$ means the\ntranspose of $\\tilde{c}$, and $\\hat{\\sigma}_{0}$ is $2\\times 2$ unit matrix.\nWe introduce the hopping integral $t$ among nearest neighbor sites\nand measure the length in the units of the lattice constant $a$.\nIn superconductors, the Hamiltonian leads\n$\n\\hat{h}_{\\boldsymbol{r},\\boldsymbol{r}'}= [ -t \\delta _{|\\boldsymbol{r}-\\boldsymbol{r}'|,1}\n+\n(-\\mu_s+4t)\\delta_{\\boldsymbol{r},\\boldsymbol{r}'} ] \\hat{\\sigma}_{0}\n$,\nthe chemical potential $\\mu_s$ is measured from the band bottom\nand $\\hat{\\Delta}=i\\Delta \\hat{\\sigma}_{2}$, where $\\Delta$ is the amplitude\nof the pair potential in the $s$-wave symmetry and $\\hat{\\sigma}_{j}$ for $j=1-3$\nare Pauli matrices. We describe a FI by\n$\n\\hat{h}_{\\boldsymbol{r},\\boldsymbol{r}'}= -t \\delta_{|\\boldsymbol{r}-\\boldsymbol{r}'|,1}\n\\hat{\\sigma}_{0}\n- (g\/2+4t) \\delta_{\\boldsymbol{r},\\boldsymbol{r}'}\\hat{\\sigma}_3\n$,\nwhere $g$ corresponds to a gap of a FI as shown in Fig.~1(b).\n\nThe Hamiltonian is diagonalized by the Bogoliubov transformation.\nThe Andreev bound state consists of subgap states whose\nwave functions decay far from the junction interface.\nIn what follows, we focus on the subspace for\nspin-$\\uparrow$ electron and spin-$\\downarrow$ hole\n[the dispersions shown by solid curves in Fig.~1(b)].\nIn superconductors, the wave function of a bound state is given by\n\\begin{align}\n\\Psi_L(\\boldsymbol{r})=&\\Phi_L \\left[\n \\left(\\begin{array}{c} u \\\\ v \\end{array} \\right)\n A e^{-ikj} +\n \\left(\\begin{array}{c} v \\\\ u \\end{array} \\right)B e^{ik^*j}\\right] \\chi_l(m),\\\\\n\\Psi_R(\\boldsymbol{r})=&\\Phi_R \\left[\n \\left(\\begin{array}{c} u \\\\ v \\end{array} \\right)\n C e^{ikj} +\n \\left(\\begin{array}{c} v \\\\ u \\end{array} \\right)D e^{-ik^*j}\\right] \\chi_l(m),\n\\end{align}\nwhere $\\nu=L$ ($R$) indicates a superconductor in the left (right) hand side,\n$\\phi_\\nu$ is the phase of a superconductor,\n$\\Phi_\\nu=\\mathrm{diag} \\left( e^{i\\phi_\\nu\/2} , e^{-i\\phi_\\nu\/2} \\right)$,\n$u(v)=[ ( 1+(-) \\sqrt{E^2-\\Delta^2}\/E )\/ 2 ]^{1\/2}$, and $A, B, C$ and $D$\nare amplitudes of the wave function for an outgoing quasiparticle.\nThe wave function in the $y$ direction is $\\chi_l(m)=\\sqrt{2\/W}\\sin[l m\\pi \/(W+1)]$,\nwhere $l$ indicates a transport channel.\nThe energy $E$ is measured from the Fermi energy\nand $k= \\cos^{-1} [2-\\mu_s\/2t - \\cos \\{ l \\pi \/ (W+1) \\} + i \\sqrt{\\Delta^2 - E^2} \/E]$ is the complex wave number.\nThese wave functions decay as $e^{-(j-L_F)\/\\xi_0}$ for $j > L_F$\nand $e^{j\/\\xi_0}$ for $j<0$ with $\\xi_0$ being the coherence length.\nIn a FI, the wave function is given by\n\\begin{align}\n\\Psi_{FI}(\\boldsymbol{r})=&\n\\left[ \\left(\\begin{array}{c} f_L e^{-iq_ej} \\\\ g_L e^{-iq_hj}\\end{array} \\right)\n+\\left(\\begin{array}{c} f_R e^{iq_ej} \\\\ g_R e^{iq_hj}\\end{array} \\right)\\right] \\chi_l(m),\\\\\n q_e=&\\pi + i\\beta_\\uparrow, \\label{wn1}\\\\\n q_h=&0+i\\beta_\\downarrow,\\label{wn2}\n\\end{align}\nwhere $\\cosh\\beta_\\uparrow= 1 + E\/2t+g\/4t + \\cos [ l \\pi \/ (W+1) ]- \\cos [ W \\pi \/ (W+1) ]$,\n $\\cosh \\beta_\\downarrow= 1 + E\/2t+g\/4t - \\cos [ l \\pi \/ (W+1) ]- \\cos [ \\pi \/ (W+1) ]$, and\n$f_L, f_R, g_L$ and $g_R$ are amplitudes of wave function in a FI.\nThe Andreev levels $\\varepsilon_{n,l} (\\phi=\\phi_L-\\phi_R)$ [$n=1, \\cdots, 4$]\ncan be calculated from boundary conditions\n$\\Psi_L(\\lambda,m)=\\Psi_{FI}(\\lambda,m)$ and\n$\\Psi_R(L_F+\\lambda,m)=\\Psi_{FI}(L_F+\\lambda,m)$ for $\\lambda =0$ and 1.\nThe Josephson current is related to $\\varepsilon_{n,l}$ via\n$ I_J (\\phi) =(2e \/ \\hbar) \\sum_{n,l} \\left[ \\partial \\varepsilon_{n,l}\n(\\phi)\/ \\partial \\phi \\right] f \\left( \\varepsilon_{n,l} (\\phi) \\right),\n$\nwhere $ f\\left( \\varepsilon \\right)$ is the Fermi-Dirac distribution function.\nThe Josephson critical current $I_C$ is defined by $I_C = I_J(\\pi\/2)$.\n\n\n\n\\begin{figure}[b]\n\\begin{center}\n\\includegraphics[width=8.7cm]{fig2.eps}\n\\end{center}\n\\caption{(color online)\n(a) The Andreev levels $\\varepsilon_{i} \\equiv \\varepsilon_{i,1}$ are plotted as functions of $\\phi$ for an one-dimensional S\/FI\/S junction with $W=1$.\n Left (right) panel shows the results for odd $L_F$ (even $L_F$) with\n$g=0.5t$.\n(b) Josephson critical current $I_C$ at $T=0.01 T_c$ as a function of the thickness $L_F$ for\n$W=1$ and $g=0.5 t$. The large red (small blue) circles indicate the $\\pi$(0) junction.\nIn the inset, the 0-$\\pi$ phase diagram on the $g$-$L_F$ plane is shown for\na two-dimensional S\/FI\/S junction with $W=10$ at $T=0.01 T_c$, where the \nred and blue regimes correspond to the $\\pi$- and 0-states, respectively.}\n\\label{fig2}\n\\end{figure}\n\nIn Fig.~\\ref{fig2}(a), we first show the Andreev levels $\\varepsilon_{n,1} \\equiv \\varepsilon_{n}$ for odd $L_F$ ($=3$ and 5) and even $L_F$ ($=4$ and 6) with $W=1$,\n$\\mu_s=2t$, and $\\Delta=0.01t$.\nThe results show that the ground state for odd $L_F$ is at $\\phi=\\pi$, whereas that for even $L_F$ is at $\\phi=0$.\nThis atomic-scale 0-$\\pi$ transition persists even if we increase $L_F$ and $W$.\nIn Fig.~2(b), we show the Josephson critical current as a function of $L_F$ for $W=1$.\nTemperature $T$ is set to be $0.01T_c \\ll T_c$, where $T_c$ is the transition temperature of a superconductor.\nThe $\\pi$(0)-state is always more stable than the 0($\\pi$)-state when the thickness of FI is an odd(even) integer.\nThe reason is as follows.\nAt low temperatures, only the Andreev levels below the Fermi energy i.e.,\n$\\varepsilon_{1}$ and\n$\\varepsilon_{2}$, contribute to $I_C$ [see Fig. 2(a)].\nIn the odd (even) $L_F$ cases, the $\\pi$- (0-) state is stable because of\n$ \\partial_\\phi \\varepsilon_1 |_{\\phi=\\pi\/2}> (<) 0 $,\n$ \\partial_\\phi \\varepsilon_2 |_{\\phi=\\pi\/2}< (>) 0 $, and\n$ | \\partial_\\phi \\varepsilon_2 |_{\\phi=\\pi\/2} |> |\\partial_\\phi \\varepsilon_1 |_{\\phi=\\pi\/2} |$.\nThe atomic-scale 0-$\\pi$ transition is insensitive to $W$ and material parameters such as $\\mu_s$, $g$, and $\\Delta$.\nAs an example, inset of Fig.~2(b) shows the phase diagram on the $g$-$L_F$ plane for $W=10$.\n\n\n\n\n\n\n\n\n\n\nThe mechanism of the 0-$\\pi$ transition in a FI is very different from that\nin a FM. The key feature is expressed by the wave number of a quasiparticle\nin a FI as shown in Eqs.~(\\ref{wn1}) and (\\ref{wn2}),\nwhere $q_e$ and $q_h$ are the wave numbers for an electron spin-$\\uparrow$\nand a hole spin-$\\downarrow$, respectively.\nThe real parts of $q_e$ and $q_h$ reflect the wave number at the $q$ points, where energy\nis closest to the Fermi energy, and differ by $\\pi$ from each other .\n As shown in Fig. 1(b), the real part of $q_e$ is\n$\\pi$ because the top of the electron band is located at $q=\\pi$.\nOn the other hand, the real part of $q_h$ is 0 because the top of the hole\nband is at $q=0$. This is the origin of the difference between $q_e$ and $q_h$ which\naccounts the atomic-scale 0-$\\pi$ transition.\nWhen we consider a usual band insulator as shown in Fig. 1(c),\nwe always obtain $q_e=q_h$ and their real parts equal $\\pi$\nbecause both the top of the electron band and the bottom of the hole band are located at $q=\\pi$.\nAs a consequence, 0-state is always stable in usual band insulators.\nThus we conclude that the characteristic band structure of a FI is the origin\nof atomic-scale 0-$\\pi$ transitions.\nThese features basically remain unchanged\neven when we consider Josephson junctions in higher dimensions. In such junctions, however, the appearance of \n0-$\\pi$ transitions depends on relative directions between the\ncurrent and the crystalline axis. We will address this issue below.\nIt should be emphasized that peculiar results presented above cannot be described by the standard quasiclassical Green's function method~\\cite{rf:Fogelstrom} where band structure structure far from the Fermi energy is is ignored.\n\n\n\nLet us reconsider atomic-scale 0-$\\pi$ transitions from a different view point of quasiparticle\ntransmission coefficient.\nIn the high barrier limit ($g \\gg t$), the Josephson critical current is perturbatively given by\n$I_C \\propto T_\\downarrow^* T_\\uparrow$~\\cite{rf:Kawabata1}.\nHere $T_{\\uparrow(\\downarrow)}$ is a transmission coefficient of a FI for\nan electron with spin-$\\uparrow$ (-$\\downarrow$).\nBy using the transfer-matrix method~\\cite{rf:Datta}, $T_\\sigma$ for\none-dimensional junctions can be obtained analytically\n$\n T_\\sigma \\approx\n\\alpha_{L_F}\\left( \\rho_\\sigma t \/ g \\right)^{L_F}.\n$\nHere $\\rho_{\\uparrow(\\downarrow)} = -(+)1$ and $\\alpha_{L_F}$ is a spin-independent complex constant.\nWe immediately find $T_\\uparrow\/T_\\downarrow = (-1)^{L_F}$.\nThus the relative phase\nof $T_\\sigma$ between spin-$\\uparrow$ and spin-$\\downarrow$\n is $\\pi$ (0) for the odd (even) number of $L_F$.\nAs a consequence, the sign of $I_C \\propto (-1)^{L_F} $ becomes negative for odd $L_F$ and positive for even $L_F$.\nIn other words, a FI acts as a $\\pi$-phase-shifter for the spin-$\\uparrow$ electron for odd $L_F$.\n\n\n\nThe transfer-matrix method in real space also enables us to extend the calculations to\nanother magnetic materials.\nUp to now, we have considered uniform magnetic moment in FI, which can be schematically expressed by\nS\/$\\uparrow_1 \\uparrow_2 \\cdots \\uparrow_{L_F}$\/S or S\/$\\downarrow_1 \\downarrow_2\n\\cdots \\downarrow_{L_F}$\/S. The arrows $\\uparrow_j$ and\n$\\downarrow_j$ indicate the $z$-axis magnetization at $j$.\nWe can extend the above simple analysis to the arbitrary\nmagnetization configuration, $e.g.,$ a\nrandom alignment described by S\/$\\downarrow_1 \\uparrow_2 \\downarrow_ 3 \\cdots\n\\uparrow_{L_F-2} \\uparrow_{L_F-1} \\downarrow_{L_F}$\/S.\nIn such junctions, we find\n$I_C \\sim \\prod_{i=1,L_F} T_{i, \\mathrm{\\downarrow}}^* T_{i, \\mathrm{\\uparrow}}\n = \\prod_{i=1,L_F} (-1)=(-1)^{L_F}$,\nwhere $T_{i,\\sigma}$ is the transmission coefficient of an FI layer at $i$.\nTherefore we obtain a noticeable result, $i.e.$,\nthe sign of\n $I_C$\nis independent of magnetization configurations and is negative (positive) for odd (even) $L_F$.\nThe appearance of the atomic-scale 0-$\\pi$ transition has been also predicted in\nS\/antiferromagnetic-interlayer\/S junctions~\\cite{rf:Barash}.\nIn their theory, however, the antiferromagnetic configuration\nis found to be essential for the atomic-scale transition.\nOn the other hand, we conclude that the magnetization symmetry is not necessary and that\nthe $\\pi$-phase difference between $T_\\uparrow$ and $T_\\downarrow$ is an essential feature\nfor the atomic-scale transition.\nTherefore our analysis provides more general view for the physics of the atomic scale 0-$\\pi$ transition.\n\n\n\n\n\n\n\nFinally, we show the possibility of the atomic-scale 0-$\\pi$ transition in a three-dimensional\njunction using realistic materials.\nHere we focus on La$_2$BaCuO$_5$ (LBCO)~\\cite{rf:Mizuno} which is an important representative FI in spintronics.\nAccording to a first-principle band calculation~\\cite{rf:LBCO}, the bottom of the minority spin band is at the $\\Gamma$ point\nwhose wave number is $(k_a, k_b, k_c) = (0,0,0)$, where $k_j$ for $j=a,b,$ and $c$ is the\nwave number along $j$ axis (see Fig.~6 in Ref.~\\onlinecite{rf:LBCO}).\nThe mirror image of the minority spin band with respect to the Fermi energy corresponds to\nthe hole band with minority spin in the Bogoliubov-de Gennes picture.\nThus the top of the minority spin hole band is at the $\\Gamma$ point.\nOn the other hand, top of the majority spin band is at the $Z$ point with\n$(k_a, k_b, k_c) = (0,0,\\pi)$.\nThus we can predict that the $\\pi$ state would be possible if one fabricates a Josephson\njunction along $c$ axis as shown in Fig.~4(a). Note that it is impossible to\nrealize the $\\pi$-state if current flows in the $ab$-plane.\nThis is because wave numbers in $ab$-plane at the bottom of the minority spin band and\nthose at the top of the majority spin band are given by the same wave number\n$(k_a, k_b) = (0,0)$~\\cite{rf:LBCO}.\n\n\n\n\nFrom the perspectives of the S\/FI interface matching and the high-temperature device-operation,\nthe usage of high-$T_c$ cuprate superconductors (HTSC), e.g.,\nYBa${}_2$Cu${}_3$O${}_{7-\\delta}$ and La${}_{2-x}$Sr${}_x$CuO${}_4$ (LSCO) is desirable.\nRecent development of the pulsed laser deposition technique enables layer-by-layer\nepitaxial-growth of oxide superlattices~\\cite{rf:Prellier}.\nIn order to show the possibility of $\\pi$-coupling in such realistic HTSC junctions, we have calculated the $c$-axis Josephson critical current $I_C$ based on a three-dimensional tight-binding model with $L_a$ and $L_b$ being the numbers of lattice sites in $a$ and $b$ directions [Fig. 3 (a)].\nIn the calculation we have taken into account the $d$-wave order-parameter symmetry in HTSC, i.e., $\\Delta= \\Delta_d (\\cos k_x a- \\cos k_y a) \/ 2$. The tight-binding parameters $t$ and $g$ have been determined by fitting to the first-principle band structure calculations along the line from $\\Gamma$ to $Z$ point~\\cite{rf:LBCO}.\nFigure 3 (c) shows the thickness $L_F$ dependence of $I_C$ at $T=0.01 T_c$ for a LSCO\/LBCO\/LSCO junction with $g\/t=20$, $\\Delta_d\/t=0.6$, and $L_a=L_b=100$.\nAs expected, the atomic-scale 0-$\\pi$ transitions can be realized in such oxide-based $c$-axis stack junctions.\n\\begin{figure}[tb]\n\\begin{center}\n\\includegraphics[width=8.8cm]{fig3.eps}\n\\end{center}\n\\caption{\n(color online) (a) Schematic picture of a $c$-axis stack high-$T_c$\nsuperconductor\/FI\/high-$T_c$ superconductor Josephson junction and (b) a $d$-wave ring to detect the $\\pi$-junction behavior experimentally.\n(c) The Josephson critical current $I_C$ as a function of the FI thickness $L_F$ at $T=0.01 T_c$ for a $c$-axis stack LSCO\/LBCO\/LSCO junction with $g\/t=20$, $\\Delta_d\/t=0.6$, and $L_a=L_b=100$.\nThe large red (small blue) circles indicate the $\\pi$(0) junction.}\n\\label{fig3}\n\\end{figure}\n\nThe experimental detection of the $\\pi$-junction is possible by using a superconducting ring\nwhich contains two Josephson junctions as shown in Fig.~\\ref{fig3}(b).\nWhen both junctions are in 0- (or $\\pi$-) state at the same time,\nthe critical current of the ring reaches its maximum at zero\nexternal magnetic flux. On the other hand, the critical current reaches its minimum\nat zero magnetic flux when the 0 state is stable in one junction and $\\pi$ is stable\nin the other~\\cite{rf:Sigrist}.\n\n\nFrom the view point of qubit applications, it is important to note that the harmful influence of midgap Andreev resonant states~\\cite{rf:Tanaka,rf:KawabataMQT1} and nodalquasiparticles due to the $d$-wave symmetry on the macroscopic quantum dynamics in $c$-axis HTSC junctions~\\cite{rf:Kleiner} is found to be weak, both theoretically~\\cite{rf:KawabataMQT2} and experimentally~\\cite{rf:MQTexperiment}.\nTherefore we conclude that HTSC\/LBCO\/HTSC\n$\\pi$-junctions would be promising candidates as basic-elements for quiet qubits.\n\n\n\n\nIn summary, we have studied the Josephson effect in S\/FI\/S junctions based on the tight-binding model.\nWe predict the formation of the atomic-scale 0-$\\pi$ transitions in such junctions.\nThis result is insensitive to the material parameters such as the gap $g$ of the FI and the superconducting gap $\\Delta$, indicating that it is a robust and universal phenomenon.\nOur findings suggest the way of realizing {\\it ideal quiet qubits} which possess both the quietness and the weak quasiparticle-dissipation nature.\n\n\n\nWe would like to thank J. Arts, A. Brinkman, M. Fogelstr\\\"om, T. Kato, P. J. Kelly, T. L\\\"ofwander, T. Nagahama, F. Nori, J. Pfeiffer, A. S. Vasenko, and M. Weides for useful discussions.\nThis work was supported by CREST-JST, a Grant-in-Aid for Scientific Research from the Ministry of Education, Science, Sports and Culture of Japan (Grant No. 19710085), and NanoNed (Grant TCS.7029).\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\IEEEPARstart{P}{hotonic}-crystal (PhC) waveguides are made by creating a line-defect in a PhC slab. The defect introduces guided modes in the photonic band-gap of the crystal and allows for an efficient propagation of optical signals. In particular, a major advantage of PhC line-defect waveguides (LDWGs), as compared to conventional waveguides, is the possibility to exploit slow-light (SL) propagation \\cite{Baba2008}. In the SL region of the waveguide dispersion relation, the group velocity is greatly reduced and ideally tends to zero at the band edge. As a consequence, in the SL region, a Bloch wave propagating within an active PhC waveguide experiences an effective gain per unit length which is greatly enhanced with respect to the modal gain coefficient of a conventional waveguide \\cite{ek2014a}. This gain-enhancement allows for the realization of shorter devices and makes PhC waveguides ideal candidates for high-density photonic integrated circuits. PhC lasers have attracted large research as efficient light-sources in on-chip and chip-to-chip interconnections. They allow for scaling the active volume while maintaining a high cavity Q-factor, thus exhibiting low threshold current and operating energy \\cite{MatsuoReview}. Different types of PhC lasers exist. A typical PhC laser consists of a so called $\\mathrm{LN}$ cavity \\cite{Okano2010}, that is formed by only omitting $N$ holes in a PhC slab. Initially, due to heat accumulating in the active region, room-temperature continuous-wave (RT-CW) operation was difficult to achieve in this type of PhC laser. In the last years, significant progress has been made and RT-CW operation has been demonstrated under optical pumping by using ultra-small cavities with embedded quantum dots (QDs) \\cite{Nomura2006}. A major breakthrough, addressing the issues of high-speed direct modulation and thermal stability, has been the introduction of the so called lambda-scale-embedded active region PhC laser (LEAP laser), working under electrical \\cite{Matsuo2012} pumping conditions. In this laser, the cavity is made of a buried heterostructure (BH) embedded in a PhC LDWG. Furthermore, differently from PhC lasers based on $\\mathrm{LN}$ cavities \\cite{Xue2015,Xue2016}, in LEAP lasers light can be emitted into in-plane waveguides. The output waveguide can be either shifted with respect to the active region \\cite{MatsuoNatPhot2013,MatsuoJSTQE2013} or placed in-line with it \\cite{Shinya}. Therefore, LEAP lasers are promising sources for photonic integrated circuits \\cite{MatsuoReview}.\n\\IEEEpubidadjcol\n\nRigorous approaches to analyze PhC devices, such as FDTD \\cite{Okano2010,Okano2009} or RCWA \\cite{LalanneRCWA2005,LalanneRCWA2007,SongRCWA}, are time- and memory-consuming and cannot always provide an intuitive understanding of the physical phenomena into play. The aim of this paper is to present an alternative and simple approach for analyzing active PhC LDWGs and lasers based on this type of waveguides. Our approach is based on coupled-mode theory (CMT) \\cite{Yariv1973,Marcuse_Book1974}, which has proved to be an effective tool to study and design lasers with periodic gain and\/or refractive index perturbation, such as edge-emitting DFB lasers \\cite{KogelnikShank}. More recently, CMT has also been used to analyze both passive \\cite{Patterson2010,MichaelisPR} and active \\cite{Chen2015} PhC waveguides. By following a simple CMT approach, we show that a weak perturbation of the gain and\/or refractive index of a PhC waveguide causes a strong coupling between the forward- and backward-propagating Bloch modes of the unperturbed waveguide, with coupling coefficients strongly increasing as we move towards the band edge. We start from the formulation already presented in \\cite{Chen2015}, where the Bloch modes of a passive, reference waveguide were used as a basis for the field expansion in a finite-length, active section; the presence of gain in this section was treated as a weak perturbation to the passive structure, coupling the otherwise independent, counter-propagating Bloch modes. However, the system of coupled propagation equations was solved numerically in \\cite{Chen2015}, thus not providing important insight on the coupling mechanism. In this work, we push further the formulation of \\cite{Chen2015} to generally take into account both a real and imaginary refractive index perturbation. Avoiding the numerical solution, we derive a simple, closed-form expression for the unit cell transmission matrix of an active PhC waveguide. Therefore, consistently with the rigorous, non-perturbative approach of \\cite{Gric2012}, we show that, in the presence of gain, the group index does not diverge at the band edge and that the maximum attainable SL gain-enhancement is limited by the gain itself. \n\nIn this work, our coupled-Bloch-mode approach is then applied to analyze the threshold condition of various types of PhC lasers. As a first example, we analyze a laser cavity made up of different sections of both passive and active PhC LDWGs. This laser is conceptually similar to the one characterized in \\cite{Shinya}. Consistenly with \\cite{Shinya}, we show that three different operating regimes can be identified, which can be explained on the basis of the interplay between the distributed feedback in the active region and the passive mirrors. This reveals the great impact of coherent distributed feedback effects in these PhC lasers. As a second example, we analyze a typical PhC laser based on a $\\mathrm{LN}$ cavity, such as that of \\cite{Xue2016}. In this case, the laser cavity is an active PhC LDWG bounded on either side by classical PhC mirrors. As a last example, we examine a structure simply consisting of an active section, with zero back reflections from the interfaces with the passive output waveguides. Interestingly, in this type of structure the distributed feedback, caused entirely by the gain perturbation in the active region, is enough to allow lasing with reasonable threshold gain.\n\nThe paper is organized as follows: in Section II, we present our model and discuss the peculiar characteristics of the self- and cross-coupling coefficients of an active PhC LDWG. In Section III, we present the numerical results applied to both active PhC waveguides and lasers. In Section IV, we finally draw the conclusions. \n\n \n\\section{Numerical model}\n\\begin{figure}[ht]\n\t\\centering\\includegraphics[width=\\linewidth]{Supercell}\n\t\\caption{\\label{fig:Supercell} Reference PhC waveguide supercell (a). \\uline{Cross section of the reference waveguide supercell at either the input or output plane. The black lines represent QWs or QDs layers} (b). Finite-length, perturbed section in red and reference waveguide in grey (c).}\n\\end{figure}\n\\begin{figure}[ht]\n\t\\centering\\includegraphics[width=0.75\\linewidth]{BandStructure_ns3171}\n\t\\caption{\\label{fig:BandStructure_ns3171} Projected band structure of the reference PhC waveguide TE-like modes. The membrane refractive index is $n_s = 3.171$, the lattice constant $a = \\mathrm{438 \\,nm}$ \\uline{and the thickness $h = \\mathrm{250 \\,nm}$; the other parameters are those of the PhC LDWG on which the lasers in \\cite{Xue2016} are based. The membrane is assumed to be suspended in air.} The fundamental guided mode is even with respect to $x$ (red, solid line), the other mode is odd (red, dashed line). \\uline{The blue lines represent a subset of the continuum of photonic crystal slab modes found by MPB.}}\n\\end{figure}\nWe consider an active, PhC LDWG of finite length bounded on either side by semi-infinite, passive PhC waveguides, as illustrated in Fig.~\\ref{fig:Supercell}(c). The passive sections have the same lattice constant and membrane thickness as the active section. \\uline{In this context, for \\textit{membrane} we simply mean a thin layer of semiconductor, suspended in a low refractive index medium and periodically patterned with holes \\cite{DeRossi2009}, as shown in cross-section in Fig.~\\ref{fig:Supercell}(b)}. \\uline{The \\textit{material}} gain \\uline{$g$} in the active section may be provided by layers of quantum wells (QWs) or QDs embedded within the PhC membrane, \\uline{shown as black lines in Fig.~\\ref{fig:Supercell}(b)}. For simplicity, the entire membrane is assumed to contain active material, as in the case of optically pumped waveguides \\cite{ek2014a} and lasers based on $\\mathrm{LN}$ cavities \\cite{Xue2015,Xue2016}, which lack a structure for lateral carrier confinement. However, we note that the coupled-Bloch-mode approach is also applicable to structures with a buried active region, such as LEAP lasers. We consider the gain of the active section and the variation of its refractive index with respect to that of the passive sections as a weak perturbation.\n\\begin{figure*}[ht]\n\n\t\\begin{subfigure}[b]{0.33\\linewidth}\n\t\t\\centering\\includegraphics[width=\\linewidth]{Gamma11}\n\t\t\\caption{\\label{fig:ConfFacta}}\n\t\\end{subfigure}%\n\n\t\\begin{subfigure}[b]{0.33\\linewidth}\n\t\t\\centering\\includegraphics[width=\\linewidth]{Gamma12ABS}\n\t\t\\caption{\\label{fig:ConfFactb}}\n\t\\end{subfigure}\n\t\\begin{subfigure}[b]{0.33\\linewidth}\n\t\t\\centering\\includegraphics[width=\\linewidth]{Gamma12ANGLE}\n\t\t\\caption{\\label{fig:ConfFactc}}\n\t\\end{subfigure}%\n\n\t\\caption{\\label{fig:ConfFact} \\sout{confinement factors} \\uline{Spatial dependence of} $\\Gamma_{11}$ and $\\Gamma_{12}$ of the reference waveguide at various $k_z$ values. The membrane refractive index is $n_s = 3.171$; the other parameters are those of the PhC LDWG on which the lasers in \\cite{Xue2016} are based. (\\ref{fig:ConfFacta}) $\\Gamma_{11}$. (\\ref{fig:ConfFactb}) Magnitude of $\\Gamma_{12}$. (\\ref{fig:ConfFactc}) Phase of $\\Gamma_{12}$.}\n\\end{figure*}\nThe forward- (+) and backward-propagating \\mbox{(-)} Bloch modes of the reference waveguide in the frequency-domain are denoted by $\\mathbf{E}_{0,\\pm}(\\mathbf{r},\\omega) = \\mathbf{e}_{0,\\pm}(\\mathbf{r},\\omega) e^{\\pm ik_z(\\omega)z}$, where $\\mathbf{e}_{0,\\pm}(x,y,z,\\omega) = \\mathbf{e}_{0,\\pm}(x,y,z+a,\\omega)$, with $k_z$ being the Bloch wave number along the length of the waveguide ($z$) and $a$ the PhC lattice constant. In the active section, the complex refractive index variation compared to the reference waveguide is $\\Delta n_s +i\\Delta n_i$, where $\\Delta n_s$ is the variation of refractive index and $\\Delta n_i$ reflects the \\uline{\\textit{modal}} gain coefficient $g_0$, with $\\Delta n_i = -(c\/\\omega) g_0\/2$. \\uline{Here, $g_0$ is given by $\\Gamma_y g$, where $\\Gamma_y$ is the optical confinement factor of the considered electric field guided mode along the y-direction and within the QWs or QDs layers.} This polarization perturbation couples to each other in the active waveguide the otherwise independent Bloch modes of the reference waveguide. Therefore, in the limit of a weak perturbation, the electric field in the active section can be expanded in the basis of the Bloch modes of the reference waveguide, with slowly-varying amplitudes $\\psi_{\\pm}(z,\\omega)$ caused by the coupling. The electric field in the active section thus reads as $\\mathbf{E}(\\mathbf{r},\\omega) = \\psi_{+}(z,\\omega)\\mathbf{E}_{0,+}(\\mathbf{r},\\omega) + \\psi_{-}(z,\\omega)\\mathbf{E}_{0,-}(\\mathbf{r},\\omega)$. At a given $\\omega$, by neglecting nonlinear effects, two coupled differential equations in $\\psi_{\\pm}$ are derived \\cite{Chen2015}:\n\\begin{equation}\n\\label{eq:CoupledBlochModeEquations}\n\\begin{aligned}\n\\partial_z\\psi_+(z) & = i\\kappa_{11}(z)\\psi_+(z) + i\\kappa_{12}(z) e^{-2ik_z z} \\psi_-(z)\\\\\n-\\partial_z\\psi_-(z) & = i\\kappa_{21}(z) e^{+2ik_z z}\\psi_+(z) + i\\kappa_{11}(z)\\psi_-(z) \n\\end{aligned}\n\\end{equation} \nThe self- and cross-coupling coefficients are written as $\\kappa_{11;12;21}(z,\\omega) \\simeq [\\Delta n_s(\\omega)+i\\Delta n_i(\\omega)](\\omega\/c)[n_{g,0}(\\omega)\/n_s]\\Gamma_{11;12;21}(z,\\omega)$, where $n_{g,0}$ and $n_s$ are the group index and the membrane material refractive index of the reference waveguide. $\\Gamma_{11;12;21} (z,\\omega)$ are given by\n\\begin{equation}\n\\label{eq:ConfFact}\n\\begin{aligned}\n\\Gamma_{11}(z,\\omega) & = \\frac{a\\int_{S} \\epsilon_0 n_s^2|\\textbf{e}_{0}(\\textbf{r},\\omega)|^2 F(\\textbf{r})dS}{\\int_{V} \\left[\\epsilon_0 n_b^2(\\textbf{r}) |\\textbf{e}_0(\\textbf{r},\\omega)|^2\\right] dV}\\\\ \\Gamma_{12}(z,\\omega) & = \\frac{a\\int_{S} \\epsilon_0 n_s^2\\left[\\textbf{e}_{0,-}(\\textbf{r},\\omega) \\cdot \\textbf{e}^*_{0,+}(\\textbf{r},\\omega)\\right] F(\\textbf{r})dS}{\\int_{V} \\left[\\epsilon_0 n_b^2(\\textbf{r}) |\\textbf{e}_0(\\textbf{r},\\omega)|^2\\right] dV}\n\\end{aligned}\n\\end{equation}\nwith $\\Gamma_{21} = \\Gamma_{12}^*$. Here, $V$ is the volume of the supercell (see Fig.~\\ref{fig:Supercell}(a)), $S$ the transverse section at position $z$ and $n_b (\\mathbf{r}) = n_s (=n_{\\mathrm{holes}})$ in the membrane (holes) is the reference waveguide background refractive index. The spatial distribution of the complex perturbation is taken into account by $F(\\mathbf{r})$, which is equal to 1 (0) in the membrane (holes) of the active section. \\uline{$\\Gamma_{11}$ is the ratio between the electric field energy in the gain region of a supercell and that stored in the whole supercell, by assuming the modal gain $g_0$ to be uniform through the semiconductor slab.} \n\\begin{figure}[ht]\n\t\\centering\\includegraphics[width=\\linewidth]{Gamma11_12Harmonics}\n\t\\caption{\\label{fig:Gamma11_12Harmonics} Magnitude of the Fourier components of the self-coupling $\\kappa_{11}(z)$ (a) and cross-coupling coefficient $\\kappa_{12}(z)$ (b) at various $k_z$ values.}\n\\end{figure}\n\\uline{We note that the self-coupling coefficient is caused by the fact that the modes used as a basis for the electric field see, in the perturbed waveguide, an average complex refractive index profile which is different from that of the reference waveguide where the modes are defined}. \\sout{We note that,} Due to the $z-$periodicity of $\\mathbf{e}_{0,\\pm}$ and $F(\\mathbf{r})$, \\sout{confinement factors} $\\Gamma_{11}$ and $\\Gamma_{12}$ and, therefore, the coupling coefficients are periodic with $z$. As an example, we consider a reference waveguide with refractive index $n_s = 3.171$; the other parameters are those of the PhC waveguide on which the lasers in \\cite{Xue2016} are based, with $a = 438\\mathrm{\\,nm}$, \\uline{$n_{\\mathrm{holes}} = 1$ and membrane thickness $h = 250\\mathrm{\\,nm}$. The membrane is assumed to be suspended in air, consistently, for instance, with \\cite{Xue2015,Xue2016} or \\cite{MatsuoNatPhot2013,MatsuoJSTQE2013}, which investigate air-bridge structures}. Band structure and TE-like Bloch modes of the reference waveguide are computed by the plane wave eigensolver MIT Photonic-Bands (MPB) \\cite{MPBpaper} (Fig.~\\ref{fig:BandStructure_ns3171}). \\uline{The blue lines represent a subset of the continuum of photonic crystal slab modes found by MPB \\cite{skorobogatiy_yang_2008}. These modes are confined to the slab along the y-direction, but are delocalized in the x- and z-direction}. We apply the coupled-Bloch-mode approach to the fundamental guided mode (red, solid line), \\uline{which is confined both along the x- and y-direction}. The magnitude and phase of $\\Gamma_{11}$ and $\\Gamma_{12}$ over a unit cell are displayed in Fig.~\\ref{fig:ConfFact} at various $k_z$ values. Given the spatial distribution of the perturbation (that is $F(\\textbf{r})$), they only depend on the reference waveguide geometry and Bloch modes and not on the magnitude of the perturbation. For uniformly pumped membranes, \\sout{confinement factors} $\\Gamma_{11}$ and $\\Gamma_{12}$ are intrinsic parameters of the reference waveguide and they determine how strong the cross-coupling is with respect to self-coupling. The coupling coefficients are indeed proportional to \\sout{confinement factors} $\\Gamma_{11}$ and $\\Gamma_{12}$ through $n_{g,0}$ and the complex refractive index perturbation \\sout{and they are periodic in $z$ with the same periodicity of $\\Gamma_{11,12,21}$}. Therefore, they can be expanded in a Fourier series as $\\kappa_{11;12}(z,\\omega) = \\sum_q \\kappa_{11,q;12,q}(\\omega) \\exp(+iq2\\pi z\/a)$. Since the phase of $\\Gamma_{12}$ is approximately linear with $z$ with a slope equal to $2\\pi\/a$, $\\kappa_{12,q=1}$ is proportional to $\\left\\langle |\\kappa_{12}(z)|\\right\\rangle$, which is comparable with $\\kappa_{11,q=0}$. This is illustrated in Fig.~\\ref{fig:Gamma11_12Harmonics}, showing the Fourier components of $\\kappa_{11}(z)$ and $\\kappa_{12}(z)$ in arbitrary units at various $k_z$ values. Interestingly, we find that the complex refractive index perturbation not only produces a self-coupling coefficient \\sout{(as expected in any standard waveguide)} \\uline{proportional to the average of the perturbation (i.e. the average of $\\Gamma_{11}(z)$ in Fig.~\\ref{fig:ConfFact}(a))}, but also a strong cross-coupling, whose magnitude is close to that of the self-coupling. This strong cross-coupling is possible thanks to the linear phase of $\\Gamma_{12}(z)$; if this linear phase component were not present, the cross-coupling would be negligible. By analyzing the contribution of the various field components ($\\hat{x}$, $\\hat{y}$ and $\\hat{z}$ component) to $\\Gamma_{12}(z)$, we have found that the linear phase variation is caused by a significant $\\hat{z}$ component of the TE-like guided mode, which is typically negligible in standard waveguides. Here the non-negligible $\\hat{z}$ component is due to the strong lateral (\\sout{$y-$} \\uline{$x-$}direction) confinement obtained by the PhC. \n\nHaving now more insight into the main properties of the coupling coefficients, we derive an analytical expression for the transmission matrix describing the field propagation over a unit cell. As illustrated in Fig.~\\ref{fig:Gamma11_12Harmonics}, all harmonics in the self-coupling coefficient, other than $q=0$, are negligible; similarly, the harmonic with $q = 1 (=-1)$ is the dominant one in the cross-coupling coefficient $\\kappa_{12}$ ($\\kappa_{21}$). This is true over a wide frequency range and especially close to the band edge. Therefore, by only retaining the dominant harmonics, Eqs.~(\\ref{eq:CoupledBlochModeEquations}) are turned into \n\\begin{equation}\n\\label{eq:CoupledBlochModeEquationsFourier}\n\\begin{aligned}\n\\partial_z\\psi_+ & \\simeq i\\kappa_{11,q=0}(\\omega)\\psi_+ + i\\kappa_{12,q=1}(\\omega) e^{+2i\\delta(\\omega) z} \\psi_-\\\\\n-\\partial_z\\psi_- & \\simeq i\\kappa_{21,q=-1}(\\omega) e^{-2i\\delta(\\omega) z}\\psi_+ + i\\kappa_{11,q=0}(\\omega)\\psi_- \n\\end{aligned}\n\\end{equation} \nwhere $\\delta(\\omega) = \\pi\/a - k_z(\\omega)$ is the detuning from the band edge. \\uline{By defining $b^{\\pm}(z,\\omega) = \\psi_{\\pm}(z,\\omega)\\exp\\{\\mp i\\delta(\\omega)z\\}$, the system of Eqs.~(\\ref{eq:CoupledBlochModeEquationsFourier}) is turned into a pair of partial differential equations with z-independent coefficients. This allows to analytically solve it over a unit cell as an initial value problem. That is, $b^{\\pm}(z_0 + a,\\omega)$ are computed by assuming $b^{\\pm}(z_0,\\omega)$ to be known, with the input coordinate $z_0$ of the unit cell conveniently chosen to be zero. Therefore, the unit cell transmission matrix in terms of $b^{\\pm}$ is obtained. However, we need the transmission matrix for $c^{\\pm}(z,\\omega)$, with $c^{\\pm}(z,\\omega) = \\psi_{\\pm}(z,\\omega)\\exp\\{\\pm ik_z(\\omega)z\\}$. Since $b^{\\pm}(z,\\omega) = c^{\\pm}(z,\\omega)\\exp\\{\\mp i(\\pi\/a)z\\}$, the unit cell transmission matrix in terms of $c^{\\pm}$ is obtained through the change of variables $b^{\\pm}(z_0 + a,\\omega) = c^{\\pm}(z_0 + a,\\omega)\\exp\\{\\mp i\\pi\\}$ and $b^{\\pm}(z_0,\\omega) = c^{\\pm}(z_0,\\omega)$. Therefore,} \\sout{By putting $\\psi_{\\pm} = c^\\pm e^{\\mp ik_z z}$ and solving the resulting equations over a unit cell}, we can relate $c^\\pm$ at the input ($N-1$) and output ($N$) of the generic $N_{\\mathrm{th}}$ cell by\n\\begin{equation}\n\\label{eq:cpm_Ta}\n\\left[\n\\begin{aligned}\n& c_N^+ (\\omega) \\\\\n& c_N^- (\\omega)\n\\end{aligned}\n\\right]\n=\n\\mathbf{T}_a (\\omega)\n\\left[\n\\begin{aligned}\n& c_{N-1}^+ (\\omega) \\\\\n& c_{N-1}^- (\\omega)\n\\end{aligned}\n\\right] \n\\end{equation}\n\\sout{$[c_N^+ (\\omega) c_N^- (\\omega)]^T = \\mathbf{T}_a (\\omega) [c_{N-1}^+ (\\omega) c_{N-1}^- (\\omega)]^T$,} where $\\mathbf{T}_a$ is the unit cell transmission matrix \\uline{in terms of $c^{\\pm}$} \\sout{and $T$ denotes the transpose operator}. The elements of $\\mathbf{T}_a$ are given by \n\\begin{equation}\n\\label{eq:Tmatrix}\n\\begin{aligned}\nT_{a,11;a,22} & = -\\cosh\\left(\\gamma a\\right) \\mp i\\left(\\tilde{\\delta}\/\\gamma\\right)\\sinh\\left(\\gamma a\\right) \\\\ \nT_{a,12;a,21} & = \\mp i\\left(\\kappa_{12,q=1;21,q=-1}\/\\gamma\\right)\\sinh\\left(\\gamma a\\right) \n\\end{aligned}\n\\end{equation}\nwith $\\tilde{\\delta}(\\omega) = \\kappa_{11,q=0}(\\omega) - \\delta(\\omega)$ and $\\gamma(\\omega) = \\sqrt{\\kappa_{12,q=1}(\\omega)\\kappa_{21,q=-1}(\\omega) - \\tilde{\\delta}^2(\\omega)}$. This approach allows to directly relate the eigenvalues of $\\mathbf{T}_a$ to the coupling coefficients and the detuning. In fact, the eigenvalues $\\lambda_{1,2}$ are readily obtained as $\\lambda_{1,2} = \\exp\\{\\pm\\mathrm{Re}\\left\\lbrace\\gamma a\\right\\rbrace\\} \\exp\\{\\pm i[\\mathrm{Im}\\left\\lbrace\\gamma a\\right\\rbrace +\\pi]\\}$. From here, we define $\\pm g_{\\mathrm{eff}} = \\pm2\\mathrm{Re}\\left\\lbrace\\gamma\\right\\rbrace$ as the effective gain per unit length, representing the gain experienced by the forward ($+$) and backward ($-$) Bloch modes of the \\textit{active} section while propagating in a unit cell. Similarly, $\\pm \\beta_{\\mathrm{eff}}a = \\pm[\\mathrm{Im}\\left\\lbrace\\gamma a\\right\\rbrace +\\pi]$ is the phase shift per cell of these Bloch modes. From this phase shift, we can then calculate the group index of the Bloch modes of the active waveguide as $n_{g}(\\omega) = \\mathrm{c}\\:d\\beta_{\\mathrm{eff}}\/d\\omega$. By applying Frobenius theorem \\cite{Frobenius}, the transmission matrix of an active waveguide of $N$ unit cells is computed as \n\\begin{equation}\n\\label{eq:FrobeniusTheorem}\n\\mathbf{T}^N_a = \\mathbf{M}\\mathbf{\\lambda_a}^N\\mathbf{M}^{-1} \n\\end{equation}\nwhere $\\mathbf{M}$ and $\\mathbf{\\lambda_a}$ are given by\n\\begin{equation}\n\\label{eq:M_Lambda}\n\\mathbf{M} = \n\\left[\n\\begin{aligned}\nu_{11} & \\quad u_{12} \\\\\nu_{21} & \\quad u_{22}\n\\end{aligned}\n\\right], \n\\quad\n\\mathbf{\\lambda_a} = \n\\left[\n\\begin{aligned}\n\\lambda_1 & \\quad 0 \\\\\n0 & \\quad \\lambda_2\n\\end{aligned}\n\\right] \n\\end{equation}\nwith $\\mathbf{u}_1 = \\left[u_{11} \\quad u_{21}\\right]^{T}$ and $\\mathbf{u}_2 = \\left[u_{12} \\quad u_{22}\\right]^{T}$ being the eigenvectors of $\\mathbf{T}_a$ \\uline{and $T$ denoting the transpose operator}. As well as being numerically efficient, this approach for computing $\\mathbf{T}^N_a$ allows for a useful physical interpretation: $\\mathbf{\\lambda_a}^N$ can be seen as the transmission matrix describing the propagation of the Bloch modes of the \\textit{active} section over the $N$ unit cells. $\\mathbf{M}^{-1}$ and $\\mathbf{M}$ can be interpreted as the transmission matrices of the interfaces between the active section and, respectively, the left and right passive waveguides; they physically account for the mismatch between the Bloch modes of the active and passive sections. \\uline{By applying the relationships between a transmission and a scattering matrix \\cite{Coldren}, the transmission matrix $\\mathbf{T}^N_a$ can be turned into the corresponding scattering matrix. The scattering parameters are given by}\n\\begin{equation}\n\\label{eq:Smatrix}\n\\begin{aligned}\nS_{11} & = \\frac{\\left(\\lambda_1^N - \\lambda_2^N\\right)T_{a,21}}{\\left(\\lambda_1^N - \\lambda_2^N\\right)T_{a,11} + \\left(\\lambda_2^{N+1} - \\lambda_1^{N+1}\\right)} \\\\ \nS_{12;21} & = \\frac{\\left(\\lambda_2 - \\lambda_1\\right)}{\\left(\\lambda_1^N - \\lambda_2^N\\right)T_{a,11} + \\left(\\lambda_2^{N+1} - \\lambda_1^{N+1}\\right)} \\\\\nS_{22} & = \\frac{\\left(\\lambda_2^N - \\lambda_1^N\\right)T_{a,12}}{\\left(\\lambda_1^N - \\lambda_2^N\\right)T_{a,11} + \\left(\\lambda_2^{N+1} - \\lambda_1^{N+1}\\right)}\n\\end{aligned}\n\\end{equation} \n\nAs outlined in \\cite{Chen2015}, the coupled-Bloch-mode approach breaks down at large values of $g_0$. Specifically, the larger $n_{g,0}$ becomes, the smaller is the value of the gain coefficient at which the model starts to fail. As a further limitation, here we also point out that the model is actually not applicable at the band edge of the reference waveguide, independently of the value of $g_0$. This is because the group velocity of the Bloch modes used as a basis for the field expansion is ideally equal to zero at the band edge of the reference waveguide. These considerations imply that, in principle, we could not analyze active PhC waveguides in the frequency range close to the critical point $k_z = \\pi\/a$. However, this limitation can be overcome with the following trick: to analyze an active PhC waveguide including the frequency range of the band edge and some frequencies in the stop-band, we start from a reference waveguide with index $n_s$ slightly \\textit{larger} than that of the waveguide to be studied. The waveguide of interest is then seen as having a real refractive index perturbation $\\Delta n_s<0$ with respect to the reference one; therefore, its dispersion relation is shifted to higher frequencies with respect to the band edge of reference waveguide, where the model is not applicable. This approach will be \\sout{illustrated} \\uline{applied} in section IIIA. \n\n\n\\section{Simulation results}\n\n\\subsection{Line-defect active waveguide}\n\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{CouplCoeff}\n\t\\caption{\\label{fig:CouplCoeff}Magnitude of self- ($\\kappa_{11,q=0}$) and cross-coupling coefficient ($\\kappa_{12,q=1}$) for $g_0 = 0$ (blue) and $g_0 = 50 \\,\\mathrm{cm^{-1}}$ (red). In both cases, $\\Delta n_s = -0.001$.}\n\\end{figure}\nFig.~\\ref{fig:CouplCoeff} shows the magnitude of the self- ($\\kappa_{11,q=0}$) and cross- coupling coefficient ($\\kappa_{12,q=1}$) for $g_0 = 0$ (blue) and $g_0 = 50 \\,\\mathrm{cm^{-1}}$ (red). In both cases, $\\Delta n_s = -0.001$. This figure proves that the cross-coupling coefficient is always comparable to the self-coupling coefficient. This is consistent with results also shown in \\cite{MichaelisPR}. \nHowever, as compared to \\cite{MichaelisPR}, we have clarified here the physical origin of this peculiar behaviour. We also observe that the coupling coefficients depend on the intensity of the perturbation, as expected, but also on frequency and they significantly increase as the frequency approaches the band edge as consequence of the SL effect. Therefore, Bloch modes at smaller frequency and\/or with higher gain of the active section experience stronger distributed feedback as compared to higher frequency Bloch modes and\/or lower active section gain. \n\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{gammaIM_MPBcomp_Updated}\n\t\\caption{\\label{fig:gammaIM_MPBcomp} Dispersion curve computed by MPB for $n_s = 3.171$ (black) and $n_s = 3.170$ (pink). Phase shift per cell of the Bloch modes of the perturbed waveguide with (light-blue, dotted) and without (light-blue, solid) cross-coupling; the reference waveguide has $n_s = 3.171$ and the real refractive index perturbation is $\\Delta n_s = -0.001$, with $g_0 = 0$. The dark-blue, dotted curve is for $\\Delta n_s = -0.002$, $g_0 = 0$.} \n\\end{figure}\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{gammaIM_ngPert}\n\t\\caption{\\label{fig:gammaIM_ngPert} Phase shift per cell of the Bloch modes of the active waveguide (a) and corresponding group index (b) at various gain values. The reference waveguide has $n_s = 3.171$ and the real refractive index perturbation is $\\Delta n_s = -0.001$.}\n\\end{figure}\nIn this section, we analyze how a generally complex refractive index perturbation impacts the dispersion relation of a PhC waveguide. To validate our model, we first study the case of a purely real refractive index perturbation, because the results obtained by our approach can be compared with MPB simulations. We consider a reference passive waveguide with $n_s = 3.171$ and we report in black in Fig.~\\ref{fig:gammaIM_MPBcomp} the corresponding dispersion relation computed by MPB. We then consider a small, real refractive index perturbation $\\Delta n_s<0$ and we compare the dispersion relation calculated with our model (i.e. $\\beta_{\\mathrm{eff}}(\\omega)a$) with that calculated by MPB for a passive waveguide with refractive index $n_s+\\Delta n_s$. As $n_s$ decreases, the dispersion curve shifts to higher frequencies, meaning that the Bloch modes become evanescent at lower frequencies. The formation of this stopband for \\textit{Bloch} modes is correctly reproduced by our approach: the light-blue, dotted curve in Fig.~\\ref{fig:gammaIM_MPBcomp} is the dispersion relation calculated by our approach for $\\Delta n_s = -0.001$ and it perfectly overlaps with that obtained by MBP for $n_s=3.170$. If the contribution of the cross-coupling coefficients in Eqs.~(\\ref{eq:CoupledBlochModeEquationsFourier}) is neglected, we find the dispersion relation shown by the light-blue, solid curve in Fig. \\ref{fig:gammaIM_MPBcomp}; in this case, the Bloch modes of the perturbed waveguide are not evanescent in the stop-band and the dispersion relation disagrees with the MPB prediction. These results validate the correctness of our approach and emphasize the role of the cross-coupling terms. Furthermore, the possibility to analyze the propagation of the Bloch-modes in a portion of the stop-band of the perturbed, passive waveguide will be exploited in the next section to study laser configurations similar to the LEAP lasers of \\cite{Matsuo2012,Matsuo2010}. \n\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{gammaRE_EnhcFact}\n\t\\caption{\\label{fig:gammaRE_EnhcFact} Effective gain as a function of frequency at various gain values (a). Gain enhancement factor at $f\\simeq\\mathrm{189.388\\,THz}$ (b). The reference waveguide has $n_s = 3.171$ and the real refractive index perturbation is $\\Delta n_s = -0.001$.}\n\\end{figure} \nAs a second example, we consider the impact of a gain perturbation, assuming now $\\Delta n_s = -0.001$ and $g_0>0$. Fig.~\\ref{fig:gammaIM_ngPert}(a) shows how the dispersion relation of the Bloch modes of the active waveguide is modified by the gain perturbation and Fig.~\\ref{fig:gammaIM_ngPert}(b) reports the corresponding group index at various gain values. For $g_0 = 0$, the group index diverges at $f\\simeq\\mathrm{189.388\\,THz}$, because this frequency corresponds to the band edge of the perturbed waveguide. At lower frequencies, the Bloch modes are evanescent and the group index is zero, \\uline{because evanescent waves do not carry any active power}. As $g_0$ increases, the group index is gradually reduced in the passband, thus limiting the SL effect; on the contrary, it gradually increases in the stopband, where $\\beta_{\\mathrm{eff}}$ is now different from zero. This is consistent with results reported in \\cite{Gric2012}, which were obtained through a non-perturbative approach. The corresponding $g_{\\mathrm{eff}}$ versus frequency is shown in Fig.~\\ref{fig:gammaRE_EnhcFact}(a) at various values of $g_0$; we also report in Fig.~\\ref{fig:gammaRE_EnhcFact}(b) the gain enhancement factor $g_{\\mathrm{eff}}\/g_0$ at $f\\simeq\\mathrm{189.388\\,THz}$ obtained by our approach. Since cross-coupling is not negligible, the gain-induced distributed feedback becomes more and more important as the gain increases, thus causing the decrease of the group index; as a consequence, the gain enhancement factor, which is based on the SL effect, is reduced by increasing the pumping of the active region. This result is consistent with \\cite{Gric2012} and confirms that a fundamental limitation to the achievable SL enhancement of the gain is imposed by the gain itself. \n\n\\subsection {PhC Lasers}\n\nIn this section, we apply the coupled-Bloch-mode approach to model the threshold characteristics of various types of PhC laser cavities. The threshold condition is found by calculating the complex loop-gain (LG) of the cavity.\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{TypeABCcavScheme}\n\t\\caption{\\label{fig:TypeABCcavScheme} Laser cavity made up of three sections, perturbed in refractive index (passive mirrors) and gain (active section) with respect to a reference, passive waveguide with $n_s = 3.171$. The section to the left of the active region (broad-band mirror) is passive, with $\\Delta n_s = -0.002$ and length fixed to $L = 30a$; the active section has $\\Delta n_s = -0.001$, $g_0>0$ and $L = 10a$. The section to the right of the active region is also passive and acts as a buffer mirror, with $\\Delta n_s = -0.001$ (Type A), $\\Delta n_s = -0.0005$ (Type B) or $\\Delta n_s = -0.002$ (Type C) and variable length $L_{\\mathrm{buffer}}$. The output waveguide coincides with the reference waveguide. \\uline{The position of the reference plane to compute the complex loop-gain is denoted by $z_{\\mathrm{ref}}$}.}\n\\end{figure} \n\nAs a first example, we analyze the cavity shown in Fig.~\\ref{fig:TypeABCcavScheme}. The cavity is made up of three sections, perturbed in refractive index (passive mirrors) and gain (active section) with respect to a reference, passive waveguide with $n_s = 3.171$. Referring to Fig.~\\ref{fig:TypeABCcavScheme}, the section to the left of the active region (rear broad-band mirror) is passive, with $\\Delta n_s = -0.002$ and length fixed to $L = 30a$; the active section has $\\Delta n_s = -0.001$, $g_0>0$ and $L = 10a$. The section to the right of the active region (front mirror) is also passive and acts as a buffer mirror, coupling the active section with the reference, output waveguide. Depending on the refractive index perturbation of this buffer region, the band edge of its dispersion relation shifts with respect to that of the active region; on the basis of the buffer refractive index perturbation, we have identified three different types of buffer, thus providing different back reflections to the active region. These will be denoted as Type A (moderate back reflection), Type B (low back reflection) and Type C (high back reflection). These laser cavities are conceptually similar to those in \\cite{Shinya}, where the relative shift of the dispersion relation of the passive sections with respect to the active section was obtained by changing the width of the corresponding waveguides \\cite{Notomi2001}. For this laser cavity, the LG is computed as the product between the left ($r_{\\mathrm{eq,L}}$) and right ($r_{\\mathrm{eq,R}}$) field reflectivity at the interface between the active section and the rear mirror (see Fig.~\\ref{fig:TypeABCcavScheme}). \\uline{Specifically, $r_{\\mathrm{eq,L}}$ is readily obtained by Eqs.~(\\ref{eq:Smatrix}) as the $S_{22}$ parameter of the broad-band mirror; $r_{\\mathrm{eq,R}}$ corresponds to the field reflectivity resulting from the cascade of the active section and buffer mirror scattering matrices, i.e.} \n\\begin{equation}\n\\begin{aligned}\n\\label{eq:reqR}\nr_{\\mathrm{eq,R}} & = S_{\\mathrm{11,active}} + r_{\\mathrm{eq,2}} \\\\\nr_{\\mathrm{eq,2}} & = \\frac{S_{\\mathrm{12,active}}S_{\\mathrm{11,buffer}}S_{\\mathrm{21,active}}}{1-S_{\\mathrm{11,buffer}}S_{\\mathrm{22,active}}}\n\\end{aligned}\n\\end{equation}\nwith $S_{ij,\\mathrm{buffer}}$ and $S_{ij,\\mathrm{active}}$ denoting the buffer and active section scattering parameters computed by Eqs.~(\\ref{eq:Smatrix}). The longitudinal resonant modes are those satisfying $\\angle \\mathrm{LG}=2m\\pi$; the threshold gain $g_{\\mathrm{0,th}}$ is the smallest $g_0$ value ensuring $|\\mathrm{LG}|=1$ at the frequency of the longitudinal modes. \\sout{$r_{\\mathrm{eq,R,L}}$ are calculated from the transmission matrices of the active section and buffer mirror as computed by Eqs.~(\\ref{eq:FrobeniusTheorem})}. \n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{MirrorsSpectra}\n\t\\caption{\\label{fig:MirrorsSpectra} Broad-band mirror reflectivity; $\\Delta n_s = -0.002$ and $L=30a$ (green). Buffer mirror reflectivity for $L_{\\mathrm{buffer}}=15a$ and $\\Delta n_s = -0.002$ (type C, black), $\\Delta n_s = -0.001$ (type A, red) and $\\Delta n_s = -0.0005$ (type B, blue). The bullets indicate the position of the lasing mode, in the three different operating regimes, both on the broad-band and buffer mirror reflection spectrum.}\n\\end{figure} \n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{gth_fTHzth_etaOut_TypeABC_DoubleAxes}\n\t\\caption{\\label{fig:gth_fTHzth_etaOut_TypeABC_DoubleAxes} Threshold gain (solid line) and lasing frequency (dashed line) as a function of the buffer mirror length (a) for type A (red), B (blue) and C (black). Output coupling efficiency (b). The active section has $\\Delta n_s = -0.001$ and $L_{\\mathrm{active}}=10a$.}\n\\end{figure}\nFig.~\\ref{fig:MirrorsSpectra} shows the reflectivity of the broad-band mirror (green) and that of the buffer mirror in the three different operating conditions: Type A (red), Type B (blue) and Type C (black). In all three cases, the buffer length is fixed to $L_{\\mathrm{buffer}} = 15a$. The bullets denote the position of the lasing mode (calculated in the following) in the three cases, with respect to both the broad-band and buffer mirror reflection spectrum. In Type C (black), the buffer refractive index perturbation coincides with that of the broad-band mirror. Therefore, at the lasing frequency, the reflectivity of both mirrors is high (because the lasing mode lies in the stop-band of the buffer mirror) and the laser threshold behaviour is mainly dominated by the mirrors feedback. In Type A, the buffer refractive index perturbation is $\\Delta n_s = -0.001$; the lasing frequency is slightly shifted with respect to Type C, but the buffer reflectivity is smaller. In Type B, the buffer refractive index perturbation is $\\Delta n_s = -0.0005$; as a result, the band edge of the buffer dispersion relation is in the stopband of the active section. The lasing frequency is practically the same as for type A, but the buffer reflectivity is even smaller. In Type A and B, we can expect the interplay of the feedback in both the active section and the mirrors to play a role in determining the laser threshold.\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{reflTermsMAG_ANGLE_TypeB_separate_WithThresholdGain}\n\t\\caption{\\label{fig:reflTermsMAG_ANGLE_TypeB_separate_WithThresholdGain}\\uline{Scattering terms of Eq.~(\\ref{eq:reqR}) for Type B configuration of Fig.~\\ref{fig:TypeABCcavScheme} evaluated at the lasing frequency and threshold gain. Magnitude of $S_{\\mathrm{11,active}}$ (blue, solid line), $r_{\\mathrm{eq,2}}$ (blue, dashed line) and $S_{\\mathrm{11,buffer}}$ (blue, dotted line); threshold gain (pink line) (a). Phase of $S_{\\mathrm{11,active}}$ (blue, solid line) and $r_{\\mathrm{eq,2}}$ (blue, dashed line) (b).}}\n\\end{figure} \n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{S11mirror_Lbuf5a17a21a28a}\n\t\\caption{\\label{fig:S11mirror_Lbuf5a17a21a28a} Type B buffer mirror reflectivity for different buffer lengths; the bullets denote, for each length, the lasing mode position.}\n\\end{figure} \nFig.~\\ref{fig:gth_fTHzth_etaOut_TypeABC_DoubleAxes}(a) shows threshold gain (solid curve) and lasing frequency (dashed curve); Fig.~\\ref{fig:gth_fTHzth_etaOut_TypeABC_DoubleAxes}(b) reports the output coupling efficiency $\\eta_{\\mathrm{out}}$, calculated as power transmission through the buffer, as a function of the buffer mirror length. Type C configuration has the smallest threshold gain, which monotonically decreases with increasing buffer length. In this case, the lasing mode is in the stop-band of the buffer mirror, whose reflectivity rapidly increases with increasing number of unit cells; as a result, the power transmission through the buffer rapidly deteriorates with increasing buffer length. This situation is similar to the operating condition of typical LEAP lasers \\cite{MatsuoNatPhot2013,MatsuoJSTQE2013}. In Type A configuration, the distributed feedback in the active section provides a back reflection comparable with that of the buffer mirror. In this case, the threshold gain is larger than in type C, but the output coupling efficiency is high and does not significantly deteriorate with increasing buffer length. In Type B configuration, the lasing mode lies outside the stop-band of the buffer mirror; consequently, this configuration exhibits the largest threshold gain, but also the largest $\\eta_{\\mathrm{out}}$. In this case, the buffer mirror reflectivity is very low and lasing is mainly possible thanks to the active section distributed feedback. Nevertheless, the buffer also slightly contributes to the laser threshold behaviour. The interplay between buffer back reflection and distributed feedback in the active region can be understood by analyzing, with reference to Fig.~\\ref{fig:TypeABCcavScheme}, the expression for $r_{\\mathrm{eq,R}}$ \\uline{given by Eqs.~(\\ref{eq:reqR})}. \\sout{For the sake of convenience, we denote by $r_{\\mathrm{eq,2}}$ the second term on the RHS of Eq.~(\\ref{eq:reqR})}. Fig.~\\ref{fig:reflTermsMAG_ANGLE_TypeB_separate_WithThresholdGain} shows in blue for Type B the magnitude (a) and phase (b) of the scattering terms, evaluated at the lasing frequency and threshold gain, which contribute to $r_{\\mathrm{eq,R}}$; it also reports the corresponding threshold gain (pink line, (a)). Fig.~\\ref{fig:S11mirror_Lbuf5a17a21a28a} shows the buffer reflectivity for various buffer lengths; the bullets denote the position of the corresponding lasing mode.\nFor $L_{\\mathrm{buffer}}=5a$, the threshold gain is $g_{\\mathrm{0,th}}\\simeq \\mathrm{70\\, cm^{-1}}$ and the lasing frequency $f\\simeq\\mathrm{189.434\\, THz}$; at $L_{\\mathrm{buffer}}=21a$, the lasing frequency is practically the same, but the threshold gain is much larger. This is because the mode is close to the zero of the buffer reflection spectrum for $L_{\\mathrm{buffer}}=21a$; as a result, although the LG phase condition is satisfied at the same frequency for these two different buffer lengths, $|r_{\\mathrm{eq,R}}|$ is smaller in the second case, thus requiring a larger gain for achieving threshold. For $L_{\\mathrm{buffer}}=28a$, the lasing frequency is the same again, but the threshold gain practically coincides with that of the structure with $L_{\\mathrm{buffer}}=5a$. This is because the mode is now close to the top of the buffer reflection spectrum side-lobe, rather than to the zero (see Fig.~\\ref{fig:S11mirror_Lbuf5a17a21a28a}); furthermore, as for $L_{\\mathrm{buffer}}=5a$, $S_{\\mathrm{11,active}}$ and $r_{\\mathrm{eq,2}}$ are nearly in-phase (see Fig.~\\ref{fig:reflTermsMAG_ANGLE_TypeB_separate_WithThresholdGain}). For the case of $L_{\\mathrm{buffer}}=17a$, although $|r_{\\mathrm{eq,2}}|$ is maximum, the threshold gain is not minimum, but rather close to its maximum value. The reason is that $S_{\\mathrm{11,active}}$ and $r_{\\mathrm{eq,2}}$ are now nearly out-of-phase. \\uline{We note that $S_{\\mathrm{11,active}}$ and $r_{\\mathrm{eq,2}}$ are also nearly out-of-phase for the case $L_{\\mathrm{buffer}}=21a$; in this case, however, the magnitude of $r_{\\mathrm{eq,2}}$ is too small to significantly affect $r_{\\mathrm{eq,R}}$}. As a result of this complex interaction between the active section distributed feedback and the buffer back reflection, the Type B configuration exhibits an optimum number of buffer cells minimizing the threshold gain. These examples prove the great impact of coherent distributed feedback effects in PhC cavities like that in Fig.~\\ref{fig:TypeABCcavScheme}, which is similar to a LEAP laser with an in-line coupled waveguide.\n \n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{HighZeroFacetReflcavScheme}\n\t\\caption{\\label{fig:HighZeroFacetReflcavScheme} Typical PhC laser based on a $\\mathrm{LN}$ cavity (a). PhC laser cavity consisting of an active section with zero back reflections \\uline{for Bloch modes} at the interfaces with the passive output waveguides (b). In both cases, the reference waveguide has $n_s = 3.171$ and the active section $\\Delta n_s = -0.001$. \\uline{The position of the reference plane to compute the complex loop-gain is denoted by $z_{\\mathrm{ref}}$}.}\n\\end{figure} \nAs a second example, we analyze the PhC lasers shown in Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(a). This configuration is similar to the optically pumped PhC laser of \\cite{Xue2016}, where the cavity mirrors are classical PhC mirrors. Referring to Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(a), $r_{\\mathrm{m,R}}$ is the reflectivity that the forward-propagating Bloch mode of the reference, passive waveguide (whose perturbation is accounted for, in the active section, by the coupled-Bloch-mode equations) undergoes when impinging on the right mirror; this mode is reflected back to the active section because it becomes evanescent within the mirror. A similar interpretation holds for $r_{\\mathrm{m,L}}$. This reflectivity cannot be computed by our approach, because the classical PhC mirror cannot be viewed as a weak perturbation to a reference waveguide (due to the high refractive index contrast between slab and air holes). However, it has been computed in \\cite{Lalanne2008} by modelling the cavity as an effective Fabry-Perot (FP) resonator and by fitting the Q-factor with that obtained through a RCWA approach \\cite{LalanneRCWA2007}. For the sake of simplicity and neglecting the impact of disorder, we assume a high, frequency-independent reflectivity $r_{\\mathrm{m,L}} = r_{\\mathrm{m,R}} = 0.98$, which represents a reasonable approximation \\cite{Xue2016,Lalanne2008,Rigal2017}.\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{rFacet098_fTHz_g0th}\n\t\\caption{\\label{fig:rFacet098_fTHz_g0th} Numerically computed resonant frequencies (a) and threshold gain ((b), solid curve) for the laser in Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(a). The threshold gain estimated by the SL-enhanced FP formula is also shown ((b), dotted curve). Each colour corresponds to a different longitudinal resonant mode.}\n\\end{figure}\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{rFacet098_resModeLocations}\n\t\\caption{\\label{fig:rFacet098_resModeLocations}. Resonant mode condition for the laser in Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(a). Each colour corresponds to a different longitudinal resonant mode. $\\beta_{\\mathrm{eff}}$ is evaluated at the numerically computed resonant frequencies and corresponding threshold gain.}\n\\end{figure}\nTo compute the LG, we choose the reference plane at the interface between the active section and the left mirror (see Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(a)).\n\\uline{As a result, $r_{\\mathrm{eq,L}}$ is equal to $r_{\\mathrm{m,L}}$ and $r_{\\mathrm{eq,R}}$ is obtained from Eq.~(\\ref{eq:reqR}) by replacing $S_{\\mathrm{11,buffer}}$ with $r_{\\mathrm{m,R}}$}. The laser threshold behaviour is summarized in Fig.~\\ref{fig:rFacet098_fTHz_g0th}, showing the numerically computed resonant frequencies (a) and threshold gain ((b), solid curve); each colour corresponds to a different longitudinal resonant mode. In the existing literature, similar cavities have been studied in \\cite{Okano2010} through FDTD simulations. In \\cite{Okano2010}, it has been shown that the resonant modes of a passive $\\mathrm{LN}$ cavity correspond to the fullfillment of the condition\n\\begin{equation}\n\\label{eq:ResCond}\n\\left(0.5 - k_za\/2\\pi\\right) = m\/2N\n\\end{equation}\nwith $N = L\/a$ being the number of unit cells, $m$ the mode order and $k_z$ the dispersion relation of the passive PhC LDWG of the cavity. For this reason, we have evaluated the quantity $0.5 - \\beta_{\\mathrm{eff}}a\/2\\pi$ at the numerically computed resonant frequencies and corresponding threshold gain of Fig.~\\ref{fig:rFacet098_fTHz_g0th}; it is shown as the solid curve in Fig.~\\ref{fig:rFacet098_resModeLocations}. We also note that \nthe quantity $0.5 - \\beta_{\\mathrm{eff}}a\/2\\pi$ \\sout{exactly} \\uline{practically} coincides, at a given cavity length, with $m\/2N$ (dashed curve in Fig.~\\ref{fig:rFacet098_resModeLocations}). Since the required threshold gain is low, the gain-induced distributed feedback is negligible. As a consequence, the gain does not impact on the position of the resonant modes. As the cavity length increases, the modes move towards the SL region along the dispersion relation of the umpumped waveguide. This \\uline{effect} is consistent with experimental \\cite{Xue2016} and numerical \\cite{Cartar2017} trends and is \\sout{just a FP effect} \\uline{independent of the perturbation-induced distributed feedback}. In fact, by setting $\\Delta n_s=0$ and $\\kappa_{12,q=1} = \\kappa_{21,q=-1} = 0$ in Eqs.~(\\ref{eq:Tmatrix}), Eq.~\\ref{eq:ResCond} can be easily obtained. The threshold gain reported in Fig.~\\ref{fig:rFacet098_fTHz_g0th}(b) is compared with the expression $\\left[1\/\\left(S\\: L_{\\mathrm{active}}\\right)\\right]\\ln\\left[1\/\\left(r_{\\mathrm{m,L}}r_{\\mathrm{m,R}}\\right) \\right]$ (dotted curve in Fig.~\\ref{fig:rFacet098_fTHz_g0th}(b)) \\cite{Xue2016}, with $S = n_{g}\/n_s$ being the slow-down factor, evaluated at the resonant frequencies, and $L_{\\mathrm{active}}$ the cavity length; again, the group index is that of the umpumped waveguide. \\uline{This expression resembles that of a standard FP laser, but the threshold gain is scaled down by the slow-down factor}. Since the gain is low, $n_g$ is not reduced and the SL enhancement of the gain is not limited. On the basis of these considerations, we conclude that the laser with classical PhC mirrors modelled as high-reflectivity, frequency-independent reflectors behaves as a SL-enhanced FP laser \\uline{for Bloch modes}.\n\n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{rFacet098Zero_g0th_fth_comparison_withMatlab}\n\t\\caption{\\label{fig:rFacet098Zero_g0th_fth_comparison_withMatlab} Threshold gain and corresponding resonant frequency for the laser in Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(a) (a) and Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(b) (b) as a function of cavity length. Each colour corresponds to a different longitudinal resonant mode. The cavity length ranges from $10a$ to $20a$.}\n\\end{figure} \n\\begin{figure}\n\t\\centering\\includegraphics[width=\\linewidth]{rFacet098Zero_GainMargin_NaEVEN_ODD}\n\t\\caption{\\label{fig:rFacet098Zero_GainMargin_NaEVEN_ODD} Difference between $\\mathrm{M_2}$ and $\\mathrm{M_1}$ threshold gain for the laser in Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(a) (red) and (b) (green) as a function of the cavity length.}\n\\end{figure}\nAs a last example, we focus on the structure in Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(b), which consists of an active section with zero back reflections \\uline{for \\textit{Bloch} modes} at the interfaces with the passive output waveguides. \\sout{We note that this condition can be practically implemented by making the active section refractive index (or width) sufficiently smaller than that of the output waveguides, such that the cavity resonant modes are in the propagation band of the output waveguides. This implies that, differently from the typical\nLEAP laser implementation, the passive sections do not\nact as mirrors}. \\uline{The practical implementation of this \\textit{matching} condition is outside the scope of this paper; various promising solutions are reported in the existing literature \\cite{DUTTAreview,Hugonin2007}.} We show for the first time, to the best of our knowledge, that this type of structure can ideed achieve lasing with reasonable threshold gain. \\sout{The output waveguides coincide with} The reference waveguide \\sout{having} has $n_s = 3.171$, while the active section has $\\Delta n_s = -0.001$ and $g_0>0$. \\uline{Similarly to the second example, here a given $\\Delta n_s$ is only considered in order to shift away the active section dispersion relation from the critical point $k_z = \\pi\/a$ and investigate the laser operation also at frequencies near the band edge (as explained in the end of section II).} To compute the LG, we choose a reference plane within the active region, at the interface between any two unit cells; $N_R$ unit cells are located on the right of the reference plane and $N_L$ on the left, with $N = N_L + N_R$ being the total number of unit cells in the whole active region. \\sout{Therefore, to obtain $r_{\\mathrm{eq,R}}$, the transmission matrix of an active section of $N_R$ unit cells is computed by Eqs.~(\\ref{eq:FrobeniusTheorem}) and turned into a scattering matrix; the $\\mathrm{S_{11}}$ parameter of this scattering matrix is $r_{\\mathrm{eq,R}}$. Similarly, $r_{\\mathrm{eq,L}}$ is computed as the $S_{22}$ parameter of the scattering matrix corresponding to the other $N_L$ cells.} \\uline{Therefore, $r_{\\mathrm{eq,R}}$ is given by the $S_{11}$ parameter of Eqs.~(\\ref{eq:Smatrix}) with $N = N_R$; similarly, $r_{\\mathrm{eq,L}}$ is computed as the $S_{22}$ parameter of Eqs.~(\\ref{eq:Smatrix}) with $N = N_L$.} The threshold gain and corresponding resonant frequencies of this laser are shown in Fig.~\\ref{fig:rFacet098Zero_g0th_fth_comparison_withMatlab}(b) and compared with those of the laser with classical PhC mirrors (Fig.~\\ref{fig:rFacet098Zero_g0th_fth_comparison_withMatlab}(a)). The cavity length ranges from $10a$ to $20a$ and each colour corresponds to a different longitudinal resonant mode. As a first remark, we note that the threshold gain is considerably larger for the laser with zero back reflections; however, the threshold gain of the lasing mode ($\\mathrm{M_1}$) turns out to be reasonable, being of the same order of magnitude as observed for the Type B configuration in Fig.~\\ref{fig:TypeABCcavScheme}. Secondly, the lasing mode frequency is essentially independent of the cavity length and it is located close to the band edge of the active section dispersion relation with $g_0 = 0$. On the contrary, the higher-order modes shift towards the SL region as the cavity length increases. This is somehow similar to what occurs in purely gain-coupled DFB lasers, whose lasing frequency is independent of the cavity length and exactly located at the Bragg frequency \\cite{KogelnikShank}. Finally, we note that the laser in Fig.~\\ref{fig:HighZeroFacetReflcavScheme}(b) exhibits a much better spectral selectivity as compared to a laser with classical PhC mirrors. This is illustrated in Fig.~\\ref{fig:rFacet098Zero_GainMargin_NaEVEN_ODD}, comparing the gain margin (defined as the difference between $\\mathrm{M_2}$ and $\\mathrm{M_1}$ threshold gain) for the lasers in Fig.~\\ref{fig:HighZeroFacetReflcavScheme} as a function of cavity length. \n\n\n\\section{Conclusions}\n\nBy starting from a set of two coupled-Bloch-mode equations \\cite{Chen2015}, we have derived a simple, closed-form expression for the unit cell transmission matrix of a PhC LDWG with a generally complex refractive index perturbation as compared to a reference waveguide. This allows for a simple and numerically efficient analysis of active PhC LDWGs and lasers based on this type of waveguide, such as LEAP lasers.\n\nIn particular, we have derived the expression of the coupling coefficients and explained that the magnitude of the cross-coupling is always comparable to that of self-coupling; this is due to the non-negligible longitudinal component of TE-like Bloch modes in PhC LDWGs. We have shown that our approach can correctly reproduce the formation of a stop-band for Bloch modes as a consequence of a purely real refractive index perturbation. We have further validated it by computing the group index and gain enhancement factor of an active PhC waveguide; consistently with the rigorous, non-perturbative approach of \\cite{Gric2012}, we have shown that the maximum attainable SL gain enhancement is limited by the gain itself. \n\nWe have then applied our coupled-Bloch-mode approach to analyze the threshold condition of three types of PhC laser cavities. The first cavity is conceptually similar to that characterized in \\cite{Shinya}. Depending on the buffer refractive index perturbation, we have identified, consistently with \\cite{Shinya}, three different operating regimes, thus proving the great impact of coherent distributed feedback effects in this type of PhC cavity. The second cavity is the one characterized in \\cite{Xue2016}. By neglecting the impact of fabrication disorder and modelling the classical PhC mirrors as standard reflectors with a high, frequency-independent reflectivity, we have shown that the gain-induced distributed feedback is negligible in this type of cavity, which simply behaves as a SL-enhanced FP laser \\uline{for Bloch modes}. As a last example, we have analyzed a structure consisting of an active section bounded on either side by passive waveguides, which are assumed to \\sout{provide zero back reflections} \\uline{be matched for the reference waveguide Bloch modes}. This means that this configuration is different from the typical LEAP laser implementation. Interestingly, we have shown that this cavity can lase with reasonable threshold gain, with lasing only sustained by the active region distributed feedback. \n\nIn conclusion, we have presented an effective approach that will be useful to provide insights on the characteristics of PhC lasers and might be also extended to study the laser dynamics of these structures. \n\n\\section{Acknowledgements}\n\nM.S. wishes to express his gratitude to Prof. I. Montrosset and Prof. R. Orta for fruitful discussions on CMT and Bloch modes. J. M\u00f8rk acknowledges support for the Villum Foundation through the NATEC Centre of Excellence (grant 8692). \n\n\\bibliographystyle{IEEEtran}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{section:introduction}\n\nThe heating of the solar chromosphere and corona is mediated by magnetic fields, which guide Poynting flux generated by plasma motions in the convection zone into the outer atmosphere and provide a means to convert the transported energy into heat. \nThe conversion mechanisms can be broadly divided into dissipation of wave energy \\citep[e.g.,][]{2020SSRv..216..140V} and dissipation of electric currents induced by the slow evolution of the magnetic field \\citep[e.g.,][]{1988ApJ...330..474P}, but their relative contribution to the energy balance is still unclear. In the quiet-Sun (QS) chromosphere, heating by acoustic waves may \\citep{2020A&A...642A..52A} or may not \\citep{2021ApJ...920..125M} act as the dominant process, but active regions (ARs) definitely require other energy sources.\n\nRecent research has focused mostly on the corona \\citep[e.g.,][]{2011Natur.475..477M,2013Natur.493..501C,2015ApJ...811..106H,2015RSPTA.37340256K} but much less on the chromosphere, which requires more sophisticated models that only recently became sufficiently realistic to allow for quantitative comparisons with observations \\citep{2019ARA&A..57..189C}. Furthermore, it is not trivial to interpret spectral diagnostics formed under optically thick, nonlocal thermodynamic equilibrium (non-LTE) conditions, such as the resonance lines of \\ion{Mg}{II} and \\ion{Ca}{II}, which require detailed three-dimensional (3D) radiative transfer calculations including partial frequency redistribution for full accuracy \\citep[see a review by][]{2017SSRv..210..109D}. \nHowever, energy losses are much greater in the chromosphere than in the corona: canonical estimates of average (total) losses for QS and AR are 4\\,kW\\,m$^{-2}$ and 20\\,kW\\,m$^{-2}$ for the chromosphere but only 0.3\\,kW\\,m$^{-2}$ and $<$\\,10\\,kW\\,m$^{-2}$, respectively, for the corona \\citep{1977ARA&A..15..363W}. In the era of high-resolution solar physics the focus should shift to obtaining new detailed models that reproduce the observed fine structure rather than averaged observables \\citep{2019ARA&A..57..189C}. Yet studies quantifying spatially and temporally resolved energy losses are scarce. Recent estimates with a time resolution of 30\\,s and a spatial resolution of $\\sim$100\\,km indicate losses that can locally be as high as 160\\,kW\\,m$^{-2}$ in the chromosphere \\citep{2020arXiv201206229D}. This was attributed to magnetic reconnection but no attempt was made at reconciling simulations with observations. \n\nIt has been well established that radiative cooling is stronger in regions where the magnetic field is more concentrated, but this relationship is not linear \\citep[e.g.,][]{1987A&A...180..241S,1999ApJ...515..812H,2018A&A...619A...5B}. Low resolution ($\\gtrsim$\\,$10^{\\prime\\prime}$) observations in the millimeter range show brightness enhancements associated with network and ARs \\citep[e.g.,][]{1991ApJ...383..443L,2009A&A...497..273L}, while high-resolution ($\\sim$\\,$0.1^{\\prime\\prime}$) optical observations show that the \\ion{Ca}{II} K brightness in ARs is dominated by an extended component that is associated with more space-filling, horizontal magnetic fields \\citep{2018A&A...612A..28L}. \n\nDissipation of electric currents induced by the magnetic field is a prime candidate for explaining this heating, but determining the electric current vector in the chromosphere is notoriously challenging. It has only been reported in sunspots using \\ion{Ca}{II} 8542\\,\\AA~observations \\citep{2005ApJ...633L..57S,2021A&A...652L...4L} and in pores using the \\ion{He}{I}\\,10830\\,\\AA~multiplet \\citep{2003Natur.425..692S}. While \\ion{Ca}{II} is partially sensitive to temperatures, the \\ion{He}{I} line is not, and thus cannot be used to establish a direct link between heating and electric currents in the atmosphere. \n\nObservational evidence for magnetic reconnection in the lower atmosphere comes from the association of small-scale optical or ultraviolet (UV) brightenings, such as Ellerman bombs, UV bursts, and jets near or above patches of opposite magnetic polarity in the photosphere \\citep[e.g.,][]{2013ApJ...774...32V,2017A&A...605A..49C,2019ApJ...887...56T,2020A&A...633A..58O}, while numerical simulations show how these events could be related to heating in current sheets \\citep[e.g.,][]{2015ApJ...799...79N,2017A&A...601A.122D,2019A&A...626A..33H,2020ApJ...891...52S}.\n\nHigh-resolution observations of the free-free millimeter (mm) continuum are now provided by the Atacama Large Millimeter\/sub-millimeter Array \\citep[ALMA,][]{2009IEEEP..97.1463W}. This radiation is optically thick in the chromopshere, and the opacity is dominated by electron-proton collisions \\citep[see review by][]{2016SSRv..200....1W}. The mm continuum provides strong temperature constraints in inversions of non-LTE lines \\citep{2018A&A...620A.124D,2020A&A...634A..56D}, which should allow us to revise estimates of radiative energy losses that can be used to benchmark numerical simulations. \n\nSmall-scale transient {\\it mm-bursts} in ARs have also been linked to magnetic reconnection events \\citep{2020A&A...643A..41D}, but the lack of chromospheric magnetic field measurements did not enable a definitive conclusion in this regard. Here, we conducted a follow-up study that combines ALMA Band\\,3 (100\\,GHz or 3\\,mm) data with optical spectropolarimetry obtained at the 1-m Swedish Solar Telescope \\citep[SST,][]{2003SPIE.4853..341S}, providing further evidence for heating in the upper chromosphere in an AR by current dissipation of up to $\\sim$5\\,kW\\,m$^{-2}$ at the current Band\\,3 spatial resolution ($\\sim$\\,1.2$^{\\prime\\prime}$). This is supported by a magnetohydrostatic extrapolation and quantitatively reproduced by a 3D radiative-magnetohydrodynamics (r-MHD) simulation.\n\n\\section{Observations}\n\\label{section:observations}\n\nWe obtained simultaneous interferometric brightness temperature, $T_{\\rm b}$, maps of the 3\\,mm continuum with ALMA and spectropolarimetric observations using the CRISP instrument \\citep{2008ApJ...689L..69S} at the SST in the Fe\\,I 6173\\,\\AA~and the Ca\\,II 8542\\,\\AA~lines (hereafter $\\lambda6173$ and $\\lambda8542$) of NOAA AR 12738 on April 13, 2019. We also use ultraviolet (UV) imaging provided by the Atmospheric Imaging Assembly \\citep[AIA,][]{2012SoPh..275...17L} and magnetogram data from the Helioseismic and Magnetic Imager \\citep[HMI,][]{2012SoPh..275..207S} on board the Solar Dynamics Observatory \\citep[SDO,][]{2012SoPh..275....3P}.\n\nThe ALMA data set consists of $T_{\\rm b}$ maps taken approximately between 18:20-18:55\\,UTC and 19:16-19:51\\,UTC at 2\\,s cadence with 140\\,s calibration intervals every 10\\,min. These data have been previously presented by \\citet{2020A&A...643A..41D} and we refer to their study for further details of the data reduction and calibration.\nIn this paper, we only use ALMA maps taken within the time span of the SST observations (see below). The field of view (FOV) has a diameter of $60^{\\prime\\prime}$ and the pixel scale is $0.3^{\\prime\\prime}$. The noise root-mean-square level is approximately 20\\,K. \n\nCoordination between ALMA in Chile and the SST in La Palma is challenging because of the difference in time zones. The SST\/CRISP observations started as soon as the seeing conditions improved in the late afternoon but it was technically unfeasible to prolong the campaign for a long period given the closeness to local sunset. Therefore, the CRISP spectropolarimetric data (full-Stokes) consist of a single line scan of sufficient quality in 17 wavelength positions in $\\lambda8542$ in the range of $\\pm700$\\,m\\AA~and 15 positions in $\\lambda6173$ within $\\pm 275$\\,m\\AA~from the line center taken between 18:48:36\\,UTC and 18:48:56\\,UTC. \n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{FigSpl1_v3.pdf}\n \\caption{Extended view of NOAA AR 12738 provided by SDO. Panel A: SDO\/AIA\\,1700\\,\\AA~intensity in square-root scale; panel B: Composite of AIA 171\\,\\AA~(red), 193\\,\\AA~(green), and 211\\,\\AA~(blue) intensities (unsharpened). Solid circle and dashed square show the ALMA and SST fields, respectively.} \n \\label{fig:Spl1}\n\\end{figure}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{Fig1_6p_v7.pdf}\n \\caption{Multiwavelength imaging of a solar active region. The leftmost panels show an extended view of the target, while the ones on the right show a closer look at the center. Panel A: AIA\\,304\\,\\AA~intensity in logarithmic scale (18:48:53\\,UTC); panel B: SST\/CRISP $\\lambda$8542\\,\\AA~core (18:48:41\\,UTC); panel C: ALMA brightness temperature at 3\\,mm (18:48:53\\,UTC); panel D: HMI LOS magnetogram clipped at +\/- 1\\,kG (white\/black, 18:49:30\\,UTC); panel E: Composite of total linear polarization (red) and total circular polarization (blue) in $\\lambda$6173 (18:48:53\\,UTC); panel F: TLP in $\\lambda$8542 (18:48:41\\,UTC). Solid circle and dashed square show the ALMA and SST fields. The cyan contours correspond to $T_{\\mathrm{b}}[3\\,\\mathrm{mm}] = 9$\\,kK.}\n \\label{fig:fig1}\n\\end{figure*}\n\nThe SST data were reduced using the \\texttt{CRISPRED} pipeline \\citep{2015A&A...573A..40D}, which includes flat-field and dark correction, cross-talk correction, polarimetric calibration, image reconstruction through Multi-Object Multi-Frame Blind Deconvolution \\citep[MOMFBD,][]{2002SPIE.4792..146L,2005SoPh..228..191V}, and fringe removal using Fourier filtering. An additional fringe removal step using principal component analysis was necessary in order to remove large-scale patterns in Stokes Q and U \\citep{2020A&A...644A..43P}. \nThe absolute intensity and wavelength calibrations were performed using the solar atlas of \\citet{1984SoPh...90..205N} as reference. The pixel scale is $0.059^{\\prime\\prime}$. \nWe co-aligned the CRISP and HMI data by cross-correlating the 6173\\,\\AA~continuum images taken by both instruments. The calibrated SST and ALMA data sets were analyzed using inversion codes (Section\\,\\ref{section:inv}).\n\nThe UV and extreme-UV (EUV) images taken by AIA and the line-of-sight (LOS) magnetograms obtained by HMI were downloaded from the Virtual Solar Observatory using the \\texttt{vso\\_search} routine in SolarSoftWare (SSW) \\citep{1998SoPh..182..497F}; they were further processed using IDL tools in SSW and the \\texttt{SunPy} package \\citep{2015CS&D....8a4009S} as described in \\citet{2020A&A...643A..41D}. \nThe LOS magnetograms were deconvolved using \\texttt{Enhance}\\footnote{\\url{https:\/\/github.com\/cdiazbas\/enhance}} \\citep{2018A&A...614A...5D}.\nIn addition, we used the full-vector magnetogram that was closest in time (taken at 18:48:01\\,UTC) to the CRISP scans for the magnetic field extrapolation (Section\\,\\ref{section:extrapol}). \nThe vector magnetogram has been processed with the \\texttt{SHARP} pipeline \\citep{2014SoPh..289.3549B} and it was obtained from the JSOC interface \\footnote{\\url{http:\/\/jsoc.stanford.edu\/HMI\/HARPS.html}}.\n\nFigure\\,\\ref{fig:Spl1} shows a context view of the AR provided by SDO. The SST and ALMA pointings were on a group of pores and an arch-filament system (AFS) southwest of a large sunspot close to disk center. The EUV composite image was generated using the \\texttt{make\\_lupton\\linebreak\\_rgb} function in \\texttt{Astropy} \\citep{astropy:2018,2004PASP..116..133L}. The focus of this paper is the area surrounding the pore at the center of the ALMA FOV, which is the location of the western footpoints of the AFS.\n\n\n\\section{Methods}\n\\label{section:methods}\n\n\\subsection{Data inversions}\n\\label{section:inv}\n\nWe ran two different inversion codes on the SST data for different purposes: \\texttt{PyMilne}\\footnote{\\url{https:\/\/github.com\/jaimedelacruz\/pyMilne}} \\citep{2019A&A...631A.153D}, a code based on the Milne-Eddington (ME) approximation for photospheric lines using analytic response functions \\citep{2007A&A...462.1137O}; and \\texttt{STiC}\\footnote{\\url{https:\/\/github.com\/jaimedelacruz\/stic}} \\citep{2019A&A...623A..74D}, a multi-atom, non-LTE inversion code based on the Rybicki-Hummer code \\citep[RH,][]{2001ApJ...557..389U} that is suitable for both photospheric and chromospheric lines. The former provides the mean magnetic field vector within the formation region of the lines in a large FOV in a quick manner, whereas the latter requires more computing power but it allows for a detailed investigation of the thermodynamic stratification of the plasma as function of logarithmic optical depth of the 500\\,nm continuum (here simply $\\log \\tau$), while taking radiative transfer effects into account \\citep[e.g.,][]{2017SSRv..210..109D}.\n\n\\subsubsection{Milne-Eddington inversions}\n\\label{section:ME}\n\nWe fitted the $\\lambda$6173 spectra taken by SST\/CRISP using \\texttt{PyMilne} in order to provide an input for the magnetic field extrapolation code (Section\\,\\ref{section:extrapol}). We took into account the spectral point-spread-function of the instrument and cavity errors \\citep{2015A&A...573A..40D}. The magnetic filing factor is assumed to be unity. The results are shown in the supplementary Fig.\\,\\ref{fig:ME}.\n\nUpper limits on the uncertainty in the parameters can be obtained from their spatial variation on scales shorter than those of typical photospheric features (e.g., granule size $\\sim$1$^{\\prime\\prime}$). Using 5$\\times$5\\,px boxes at five different locations in the magnetic areas of the region of interest (ROI; see Fig.\\,\\ref{fig:fig2}), we found average standard deviations of $\\delta\\norm{B}=27$\\,G, $\\delta v_{\\rm LOS}=0.07\\,\\rm km\\,s^{-1}$, $\\delta \\theta=2^{\\circ}$, and $\\delta \\phi=8^{\\circ}$, in the magnetic field strength, line-of-sight velocity, inclination angle, and azimuth angle, respectively.\n\n\\subsubsection{Non-LTE inversions}\n\\label{section:nlte}\n\n\\begin{figure*}[t]\n \\centering\n \\sidecaption\n \\includegraphics[width=120mm]{Fig2_v9.pdf}\n \\caption{ Non-LTE inversions of the spectral data and radiative losses in the upper chromosphere. Temperature, integrated radiative losses within the contribution function of the 3\\,mm continuum, longitudinal field strength, and transverse field strength at selected optical depths from the photosphere to the chromosphere are shown. The cyan contours correspond to $T_{\\mathrm{b}}[3\\,\\mathrm{mm}] = 9$ and 10\\,kK. The middle row shows example observed (markers) and fitted (solid lines) intensity of the $\\lambda6173$ and $\\lambda8542$ lines at three locations indicated by different markers overlaid on the other panels; the intensity is normalized by $\\bar{I_{0}}$ -- the mean intensity at the bluest sampled wavelength of each line.}\n \\label{fig:fig2}\n\\end{figure*}\n\nWe also ran non-LTE inversions of the $\\lambda$6173 and $\\lambda$8542 lines along with the ALMA $T_{\\rm b}[\\rm 3\\,mm]$ map using the \\texttt{STiC} code similarly to the approach described in \\citet{2018A&A...620A.124D}.\nThe Band\\,3 $T_{\\rm b}$ maps were converted into intensity in c.g.s. units using the Planck function and linearly resampled to the pixel scale of the CRISP data. The inherent inversion uncertainties (see below) outweigh the interpolation uncertainties.\nWe note that there is a factor of ten difference in spatial resolution between the optical\/infrared and mm diagnostics. Multiresolution spectral data can be dealt with using linear operators and global inversion schemes \\citep[e.g.,][]{2019A&A...631A.153D} but this has not yet been implemented into \\texttt{STiC}. Therefore, we have decided to preserve the high-resolution information provided by the SST\/CRISP spectropolarimetry and oversample the ALMA maps. This approach worked well here because of the distinct formation heights of $\\lambda$8542 and the 1.25mm continuum. The \nhigh-resolution information in the $\\lambda$6173 and $\\lambda$8542 lines provides the temperature stratification at high resolution in the lower atmosphere. Because the chromosphere at depths lower than $\\log \\tau \\sim-5$ is so unconstrained from inversions of those spectral lines \\citep{2018A&A...620A.124D}, \\texttt{STiC} will essentially provide a low-resolution upper chromosphere constrained by ALMA on top of the high-resolution lower atmosphere constrained by the spectral lines.\nWe restricted the inversions to a $\\sim$$16.5^{\\prime\\prime}$$\\times$$24.8^{\\prime\\prime}$ subfield that encloses the ROI in order to reduce the computational cost. This analysis step required a few million core-hours.\n\nWe treated each pixel independently (1.5-D approximation) by simultaneously solving the statistical equilibrium equation for non-LTE populations of a 4-level H atom, a 6-level Ca\\,II atom, and a 16-level Fe\\,I atom along with the charge conservation equation, while other atoms and molecules are treated in LTE. Compared to LTE, treating the hydrogen atom in non-LTE and correcting the electron densities using charge conservation provides more realistic values that directly impact the calculation of the opacity in the mm continuum \\citep[e.g.,][]{2020A&A...634A..56D}. \nWe find that treating the Fe\\,I atom in non-LTE leads to average differences in the temperature and magnetic field strength of the order of a few percent at $\\log \\tau=0$ (relative to LTE). Differences in the field strength can be up to $\\sim$\\,60\\% in some patches. \\citet{2020A&A...633A.157S} investigated this in detail for the \\ion{Fe}{I} 6301, 6302\\,\\AA~lines using a more complete atomic model, but this would further increase the computational cost of our inversions. It is not critical for our conclusions as we are mainly interested in estimating radiative losses in the chromosphere, however, these effects contribute to the uncertainties of the ME inversions and field extrapolations (Section\\,\\ref{section:extrapol}).\nWe then ran another spectral synthesis solving the statistical equilibrium equation for a 11-level Mg\\,II atom in order to obtain population densities and radiative rates for computing radiative energy losses (Section\\,\\ref{section:enlosses}). The calculations include PRD in the \\ion{Mg}{II} h and k lines. The gas pressure is obtained by integration of the hydrostatic equilibrium equation and the value at the top boundary is treated as a free parameter \\citep{2019A&A...623A..74D}. \n\nThe polarization signals are assumed to be dominated by the Zeeman effect and we do not take the Hanle effect into account. This is a good approximation since the ROI features magnetic fields that are stronger than 500\\,G \\citep{2021arXiv210604478C}.\n\n\\texttt{STiC} uses parameterization by nodes that are interpolated using B\u00e9zier splines in an optical depth grid. We used nine nodes in temperature, four nodes in line-of-sight velocity, and two nodes in microturbulence, parallel and transverse magnetic field, and magnetic azimuth angle.\n\nThe uncertainties were estimated using a Monte Carlo approach \\citep{1992nrca.book.....P} on selected pixels in the ROI (supplementary Fig.\\,\\ref{fig:STiC_spl}). After obtaining good fits to the SST and ALMA observations, we generated up to 100 different synthetic spectra adding a random component that is the sum in quadrature of white noise and flux calibration uncertainties. The latter amounts to less than 1\\% for the spectral lines \\citep{1984SoPh...90..205N} and about 5\\% for the 3\\,mm continua. The synthetic spectra were inverted using different randomly generated atmospheres as initial guesses. This provides probability distributions for each parameter from which we computed the 16th, 50th and 84th percentiles at every optical depth (supplementary Fig.\\,\\ref{fig:STiC_spl}). \nTypical uncertainties in temperature are of the order of $\\sim$50\\,K or lower within $\\log\\tau=[-4,0]$, but they increase at lower optical depths reaching $\\sim$600\\,K at $\\log\\tau=-6$; below these values, the response functions of the diagnostics are weak. The uncertainty in the magnetic field strength is $\\sim$10-20\\,G for the longitudinal (or LOS) component and $\\sim$10-70\\,G for the transverse component within $\\log\\tau=[-5,0]$. The sensitivity of the $\\lambda$6173 and $\\lambda$8542 lines to magnetic fields is weak at optical depths lower than $\\log\\tau=-5$.\n\n\n\\subsection{Magnetic field extrapolation}\n\\label{section:extrapol}\n\nWe performed a magnetic field extrapolation using a magnetohydrostatic (MHS) model based on the SST and HMI data. Since the SST\/CRISP FOV covers only part of the magnetic connectivity, we embedded a cutout of the FOV in the SHARP-processed (disambiguated) HMI vector magnetogram, which covers the entire AR (top panels in supplementary Fig.\\,\\ref{fig:extrapolSpl}). \nIn this way we make use of the high-resolution information provided by the SST data in the ROI and we take advantage of the more extended HMI FOV for context. This is necessary to interpret the interaction of small- and large-scale loop systems. The resulting magnetogram is in cylindrical-equal-area projection (CEA) coordinates.\n\nTo ensure consistency between the CRISP and HMI magnetograms, we disambiguated the azimuth angle provided by the ME inversion of the CRISP data by imposing an acute angle with the HMI\/SHARP magnetogram. Both magnetograms are derived from the same spectral line ($\\lambda6173$). The borders of the CRISP cutout were smoothed with a Gaussian kernel. \nFinally, the maps of the three components of the magnetic field vector, $\\vec{B}$, were convolved with a median filter to reduce the impact of bad pixels and inversion noise in the extrapolation. \n\nThe MHS model takes into account the effect of plasma forces which cannot be ignored in the lower atmosphere. An optimization approach is used to numerically solve the equations \\citep{2019A&A...631A.162Z}:\n\\begin{eqnarray}\n \\frac{1}{\\mu_{0}}(\\nabla \\times \\vec{B}) \\times \\vec{B} - \\nabla p + \\rho\\,\\vec{g} = 0,\\\\\n \\nabla \\cdot \\vec{B} = 0,\n\\end{eqnarray}\nstarting from a nonlinear force-free field extrapolation as initial guesses \n\\citep{2004SoPh..219...87W}.\nHere, $\\mu_0$ is the magnetic permeability, $p$ is the gas pressure, $\\rho$ is the mass density, and $\\vec{g}$ is the gravitational acceleration. The optimization procedure aims to reach a static equilibrium state between the Lorentz force, the pressure gradient, and gravity.\n\nThe model uses the photospheric vector magnetogram (Section\\,\\ref{section:ME}) as the lower boundary condition. The plasma pressure at the lower boundary is calculated from:\n\\begin{equation}\n p+\\frac{B^2_{z}}{2} = P_\\mathrm{ph},\n\\end{equation}\nwhere $P_\\mathrm{ph},$ is a typical photospheric pressure. Further details of the MHS method can be found in \\citet{2018ApJ...866..130Z,2020A&A...640A.103Z}.\nThe resulting model has a size of $2000\\times1800\\times256$ pixels with a uniform pixel scale of 40\\,km.\n\n\\subsection{3D radiative-magnetohydrodynamics simulation}\n\\label{section:simulation}\n\nThe simulation presented here was performed with the \\texttt{MURaM} code \\citep{2005A&A...429..335V,2017ApJ...834...10R}, which includes the following physics: single fluid MHD, 3D grey LTE radiative transfer, a tabulated LTE equation of state, Spitzer heat conduction, and optically thin radiative losses in the corona based on \\texttt{CHIANTI} \\citep{2012ApJ...744...99L}. The chromospheric and coronal parts of the simulation domain is heated by the Poynting flux generated through magnetoconvection in the photosphere and convection zone. Earlier simulations with this code have reproduced many observed chromospheric phenomena \\citep[e.g.,][]{2019A&A...631A..33B,2020LRSP...17....3L}.\n\nThe simulation domain has an extent of $40\\times 40 \\times 22$\\,Mm, spanning the vertical direction from $-8$\\,Mm to 14\\,Mm above the average $\\tau_{500\\,\\mathrm{nm}}$\\,$=$\\,1 height. The pixel size is 0.078\\,Mm. The run was initialized with a bipolar uniform magnetic field of 200\\,G \\citep{2011A&A...533A..86C}, which is added to the well-developed nonmagnetic convection simulation to form extended magnetic field concentrations at meso- to super-granular scales. This was done with the aim of reproducing the plasma dynamics of solar plage -- regions of moderate magnetic activity. The computational domain was then extended to include the upper solar atmosphere, and the magnetic field from the preexisting simulation was used for potential field extrapolation into the rest of the domain. The new simulation was then run until a relaxed state was achieved. An additional bipolar flux system was advected through the bottom boundary through an ellipsoidal region with major axes (a, b) = (3, 1) Mm and a field strength of 8000\\,G \\citep{2019NatAs...3..160C}, which emerged from the convection zone into the chromosphere directly beneath the preexisting filaments. Once the flux reaches the photosphere, its field strength decreased to around 1.5\\,kG.\n\nSince the non-LTE spectral synthesis is quite computationally intensive and memory-demanding, here we analyzed one single simulation snapshot where we identified a region of enhanced $T_{\\rm b}[3\\rm\\,mm]$ at a later stage of the flux emergence $t=17$\\,min after the magnetic flux reached the photosphere. Anyhow, the lack of spectropolarimetric time series in $\\lambda6173$ and $\\lambda8542$ does not allow us to compare the observations and the unraveling of the simulated flux emergence. \n\nWe computed the emerging intensities in $\\lambda$8542 from the simulation in non-LTE 1.5D (full-Stokes) using \\texttt{STiC} and in 3D (intensity only) using \\texttt{Multi3D} \\citep{2009ASPC..415...87L}. The synthetic \\ion{Ca}{II} lines were convolved with the CRISP response function.\nThe 3\\,mm continuum intensities were computed assuming statistical equilibrium and charge conservation using \\texttt{STiC} \\citep[see also][]{2020A&A...634A..56D}.\nBesides the synthetic intensities, we also stored the opacities $\\alpha (\\nu, z)$ as a function of frequency, $\\nu$, and height, $z$ for each pixel. We investigated the formation of the 3\\,mm continuum in detail using contribution functions (CF) defined as\n\\begin{equation}\n \\mathrm{CF}_{\\nu} (z)=\\alpha_{\\nu}(z)\\,S_{\\nu}(z)\\,e^{-\\tau_{\\nu}(z)},\n\\end{equation}\n\\noindent where $\\nu=100$\\,GHz, $S_{\\nu}$ is the source function, which is given by the Planck function, and $\\tau_{\\nu}$ is the optical depth. This quantity essentially quantifies the contribution from different heights of the atmosphere to the emerging intensities.\n\n\\subsection{Radiative energy losses}\n\\label{section:enlosses}\n\nBesides the radiative losses, $Q_{\\rm l}$, that can be directly retrieved from the \\texttt{MURaM} simulation output \\citep{2017ApJ...834...10R}, we also obtained $Q_{\\rm l}[\\rm STiC]$ from the non-LTE synthesis with \\texttt{STiC} by summing the contributions to the radiative cooling from the Ca\\,II H, K, infrared triplet, Mg\\,II h, k, UV triplet, H$\\alpha$, and Ly$\\alpha$ lines as follows:\n\\begin{equation}\n Q_{\\rm l}[\\mathrm{STiC}] = h\\nu_{0}(n_{\\rm u}R_{\\rm ul} - n_{\\rm l}R_{\\rm lu}),\n\\end{equation}\n\\noindent where $h$ is the Planck constant, $\\nu_{0}$ is the frequency of the transition, $n_{\\rm u}$ and $n_{\\rm l}$ are the population densities of the upper and lower levels, and $R_{\\rm ul}$ and $R_{\\rm lu}$ are the radiative rates of the transitions\n\\citep[e.g.,][]{2020arXiv201206229D}. These chromospheric losses are more suitable for comparison with the losses that can be inferred from observations using data inversions (Section\\,\\ref{section:nlte}).\n\nTotal radiative losses were obtained by integration in the height range spanned by the CF of the 3\\,mm continuum using a threshold of 1\\% of the maximum in each pixel. This criterion was used both for the CFs obtained from the simulation and observations. \n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\linewidth]{extrapol_v4.png}\n \\caption{Magnetohydrostatic extrapolation of the SST and HMI composite magnetogram and millimeter continuum brightness. The background shows a SST\/CRISP and HMI magnetogram composite image clipped at +\/-1\\,kG (white\/black). The magnetic field lines are computed from the MHS extrapolation and they are color-coded with the horizontal field strength. The pink shade shows regions where $T_{\\rm b}\\rm\\,[3\\,mm]>9$\\,kK.} \n \\label{fig:fig3}\n\\end{figure}\n\n\\begin{figure*}\n \\centering\n \\includegraphics[width=\\linewidth]{FigSpl5_v7.pdf}\n \\caption{Simulated magnetograms and synthetic emission. Strength of the vertical (panel A) and horizontal (panel B) components of the magnetic field at $z=0$\\,Mm; panel B has been gamma-adjusted for display purposes. Panel C: Intensity in the core of $\\lambda 8542$; the range is capped for display purposes. Panel D and panel E: Continuum $T_{\\rm b}[\\rm 3\\,mm]$ at full resolution and convolved with a Gaussian kernel with full-width-at-half-maximum of 1.2$^{\\prime\\prime}$; the cyan contours show $T_{\\rm b}[\\rm 3\\,mm]=9$\\,kK. The dashed box delimits the area displayed in Fig.\\,\\ref{fig:fig4}\\textcolor{blue}{B}, \\ref{fig:fig4}\\textcolor{blue}{C}. The lower right panels show selected $\\lambda 8542$ profiles (normalized intensity) in the flux emergence region. Vertical cuts through various parameters of the simulated atmosphere along the slices {\\it S3}, {\\it S4}, and {\\it S5} are displayed in the supplementary Fig.\\,\\ref{fig:simSlices}.}\n \\label{fig:simextra0} \n\\end{figure*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=0.85\\linewidth]{merge_test9_v6.pdf}\n \\caption{Magnetic topology, heating rates, and the formation of the millimeter continuum in a 3-D radiative-magnetohydrodynamics simulation. Panel A: Photospheric magnetogram clipped at +\/- 1\\,kG; the yellow shade shows where $T_{\\rm b}[3 \\mathrm{mm}]>9$\\,kK. The area inside the dotted box is displayed in panels B and C, which show $T_{\\rm b}[3 \\mathrm{mm}]$ and the sum of viscous and resistive heating integrated within the CF of the 3\\,mm continuum. The group of panels D show from the left to the right: Current density squared per mass unit (square-root scaling), logarithm of temperature, and CF$_{\\rm 3\\,mm}$ slices along the dotted paths {\\it S1} and {\\it S2} overlaid on panels B and C; the white dashed lines show where $\\tau_{\\rm 3\\,mm}=1$, and the dotted black lines show where CF is at 1\\% of the maximum in each column. } \n \\label{fig:fig4}\n\\end{figure*}\n\n\n\\section{Results}\n\\label{section:results}\n\nAR 12738 showed ongoing magnetic flux emergence into a preexisting AFS. The ALMA map (Fig.\\,\\ref{fig:fig1}\\textcolor{blue}{C}) reveals elongated patches of enhanced $T_{\\rm b}\\rm [3\\,mm]$ by $\\sim$3,000\\,K relative to QS values, which is indicative of local heating in the upper chromosphere. The ALMA time series (supplementary movie) shows recurring brightenings exhibiting several hundred kelvin $T_{\\rm b}\\rm [3\\,mm]$ variations, which last from a few tens of seconds to a few minutes with no clear periodicity. These hot spots are also fully visible in the AIA\\,304\\,\\AA~images (Fig.\\,\\ref{fig:fig1}\\textcolor{blue}{A}) and partially in AIA\\,171\\,\\AA~(Fig.\\,\\ref{fig:Spl1}) -- but not in hotter channels. Correlation between Band\\,3 brightenings and coronal emission in ARs has been reported before \\citep{2020A&A...643A..41D,2021A&A...651A...6B}. \nThere are other bright regions in the FOV in the 304\\,\\AA~images that have no counterpart in the ALMA maps. This may imply varying relative differences in formation heights of both diagnostics at different locations.\n\nA comparison to the HMI LOS magnetogram (Fig.\\,\\ref{fig:fig1}\\textcolor{blue}{D}) shows that this region is located between concentrations of magnetic field of opposite polarity, which are the footpoints of chromospheric loops that connect the two patches. The SST data indeed show short bright loop-like structure in the $\\lambda$8542 core (Fig.\\,\\ref{fig:fig1}\\textcolor{blue}{B}) and significant total linear polarization signals ($\\mathrm{TLP}=\\int\\sqrt{Q^2+U^2}\/I\\,d\\lambda$) indicative of strong transverse magnetic field in the photosphere and chromosphere (Figs.\\,\\ref{fig:fig1}\\textcolor{blue}{E}, \\ref{fig:fig1}\\textcolor{blue}{F}). The composite image of other AIA EUV channels shows longer dark filaments overlying the small-scale loops (Fig.\\,\\ref{fig:Spl1}).\n\n\\subsection{Physical properties from inversions}\n\nThe non-LTE inversions provide a well-resolved temperature structure from the photosphere to the top of the chromosphere where the 3\\,mm continuum is formed. The sensitivity of the spectra to magnetic fields is lower, and the inversions provide the vector magnetic field in the photosphere and mid-chromosphere. \n\nFigure~\\ref{fig:fig2} shows the results of the \\texttt{STiC} inversions. We find increased temperatures in the chromosphere at least up to optical depth $\\log \\tau$\\,$\\sim$\\,$-6$ where $T$\\,$\\sim$\\,10,000\\,K, which is on the order of the observed $T_{\\rm b}\\rm\\,[3\\,mm]$. The warm locations feature $\\lambda$8542 profiles with central reversals or raised intensities in the wings and small Doppler shifts ($\\lesssim$\\,$4\\,\\rm km\\,s^{-1}$), which are well-reproduced by our models (supplementary Fig.\\,\\ref{fig:STiC_spl}). \n\nWe computed the radiative energy losses as a proxy for the heating rate, which cannot be directly observed. In Fig.\\,\\ref{fig:fig2} we also show integrated losses from the inferred atmosphere in the strongest lines of H\\,I, Ca\\,II, and Mg\\,II (Section\\,\\ref{section:enlosses}) in the height range spanned by the CF of the 3\\,mm continuum. This is done in geometrical height scale assuming hydrostatic equilibrium.\nEnergy losses range from 2.6 to 4.9\\,$\\rm kW\\,m^{-2}$ with a mean value of $\\sim$4\\,$\\rm kW\\,m^{-2}$ within the $T_{\\rm b}\\rm\\,[3\\,mm]=9$\\,kK contours, which is higher than previous estimates of $\\sim$2\\,$\\rm kW\\,m^{-2}$ in the upper chromosphere \n\\citep{1977ARA&A..15..363W}. \n\nThe longitudinal and transverse photospheric field in the ROI have a maximum strength of $\\lvert B_{\\rm ln}\\rvert$\\,$=$\\,$1890$\\,G and $\\lvert B_{\\rm tr}\\rvert$\\,$=$\\,$1380$\\,G and the uncertainty is of the order of 10\\,G. The chromospheric magnetic field is overall weaker, but the transverse component remains strong with an average (maximum) value of $\\lvert B_{\\rm tr}\\rvert\\sim480$\\,G (1060\\,G) with an uncertainty of $\\sim$70\\,G at $\\log \\tau=-5$ within the $T_{\\rm b}\\rm\\,[3\\,mm]=9$\\,kK contours. The transverse field traces the near-horizontal tops of the emerging loops and coincides with higher temperature regions. \n\n\n\\subsection{Magnetic topology}\n\nThe fidelity of the magnetic field derived from the inversions is high, but the vertical resolution is low and the FOV is small. Therefore, we complement our analysis with a MHS extrapolation based on a high-resolution magnetogram derived from the SST $\\lambda$6173 data (supplementary Fig.\\,\\ref{fig:ME}) embedded in a lower resolution magnetogram provided by HMI (Section\\,\\ref{section:extrapol}). \n\nFigure~\\ref{fig:fig3} was produced using the \\texttt{VAPOR} software\\footnote{\\url{www.vapor.ucar.edu}} \\citep{atmos10090488} and it shows a 3D rendering of the overlying field connecting the pore and the plage region, whose direction is the same as the AFS in the EUV images (c.f. Fig.\\,\\ref{fig:Spl1}).\n\nA comparison with Fig.\\,\\ref{fig:fig1}\\textcolor{blue}{B} confirms that the field lines align with the direction of the bright $\\lambda8542$ fibrils inside the ALMA $T_{\\rm b}$ contours, in agreement with the picture drawn from the imaging and inversions. The horizontal magnetic field strength in the short loops derived from the extrapolation (as high as $\\sim$\\,800\\,G around $z$\\,$\\sim$\\,1\\,Mm) is consistent with the inversion results. \nPatches of high $T_{\\rm b}\\rm\\,[3\\,mm]$ coincide with the interaction region between the loop systems where current sheets (tangential discontinuities) must exist between them. \n\nWe computed the current density ($\\vec{j}=\\nabla \\times \\vec{B}\/\\mu_{0}$) from the extrapolation for completeness (supplementary Fig.\\,\\ref{fig:extrapolSpl}), but we note that spurious currents arise from the lack of smoothness in the magnetic field introduced by inversion noise in the magnetograms and the azimuth angle disambiguation, as well as limitations of the MHS extrapolation algorithm itself \\citep{2018ApJ...866..130Z}. We find current strands over a range of heights relevant to the formation of the 3\\,mm continuum ($\\sim$1-4\\,Mm, Fig.\\,\\ref{fig:fig4} and Fig.\\,\\ref{fig:simextra2}\\textcolor{blue}{C}) that is cospatial with both the short $\\lambda8542$ loops and the long, overlying ones seen in the AIA images, but we could not identify a single height that correlates strongly with $T_{\\rm b}\\rm[3\\,mm]$. However, we do expect spatial and temporal variations of the formation height of the mm continuum \\citep[e.g.,][]{2015A&A...575A..15L,2020ApJ...891L...8M}.\n\nWe attempted to correlate $T_{\\rm b}[\\rm 3\\,mm]$ with $j^{2}\/\\rho$ in the MHS extrapolation at the height where $\\tau_{\\rm 3\\,mm}=1$, obtained from the non-LTE inversions assuming hydrostatic equilibrium (typically $z$\\,$\\sim$\\,1.5-1.8\\,Mm) and taking into account the fact that the zero point of the MHS extrapolation is defined by the mean formation height of $\\lambda6173$; however, this turned out to be inconclusive. This comparison was also made in column mass scale. \nThis is partly due to the fact that the height scales obtained from these two methods are different, but we also expect the MHS extrapolation to underestimate the magnetic structure and field strength compared to $\\lambda8542$ spectropolarimetry \\citep{2021arXiv210902943V}, so the electric current values carry some uncertainty. Moreover, the height-integration effect of the CF of the 3\\,mm continuum may smear such correlation, as further discussed in Section\\,\\ref{section:formation}.\n\nTherefore, we cannot unambiguously link the observed millimeter emission to heating in current sheets based on these data alone. Nonetheless, the 3\\,mm continuum forms above $\\lambda8542$ \\citep{2018A&A...620A.124D}, but below the He\\,II\\,304\\,\\AA~line, which strongly suggests that the mm continuum originates in the the shear layer. \n\n\\subsection{Investigating the formation of the mm continuum}\n\\label{section:formation}\n\n\\begin{figure*}[t]\n \\centering\n \\sidecaption\n \\includegraphics[width=120mm]{FigSpl7_v7.pdf}\n \\caption{Heating rates and the formation of the millimeter continuum in the simulation. Panel A: Integrated $j^{2}\/\\rho$ weighted by the contribution function of the 3\\,mm continuum (CF$_{\\rm 3\\,mm}$). Panel B: Photospheric magnetogram (range $\\pm1$\\,kG) with 3-D rendering of magnetic field lines and CF$_{\\rm 3\\,mm}$ (blue shade) of the region indicated by the arrow in panel A. A threshold was applied to the CF values not to hide the field lines. Panel C: Height at which $\\tau=1$ at 3\\,mm; the errorbar overlaid on the colormap shows the median and the range between the 16th and 84th percentiles of the distribution. Panel D: Temperature (dashed lines), total heating rates per mass (yellow lines), and CF$_{\\rm 3\\,mm}$ (blue lines) as a function of height at two locations indicated in panels A and C. }\n \\label{fig:simextra2} \n\\end{figure*}\n\\begin{figure*}\n \\centering\n \\sidecaption\n \\includegraphics[width=120mm]{FigSpl12_v4.pdf}\n \\caption{Radiative losses in the simulation. Total radiative losses integrated within CF$_{\\rm 3 mm}$ from the \\texttt{MURaM} output (panel A) and non-LTE values calculated using \\texttt{STiC} (panel B) at full resolution; panels C and D show the corresponding quantities convolved with a Gaussian kernel with FWHM of 1.2$^{\\prime\\prime}$. Cyan contours show $T_{\\rm b}[\\rm 3\\,mm]=9$\\,kK. Top row panels are displayed in square-root scale.}\n \\label{fig:simLosses}\n\\end{figure*}\n\nTo gain further insight into the magnetic flux emergence and how electric currents heat the atmosphere in that process, we ran a 3D r-MHD simulation using \\texttt{MURaM}, as explained in Section\\,\\ref{section:simulation}. Figure~\\ref{fig:simextra0} displays the strength of the vertical and horizontal components of the photospheric magnetic field vector along with the synthetic emission in the core of $\\lambda8542$ (corrected for Doppler shifts) and $T_{\\rm b}[\\rm 3\\,mm]$. In order to simulate the effect of the ALMA beam to first order, we convolved $T_{\\rm b}[\\rm 3\\,mm]$ at the simulation resolution (Fig.\\,\\ref{fig:simextra0}\\textcolor{blue}{D}) with a Gaussian kernel with full-width-at-half-maximum $\\rm FWHM$\\,$=$\\,1.2$^{\\prime\\prime}$ (Fig.\\,\\ref{fig:simextra0}\\textcolor{blue}{E}). In this paper we are primarily interested in the region inside the dashed box that encloses a patch of emerging flux and where we see some compact mm brightenings as well as bright fibrils. At the ALMA resolution the bright strands appear more blob-like as in the observations (Fig.\\,\\ref{fig:fig1}\\textcolor{blue}{C}). The simulation is also able to reproduce $\\lambda$8542 profiles qualitatively similar to the observations featuring central reversals and raised intensities in the wings. There are patches where the line core is in full emission, which we do not find in the single CRISP scan that we have. Emission profiles are uncommon in the simulation, and they occur at sites of enhanced heating at the $\\tau=1$ layer of the line core (e.g., panels S4 in Fig.\\,\\ref{fig:simLosses}). We would need a longer $\\lambda$8542 time series to investigate whether these kinds of emission profiles can occur as in the simulation.\n\nFigure~\\ref{fig:fig4}\\textcolor{blue}{A} shows the footprint of the emerging bubble together with field lines highlighting the direction of the emerging loops (green), the overlying filaments (red), and the two sets of reconnected field lines (blue and orange). The synthetic 3\\,mm emission shows a bifurcated structure following the reconnected field lines in the interaction region \n(Fig.\\,\\ref{fig:fig4}\\textcolor{blue}{B}). \n\nWe find enhanced total heating rates, defined as the sum of the viscous, $Q_{\\rm v}$, and resistive, $Q_{\\rm r}$, heating within CF$_{\\rm 3\\,mm}$ in thin strands in the interaction region (Fig.\\,\\ref{fig:fig4}\\textcolor{blue}{C}). The mean (maximum) values at the location where $T_{\\rm b}\\rm\\,[3\\,mm]>10,000$~K are 12\\,kW\\,m$^{-2}$ (184\\,kW\\,m$^{-2}$).\nThis heating is caused by dissipation of currents and viscous dissipation of mass flows, both of which are driven by the interaction of the magnetic loop systems. This does not only directly cause Joule heating, but also drives flows through the Lorentz force that dissipate through viscosity. Our simulation employs numerical diffusive and resistive terms with an effective magnetic Prandtl number $P_\\mathrm{m} > 1$ so that viscous heating is the largest contributor. However, in the real chromosphere $P_\\mathrm{m} < 1$ and the heating is dominated by electric resistivity. The 3\\,mm continuum is optically thick where $P_\\mathrm{m} < 1$ (supplementary Fig.\\,\\ref{fig:simSlices2}).\n\nThe vertical cuts along the slices {\\it S1} and {\\it S2} (Fig.\\,\\ref{fig:fig4}\\textcolor{blue}{D}) underscore that CF$_{\\rm 3\\,mm}$ peaks at locations of high $j^2\/\\rho$ in the chromosphere where there are magnetic field gradients, but the resulting $T_{\\rm b}\\rm\\,[3\\,mm]$ may reflect contributions from multiple strands along the LOS. In supplementary Fig.\\,\\ref{fig:simSlices} we also provide additional 2D slices of the total heating rates per mass unit, which show the same qualitative picture. \nThe CF$_{\\rm 3\\,mm}$ shows a loop-like structure following the shape of the chromosphere-transition region boundary where the gas is still partially ionized. Locations of larger $j^2\/\\rho$ at transition region and coronal temperatures have a negligible (3\\,mm) opacity so they do not contribute to $T_{\\rm b}\\rm\\,[3\\,mm]$. However, the heating rate in those strands can be very large, and hot pockets embedded in the 3\\,mm formation height range lead to the large peak values in Fig.\\,\\ref{fig:fig4}\\textcolor{blue}{C}. \n\nThe simulated chromosphere is pervaded by multiple current sheets at different heights and shows a complicated thermal structure (Figs.\\,\\ref{fig:fig4}\\textcolor{blue}{D}), while the analysis of the opacity data shows that the formation height of the 3\\,mm continuum varies significantly across the flux emergence region (Fig.\\,\\ref{fig:simextra2}\\textcolor{blue}{C}). The CF of the 3\\,mm continuum peaks where the loop systems meet an angle (Fig.\\,\\ref{fig:simextra2}\\textcolor{blue}{B}) and the atmosphere is locally heated (see also the supplementary Fig.\\,\\ref{fig:simSlices}). Figure~\\,\\ref{fig:simextra2}\\textcolor{blue}{A} shows integrated $j^{2}\/\\rho$ weighted by CF$_{\\rm 3\\,mm}$, which reveals the filamentary structure of the heating that is very similar to the synthetic emission itself (c.f. Fig.\\,\\ref{fig:simextra0}\\textcolor{blue}{D}).\n\nThe layer where optical depth is unity in the core of $\\lambda$8542 is typically located below that of the 3\\,mm continuum in the flux emergence region (supplementary Fig.\\,\\ref{fig:simSlices}). This suggests that the former traces the top of the low-lying fibrils, whereas the latter sees the heating at or above the $\\lambda$8542 canopy. \n\nFigure~\\,\\ref{fig:simLosses} shows a comparison between the total radiative losses $Q_{\\rm l}$ and $Q_{\\rm l}[\\rm STiC]$ in the simulated chromosphere. At the native resolution of the simulation, the losses show the same bifurcated structure in the interaction region (c.f. Fig.\\,\\ref{fig:fig4}\\textcolor{blue}{B}, \\ref{fig:fig4}\\textcolor{blue}{C}). Total $Q_{\\rm l}$ in the simulation reach above 100\\,$\\rm kW\\,m^{-2}$ in some pixels due to the inclusion of losses at transition-region and coronal temperatures in thin pockets within the formation range of the millimeter continuum (c.f. the supplementary Fig.\\,\\ref{fig:simSlices}). These are not included in the \\texttt{STiC} losses, hence, the lower values. Much of the filamentary structure is lost at the ALMA resolution, and the values of the radiative losses decrease significantly; the mean(standard deviation) is $\\sim$\\,$6(\\pm 2)\\,\\rm kW\\,m^{-2}$, where $T_{\\rm b}\\rm\\,[3\\,mm]$\\,>\\,9\\,kK, which is similar to the values derived from the non-LTE inversions of the SST and ALMA data (Fig.\\,\\ref{fig:fig2}). \n\n\\section{Discussion and conclusions}\n\\label{section:conclusions}\n\nIn this paper we present an analysis of joint SST and ALMA observations of an AR on the Sun using inversion methods, field extrapolations, and a numerical simulation. We find enhanced $T_{\\rm b}[\\rm 3\\,mm]$ and bright $\\lambda$8542 profiles between a pore and parasitic opposite polarity patch in the photosphere. This is also the location of short, low-lying chromospheric fibrils, where the magnetic field is more horizontal to the solar surface, underneath an overlying AFS seen in absorption in the AIA EUV channels.\n\nOur analysis validates dissipation in current sheets as, at least, a locally dominant source of atmospheric heating, which produces brightenings in chromospheric diagnostics within ARs. Integrated radiative losses in the strongest chromospheric lines obtained from the simulation can be an order of magnitude higher than the ones determined from the observations. However, degrading the former to the spatial resolution of ALMA yields a mean(standard deviation) value of $\\sim$\\,6\\,$(\\pm 2)\\,\\rm kW\\,m^{-2}$, which is consistent with the observed values. The heating occurs on spatial scales that are not resolved in the ALMA Band\\,3 data. We note that this is only an approximate way of comparing losses obtained from observations and simulations at different spatial resolutions. In principle we would need to investigate the impact of the lack of constraints in the inversions on the estimates of the radiative losses by inverting synthetic data from the simulation at different resolutions, but this is beyond the scope of this paper.\n\nOur estimates are much lower than the values reported in an observed magnetic reconnection event of up to $\\sim$\\,160$\\,\\rm kW\\,m^{-2}$ \\citep{2020arXiv201206229D}. This is partly due to differences in the integration method but, most importantly, that event was much stronger and showed strong flows and an associated surge, which we did not detect. However, our limited spectropolarimetric data only provide one time frame and do not allow us to investigate the dynamics in detail. The ALMA time series does show recurring elongated brightenings at that location prior to the SST campaign, which would be consistent with the bright strands that we see in the simulation.\n\nWe note that the simulation makes use of numerical diffusivity and viscosity terms that are larger than their physical values. This is common in numerical simulations of the type employed here, and experiments show that this has only a marginal effect on the total energy dissipation rate \n\\citep{1996JGR...10113445G,2017ApJ...834...10R}, \nwhich is mainly determined by the Poynting flux input in the photosphere. \nThe simulation was run with an effective numerical magnetic Prandtl number $P_\\mathrm{m}>1$; about two thirds of the energy dissipated in the chromosphere is through viscosity and one third by electric resistivity. The chromosphere has a $P_\\mathrm{m}$ that is significantly below unity, especially if ambipolar diffusion is taken into account \n\\citep{2012ApJ...753..161M}, which appears as an additional cross-field resistivity in the MHD approximation. \nWhile the majority of the dissipation of magnetic energy in the simulated chromosphere occurs through the Lorentz force, which drives flows that are then damped through viscosity, the solar chromosphere causes the majority of the energy to dissipate through currents \\citep[e.g.,][]{2017ApJ...834...10R,2019ApJ...879...57B}.\n\nAlthough our simulation lacks some chromospheric physics such as ion-neutral interactions and non-equilibrium ionization \\cite[e.g.,][]{2012ApJ...747...87K,2020A&A...633A..66N}, it reproduces well the observed magnetic configuration, infrared and radio spectra, and energetics remarkably. It also shows how the flux emergence leads to enhanced emission in the mm continuum, which is a good proxy for local chromospheric heating, and how the opacity may vary across the flux emergence region. However, the height-integration effect of the contribution function implies that the observed brightness temperatures may be a weighted average of contributions from several different layers along the LOS \\cite[see also][]{2020ApJ...891L...8M}, which complicates the interpretation of observations.\n\nThe MHS extrapolation reveals a textbook magnetic topology similar to previously proposed models of the interaction of emergent flux and the canopy \\cite[e.g.,][]{2003Natur.425..692S,2014LRSP...11....3C,2016ApJ...825...93O}, leading to the formation of current sheets between the two flux systems \\cite[e.g.,][]{2007ApJ...666..516G,2014ApJ...788L...2A}. If the loop interaction occurs at coronal heights this may lead to much higher temperatures ($\\gtrsim$\\,1\\,MK) and produce the recently discovered {\\it campfire} EUV signatures, as simulations suggest \\citep{2021A&A...656L...7C}.\n\nThese findings may play a role in explaining the solar cycle modulation of brightness temperatures in the millimeter range and their correlation with the sunspot number \\citep{2020ApJ...902..136G}, as well as the excess millimeter brightness in chromospheres of other stars \\citep{2015ApJ...809...47M,2015A&A...573L...4L,2017A&A...602L..10O}. Our analysis quantifies radiative losses, which can also be used to benchmark simulations of solar and stellar atmospheres. \n\n\\begin{acknowledgements}\n\nThis paper makes use of the following ALMA data: ADS\/JAO.ALMA\\#2018.1.01518.S. The Swedish 1-m Solar Telescope is operated on the island of La Palma by the Institute for Solar Physics of Stockholm University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrof\u00edsica de Canarias. \nThe Institute for Solar Physics is supported by a grant for research infrastructures of national importance from the Swedish Research Council (registration number 2017-00625). ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI\/NRAO and NAOJ. \nPart of the calculations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre (NSC) at Link\\\"{o}ping University and the PDC Centre for High Performance Computing (PDC-HPC) at the Royal Institute of Technology in Stockholm. \nThis project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (SUNMAG grant agreement 759548 and SOLARNET grant agreement 824135), the Knut and Alice Wallenberg Foundation, Swedish Research Council (2021-05613) and Swedish National Space Agency (2021-00116); this material is partly based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977; X.Z. is supported by the mobility program (M-0068) of the Sino-German Science Center. \n\nThis research has made use of \\texttt{Astropy} (\\url{https:\/\/astropy.org}) -- a community-developed core Python package for Astronomy \\citepads{astropy:2018} and \\texttt{SunPy} (\\url{https:\/\/sunpy.org}) -- an open-source and free community-developed solar data analysis Python package \\citep{2015CS&D....8a4009S}.\n\n\\end{acknowledgements}\n\n\\bibliographystyle{aa}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzmahj b/data_all_eng_slimpj/shuffled/split2/finalzzmahj new file mode 100644 index 0000000000000000000000000000000000000000..4d39f913f3ac5cfed7e16719126234d81e595943 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzmahj @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nE-mail, or electronic mail, is one of the most popular forms of communication in the world, with\nover~3.9 billion active email users~\\cite{campaign_monitor}. As a side effect of this rapid growth, \nthe number of unwanted bulk email messages---i.e., spam messages---sent with commercial \nor malicious intent has also grown. According to~\\cite{campaign_monitor},\n60 billion spam emails will be sent each \nday for the next three years. \n\nWhile text-based spam filtering systems are in use by most, \nif not all, e-mail clients~\\cite{machine_learning_for_email_spam_filtering}, spammers \ncan embed messages in attached images to evade such systems---such messages are known as \nimage spam. Image spam detectors based on optical character recognition (OCR) have been \ndeployed to combat such e-mail. As a countermeasure, spammers can modify images \nso as to disrupt OCR based techniques~\\cite{image_spam_hunter}. \n\nIn recent years, deep learning models, such as multi-layer perceptrons and convolutional neural networks, \nhave been successfully applied to the image spam problem~\\cite{image_spam_hunter, \nimage_spam_analysis_and_detection, statistical_feature_extraction_for_classification, \nsupport_vector_machines_for_image_spam, deepimagespam, image_spam_filtering_using_conv, \nconvolutional_neural_networks_for_image_spam}. Note that these techniques do not rely\non OCR, but instead detect image spam directly, based on characteristics of the images.\n\nWith the recent development of perturbation methods, the possibility exists for spammers \nto utilize adversarial techniques to defeat image-based machine learning \ndetectors~\\cite{intriguing_properties}. To date, we are not aware of perturbation techniques\nhaving been used by image spammers, but it is highly likely that this will occur in the\nnear future.\n\nThe main contributions of our research are the following.\n\\begin{itemize}\n\\item We show that the universal perturbation adversarial attack is best suited for the \ntask of bypassing deep learning-based image spam filters.\n\\item We propose a new image transformation-based attack that utilizes the maximization \nof layer activations to produce spam images containing universal perturbations. This technique\nfocuses perturbations in the most salient regions, as well as concentrating natural features \nin the remaining regions.\n\\item We compare our proposed adversarial technique to existing attacks and find that our \napproach outperforms all others in terms of accuracy reduction, computation time per example, \nand perturbation magnitude.\n\\item We generate a large dataset containing both non-spam and adversarial spam images using \nour proposed attack. The authors will make this dataset available to researchers.\n\\end{itemize}\n\nThe remainder of this chapter is organized as follows. In Section~\\ref{sec:bac}, we provide\nan overview of relevant research and related work.\nIn Section~\\ref{sec:adv}, we evaluate adversarial attacks in the context of image spam, \nand in Section~\\ref{sec:univ}, we present our proposed attack.\nFinally, Section~\\ref{sec:con} concludes this chapter, where we have included\nsuggestions for future work.\n\n\n\\section{Background}\\label{sec:bac}\n\n\\subsection{Image Spam Filtering}\n\nThe initial defenses against image spam relied on\noptical character recognition (OCR). In such OCR-based systems, \ntext is extracted from an image, at which point\na traditional text-based spam filter can be used~\\cite{spam_assassin}. \nAs a reaction to OCR-based techniques,\nspammers introduced images with slight modifications, such as \noverlaying a light background of random artifacts on images, which are\nsufficient to render OCR ineffective. The rise of learning algorithms, however, \nhas enabled the creation of image spame filtering systems based directly on \nimage features. \n\nIn~2008, a filtering system using a global image feature-based probabilistic boosting \ntree was proposed, and achieved an~89.44\\%\\ detection rate with a false positive rate \nof~0.86\\%~\\cite{image_spam_hunter}. Two years later, an artificial neural network for \nimage classification was proposed~\\cite{statistical_feature_extraction_for_classification}. \nThese latter authors used were able to classify image spam\nwith~92.82\\%\\ accuracy based on color histograms, \nand~89.39\\%\\ accuracy based on image composition extraction. \n \nThe two image spam detection methods presented \nin~\\cite{image_spam_analysis_and_detection} rely on principal component analysis (PCA)\nand support vector machines (SVM). In addition, the authors \nof~\\cite{image_spam_analysis_and_detection} introduce a new dataset that their \nmethods cannot reliably detect. \nTwo years later, the authors of~\\cite{support_vector_machines_for_image_spam} improved \non the results in~\\cite{image_spam_analysis_and_detection} by training a linear SVM \non~38 image features, achieving~98\\%, \naccuracy in the best case. The authors also introduce a challenge dataset that is even \nmore challenging than the analogous dataset presented \nin~\\cite{image_spam_analysis_and_detection}. \n \nThe recent rise of deep learning, a subfield of machine learning, coupled with advances \nin computational speed has enabled the creation of filtering systems capable of considering \nnot only image features, but entire images at once. In particular, \nconvolutional neural networks (CNN) \nare well suited to computer vision tasks due to their powerful feature extraction capabilities. \n\nIn recent years, CNNs have been applied to the task of image spam detection.\nFor example, in~\\cite{image_spam_filtering_using_conv} a CNN is trained on \nan augmented dataset of spam images, achieving a~6\\%\\ improvement in accuracy,\nas compared to previous work. Similarly, the authors of~\\cite{deepimagespam} \nconsider a CNN, which achieved~91.7\\%\\ accuracy. \nIn~\\cite{convolutional_neural_networks_for_image_spam},\na CNN-based system is proposed, which achieves an accuracy of~99\\%\\ on \na real-world image spam dataset,~83\\%\\ accuracy \non the challenge dataset in~\\cite{image_spam_analysis_and_detection}\n(an improvement over previous works), \nand~68\\%\\ on the challenge dataset in~\\cite{support_vector_machines_for_image_spam}. \n\nFrom the challenge datasets introduced in~\\cite{image_spam_analysis_and_detection} and~\\cite{support_vector_machines_for_image_spam}, we see that\nthe accuracy of machine learning-based filtering systems can be \nreduced significantly with appropriate modifications to spam images. In this research, \nwe show that the accuracy of \nsuch systems can be reduced far more by using the adversarial learning\napproach that we present below. \n\n\\subsection{Adversarial Learning}\n\nThe authors of~\\cite{intriguing_properties} found that by applying an imperceptible \nfilter to an image, a given neural network's prediction can be arbitrarily changed. \nThis filter can be generated from the optimization problem\n\\begin{equation}\\nonumber\n \\begin{split}\n& \\textbf{minimize } \\|r\\|_2 \\\\\n&\\textbf{subject to } f(x+r) = l \\text{ and } x+r \\in [0, 1]^m\n \\end{split}\n\\end{equation}\nwhere $f$ is the classifier, $r$ is the minimizer, $l$ is the target label, and $m$ is the dimension of the image. \nThe resulting modified images are said to be \\textit{adversarial examples}, and the attack presented in~\\cite{intriguing_properties} is known as the \\textit{L-BFGS Attack}. These adversarial examples generalize well to different network architectures and networks.\n \nMore recently, many advances have been made in both adversarial example generation and \ndetection. For example, in~\\cite{adversarial_examples_attacks_and_defenses} a taxonomy \nis proposed\nfor generation and detection methods, as well as a threat model. Based on this threat model, \nthe task of attacking neural network-based image spam detectors requires an attack that is \nfalse-negative (i.e. generative of positive samples misclassified as negative) and black-box \n(i.e. the attacker does not have access to the trained model). \nAttacks on image spam classifiers must satisfy these two criteria. \n\nAfter the introduction of the L-BFGS Attack, the authors of~\\cite{explaining_and_harnessing} \nbuilt on their work in~\\cite{intriguing_properties} by introducing \nthe \\textit{Fast Gradient Sign Method} (FGSM). This method uses the gradient of the \nloss function with respect to a given input image to efficiently create a new image that \nmaximizes the loss, via backpropagation. This can be summarized with the expression\n$$\n \\mbox{adv}_x = x + \\epsilon\\,\\mbox{sign}\\bigl(\\nabla_x J(\\theta, x, y)\\bigr)\n$$\nwhere $\\theta$ is the parameters of the model, $x$ is the input image, $y$ is the target label, \nand $J$ is the cost function used to train the model.\nThese authors also introduce the notion that adversarial examples result from linear \nbehavior in high-dimensional spaces. \n\nThe authors of~\\cite{towards_evaluating_the_robustness} introduce \\textit{C\\&W's Attack}, \na method designed to combat \\textit{defensive distillation}, which consists of training a \npair of models such that there is a low probability of successively attacking both models. \nC\\&W's Attack is a non-box constrained variant of the L-BFGS Attack that is more easily \noptimized and effective against both distilled and undistilled networks. \nThey formulate adversarial example generation as the optimization problem\n\\begin{equation}\\nonumber\n \\begin{split}\n& \\textbf{minimize } D(x, x + \\delta) + c \\cdot f(x + \\delta) \\\\ \n& \\textbf{such that } x + \\delta \\in [0, 1]^n \n\\end{split} \n\\end{equation}\nwhere $x$ is the image, $D$ is one of the three distance metrics described below, and $c$ is a suitably chosen constraint (the authors choose $c$ with binary search). \nThe authors also utilize three distance metrics for measuring perturbation: $L_0$ (the number of altered pixels), $L_2$ (the Euclidean distance), and $L_{\\infty}$ (the maximum change to any of the coordinates), and introduced three subvariants of their attack that aim to minimize each of these distance metrics. \n \nIt is important to note that the previously mentioned attacks require knowledge of the classifier's gradient and, as such, cannot be directly deployed in a black-box attack. In~\\cite{practical_black_box}, the authors propose using a surrogate model for adversarial example generation to enable the transferability of adversarial examples to attack black-box models. Differing from gradient-based methods, the authors of~\\cite{zoo} introduced a method, \n\\textit{Zeroth Order Optimization} (ZOO), which is inspired by\nthe work in~\\cite{towards_evaluating_the_robustness}.\nThe ZOO technique employs gradient estimation, with the most significant downside \nbeing that it is computationally expensive. \n\nThe paper~\\cite{deepfool} introduces the \\textit{DeepFool} attack, which aims to find the minimum\ndistance from the original input images to the decision boundary for adversarial examples. \nThey found that the minimal perturbation needed for an affine classifier is the distance to the separating affine hyperplane, which is expressed (for differentiable binary classifiers) as \n\\begin{equation}\\nonumber\n\\begin{split}\n& \\textbf{argmin}_{\\eta_i} \\|\\eta_i\\|_2 \\\\\n& \\textbf{such that } f(x_i) + \\nabla f(x_i)^T \\eta_i = 0 \n\\end{split}\n\\end{equation}\nwhere $i$ denotes the iteration, $\\eta$ is the perturbation, and $f$ is the classifier.\nIn comparison to FGSM, DeepFool minimizes the magnitude of the perturbation, instead of the number of selected features. This would appear to be ideal for spammers, since it would tend to minimize the effect on an image.\n \nThe \\textit{universal perturbation} attack presented in~\\cite{universal} is also suited to \nthe task at hand. We believe that universal adversarial examples are most likely \nto be deployed by spammers against black-box models due to their simplicity \nand their transferability across architectures. Generating universal \nperturbations is an iterative process, as the goal is to find a vector $v$ that satisfies \n$$\n \\|v\\|_p \\leq \\xi \\mbox{\\ \\ and\\ \\ }\n \\mathbb{P}_{x\\sim \\mu} (\\hat{k}(x+v) \\neq \\hat{k}(x)) \\geq 1 - \\delta\n$$\nwhere $\\mu$ is a distribution of images, $\\hat{k}$ is a \nclassification function that outputs for each image $x$ and a label $\\hat{k}(x)$.\nThe results in~\\cite{universal} show that universal perturbations are misclassified with high probability, suggesting that the existence of such perturbations are correlated to certain regions of the decision boundary of a deep neural network.\n\nFinally, the authors of~\\cite{restoration_as_a_defense} propose input restoration with a \npreprocessing network to defend against adversarial attacks. \nThe authors' defense improved the classification precision of a CNN \nfrom~10.2\\%\\ to~81.8\\%, on average. These results outperform \nexisting input transformation-based defenses. \n\n\\section{Evaluating Adversarial Attacks}\\label{sec:adv}\n\n\\subsection{Experimental Design}\n\nThe two multi-layer perceptron and convolutional neural network architectures presented in~\\cite{convolutional_neural_networks_for_image_spam} are each trained on both of\nthe dataset presented in~\\cite{image_spam_hunter}, which henceforth will be referred to \nas the \\textit{ISH Dataset}, and the dataset presented \nin~\\cite{support_vector_machines_for_image_spam}, which henceforth will be referred to as \nthe \\textit{MD Dataset} (modified Dredze). We use TensorFlow~\\cite{tensorflow} to train\nour models---both architectures have been trained as they were presented in their \nrespective articles on each of the datasets. NumPy~\\cite{numpy} and \nOpenCV~\\cite{opencv} are used for numerical operations and image processing \ntasks, respectively. All computation are performed on a\nlaptop with~8GB ram, using Google Colaboratory's Pro GPU. \n\nThe ISH Dataset contains~928 spam images and~830 non-spam images, \nwhile the MD Dataset contains~810 spam images and~784 non-spam images;\nall images in both datasets are in \\textit{jpg} format. These datasets are summarized\nin Table~\\ref{tab:data}.\n\n\\begin{table}[!htb]\n\\caption{Image spam datasets}\\label{tab:data}\n\\centering\n\\begin{tabular}{ccc}\n\\midrule\\midrule\n\\textbf{Name} & \\textbf{Spam images} & \\textbf{Non-spam images} \\\\\n\\midrule\nISH dataset & \\zz928 & \\zz830 \\\\\nMD dataset & \\zz810 & \\zz784 \\\\\n\\midrule\nTotal & 1738 & 1613 \\\\\n\\midrule\\midrule\n\\end{tabular}\n\\end{table}\n\nDataset preprocessing for the networks \npresented in~\\cite{convolutional_neural_networks_for_image_spam} consist \nof downsizing each of the images such that their dimensions are~$32\\time 32\\times 3$, \napplying zero-parameter Canny edge detection~\\cite{canny} to a copy of the downsized \nimage, and concatenating the downsized image with the copy that had \nCanny edge detection applied. This process results in~$64\\time 32\\time 3$ images, \nwhich are used to train the two neural networks, one for the ISH dataset, and one for the MD dataset. \nThe four resulting models achieved accuracies within roughly~7\\%\\ of the accuracies \nreported in~\\cite{convolutional_neural_networks_for_image_spam}. \n\nTo enable the generation of adversarial examples, four larger models with an input size of 400x400 are also trained on \nthe original datasets. The first few layers of each of these models are simply used to \ndownscale input images such that the original architectures can be used after downscaling. \nThese four alternative models achieve accuracy roughly equivalent to the \noriginal models. The four adversarial attacks \n(FGSM, C\\&W's Attack, DeepFool, and Universal Perturbation) \nutilize these four alternative models to generate adversarial examples that can then be formatted \nas the original datasets to attack the original four models. This procedure attempts to exploit \nthe transferability of adversarial examples to similar architectures. \n\nThe IBM Adversarial Robustness Toolbox (ART)~\\cite{adversarial_robustness_toolbox} is \nused to implement C\\&W's Attack, DeepFool, \nand Universal Perturbations, while FGSM was implemented independently from scratch.\nAn attempt was made to optimize the parameters of each technique---the resulting\nparameters are summarized in Table~\\ref{tab:parms}. Note that\nfor the Universal Perturbation attack, FGSM was used as the base attack, as the IBM ART \nallows any adversarial attack to be used for computing universal perturbations.\n\n\\begin{table}[!htb]\n\\advance\\tabcolsep by 4pt\n\\caption{Attack parameters}\\label{tab:parms}\n\\centering\n\\begin{tabular}{c|ll}\n\\midrule\\midrule\n\\textbf{Attack} & \\textbf{Description} & \\textbf{Value} \\\\\n\\midrule\nFGSM \n & perturbation magnitude & 0.1 \\\\ \\midrule\n\\multirow{6}{*}{C\\&W's attack} \n & target confidence & 0 \\\\ \n & learning rate & 0.001 \\\\\n & binary search steps & 20 \\\\ \n & maximum iterations & 250 \\\\\n & initial trade-off & 100 \\\\\n & batch size & 1 \\\\ \\midrule\n\\multirow{4}{*}{DeepFool}\n & max iterations & 500 \\\\ \n & overshoot parameter & $10^{-6}$ \\\\ \n & class gradients & 10 \\\\\n & batch size & 1 \\\\ \\midrule\n\\multirow{4}{*}{Universal Perturbation}\n & target accuracy & 0\\% \\\\ \n & max iterations & 250 \\\\\n & step size & 64 \\\\\n & norm & $\\infty$ \\\\\n\\midrule\\midrule\n\\end{tabular}\n\\end{table}\n\nThe metrics used to evaluate each of the four attacks are the average accuracy, \narea under the curve (AUC) of the receiver operating characteristic (ROC) curve, average~$L_2$ perturbation measurement \n(Euclidean distance), and average computation time per example for each of the four models. \nScikit-learn~\\cite{scikit-learn} was used to generate the ROC curves for each attack. \n\nWe use~251 data points for accuracy \nand $L_2$ distances collected for the FGSM, DeepFool, and Universal Perturbation experiments, \nin accordance with the full size of the test dataset, which contains~251 examples for generating adversarial examples. However, only~$28$ data points were \ncollected from the C\\&W's Attack experiment due to \nthe large amount of time required to generate each data point (roughly five minutes per data point). \nThe technique that will be used as the basis of our proposed attack will be selected based \non the performance of each attack, as presented in the next section.\n\n\\subsection{Analysis}\n\n\nThe mean accuracy, computation time per example, and~$L_2$ distance were \nrecorded for each of the four models attacked by each of the attack methods. \nThis data was compiled into the tables discussed in this section. \n\nFrom Table~\\ref{tab:meanacc1} we see that for FGSM, the accuracy of the attacked models \nis shown to vary inconsistently while Figure~\\ref{fig:fgsml2} shows that the distribution of \nthe~$L_2$ distances of the generated adversarial examples skew right. \nBased on these results \nand corresponding density plots of the accuracy and~$L_2$ distance distributions, \nthe FGSM attack can be ruled out as a candidate due to poor accuracy. \n\n\\begin{table}[!htb]\n\\advance\\tabcolsep by 4pt\n \\centering\n \\caption{Mean accuracy per adversarial example}\n \\label{tab:meanacc1}\n \\begin{tabular}{c|cccc}\n \\midrule\\midrule\n \n \\multirow{2}{*}{\\textbf{Model}}\n & \\multirow{2}{*}{\\textbf{FGSM}}\n & \\textbf{C\\&W's}\n & \\multirow{2}{*}{\\textbf{DeepFool}}\n & \\textbf{Universal}\\\\\n & & \\textbf{Attack} & & \\textbf{Perturbation}\\\\\n \\midrule\n MLP (ISH) & 95.2\\% & 89.2\\% & 98.8\\% & 98.7\\%\\\\\n CNN (ISH) & 36.2\\% & 49.6\\% & 61.5\\% & 49.9\\%\\\\\n MLP (MD) & 69.7\\% & 75.6\\% & 93.5\\% & 94.3\\%\\\\\n CNN (MD) & 82.8\\% & 77.2\\% & 14.5\\% & \\zz8.4\\%\\\\\n \\midrule\\midrule\n \\end{tabular}\n\\end{table}\n\nThe mean~$L_2$ (Euclidean) distances of the adversarial examples\nare given in Table~\\ref{tab:meandist1}. The distribution of distances appears to be roughly equivalent across all attacks.\n\n\\begin{table}[!htb]\n\\advance\\tabcolsep by 4pt\n \\centering \n \\caption{Mean $L_2$ (Euclidean) distance of adversarial examples from original images}\n \\label{tab:meandist1}\n \\begin{tabular}{c|cccc}\n \\midrule\\midrule\n \n \\multirow{2}{*}{\\textbf{Model}}\n & \\multirow{2}{*}{\\textbf{FGSM}}\n & \\textbf{C\\&W's}\n & \\multirow{2}{*}{\\textbf{DeepFool}}\n & \\textbf{Universal}\\\\\n & & \\textbf{Attack} & & \\textbf{Perturbation}\\\\\n \\midrule\n MLP (ISH) & 11537.55 & 10321.77 & 11513.26 & 11483.72\\\\\n CNN (ISH) & 11108.44 & 10924.14 & 11216.19 & 11416.58\\\\\n MLP (MD) & \\zz8998.71 & \\zz9185.04 & \\zz9566.02 & \\zz9490.56\\\\\n CNN (MD) & \\zz9144.49 & \\zz9009.91 & \\zz9128.99 & \\zz9381.15\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\n\\begin{figure}[!htb]\n\\centering\n\\includegraphics[width=7.8cm]{images\/FGSMl2.png}\n\\caption{Density plot of~$L_2$ (Euclidean) distances (Fast Gradient Sign Method)}\n\\label{fig:fgsml2}\n\\centering\n\\end{figure}\n\nDeepFool can also be ruled as a candidate, as the attack has been seen to be \nonly marginally better than the FGSM attack in terms of performance, while also \nhaving a significantly higher average computation time per adversarial example. This\ncan be observed in Table~\\ref{tab:meancomp1}, where the computation time \nper example varies greatly. \n\n\\begin{table}[!htb]\n\\advance\\tabcolsep by 4pt\n \\centering\n \\caption{Mean computation time per adversarial example}\n \\label{tab:meancomp1}\n \\begin{tabular}{c|cccc}\n \\midrule\\midrule\n \n \\multirow{2}{*}{\\textbf{Model}}\n & \\multirow{2}{*}{\\textbf{FGSM}}\n & \\textbf{C\\&W's}\n & \\multirow{2}{*}{\\textbf{DeepFool}}\n & \\textbf{Universal}\\\\\n & & \\textbf{Attack} & & \\textbf{Perturbation}\\\\\n \\midrule\n MLP (ISH) & 0.180 & 269.65 & 19.90 & 4.37\\\\\n CNN (ISH) & 0.038 & 251.01 & \\zz4.75 & 2.87\\\\\n MLP (MD) & 0.164 & 270.58 & 36.30 & 3.71\\\\\n CNN (MD) & 0.165 & 244.47 & \\zz1.48 & 5.23\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\nIn contrast, C\\&W's Attack shows consistent performance in all three metrics at the cost of \nhigh computation time (roughly five minutes per adversarial example).\nThe consistency of this attack is ideal from a spammer's perspective, \nthough the trade-off is a relatively high computation time. In addition, the \nleft skew of this attack with respect to~$L_2$ distance, as presented in Figure~\\ref{fig:cwl2}, \nindicates that the perturbation made to spam images is much lower in comparison to the other attacks.\n\n\\begin{figure}[!htb]\n\\centering\n\\includegraphics[width=8cm]{images\/CWl2.png}\n\\caption{Density plot of~$L_2$ (Euclidean) distances (C\\&W's Attack)}\n\\label{fig:cwl2}\n\\centering\n\\end{figure}\n\nThe Universal Perturbation attack is inconsistent in terms of accuracy, as shown \nin Table~\\ref{tab:meanacc1} where the mean accuracy across the four models is clearly \nshown to fluctuate wildly, but this \nis simply due to the fact that only one perturbation (albeit with varying success across architectures) is applied to all spam images, which is highly advantageous for spammers.\nThe generation and application of this perturbation to an image takes roughly four seconds,\nwhich would result in greater performance in a real-world spam setting in comparison \nto C\\&W's Attack. \n \nTo further compare C\\&W's Attack and the Universal Perturbation attack, \nthe ROC curves of the two are presented in Figure~\\ref{fig:cwroc_uproc}.\nThese ROC curves can be used to quantify the diagnostic ability of the \nmodels attacked by each method.\n\n\\begin{figure}[!htb]\n \\centering\n \\begin{minipage}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{images\/CWroc.png}\n \\end{minipage}\n \\begin{minipage}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{images\/UProc.png}\n \\end{minipage}\n \\caption{ROC curves of C\\&W's Attack and the Universal Perturbation when used to attack the four classifiers}\n \\label{fig:cwroc_uproc}\n\\end{figure}\n\nThe ROC curve for C\\&W's attack is much noisier due to being generated from only~28 \ndata points. Taking this into consideration, it can be inferred that both C\\&W's Attack \nand the Universal Perturbation attack are able to reduce the areas under the ROC curve (AUC) \nof the attacked models to values close to~0.5. This suggests that both attacks are able to reduce the class separation capacity of attacked image spam classifiers to essentially random. \n\nTo analyze the differences in distribution of the accuracy and~$L_2$ distance data collected from \nthe trials conducted on C\\&W's Attack and the Universal Perturbation attack, the Mann-Whitney U Test was utilized via its implementation \nin SciPy~\\cite{scipy}. The Mann-Whitney U Test compares two populations---in \nthis case, the accuracy and~$L_2$ distance data from both attacks for each attacked model.\nThe null hypothesis (H0) for the test is that the probability is~50\\%\\ that a randomly drawn \nvalue from the first population will exceed a value from the second population. The result of each \ntest is a Mann-Whitney U Statistic (not relevant in our case) and a~$p$-value. We use\nthe~$p$-value to determine whether the difference between the data is statistically \nsignificant, where the standard threshold is~$p = 0.05$. The results of these tests are \ngiven in Table~\\ref{tab:mannwhitneyu}.\n\n\\begin{table}[!htb]\n\\advance\\tabcolsep by 4pt\n \\centering\n \\caption{Mann-Whitney U Test results comparing C\\&W's Attack and the Universal Perturbation attack}\n \\label{tab:mannwhitneyu}\n \\begin{tabular}{l|ll}\n \\midrule\\midrule\n \\multicolumn{1}{c|}{\\textbf{Model}}\n & \\multicolumn{1}{c}{\\textbf{Accuracy $p$-value}} \n & \\multicolumn{1}{c}{\\textbf{$L_2$ distance $p$-value}} \\\\\n \\midrule\n MLP ISH & 0.000 (H0 is rejected) & 0.034 (H0 is rejected)\\\\\n CNN ISH & 0.384 (H0 is not rejected) & 0.098 (H0 is not rejected)\\\\\n MLP MD & 0.000 (H0 is rejected) & 0.057 (H0 is not rejected)\\\\\n CNN MD & 0.000 (H0 is rejected) & 0.016 (H0 is rejected)\\\\\n \\midrule\\midrule\n \\end{tabular}\n\\end{table}\n\nThe results in Table~\\ref{tab:mannwhitneyu} imply that the performance of these two attacks (C\\&W's Attack and the Universal Perturbation attack)\nare nearly identical when attacking a CNN trained on the ISH dataset, \nas evidenced in the second row, where the null hypothesis is not rejected. \nHowever, the~$L_2$ distance measurement for spam images \nthat have had the universal perturbation applied should remain constant relative \nto the original spam image. Therefore, the results of these tests suggest that \nthe Universal Perturbation attack is able to achieve similar performance \nto C\\&W's Attack, in terms of perturbation magnitude, \nwith a much lower computation time per example in comparison to C\\&W's Attack. \n\nGiven the above evidence, the Universal Perturbation attack is the best choice for \nimage spam, as it is unrivaled in terms of potential performance in a real-world \nsetting. The key advantages of the Universal Perturbation attack include\nthat it generates a single perturbation to be applied to all spam images, \nand its relatively fast computation time per adversarial example. Therefore,\nUniversal Perturbation will be used as a basis for our image transformation \ntechnique, as discussed and analyzed in the remainder of this paper. A sample adversarial spam image generated with the Universal Perturbation attack is presented in Figure~\\ref{fig:sampleadv}.\n\n\n\\begin{figure}[!htb]\n \\centering\n \\includegraphics[width=8cm]{images\/newspamimage.png}\n \\caption{Adversarial spam image generated with the Universal Perturbation attack}\n \\label{fig:sampleadv}\n\\end{figure}\n\n\\section{Inceptionism-Augmented Universal Perturbations}\\label{sec:univ}\n\n\\subsection{Procedure}\n\nBased on the results and discussion above, a transformation that\nis applied to spam images prior to generating adversarial examples, since perturbations cannot be transformed after application, should \nmeet the following conditions.\n\\begin{itemize}\n \\item Lower the misclassification rate\n \\item Preserve adversarial effects after image resizing\n \\item Make non-spam features more prominent, while retaining legibility\n\\end{itemize}\nGiven the above criteria, a reasonable approach would be to maximize the presence \nof \"natural features\" in a given spam image. That is, the features characteristic of non-spam images learned by classifiers should be maximized whilst retaining legibility. To accomplish this, the procedure for maximizing \nthe activation of a given output neuron (in this case, the non-spam output neuron), \nas introduced in~\\cite{deepdream}, dubbed \"DeepDream\", can be used to increase the number of natural features in all images from the non-spam subsets of the ISH and MD datasets.\nThis is accomplished by maximizing the activations of the convolutional layer \nwith the greatest number of parameters and output layer in the corresponding CNNs. \nThe resulting two sets of images that have had DeepDream applied (``dreamified'' images) are then grouped into batches of four images. The weighted average of the four images in each batch can then be taken to produce two \nprocessed non-spam datasets of images with high concentrations of natural features, as batches of greater than four images may result in high noise. Each of the images in the resulting two non-spam datasets \nare henceforth referred to as \\textit{natural perturbations}.\n\nTo preserve the adversarial effect that the universal perturbation introduces, \nthe Gradient-weighted Class Activation Mapping (Grad-CAM) technique \nintroduced in~\\cite{gradcam} \nis used to generate a class activation map for each spam \nimage in each dataset. The inverse of each such map is used with a natural perturbation \ngenerated from the same dataset to remove the regions of the \nnatural perturbation where the class activation map is highest. \nBy superimposing the resulting natural perturbations onto the corresponding spam images, \nthe regions where the universal perturbation is most effective are left intact while the \nregions of the spam images affected by the natural perturbations benefit by being\nmore non-spam-like. The presence of natural features in the resulting spam images \nshould also result in robustness against resizing prior to inference by a deep \nlearning-based image spam detection model, as the natural features should be still be somewhat preserved even after being shrunken. \n\nThe universal perturbation is then applied to each of the resulting spam images. \nThe result is that we potentially reduce a deep learning-based image spam \ndetector's accuracy due to the presence of a natural perturbation and a universal adversarial perturbation and retain some sort of adversarial effect in the case of resizing.\nThis procedure also allows for the retention of legible text within spam images. \n\n\\subsection{Implementation}\n\nTo generate our two sets of ``dreamified'' images, the CNN architecture presented in~\\cite{convolutional_neural_networks_for_image_spam} is trained on both \nthe ISH and MD datasets, with inverted labels to allow for the maximization of the \nactivations of the neurons corresponding to non-spam images, as the activations for spam images would be maximized if the labels weren't inverted. \nThese two models are trained with the TensorFlow Keras API, with the \nhyperparameters given in~\\cite{convolutional_neural_networks_for_image_spam}. \nFor each of the models, the convolutional layer with the highest number of parameters \nand the output layer were chosen as the layers in which the activation should be maximized \nvia gradient ascent, as the aforementioned convolutional layer is responsible for recognizing the most complex natural features. Each of the images from the non-spam subsets of the ISH and MD datasets were used for inference on the two CNN models. The CNN models use \nthe losses of the chosen layers to iteratively update the non-spam images with gradient \nascent so that the number of non-spam features is maximized. \nEach non-spam image \nis updated for~64 iterations with an update size of~$0.001$. The resulting ``dreamified'' \nimages are then grouped into batches of~4 and blended via evenly distributed weighted \naddition to produce a total of~392 grayscale images, each of size~$400\\times 400\\times 1$.\nThese~392 grayscale images are evenly split between the ISH dataset and MD datasets.\n \nTo utilize GradCAM, \nthe CNN architecture presented in~\\cite{convolutional_neural_networks_for_image_spam} \nis trained on both the ISH and MD datasets with normal labels. \nFor each image from the spam subsets of the ISH and MD datasets, \nGradCAM is used to generate a corresponding class activation map based \non the activations of the last convolutional layer in each of the two models.\nThis is accomplished by computing the gradient of the top predicted class with \nrespect to the output feature map of the last convolutional layer, \nusing the mean intensity of the gradient over specific feature map channels. \nOpenCV~\\cite{opencv} is then used to upscale each of the class activation maps \nto~$400\\times 400$, convert them to binary format, and invert the result to allow the class activation maps to be applied to the natural perturbations such that only the areas with highest activation will contain the natural perturbations. \nThe bitwise AND of each processed class activation map and a randomly selected \nnatural perturbation can then be used to generate two sets of processed \nnatural perturbations, which are superimposed on the corresponding spam images \nfrom each of the two spam subsets. This procedure results in two subsets \nof spam images with natural perturbations. \n\nLastly, the universal perturbation is generated and applied to all images within \nthe two spam image subsets that have had natural perturbations applied. For this\noperation, we use the IBM Adversarial Robustness Toolbox~\\cite{adversarial_robustness_toolbox}.\nThe hyperparameters for the Universal Perturbation attack remain the same \nas those given in Table~\\ref{tab:parms}, above.\n\n\\subsection{Performance Evaluation}\n\nThe mean accuracy, computation time per example, and~$L_2$ distance were recorded for \neach of the four models attacked using spam images with modified universal perturbations.\nThis is analogous to what was done during the attack selection process. \nThis data has been compiled into the tables discussed in this section.\n\nAs can be seen from the results in Table~\\ref{tab:meanacc2},\nthe proposed method for generating adversarial \nspam images is capable of lowering a learning-based model's accuracy \nto~23.7\\%. In addition, on average, our proposed technique is\nmuch more effective while being evenly distributed in terms of accuracy \non similar learning-based models. \n\n\\begin{table}[!htb]\n\\advance\\tabcolsep by 4pt\n \\centering\n \\caption{Mean accuracy of each model with spam images created by the proposed method}\n \\label{tab:meanacc2}\n \\resizebox{0.85\\textwidth}{!}{\n \\begin{tabular}{c|cccc}\n \\midrule\n \\midrule\n \\textbf{Images} & \\textbf{MLP (ISH)} & \\textbf{CNN (ISH)} & \\textbf{MLP (MD)} & \\textbf{CNN (MD)} \\\\\n \\midrule\n Modified spam images & 80.1\\% & 98.8\\% & 98.4\\% & 75.3\\% \\\\\n Modified spam images with & \\multirow{2}{*}{72.2\\%} \n \t\t\t\t\t\t& \\multirow{2}{*}{50.4\\%} \n\t\t\t\t\t\t& \\multirow{2}{*}{78.7\\%} \n\t\t\t\t\t\t& \\multirow{2}{*}{23.7\\%} \\\\\n Universal Perturbations \\\\\n \\midrule\\midrule\n \\end{tabular}}\n\\end{table}\n\nFrom Table~\\ref{tab:meancomp2}, we see that\nin contrast to C\\&W's Attack, which on average takes 258.93 seconds per example, the time necessary to generate adversarial spam images \nwith natural perturbations is significantly lower and comparable to \nthat of the original Universal Perturbation attack.\nThis is another advantage of our proposed attack.\n\n\\begin{table}[!htb]\n\\advance\\tabcolsep by 4pt\n \\centering\n \\caption{Mean computation time per adversarial spam image (in seconds)}\n \\label{tab:meancomp2}\n \\begin{tabular}{cccc}\n \\midrule\\midrule\n \\textbf{MLP (ISH)} & \\textbf{CNN (ISH)} & \\textbf{MLP (MD)} & \\textbf{CNN (MD)} \\\\\n \\midrule\n 5.46 & 5.15 & 5.87 & 4.80 \\\\\n \\midrule\\midrule\n \\end{tabular}\n\\end{table}\n\nThe mean~$L_2$ distances and the distribution of the~$L_2$ distances of the modified adversarial spam \nimages are given in Table~\\ref{tab:meandist2}. From Figure~\\ref{fig:lastl2}, we see that the distributions \nof these distances are, on average, not skewed, indicating that the natural perturbations have \nhad a slight negative effect on the spam image $L_2$ distances, as the distributions for the original Universal Perturbation attack were skewed to the left. \n\n\\begin{table}[!htb]\n\\advance\\tabcolsep by 4pt\n \\centering\n \\caption{Mean~$L_2$ (Euclidean) distance of modified adversarial spam images from original images}\n \\label{tab:meandist2}\n \\begin{tabular}{cccc}\n \\midrule\\midrule\n \\textbf{MLP (ISH)} & \\textbf{CNN (ISH)} & \\textbf{MLP (MD)} & \\textbf{CNN (MD)} \\\\\n \\midrule\n 11392.02 & 11309.40 & 9440.69 & 9628.61 \\\\\n \\midrule\\midrule\n \\end{tabular}\n\\end{table}\n\n\\begin{figure}[!htb]\n \\centering\n \\includegraphics[width=8cm]{images\/lastl2.png}\n \\caption{Density plot of~$L_2$ (Euclidean) distances of the modified adversarial spam images from the original images}\n \\label{fig:lastl2}\n\\end{figure}\n\nThe ROC curves of the models attacked by the proposed method, \nwhich appear in Figure~\\ref{fig:lastroc}, are slightly worse in comparison to \nthat of the original Universal Perturbation attack, suggesting once more that the \nattack is capable of reducing the class separation capacity of attacked image spam \nclassifiers to essentially random.\n\n\\begin{figure}[!htb]\n \\centering\n \\includegraphics[width=8cm]{images\/lastroc.PNG}\n \\caption{ROC curves of each of the four models attacked by the modified spam images generated with the proposed method}\n \\label{fig:lastroc}\n\\end{figure}\n\n\\subsection{Proposed Dataset Analysis}\n\nFigure~\\ref{fig:spamimage2} contains an example of a modified adversarial spam images.\nFrom this image, we observe that\nthe proposed method was able to effectively utilize class activation maps \ngenerated with GradCAM to selectively apply a random natural perturbation \nto the spam image. As discussed in the previous section, this decreases \nclassification accuracy even prior to the application of a universal perturbation. \n\n\\begin{figure}[!htb]\n \\centering\n \\includegraphics[width=6cm]{images\/img213.jpg}\n \\caption{Example of modified adversarial spam image generated with the proposed method}\n \\label{fig:spamimage2}\n\\end{figure}\n\nTo fully evaluate the effect of the modified adversarial spam images from the two \nmodified datasets, two sets of class activation maps are generated from the spam \nsubsets of the two datasets using GradCAM and the corresponding CNN models.\nThese activation maps are then averaged to obtain two heatmaps \nfrom the class activation maps, as shown in Figures~\\ref{fig:origishheatmap1} \nand~\\ref{fig:origsvmheatmap1}. For comparison, the same process was applied to \nthe original datasets to obtain Figures~\\ref{fig:ishheatmap1} and~\\ref{fig:svmheatmap1}.\n\n\\begin{figure}[!htb]\n \\centering\n \\begin{minipage}[b]{0.425\\textwidth}\n \\includegraphics[width=\\textwidth]{images\/ishheatmap1.png}\n \\caption{ISH spam data}\n \\label{fig:ishheatmap1}\n \\end{minipage}\n \\begin{minipage}[b]{0.425\\textwidth}\n \\includegraphics[width=\\textwidth]{images\/svmheatmap1.png}\n \\caption{MD spam data}\n \\label{fig:svmheatmap1}\n \\end{minipage}\n\\end{figure}\n\n\\begin{figure}[!htb]\n \\centering\n \\begin{minipage}[b]{0.425\\textwidth}\n \\includegraphics[width=\\textwidth]{images\/origishheatmap1.png}\n \\caption{Modified ISH spam data}\n \\label{fig:origishheatmap1}\n \\end{minipage}\n \\begin{minipage}[b]{0.425\\textwidth}\n \\includegraphics[width=\\textwidth]{images\/origsvmheatmap1.png}\n \\caption{Modified MD spam data}\n \\label{fig:origsvmheatmap1}\n \\end{minipage}\n\\end{figure}\n\nAs can be seen in Figures~\\ref{fig:ishheatmap1} and~\\ref{fig:svmheatmap1}, \nthe activation regions for spam images from the original ISH and MD datasets \nare skewed towards the top and bottom. The narrow shape of these regions represent \nthe regions in spam images that generate the highest activations in \nthe neurons of the deep learning-based classifier. \nThe central region of the average class activation map for spam images from the \nMD dataset is much darker in comparison to that of spam images \nfrom the ISH dataset due to the superimposition of natural images \ndirectly onto spam features, \nas described in~\\cite{support_vector_machines_for_image_spam}. \n\nIn contrast, Figures~\\ref{fig:origishheatmap1} and~\\ref{fig:origsvmheatmap1} indicate \nthat the introduction of natural and universal adversarial perturbations \nare able to more evenly distribute the activation regions. This result shows \nthat the spam images from the modified datasets are much closer---in terms \nof natural features---to non-spam images. This also suggests that the proposed \nmethod outperforms the procedure used to generate the original MD dataset \nas outlined in~\\cite{support_vector_machines_for_image_spam}. \n\n\\section{Conclusion and Future Work}\\label{sec:con}\n\nModern deep learning-based image spam classifiers can accurately classify \nimage spam that has appeared to date in the wild. \nHowever, spammers are constantly creating new countermeasures to\ndefeat anti-spam technology. Consequently, the eventual use of adversarial examples \nto combat deep learning-based image spam filters is inevitable. \n\nIn this chapter, four adversarial attacks were selected based on specific restrictions \nand constraints of the image spam problem. These adversarial attacks \nwere evaluated on the CNN and MLP architectures introduced in~\\cite{convolutional_neural_networks_for_image_spam}. For training data,\nwe used the dataset presented in~\\cite{image_spam_hunter} \nand~\\cite{support_vector_machines_for_image_spam}. \nThe Fast Gradient Sign Method (FGSM) attack, C\\&W's Attack, DeepFool, and the \nUniversal Perturbation attack were all evaluated based on mean accuracy reduction, \nmean computation time per adversarial spam image, mean $L_2$ distance from the original spam \nimages, and ROC curves of the attacked classifiers. Through further statistical analysis, \nthe Universal Perturbation was chosen as a base for our proposed image transformation \nattack, due to its versatility and overall high performance \nin terms of accuracy reduction and computation time.\n\nTo maximize the number and intensity of natural features in an attack, the approach \nintroduced in~\\cite{deepdream} for maximizing activations of certain layers in \na deep neural network was used. This technique serves to generate sets of ``natural perturbations'' \nfrom the non-spam subsets of the image spam datasets. These natural perturbations \nwere then modified via the class activation maps of all spam images in both datasets. \nThe class activations were generated using GradCAM from the two convolutional \nneural networks trained on the ISH and MD datasets. These activation maps allow the regions in spam images recognized to contribute most to the spam classification to benefit from a universal adversarial perturbation. \n\nOur technique resulted in comparable---if not greater---accuracy reduction as\ncompared to C\\&W's Attack. In addition, our approach is \ncomputation much more efficient than C\\&W's Attack. \nFurthermore, the nature of our attack implies that the only potential \ncomputational bottleneck is generating the modified natural perturbations.\nThis aspect of the attack\nwould not be an issue in practice, unless a spammer generates \nvast numbers (i.e., in the millions) of modified \nadversarial spam images. \n\nA dataset of modified adversarial \nspam images has been generated by the authors by applying the proposed attack to the spam subsets of the ISH and MD datasets. This dataset will be made freely available to researchers.\n\nFuture work will include evaluating the ability of adversarial attack defense \nmethods. We will consider defensive distillation against adversarial spam images \ngenerated with our proposed attack. The goal of this research will be to develop defenses specifically designed for natural perturbation-augmented adversarial spam images. For example,\nthe subtraction of predicted adversarial perturbations is one path that we intend to pursue. \n\n\n\n\n\\bibliographystyle{plain}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction Definitions and Results}\nOriginally, the Chuang's inequality is a standard estimate in Nevanlinna theory but later on this inequality is used as a valuable tool in the study\nof value distribution of differential polynomials. For example, Recently, using this inequality, some sufficient conditions are obtained for which two differential polynomials sharing a small function satisfies the conclusions of Br\\\"{u}ck conjecture (\\cite{bb}, \\cite{bc1}, \\cite{bc2}, \\cite{bm}). \\par\nAt this point, we recall some notations and definitions to proceed further.\\par\nIt will be convenient to let $E$ denote any set of positive real numbers of finite linear measure, not necessarily the same at each occurrence.\\par\nLet $f$ be a non constant meromorphic function in the open complex plane $\\mathbb{C}$. For any non-constant meromorphic function $f$, we denote by $S(r, f)$ any quantity satisfying $$S(r, f) = o(T(r, f))\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\ (r\\lra \\infty, r\\not\\in E).$$\nA meromorphic function $a(\\not\\equiv \\infty)$ is called a small function with respect to $f$ provided that $T(r,a)=S(r,f)$ as $r\\lra \\infty, r\\not\\in E$.\\par\nWe use $I$ to denote any set of infinite linear measure of $0&=& \\sum_{n=0}^N \\genfrac{(}{)}{0pt}{}{N}{n}^{1\/2}\\cos^{n}(\\theta\/2)\n \\sin^{N-n}(\\theta\/2)\n \\nonumber\\\\\n &\\times& e^{i(N-n)\\phi}| n, N-n \\rangle\\;.\n\\end{eqnarray}\n\nThe Gross-Pitaevskii dynamics can be mapped to that of a nonrigid\npendulum~\\cite{SmerziEtAl97}. Including the term describing the periodic shaking, the \nHamilton function is given by ($z=\\cos^2(\\theta\/2)-\\sin^2(\\theta\/2)$):\n\\begin{eqnarray}\nH_{\\rm mf}& = &\\frac{N\\kappa}{\\Omega}z^2-\\sqrt{1-z^2}\\cos(\\phi)\\nonumber\\\\\n&-&2z\\left(\\frac{\\mu_0}{\\Omega}+\n\\frac{\\mu_1}{\\Omega}\\sin\\left({\\textstyle\\frac{\\omega}{\\Omega}}\\tau\\right)\\right)\\;,\\quad \\tau =t\\Omega\\;.\n\\label{eq:mean}\n\\end{eqnarray}\nIn our case, $z\/2$ is \nthe experimentally measurable~\\cite{GatiOberthaler07} population imbalance which can\nbe used\nto characterise the mean-field dynamics. The dynamics for this system are known to become\nchaotic~\\cite{GuckenheimerHolmes83}. \n\n\n\\section{Multi-particle-entanglement \\& Quantum Fisher information\\label{sec:multi}}\n\nMulti-particle-entanglement~\\cite{Yukalov2003,Vaucher08,DunninghamEtAl05,PoulsenEtAl05,CiracEtAl98,MicheliEtAl03,MahmudEtAl03,Dounas-frazerEtAl07}\nis a hot topic of current research; to experimentally realise ``Schr\\\"odinger-cat'' like\nsuperpositions of BECs is still a challenge of fundamental research. For the periodically driven\ndouble-well potential, the relation between emergence of ``Schr\\\"odinger-cat'' like mesoscopic superpositions\nin phase-space and mean-field chaos has been discovered in Ref.~\\cite{WeissTeichmann08}. An ideal\nexample of such a mesoscopic superposition is shown\nin Fig.~\\ref{fig:cat}; the fidelity~\\cite{fidelity} of some of the highly entangled states found numerically\nwas well above 50\\%~\\cite{WeissTeichmann08}. In this manuscript, we employ the fact that the\nquantum Fisher information can be used to detect multi-particle entanglement~\\cite{PezzeSmerzi2007}.\n\\begin{figure}\n\\vspace{-1cm}\n\\includegraphics[angle=-90,width=\\linewidth]{cat_perfect.jpg}\n\\caption{\\label{fig:cat}(Colour online) An ideal ``Schr\\\"odinger-cat'' like state which is the superposition of\n two atomic coherent states~(\\ref{eq:atomic}) with hardly any overlap. \nThe figure\nshows the projection \n$|\\langle \\psi_{\\rm cat}| \\theta, \\phi\\rangle|^2$\n of the\n``Schr\\\"odinger-cat'' like state $\\psi_{\\rm cat} = \\frac{1}{\\sqrt{2}} \\left( |z=-0.6, \\phi = 1.2 \\rangle + |z = 0.65, \\phi=-2.20 \\rangle\n\\right)$ onto the atomic\ncoherent states $|\\theta, \\phi \\rangle$ (e.q. \\ref{eq:atomic}) in dependece of\n$z$ and $\\phi$.\nWhile\n Ref.~\\cite{WeissTeichmann08} concentrated on identifying such highly entangled state, the\n present manuscript uses a different approach to search highly entangled states: the sufficient condition~(\\ref{eq:sufficient}) derived in\n Ref.~\\cite{PezzeSmerzi2007} by using the quantum Fisher information~(\\ref{eq:fisher})\n (Ref.~\\cite{PezzeSmerzi2007} and references therein).}\n\\end{figure}\n \nBefore defining the quantum Fisher information, we note that the creation and annihilation\noperators can be used to define\n\\begin{eqnarray}\n\\hat{J}_x =& &\\frac12\\left(\\hat{a}^{\\dag}_1\\hat{a}_2^{\\phantom{\\dag}}+\\hat{a}^{\\dag}_2\\hat{a}^{\\phantom{\\dag}}_1\\right)\\;,\\\\\n\\hat{J}_y =& -&\\frac i2\\left(\\hat{a}_1^{\\dag}\\hat{a}_2^{\\phantom{\\dag}}-\\hat{a}_2^{\\dag}\\hat{a}^{\\phantom{\\dag}}_1\\right)\\;,\\\\\n\\hat{J}_z =& &\\frac 12\\left(\\hat{a}_1^{\\dag}\\hat{a}_1^{\\phantom{\\dag}}-\\hat{a}_2^{\\dag}\\hat{a}_2^{\\phantom{\\dag}}\\right)\\;,\n\\end{eqnarray}\nwhich satisfy angular momentum commutation rules. Except for a factor of $N$, the operator $\\hat{J}_z$ is the operator\nof the (experimentally measurable~\\cite{GatiOberthaler07}) population imbalance.\n\nWhile the quantum Fisher information~$F_{\\rm Q}$ can be defined for statistical mixtures, it is\nparticularly simple for pure states~\\cite{PezzeSmerzi2007}:\n\\begin{equation}\nF_{\\rm Q} = 4(\\Delta \\hat{J}_n)^2\\;;\n\\end{equation}\nwhere $\\Delta \\hat{J}_n$ are the mean-square fluctuations of $\\hat{J}$ in direction $n$. In\nthe following we choose the $z$-direction, thus\n\\begin{equation}\n\\label{eq:fisher}\nF_{\\rm Q} = 4(\\Delta \\hat{J}_z)^2\\;.\n\\end{equation}\nFor $N$ particles, a sufficient condition for multi-particle entanglement is given\nby~\\cite{PezzeSmerzi2007}:\n\\begin{eqnarray}\n\\label{eq:sufficient}\nF_{\\rm ent}&>&1\\;,\\\\\nF_{\\rm ent}&\\equiv&\\frac{F_{\\rm Q}}N\\;.\n\\label{eq:flag}\n\\end{eqnarray}\nFor the ideal ``Schr\\\"odinger-cat'' state in real space,\n\\begin{equation}\n|\\psi_{\\rm NOON}\\rangle=\\frac1{\\sqrt{2}}\\left(|N,0\\rangle+|0,N\\rangle\\right)\\;,\n\\end{equation}\nthis entanglement flag reaches a value of $N$. Thus, while Eq.~(\\ref{eq:sufficient}) already\nindicates multi-particle entanglement, values of \n\\begin{equation}\n\\label{eq:highly}\nF_{\\rm ent}\\gg 1\n\\end{equation}\n demonstrate highly entangled\nstates.\n\n\n\n\\section{Results\\label{sec:results}}\nIn order to characterise whether or not the mean-field dynamics are chaotic, Poincar\\'e\nsurface of sections are particularly suited: for a set of initial conditions, the mean-field\ndynamics of the periodically driven system characterised by the angular frequency $\\omega$ is\nplotted at integer multiples of $2\\pi\/\\omega$. Figure~\\ref{fig:sosa} shows a phenomenon which\nis very characteristic for the non-rigid pendulum on which the BEC in a double well was\nmapped within mean-field: the coexistence between chaotic and regular regions.\n\\begin{figure}[th]\n\\includegraphics[angle=-90,width=\\linewidth]{sos_a.jpg}\n\\caption{\\label{fig:sosa}Poincar{\\'e} surface of section for the mean-field system. Closed\n loops characterise stable orbits whereas chaos is represented by irregular dots. The\n parameters are chosen such that they correspond to a one-photon resonance: a tilt of $2\\mu_0\/\\Omega=3.0$, a driving frequency of~$\\omega=3\\Omega$, an interaction of $N\\kappa\/\\Omega=0.8$ and a driving amplitude of $2\\mu_1\/\\Omega=0.9$ (cf.\\ Ref.~\\cite{EckardtEtAl05}).}\n\\end{figure}\n\n While each\ninitial condition will, in general, lead to many dots in plots like Fig.~\\ref{fig:sosa}, we\nproceed in the spirit of Ref.~\\cite{WeissTeichmann08} to characterise the $N$-particle\ndynamics: In Fig.~\\ref{fig:entfig1a} each initial condition leads to only one point: the\nentanglement flag~(\\ref{eq:flag}) for this initial condition after a fixed time~$t\\Omega$. Highly entangled states\n(Eq.~(\\ref{eq:highly})) can be found on the boundary of stable regions; already for short\ntimes (Fig.~\\ref{fig:entfig1a}~a) such highly entangled states can occur. For larger times\n(Fig.~\\ref{fig:entfig1a}~b) many features of the Poincar\\'e surface of section in\nFig.~\\ref{fig:sosa} are visible in the entanglement generation which essentially spreads over\nthe entire chaotic part of the initial conditions.\n\\begin{figure}\n\\vspace*{-1cm}\n\\includegraphics[width=\\linewidth]{plotfig1a.jpg}\n\\caption{\\label{fig:entfig1a}(Colour online) The entanglement flag~(\\ref{eq:flag}) in a two-dimensional\n projection as a function of the initial condition for $N=100$ particles. All other\n parameter as in Fig.~\\ref{fig:sosa}. The $N$-particle wave-function which corresponds to\n the mean-field initial conditions ($z_0$,$\\phi_0$) can be found in\n Eq.~(\\ref{eq:atomic}). In the upper panel, the (experimentally measurable) entanglement\n flag~(\\ref{eq:flag}) is shown after a time of $t\\Omega=10$, in the lower panel the time is\n $t\\Omega=100$. Highly entangled states (cf.\\ Eq.~(\\ref{eq:highly})) occur in the entire\n chaotic regime.\n }\n\\end{figure}\n\n\n\\begin{figure}\n\\includegraphics[angle=-90,width=\\linewidth]{sos_b.jpg}\n\\caption{\\label{fig:sosb}Poincar{\\'e} surface of section for the mean-field system (cf.\\\n Fig.~\\ref{fig:sosa}). The\n parameters are chosen such that they correspond to a $3\/2$-photon resonance~\\cite{EckardtEtAl05} with\n $N\\kappa\/\\Omega=0.1$, $2\\mu_0\/\\Omega=3.0$, $\\omega\/\\Omega=2.08$ and~$2\\mu_1\/\\Omega=1.8$.}\n\\end{figure}\n\nFor parameters which display no chaotic parts in the Poincar\\'e surface of section\n(Fig.~\\ref{fig:sosb}), on short time-scales hardly any entanglement emerges\n(Fig.~\\ref{fig:entfig1b} a). For larger time-scales, entanglement generation occurs on the\nboundaries of stable regions (Fig.~\\ref{fig:entfig1b} b).\n\\begin{figure}\n\\vspace*{-2cm}\n\\includegraphics[width=\\linewidth]{plotfig1b.jpg}\n\\caption{\\label{fig:entfig1b}(Colour online) The entanglement flag~(\\ref{eq:flag}) in a two-dimensional\n projection as a function of the initial condition (cf.\\ Fig.~\\ref{fig:entfig1a}) for $N=100$ particles; all other\n parameter as in Fig.~\\ref{fig:sosb}. For the regular regime given by the parameters of\n Fig.~\\ref{fig:sosb}, after short time-scales of $t\\Omega=10$ (upper panel)\n entanglement-production is very weak (note that the brightness-entanglement-coding differs by\n a factor of 10 from Fig.~\\ref{fig:entfig1b} and the longer time-scales $t\\Omega=100$\n depicted in the lower panel). For larger time-scales, entanglement production mainly occurs\non the boundary of stable regions (lower panel).}\n\\end{figure}\n\n\\section{Conclusion}\n\nIn Ref.~\\cite{WeissTeichmann08} we discovered the relation between mesoscopic ``Schr\\\"odinger-cat''\nlike superpositions in phase space and mean-field entanglement. In the present paper, we\ndemonstrated that also for more general entangled states, multi-particle entanglement can be\na quantum signature of chaos. For regular systems, the general entangled states also\noccur. However, it is restricted to the boundaries of stable regions and only occurs on\nlonger time-scales. While the focus in\nRef.~\\cite{WeissTeichmann08} was on finding particularly highly entangled states for each\ninitial condition, in the present manuscript the entanglement production was shown for each\ninitial condition at the same point in time, both for short and longer times between the\nonset of the computer experiment and the read-out. \n\nAs an entanglement-flag, we apply the quantum Fisher information for pure states.\nIn this paper we use it in a way which is\nparticularly easy to measure experimentally. However, using Eq.~(\\ref{eq:flag}) assumes a pure state and\nwould thus only be valid in an ideal system without decoherence. For\nmore realistic situations, \nexperimental signatures as in Refs.~\\cite{PiazzaEtAl2008,WeissCastin08} should be\ninvestigated in the future.\n\n\n\n\n\\acknowledgments\n\nWe would like to thank A.~Smerzi for useful discussions and M.~Holthaus for his\ncontinuous support. N.T.\\ acknowledges funding by the Studienstiftung des deutschen Volkes.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nWhile within-category demand effects of the marketing mix have been studied extensively, cross-category effects are less well understood \\citep{leeflang:12}. Nevertheless, cross-category effects might be substantial. Some categories are complements, e.g.\\ bacon and eggs studied by \\cite{Niraj08} or cake mix and cake frosting studied by \\cite{Manchanda99}, while others are substitutes, e.g.\\ frozen, refrigerated and shelf-stable juices \\citep{Wedel2004}. But cross-effects also exist among categories that are not complements or substitutes for several reasons. First, as a result of brand extensions, brands are no longer limited to one category \\citep{Erdem98,Kamakura2007,Ma2012}. So advertising and promotion of a brand within one category might spill over to own brand sales in other categories.\nSecond, advertising and promotions generate more store traffic and therefore more sales in other categories \\citep{Bell1998}. \nAnd third, lower expenditures in one category alleviate the budget constraint such that consumers are able to spend more on other, seemingly unrelated, categories \\citep{song:07, Lee2013}.\n\nWhile cross-category effects might be substantial for these reasons, we do not expect that each category's marketing mix variables influence each and every other category. Instead, we expect some cross-category effects to be zero -- or very close to zero -- but we can not a priori exclude them. Therefore, we use an exploratory modeling approach for parsimonious estimation of a product category network. The network allows us to easily identify categories that are influential for or responsive to changes in other categories. Building on a widely used category typology of destination, routine, occasional and convenience categories \\citep{Blattberg95,Briesch2013}, we find that destination categories are most influential, convenience and occasional categories most responsive, and routine categories moderately influential and moderately responsive. \n\nIn order to estimate the cross-category network, this paper presents sparse estimation of the Vector AutoRegressive (VAR) model. The estimation is {\\it sparse} in the sense that some of the within-and cross-category effects in the model can be estimated as exactly zero. Initiated by the work of \\cite{Baghestani91} and \\cite{Dekimpe95}, the VAR Market Response Model has become a standard, flexible tool to measure own- and cross-effects of marketing actions in a competitive environment. The main drawback of the VAR model is the risk of overparametrization because the number of parameters increases quadratically with the number of included categories. \nEarlier studies using the VAR model, like e.g. \\citeauthor{Nijs01} (\\citeyear{Nijs01}; \\citeyear{Nijs2007}); \\cite{Pauwels2002}; \\citeauthor{Srinivasan00} (\\citeyear{Srinivasan00}; \\citeyear{Srinivasan04}); \\cite{Steenkamp05}, were often limited by this overparametrization problem.\nTo overcome this problem, previous research on cross-category effects has limited its attention to a small number of categories by studying substitutes or complements \\citep{Kamakura2007,song:07,Leeflang:08,Bandyopadhyay2009,Ma2012}. We present an estimation technique for cross-category effects in much larger product category networks. The technique allows many parameters to be estimated even with short observation periods. Short observation periods are commonplace in marketing practice since many firms discard data that are older than one year \\citep{Lodish07}. \n\nThis paper contributes to the extant retail literature in a number of important ways. \n(1) Previous cross-category literature largely limits attention to categories that are directly related through substitution, complementarity or brand extensions. We provide evidence that cross-category effects go beyond such directly related categories. \n(2) We introduce the concepts of influence and responsiveness of a product category and position different category types (destination, routine, occasional and convenience) according to these dimensions. \n(3) To identify the cross-category effects, we estimate a large VAR model using an extension of the lasso approach of \\cite{Tibshirani96}. \n\n\nThe remainder of this article is organized as follows. Section 2 positions this paper in the cross-category management literature and describes the conceptual framework that positions category types according to their influence and responsiveness. Section 3 discusses the methodology. We describe the sparse estimator of the VAR model, discuss how to construct impulse response functions ans compare the sparse estimation technique with two Bayesian estimators. In Section 4, a simulation study shows the excellent performance of the proposed methodology in terms of estimation reliability and prediction accuracy. Section 5 presents our data and model, Section 6 our findings on cross-category demand effects. \nWe first identify which categories are most influential and which are most responsive to changes in other categories. Then, we identify the main cross-category effects based on estimated cross-price, promotion and sales elasticities.\n\n\\section{Cross-Category Management}\nThe importance of category management for retailers is widely acknowledged, both as a marketing tool for category performance \\citep{Fader1990, Basuroy2001, Dhar2002} and as an operational tool for planning and logistics \\citep{Rajagopalan2012}. \nSuccessful category management requires retailers to understand cross-category effects of prices, promotions and sales.\nAmong these, the cross-category effects of prices on sales -- which define substitutes and complements -- are the most extensively studied \\citep{Song:06, Bandyopadhyay2009, leeflang:12, Sinistyn2012}. \nCross-category effects of promotions, e.g.\\ feature and display promotions, on sales result from many brands being active in multiple categories \\citep{Erdem02}. Brand associations carry over to products of the same brand in other categories, e.g.\\ through umbrella branding \\citep{Erdem98} or horizontal product line extensions \\citep{Aaker90}. \nLess well understood than the effects of prices and promotions, are the effects of sales in one category on sales in other categories. Such effects might exist because categories are related based on affinity in consumption \\citep{Shankar2014}, because products from various categories are placed close to each other in the shelves \\citep{Bezawada09, Shankar2014}, or because of the budget constraint \\citep{Du2008}. If consumers spend more in a certain category they might, all else equal, spend less in other categories simply because they hit their budget constraint. As a result, cross-category effects might exist between seemingly unrelated categories. \n\nWhen studying these cross-category effects of price, promotion and sales on sales, several asymmetries might arise. \nA first asymmetry concerns within- versus cross-category effects. We expect within-category effects to be more prevalent and larger in size than cross-category effects (e.g. \\citealp{Song:06}; \\citealp{Bezawada09}).\nA second asymmetry concerns category influence versus category responsiveness.\nInfluential categories are important drivers of other category's sales, while sales of responsive categories react to changes in other categories. To identify which categories are more influential or more responsive, we build on a widely used typology of categories described in \\cite{Blattberg95}. \n\n\\cite{Blattberg95} define 4 category types from the consumer perspective: destination, routine, occasional and convenience. \nDestination categories contain goods that consumers plan to buy before they go on a shopping trip, such as soft drinks. \\cite{Briesch2013} show that destination categories are generally categories in which consumers spend a lot of their budget. Retailers typically use a price aggressive promotion strategy and high promotion intensity for these destination categories with the goal of increasing store traffic.\nBecause consumers shop to buy products in the destination categories, destination categories are likely to influence sales in other categories. However, since consumers already plan to buy in the destination categories before entering the store, destination category sales will not be highly responsive \\citep{Shankar2014}. \n\nAbout 55\\% to 60\\% of categories are routine categories \\citep{Pradhan09}. Routine categories are regularly and routinely purchased, such as juices and biscuits. Retailers typically use a consistent pricing strategy and average level of promotion intensity. Because purchases in routine categories can more easily be delayed than purchases in destination categories, we expect routine categories to be more responsive. But, since purchases in routine categories altogether still account for a large portion of the budget, they are also likely to influence sales in other categories. \n\nOccasional categories follow a seasonal pattern or are purchased infrequently. These categories comprise a small proportion of retail expenditures while they contain typically more expensive items, like oatmeal. We therefore expect occasional categories to be less influential and more responsive than destination or routine categories. \n\nFinally, convenience categories are categories that consumers find convenient to pick up during their one-stop shopping trip, like ready-to-eat-meals. These purchase decisions are typically made in the store. Since convenience categories are geared towards consumer convenience and filling impulse needs, we expect them to be highly responsive.\n\n\n\\section{Sparse Vector Auto-Regressive Modeling}\n\n\\subsection{Motivation}\n\nThe aim of this paper is to identify cross-category demand effects in a large product category network.\nTo this end, we use the Vector AutoRegressive (VAR) model. The VAR is ideal for measuring within- and cross-category effects of marketing actions since it accounts for both inertia in marketing spending and performance feedback effects by treating marketing variables as endogenous \\citep{Dekimpe95}. \nOther studies on cross-category effects, like e.g.\\ \\cite{Wedel2004} use a demand model with exogenous prices, or a simultaneous equations model without lagged effects like \\cite{Shankar2014}. However, managers may set marketing instruments strategically in response to market performance and market response expectations. Not accounting for time inertia or feedback effects limits our understanding of how the market functions and misleads managerial insights and prediction.\n\nIdentifying cross-category demand effects using VAR analysis remains challenging because the sheer number of such effects makes them hard to estimate. The number of parameters to be estimated in the VAR rapidly explodes, making standard estimation inaccurate. This undermines the ability to identify important relationships in the data.\nTo overcome an explosion of the number of parameters in the VAR, marketing researchers have used pre-estimation dimension reduction techniques, i.e.\\ they first impose restrictions on the model and then estimate the reduced model. Four such common techniques are (i) treating marketing variables as exogenous (e.g. \\citealp{Nijs01}; \\citealp{Pauwels2002} and \\citealp{Nijs2007}), (ii) estimating submodels rather than a full model (e.g. \\citealp{Srinivasan00}; \\citealp{Srinivasan04}), (iii) aggregating or pooling over, for instance, stores or competitors (e.g. \\citealp{Horvath08}; \\citealp{Slotegraaf2008}), and (iv) applying Least Squares to a restricted model (e.g. \\citealp{Dekimpe95, Dekimpe99_b}; \\citealp{Nijs2007}). Most researchers applying pre-estimation dimension reduction techniques recognize that they do so because of the practical limitations of standard estimation techniques rather than for theoretical reasons (e.g. \\citealp{Srinivasan04} and \\citealp{Bandyopadhyay2009}). \n\nTo address the overparametrization of the VAR, we use sparse estimation. Sparsity means that some of the within- and cross-category effects in the VAR are estimated as exactly zero. \nAs argued in the previous section, from a substantive perspective, we cannot exclude cross-category effects before estimation because cross-category effects might occur between seemingly unrelated categories. \nFrom a methodological perspective, sparse estimation is a powerful solution to handle the overparametrization of the VAR.\nIn our cross-category model, we endogenously model sales, promotion and prices of 17 product categories. Hence, already in a VAR model with one lag, as much as $(3 \\times 17) \\times (3 \\times 17) = 2601$ within- and cross-category effects need to be estimated. Since the sparse estimation procedure puts some of these effects to zero, a more parsimonious model is obtained. Results are easier to interpret and, therefore, the sparse estimation procedure provides actionable insights to managers.\n\n\n\n\\subsection{Extending the Lasso to the VAR model}\nIn situations where the number of parameters to estimate is large relative to the sample size, the Lasso proposed by \\cite{Tibshirani96} provides a solution within the multiple regression model. The Lasso minimizes the least squares criterion penalized for the sum of the absolute values of the regression parameters. This penalization forces some of the estimated regression coefficients to be exactly zero, which results in selection of the pertinent variables in the model. The Lasso method is well established \\citep{Buhlmann2010, Chatterjee2011} and shows good performance in various applied fields \\citep{Wu2009, Fan2011}.\n\nThe Lasso technique can not be directly applied to the VAR model because the VAR model differs from a multiple regression model in two important aspects. First, a VAR model contains several equations, corresponding to a multivariate regression model. Correlations between the error terms of the different equations need to be taken into account.\nSecond, a VAR model is dynamic, containing lagged versions of the same time series as right-hand side variables of the regression equation. Both aspects of VAR models make it necessary to extend the lasso to the VAR context, what the sparse estimator in this paper does.\n\nIt builds further on a sparse estimator of the multivariate regression model \\citep{Rothman10}, and the groupwise lasso for categorical variables \\citep{Yuan06, Meier07}. The estimator is consistent for the unknown model parameters, see \\citet{Meier07} and \\citet{Friedman07}. \n\n\n\\subsection{Model Specification}\nSales, price and promotion are measured for several categories over a certain time period. We collect all these time series in a multivariate time series ${\\bf y}_t$ with $q$ components. In our cross-category demand effects study, ${\\bf y}_t$ contains sales, price and promotion for 17 product categories, hence $q= 3 \\times 17 = 51$. The VAR Market Response Model is given by\n\\begin{equation}\\label{varp}\n{\\bf y}_t = B_1 {\\bf y}_{t-1} + B_2 {\\bf y}_{t-2} + \\ldots + B_p {\\bf y}_{t-p} + {\\bf e}_t \\, ,\n\\end{equation}\nwhere $p$ is the lag length. The autoregressive parameters $B_1$ to $B_p$ are $(q \\times q)$ matrices, which capture both within- and cross-category effects. The elements of these matrices measure the effect of sales, price and promotion in one category on the sales, price and promotion in other categories (including its own). The error term ${\\bf e}_t$ is assumed to follow a $N_q(0,\\Sigma)$ distribution. We assume, without loss of generality, that all time series are mean centered such that no intercept is included.\n\nIf the number of components $q$ in the multivariate time series is large, the number of unknown elements in the sequence of matrices $B_1,\\ldots,B_p$ explodes to $p q^2$, and accurate estimation by standard methods is no longer possible. Sparse estimation, with many elements of the matrices $B_1,\\ldots,B_p$ estimated as zero, brings an outcome: it will not only provide estimates with smaller mean squared error, but also substantially improve model interpretability.\nThe method we propose does not require the researcher to prespecify which entries in the $B_j$ matrices are zero and which are not. Instead, the estimation and variable selection are simultaneously performed. This is particularly of interest in situations where there is no a priori information on which time series is driving which.\n\nThe instantaneous correlations in model \\eqref{varp} are captured in the error covariance matrix $\\Sigma$. If the dimension $q$ is large relative to the number of observations, estimation of $\\Sigma$ becomes problematic. The estimated covariance matrix risks getting singular, i.e.\\ its inverse does not exist. Hence, we also induce sparsity in the estimation of the inverse error covariance matrix $\\Omega=\\Sigma^{-1}$. The elements of $\\Omega$ have a natural interpretation as partial correlations between the error components of the $q$ equations in model \\eqref{varp}. If the $ij$-th element of the inverse covariance matrix is zero this means that, conditional on the other error terms, there is no correlation between the error terms of equations $i$ and $j$.\n\n\n\\subsection{Penalized Likelihood Estimation}\nThis section defines the sparse estimation procedure for the VAR model. The Sparse VAR estimator is defined by minimizing a measure of goodness-of-fit to the data combined with a {\\it penalty} for the magnitude of the model parameters. It is convenient to first recast model \\eqref{varp} in stacked form as\n\\begin{equation}\\label{stacked}\ny = X \\beta + e \\, ,\n\\end{equation}\nwhere $y$ is a vector of length $n q$ containing the stacked values of the time series. If the multivariate time series has length $T$, then $n=T-p$ is the number of time points for which all current and lagged observations are available. The vector $\\beta$ contains the stacked\nvectorized matrices $B_1,\\ldots,B_p$, and $e$ the vector of stacked error terms.\nThe matrix $X=I_q \\otimes X_0$, with $ X_0 = (\\underline{{\\bf Y}}_1, \\ldots, \\underline{{\\bf Y}}_p)$, is of dimension $(n q \\times p q^2)$.\nHere $\\underline{{\\bf Y}}_j$ is an $(n \\times q)$ matrix, containing the values of the $q$ series at lag $j$ in its columns, for $1 \\leq j \\leq p$, with $p$ the maximum lag. The symbol $\\otimes$ stands for the Kronecker product.\n\nThe sparse estimator of the autoregressive parameters $\\beta$ and the inverse covariance matrix $\\Omega=\\Sigma^{-1}$ are obtained by minimizing the negative log likelihood with a groupwise penalization on the $\\beta$ and a penalization on the off-diagonal elements of $\\Omega$:\n\\begin{equation}\\label{mincrit}\n(\\hat{\\beta},\\hat{\\Omega}) = \\underset{(\\beta,\\Omega)}{\\operatorname{argmin}} \\, \\frac{1}{n} (y-X \\beta)^{\\prime} \\tilde{\\Omega} (y-X \\beta) - \\log|\\Omega| + \\lambda_1 \\sum_{g=1}^{G} ||\\beta_g|| + \\lambda_2 \\sum_{k \\neq k'} |\\Omega_{kk'}| \\, ,\n\\end{equation}\nwhere $||u||= (\\sum_{i=1}^{n} u_i^2)^{1\/2}$ is the Euclidean norm and $\\tilde{\\Omega}= \\Omega \\otimes I_n$. \nBy simultaneously estimating $\\beta$ and $\\Omega$, we take the correlation structure between the error terms into account.\nThe vector $\\beta_g$ in \\eqref{mincrit} is a subvector of $\\beta$, containing the regression coefficients for the lagged values of the same time series in one of the $q$ equations in model \\eqref{varp}. The coefficients of the lagged values of the same time series form a group. The total number of groups is $G=q^2$ because there are $q$ groups within each of the $q$ equations.\nThe penalty on the regression coefficients enforces that either \\textit{all} elements of the group $\\hat\\beta_g$ are zero or \\textit{none}. As a result, we take the dynamic nature of the VAR model into account since the estimated $B_j$ matrices, for $j=1,\\ldots,p$, have their zero elements in exactly the same cells.\nThe penalization on the off-diagonal elements of $\\Omega$ induces sparsity in the estimate $\\hat\\Omega$. Finally, the scalars $\\lambda_1$ and $\\lambda_2$ control the degree of sparsity of the regression estimator and the inverse covariance matrix estimator, respectively. The larger these values, the more sparsity is imposed.\nDetails on the algorithm to perform penalized likelihood estimation and the selection of the sparsity parameters $\\lambda_1$ and $\\lambda_2$ can be found in Appendix A.\n\nOur approach is similar to \\cite{Hsu08} who use the Lasso within a VAR context. However, they do not account for the group-structure in the VAR model, nor do they impose sparsity on the error covariance matrix. \n\\cite{Davis12} propose another sparse estimation procedure for the VAR. They infer the sparsity structure of the autoregressive parameters from an estimate of the partial spectral coherence using a two-step procedure. Since variable selection is performed prior to model estimation, the resulting estimator suffers from pre-testing bias. Moreover, the number of parameters might still approach the sample size, leading to unstable estimation or even making estimation infeasible if the number of parameters still exceeds the sample size.\nSparse estimation in economics is a growing field, see \\cite{Fan2011} and references therein for an overview. \n\n\\subsection{Alternative: Bayesian Estimators}\nAn alternative to the sparse estimation technique is to impose prior information in a Bayesian setting. Bayesian regularization techniques have been proposed for the VAR model in \\cite{Litterman80} and are used in various applied fields such as macroeconomics \\citep{Gefang14, Banbura10}, finance \\citep{Carriero12} and marketing \\citep{Lenk09,Fok:12,Bandyopadhyay2009}. They are also applicable to a situation like ours where there are many parameters to be estimated with a limited observation period, and are thus a good benchmark. However, these methods are not sparse, they do not perform variable selection simultaneously with model estimation. The following two paragraphs elaborate on two Bayesian estimators which serve as non-sparse alternatives.\n\\bigskip\n\n{\\it Minnesota Prior.} The original Minnesota prior only specifies a prior distribution for the regression parameters of the VAR model. The error covariance matrix $\\Sigma$ is assumed to be diagonal, and estimated by $\\hat{\\Sigma}_{ii} = \\hat{\\sigma}_{i}^{2}$ with $\\hat{\\sigma}_{i}^{2}$ the standard OLS estimate of the error variance in an AR$(p)$ model for the $i^{th}$ time series \\citep{koop:09}. The prior distribution of the regression parameters is taken to be multivariate normal:\n\\begin{equation}\n\\beta \\sim N(\\underline{\\beta}_{M},\\underline{V}_{M}) \\label{Minnesotaprior}.\n\\end{equation}\nFor the prior mean, the common choice is $\\underline{\\beta}_{M}=0_{Kq}$ for stationary series. The prior covariance matrix $\\underline{V}_{M}$ is diagonal. The posterior distribution is again multivariate normal. Full technical details can be found in \\cite{koop:09}.\n\nThe main advantage of the Minnesota prior is its ease of implementation, since posterior inference only involves the multivariate normal distribution. However, imposing the Minnesota prior only ensures that the parameter estimates are \\textit{shrunken} towards zero, while the Sparse VAR ensures that some parameters will be estimated as \\textit{exactly} zero.\n\\bigskip\n\n{\\it Normal Inverted Wishart Prior.}\nThe Minnesota prior takes the error covariance matrix $\\Sigma$ as fixed and diagonal and, hence, not as an unknown parameter. To overcome this problem, \\cite{Banbura10} impose an inverse Wishart prior on the $\\Sigma$ matrix. More precisely,\n\n\\begin{equation}\n\\beta \\mid \\Sigma \\sim N(\\underline{\\beta}_{NIW},\\Sigma \\otimes \\Omega_{0}) \\text{\\ \\ and \\ \\ } \\Sigma \\sim iW(S_{0},\\nu_{0}), \\label{LBVArRprior}\n\\end{equation}\nwhere $\\underline{\\beta}_{NIW},\\Omega_{0},S_{0}$ and $\\nu_{0}$ are hyperparameters. Under this normal inverted Wishart prior (labeled in the remainder of this paper as ``NIW\"), the posterior for $\\beta$, conditional on $\\Sigma$ is normal, and the posterior for $\\Sigma$ is again inverted Wishart. Full technical details can be found in \\cite{Banbura10}.\n\n\\subsection{Impulse Response Functions}\nImpulse response functions (IRFs) are extensively used to assess the dynamic effect of external shocks to the system such as changes in the marketing mix. An IRF pictures how a change to a certain variable at moment $t$ impacts the value of any other time series at time $t+k$, accounting for interrelations with all other variables. The magnitude of the effect is plotted as a function of $k$. An extensive discussion on the interpretation of the IRF in marketing modeling can be found in \\cite{Dekimpe95}. We use IRFs to gain insight in the dynamics of within and cross-category sales, promotion and price effects on each of the 17 product category sales. The IRFs are easily computed as a function of the Sparse VAR estimator (see \\citealp{Hamilton91}). Since we want to account for correlated error terms, we use generalized IRFs \\citep{Pesaran1998, Dekimpe99a}.\n\nTo obtain confidence bounds for the generalized IRFs estimated by Sparse VAR, we use a residual parametric bootstrap procedure \\citep{Chatterjee2011}. We generate $N_{b} = 1000$ time series of length $ T $ from the VAR model (\\ref{stacked}). The invertible estimate of $ \\Sigma $ delivered by the Sparse VAR estimation procedure is needed to draw random numbers for the $ N_{q}(0,\\Sigma) $ error distribution. For each of these $ N_{b} $ multiple time series, the estimates of the regression parameters are computed. We compute the covariance matrix of the $N_{b}$ bootstrap replicates. For each of the $N_b$ generated series impulse response functions are computed; the 90\\% confidence bounds are then obtained by taking the 5\\% and 95\\% percentiles.\n\n\\section{Estimation and prediction performance}\nWe conduct a simulation study to compare the proposed Sparse VAR with Bayesian methods using the Minnesota and NIW prior. As benchmarks, we include the classical Least Squares (LS) estimator and two restricted versions of LS which are often used in practice. In the 1-step Restricted LS \\citep{Dekimpe95,Dekimpe99_b}, we estimate the model with classical LS, delete all variables with $|$$t$-statistic$|$ $ \\leq 1 $, and re-estimate the model with the remaining variables. We also consider an iterative Restricted LS method described in \\cite{Lutkepohl04} where we fit the full model using LS and sequentially eliminate the variables leading to the largest reduction of BIC until no further improvement is possible, of which a close variant was used by \\cite{Nijs2007}.\n\nWe simulate from a VAR model with $q=10$ dimensions and $p=2$ lags. Each time series has an own auto-regressive structure and we include system dynamics among the different series. The first series leads series two to five, while the sixth series leads time series 7 to 10. Specifically, the data generating processes are given by\n$${\\bf y}_t =\n\\begin{bmatrix}\nB_{1} & 0\\\\\n0 & B_{1}\\\\\n\\end{bmatrix} {\\bf y}_{t-1}\n+\n\\begin{bmatrix}\nB_2 & 0\\\\\n0 & B_2\\\\\n\\end{bmatrix} {\\bf y}_{t-2}\n+ {\\bf e}_t \\, ,\n$$\nwith\n\\footnotesize\n$$\nB_{1} =\n\\begin{bmatrix}\n0.4 & 0.0 & 0.0 & 0.0 & 0.0 \\\\\n0.4 & 0.4 & 0.0 & 0.0 & 0.0 \\\\\n0.4 & 0.0 & 0.4 & 0.0 & 0.0 \\\\\n0.4 & 0.0 & 0.0 & 0.4 & 0.0 \\\\\n0.4 & 0.0 & 0.0 & 0.0 & 0.4 \\\\\n\\end{bmatrix} \\text{and}\n\\hspace{0.2cm} \\hspace{0.2cm}\nB_{2} =\n\\begin{bmatrix}\n0.2 & 0.0 & 0.0 & 0.0 & 0.0 \\\\\n0.2 & 0.2 & 0.0 & 0.0 & 0.0 \\\\\n0.2 & 0.0 & 0.2 & 0.0 & 0.0 \\\\\n0.2 & 0.0 & 0.0 & 0.2 & 0.0 \\\\\n0.2 & 0.0 & 0.0 & 0.0 & 0.2 \\\\\n\\end{bmatrix}.\n$$\n\\smallskip\n\\normalsize\n\nIn total, there are $p q^2=200$ regression parameters to be estimated with 36 true parameter values different from zero. The 10-dimensional error term ${\\bf e}_t $ is drawn from a multivariate normal with mean zero and covariance matrix $\\Sigma=0.1 I_{10}$. We generate $N_s=1000$ multivariate time series of length 50 according to the above simulation scheme.\n\n\\subsection{Performance measures}\n\nWe evaluate the different estimators in terms of (i) estimation accuracy, (ii) sparsity recognition performance, and (iii) forecast performance.\n\nTo evaluate estimation accuracy, we compute the mean absolute estimation error (MAEE), averaged over the simulation runs and over the 200 parameters\n$$\n\\mbox{MAEE} = \\frac{1}{N_s} \\frac{1}{pq^2} \\sum_{s=1}^{N_s} \\sum_{j=1}^p \\sum_{k,l=1}^q | \\hat{b}^s_{klj} - b_{klj} |,\n$$\nwhere $\\hat{b}^s_{klj}$ is the estimate of $b_{klj}$, the $kl^{th}$\\ element of the matrix $B_j$ corresponding to lag $j$, for the $s^{th}$ simulation run.\n\nConcerning sparsity recognition, we compute the true positive rate and true negative rate\n\\begin{gather}\n\\text{TPR}(\\hat{b},b) = \\dfrac{ \\# \\{ (k,l,j) : \\hat{b}_{klj} \\neq 0 \\ and \\ b_{klj} \\neq 0 \\}}{\\# \\{ (k,l,j) : \\ b_{klj} \\neq 0 \\}} \\nonumber \\\\\n\\text{TNR}(\\hat{b},b) = \\dfrac{ \\# \\{ (k,l,j) : \\hat{b}_{klj} = 0 \\ and \\ b_{klj} = 0 \\}}{\\# \\{ (k,l,j) : \\ b_{klj} = 0 \\}}. \\nonumber\n\\label{sparsityperformance} \n\\end{gather}\nThe true positive rate (TPR) gives an indication on the number of true relevant regression parameters detected by the estimation procedure. The true negative rate (TNR) measures the hit rate of detecting a true zero regression parameter. Both should be as large as possible.\n\nFinally, we conduct an out-of-sample rolling window forecasting exercise. Using the same simulation design as before, we generate multivariate time series of length $T=60$, and use a rolling window of length $S=50$. For all estimation methods, 1-step-ahead forecasts are computed for $t=S,\\ldots,T-1$. Next, we compute the Mean Absolute Forecast Error (MAFE), averaged over all time series and across time\n\\begin{equation}\n\\text{MAFE} = \\frac{1}{T-S} \\frac{1}{q} \\sum_{t=S}^{T-1}\\sum_{i=1}^{q} | \\ \\hat{y}^{(i)}_{t+1} - y^{(i)}_{t+1} \\ | ,\n\\end{equation}\nwhere $y^{(i)}_{t+1}$ is the value of the $i^{th}$ time series at time ${t+1}$.\n\n\\subsection{Results}\nTable \\ref{simulationresults} presents the performance measures of the Sparse VAR, the Bayesian and benchmark methods. The Sparse VAR estimator performs best in terms of estimation accuracy. It attains the lowest value of the MAEE (0.041). A paired $t$-test confirms that the Sparse VAR significantly outperforms the other methods (all $p$-values $<0.001$).\n\n\n\\linespread{1.2}\n\\begin{table}\n\\begin{center}\n\\caption{Mean Absolute Estimation Error (MAEE), True Positive Rate (TPR), True Negative Rate (TNR) and Mean Absolute Forecast Error (MAFE), averaged over 1000 simulation runs, are reported for every method. \\label{simulationresults}}\n\\begin{tabular}{lcccccccccccc}\n \\hline\nMethod \t\t\t\t\t&&& MAEE &&& TPR &&& TNR &&& MAFE \\\\ \\hline\nSparse VAR \t\t\t\t\t&&& 0.041 &&& 0.860 &&& 0.848 &&& 0.359 \\\\\nLS \t\t\t\t\t\t\t&&& 0.157 &&& 1 &&& 0 &&& 0.540 \\\\\nRestricted LS: 1-step \t\t&&& 0.121 &&& 0.709 &&& 0.541 &&& 0.520 \\\\\nRestricted LS: Iterative \t&&& 0.116 &&& 0.261 &&& 0.775 &&& 0.516 \\\\\nBayesian: Minnesota \t\t&&& 0.044 &&& 1 &&& 0 &&& 0.355 \\\\\nBayesian: NIW \t\t\t\t&&& 0.077 &&& 1 &&& 0 &&& 0.476 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\end{table}\n\\linespread{1.5}\n\nSparsity recognition performance is evaluated using the true positive rate and the true negative rate, reported in Table \\ref{simulationresults}. For the LS and Bayesian estimators, all parameters are estimated as non-zero, resulting in a perfect true positive rate and zero true negative rate. Among the variable selection methods, the Sparse VAR performs best. Sparse VAR achieves a value of the true positive rate of 0.86; 0.85 for the true negative rate.\n\nFinally, we evaluate the forecast performance of the different estimators by the Mean Absolute Forecast Error in Table \\ref{simulationresults}. The Sparse VAR and the Bayesian estimator with Minnesota prior achieve the best forecast performance. A Diebold-Mariano test confirms that these two methods perform significantly better than the others ($p$-values $<0.001$). There is no significant difference in forecast performance between Sparse VAR and the Bayesian estimator with Minnesota prior.\n\n\\subsection{Robustness checks}\n\\textit{Alternative penalty function.} We investigate the robustness of Sparse VAR to the choice of the penalty function. We replace the grouplasso penalty on the regression coefficients with the elastic net penalty \\citep{Zou05}. Elastic net is a regularized regression method that linearly combines the $L_1$ and $L_2$ penalties of respectively lasso and ridge regression. Like the grouplasso, elastic net produces a sparse estimate of the regression coefficients. All other steps of the methodology remain unchanged. We find that the grouplasso penalty performs slightly better than the elastic net penalty in terms of estimation accuracy, sparsity recognition and prediction performance. \n\n\\medskip\n\n\\textit{Sensitivity to the order of the VAR.} We estimate the model with Sparse VAR for different values of $p$ and evaluate the performance. As expected, Sparse VAR attains the best estimation accuracy for the true value $p=2$. The results are, however, very robust to the choice of the order of the VAR. Selecting $p$ too low is slightly worse than selecting $p$ too high. \n\n\\medskip\n\n\\textit{Sensitivity to the sparsity parameters.} The sparsity parameters are selected according to the BIC and this selection is an integral part of the estimation procedure.\nThe results are not sensitive to the value of $\\lambda_2$, which controls the sparsity of $\\widehat{\\Omega}$. The results are more sensitive to the choice of $\\lambda_1$, since it directly influences the sparsity of the autoregressive parameters. It turms out that Sparse VAR still outperforms the other estimators for a large range of $\\lambda_1$ values.\n\n\n\\section{Data and Model}\nWe use the sparse estimation technique for large VARs described in Section 3 to identify cross-category demand effects across 17 categories in the Dominick's Finer Foods database. This database is a well-established source of weekly scanner data from a large Midwestern supermarket chain, Dominick's Finer Foods (e.g. \\citealp{Kamakura2007, Pauwels07}). We first describe the data and model in more detail, and then report on the insights the Sparse VAR generates in the next section.\n\n\nWe use all 17 product categories in the Dominick's Finer Foods database containing food and drink items, a much broader selection of categories than previous studies on cross-category demand effects have considered. A description of each product category can be found in Table \\ref{Categories}. For 15 stores, we obtain weekly sales, pricing and promotional feature and display data for the 17 product categories.\n\n\\linespread{1.2}\n\\begin{table}\n\\small\n\\begin{center}\n\\caption{Description of the 17 categories from Dominick's Finer Foods database that are analyzed in this paper. For each category, we report the proportion of food and drink expenditures. \\label{Categories}}\n\\small\n\\begin{tabular}{lclllc} \\hline\nCategory & Expenditures &&& Category & Expenditures \\\\ \\hline\nSoft Drinks & 22.24\\% \t\t&&& Snack Crackers & 3.04\\%\\\\\nCereals & 13.92\\% \t\t\t&&& Frozen Juices & 2.88\\% \\\\\nCheeses & 10.46\\% \t\t&&& Canned Tuna & 2.80\\% \\\\\nRefrigerated Juices & 7.36\\% \t\t\t&&& Frozen Dinners & 2.00\\% \\\\\nFrozen Entrees & 6.98\\% \t&&& Front-end-candies & 2.00\\% \\\\\nBeer & 6.35\\% \t\t\t\t&&& Cigarettes & 1.49\\% \\\\\nCookies & 6.21\\%\t\t\t\t&&& Oatmeal & 1.43\\%\\\\\nCanned Soup & 4.82\\% \t\t\t&&& Crackers & 1.37\\%\\\\\nBottled Juices& 4.66\\% \t\t&&& & \\\\ \\hline\n\\end{tabular} \\\\\n\\end{center}\n\\end{table}\n\\linespread{1.5}\n\\normalsize\n\n\n\\noindent\n{\\bf Sales.} Category sales volumes for the 17 categories, measured in dollars per week.\n\n\\noindent\n{\\bf Promotion.} The promotional data include the percentage of SKUs of each category that are promoted (feature and display) in a given week, following \\citet{Srinivasan04}.\n\n\\noindent\n{\\bf Prices.} To aggregate pricing data from the SKU level to the product category level, we follow \\citet{Srinivasan04} and \\citet{Pauwels2002} in using SKU market shares as weights. Prices are not deflated because there is strong evidence that people are sensitive to nominal rather than real price changes \\citep{Shafir1997} over short time periods.\n\n\\medskip\n\nWe use data from January 1993 to July 1994, 77 weeks in total. We neither use \ndata before 1993 since they contain missing observations, nor\nobservations after 1994 since \\citet{Srinivasan04} pointed out that manufacturers made extensive use of `pay-for-performance' price promotions as of 1994, which are not fully reflected in the Dominick's database. This data range is short relative to the dimension of the VAR, which calls for a regularization approach such as the Sparse VAR. For all stores, we collect data on sales, promotion and pricing for all 17 categories. Only for cigarettes, no promotion variable is included in the VAR since none of the SKUs in that category were promoted during the observation period.\n\nWe estimate a separate VAR model for each store, which allows to evaluate the robustness of the findings. The multivariate time series entering the VAR model are the log-differenced sales ($\\mathbf{Y_t}$), differenced promotion ($\\mathbf{M_t}$), and log-differenced prices ($\\mathbf{P_t}$).\\footnote{Following standard practice, we first test for stationarity. A stationarity test of all individual time series using the Augmented Dickey-Fuller test indicates that most time series in levels are integrated of order 1.} The dimensions of the time series are represented in Table \\ref{data}. We use the Vector Autoregressive model, with endogenous promotion and prices,\n\\begin{equation}\\label{eq: application model}\n\\left[ \\begin{matrix} \\mathbf{Y_t} \\\\ \\mathbf{P_t} \\\\ \\mathbf{M_t} \\end{matrix} \\right] =\nB_0 + B_1 \\left[ \\begin{matrix} \\mathbf{Y_{t-1}} \\\\ \\mathbf{P_{t-1}} \\\\ \\mathbf{M_{t-1}} \\end{matrix} \\right]\n+ \\ldots + B_p \\left[ \\begin{matrix} \\mathbf{Y_{t-p}} \\\\ \\mathbf{P_{t-p}} \\\\ \\mathbf{M_{t-p}} \\end{matrix} \\right] + \\mathbf{e_t}.\n\\end{equation}\nAveraged across stores, the selected value of $p$ is two for the Sparse VAR.\nAlso for the Bayesian estimators, the lag order of the VAR is selected using the BIC criterion, which is one for the majority of the stores. \n\n\\linespread{1.2}\n\\begin{table}\n\\begin{center}\n\\caption{Description of the 15 data sets. Each data set contains multivariate time series for sales ($\\textbf{Y}_{t}$), promotion ($\\textbf{M}_{t}$) and prices ($\\textbf{P}_{t}$). \\label{data}}\n\\small\n\\begin{tabular}{cccccc} \\hline\n\\rule{0pt}{3ex} Store & Number of & \\multicolumn{3}{c}{Dimension} & \\\\\n & Time Points & $\\textbf{Y}_{t}$ & $\\textbf{M}_{t}$ & $\\textbf{P}_{t}$ & Total \\\\ \\hline\nStore 1-15 \\rule{0pt}{3ex} & 77 & 17 & 16 & 17 & 50\\\\ \\hline\n\\end{tabular} \\\\\n\\end{center}\n\\end{table}\n\\linespread{1.5}\n\\section{Empirical Results}\n\nWe focus on the effects of prices, promotions and sales in category A on the sales (or demand) in category B, where A and B belong to the product category network. We first study the direct effects. \nFor instance, there is no direct effect of price of A on sales of B if the corresponding estimated regression coefficients are equal to zero at all lags. \nThen we turn to the complete chain of direct and indirect effects using Impulse Response Functions. \nFor instance, price in category A indirectly influences sales in category B when the price of category A influences the price, promotion or sales in a certain other category C which, in turn, influences the sales of category B. Since we work in a time series setting, both direct and indirect effects are dynamic in the sense that the effect occurs with a certain delay.\n\n\\subsection{A network of product categories}\nWe analyze cross-category demand effects as a network of interlinked product categories of which prices, promotions and sales in one category have an effect on sales in other categories. Recently, network perspectives have been increasingly used by marketing researchers to model, for example, the network value of a product in a product network \\citep{Singer2013} or to investigate the flow of influence in a social network \\citep{Zubcsek11}. In our case, the 17 product categories are the nodes of the network. We estimate the Sparse VAR for 15 stores separately. If the Sparse VAR estimation results indicate, by giving a non-zero estimate, that prices in one category have a direct influence on sales in another category in the majority of the 15 stores, a directed edge is drawn between them. The resulting directed network is plotted in Figure \\ref{crosscatPrice}. Similarly, Figures \\ref{crosscatPromo} and \\ref{crosscatSLS} present cross-category effects of respectively promotion and sales on sales. If promotion or sales in one category directly influence sales in another category, respectively, this is indicated by a directed edge. \n\n\n\\linespread{1.2}\n\\begin{table}\n\\centering\n\\caption{Proportion of nonzero within and cross-category effects of price, promotion and sales on sales, averaged across 15 stores and 17 product categories.} \\label{Within-Cross}\n\\begin{tabular}{lccccccccc}\n \\hline\n &&& Price &&& Promotion &&& Sales \\\\\n \\hline\nWithin-category &&& 34\\% &&& 30\\% &&& 96\\% \\\\\nCross-category &&& 19\\% &&& 21\\% &&& 21\\% \\\\\n \\hline \n\\end{tabular}\n\\end{table}\n\\linespread{1.5}\n\n\nA first important finding is that the cross-category networks are sparse -- not each category influences each and every other category. While the sparse VAR estimation favors zero-effects, it does not enforce them. Here, as many as 78\\% of all estimated effects are zero-effects. Table \\ref{Within-Cross} summarizes the prevalence of within-and cross-category effects. As expected, within-category effects are more common than cross-category effects. For all categories, past values of the own category's sales are selected for almost all stores. Cross-category effects of price on sales (19\\%), promotion on sales (21\\%) and sales on sales (21\\%) are about equally prevalent.\n\n\\begin{figure}\n\t\\begin{center}\n\t\t\\includegraphics[width=9cm, angle=-90]{PRICE}\n\t\\end{center}\n\t\\caption{Cross-category effect network of prices on sales: a directed edge is drawn from one category to another if its price influences sales in the other category for the majority of stores. \\label{crosscatPrice}} \n\\end{figure}\n\n\\begin{figure}\n\t\\begin{center}\n\t\t\\includegraphics[width=8.5cm, angle=-90]{PROMO}\n\t\\end{center}\n\t\\caption{Cross-category effect network of promotions on sales: a directed edge is drawn from one category to another if its promotion influences sales in the other category for the majority of stores. \\label{crosscatPromo}} \n\\end{figure}\n\n\\begin{figure}\n\t\\begin{center}\n\t\t\\includegraphics[width=8.5cm, angle=-90]{SLS}\n\t\\end{center}\n\t\\caption{Cross-category effect network of sales on sales: a directed edge is drawn from one category to another if its sales influences sales in the other category for the majority of stores.\\label{crosscatSLS}} \n\\end{figure}\n\n\n\nNext, we focus on category influence and responsiveness in the cross-category network, measured by the number of edges originating from and pointing to a category respectively.\nAs discussed in Section 2, destination categories are expected to be more influential, while convenience categories are expected to be more responsive. \n We discuss which types of categories we find to be most influential and\/or responsive in the cross-category networks of prices on sales, promotion on sales, and sales on sales. \n\nThe most influential categories in the cross-category network of prices on sales are destination categories such as Soft Drinks and Cheeses (cfr.\\ each four outgoing edges in Figure \\ref{crosscatPrice}). This is consistent with our expectations, as Soft Drinks is known to be a destination category \\citep{Briesch2013,Shankar2014,Blattberg95}. Soft Drinks is ranked first and Cheeses third in terms of food and drink expenditures (see Table \\ref{Categories}) and are both heavily promoted by retailers. A price change in either of these categories thus strongly influences the budget constraint, which in turn influences purchase decisions in other categories. In the cross-category network of promotions on sales, Cereals is the most influential category (cfr. five outgoing edges in Figure \\ref{crosscatPromo}). \\cite{Briesch2013} identified Cereals as highly ranked among the destination categories. This is not surprising as cereals are part of daily consumption patterns and are ranked second in terms of food and drink expenditures. In the cross-category effects network of sales on sales in Figure \\ref{crosscatSLS}, we identify again Cheeses as the most influential category. \n\nWe find convenience categories to be highly responsive to changes in other categories.\nThe most prominent price effects are observed for Canned Soup (cfr.\\ five incoming edges in Figure \\ref{crosscatPrice});\nthe most prominent promotion effects for Frozen Dinners, Crackers and Canned Soup (cfr. each three incoming edges in Figure \\ref{crosscatPromo}); \nand the most prominent sales effects for Oatmeal and Crackers (cfr.\\ each four incoming edges in Figure \\ref{crosscatSLS}). \nThese categories are typically bought out of convenience, such as Frozen Dinners and Canned Soup; or bought on occasion, such as Oatmeal and Crackers, counting for a very small percentage of food and drink expenditures (see Table \\ref{Categories}). \n\nRoutine categories such as Bottled Juices, Refrigerated Juices, Frozen Juices and Cookies score moderate-to-high on category influence but are also responsive. \nThis is in line with our expectation of many grocery categories being routine categories that are moderately influential and moderately responsive. Finally, the cigarettes category is least responsive and least influential. This finding is not surprising as cigarettes are addictive, hence, smokers probably have a stable consumption unrelated to food and drinks.\n\nTo confirm the robustness of the results obtained by Sparse VAR, we check whether category responsiveness and influence are consistent across stores. We compute Kendall's coefficient of concordance $W$ for category influence and responsiveness calculated from the graphs in Figures 2-4 at the store level. As $W$ increases from 0 to 1, there is stronger consistency across stores. Table \\ref{KendallW} indicates that all values of Kendall's $W$ are significant.\n\n\n\\linespread{1.2}\n\\begin{table}\n\\begin{center}\n\\caption{Kendall's coefficient of concordance across stores of cross-category effects of price, promotion and sales on sales for both category influence and responsiveness. $P$-values are indicated between parentheses. \\label{KendallW}}\n\\begin{tabular}{lccccccccc}\n\\hline\n\n &&& Price &&& Promotion &&& Sales \\\\ \\hline\n Influence \t\t&&& $\\underset{(< 0.001)}{0.40}$ &&& $\\underset{(< 0.001)}{0.56}$ &&& $\\underset{(< 0.001)}{0.30}$ \\\\\n Responsiveness \\rule{0pt}{3ex}\t&&& $\\underset{(<0.001)}{0.30}$ &&& $\\underset{(0.001)}{0.16}$ &&& $\\underset{(< 0.001)}{0.17}$ \\\\\\hline\n\\end{tabular}\n\\end{center}\n\\end{table}\n\\linespread{1.5}\n\n\\subsection{Impulse Response Functions}\nFor each store, we estimate the Sparse VAR and compute the corresponding Impulse Response Functions (IRFs). The effect size of an impulse is obtained by summing the absolute values of the responses across the first 10 lags of the IRF, where we take absolute values in order not to average out positive and negative response. We compute effect sizes of impulses in price, promotion or sales in one product category on the sales in the same (within) category or another (cross) category. In Table 6, we report the within and cross-category price, promotion and sales effect sizes, averaged across the 15 stores and the product categories. \n\n\nTable \\ref{Effectsizes} indicates that, for example, a one standard deviation price shock leads to an accumulated absolute change of .004 in own sales growth over a time period of 10 lags. As for the direct effects, we systematically find that within-category effects are larger in magnitude than cross-category effects, especially for sales and prices. For the marketing mix, promotions exert stronger within- as well as cross-category effects than price changes.\n\n\\linespread{1.2}\n\\begin{table}\n\\centering\n\\caption{Size of within and cross-category effects of price, promotion and sales on sales, summed across 10 lags of the IRF, averaged across stores and product categories, and in absolute value.} \\label{Effectsizes}\n\\begin{tabular}{lccccccccc}\n \\hline\n &&& Price &&& Promotion &&& Sales \\\\\n \\hline\nWithin-category &&& 0.004 &&& 0.006 &&& 0.057 \\\\\nCross-category &&& 0.002 &&& 0.005 &&& 0.002 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\linespread{1.5}\n\n\\begin{table}\n\\caption{Cross-category price, promotion and sales effects on sales summed across 10 lags of IRFs and averaged across stores. We present only the five largest positive and negative effects. \\label{Crosscateffects}}\n\\footnotesize\n\\resizebox{0.95\\textwidth}{!}{\\begin{minipage}{\\textwidth}\n\\centering\n\\begin{tabular}{lllcc|llllcc}\n\\hline\n\\multicolumn{6}{l}{\\textbf{Cross-category price effects}} & \\multicolumn{5}{l}{\\textbf{ }}\\\\ \\hline\nPrice && Sales && Effect && Price && Sales && Effect \\\\ \nimpulse && response & & && impulse && response & & \\\\ \\hline\n \\multicolumn{4}{c}{\\underline{Perceived complements}} &&& \\multicolumn{4}{c}{\\underline{Perceived substitutes}} &\\\\\nSoft Drinks && Canned Tuna && -0.0209 && Front-end-candies && Bottled Juices && 0.0120\\\\\nSoft Drinks && Frozen Entrees &&-0.0182&& Soft Drinks && Frozen Juices && 0.0060\\\\\nCanned Tuna && Canned Soup && -0.0173 && Snack Crackers && Beer && 0.0058\\\\\nCereals && Frozen Dinners && -0.0104 && Cookies && Oatmeal && 0.0056\\\\\nBottled Juices && Crackers && -0.0074 && Frozen Juices && Bottled Juices && 0.0023\\\\ \\hline\n\\multicolumn{6}{l}{\\textbf{Cross-category promotion effects}} & \\multicolumn{5}{l}{\\textbf{ }}\\\\ \\hline\nPromotion && Sales && Effect && Promotion && Sales && Effect \\\\ \nimpulse && response && && impulse && response & & \\\\ \\hline\nBottled Juices && Frozen Entrees && 0.0586 && Oatmeal && Canned Tuna && -0.0214\\\\\nCheeses && Frozen Entrees && 0.0421 && Cheeses && Cookies && -0.0160 \\\\\nCrackers && Frozen Entrees && 0.0246 && Bottles Juices && Canned Tuna && -0.0158 \\\\\nFrozen Dinners && Frozen Entrees && 0.0170&& Refrigerated Juices && Canned Tuna && -0.0128 \\\\\nSnack Crackers && Frozen Entrees && 0.0127 && Cereals && Cheeses && -0.0127 \\\\ \\hline\n\n\\multicolumn{6}{l}{\\textbf{Cross-category sales effects}} & \\multicolumn{5}{l}{\\textbf{ }}\\\\ \\hline\nSales && Sales && Effect && Sales && Sales && Effect \\\\\nimpulse && response & & && impulse && response && \\\\ \\hline\nFront-end-candies && Soft Drinks && 0.0191 && Snack Crackers && Oatmeal && -0.0154 \\\\\nOatmeal && Frozen Entrees && 0.0123 && Frozen Juices && Frozen Entrees && -0.0120 \\\\\nCanned Tuna && Crackers && 0.0094 && Cereals && Frozen Dinners && -0.0099 \\\\\nFront-end-candies && Beer && 0.0086 && Snack Crackers && Cookies && -0.0087\\\\\nSnack Crackers && Frozen Dinners && 0.0064 && Refrigerated Juices && Canned Tuna &&-0.0084 \\\\\\hline\n\\end{tabular}\n\\end{minipage} }\n\\end{table}\n\nTo get more insight in the sign of the cross-category effects, we summarize each IRF by the sum of the first 10 responses, and average this number over all stores. Table \\ref{Crosscateffects} reports the five largest positive and negative cross-category effects of price, promotion and sales on sales.\n\n\\medskip\n\n\\textit{Cross-category price effects.} We investigate whether consumers perceive categories as complements or as substitutes. Complementary and substitution effects occur between categories because they are consumed together or separately. Following the standard economic definition \\citep{Pashigian98}, complements are defined as goods having a negative cross-price elasticity, whereas substitutes are defined as goods having a positive cross-price elasticity. \n We find evidence of two important drivers of cross-category price effects: consumption relatedness and the budget constraint. \n \nAs an example of consumption relatedness, consider Soft Drink prices and Frozen Juices. An increase in Soft Drink prices makes consumers spend more on other drinks as a compensation, in particular Frozen Juices (see Table \\ref{Crosscateffects}). The joint dynamic effect of a one standard deviation price impulse of Soft Drinks on the sales response growth of Frozen Juices is depicted in Figure \\ref{IRF_PRICE} for the first three stores in the data set. Note that the instantaneous effect is estimated as exactly zero since the Sparse VAR puts the corresponding effect in the $\\widehat\\Sigma$ matrix to zero. We see a sharp increase in Frozen Juices sales growth one week after the soft drink price increase, indicating substitution. However, the next two weeks, sales growth of Frozen Juices slows down, which could indicate stockpiling behavior \\citep{Gangwar13}.\n\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=14.1cm]{IRFS_SLStoPRICE}\n\\end{center}\n\\caption{Impulse response function: response of frozen juices sales growth to a one standard deviation impulse in the price of soft drinks. \\label{IRF_PRICE}}\n\\end{figure}\n\nAnother example of consumption relatedness is Soft Drinks and Frozen Entrees. As can be seen from Table \\ref{Crosscateffects}, we find a strong negative effect of Soft Drink prices on Frozen Entrees. This might be due to the fact that Soft Drinks and Frozen Entrees are consumed together. We do not find the opposite effect of price changes in Frozen Entrees on the sales of Soft Drinks. This asymmetry arises because Soft Drinks is a destination category (high influence), while Frozen Entrees is a convenience category (highly responsiveness). \n\n\n\n\nConcerning the budget constraint, prominent cross-category price effects are observed for Soft Drinks and Cereals, both destination categories. Soft Drinks and Cereals account for a relatively large proportion of the expenditures of US families (respectively 22\\% and 14\\% of spending on food and drinks, see Table \\ref{Categories}), which indicates that the budget constraint is an important source of cross-category effects. \n\n\\medskip\n\n\\textit{Cross-category promotion effects.}\nThe results in Table \\ref{Crosscateffects} indicate that branding and promotion intensity are important drivers of cross-category promotion effects.\nConcerning branding, cross-category promotion effects are observed for categories that share brands such as Frozen Dinners and Frozen Entrees (e.g. the frozen prepared foods brand ``Stouffer's\").\nConcerning promotion intensity, prominent cross-category promotion effects are observed for categories in which a high percentage of the SKUs is promoted, such as Cheeses and Bottled Juices (respectively 28\\% and 26\\% of SKUs, on average, are promoted in our data.) A promotion impulse in such categories might either trigger join consumption (e.g. Bottled Juices and Frozen Entrees), or deter consumption (e.g. Cheeses and Cookies).\n\n\n\\medskip\n\n\\textit{Cross-category sales effects.} \nIn Table \\ref{Crosscateffects}, we find evidence of two important drivers of cross-category effects of sales on sales: affinity in consumption and the budget constraint. \nProminent cross-category sales effects occur because of affinity in consumption. Some categories are jointly consumed towards a common goal, such as Front-end-candies and Soft Drinks\/Beer (for a light meal); while others such as Snack Crackers and Cookies are purchased as replacements since consumers might perceive them to have a similar functionality.\nConcerning the budget constraint, we find some cross-category sales effects between seemingly unrelated categories such as Refrigerated Juices and Canned Tuna.\n\n\n\\medskip\n\n\nImportantly, the results from Table \\ref{Crosscateffects} are in line with our findings on category influence and responsiveness. Destination categories such as Soft Drinks, Cereals and Cheeses mainly influence sales in other categories through their price, promotion or sales impulses. Convenience categories such as Frozen Entrees and Frozen Dinners are more responsive to changes in other categories. Routine categories, such as Cookies, are moderately influential and moderately responsive, while occasional categories, such as Oatmeal, are highly responsive. \n\n\n\\subsection{Robustness checks}\n\\textit{Alternative penalty function.} We investigate the robustness of the results to the choice of the penalty function. We re-estimate the models using the Sparse VAR with elastic net instead of the grouplasso penalty (a short explanation of the elastic net is given in Section 4). The managerial insights obtained by Sparse VAR with either grouplasso or elastic net are very similar. \nSimilarities are that \n(i) within-category effects are more common and larger in magnitude than cross-category effects, \n(ii) destination categories such as Cheeses and Cereals are very influential,\n(iii) convenience categories such as Frozen Entrees, and occasional categories such as Crackers are very responsive\n(iv) routine categories such as Bottled Juices, Refrigerated Juices and Cookies are both influential and responsive\n(v) the most prominent cross-category effects of price, promotion and sales on sales are highly overlapping.\n\n\\medskip\n\n\\textit{Alternative data period.} We also check the performance of the Sparse VAR on the post-1994 data. Retailers made extensive use of ``pay-for-performance\" price promotions that are not fully reflected in the Dominick's database. The data generating process might have changed in this period. Therefore, we should not assume constant parameter values. We re-estimate the model on the post-1994 data (data from October 1995 until May 1997) and verify its performance. In the post-1994 period, similar conclusions can be drawn with respect to within versus cross-category effects and category influence and responsiveness.\nSome differences are observed in the post-1994 period concerning the impulse response functions. These differences occur due to an altered strategy concerning average pricing and promotion intensity in the 17 product categories in the post-1994 period compared to the 1993-1994 period. Detailed results are available from the authors upon request.\n\n\\medskip\n\n\\textit{Alternative sparsity parameter selection.} Our results are based on the BIC to select the penalty parameters. We also ran the analysis using AIC as a selection criterion for the penalty function. While the model selected by AIC are slightly less sparse, the substantive insights do not change. \n\n\\subsection{Forecast Performance}\nAlthough prediction is not the main goal of the proposed methodology, we deem it important to show that the Sparse VAR can compete with other methods in terms of prediction accuracy. We estimate model \\eqref{eq: application model} for each store and perform a forecast exercise (cfr. Section 4), using a rolling window of length $S=67$. One-step-ahead forecasts of sales for each product category are computed for $t=S,\\ldots,T-1$, with $T=77$. The same estimation methods as in Section 4 are used.\n\nResults on the sales predictions are summarized in Table \\ref{Forecasts} by the Mean Absolute Forecast Error (MAFE), averaged across time and over the 17 product categories and 15 stores. The MAFE should be seen as a measure of forecast accuracy, not as a measure of managerial relevance of the obtained results.\nThe variable selection methods Sparse VAR, 1-step and Iterative Restricted LS perform, on average, better than the methods that don't perform variable selection. This indicates that sparsity improves prediction accuracy. Sparse VAR and Iterative Restricted LS achieve the best forecasting performance. A Diebold-Mariano test \\citep{Diebold95} confirms that latter two methods significantly outperform the other methods. We conclude that the improvement in interpretability of the model obtained by Sparse VAR, as discussed in the previous section, does not come at the cost of lower forecast performance.\n\n\\linespread{1.2}\n\\begin{table}\n\\begin{center}\n\\caption{Mean Absolute Forecast Error (MAFE) for category-specific sales, averaged over the 15 stores and the 17 product categories. $P$-values of a Diebold-Mariano test comparing the Sparse VAR to its alternatives are indicated between parentheses. \\label{Forecasts}}\n\\small\n\\begin{tabular}{lcccccc}\n \\hline\n \\rule{0pt}{3ex} & Sparse VAR & & \\multicolumn{2}{c}{Restricted LS} & \\multicolumn{2}{c}{Bayesian Methods} \\\\\n \\rule{0pt}{3ex} & & LS & 1-step & Iterative & Minnesota & NIW \\\\\n \\hline\nMAFE & 736.80 & $\\underset{(<0.01)}{1298.54}$ & $\\underset{(<0.01)}{784.96}$ & $\\underset{(0.38)}{734.82}$ & $\\underset{(<0.01)}{875.47}$ & $\\underset{(<0.01)}{1078.03}$ \\\\ \\hline\n\\end{tabular}\n\\end{center}\n\\end{table}\n\\linespread{1.5}\n\n\\section{Discussion}\n\nThis paper presents a Sparse VAR methodology to detect the inter-relationships in a large product category network. In the cross-category demand effects application, we detect an important number of cross-category demand effects for a large number of categories. We find that categories have asymmetric roles: While destination categories are more influential, convenience categories are more responsive.\nWe identify main perceived cross-category effects but also detect cross-category effects between categories that are not directly related at first sight. Hence, the need to study -- potentially a large number of -- product categories simultaneously. While cross-category effects are prevalent, many of them are still absent, calling for a sparse estimation procedure that succeeds in highlighting the main inter-relationships in the product category network.\n\nWe identify category influence and responsiveness in our cross-category demand effects application using aggregate store level data. Other cross-category studies, such as \\cite{Russell97,Ainslie98,Russell99,Russell00,Elrod02} use market basket data. Since the availability and use of such market basket data pose difficulties to managers, they rarely use market basket data for category analysis \\citep{Shankar2014}. As managerial decisions are often made at the category level, managers prefer to work with more readily available aggregate store level data. Hence, using aggregate category store level is managerially relevant \\citep{ailawadi:09, leeflang:12}.\n\nA first limitation of our approach is that we use aggregate category data, which might lead to biased estimates when there is heterogeneity on the SKU level \\citep{Dekimpe00}. Second, our model does not allow to estimate cross-category effects on the individual consumer level. Insights into the behavior of consumers are revealed using market basket data, which requires a very different modeling approach. Despite these limitations, aggregate category data are highly relevant from the perspective of category management within the store.\n\nAn important advantage of the Sparse VAR is that it overcomes the dimensionality problem -- it results in a parsimonious model with minimal structural constraints. We show that this leads to more accurate estimation and prediction results as compared to standard Least Squares methods.\nIf the researcher wishes to restrict some of the parameters to zero a priori, using marketing theory, this is of course still possible to implement with the Sparse VAR. The same holds for the reverse, i.e.\\ forcing some variables to be included in the model, which can be done by adjusting the penalty on the regression coefficients in \\eqref{mincrit}.\n\nThe methodology presented in this paper is relevant in a variety of other settings. First, Sparse VAR can be used to study competitive demand effects across many competitors. The VAR is ideal for measuring competitive effects since it is able to capture own- and cross-elasticity of sales to both pricing and marketing spending \\citep{Srinivasan04, Horvath08}.\nTypically only three competitors are included in such studies, while using the Sparse VAR allows for a much larger number to be included. Second, in the field of international marketing research there is an increased interest in studying cross-country spill-over effects, as for example in \\cite{Albuquerque07}, \\cite{VanEverdingen09} and \\cite{Kumar02}. Every country that is added to the data set leads to an increase in the number of cross-country parameters to be estimated. Using the proposed methodology, a large VAR model could be built which allows spill-over effects between many countries. Finally, the Market Response Model could be extended with data on online word of mouth or online search, which are now readily available. Especially in the Big Data era, most companies collect an abundance of variables \\citep{BigData2013}, such that large VAR models will become even larger as more granular data become available. \n\n\\bigskip \\noindent\n{\\bf Acknowledgments.} The authors thank the Editors, Shankar Ganesan and Murali K. Mantrala, and two anonymous referees for their valuable comments that have improved the paper significantly. Financial support from the FWO (Research Foundation Flanders) is also gratefully acknowledged (FWO, contract number 11N9913N).\n\n\n\\begin{appendices}\n\\numberwithin{equation}{section}\n\\section{Penalized Likelihood Estimation}\n\\noindent\nWe iteratively solve the minimization problem \\eqref{mincrit} for $\\beta$ conditional on $\\Omega$ and then for $\\Omega$ conditional on $\\beta$.\n\n\\medskip \\noindent\n{\\it Solving for $\\beta|\\Omega$:} When $\\Omega$ is fixed, the minimization problem in \\eqref{mincrit} is equivalent to minimizing\n\\begin{equation}\\label{mincritbeta}\n\\hat{\\beta}|\\Omega = \\underset{\\beta}{\\operatorname{argmin}} \\frac{1}{n} (\\tilde{y}-\\tilde{X} \\beta)^{\\prime} (\\tilde{y}-\\tilde{X} \\beta) + \\lambda_1 \\sum_{g=1}^{G} ||\\beta_g||_2 \\, ,\n\\end{equation}\nwhere $\\tilde{y}= Py$, $\\tilde{X}=PX$, and $P$ is a matrix such that $P^{\\prime}P=\\tilde{\\Omega}$. \nThe transformation of the data to $\\tilde{y}$ and $\\tilde{X}$ ensures that the resulting model has uncorrelated and homoscedastic error terms. The above minimization problem is convex if $\\Omega$ is nonnegative definite. The minimization problem is equivalent to the groupwise lasso of \\cite{Yuan06}, implemented in the R package \\verb+grplasso+ \\citep{Rgrplasso}.\n\n\n\\noindent\n{\\it Solving for $\\Omega|\\beta$:} When $\\beta$ is fixed, the minimization problem in \\eqref{mincrit} reduces to\n\\begin{equation}\\label{mincritOmega}\n\\hat{\\Omega}|\\beta = \\underset{\\Omega}{\\operatorname{argmin}} \\, \\frac{1}{n} (y-X \\beta)^{\\prime} \\tilde{\\Omega} (y-X \\beta)- \\log|\\Omega| + \\lambda_2 \\sum_{k \\neq k'} |\\Omega_{kk'}| \\, ,\n\\end{equation}\nwhich corresponds to penalized covariance estimation. Using the glasso algorithm\nof \\cite{Friedman07}, available in the R package \\verb+glasso+ \\citep{Rglasso}, the optimization problem in \\eqref{mincritOmega}\nis solved.\n\nWe start the algorithm by taking $\\widehat{\\Omega}=I_q$ and iterate until convergence. We iterate until $max_s |\\hat{\\beta}_{s,i}-\\hat{\\beta}_{s,i-1}|<\\epsilon$, with $\\hat{\\beta}_{s,i}$ the $s^{th}$ parameter estimate in iteration $i$ (same for $\\hat{\\Omega}$) and the tolerance $\\epsilon$ set to $10^{-3}$. \n\n\\paragraph{Selecting the Sparsity Parameters and the order of the VAR}\nWe first determine the optimal values of $\\lambda_1$ and $\\lambda_2$ for a fixed value of $p$, the order of the VAR. The sparsity parameters $\\lambda_1$ and $\\lambda_2$ are selected according to a minimal Bayes Information Criterion (BIC).\nIn the iteration step where $\\beta$ is estimated conditional on $\\Omega$, we solve \\eqref{mincritbeta} over a range of values for $\\lambda_1$ and select the one with lowest value of\n\\begin{equation}\\label{eq: BICbeta}\nBIC_{\\lambda_1} = -2 \\log L_{\\lambda_1} + k_{\\lambda_1} \\log(n),\n\\end{equation}\nwhere $L_{\\lambda_1}$ is the estimated likelihood, corresponding to the first term in \\eqref{mincritbeta}, using sparsity parameter $\\lambda_1$. Furthermore, $k_{\\lambda_1}$ is the number of non-zero estimated regression coefficients and $n$ the number of observations.\nSimilarly, for selecting $\\lambda_2$, we use the BIC given by\n\\begin{equation}\\label{eq: BIComega}\nBIC_{\\lambda_2} = -2 \\log L_{\\lambda_2} + k_{\\lambda_2} \\log(n) \\, .\n\\end{equation}\nFinally, we select the order $p$ of the VAR. We estimate the VAR for different values of $p$. The optimal values of $\\lambda_1$ and $\\lambda_2$ are determined for a each of those values of $p$. We select the order $p$ of the VAR using BIC:\n\\begin{equation}\\label{eq: BICp}\nBIC_{(p, \\lambda_1(p), \\lambda_2(p))} = -2 \\log L_{(p, \\lambda_1(p), \\lambda_2(p))} + k_{(p, \\lambda_1(p), \\lambda_2(p))} \\log(n) \\, , \n\\end{equation}\nwhere $L_{(p, \\lambda_1(p), \\lambda_2(p))}$ and $k_{(p, \\lambda_1(p), \\lambda_2(p))}$ depend on the value $p$ and the optimally chosen values of $\\lambda_1(p)$ and $\\lambda_2(p)$ for that specific value of $p$.\n\\end{appendices}\n\n\n\\linespread{1}\n\\bibliographystyle{asa}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\subsection{Primitive trace map, local injectivity}\n\nLet $(M,g)$ be a smooth closed Riemannian Anosov manifold such as a manifold of negative sectional curvature \\cite{Anosov-67}. Recall that this means that there exists a continuous flow-invariant splitting of the tangent bundle to the unit tangent bundle $\\mathcal{M} := SM$:\n\\[\nT\\mathcal{M} = \\mathbb{R} X \\oplus E_s \\oplus E_u,\n\\]\nsuch that:\n\\begin{equation}\n\\label{equation:anosov}\n\\begin{array}{l}\n\\forall t \\geq 0, \\forall v \\in E_s, ~~ |\\dd\\varphi_t(v)| \\leq Ce^{-t\\theta}|v|, \\\\\n\\forall t \\leq 0, \\forall v \\in E_u, ~~ |\\dd\\varphi_t(v)| \\leq Ce^{-|t|\\theta}|v|,\n\\end{array}\n\\end{equation}\nwhere $(\\varphi_t)_{t \\in \\mathbb{R}}$ is the geodesic flow on $\\mathcal{M}$, and the constants $C,\\theta > 0$ are uniform and the metric $|\\cdot|$ is arbitrary. \n\nLet $\\E \\rightarrow M$ be a smooth Hermitian vector bundle. We denote by $\\mathcal{A}_{\\E}$ the affine space of smooth unitary connections on $\\E$ and $\\mathbb{A}_{\\E}$ the moduli space of connections up to gauge-equivalence, namely a point $\\mathfrak{a} \\in \\mathbb{A}_{\\E}$ is an orbit $\\mathfrak{a} := \\left\\{p^*\\nabla^{\\E} ~|~p \\in C^\\infty(M,\\mathrm{U}(\\E))\\right\\}$ of gauge-equivalent connections, where $\\nabla^{\\E} \\in \\mathfrak{a}$ is arbitrary. We let $\\mathcal{C} = \\left\\{c_1,c_2,...\\right\\}$ be the set of free homotopy classes of loops on $M$ which is known to be in one-to-one correspondence with closed geodesics \\cite{Klingenberg-74}. More precisely, given $c \\in \\mathcal{C}$, there exists a unique closed geodesic $\\gamma_g(c) \\subset M$ in the class $c$. It will be important to make a difference between \\emph{primitive} and \\emph{non-primitive} homotopy classes (resp. closed geodesics): a free loop is said to be primitive if it cannot be homotoped to a certain power (greater or equal than $2$) of another free loop. The set of primitive classes defines a subset $\\mathcal{C}^\\sharp = \\left\\{c_1^\\sharp,c_2^\\sharp,...\\right\\} \\subset \\mathcal{C}$.\n\nGiven a class $\\mathfrak{a} \\in \\mathbb{A}_{\\E}$, a unitary connection $\\nabla^{\\E} \\in \\mathfrak{a}$ and an arbitrary point $x_{c^\\sharp} \\in \\gamma_g(c^\\sharp)$ (for some $c^\\sharp \\in \\mathcal{C}^\\sharp$), the parallel transport $\\mathrm{Hol}_{\\nabla^{\\E}}(c^\\sharp) \\in \\mathrm{U}(\\E_{x_{c^\\sharp}})$, starting at $x_{c^\\sharp}$, with respect to $\\nabla^{\\E}$ and along $\\gamma_g({c^\\sharp})$ depends on the choice of representative $\\nabla^{\\E} \\in \\mathfrak{a}$ since two gauge-equivalent connections have conjugate holonomies. However, the trace does not depend on a choice of $\\nabla^{\\E} \\in \\mathfrak{a}$ and the \\emph{primitive trace map}:\n\\begin{equation}\n\\label{equation:trace}\n\\mathcal{T}^\\sharp : \\mathbb{A}_{\\E} \\ni \\mathfrak{a} \\mapsto \\left(\\Tr\\left(\\mathrm{Hol}_{\\nabla^{\\E}}(c^\\sharp_1)\\right), \\Tr\\left(\\mathrm{Hol}_{\\nabla^{\\E}}(c^\\sharp_2)\\right), ...\\right) \\in \\ell^\\infty(\\mathcal{C}^\\sharp),\n\\end{equation}\nis therefore well-defined. Observe that the data of the primitive trace map is a rather weak information: in particular, it is \\emph{not} (\\emph{a priori}) equivalent to the data of the conjugacy class of the holonomy along each closed geodesic (and the latter is the same as the non-primitive trace map, where one considers \\emph{all} closed geodesics). One of the main results of this paper is the following:\n\n\\begin{theorem}\n\\label{theorem:injectivity}\nLet $(M,g)$ be a smooth Anosov Riemannian manifold of dimension $\\geq 3$ and let $\\E \\rightarrow M$ be a smooth Hermitian vector bundle. Let $\\mathfrak{a}_0 \\in \\mathbb{A}_{\\E}$ be a \\emph{generic} point. Then, the primitive trace map is \\emph{locally injective} near $\\mathfrak{a}_0$.\n\\end{theorem}\n\nBy \\emph{local injectivity}, we mean the following: there exists $N \\in \\mathbb{N}$ (independent of $\\mathfrak{a}_0$) such that $\\mathcal{T}^\\sharp$ is locally injective in the $C^N$-quotient topology on $\\mathbb{A}_{\\E}$. In other words, for any element $\\nabla^{\\E}_0 \\in \\mathfrak{a}_0$, there exists $\\varepsilon > 0$ such that the following holds; if $\\nabla_{1,2}^{\\E}$ are two smooth unitary connections such that $\\|p_i^*\\nabla_{i}^{\\E} - \\nabla^{\\E}_0\\|_{C^N} < \\varepsilon$ for some $p_i \\in C^\\infty(M,\\mathrm{U}(\\E))$, and $\\mathcal{T}^\\sharp(\\nabla_1^{\\E}) = \\mathcal{T}^\\sharp(\\nabla_2^{\\E})$, then $\\nabla_1^{\\E}$ and $\\nabla_2^{\\E}$ are gauge-equivalent.\n\nWe say that a point $\\mathfrak{a}$ is \\emph{generic} if it enjoys the following two features: \n\n\\begin{itemize}\\label{def:generic}\n\\item[\\textbf{(A)}] $\\mathfrak{a}$ is \\textbf{opaque}. By definition (see \\cite[Section 5]{Cekic-Lefeuvre-20}), this means that for all $\\nabla^{\\E} \\in \\mathfrak{a}$, the parallel transport map along geodesics does not preserve any non-trivial subbundle $\\mathcal{F} \\subset \\E$ (i.e. $\\mathcal{F}$ is preserved by parallel transport along geodesics if and only if $\\mathcal{F} = \\left\\{0\\right\\}$ or $\\mathcal{F} = \\E$). This was proved to be equivalent to the fact that the Pollicott-Ruelle resonance at $z=0$ of the operator $\\mathbf{X} := \\pi^* \\nabla^{\\mathrm{End}}_X$ has multiplicity equal to $1$, with resonant space $\\mathbb{C} \\cdot \\mathbbm{1}_{\\E}$ (here $\\pi : SM \\rightarrow M$ is the projection; $\\nabla^{\\mathrm{End}}$ is the induced connection on the endomorphism bundle, see \\S\\ref{ssection:connections} for further details);\n\n\n\\item[\\textbf{(B)}] $\\mathfrak{a}$ has \\textbf{solenoidally injective generalized X-ray transform} $\\Pi^{\\mathrm{End}(\\E)}_1$ on twisted $1$-forms with values in $\\mathrm{End}(\\E)$. This last assumption is less easy to describe in simple geometric terms: roughly speaking, the X-ray transform is an operator of integration of symmetric $m$-tensors along closed geodesics. For vector-valued symmetric $m$-tensors, this might not be well-defined, and one needs a more general (hence, more abstract) definition involving the residue at $z=0$ of the meromorphic extension of the family $\\mathbb{C} \\ni z \\mapsto (-\\mathbf{X}-z)^{-1}$, see \\S\\ref{section:twisted}. \n\\end{itemize}\nIt was shown in previous articles \\cite{Cekic-Lefeuvre-20,Cekic-Lefeuvre-21-2} that in dimension $n \\geq 3$, properties \\textbf{(A)} and \\textbf{(B)} are satisfied on an open dense subset $\\omega \\subset \\mathbb{A}_{\\E}$ with respect to the $C^N$-quotient topology.\\footnote{More precisely, there exists $N \\in \\mathbb{N}$ and a subset $\\Omega \\subset \\mathcal{A}_{\\E}$ of the (affine) Fr\\'echet space of smooth affine connections on $\\E$ such that $\\omega = \\pi_{\\E}(\\Omega)$ (where $\\pi_{\\E} : \\mathcal{A}_{\\E} \\rightarrow \\mathbb{A}_{\\E}$ is the projection) and\n\\begin{itemize}\n\\item $\\Omega$ is invariant by the action of the gauge-group, namely $p^*\\Omega = \\Omega$ for all $p \\in C^\\infty(M,\\mathrm{U}(\\E))$;\n\\item $\\Omega$ is open, namely for all $\\nabla^{\\E}_0 \\in \\Omega$, there exists $\\varepsilon > 0$ such that if $\\nabla^{\\E} \\in \\mathcal{A}_{\\E}$ and $\\|\\nabla^{\\E}-\\nabla^{\\E}_0\\|_{C^N} < \\varepsilon$, then $\\nabla^{\\E} \\in \\Omega$;\n\\item $\\Omega$ is dense, namely for all $\\nabla^{\\E}_0 \\in \\mathcal{A}_{\\E}$, for all $\\varepsilon > 0$, there exists $\\nabla^{\\E} \\in \\Omega$ such that $\\|\\nabla^{\\E}-\\nabla^{\\E}_0\\|_{C^N} < \\varepsilon$;\n\\item Connections in $\\Omega$ satisfy properties \\textbf{(A)} and \\textbf{(B)}.\n\\end{itemize}} When the reference connection $\\mathfrak{a}$ satisfies only the property \\textbf{(A)} (this is the case for the product connection on the trivial bundle for instance), we are able to show a \\emph{weak local injectivity} result, see Theorem \\ref{thm:weaklocal}. \n\nWe note that the gauge class of a connection is uniquely determined from the holonomies along \\emph{all} closed loops \\cite{Barrett-91, Kobayashi-54} and that within the mathematical physics community our primitive trace map $\\mathcal{T}^\\sharp$ is known as the \\emph{Wilson loop} operator \\cite{Beasley-13, Giles-81, Loll-93, Wilson-74}. In stark contrast, our Theorem \\ref{theorem:injectivity} says that the \\emph{restriction to closed geodesics} of this operator already determines (locally) the gauge class of the connection.\n\n\\subsection{Global injectivity}\n\n\\label{ssection:global-inj}\n\nWe now mention some global injectivity results. We let $\\mathbb{A}_r := \\bigsqcup_{\\E_r \\in \\mathrm{Vect}_r(M)} \\mathbb{A}_{\\E_r}$, where the disjoint union runs over all Hermitian vector bundles $\\E_r \\in \\mathrm{Vect}_r(M)$ of rank $r$ over $M$ up to isomorphisms, and we set:\n\\[\n\\mathbb{A} := \\bigsqcup_{r \\geq 0} \\mathbb{A}_r,\n\\]\nand $\\mathrm{Vect}(M) = \\bigsqcup_{r \\geq 0} \\mathrm{Vect}_r(M)$ be the space of all topological vector bundles up to isomorphisms. A point $\\mathrm{x} \\in \\mathbb{A}$ corresponds to a pair $([\\mathcal{E}],\\mathfrak{a})$, where $[\\mathcal{E}] \\in \\mathrm{Vect}(M)$ is an equivalence class of Hermitian vector bundles and $\\mathfrak{a}$ a class of gauge-equivalent unitary connections.\\footnote{Note that if two smooth Hermitian vector bundles $\\E_1$ and $\\E_2$ are isomorphic as topological vector bundles (i.e. there exists an invertible $p \\in C^\\infty(M,\\mathrm{Hom}(\\E_1,\\E_2))$), then they are also isomorphic as Hermitian vector bundles, that is $p$ can be taken unitary; the choice of Hermitian structure is therefore irrelevant.} \n\nThe space $\\mathbb{A}$ has a natural monoid structure given by the $\\oplus$-operator of taking direct sums (both for the vector bundle part and the connection part). The primitive trace map can then be seen as a \\emph{global} (monoid) homomorphism: \n\\begin{equation}\n\\label{equation:trace-total}\n\\mathcal{T}^\\sharp : \\mathbb{A} \\longrightarrow \\ell^\\infty(\\mathcal{C}^\\sharp),\n\\end{equation}\nwhere $\\ell^\\infty(\\mathcal{C}^\\sharp)$ is endowed with the obvious additive structure. We actually conjecture that the generic assumption of Theorem \\ref{theorem:injectivity} is unnecessary and that the primitive trace map \\eqref{equation:trace-total} should be globally injective if $\\dim(M) \\geq 3$ and $\\dim (M)$ is odd. Let us discuss a few partial results supporting the validity of this conjecture:\n\n\\begin{enumerate}\n\t\\item In \\S\\ref{sssection:line}, we show that the primitive trace map is injective when restricted to \\emph{direct sums of line bundles} when $\\dim(M) \\geq 3$, see Theorem \\ref{theorem:sum}. Note that it was proved by Paternain \\cite{Paternain-09} that the primitive trace map restricted to line bundles $\\mathcal{T}_1^\\sharp : \\mathbb{A}_1 \\longrightarrow \\ell^\\infty(\\mathcal{C}^\\sharp)$ is injective when $\\dim(M) \\geq 3$. \n\n\t\\item In \\S\\ref{sssection:flat}, under the restriction of $\\mathcal{T}^\\sharp$ to \\emph{flat} connections, we show that the primitive trace map $\\mathcal{T}^\\sharp$ is globally injective, see Proposition \\ref{proposition:flat}.\n\t\n\t\\item In \\S\\ref{sssection:negative}, we also obtain a global result in negative curvature under an extra \\emph{spectral condition}, see Proposition \\ref{proposition:negative}. This condition is generic (see Appendix \\ref{appendix:ckts}) and is also satisfied by connections with \\emph{small curvature}, i.e. whose curvature is controlled by a constant depending only on the dimension and an upper bound on the sectional curvature of $(M,g)$ (see Lemma \\ref{lemma:small-curvature}).\n\t\n\t\n\t\\item In \\S\\ref{sssection:topology}, as a consequence of Corollary \\ref{corollary:iso} below, we have that the primitive trace map $\\mathcal{T}^{\\sharp}([\\mathcal{E}],\\mathfrak{a})$ allows to recover the isomorphism class $\\pi^*[\\E]$. In particular if $\\dim M$ is odd, this suffices to recover $[\\E]$, see Proposition \\ref{proposition:topology}.\n\n\\end{enumerate}\n\n\n\n\n\n\n\n\nTheorem \\ref{theorem:injectivity} is inspired by earlier work on the subject, see \\cite{Paternain-09,Paternain-12,Paternain-13,Paternain-lecture-notes,Guillarmou-Paternain-Salo-Uhlmann-16} for instance. Nevertheless, it goes beyond the aforementioned literature thanks to an \\emph{exact Liv\\v{s}ic cocycle Theorem} (see Theorem \\ref{theorem:weak-intro}), explained in the next paragraph \\S\\ref{ssection:exact}. It also belongs to a more general family of \\emph{geometric inverse results} which has become a very active field of research in the past twenty years, both on closed manifolds and on manifolds with boundary, see \\cite{Pestov-Uhlmann-05, Stefanov-Uhlmann-04,Paternain-Salo-Uhlmann-13, Uhlmann-Vasy-16,Stefanov-Uhlmann-Vasy-17,Guillarmou-17-2} among other references.\n\n\n\nTheorem \\ref{theorem:injectivity} can also be compared to a similar problem called the \\emph{marked length spectrum} (MLS) rigidity conjecture, also known as the Burns-Katok \\cite{Burns-Katok-85} conjecture. The latter asserts that if $(M,g)$ is Anosov, then the marked length spectrum\n\\begin{equation}\n\\label{equation:mls}\nL_g : \\mathcal{C} \\rightarrow \\mathbb{R}_+, ~~~L_g(c) := \\ell_g(\\gamma_g(c)),\n\\end{equation}\n(where $\\ell_g(\\gamma)$ denotes the Riemannian length of the curve $\\gamma \\subset M$ computed with respect to the metric $g$), namely the length of all closed geodesics marked by the free homotopy classes of $M$, should determine the metric up to isometry. Despite some partial answers \\cite{Katok-88, Croke-90,Otal-90,Besson-Courtois-Gallot-95,Hamenstadt-99,Guillarmou-Lefeuvre-18}, this conjecture is still widely open. Recently, Guillarmou and the second author proved a local version of the Burns-Katok conjecture \\cite{Guillarmou-Lefeuvre-18} using techniques from microlocal analysis and the theory of Pollicott-Ruelle resonances. \n\n\\subsection{Inverse Spectral problem}\n\n\nThe \\emph{length spectrum} of the Riemannian manifold $(M,g)$ is the collection of lengths of closed geodesics \\emph{counted with multiplicities}. It is said to be \\emph{simple} if all closed geodesics have distinct lengths and this is known to be a generic condition (with respect to the metric), see \\cite{Abraham-70,Anosov-82} (even in the non-Anosov case). Given $\\nabla^{\\E} \\in \\mathfrak{a}$, one can form the \\emph{connection Laplacian} $\\Delta_{\\nabla^{\\E}} := (\\nabla^{\\E})^*\\nabla^{\\E}$ (also known as the Bochner Laplacian) which is a differential operator of order $2$, non-negative, formally self-adjoint and elliptic, acting on $C^\\infty(M,\\E)$. While $\\Delta_{\\nabla^{\\E}}$ depends on a choice of representative $\\nabla^{\\E}$ in the class $\\mathfrak{a}$, its spectrum does not and there is a well-defined \\emph{spectrum map}:\n\\begin{equation}\\label{eq:spectrummap}\n\\mathcal{S} : \\mathbb{A}_{\\E} \\ni \\mathfrak{a} \\mapsto \\mathrm{spec}(\\Delta_{\\mathfrak{a}}),\n\\end{equation}\nwhere $\\mathrm{spec}(\\Delta_{\\mathfrak{a}}) = \\left\\{0 \\leq \\lambda_0(\\mathfrak{a}) \\leq \\lambda_1(\\mathfrak{a}) \\leq ...\\right\\}$ is the spectrum counted with multiplicities. Note that more generally, the spectrum map \\eqref{eq:spectrummap} can be defined on the whole moduli space $\\mathbb{A}$ (just as the primitive trace map \\eqref{equation:trace}). The trace formula of Duistermaat-Guillemin \\cite{Duistermaat-Guillemin-75, Guillemin-73} applied to $\\Delta_{\\mathfrak{a}}$ reads (when the length spectrum is simple):\n\\begin{equation}\n\\label{equation:trace-formula}\n\\lim_{t \\to \\ell(\\gamma_g(c))} \\left(t-\\ell(\\gamma_g(c))\\right) \\sum_{j \\geq 0} e^{-i \\sqrt{\\lambda_j}(\\mathfrak{a})t} = \\dfrac{\\ell(\\gamma_g(c^\\sharp)) \\Tr\\left(\\mathrm{Hol}_{\\nabla^{\\E}}(c)\\right)}{2\\pi |\\det(\\mathbbm{1}-P_{\\gamma_g(c)})|^{1\/2}} ,\n\\end{equation}\nwhere $\\sharp : \\mathcal{C} \\rightarrow \\mathcal{C}^\\sharp$ is the operator giving the primitive orbit associated to an orbit; $P_\\gamma$ is the Poincar\\'e map associated to the orbit $\\gamma$ and $\\ell(\\gamma)$ its length. Theorem \\ref{theorem:injectivity} therefore has the following straightforward consequence:\n\n\\begin{corollary}\n\\label{corollary:spectral}\nLet $(M,g)$ be a smooth Anosov Riemannian manifold of dimension $\\geq 3$ \\emph{with simple length spectrum}. Then:\n\\begin{itemize}\n\\item Let $\\E \\rightarrow M$ be a smooth Hermitian vector bundle and $\\mathfrak{a}_0 \\in \\mathbb{A}_{\\E}$ be a generic point. Then, the spectrum map $\\mathcal{S}$ is locally injective near $\\mathfrak{a}_0$.\n\\item The spectrum map $\\mathcal{S}$ is also globally injective when restricted to the cases \\emph{(1)-(4)} of the previous paragraph \\S\\ref{ssection:global-inj}.\n\\end{itemize}\n\\end{corollary}\n\nThis corollary simply follows from Theorem \\ref{theorem:injectivity} by observing that under the simple length spectrum assumption, the primitive trace map can be recovered from the equality \\eqref{equation:trace-formula}. Corollary \\ref{corollary:spectral} is analogous to the Guillemin-Kazhdan \\cite{Guillemin-Kazhdan-80,Guillemin-Kazhdan-80-2} rigidity result in which a potential $q \\in C^\\infty(M)$ is recovered from the knowledge of the spectrum of $-\\Delta_g + q$ (see also \\cite{Croke-Sharafutdinov-98,Paternain-Salo-Uhlmann-14-1}). As far as the connection Laplacian is concerned, it seems that Corollary \\ref{corollary:spectral} is the first positive result in this direction. Counter-examples were constructed by Kuwabara \\cite{Kuwabara-90} using the Sunada method \\cite{Sunada-85} but on coverings of a given Riemannian manifolds; hence the simple length spectrum condition is clearly violated. Up to our knowledge, it is also the first positive general result in an inverse spectral problem on a closed manifold of dimension $> 1$ with an \\emph{infinite} gauge-group. \n\nThis gives hope that similar methods could be used in the classical problem of recovering the isometry class of a metric from the spectrum of its Laplace-Beltrami operator \\emph{locally} (similarly to a conjecture of Sarnak for planar domains \\cite{Sarnak-90}). Such a result was already obtained in a neighbourhood of negatively-curved locally symmetric spaces by Sharafutdinov \\cite{Sharafutdinov-09}. See also \\cite{Croke-Sharafutdinov-98} for the weaker deformational spectral rigidity results or \\cite{DeSimoi-Kaloshin-Wei-17, Hezari-Zelditch-19} for recent results in the plane.\n\n\\subsection{Exact Liv\\v{s}ic cocycle theorem}\n\n\\label{ssection:exact}\n\nThe main ingredient in the proof of Theorem \\ref{theorem:injectivity} is the following Liv\\v{s}ic-type result in hyperbolic dynamical systems, which may be of independent interest. It shows that the cohomology class of a unitary cocycle over a transitive Anosov flow is determined by its trace along primitive periodic orbits. We phrase it in a somewhat more general context where we allow non-trivial vector bundles\n\n\\begin{theorem}\n\\label{theorem:weak-intro}\nLet $\\mathcal{M}$ be a smooth manifold endowed with a smooth transitive Anosov flow $(\\varphi_t)_{t \\in \\mathbb{R}}$. For $i\\in \\left\\{1,2\\right\\}$, let $\\E_i \\rightarrow \\mathcal{M}$ be a Hermitian vector bundle over $\\mathcal{M}$ equipped with a unitary connection $\\nabla^{\\mathcal{E}_i}$, and denote by $C_i(x,t) : (\\E_i)_x \\rightarrow (\\E_i)_{\\varphi_t(x)}$ the parallel transport along the flowlines with respect to $\\nabla^{\\E_i}$. If the connections have \\emph{trace-equivalent holonomies} in the sense that for all \\emph{primitive} periodic orbits $\\gamma$, one has\n\\begin{equation}\n\\label{equation:trace-intro}\n\\Tr\\left(C_1(x_\\gamma,\\ell(\\gamma))\\right) = \\Tr\\left(C_2(x_\\gamma,\\ell(\\gamma))\\right),\n\\end{equation}\nwhere $x_\\gamma \\in \\gamma$ is arbitrary and $\\ell(\\gamma)$ is the period of $\\gamma$, then the following holds: there exists $p \\in C^\\infty(\\mathcal{M},\\mathrm{U}(\\E_2,\\E_1))$ such that for all $x \\in \\mathcal{M}, t \\in \\mathbb{R}$,\n\\begin{equation}\n\\label{equation:cohomologous}\nC_1(x,t) = p(\\varphi_t x) C_2(x,t) p(x)^{-1}.\n\\end{equation}\n\\end{theorem}\n\nIn the vocabulary of dynamical systems, note that each unitary cocycle is given by parallel transport along some unitary connection and \\eqref{equation:cohomologous} says that the cocycles induced by parallel transport are \\emph{cohomologous}. In particular, in the case of the trivial principal bundle $\\mathrm{U}(r) \\times \\mathcal{M} \\rightarrow \\mathcal{M}$ our theorem can be restated just in terms of $\\mathrm{U}(r)$-cocycles. Note that the bundles $\\E_1$ and $\\E_2$ could be \\emph{a priori} distinct (and have different ranks) but Theorem \\ref{theorem:weak-intro} shows that they are actually isomorphic:\n\n\\begin{corollary}\n\\label{corollary:iso}\nLet $\\E_1,\\E_2 \\rightarrow \\mathcal{M}$ be two Hermitian vector bundles equipped with respective unitary connection $\\nabla^{\\mathcal{E}_1}$ and $\\nabla^{\\mathcal{E}_2}$. If the traces of the holonomy maps agree as in \\eqref{equation:trace-intro}, then $\\E_1 \\simeq \\E_2$ are isomorphic.\n\n\\end{corollary}\n\nTheorem \\ref{theorem:weak-intro} has other geometric consequences which are further detailed in \\S\\ref{ssection:intro}. Liv\\v{s}ic-type theorems have a long history in hyperbolic dynamical systems going back to the seminal paper of Liv\\v{s}ic \\cite{Livsic-72} and appear in various settings. They were both developed in the Abelian case i.e. for functions (see \\cite{Livsic-72,DeLaLlave-Marco-Moryon-86, Lopes-Thieullen-05,Guillarmou-17-1,Gouezel-Lefeuvre-19} for instance) and in the cocycle case.\n\nSurprisingly, we could not locate any result such as Theorem \\ref{theorem:weak-intro} in the literature. The closest works (in the discrete-time case) are that of Parry \\cite{Parry-99} and Schmidt \\cite{Schmidt-99} which mainly inspired the proof of Theorem \\ref{theorem:weak-intro}. Nevertheless, when considering compact Lie groups, Parry's and Schmidt's results seem to be weaker as they need to assume that the conjugacy classes of the cocycles agree (and not only the traces) and that a certain additional cocycle is transitive in order to derive the same conclusion. The literature is mostly concerned with the discrete-time case, namely hyperbolic diffeomorphisms: in that case, a lot of articles are devoted to studying cocycles with values in a non-compact Lie group (and sometimes satisfying a ``slow-growth\" assumption), see \\cite{DeLaLlave-Windsor-10,Kalinin-11, Sadovskaya-13, Sadvoskaya-17, Avila-Kocsard-Liu-18}. One can also wonder if Theorem \\ref{theorem:weak-intro} could be proved in the non-unitary setting. Other articles such as \\cite{Nitica-Torok-95,Nitica-Torok-98,Nicol-Pollicott-99,Walkden-00,Pollicott-Walkden-01} seem to have been concerned with regularity issues on the map $p$, namely bootstrapping its regularity under some weak \\emph{a priori} assumption (such as measurability only). Let us also point out at this stage that some regularity issues will appear while proving Theorem \\ref{theorem:weak-intro} but this will be bypassed by the use of a recent regularity statement \\cite[Theorem 4.1]{Bonthonneau-Lefeuvre-20} in hyperbolic dynamics.\n\n\n\\subsection{Organization of the paper}\n\nThe paper is divided in three parts:\n\n\\begin{itemize}\n\n\\item First of all, we prove in Section \\S\\ref{section:livsic} the \\emph{exact Liv\\v{s}ic cocycle} Theorem \\ref{theorem:weak-intro} for general Anosov flows showing that the cohomology class of a unitary cocycle is determined by its trace along closed orbits. The proof is based on the introduction of a new tool which we call \\emph{Parry's free monoid}, denoted by $\\mathbf{G}$, and formally generated by orbits homoclinic to a given closed orbit. We show that any unitary connection induces a unitary representation of the monoid $\\mathbf{G}$ and that trace-equivalent connections have the same character; we can then apply tools from representation theory to conclude. We believe that this notion could be used in other contexts. It is reinvested in a companion paper \\cite{Cekic-Lefeuvre-21-3} to study \\emph{transparent manifolds}, namely Riemannian manifolds with trivial holonomy along closed geodesics.\n\n\\item In subsequent sections, we develop a microlocal framework, based on the theory of Pollicott-Ruelle resonances. We define a notion of \\emph{generalized X-ray transform with values in a vector bundle} which is mainly inspired by \\cite{Guillarmou-17-1,Guillarmou-Lefeuvre-18,Gouezel-Lefeuvre-19}. In \\S\\ref{section:geometry}, we relate the geometry of the moduli space of gauge-equivalent connections with the leading Pollicott-Ruelle resonance of a certain natural operator (called the mixed connection).\n\n\\item Eventually, the main results such as Theorem \\ref{theorem:injectivity} are proved in \\S\\ref{section:proofs}, where we also deduce the global properties of $\\mathcal{T}^\\sharp$ involving line bundles, flat bundles, negatively curved base manifolds, and the topology of bundles.\n\n\n\\end{itemize}\nSome technical preliminaries are provided in Section \\S\\ref{section:tools}. \n\n\\subsection{Perspectives}\n\nWe intend to pursue this work in different directions:\n\n\\begin{itemize}\n\n\\item We expect to be able to show a stability estimate version of Theorem \\ref{theorem:injectivity}, namely that the distance between connections up to gauge-equivalence is controlled (at least locally) by the distance between their images under the primitive trace map $\\mathcal{T}^\\sharp$. We believe that such an estimate should prove that the moduli space of connections up to gauge-equivalence $\\mathbb{A}_{\\E}$ (locally) embeds naturally via the primitive trace map as a closed topological (H\\\"older-continuous) Banach submanifold of $\\ell^\\infty(\\mathcal{C}^\\sharp)$.\n\n\\item We believe that the representation-theoretic approach developed in \\S\\ref{section:livsic} using Parry's free monoid $\\mathbf{G}$ could be pushed further. In a subsequent article \\cite{Cekic-Lefeuvre-21-3}, we will study the particular case of the Levi-Civita connection on the tangent bundle of Anosov manifolds and try to classify the possible images $\\rho(\\mathbf{G}) \\subset \\mathrm{O}(n)$. This problem is intimately connected to the dynamics of the frame flow.\n\n\\item Eventually, the arguments developed in \\S\\ref{section:livsic} mainly rely on the use of homoclinic orbits; to these orbits, we will associate a notion of \\emph{length} which is well-defined as an element of $\\mathbb{R}\/T_\\star\\mathbb{Z}$, for some real number $T_\\star > 0$. We believe that, similarly to the set of periodic orbits where one defines the \\emph{Ruelle zeta function}\n\\[\n\\zeta(s) := \\prod_{\\gamma^\\sharp \\in \\mathcal{G}^\\sharp} \\left(1 - e^{-s \\ell(\\gamma^\\sharp)}\\right), \n\\]\nwhere the product runs over all primitive periodic orbits $\\gamma^\\sharp \\in \\mathcal{G}^\\sharp$ (and $\\ell(\\gamma^\\sharp)$ denotes the orbit period) and shows that this extends meromorphically from $\\left\\{\\Re(s) \\gg 0 \\right\\}$ to $\\mathbb{C}$ (see \\cite{Giulietti-Liverani-Pollicott-13,Dyatlov-Zworski-16}), one could also define a complex function for homoclinic orbits by means of a Poincar\\'e series (rather than a product). It should be a consequence of \\cite[Theorem 4.15]{Dang-Riviere-20} that this function extends meromorphically to $\\mathbb{C}$. It might then be interesting to compute its value at $0$; the latter might be independent of the choice of representatives for the length of homoclinic orbits (two representatives differ by $mT_\\star$, for some $m \\in \\mathbb{Z}$) and could be (at least in some particular cases) an interesting topological invariant as for the Ruelle zeta function on surfaces, see \\cite{Dyatlov-Zworski-17}. \\\\\n\n\\end{itemize} \n\n\\noindent \\textbf{Acknowledgement:} M.C. has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 725967). We warmly thank Yannick Guedes Bonthonneau, Yann Chaubet, Colin Guillarmou, Julien March\\'e, Gabriel Paternain, Steve Zelditch for fruitful discussions. We also thank S\\'ebastien Gou\\\"ezel, Boris Kalinin, Mark Pollicott, Klaus Schmidt for answering our questions on Liv\\v{s}ic theory, and Nikhil Savale for providing us with the reference \\cite{Guillemin-73}. Special thanks to Danica Kosanovi\\'c for helping out with the topology part.\n\n\n\\section{Setting up the tools}\n\n\\label{section:tools}\n\n\\subsection{Microlocal calculus and functional analysis} \n\nLet $\\mathcal{M}$ be a smooth closed manifold. Given a smooth vector bundle $\\E \\rightarrow \\mathcal{M}$, we denote by $\\Psi^m(\\mathcal{M}, \\E)$ the space of pseudodifferential operators of order $m$ acting on $\\E$. When $\\E$ is the trivial line bundle, such an operator $P \\in \\Psi(\\mathcal{M})$ can be written (up to a smoothing remainder) in local coordinates as\n\\begin{equation}\n\\label{equation:quantization}\nPf(x) = \\int_{\\mathbb{R}^{n}} \\int_{\\mathbb{R}^{n}} e^{i\\xi \\cdot(x-y)}p(x,\\xi)f(y)dyd\\xi,\n\\end{equation}\nwhere $f$ is compactly supported in the local patch and $p \\in S^m(T^*\\mathbb{R}^{n})$ is a \\emph{symbol} i.e. it satisfies the following estimates in coordinates:\n\\begin{equation}\n\\label{equation:bounds}\n\\sup_{|\\alpha'| \\leq \\alpha, |\\beta'|\\leq \\beta} \\sup_{(x,\\xi) \\in T^*\\mathbb{R}^{n+1}} \\langle \\xi \\rangle^{m - |\\alpha'|} |\\partial_\\xi^{\\alpha'} \\partial_x^{\\beta'} p(x,\\xi)| < \\infty,\n\\end{equation}\nfor all $\\alpha, \\beta \\in \\mathbb{N}^n$, with $\\langle \\xi \\rangle = \\sqrt{1+|\\xi|^2}$. When $\\E$ is not the trivial line bundle, the symbol $p$ is a matrix-valued symbol. Given $p \\in S^m(T^*\\mathcal{M})$, one can define a (non-canonical) \\emph{quantization procedure} $\\Op : S^m(T^*\\mathcal{M}) \\rightarrow \\Psi^m(\\mathcal{M})$ thanks to \\eqref{equation:quantization} in coordinates patches. This also works more generally with a vector bundle $\\E \\rightarrow \\mathcal{M}$ and one has a quantization map $\\Op : S^m(T^*\\mathcal{M}, \\mathrm{End}(\\E)) \\rightarrow \\Psi^m(\\mathcal{M},\\E)$ (note that the symbol is then a section of the pullback bundle $\\mathrm{End}(\\E) \\rightarrow T^*\\mathcal{M}$ satisfying the bounds \\eqref{equation:bounds} in coordinates). There is a well-defined (partial) inverse map $\\sigma_{\\mathrm{princ}} : \\Psi(\\mathcal{M},\\E) \\rightarrow S^m(T^*\\mathcal{M}, \\mathrm{End}(\\E))\/S^{m-1}(T^*\\mathcal{M}, \\mathrm{End}(\\E))$ called the \\emph{principal symbol} and satisfying $\\sigma_{\\mathrm{princ}}(\\Op(p)) = [p]$ (the equivalence class as an element of $S^m(T^*\\mathcal{M}, \\mathrm{End}(\\E))\/S^{m-1}(T^*\\mathcal{M}, \\mathrm{End}(\\E))$).\n\nWe denote by $H^s(\\mathcal{M}, \\E)$ the space of Sobolev sections of order $s \\in \\mathbb{R}$ and by $C^s_*(\\mathcal{M}, \\E)$ the space of H\\\"older-Zygmund sections of order $s \\in \\mathbb{R}$. It is well-known that for $s \\in \\mathbb{R}_+ \\setminus \\mathbb{N}$, $C^s_*$ coincide with the space of H\\\"older-continuous sections $C^s$ of order $s$. Recall that $C^s_*$ is an algebra as long as $s > 0$ and $H^s$ is an algebra for $s > n\/2$.\nIf $P \\in \\Psi^m(\\mathcal{M}, \\E)$ is a pseudodifferential operator of order $m \\in \\mathbb{R}$, then $P : X^{s+m}(\\mathcal{M}, \\E) \\rightarrow X^s(\\mathcal{M}, \\E)$, where $X = H$, $C_*$, is bounded. We refer to \\cite{Shubin-01,Taylor-91} for further details.\n\n\\subsection{Connections on vector bundles}\n\n\\label{ssection:connections}\n\nWe refer the reader to \\cite[Chapter 2]{Donaldson-Kronheimer-90} for the background on connections on vector bundles.\n\n\n\n\n\n\n\\subsubsection{Mixed connection on the homomorphism bundle}\n\n\\label{sssection:connection-induced}\n\nIn this paragraph, we consider two Hermitian vector bundles $\\E_1, \\E_2 \\rightarrow \\mathcal{M}$ equipped with respective unitary connections $\\nabla_1 = \\nabla^{\\E_1}$ and $\\nabla_2 = \\nabla^{\\E_2}$ which can be written in some local patch $U \\subset \\mathbb{R}^n$ of coordinates $\\nabla^{\\E_i} = d +\\Gamma_i$, for some $\\Gamma_i \\in C^\\infty(U,T^*U \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E_i))$. Let $\\Hom(\\E_1,\\E_2)$ be the vector bundle of homomorphisms from $\\E_1$ to $\\E_2$, endowed with the natural Hermitian structure.\n\n\\begin{definition}\\label{definition:mixed}\nWe define the (unitary) \\emph{homomorphism} or \\emph{mixed} connection $\\nabla^{\\Hom(\\nabla^{\\E_1}, \\nabla^{\\E_2})}$ on $\\Hom(\\E_1, \\E_2)$, induced by $\\nabla^{\\E_1}$ and $\\nabla^{\\E_2}$, by the Leibnitz property:\n\\[\n\\forall u \\in C^\\infty(\\mathcal{M}, \\Hom(\\E_1, \\E_2)), \\forall s \\in C^\\infty(\\mathcal{M}, \\E_1), \\quad \\nabla^{\\E_2}(us) = (\\nabla^{\\Hom(\\nabla^{\\E_1}, \\nabla^{\\E_2})}u) \\cdot s + u\\cdot (\\nabla^{\\E_1}s).\n\\]\n\\end{definition}\nEquivalently, it is straightforward to check that this is the canonical tensor product connection induced on $\\Hom(\\E_1, \\E_2) \\cong \\E_2 \\otimes \\E_1^*$ and that in local coordinates we have\n\\begin{equation}\\label{eq:localhom}\n\t\\nabla^{\\Hom(\\nabla^{\\E_1}, \\nabla^{\\E_2})} u := du + \\Gamma_2 (\\bullet) u - u \\Gamma_1(\\bullet).\n\\end{equation}\nNote that this definition does not require the bundles to have same rank;\nwe insist on the fact that the mixed connection $\\nabla^{\\Hom(\\nabla_1, \\nabla_2)}$ \\emph{depends on a choice} of connections $\\nabla_1$ and $\\nabla_2$. In the particular case when $\\E_1 = \\E_2 = \\E$ and $\\nabla_1^{\\E} = \\nabla_2^{\\E} = \\nabla^{\\E}$ we will write $\\nabla^{\\mathrm{End}(\\nabla^{\\E})} = \\nabla^{\\Hom(\\nabla^{\\E}, \\nabla^{\\E})}$ for the induced \\emph{endomorphism} connection on $\\mathrm{End}(\\E)$. When clear from the context, we will also write $\\nabla^{\\mathrm{End}(\\E)}$ for the endomorphism connection induced by $\\nabla^{\\E}$.\n\n\n\nGiven a flow $(\\varphi_t)_{t \\in \\mathbb{R}}$, we will denote by $P(x,t) : \\Hom({\\E_1}_x, {\\E_2}_x) \\rightarrow \\Hom({\\E_1}_{\\varphi_t(x)}, {\\E_2}_{\\varphi_t(x)})$ the parallel transport with respect to the mixed connection along the flowlines of $(\\varphi_t)_{t \\in \\mathbb{R}}$. Observe that for $u \\in \\Hom({\\E_1}_x, {\\E_2}_x)$, we have:\n\\begin{equation}\n\\label{equation:tp-mixed}\nP(x,t) u = C_2(x,t) u C_1(x,t)^{-1},\n\\end{equation}\nwhere $C_i(x,t) : {\\E_i}_x \\rightarrow {\\E_i}_{\\varphi_t(x)}$ is the parallel transport with respect to $\\nabla^{\\E_i}$ along the flowlines of $(\\varphi_t)_{t \\in \\mathbb{R}}$.\n\nRecall that the curvature tensor $F_\\nabla \\in C^\\infty(\\mathcal{M}, \\Lambda^2(T^*M) \\otimes \\mathrm{End}(\\E))$ of $\\nabla = \\nabla^{\\E}$ is defined as, for any vector fields $X, Y$ on $\\mathcal{M}$ and sections $S$ of $\\E$\n\\begin{equation*\n\tF_\\nabla(X, Y)S = \\nabla_X \\nabla_Y S - \\nabla_Y \\nabla_X S - \\nabla_{[X, Y]}S.\n\\end{equation*} \nThen a quick computation using \\eqref{eq:localhom} reveals that:\n\\begin{equation}\n\\label{equation:induced-curvature}\nF_{\\nabla^{\\mathrm{Hom}(\\nabla_1,\\nabla_2)}} u = F_{\\nabla_2} \\cdot u - u \\cdot F_{\\nabla_1}.\n\\end{equation}\nEventually, using the Leibnitz property, we observe that if $p_i \\in C^\\infty(\\mathcal{M},\\mathrm{U}(\\E_i',\\E_i))$ is an isomorphism, then:\n\\begin{align}\n\\label{equation:lien}\n\\begin{split}\n\\nabla^{\\Hom(\\nabla_1, p_2^*\\nabla_2)} u &= (p_2)^{-1} \\nabla^{\\Hom(\\nabla_1, \\nabla_2)} (p_2 u), \\quad \\forall u \\in C^\\infty(\\mathcal{M}, \\Hom(\\E_1, \\E_2')),\\\\\n\\nabla^{\\Hom(p_1^*\\nabla_1, \\nabla_2)} u &= \\nabla^{\\Hom(\\nabla_1, \\nabla_2)} (u p_1^{-1}) \\cdot p_1,\\quad\\,\\, \\forall u \\in C^\\infty(\\mathcal{M}, \\Hom(\\E_1', \\E_2)).\n\\end{split}\n\\end{align}\n\n\\subsubsection{Ambrose-Singer formula}\n\nRecall that the celebrated Ambrose-Singer formula (see eg. \\cite[Theorem 8.1]{Kobayashi-Nomizu-69}) determines the tangent space at the identity of the holonomy group with respect to an arbitrary connection, in terms of its curvature tensor. Here we give an integral version of this fact. We start with a Hermitian vector bundle $\\E$ over the Riemannian manifold $(\\mathcal{M}, g)$. Equip $\\E$ with unitary connection $\\nabla = \\nabla^{\\E}$. \n\n\n\nConsider\na smooth homotopy $\\Gamma: [0, 1]^2 \\to \\mathcal{M}$ such that $\\Gamma(0, 0) = p$.\nThe ``vertical'' map $C_{\\uparrow}(s, t): \\E_p \\to \\E_{\\Gamma(s, t)}$ is obtained by parallel transporting with respect to $\\nabla$ from $\\E_p$ to $\\E_{\\Gamma(0, 1)}$, then $\\E_{\\Gamma(0, 1)}$ to $\\E_{\\Gamma(s, 1)}$ and $\\E_{\\Gamma(s, 1)}$ to $\\E_{\\Gamma(s, t)}$, along $\\Gamma(0, \\bullet)$, $\\Gamma(\\bullet, 1)$ and $\\Gamma(s, \\bullet)$, respectively. Next, define the ``horizontal'' map $C_{\\rightarrow}(s, t): \\E_p \\to \\E_{\\Gamma(s, t)}$ by parallel transport with respect to $\\nabla$ from $\\E_p$ to $\\E_{\\Gamma(s, 0)}$ and $\\E_{\\Gamma(s, 0)}$ to $\\E_{\\Gamma(s, t)}$, along $\\Gamma(\\bullet, 0)$ and $\\Gamma(s, \\bullet)$, respectively. For a better understanding, see Figure \\ref{fig:AS1}.\n\t\t\n\t\t\n\t\t\n\nWe are ready to prove the formula:\n\n\n\\begin{lemma}\\label{lemma:ambrosesinger}\n\tThe following formula holds\n\t\\begin{equation}\n\t\tC_{\\uparrow}^{-1}(1, 1) C_{\\rightarrow}(1, 1) - \\mathbbm{1}_{\\E_{p}} = \\int_0^1 \\int_0^1 C_{\\uparrow}(s, t)^{-1} F_\\nabla(\\partial_t, \\partial_s) C_{\\rightarrow}(s, t) \\, dt \\, ds.\n\t\\end{equation}\t\n\\end{lemma}\n\n\n\n\n\n\\begin{proof}\n\tLet $w_1, w_2 \\in \\E_p$; formally, we will identify the connection $\\nabla$ with its pullback $\\Gamma^*\\nabla$ on the pullback bundle $\\Gamma^*\\E$ over $[0, 1]^2$, as well as the curvature $F_\\nabla$ with $\\Gamma^*F_\\nabla$. Then we have the following chain of equalities:\n\t\\begin{align*}\n\t\t&\\langle{w_1, (C_{\\uparrow}(1, 1)^{-1} C_{\\rightarrow}(1, 1) - \\mathbbm{1}_{\\E_p}) w_2}\\rangle = \\langle{C_{\\uparrow}(1, 1)w_1, C_{\\rightarrow}(1, 1)w_2}\\rangle - \\langle{C_{\\uparrow}(0, 1)w_1, C_{\\rightarrow}(0, 1)w_2}\\rangle\\\\\n\t\t &= \\int_0^1 \\partial_s \\langle{C_{\\uparrow}(s, 1) w_1, C_{\\rightarrow}(s, 1) w_2}\\rangle ds\\\\\n\t\t&= \\int_0^1 \\langle{C_{\\uparrow}(s, 1) w_1, \\nabla_{\\partial_s} C_{\\rightarrow}(s, 1) w_2}\\rangle ds\\\\\n\t\t&= \\int_0^1 \\Big[\\int_0^1 \\Big(\\partial_t \\langle{C_{\\uparrow}(s, t) w_1, \\nabla_{\\partial_s} C_{\\rightarrow}(s, t)w_2}\\rangle + \\langle{C_{\\uparrow}(s, 0) w_1, \\nabla_{\\partial_s} C_{\\rightarrow}(s, 0) w_2}\\rangle\\Big)dt\\Big] ds\\\\\n\t\t&= \\int_0^1 \\int_0^1 \\langle{C_{\\uparrow}(s, t) w_1, \\nabla_{\\partial_t} \\nabla_{\\partial_s} C_{\\rightarrow}(s, t) w_2}\\rangle \\,ds\\, dt\\\\\n\t\t&= \\int_0^1 \\int_0^1 \\langle{w_1, C_{\\uparrow}(s, t)^{-1}\\underbrace{(\\nabla_{\\partial_t} \\nabla_{\\partial_s} - \\nabla_{\\partial_s} \\nabla_{\\partial_t})}_{=F_\\nabla(\\partial_t, \\partial_s)} C_{\\rightarrow}(s, t) w_2}\\rangle \\,ds\\, dt,\n\t\\end{align*}\n\tas the Lie bracket $[\\partial_s, \\partial_t] = 0$ and we used the unitarity of $\\nabla$ throughout. This completes the proof, since $w_1$ and $w_2$ were arbitrary.\n\\end{proof}\n\n\\begin{figure}\n \\centering\n\\begin{tikzpicture}[scale = 0.8, everynode\/.style={scale=0.5}]\n\\tikzset{cross\/.style={cross out, draw=black, minimum size=2*(#1-\\pgflinewidth), inner sep=0pt, outer sep=0pt},\ncross\/.default={1pt}}\n\n \t\\draw[thick, ->] (0, 0) -- (6,0) node[right] {\\small $s$};\n\t\t\\draw[thick, ->] (0, 0) -- (0,6) node[left] {\\small $t$};\n\t\t\n\t\n\t\t\\draw[thick] (3, 5) -- (5, 5) -- (5, 0);\n\t\t\n\t\n\t\t\\draw[thick, blue] (0, 0) -- (0, 5) -- (3, 5) -- (3, 3);\n\t\t\n\t\n\t\t\\draw[thick, red] (0, 0) -- (3, 0) -- (3, 3);\n\t\t\n\t\n\t\t\\draw[thick, ->] (3, 1.5)--(3, 1.501);\n\t\t\\draw[thick, ->] (1.5, 0)--(1.501, 0);\n\t\t\n\t\n\t\t\\draw[thick, ->] (0, 2.5)--(0, 2.501);\n\t\t\\draw[thick, ->] (1.5, 5)--(1.501, 5);\n\t\t\\draw[thick, ->] (3, 4)--(3, 3.999);\n\t\t\n\t\n\t\t\\fill (0, 0) node[below left] {\\tiny $p$} circle (1.5pt);\n\t\t\\fill (5, 0) node[below] {\\tiny $\\Gamma(1, 0)$} circle (1.5pt);\n\t\t\\fill (0, 5) node[left] {\\tiny $\\Gamma(0, 1)$} circle (1.5pt);\n\t\t\\fill (5, 5) node[right] {\\tiny $\\Gamma(1, 1)$} circle (1.5pt);\n\t\t\n\t\n\t\t\\fill (3, 3) node[right] {\\small $\\Gamma(s, t)$} circle (1.5pt);\n\t\t\\fill (3, 0) node[below] {\\small $\\Gamma(s, 0)$} circle (1.5pt);\n\t\t\\fill (3, 5) node[above] {\\small $\\Gamma(s, 1)$} circle (1.5pt);\n\\end{tikzpicture}\n \\caption{\\small The homotopy $\\Gamma$ in Lemma \\ref{lemma:ambrosesinger} with the corresponding points in $\\mathcal{M}$: in blue and red are the trajectories along which the parallel transport maps $C_{\\uparrow}$ (vertical) and $C_{\\rightarrow}$ (horizontal) are taken, respectively.}\n \\label{fig:AS1}\n\\end{figure}\n\n\nWe have two applications in mind for this lemma: one if $\\gamma$ is in a neighbourhood of $p$ and we use the radial homotopy via geodesics emanating from $p$, and the second one for the ``thin rectangle'' obtained by shadowing a piece of the flow orbit, see Lemma \\ref{lemma:ASgeometry}.\n\n\n\n\\subsection{Fourier analysis in the fibers} In this paragraph, we recall some elements of Fourier analysis in the fibers and refer to \\cite{Guillemin-Kazhdan-80, Guillemin-Kazhdan-80-2, Paternain-Salo-Uhlmann-14-2, Paternain-Salo-Uhlmann-15, Guillarmou-Paternain-Salo-Uhlmann-16} for further details.\n\n\n\n\n\\subsubsection{Analysis on the trivial line bundle}\n\n\\label{sssection:line-bundle}\n\n\nLet $(M,g)$ be a smooth Riemannian manifold of arbitrary dimension $n \\geq 2$. The unit tangent bundle is endowed with the natural Sasaki metric and we let $\\pi : SM \\rightarrow M$ be the projection on the base. There is a canonical splitting of the tangent bundle to $SM$ as:\n\\[\nT(SM) = \\mathbb{H} \\oplus \\mathbb{V} \\oplus \\mathbb{R} X,\n\\] \nwhere $X$ is the geodesic vector field, $\\mathbb{V} := \\ker \\dd \\pi$ is the vertical space and $\\mathbb{H}$ is the horizontal space: it can be defined as the orthogonal to $\\mathbb{V} \\oplus \\mathbb{R} X$ with respect to the Sasaki metric (see \\cite[Chapter 1]{Paternain-99}). Any vector $Z \\in T(SM)$ can be decomposed according to the splitting\n\\[\nZ = \\alpha(Z) X + Z_{\\mathbb{H}} + Z_{\\mathbb{V}},\n\\]\nwhere $\\alpha$ is the Liouville $1$-form, $Z_{\\mathbb{H}} \\in \\mathbb{H}, Z_{\\mathbb{V}} \\in \\mathbb{V}$. If $f \\in C^\\infty(SM)$, its gradient computed with respect to the Sasaki metric can be written as:\n\\[\n\\nabla_{\\mathrm{Sas}}f = (Xf) X + \\nabla_{\\mathbb{H}} f + \\nabla_{\\mathbb{V}} f,\n\\]\nwhere $\\nabla_{\\mathbb{H}} f \\in \\mathbb{H}$ is the horizontal gradient, $\\nabla_{\\mathbb{V}} f \\in \\mathbb{V}$ is the vertical gradient. We also let $\\mathcal{N} \\to SM$ be the \\emph{normal bundle} whose fiber over each $(x,v) \\in SM$ is given by $(\\mathbb{R} \\cdot v)^\\bot$. The bundles $\\mathbb{H}$ and $\\mathbb{V}$ may be naturally identified with the bundle $\\mathcal{N}$ (see \\cite[Section 1]{Paternain-99}).\n\nFor every $x \\in M$, the sphere $S_xM = \\left\\{ v \\in T_xM ~|~ |v|^2_x = 1\\right\\} \\subset SM$ endowed with the Sasaki metric is isometric to the canonical sphere $(\\mathbb{S}^{n-1},g_{\\mathrm{can}})$. We denote by $\\Delta_{\\mathbb{V}}$ the vertical Laplacian obtained for $f \\in C^\\infty(SM)$ as $\\Delta_{\\mathbb{V}} f(x,v) = \\Delta_{g_{\\mathrm{can}}}(f|_{S_xM})(v)$, where $\\Delta_{g_{\\mathrm{can}}}$ is the spherical Laplacian. For $m \\geq 0$, we denote by $\\Omega_m$ the (finite-dimensional) vector space of spherical harmonics of degree $m$ for the spherical Laplacian $\\Delta_{g_{\\mathrm{can}}}$: they are defined as the elements of $\\ker(\\Delta_{g_{\\mathrm{can}}} + m(m+n-2))$. We will use the convention that $\\Omega_m = \\left\\{0\\right\\}$ if $m< 0$. If $f \\in C^\\infty(SM)$, it can then be decomposed as $f = \\sum_{m \\geq 0} f_m$, where $f_m \\in C^\\infty(M,\\Omega_m)$ is the $L^2$-orthogonal projection of $f$ onto the spherical harmonics of degree $m$.\n\nThere is a one-to-one correspondence between trace-free symmetric tensors of degree $m$ and spherical harmonics of degree $m$. More precisely, the map\n\\[\n\\pi_m^* : C^\\infty(M,\\otimes^m_S T^*M|_{0-\\Tr}) \\rightarrow C^\\infty(M,\\Omega_m),\n\\]\ngiven by $\\pi_m^*f(x,v) = f_x(v,...,v)$ is an isomorphism. Here, the index $0-\\Tr$ denotes the space of trace-free symmetric tensors, namely tensors such that, if $(\\e_1,...,\\e_n)$ denotes a local orthonormal frame of $TM$:\n\\[\n\\Tr(f) := \\sum_{i=1}^n f(\\e_i,\\e_i,\\cdot, ...,\\cdot) = 0.\n\\]\nWe will denote by ${\\pi_m}_* : C^\\infty(M,\\Omega_m) \\rightarrow C^\\infty(M,\\otimes^m_S T^*M|_{0-\\Tr})$ the adjoint of this map. More generally, the mapping\n\\begin{equation}\\label{eq:pi_muntwisted}\n\\pi_m^* : C^\\infty(M,\\otimes^m_S T^*M) \\rightarrow \\oplus_{k \\geq 0} C^\\infty(M,\\Omega_{m-2k})\n\\end{equation}\nis an isomorphism. We refer to \\cite[Section 2]{Cekic-Lefeuvre-20} for further details.\n\nThe geodesic vector field acts as $X : C^\\infty(M,\\Omega_m) \\rightarrow C^\\infty(M,\\Omega_{m-1}) \\oplus C^\\infty(M,\\Omega_{m+1})$ (see \\cite{Guillemin-Kazhdan-80-2, Paternain-Salo-Uhlmann-15}). We define $X_+$ as the $L^2$-orthogonal projection of $X$ on the higher modes $\\Omega_{m+1}$, namely if $u \\in C^\\infty(M,\\Omega_m)$, then $X_+u := (Xu)_{m+1}$ and $X_-$ as the $L^2$-orthogonal projection of $X$ on the lower modes $\\Omega_{m-1}$. For $m \\geq 0$, the operator $X_+ : C^\\infty(M,\\Omega_m) \\rightarrow C^\\infty(M,\\Omega_{m+1})$ is elliptic and thus has a finite dimensional kernel (see \\cite{Dairbekov-Sharafutdinov-10}). The operator $X_- : C^\\infty(M,\\Omega_m) \\rightarrow C^\\infty(M,\\Omega_{m-1})$ is of divergence type. The elements in the kernel of $X_+$ are called \\emph{Conformal Killing Tensors (CKTs)}, associated to the trivial line bundle. For $m=0$, the kernel of $X_+$ on $C^\\infty(M,\\Omega_0)$ always contains the constant functions. We call \\emph{non trivial CKTs} elements in $\\ker X_+$ which are not constant functions on $SM$. The kernel of $X_+$ is invariant by changing the metric by a conformal factor (see \\cite[Section 3.6]{Guillarmou-Paternain-Salo-Uhlmann-16}). It is known (see \\cite{Paternain-Salo-Uhlmann-15}) that there are no non trivial CKTs in negative curvature and for Anosov surfaces but the question remains open for general Anosov manifolds. We provide a positive answer to this question \\emph{generically} as a byproduct of our work \\cite{Cekic-Lefeuvre-21-2}.\n\n\n\\subsubsection{Twisted Fourier analysis}\n\n\\label{sssection:twisted-fourier}\n\nWe now consider a Hermitian vector bundle with a unitary connection $(\\mathcal{E},\\nabla^{\\mathcal{E}})$ over $(M,g)$ and define the operator $\\mathbf{X} := (\\pi^*\\nabla^{\\mathcal{E}})_X$ acting on $C^\\infty(SM,\\pi^*\\mathcal{E})$, where $\\pi : SM \\rightarrow M$ is the projection. For the sake of simplicity, we will drop the $\\pi^*$ in the following. If $f \\in C^\\infty(SM,\\mathcal{E})$, then $\\nabla^{\\mathcal{E}}f \\in C^\\infty(SM,T^*(SM) \\otimes \\mathcal{E})$ and we can write\n\\[\n\\nabla^{\\mathcal{E}}f = (\\mathbf{X} f, \\nabla_{\\mathbb{H}} f, \\nabla_{\\mathbb{V}} f),\n\\]\nwhere $\\nabla_{\\mathbb{H}} f \\in C^\\infty(SM, \\mathbb{H}^* \\otimes \\mathcal{E}), \\nabla_{\\mathbb{V}} f \\in C^\\infty(SM, \\mathbb{V}^* \\otimes \\mathcal{E})$. For future reference, we introduce a bundle endomorphism map $R$ on $\\mathcal{N} \\otimes \\E$, derived from the Riemann curvature tensor via the formula $R(x, v)(w \\otimes e) = (R_x(w, v)v) \\otimes e$.\n\nIf $(e_1,...,e_r)$ is a local orthonormal frame of $\\mathcal{E}$, then we define the vertical Laplacian as\n\\[\n\\Delta_{\\mathbb{V}}^{\\mathcal{E}}(\\sum_{k=1}^r u_k e_k) := \\sum_{k=1}^r (\\Delta_{\\mathbb{V}} u_k) e_k.\n\\]\nAny section $f \\in C^\\infty(SM,\\mathcal{E})$ can be decomposed according to $f = \\sum_{m \\geq 0} f_m$, where $f_m \\in \\ker(\\Delta_{\\mathbb{V}}^{\\mathcal{E}} + m(m+n-2))$ and we define $C^\\infty(M,\\Omega_m \\otimes \\mathcal{E}) := \\ker(\\Delta_{\\mathbb{V}}^{\\mathcal{E}} + m(m+n-2)) \\cap C^\\infty(SM,\\mathcal{E})$.\n\nHere again, the operator $\\mathbf{X} : C^\\infty(M,\\Omega_m \\otimes \\E) \\rightarrow C^\\infty(M,\\Omega_{m-1} \\otimes \\E) \\oplus C^\\infty(M,\\Omega_{m+1} \\otimes \\E)$ can be split into the corresponding sum $\\mathbf{X} = \\mathbf{X}_- + \\mathbf{X}_-$. For every $m \\geq 0$, the operator $\\mathbf{X}_+$ is elliptic and has finite dimensional kernel, whereas $\\mathbf{X}_-$ is of divergence type. The kernel of $\\mathbf{X}_+$ is invariant by a conformal change of the metric (see \\cite[Section 3.6]{Guillarmou-Paternain-Salo-Uhlmann-16}) and elements in its kernel are called \\emph{twisted Conformal Killing Tensors} (CKTs). There are examples of vector bundles with CKTs on manifolds of arbitrary dimension. We proved in a companion paper \\cite{Cekic-Lefeuvre-20} that the non existence of CKTs is a generic condition, no matter the curvature of the manifold (generic with respect to the connection, i.e. there is a residual set of the space of all unitary connections with regularity $C^k$, $k \\geq 2$, which has no CKTs)\n\nIt is also known by \\cite{Guillarmou-Paternain-Salo-Uhlmann-16}, that in negative curvature, there is always a \\emph{finite number of degrees} with CKTs (and this number can be estimated thanks to a lower bound on the curvature of the manifold and the curvature of the vector bundle). In other words, $\\ker \\mathbf{X}_+|_{C^\\infty(SM,\\mathcal{E})}$ is finite-dimensional. The proof relies on an energy identity called the Pestov identity. This is also known for Anosov surfaces since any Anosov surface is conformally equivalent to a negatively-curved surface and CKTs are conformally invariant. Nevertheless, and to the best of our knowledge, it is still an open question to show that for Anosov manifolds of dimension $n \\geq 3$, there is at most a finite number of CKTs.\n\n\n\n\n\n\\subsection{Twisted symmetric tensors}\\label{section:twisted}\n\nGiven a section $u \\in C^\\infty(M,\\otimes^m_S T^*M \\otimes \\mathcal{E})$, the connection $\\nabla^{\\mathcal{E}}$ produces an element $\\nabla^{\\mathcal{E}}u \\in C^\\infty(M, T^*M \\otimes (\\otimes^m_S T^*M) \\otimes \\mathcal{E})$. In coordinates, if $(e_1, ..., e_r)$ is a local orthonormal frame for $\\mathcal{E}$ and $\\nabla^{\\mathcal{E}} = d + \\Gamma$, for some one-form with values in skew-Hermitian matrices $\\Gamma$, such that $\\nabla^{\\E}e_k = \\sum_{i = 1}^n\\sum_{l = 1}^r \\Gamma_{ik}^{l} dx_i \\otimes e_l$, we have:\n\\begin{equation}\n\\label{equation:nabla-e}\n\\begin{split}\n\\nabla^{\\mathcal{E}}(\\sum_{k=1}^r u_k \\otimes e_k) & = \\sum_{k=1}^r \\big(\\nabla u_k \\otimes e_k + u_k \\otimes \\nabla^{\\mathcal{E}} e_k\\big)\\\\\n& = \\sum_{k=1}^r \\left(\\nabla u_k + \\sum_{l=1}^r \\sum_{i=1}^n \\Gamma_{il}^k u_l \\otimes dx_i \\right) \\otimes e_k,\n\\end{split}\n\\end{equation}\nwhere $u_k \\in C^\\infty(M,\\otimes^m_S T^*M)$ and $\\nabla$ is the Levi-Civita connection. The symmetrization operator $\\mathcal{S}^{\\mathcal{E}} : C^\\infty(M,\\otimes^m T^*M \\otimes \\mathcal{E}) \\rightarrow C^\\infty(M,\\otimes^m_S T^*M \\otimes \\mathcal{E})$ is defined by:\n\\[\n\\mathcal{S}^{\\mathcal{E}}\\left(\\sum_{k=1}^r u_k \\otimes e_k\\right) = \\sum_{k=1}^r \\mathcal{S}(u_k) \\otimes e_k,\n\\]\nwhere $u_k \\in C^\\infty(M,\\otimes^m_S T^*M)$ and in coordinates, writing $u_k = \\sum_{i_1, ..., i_m=1}^n u_{i_1...i_m}^{(k)} dx_{i_1} \\otimes ... \\otimes dx_{i_m}$, we have\n\\[\n\\mathcal{S}(dx_{i_1} \\otimes ... \\otimes dx_{i_m}) = \\dfrac{1}{m!} \\sum_{\\pi \\in \\mathfrak{S}_m} dx_{\\pi(i_1)} \\otimes ... \\otimes dx_{\\pi(i_m)},\n\\]\nwhere $\\mathfrak{S}_m$ denotes the group of permutations of order $m$. For the sake of simplicity, we will write $\\mathcal{S}$ instead of $\\mathcal{S}^{\\mathcal{E}}$. We can symmetrize \\eqref{equation:nabla-e} to produce an element $D^{\\E} := \\mathcal{S} \\nabla^{\\mathcal{E}}u \\in C^\\infty(M, \\otimes^{m+1}_S T^*M \\otimes \\mathcal{E})$ given in coordinates by:\n\\begin{equation}\n\\label{equation:formula-de}\nD^{\\E} \\left(\\sum_{k=1}^r u_k \\otimes e_k\\right) = \\sum_{k=1}^r \\left( Du_k + \\sum_{l=1}^r \\sum_{i=1}^n \\Gamma_{il}^k \\sigma(u_l \\otimes dx_i) \\right) \\otimes e_k,\n\\end{equation}\nwhere $D := \\mathcal{S} \\nabla$ is the usual symmetric derivative of symmetric tensors\\footnote{Beware of the notation: $\\nabla^{\\mathcal{E}}$ is for the connection, $D^{\\E}$ for the symmetric derivative of tensors and $\\nabla^{\\mathrm{End}(\\E)}$ is the connection induced by $\\nabla^{\\mathcal{E}}$ on the endomorphism bundle.}. Elements of the form $Du \\in C^\\infty(M,\\otimes^{m+1}_S T^*M)$ are called \\emph{potential tensors}. By comparison, we will call elements of the form $D^{\\E}f \\in C^\\infty(M,\\otimes^{m+1}_S T^*M \\otimes \\mathcal{E})$ \\emph{twisted potential tensors}. The operator $D^{\\E}$ is a first order differential operator and its expression can be read off from \\eqref{equation:formula-de}, namely:\n\\[\n\\begin{split}\n\\sigma_{\\mathrm{princ}}(D^{\\E})(x,\\xi) \\cdot \\left(\\sum_{k=1}^r u_k(x) \\otimes e_k(x) \\right) & = \\sum_{k=1}^r \\left(\\sigma_{\\mathrm{princ}}(D)(x,\\xi) \\cdot u_k(x)\\right) \\otimes e_k(x) \\\\\n& = i \\sum_{k=1}^r \\sigma(\\xi \\otimes u_k(x)) \\otimes e_k(x),\n\\end{split}\n\\]\nwhere $e_k(x) \\in \\mathcal{E}_x, u_k(x) \\in \\otimes^m_S T^*_xM$ and the basis $(e_1(x),...,e_r(x))$ is assumed to be orthonormal for the metric $h$ on $\\mathcal{E}$. One can check that this is an injective map, which means that $D^{\\E}$ is a left-elliptic operator and can be inverted on the left modulo a smoothing remainder. Its kernel is finite-dimensional and consists of smooth elements\n\nBefore that, we introduce for $m \\in \\mathbb{N}$, the operator\n\\[\n\\pi_m^* : C^\\infty(M,\\otimes^m_S T^*M \\otimes \\mathcal{E}) \\rightarrow C^\\infty(SM,\\pi^*\\mathcal{E}), \n\\]\ndefined by\n\\[\n\\pi_m^*\\left(\\sum_{k=1}^r u_k \\otimes e_k\\right) (x,v) := \\sum_{k=1}^r ({u_k})_x(v,...,v) e_k(x).\n\\]\nSimilarly to \\eqref{eq:pi_muntwisted}, the following mappings are isomorphisms (see \\cite[Section 2]{Cekic-Lefeuvre-20}):\n\\begin{align*}\n\\pi_m^* &: C^\\infty(M,\\otimes^m_S T^*M \\otimes \\mathcal{E}) \\rightarrow \\oplus_{k \\geq 0} C^\\infty(M,\\Omega_{m-2k} \\otimes \\E),\\\\\n\\pi_m^* &: C^\\infty(M,\\otimes^m_S T^*M|_{0-\\Tr} \\otimes \\mathcal{E}) \\rightarrow C^\\infty(M,\\Omega_m \\otimes \\E).\n\\end{align*}\nWe recall the notation $(\\pi^* \\nabla^{\\mathcal{E}})_X := \\mathbf{X}$. The following remarkable commutation property holds (see \\cite[Section 2]{Cekic-Lefeuvre-20}):\n\\begin{equation}\\label{eq:pullback}\n\t\\forall m \\in \\mathbb{Z}_{\\geq 0}, \\quad \\pi_{m+1}^* D^{\\E} = \\mathbf{X} \\pi_m^*.\n\\end{equation}\n\n\nThe vector bundle $\\otimes^m_S T^*M \\otimes \\mathcal{E}$ is naturally endowed with a canonical fiberwise metric induced by the metrics $g$ and $h$ which allows to define a natural $L^2$ scalar product. The $L^2$ formal adjoint $(D^{\\E})^*$ of $D^{\\E}$ is of divergence type (in the sense that its principal symbol is surjective for every $(x,\\xi) \\in T^*M \\setminus \\left\\{ 0 \\right\\}$, see \\cite[Definition 3.1]{Cekic-Lefeuvre-20} for further details). We call \\emph{twisted solenoidal tensors} the elements in its kernel.\n\nBy ellipticity of $D^{\\E}$, for any twisted $m$-tensor $f$ there exists a unique $p \\in (\\ker D^{\\E})^\\perp \\cap C^\\infty(M,\\otimes^{m-1}_S T^*M \\otimes \\mathcal{E}), h \\in C^\\infty(M,\\otimes^{m}_S T^*M \\otimes \\mathcal{E})$ such that:\n\\begin{equation}\\label{eq:decomposition-tt}\nf = D^{\\E}p + h, \\quad (D^{\\E})^*h = 0.\n\\end{equation}\nThe previous decomposition could be extended to other regularities. We define $\\pi_{\\ker (D^{\\E})^*}f := h$ as the $L^2$-orthogonal projection on twisted solenoidal tensors. This can be expressed as:\n\\begin{equation}\n\\label{equation:projection}\n\\pi_{\\ker (D^{\\E})^*} = \\mathbbm{1} - D^{\\E} [(D^{\\E})^*D^{\\E}]^{-1} (D^{\\E})^*,\n\\end{equation}\nwhere $[(D^{\\E})^*D^{\\E}]^{-1}$ is the resolvent of the operator.\n\n\n\n\n\n\n\n\n\n\n\n\\subsection{Pollicott-Ruelle resonances}\n\nWe explain the link between the widely studied notion of Pollicott-Ruelle resonances (see for instance \\cite{Liverani-04, Gouezel-Liverani-06,Butterley-Liverani-07,Faure-Roy-Sjostrand-08,Faure-Sjostrand-11,Faure-Tsuji-13,Dyatlov-Zworski-16}) and the notion of (twisted) Conformal Killing Tensors introduced in the last paragraph. We also refer to \\cite{Cekic-Lefeuvre-20} for an extensive discussion about this.\n\n\n\n\\subsubsection{Definition of the resolvents}\n\n\n\\label{ssection:resonances}\n\nLet $\\mathcal{M}$ be a smooth manifold endowed with a vector field $X \\in C^\\infty(\\mathcal{M},T\\mathcal{M})$ generating an Anosov flow in the sense of \\eqref{equation:anosov}. Throughout this paragraph, we will always assume that the flow is volume-preserving. It will be important to consider the dual decomposition to \\eqref{equation:anosov}, namely\n\\[\nT^*(\\mathcal{M}) = \\mathbb{R} E_0^* \\oplus E_s^* \\oplus E_u^*,\n\\]\nwhere $E_0^*(E_s \\oplus E_u) = 0, E_s^*(E_s \\oplus \\mathbb{R} X) = 0, E_u^*(E_u \\oplus \\mathbb{R} X) = 0$. As before, we consider a vector bundle $\\mathcal{E} \\rightarrow \\mathcal{M}$ equipped with a unitary connection $\\nabla^{\\E}$ and set $\\mathbf{X} := \\nabla^{\\mathcal{E}}_X$. Since $X$ preserves a smooth measure $\\dd \\mu$ and $\\nabla^{\\mathcal{E}}$ is unitary, the operator $\\mathbf{X}$ is skew-adjoint on $L^2(\\mathcal{M},\\mathcal{E};\\dd\\mu)$, with dense domain\n\\begin{equation}\n\\label{equation:domaine-p}\n\\mathcal{D}_{L^2} := \\left\\{ u \\in L^2(\\mathcal{M},\\mathcal{E};\\dd\\mu) ~|~ \\mathbf{X} u \\in L^2(\\mathcal{M},\\mathcal{E};\\dd\\mu)\\right\\}.\n\\end{equation}\nIts $L^2$-spectrum consists of absolutely continuous spectrum on $i\\mathbb{R}$ and on embedded eigenvalues. We introduce the resolvents\n\\begin{equation}\n\\label{equation:resolvent}\n\\begin{split}\n&\\RR_+(z) := (-\\mathbf{X}-z)^{-1} = - \\int_0^{+\\infty} e^{-t z} e^{-t\\mathbf{X}} \\dd t, \\\\\n&\\RR_-(z) := (\\mathbf{X}-z)^{-1} = - \\int_{-\\infty}^0 e^{z t} e^{-t\\mathbf{X}} \\dd t,\n\\end{split}\n\\end{equation}\ninitially defined for $\\Re(z) > 0$. (Let us stress on the conventions here: $-\\mathbf{X}$ is associated to the positive resolvent $\\RR_+(z)$ whereas $\\mathbf{X}$ is associated to the negative one $\\RR_-(z)$.) Here $e^{-t\\mathbf{X}}$ denotes the propagator of $\\mathbf{X}$, namely the parallel transport by $\\nabla^{\\mathcal{E}}$ along the flowlines of $X$. If $\\mathbf{X} = X$ is simply the vector field acting on functions (i.e. $\\mathcal{E}$ is the trivial line bundle), then $e^{-tX}f(x) = f(\\varphi_{-t}(x))$ is nothing but the composition with the flow.\n\nThere exists a family $\\mathcal{H}^s_\\pm$ of Hilbert spaces called \\emph{anisotropic Sobolev spaces}, indexed by $s > 0$, such that the resolvents can be meromorphically extended to the whole complex plane by making $\\mathbf{X}$ act on $\\mathcal{H}^s_\\pm$. The poles of the resolvents are called the \\emph{Pollicott-Ruelle resonances} and have been widely studied in the aforementioned literature \\cite{Liverani-04, Gouezel-Liverani-06,Butterley-Liverani-07,Faure-Roy-Sjostrand-08,Faure-Sjostrand-11,Faure-Tsuji-13,Dyatlov-Zworski-16}. Note that the resonances and the resonant states associated to them are intrinsic to the flow and do not depend on any choice of construction of the anisotropic Sobolev spaces. More precisely, there exists a constant $c > 0$ such that $\\RR_\\pm(z) \\in \\mathcal{L}(\\mathcal{H}_\\pm^s)$ are meromorphic in $\\left\\{\\Re(z) > -cs\\right\\}$. For $\\RR_+(z)$ (resp. $\\RR_-(z)$), the space $\\mathcal{H}^s_+$ (resp. $\\mathcal{H}^s_-$) consists of distributions which are microlocally $H^s$ in a neighborhood of $E_s^*$ (resp. microlocally $H^{s}$ in a neighborhood of $E_u^*$) and microlocally $H^{-s}$ in a neighborhood of $E_u^*$ (resp. microlocally $H^{-s}$ in a neighborhood of $E_s^*$), see \\cite{Faure-Sjostrand-11,Dyatlov-Zworski-16}. These spaces also satisfy $(\\mathcal{H}^s_+)' = \\mathcal{H}^s_-$ (where one identifies the spaces using the $L^2$-pairing).\n\nFrom now on, we will assume that $s$ is fixed and small enough, and set $\\mathcal{H}_\\pm := \\mathcal{H}_\\pm^s$. We have\n\\begin{equation}\n\\label{equation:betaan}\nH^{s} \\subset \\mathcal{H}_{\\pm} \\subset H^{-s}.\n\\end{equation}\nand there is a certain strip $\\left\\{\\Re(z) > -\\varepsilon_{\\mathrm{strip}}\\right\\}$ (for some $\\varepsilon_{\\mathrm{strip}} > 0$) on which $z \\mapsto \\RR_{\\pm}(z) \\in \\mathcal{L}(\\mathcal{H}_{\\pm})$ is meromorphic (and the same holds for small perturbations of $\\mathbf{X}$).\n\nThese resolvents satisfy the following equalities on $\\mathcal{H}_\\pm$, for $z$ not a resonance:\n\\begin{equation}\n\\label{equation:resolvent-identity}\n\\RR_\\pm(z)(\\mp\\mathbf{X}- z) = (\\mp\\mathbf{X}- z) \\RR_\\pm(z) = \\mathbbm{1}_{\\mathcal{E}}.\n\\end{equation}\nGiven $z \\in \\mathbb{C}$ which not a resonance, we have:\n\\begin{equation}\n\\label{equation:adjoint}\n\\RR_+(z)^* = \\RR_-(\\overline{z}),\n\\end{equation}\nwhere this is understood in the following way: given $f_1, f_2 \\in C^\\infty(\\mathcal{M},\\mathcal{E})$, we have\n\\[\n\\langle \\RR_+(z) f_1, f_2 \\rangle_{L^2} = \\langle f_1, \\RR_-(\\overline{z}) f_2 \\rangle_{L^2}.\n\\]\n(We will always use this convention for the definition of the adjoint.) Since the operators are skew-adjoint on $L^2$, all the resonances (for both the positive and the negative resolvents $\\RR_\\pm$) are contained in $\\left\\{\\Re(z) \\leq 0 \\right\\}$. A point $z_0 \\in \\mathbb{C}$ is a resonance for $-\\mathbf{X}$ (resp. $\\mathbf{X}$) i.e. is a pole of $z \\mapsto \\RR_+(z)$ (resp. $\\RR_-(z)$) if and only if there exists a non-zero $u \\in \\mathcal{H}^s_+$ (resp. $\\mathcal{H}^s_-$) for some $s > 0$ such that $-\\mathbf{X} u = z_0 u$ (resp. $\\mathbf{X} u = z_0 u$). If $\\gamma$ is a small counter clock-wise oriented circle around $z_0$, then the spectral projector onto the resonant states is\n\\[\n\\Pi_{z_0}^{\\pm} = - \\dfrac{1}{2\\pi i} \\int_{\\gamma} \\RR_{\\pm}(z) \\dd z = \\dfrac{1}{2\\pi i} \\int_{\\gamma} (z \\pm \\mathbf{X})^{-1} \\dd z,\n\\]\nwhere we use the abuse of notation that $-(\\mathbf{X}+z)^{-1}$ (resp. $(\\mathbf{X}-z)^{-1}$) to denote the meromorphic extension of $\\RR_+(z)$ (resp. $\\RR_-(z)$). \n\n\n\\subsubsection{Resonances at $z=0$}\n\n\\label{sssection:resonances-zero}\n\nBy the previous paragraph, we can write in a neighborhood of $z=0$ the following Laurent expansion (beware the conventions):\n\\[\n\\RR_+(z) = - \\RR_0^+ - \\dfrac{\\Pi_0^+}{z} + \\mathcal{O}(z).\n\\]\n(Or in other words, using our abuse of notations, $(\\mathbf{X}+z)^{-1} = \\RR_0^+ + \\Pi_0^+\/z + \\mathcal{O}(z)$.) And:\n\\[\n\\RR_-(z) = -\\RR_0^- - \\dfrac{\\Pi_0^-}{z} + \\mathcal{O}(z).\n\\]\n(Or in other words, $(z - \\mathbf{X})^{-1} = \\RR_0^- + \\Pi_0^-\/z + \\mathcal{O}(z)$.) As a consequence, these equalities define the two operators $\\RR_0^{\\pm}$ as the holomorphic part (at $z=0$) of the resolvents $-\\RR_\\pm(z)$. We introduce:\n\\begin{equation}\n\\label{equation:pi}\n\\Pi := \\RR_0^+ + \\RR_0^-.\n\\end{equation}\nWe have:\n\n\\begin{lemma}\n\\label{lemma:relations-resolvent}\nWe have $(\\RR_0^+)^* = \\RR_0^{-}, (\\Pi_0^+)^* = \\Pi_0^- = \\Pi_0^+$. Thus $\\Pi$ is formally self-adjoint. Moreover, it is nonnegative in the sense that for all $f \\in C^\\infty(\\mathcal{M},\\mathcal{E})$, $\\langle \\Pi f, f \\rangle_{L^2} = \\langle f, \\Pi f \\rangle_{L^2} \\geq 0$. Also, $\\langle \\Pi f, f \\rangle = 0$ if and only if $\\Pi f = 0$ if and only if $f = \\mathbf{X} u + v $, for some $u \\in C^\\infty(\\mathcal{M},\\E), v \\in \\ker(\\mathbf{X}|_{\\mathcal{H}_\\pm})$.\n\\end{lemma}\n\n\\begin{proof} See \\cite[Lemma 5.1]{Cekic-Lefeuvre-20}. \\end{proof}\n\n\nWe also record here for the sake of clarity the following identities:\n\\begin{equation}\\label{eq:resolventrelations}\n\\begin{split}\n& \\Pi_0^{\\pm} \\RR_0^+ = \\RR_0^+ \\Pi_0^{\\pm} = 0, \\Pi_0^{\\pm} \\RR_0^- = \\RR_0^- \\Pi_0^{\\pm} = 0,\\\\\n& \\mathbf{X} \\Pi_0^\\pm = \\Pi_0^\\pm \\mathbf{X} = 0, \\mathbf{X} \\RR_0^+ = \\RR_0^+ \\mathbf{X} = \\mathbbm{1} - \\Pi_0^+, -\\mathbf{X} \\RR_0^- = -\\RR_0^- \\mathbf{X} = \\mathbbm{1} - \\Pi_0^-.\n\\end{split}\n\\end{equation}\n\n\n\n\n\\subsection{Generalized X-ray transform}\n\\label{ssection:generalized-x-ray}\nThe discussion is carried out here in the closed case, but could also be generalized to the case of a manifold with boundary.\nWe introduce the operator\n\\[\n\\Pi := \\RR_0^+ + \\RR_0^-,\n\\]\nwhere $\\RR_0^+$ (resp. $\\RR_0^-$) denotes the holomorphic part at $0$ of $-\\RR_+(z)$ (resp. $-\\RR_-(z)$) and $\\Pi^+_0$ is the $L^2$-orthogonal projection on the (smooth) resonant states at $0$. Such an operator was first introduced in the non-twisted case by Guillarmou \\cite{Guillarmou-17-1}. The operator $\\Pi + \\Pi^+_0$ is the derivative of the (total) $L^2$-spectral measure at $0$ of the skew-adjoint operator $\\mathbf{X}$.\n\n\\begin{definition}[Generalized X-ray transform of twisted symmetric tensors]\n\\label{definition:generalized-xray}\nWe define the generalized X-ray transform of twisted symmetric tensors as the operator:\n\\[\n\\Pi_m := {\\pi_m}_* \\left(\\Pi + \\Pi^+_0\\right) \\pi_m^*.\n\\]\n\\end{definition}\n\n\\begin{remark}\\rm\n\tThis allows to define a generalized (twisted) X-ray transform $\\Pi_m$ for an arbitrary unitary connection $\\nabla^{\\mathcal{E}}$ on $\\mathcal{E}$. Indeed, it is not clear a priori if one sticks to the usual definition of the X-ray transform that one can find a ``natural'' candidate for the X-ray transform on twisted tensors. For instance, one could consider the map\n\\[\n\\mathcal{C} \\ni \\gamma \\mapsto I_m^{\\nabla^{\\E}}f (\\gamma) := \\dfrac{1}{\\ell(\\gamma)} \\int_0^{\\ell(\\gamma)} (e^{-t \\mathbf{X}} f) (x_\\gamma, v_\\gamma) \\dd t \\in \\mathcal{E}_{x_\\gamma},\n\\]\nwhere $\\gamma \\in \\mathcal{C}$ is a closed geodesic and $(x_\\gamma,v_\\gamma) \\in \\gamma$. However, this definition does depend on the choice of base point $(x_\\gamma,v_\\gamma) \\in \\gamma$ and it would no longer be true that $I^{\\nabla^{\\mathcal{E}}}_m (D^{\\E}p) (\\gamma) = 0$ unless the connection is transparent.\n\\end{remark}\n\n\nBy \\eqref{eq:pullback} and \\eqref{eq:resolventrelations}, we have the equalities:\n\\begin{equation}\\label{eq:Pi_mproperties}\n(D^{\\E})^* \\Pi_m = 0 = \\Pi_m D^{\\E},\n\\end{equation}\nshowing that $\\Pi_m$ maps the set of twisted solenoidal tensors to itself. We say that the generalised $X$-ray transform is \\emph{solenoidally injective} ($s$-injective) on $m$-tensors, if for all $u \\in C^\\infty(SM, \\E)$ and $f \\in C^\\infty(M, \\otimes_S^mT^*M \\otimes \\E)$\n\\begin{equation}\\label{eq:cohomeqn}\n\t\\mathbf{X} u = \\pi_m^* f \\implies \\exists p \\in C^\\infty(M, \\otimes_S^{m-1}T^*M \\otimes \\E)\\,\\, \\mathrm{such\\,\\, that}\\,\\,f = D^{\\E} p.\n\\end{equation}\nWe have the following:\n\n\\begin{lemma}\\label{lemma:x-ray}\n\tThe generalised $X$-ray transform is $s$-injective of $m$-tensors if and only if $\\Pi_m$ is injective on solenoidal tensors (if this holds, we say $\\Pi_m$ is \\emph{$s$-injective}).\n\\end{lemma}\n\\begin{proof}\n\tAssume that $\\Pi_m f = 0$ and $f$ is a twisted solenoidal $m$-tensor. Then\n\\[\n\\langle \\Pi_m f, f \\rangle_{L^2} = \\langle \\Pi \\pi_m^*f , \\pi_m^*f \\rangle_{L^2} + \\langle \\Pi^+_0 \\pi_m^*f, \\pi_m^* f \\rangle_{L^2} = 0.\n\\]\nBoth terms on the right hand side are non-negative by Lemma \\ref{lemma:relations-resolvent}, hence both of them vanish, and the same Lemma implies that $\\Pi \\pi_m^* f = 0$ and $\\Pi^+_0 \\pi_m^*f = 0$. Thus $\\mathbf{X} u = \\pi_m^*f$ for some smooth $u$, so by the $s$-injectivity of generalised $X$-ray transform we obtain $f$ is potential, which implies $f = 0$.\n\nThe other direction is obvious by \\eqref{eq:resolventrelations}.\n\\end{proof}\n\nNext, we show $\\Pi_m$ enjoys good analytical properties:\n\n\\begin{lemma}\n\\label{lemma:generalized-xray}\nThe operator $\\Pi_m : C^\\infty(M,\\otimes^m_S T^*M \\otimes \\mathcal{E}) \\rightarrow C^\\infty(M,\\otimes^m_S T^*M \\otimes \\mathcal{E})$ is:\n\\begin{enumerate}\n\\item A pseudodifferential operator of order $-1$,\n\\item Formally self-adjoint and elliptic on twisted solenoidal tensors (its Fredholm index is thus equal to $0$ and its kernel\/cokernel are finite-dimensional),\n\\item Under the assumption that $\\Pi_m$ is $s$-injective, the following stability estimates hold:\n\\[\n\\forall s \\in \\mathbb{R},\\,\\, \\forall f \\in H^s(M,\\otimes^m_S T^*M \\otimes \\mathcal{E}) \\cap \\ker (D^{\\E})^*, ~~~ \\|f\\|_{H^s} \\leq C_s \\|\\Pi_m f \\|_{H^{s+1}},\n\\]\nfor some $C_s > 0$ and for some $C > 0$:\n\\[\n\\forall f \\in H^{-1\/2}(M,\\otimes^m_S T^*M \\otimes \\mathcal{E}) \\cap \\ker (D^{\\E})^*, ~~~ \\langle \\Pi_m f, f \\rangle_{L^2} \\geq C \\|\\pi_{\\ker (D^{\\E})^*} f\\|^2_{H^{-1\/2}}.\n\\]\nIn particular, these estimate hold if $(M,g)$ has negative curvature and $\\nabla^{\\E}$ has no twisted CKTs.\n\\end{enumerate}\n\\end{lemma}\n\n\n\\begin{proof}\nThe proof of the first two points follows from a rather straightforward adaptation of the proof of \\cite[Theorem 2.5.1]{Lefeuvre-thesis} (see also \\cite{Guillarmou-17-1} for the original arguments); we omit it. It remains to prove the third point. \n\nThe first estimate follows from $(2)$, the elliptic estimate and the fact that $\\Pi_m$ is $s$-injective. The last estimate in the non-twisted case follows from \\cite[Lemma 2.1]{Guillarmou-Knieper-Lefeuvre-19} (or \\cite[Theorem 2.5.6]{Lefeuvre-thesis}) and subsequent remarks; the twisted case follows by minor adaptations.\n\n\nIf $(M,g)$ has negative curvature and $\\nabla^{\\E}$ has no twisted CKTs, using Lemma \\ref{lemma:x-ray} and by \\cite[Sections 4, 5]{Guillarmou-Paternain-Salo-Uhlmann-16} we get that $\\Pi_m$ is $s$-injective, proving the claim.\n\\end{proof}\n\n\n\n\n\n\n\n\n\\section{Exact Liv\\v{s}ic cocycle theory}\n\nWe phrase this section in a very general context which is that of a transitive Anosov flow on a smooth manifold. It is of independent interest to the rest of the article. \n\n\\label{section:livsic}\n\n\\subsection{Statement of the results}\n\n\\label{ssection:intro}\n\n\\subsubsection{A weak exact Liv\\v{s}ic cocycle theorem}\n\nLet $\\mathcal{M}$ be a smooth closed manifold endowed with a flow $(\\varphi_t)_{t \\in \\mathbb{R}}$ with infinitesimal generator $X \\in C^\\infty(\\mathcal{M},T\\mathcal{M})$. We assume that the flow is Anosov in the sense of \\eqref{equation:anosov} and that it is \\emph{transitive}, namely it admits a dense orbit\\footnote{Note that there are examples of non-transitive Anosov flows, see \\cite{Franks-Williamas-80}.}. We denote by $\\mathcal{G}$ the set of all periodic orbits for the flow and by $\\mathcal{G}^\\sharp$ the set of all \\emph{primitive} orbits, namely orbits which cannot be written as a shorter orbit to some positive power greater or equal than $2$. \n\n\n\n\nLet $(\\mathcal{E},\\nabla^{\\mathcal{E}})$ be a smooth Hermitian vector bundle of rank $r$ equipped with a unitary connection $\\nabla^{\\mathcal{E}}$. We will denote by \n\\[\nC(x,t) : \\mathcal{E}_x \\rightarrow \\mathcal{E}_{\\varphi_t(x)},\n\\]\n the parallel transport along the flowlines of $(\\varphi_t)_{t \\in \\mathbb{R}}$ with respect to the connection $\\nabla^{\\mathcal{E}}$. In the more general setting, we may consider $\\E_1, \\E_2 \\rightarrow \\mathcal{M}$, two Hermitian vector bundles, equipped with two respective unitary connections $\\nabla^{\\mathcal{E}_1}$ and $\\nabla^{\\mathcal{E}_2}$.\nRecall that if $\\nabla^{\\E_2} = p^*\\nabla^{\\E_1}$, for some unitary map $p \\in C^\\infty(\\mathcal{M},\\mathrm{U}(\\E_2, \\E_1))$\\footnote{Here, we denote by $\\mathrm{U}(\\E_2, \\E_1) \\rightarrow \\mathcal{M}$ the bundle of unitary maps from $\\E_2 \\rightarrow \\E_1$. Of course, it may be empty if the bundles are not isomorphic.}, i.e. the connections are gauge-equivalent, then\nparallel transport along the flowlines of $(\\varphi_t)_{t \\in \\mathbb{R}}$ satisfies the commutation relation:\n\\[\nC_1(x,t)= p(\\varphi_t x) C_2(x,t) p(x)^{-1}.\n\\]\nWe say that such cocycles are \\emph{cohomologous}. In particular, given a closed orbit $\\gamma = (\\varphi_t x_0)_{t \\in [0,T]}$ of the flow, one has\n\\[\nC_1(x_0,T) = p(x_0) C_2(x_0,T) p(x_0)^{-1},\n\\]\ni.e. the parallel transport map are conjugate.\n\n\\begin{definition}\n\\label{definition:equivalence}\nWe say that the connections $\\nabla^{\\E_{1,2}}$ have \n\\emph{trace-equivalent holonomies} if for all primitive closed orbits $\\gamma \\in \\mathcal{G}^\\sharp$, we have:\n\\begin{equation}\n\\label{equation:trace-equivalence}\n\\Tr(C_1(x_\\gamma,\\ell(\\gamma))) = \\Tr(C_2(x_\\gamma,\\ell(\\gamma))),\n\\end{equation}\nwhere $x_\\gamma \\in \\gamma$ is arbitrary and $\\ell(\\gamma)$ is the period of $\\gamma$.\n\\end{definition}\n\nThis condition could be \\emph{a priori} obtained with $\\rk(\\E_1) \\neq \\rk(\\E_2)$. We shall see that this cannot be the case.\nThe following result is one of the main theorems of this paper. It seems to improve known results on Liv\\v{s}ic cocycle theory (in particular \\cite{Parry-99,Schmidt-99}), see \\S\\ref{ssection:exact} for a more extensive discussion on the literature.\n\n\\begin{theorem}\n\\label{theorem:weak}\nAssume $\\mathcal{M}$ is endowed with a smooth transitive Anosov flow. Let $\\E_1, \\E_2 \\rightarrow \\mathcal{M}$ be two Hermitian vector bundles over $\\mathcal{M}$ equipped with respective unitary connections $\\nabla^{\\mathcal{E}_1}$ and $\\nabla^{\\mathcal{E}_2}$. If the connections have \\emph{trace-equivalent holonomies} in the sense of Definition \\ref{definition:equivalence}, then there exists $p \\in C^\\infty(\\mathcal{M},\\mathrm{U}(\\E_2,\\E_1))$ such that: for all $x \\in \\mathcal{M}, t \\in \\mathbb{R}$,\n\\begin{equation*}\nC_1(x,t) = p(\\varphi_t x) C_2(x,t) p(x)^{-1},\n\\end{equation*}\ni.e. the cocycles induced by parallel transport are cohomologous. Moreover, $\\E_2 \\simeq \\E_1$ are isomorphic.\n\\end{theorem}\n\nAs we shall see in the proof, for any given $L > 0$, it suffices to assume that the trace-equivalent holonomy condition \\eqref{equation:trace} holds for all primitive periodic orbits of length $\\geq L$ in order to get the conclusion of the theorem. Surprisingly, the rather weak condition \\eqref{equation:trace} implies in particular that the bundles are isomorphic as stated in Corollary \\ref{corollary:iso} and the trace of the holonomy of unitary connections along closed orbits should allow one in practice to classify vector bundles over manifolds carrying Anosov flows. Even more suprisingly, the rank of $\\E_1$ and $\\E_2$ might be \\emph{a priori} different and Theorem \\ref{theorem:weak} actually shows that the ranks have to coincide.\n\nThe idea relies on a key notion which we call \\emph{Parry's free monoid}, whose introduction goes back to Parry \\cite{Parry-99}. This free monoid $\\mathbf{G}$ corresponds (at least formally) to the free monoid generated by the set of homoclinic orbits to a given periodic orbit of a point $x_{\\star}$ (see \\S\\ref{sssection:homoclinic} for a definition) and we shall see that a connection induces a unitary representation $\\rho : \\mathbf{G} \\rightarrow \\mathrm{U}(\\E_{x_{\\star}})$ (it is not canonical but we shall see that its important properties are). Geometric properties of the connection can be read off this representation, see Theorem \\ref{theorem:iso} below. Moreover, tools from representation theory can be applied and this is eventually how we will prove Theorem \\ref{theorem:weak}. \n\n\n\\subsubsection{Opaque and transparent connections}\n\nTheorem \\ref{theorem:weak} has an interesting straightforward corollary. Recall that a unitary connection is said to be \\emph{transparent} if the holonomy along all closed orbits is trivial.\n\n\\begin{corollary}\nAssume $\\mathcal{M}$ is endowed with a smooth transitive Anosov flow. Let $\\E \\rightarrow \\mathcal{M}$ be a Hermitian vector bundle over $\\mathcal{M}$ of rank $r$ equipped with a unitary connection $\\nabla^{\\mathcal{E}}$. If the connection is transparent, then $\\E$ is trivial and trivialized by a smooth orthonormal family $e_1,...,e_r \\in C^\\infty(\\mathcal{M},\\E)$ such that $\\nabla^{\\E}_X e_i = 0$.\n\\end{corollary}\n\nThis result will be used in \\cite{Cekic-Lefeuvre-21-3} to study the Levi-Civita connection on Anosov manifolds and the notion of \\emph{transparent manifolds}, namely manifolds with trivial holonomy along closed geodesics. In order to prove the previous corollary, it suffices to apply Theorem \\ref{theorem:weak} with $\\E_1 = \\E$ equipped with $\\nabla^{\\E}$ and $\\E_2 = \\mathbb{C}^r \\times M$ equipped with the trivial flat connection. Then $C_2(x,t) = \\mathbf{1}$ and $(e_1,...,e_n)$ is obtained as the image by $p$ of the canonical basis of $\\mathbb{C}^n$. This corollary seems to be known in the folklore but nowhere written. It is stated in \\cite[Proposition 9.2]{Paternain-12} under the extra-assumption that $\\E \\oplus \\E^*$ is trivial.\n\nThe ``opposite\" notion of transparent connections is that of \\emph{opaque} connections which are connections that do not preserve any non-trivial subbundle $\\mathcal{F} \\subset \\E$ by parallel transport along the flowlines of $(\\varphi_t)_{t \\in \\mathbb{R}}$. It was shown in \\cite[Section 5]{Cekic-Lefeuvre-20} that the opacity of a connection is equivalent to the fact that\n\\[\n\\ker (\\nabla^{\\mathrm{End}}_X|_{C^\\infty(\\mathcal{M},\\mathrm{End}(\\E))}) = \\mathbb{C} \\cdot \\mathbf{1}_{\\E}.\n\\]\nAlso note that when $X$ is volume-preserving, this corresponds to the Pollicott-Ruelle (co)resonant states at $0$ associated to the operator $\\nabla^{\\mathrm{End}}_X$. We shall also connect this notion with the representation $\\rho : \\mathbf{G} \\rightarrow \\mathrm{U}(\\E_{x_{\\star}})$ of the free monoid:\n\n\\begin{prop}\n\\label{proposition:opaque}\nThe following statements are equivalent:\n\\begin{enumerate}\n\\item The connection $\\nabla^{\\E}$ is opaque;\n\\item $\\ker (\\nabla^{\\mathrm{End}}_X|_{C^\\infty(\\mathcal{M},\\mathrm{End}(\\E))}) = \\mathbb{C} \\cdot \\mathbf{1}_{\\E}$;\n\\item The representation $\\rho : \\mathbf{G} \\rightarrow \\mathrm{U}(\\E_{x_{\\star}})$ is irreducible.\n\\end{enumerate}\n\\end{prop}\n\n\n\n\\subsubsection{Kernel of the endomorphism connection}\n\nThe previous proposition actually follows from a more general statement which we now describe. The representation $\\rho : \\mathbf{G} \\rightarrow \\mathrm{U}(\\E_{x_{\\star}})$ gives rise to an orthogonal splitting \n\\[\n\\E_{x_{\\star}} = \\oplus_{i=1}^K \\E_i^{\\oplus n_i},\n\\]\nwhere $\\E_i \\subset \\E_{x_\\star}$ and $n_i \\geq 1$; each factor $\\E_i$ is $\\mathbf{G}$-invariant and the induced representation on each factor is irreducible; furthermore, for $i \\neq j$, the induced representations on $\\E_i$ and $\\E_j$ are not isomorphic. Let $\\mathbb{C}[\\mathbf{G}]$ be the formal algebra generated by $\\mathbf{G}$ over $\\mathbb{C}$ and let $\\mathbf{R} := \\rho\\left( \\mathbb{C}[\\mathbf{G}] \\right)$. By Burnside's Theorem (see \\cite[Corollary 3.3]{Lang-02} for instance), one has that:\n\\[\n\\mathbf{R} = \\oplus_{i=1}^K \\Delta_{n_i} \\mathrm{End}(\\E_i),\n\\]\nwhere $\\Delta_{n_i} u = u \\oplus ... \\oplus u$ for $u \\in \\mathrm{End}(\\E_i)$, the sum being repeated $n_i$-times. We introduce the \\emph{commutant} $\\mathbf{R}'$ of $\\mathbf{R}$, defined as:\n\\[\n\\mathbf{R}' := \\left\\{ u \\in \\mathrm{End}(\\E_{x_\\star}) ~|~ \\forall v \\in \\mathbf{R}, uv = vu\\right\\}.\n\\]\nWe then have:\n\n\\begin{theorem}\n\\label{theorem:iso}\nThere exists a natural isomorphism:\n\\[\n\\Phi : \\mathbf{R}' \\rightarrow \\ker \\nabla^{\\mathrm{End}(\\E)}_X|_{C^\\infty(\\mathcal{M},\\mathrm{End}(\\E))}.\n\\]\nIn particular these spaces have same dimension, that is\n\\[\n\\dim\\left( \\ker \\nabla^{\\mathrm{End}(\\E)}_X|_{C^\\infty(\\mathcal{M},\\mathrm{End}(\\E))} \\right) = \\dim(\\mathbf{R}') = \\sum_{i=1}^K n_i^2.\n\\]\n\\end{theorem}\n\n\n\n\\subsubsection{Invariant sections} To conclude this paragraph, we now investigate the existence of smooth \\emph{invariant} sections of the bundle $\\E \\to \\mathcal{M}$, namely elements of $\\ker \\nabla^{\\E}_X|_{C^\\infty(\\mathcal{M},\\E)}$. First of all, observe that if $u \\in C^\\infty(\\mathcal{M},\\E)$ is an invariant section, then $u_\\star:= u(x_\\star)$ is invariant by the $\\mathbf{G}$-action. The converse is also true:\n\n\n\n\\begin{lemma}\n\\label{lemma:invariant-section}\nAssume that there exists $u_\\star \\in \\E_{x_\\star}$ such that $\\rho(g)u_\\star = u_\\star$ for all $g \\in \\mathbf{G}$. Then, there exists (a unique) $u \\in C^\\infty(\\mathcal{M},\\E)$ such that $u(x_\\star)=u_\\star$ and $\\nabla^{\\E}_X u = 0$.\n\\end{lemma}\n\nSuch an approach turns out to be useful when trying to understand a sort of \\emph{weak version} of Liv\\v{s}ic theory, such as the following: if $\\E \\rightarrow \\mathcal{M}$ is a vector bundle equipped with the unitary connection $\\nabla^{\\E}$ and for each periodic orbit $\\gamma \\in \\mathcal{G}$, there exists a section $u_\\gamma \\in C^\\infty(\\gamma,\\E|_{\\gamma})$ such that $\\nabla^{\\E}_X u_{\\gamma} = 0$, then one can wonder if this implies the existence of a global invariant smooth section $u \\in C^\\infty(\\mathcal{M},\\E)$? It turns out that the answer depends on the rank of $\\E$:\n\n\\begin{lemma}\n\\label{lemma:rank2}\nAssume that $\\rk(\\E) \\leq 2$ and that for all periodic orbits $\\gamma \\in \\mathcal{G}$, there exists $u_\\gamma \\in C^\\infty(\\gamma,\\E|_{\\gamma})$ such that $\\nabla^{\\E}_X u_{\\gamma} = 0$. Then, there exists $u \\in C^\\infty(\\mathcal{M},\\E)$ such that $\\nabla^{\\E}_X u = 0$.\n\\end{lemma}\n\nWe shall see that the proof of the previous Lemma is purely representation-theoretic and completely avoids the need to understand dynamics and the distribution of periodic orbits. We leave as an exercise for the reader the fact that Lemma \\ref{lemma:rank2} does not hold when $\\rk(\\E) \\geq 3$. A simple counter-example can be built using the following argument: any matrix in $\\mathrm{SO}(3)$ preserves an axis; hence, taking any $\\mathrm{SO}(3)$-connection on a \\emph{real} vector bundle of rank $3$ and then complexifying the bundle, one gets a vector bundle and a connection satisfying the assumptions of Lemma \\ref{lemma:rank2}; it then suffices to produce an $\\mathrm{SO}(3)$-connection without any invariant sections.\n\nWe believe that other links between properties of the representation $\\rho$ and the geometry and\/or dynamics of the parallel transport along the flowlines could be discovered. To conclude, let us also mention that all the results are presented here for complex vector bundles; most of them could be naturally restated for real vector bundles modulo the obvious modifications in the statements.\n\n\n\\subsection{Dynamical preliminaries on Anosov flows}\n\n\\label{section:anosov}\n\n\\subsubsection{Shadowing lemma and homoclinic orbits}\n\n\n\nFix an arbitrary Riemannian metric $g$ on $\\mathcal{M}$. As usual, we define the \\emph{local strong (un)stable manifolds} as:\n\\[\n\\begin{array}{l}\nW^{s}_{\\delta}(x) :=\\left\\{y \\in \\mathcal{M}, ~ \\forall t \\geq 0, d(\\varphi_t y, \\varphi_t x) < \\delta, d(\\varphi_t x, \\varphi_t y) \\rightarrow_{t \\rightarrow +\\infty} 0 \\right\\}, \\\\\nW^{u}_{\\delta}(x) :=\\left\\{y \\in \\mathcal{M}, ~ \\forall t \\leq 0, d(\\varphi_t y, \\varphi_t x) < \\delta, d(\\varphi_t x, \\varphi_t y) \\rightarrow_{t \\rightarrow -\\infty} 0 \\right\\},\n\\end{array}\n\\]\nwhere $\\delta > 0$ is chosen small enough. For $\\delta = \\infty$, we obtain the sets $W^{s,u}(x)$ which are the strong stable\/unstable manifolds of $x$. We also set $W^{s,u}_{\\mathrm{loc}}(x) =: W^{s,u}_{\\delta_0}(x)$ for some fixed $\\delta_0 > 0$ small enough. The \\emph{local weak (un)stable manifolds} $W^{ws,wu}_{\\delta}(x)$ are the set of points $y \\in B(x,\\delta)$ such that there exists $t \\in \\mathbb{R}$ with $|t| < \\delta$ and $\\varphi_t y \\in W^{s,u}_{\\mathrm{loc}}(x)$.\nThe following lemma is known as the \\emph{local product structure} (see \\cite[Theorem 5.1.1]{Fisher-Hasselblatt-19} for more details):\n\n\n\\begin{lemma}\n\\label{lemma:product-structure}\nThere exists $\\varepsilon_0, \\delta_0 > 0$ small enough such that for all $x,y \\in \\mathcal{M}$ such that $d(x,y) < \\varepsilon_0$, the intersection $W^{wu}_{\\delta_0}(x) \\cap W^{s}_{\\delta_0}(y)$ is precisely equal to a unique point $\\left\\{z\\right\\}$. We write $z := [x,y]$.\n\\end{lemma}\n\n\nThe main tool we will use to construct suitable \\emph{homoclinic orbits} is the following classical shadowing property of Anosov flows for which we refer to \\cite[Corollary 18.1.8]{Hasselblatt-Katok-95}, \\cite[Theorem\n5.3.2]{Fisher-Hasselblatt-19} and \\cite[Proposition 6.2.4]{Fisher-Hasselblatt-19}. For the sake of simplicity, we now write $\\gamma=[xy]$ if $\\gamma$ is an orbit segment of the flow with endpoints $x$ and $y$.\n\n\\begin{theorem}\n\\label{theorem:shadowing} There exist $\\varepsilon_0>0$, $\\theta>0$ and $C>0$ with the\nfollowing property. Consider $\\varepsilon<\\varepsilon_0$, and a finite or infinite sequence\nof orbit segments $\\gamma_i = [x_iy_i]$ of length $T_i$ greater than $1$ such\nthat for any $n$, $d(y_n,x_{n+1}) \\leq \\varepsilon$. Then there exists a genuine\norbit $\\gamma$ and times $\\tau_i$ such that $\\gamma$ restricted to $[\\tau_i,\n\\tau_i+T_i]$ \\emph{shadows} $\\gamma_i$ up to $C\\varepsilon$. More precisely, for all $t\\in\n[0, T_i]$, one has\n\\begin{equation}\n\\label{eq:d_hyperbolic}\n d(\\gamma(\\tau_i+t), \\gamma_i(t)) \\leq C \\varepsilon e^{-\\theta \\min(t, T_i-t)}.\n\\end{equation}\nMoreover, $|\\tau_{i+1} - (\\tau_i + T_i)| \\leq C \\varepsilon$. Finally, if the\nsequence of orbit segments $\\gamma_i$ is periodic, then the orbit $\\gamma$ is\nperiodic.\n\\end{theorem}\n\nLet us also make the following important comment. In the previous theorem, one can also allow the first orbit segment $\\gamma_i$ to be\ninfinite on the left, and the last orbit segment $\\gamma_j$ to be infinite on\nthe right. In this case,~\\eqref{eq:d_hyperbolic} should be replaced by: assuming that $\\gamma_i$ is defined on $(-\\infty, 0]$\nand $\\gamma_j$ on $[0,+\\infty)$, we would get for some $\\tilde\\tau_{i+1}$\nwithin $C\\varepsilon$ of $\\tau_{i+1}$, and all $t\\geq 0$\n\\begin{equation*}\n d(\\gamma(\\tilde\\tau_{i+1}-t), \\gamma_i(-t)) \\leq C \\varepsilon e^{-\\theta t}, \\quad d(\\gamma(\\tau_{j}+t), \\gamma_j(t)) \\leq C \\varepsilon e^{-\\theta t}.\n\\end{equation*}\n\n\n\n\n\n\n\\label{sssection:homoclinic}\n\nFix an arbitrary periodic point $x_{\\star} \\in \\mathcal{M}$ of period $T_{\\star}$ and denote by $\\gamma_{\\star}$ its primitive orbit.\n\n\\begin{definition}[Homoclinic orbits]\n\\label{definition:homoclinic}\nA point $p \\in \\mathcal{M}$ is said to be \\emph{homoclinic} to $x_{\\star}$ if $p \\in W^{ws}(x_{\\star}) \\cap W^{wu}(x_{\\star})$ (in other words, $d(\\varphi_{t+t^\\pm_0} p, \\varphi_t x_{\\star}) \\rightarrow_{t \\rightarrow \\pm \\infty} 0$ for some $t^\\pm_0 \\in \\mathbb{R}$). We say that an orbit $\\gamma$ is homoclinic to $x_{\\star}$ if it contains a point $p \\in \\gamma$ that is homoclinic to $x_{\\star}$ and we denote by $\\mathcal{H} \\subset \\mathcal{M}$ the set of homoclinic orbits to $x_{\\star}$.\n\\end{definition}\n\n\nNote that due to the hyperbolicity, the convergence of the point $p$ to $x_{\\star}$ is exponentially fast. More precisely, let $\\gamma$ be the orbit of $p$ and let $\\mathbb{R} \\ni t \\mapsto \\gamma(t)$ be the flow parametrization of $\\gamma$. Then, there exists uniform constants $C,\\theta > 0$ (independent of $\\gamma$) and $A_\\pm \\in \\mathbb{R}$ (depending on $\\gamma$) such that the following holds:\n\\begin{equation}\n\\label{equation:distance}\nd(\\gamma(A_\\pm \\pm n T_{\\star}), x_{\\star}) \\leq C e^{-\\theta n}.\n\\end{equation}\nThe points $\\gamma(A_\\pm)$ correspond to an arbitrary choice of points in $W^{s,u}_{\\delta_0}(x_{\\star})\\cap \\gamma$ (for some arbitrary $\\delta_0 > 0$ small enough). Homoclinic orbits have infinite length (except the orbit of $x_{\\star}$ itself) but it will be convenient to introduce a notion of \\emph{length} $T_\\gamma$ which we define to be equal to $T_\\gamma:=A_+-A_-$ (note that this is a highly non-canonical definition). We define the trunk to be equal to the central segment $\\gamma([A_-,A_+])$. In other words, the length of $\\gamma$ is equal to the length of its trunk. We also define the points: $x_n^\\pm := \\gamma(A_\\pm \\pm nT_{\\star})$. Note that another choice of values $A'_\\pm$ has to differ from $A_\\pm$ by $mT_{\\star}$ for some $m \\in \\mathbb{Z}$. Homoclinic orbits will play a key role as we shall see in due course. \n\n\\begin{lemma}\n\\label{lemma:dense}\nAssume that the flow is transitive. Then the set $\\mathcal{W}$ of points belonging to a homoclinic orbit in $\\mathcal{H}$ is dense in $\\mathcal{M}$.\n\\end{lemma}\n\n\n\\begin{proof}\nThis is a straightforward consequence of the shadowing Theorem \\ref{theorem:shadowing}: one concatenates a long segment $S$ of a transitive orbit with $\\gamma_{\\star}$ i.e. one applies Theorem \\ref{theorem:shadowing} with $... \\gamma_{\\star} \\gamma_{\\star} S \\gamma_{\\star} \\gamma_{\\star} ...$.\n\\end{proof}\n\n\\begin{remark}\n\\rm\nIn the particular case of an Anosov geodesic flow on the unit tangent bundle, one can check that $\\mathcal{H}$ is in one-to-one correspondence with $\\pi_1(M)\/\\langle \\widetilde{\\gamma_{\\star}} \\rangle$, where $\\widetilde{\\gamma_{\\star}} \\in \\pi_1(M)$ is any element such that the conjugacy class of $\\widetilde{\\gamma_{\\star}}$ in $\\pi_1(M)$ corresponds\\footnote{Recall that the set of free homotopy classes $\\mathcal{C}$ is in one-to-one correspondence with conjugacy classes of $\\pi_1(M)$, see \\cite[Chapter 1]{Hatcher-02}.} to the free homotopy class $c \\in \\mathcal{C}$ whose unique geodesic representative is $\\gamma_{\\star}$. \n\\end{remark}\n\n\n\n\n\\subsubsection{Applications of the Ambrose-Singer formula}\n\n\nConsider a Hermitian vector bundle $\\E$ over $(\\mathcal{M}, g)$ equipped with a unitary connection $\\nabla = \\nabla^{\\E}$. If $x, y \\in \\mathcal{M}$ are at a distance less than the injectivity radius of $\\mathcal{M}$, denote by $C_{x \\to y}: \\E_x \\to \\E_y$ the parallel transport with respect to $\\nabla^{\\E}$ along the shortest geodesic from $x$ to $y$, by $C(x, t): \\E_x \\to \\E_{\\varphi_tx}$ the parallel transport along the flow and by $C_\\gamma$ the parallel transport along a curve $\\gamma$. For $U \\in C^\\infty(\\mathcal{M},\\mathrm{End}(\\mathcal{E}))$, we define, for all $x \\in \\mathcal{M}$, \n\\[\n\\|U\\|_x := \\Tr(U^*(x)U(x))^{1\/2},\n\\]\nand $\\|U\\|_{L^\\infty} := \\sup_{x \\in \\mathcal{M}} \\|U\\|_x$. In particular, if $U \\in C^\\infty(\\mathcal{M},\\mathrm{U}(\\mathcal{E}))$ is unitary, then $\\|U\\|_{L^\\infty} = \\sqrt{\\rk(\\E)}$. We record the following consequences of Lemma \\ref{lemma:ambrosesinger}:\n\n\n\n\\begin{lemma}\\label{lemma:ASgeometry}\nThe following consequences of the Ambrose-Singer formula hold:\n\\begin{enumerate}\n\t\\item Assume we are in the setting of Theorem \\ref{theorem:shadowing}: for some $C, \\varepsilon, T > 0$, let $x, p \\in \\mathcal{M}$ satisfy $d(\\varphi_t x, \\varphi_t p) \\leq C \\varepsilon e^{-\\theta \\min(t,T-t)}$ for all $t \\in [0, T]$. Then for any $0 \\leq T_1 \\leq T$:\n\t\\[\\|C(\\varphi_{T_1}x, -T_1)C_{\\varphi_{T_1}p \\to \\varphi_{T_1}x}C(p, T_1) C_{x \\to p} - \\mathbbm{1}_{\\E_x}\\|_x \\leq \\frac{c_0C \\varepsilon}{\\theta} \\times \\|F_\\nabla\\|_{C^0},\\]\n\twhere $c_0 = c_0(X, g) > 0$ depends only on the flow $X$ and the metric.\n\t\n\t\\item Assume $\\gamma \\subset B(p, \\imath\/2)$ is a closed piecewise smooth curve at $p$ of length $L$. Then for some $C = C(g) > 0$ depending on the metric:\n\t\\[\\|C_\\gamma - \\id_p\\|_p \\leq C L \\times \\sup_{y \\in \\gamma} d(p, y) \\times \\|F_\\nabla\\|_{C^0}.\\]\n\t\n\t\\item Let $\\gamma: [0, L] \\to M$ be a unit speed curve based at $p$, and $\\nabla'$ be a second unitary connection on $\\E$, whose parallel transport along $\\gamma$ we denote by $C'_\\gamma$. Then:\n\t\\[\\|C_{\\gamma}^{-1}C'_{\\gamma} - \\id_p\\|_p \\leq L \\times \\|\\nabla - \\nabla'\\|_{C^0}.\\]\n\t\\end{enumerate}\n\\end{lemma}\nThe geometries appearing in (1), (2) and (3) are depicted in Figure \\ref{fig:ASgeometry} (A), (B) and (C), respectively.\n \n \\begin{figure}\n \\centering\n\\begin{subfigure}{0.39\\linewidth}\n \\centering\n\\begin{tikzpicture}[scale = 0.55, everynode\/.style={scale=0.5}]\n\\tikzset{cross\/.style={cross out, draw=black, minimum size=2*(#1-\\pgflinewidth), inner sep=0pt, outer sep=0pt},\ncross\/.default={1pt}}\n\n \n \t\n \n\t\t\\fill[pattern=vertical lines, pattern color=blue, draw = black] (0, 0) -- (0, 1.75) to[out = -35, in= -145, distance=75] (10, 1.75) -- (10, 0) -- (0,0);\n\t\t\n\t\t\n\t\t\\fill (0, 0) node[below left] {\\small $x$} circle (1.5pt);\n\t\t\\fill (10, 0) node[below right] {\\small $\\varphi_Tx$} circle (1.5pt);\n\t\t\\fill (0, 1.75) node[left] {\\small $p$} circle (1.5pt);\n\t\t\\fill (10, 1.75) node[right] {\\small $\\varphi_T p$} circle (1.5pt);\n\n\t\t\\draw[thick, ->] (5, 1.1) -- (5, 0.6) node[above left] {\\tiny $\\varphi_t p$};\n\t\t\\draw (5, 1.25) node[right] {$\\mathcal{O}(e^{-\\theta t})$};\n\t\t\\fill (5, 0.6) circle (1pt);\n\t\t\\draw[thick, ->] (5, -0.5) -- (5, 0) node[below left] {\\tiny $\\varphi_t x$};\t\t\n\t\t\\fill (5, 0) circle (1pt);\n\\end{tikzpicture}\n \\caption{\\small Shadowing homotopy.}\n \n \\end{subfigure}\n \\begin{subfigure}{0.29\\linewidth}\n \\centering\n \t\t\\begin{tikzpicture}[scale=0.8]\n \t\t\\draw (0, 0) circle (1.5);\n \t\t\t\\draw (0, 0) node[below]{$p$}--(-1, 1)--(1,1)--(0,0);\n \t\t\t\\fill (0, 0) circle (1pt) (1, 1) circle (1pt) (-1, 1) circle (1pt);\n \t\t\t\\draw[blue] (0, 0)--(-0.8, 1) (0, 0)--(-0.6,1) (0, 0)--(-0.4,1) (0, 0)--(-0.2,1) (0, 0)--(-0,1) (0, 0)--(0.2,1) (0, 0)--(0.4,1) (0, 0)--(0.6,1) (0, 0)--(0.8,1);\n \t\t\\draw (0.5, 0.4) node[right]{$\\gamma$};\n \t\t\\end{tikzpicture}\n \t\t \\caption{\\small Radial homotopy.}\n \n \\end{subfigure}\n \\begin{subfigure}{0.29\\linewidth}\n \\centering\n \t\t\\begin{tikzpicture}\n \t\t\t\\draw[thick, ->] (0, 0) node[below]{\\small $p = \\gamma(0)$}--(0.5, 0.5);\n \t\t\t\\draw[thick] (0.5, 0.5)--(1, 1) node[right]{\\small $\\gamma(L)$};\n \t\t\t\\fill (0, 0) circle(1pt) (1, 1) circle(1pt);\n \t\t\n \t\t\t\\draw[->] (0.5, 0.2)--(0.8, 0.5) node[below right]{\\small $C'_\\gamma$};\n \t\t\t\\draw[->] (0.5, 0.8)--(0.2, 0.5) node[above left]{\\small $C_\\gamma^{-1}$};\n \t\t\\end{tikzpicture}\n \t\t \t\t \\caption{\\small Straight-line homotopy.}\n \n \\end{subfigure}\n \\caption{\\small Presentation of the geometries considered in Lemma \\ref{lemma:ASgeometry}.}\n \\label{fig:ASgeometry}\n\\end{figure}\n\\begin{proof\n\tWe first prove (1). For $C, \\varepsilon$ small enough, for all $t \\in [0, T]$ we denote by $\\tau_t$ the unit speed shortest geodesic, of length $\\ell(t)$, from $\\varphi_tx$ to $\\varphi_t p$. Define a smooth homotopy $\\Gamma: [0, 1]^2 \\to \\mathcal{M}$ by setting:\n\t\\[\\Gamma(s, t) := \\tau_{tT_1}(s \\ell(t)),\\]\n\tand note that by assumption $\\ell(t) \\leq C\\varepsilon e^{-\\theta \\min(t, T - t)}$. We apply Lemma \\ref{lemma:ambrosesinger} to the homotopy $\\Gamma$ to obtain, after a rescaling of parameters $s$ and $t$:\n\t\\begin{multline*}\n\t\tC(\\varphi_{T_1}x, -T_1)C_{\\varphi_{T_1}p \\to \\varphi_{T_1}x}C(p, T_1) C_{x \\to p} - \\mathbbm{1}_{\\E_x}\\\\\n\t\t= \\int_0^{T_1} \\int_0^{\\ell(t)} C_{\\uparrow}^{-1}(s, t) F_\\nabla(\\partial_t \\tau_t(s), \\partial_s \\tau_t(s)) C_{\\rightarrow}(s, t) \\, ds \\, dt.\n\t\\end{multline*}\n\tHere we recall $C_{\\uparrow}$ and $C_{\\rightarrow}$ are parallel transport maps obtained by parallel transport along curves as in Figure \\ref{fig:AS1}. Since $C_{\\uparrow}$ and $C_{\\rightarrow}$ are isometries, and since by compactness $|\\partial_t \\tau_t(s)| \\leq D$ for some $0 < D = D(X, g)$, we have:\n\t\\begin{multline*}\n\t\t\\|C(\\varphi_{T_1}x, -T_1)C_{\\varphi_{T_1}p \\to \\varphi_{T_1}x}C(p, T_1) C_{x \\to p} - \\mathbbm{1}_{\\E_x}\\|_x\\\\\n\t\t \\leq CD\\varepsilon \\|F_{\\nabla}\\|_{C^0} \\int_0^T e^{-\\theta \\min(t, T - t)} dt \\leq \\frac{2D C}{\\theta} \\times \\varepsilon \\|F_\\nabla\\|_{C^0}.\n\t\\end{multline*}\n\t\n\tFor (2), we may assume by approximation that $\\gamma$ is smooth. Then taking the homotopy \n\t\\[\\Gamma(s, t) = \\exp_x( t \\exp_x^{-1} (\\gamma(sL))),\\]\n\tand applying Lemma \\ref{lemma:ambrosesinger}, we obtain by a rescaling of $s$ and writing $\\widetilde{\\Gamma}(s, t) = \\Gamma(s\/L, t)$:\n\t\\[C_\\gamma - \\id_x = \\int_0^L \\int_0^1 C_1(s, t)^{-1} F_\\nabla(\\partial_s \\widetilde{\\Gamma}, \\partial_t \\widetilde{\\Gamma}) C_2(s, t) \\,dt \\,ds.\\]\n\tThe estimate now follows by using $\\|\\partial_t \\widetilde{\\Gamma}\\| \\leq C d(x, \\gamma(s))$, where we introduce the positive constant $C = \\sup_{x \\in M} \\sup_{|y|_{g_x} < \\imath\/2}\\|d\\exp_x(y)\\|_{T_xM \\to T_{\\exp_x(y)}}$.\n\t\n\tFor the final item, denote by $C_t, C_t'$ the parallel transports along $\\gamma|_{[0, t]}$ with the connection $\\nabla, \\nabla'$, respectively. Then it is straightforward that $\\partial_t(C_t^{-1}C'_t) = C_t^{-1}(\\nabla- \\nabla')(\\dot{\\gamma}(t)) C_t'$, so\n\t\\[C_\\gamma^{-1} C_\\gamma' - \\id_p = \\int_0^L C_t^{-1}(\\nabla- \\nabla')(\\dot{\\gamma}(t)) C_t' \\,dt.\\]\n\tThe required estimate follows\n\\end{proof}\n\nWe also have the following result to which we will refer to as the \\emph{spiral Lemma}:\n\n\\begin{lemma}\n\\label{lemma:spiral}\nLet $x_{\\star} \\in \\mathcal{M}$ be a periodic point of period $T_{\\star}$ and let $x_0 \\in W^s_{\\mathrm{loc}}(x_{\\star})$. Define $x_n := \\varphi_{nT_{\\star}}x_0$ and write $q_n := C(x_{\\star},nT_{\\star})^{-1}C_{x_n \\rightarrow x_{\\star}} C(x_0, n T_{\\star})C_{x_{\\star} \\rightarrow x_0}$. Then:\n\\[\n\\rho(x_0) := \\lim_{n \\rightarrow +\\infty} q_n \\in \\mathrm{U}(\\E_{x_{\\star}})\n\\]\nexists. Moreover, there exist some uniform constants $C,\\theta > 0$ such that\n\\[\n|q_n - \\rho(x_0)| \\leq C e^{-\\theta n}.\n\\]\n\\end{lemma}\n\n\\begin{center}\n\\begin{figure}[htbp!]\n\\includegraphics{spiral.eps}\n\\caption{The spiral Lemma: the set $\\Omega_1$ corresponds to the area over which the integral in the Ambrose-Singer formula is computed for $n=1$.}\n\\label{fig:spiral}\n\\end{figure}\n\\end{center} \n\n\\begin{proof}\nApply the Ambrose-Singer formula as in the first item of the previous Lemma (same notations as in the previous proof):\n\\[\nq_n - \\mathbbm{1}_{\\E_{x_{\\star}}} = \\int_0^{nT_{\\star}} \\int_0^{\\ell(t)} C_{\\uparrow}^{-1}(s, t) F_\\nabla(\\partial_t \\tau_t(s), \\partial_s \\tau_t(s)) C_{\\rightarrow}(s, t) \\, ds \\, dt,\n\\]\nwhere $\\tau_t$ is the unit speed shortest geodesic of length $\\ell(t)$ from $\\varphi_t x_0$ and $\\varphi_t x_{\\star}$. Observe that this integral converges absolutely as (see \\eqref{equation:distance}):\n\\[\n\\int_0^{nT_{\\star}} \\norm{ \\int_0^{\\ell(t)} C_{\\uparrow}^{-1}(s, t) F_\\nabla(\\partial_t \\tau_t(s), \\partial_s \\tau_t(s)) C_{\\rightarrow}(s, t) \\, \\dd s \\, } \\dd t \\leq \\int_0^{n T_{\\star}} C \\|F_{\\nabla}\\|_{C^0} e^{-\\theta t} \\dd t < \\infty,\n\\]\nand thus the limit exists. Moreover, it is clear that the convergence is exponential.\n\\end{proof}\n\n\n\n\n\n\n\\subsection{Proof of the exact Liv\\v{s}ic cocycle Theorem}\n\n\\label{section:weak-strong}\n\n\n\n\\subsubsection{Parry's free monoid}\n\nAs we shall see, Parry's free monoid is the key notion to understand the holonomy of unitary connections.\nWhereas flat connections up to gauge equivalence correspond to representations of the fundamental group up to conjugacy, in the setting of hyperbolic dynamics, we will show that \\emph{arbitrary} connections up to cocycle equivalence correspond to representations of Parry's free monoid. \nRecall from \\S\\ref{sssection:homoclinic} that $x_{\\star} \\in \\mathcal{M}$ is a periodic point of period $T_{\\star}$. Let $\\mathbf{G}$ be the free monoid generated by $\\mathcal{H}$ (homoclinic orbits to $x_{\\star}$), namely the formal set of words\n\\[\n\\mathbf{G} := \\left\\{\\gamma_1^{m_1} ... \\gamma_k^{m_k} ~|~k \\in \\mathbb{N}, m_1,...,m_k \\in \\mathbb{N}_0, \\gamma_1,...,\\gamma_k \\in \\mathcal{H}\\right\\},\n\\]\nendowed with the obvious monoid structure. The empty word corresponds to the identity element denoted by $\\mathbf{1}_{\\mathbf{G}}$. Note the periodic orbit corresponding to $x_{\\star}$ also belongs to the set of homoclinic orbits. We call $\\mathbf{G}$ \\emph{Parry's free monoid} as the idea (although not written like this) was first introduced in his work \\cite{Parry-99} (see also \\cite{Schmidt-99} for a related approach).\nThe main result of this paragraph is the following:\n\n\\begin{prop}\n\\label{proposition:representation0}\nLet $\\nabla^{\\E}$ be a unitary connection on the Hermitian vector bundle $\\E \\rightarrow \\mathcal{M}$. Then $\\nabla^{\\E}$ induces a representation\n\\[\n\\rho : \\mathbf{G} \\rightarrow \\mathrm{U}(\\E_{x_{\\star}}).\n\\]\n\\end{prop}\n\nFormally, this proposition could have also been stated as a definition.\n\n\\begin{proof}\nSince $\\mathbf{G}$ is a free monoid, it suffices to define $\\rho$ on the set of generators of $\\mathbf{G}$, namely for all homoclinic orbits $\\gamma \\in \\mathcal{H}$. For the neutral element we set $\\rho(\\mathbf{1}_{\\mathbf{G}}) = \\mathbbm{1}_{\\E_{x_{\\star}}}$. For the periodic orbit $\\gamma_{\\star}$ of $x_{\\star}$, we set $\\rho(\\gamma_{\\star}) := C(x_{\\star},T_{\\star})$.\n\nLet $\\gamma \\in \\mathcal{H}$ (and $\\gamma \\neq \\gamma_{\\star}$) and consider a parametrization $\\mathbb{R} \\ni t \\mapsto \\gamma(t)$. Following the notations of \\S\\ref{sssection:homoclinic}, we let $x_n^\\pm := \\gamma(A_\\pm \\pm nT_{\\star}), x_n^+ = \\varphi_{T_n}(x_n^-)$ for some $T_n = A_+-A_-+2n T_{\\star}$, where $T_\\gamma := A_+ - A_-$ (length of the trunk), and the points $(x_n^\\pm)_{n \\in \\mathbb{N}}$ converge exponentially fast to $x_{\\star}$ as $n \\rightarrow \\infty$. As we shall see, there is a small technical issue coming from the fact that $C(x_{\\star},T_{\\star})$ is not trivial and this can be overcome by considering a subsequence $k_n \\rightarrow \\infty$ such that\\footnote{For any compact metric group $G$, if $g \\in G$, there exists a sequence $k_n \\in \\mathbb{N}$ such that $g^{k_n} \\rightarrow_{n \\rightarrow \\infty} \\mathbf{1}_G$.}\n\\begin{equation}\\label{eq:k_n}\n\tC(x_{\\star},T_{\\star})^{k_n} \\rightarrow \\mathbbm{1}_{\\E_{x_{\\star}}}, \\quad n \\to \\infty.\\end{equation}\n\n\n\nFor $n,m \\in \\mathbb{N}$, we define $\\rho_{m, n}(\\gamma) \\in \\mathrm{U}(\\E_{x_{\\star}})$ as follows: \n\\begin{equation}\\label{eq:rhomn}\n\\rho_{m,n}(\\gamma) := C_{x_{k_m}^+ \\rightarrow x_{\\star}} C(x_0^+, k_m T_{\\star}) C(x_0^-, T_\\gamma)C(x_{k_n}^-,k_nT_{\\star}) C_{x_{\\star} \\rightarrow x_{k_n}^-},\n\\end{equation}\nand we will write $\\rho_{n}(\\gamma) := \\rho_{n,n}(\\gamma)$.\n\n\\begin{lemma}\n\\label{lemma:convergence}\nThere exists $\\rho(\\gamma) \\in \\mathrm{U}(\\E_{x_{\\star}})$ such that:\n\\[\n\\rho_{m,n}(\\gamma) \\rightarrow_{n,m \\rightarrow \\infty} \\rho(\\gamma),\n\\]\nand $\\rho(\\gamma)$ does not depend in which sense the limit in $n,m$ is taken. \n\\end{lemma}\n\n\n\\begin{proof}\nWe have by construction:\n\\begin{equation}\n\\label{equation:cv0}\n\\begin{split}\n\\rho_{m,n}(\\gamma) & = C_{x_{k_m}^+ \\rightarrow x_{\\star}} C(x^+_0, k_m T_{\\star})C(x^-_0, T_\\gamma)C(x_{k_n}^-,k_n T_{\\star}) C_{x_{\\star} \\rightarrow x_{k_n}^-}\\\\\n& = \\left[C_{x_{k_m}^+ \\rightarrow x_{\\star}} C(x^+_0, k_m T_{\\star}) C_{x_{\\star} \\rightarrow x^+_0} C(x_{\\star},k_mT_{\\star})^{-1}\\right] \\\\\n& \\hspace{2cm} \\times C(x_{\\star},k_mT_{\\star}) C_{x^+_0 \\rightarrow x_{\\star}} C(x^-_0, T_\\gamma)C_{x_{\\star} \\rightarrow x^-_0} C(x_{\\star},k_nT_{\\star}) \\\\\n& \\hspace{2cm} \\times \\left[C(x_{\\star},k_nT_{\\star})^{-1}C_{x^-_0 \\rightarrow x_{\\star}}C(x_{k_n}^-,k_n T_{\\star}) C_{x_{\\star} \\rightarrow x_{k_n}^-}\\right],\n\\end{split}\n\\end{equation}\nwhere $T_\\gamma$ is independent of $n$ (trunk of $\\gamma$). For the middle term we have by \\eqref{eq:k_n}\n\\[\nC(x_{\\star},k_mT_{\\star}) C_{x^+_0 \\rightarrow x_{\\star}} C(x^-_0, T_\\gamma)C_{x_{\\star} \\rightarrow x^-_0} C(x_{\\star},k_nT_{\\star}) = C_{x^+_0 \\rightarrow x_{\\star}} C(x^-_0, T_\\gamma)C_{x_{\\star} \\rightarrow x^-_0} + o(1),\n\\]\nas $n,m$ go to $+\\infty$. Moreover the convergence of the terms between brackets follow from the spiral Lemma \\ref{lemma:spiral} (the convergence is exponentially fast).\n\\end{proof}\n\nThis concludes the proof.\n\n\\end{proof}\n\n\\begin{remark}\n\\rm\nFor $\\gamma \\in \\mathcal{H}$, \\eqref{equation:cv0} shows that $\\rho(\\gamma)$ does not depend on the choice of subsequence $(k_n)_{n \\in \\mathbb{N}}$ as long as it satisfies $C(x_{\\star},T_{\\star})^{k_n} \\rightarrow \\mathbbm{1}$. However, $\\rho(\\gamma)$ does depend on the choice of trunk $[x_0^-x_0^+]$ for $\\gamma$ and another choice of trunk produces a $\\rho'(\\gamma)$ which differs from $\\rho(\\gamma)$ by:\n\\begin{equation}\n\\label{equation:differs}\n\\rho'(\\gamma) = C(x_{\\star},T_{\\star})^{m_L(\\gamma)} \\rho(\\gamma) C(x_{\\star},T_{\\star})^{m_R(\\gamma)},\n\\end{equation}\nwhere $m_L(\\gamma), m_R(\\gamma) \\in \\mathbb{Z}$. \n\\end{remark}\n\n\n\\subsubsection{Conjugate representations}\n\nWe introduce the submonoid $\\mathbf{G}^* := \\mathbf{G} \\setminus \\left\\{ \\gamma_{\\star}^k, k \\geq 1\\right\\}$ that is $\\mathbf{G}$ minus powers of $\\gamma_{\\star}$. Recall that the character of a representation $\\rho$ is defined by $\\chi_\\rho(\\bullet) := \\Tr(\\rho(\\bullet))$. This paragraph is devoted to proving the following:\n\n\\begin{prop}\n\\label{proposition:representation}\nLet $\\nabla^{\\E_{1,2}}$ be two unitary connections on the Hermitian vector bundles $\\E_1,\\E_2 \\rightarrow \\mathcal{M}$. Assume that the connections have trace-equivalent holonomies in the sense of Definition \\ref{definition:equivalence}. Then, the induced representations $\\rho_{1,2} : \\mathbf{G}^* \\rightarrow \\mathrm{U}({\\E_{1,2}}_{x_{\\star}})$ have the same character. In particular, this implies that they are isomorphic, i.e. there exists $p_{\\star} \\in \\mathrm{U}({\\E_2}_{x_{\\star}}, {\\E_1}_{x_{\\star}})$ such that: \n\\begin{equation}\\label{eq:repconj}\n\\forall \\gamma \\in \\mathbf{G}, ~~ \\rho_1(\\gamma) = p_{\\star} \\rho_2(\\gamma) p_{\\star}^{-1}.\n\\end{equation}\n\\end{prop}\n\nFollowing Lemma \\ref{lemma:convergence}, we consider a subsequence $(k_n)_{n \\in \\mathbb{N}}$ such that $C_{1,2}(x_{\\star},T_{\\star})^{k_n} \\rightarrow \\mathbbm{1}$. \n\n\\begin{proof}\nOnce we know that the representations have the same character, the conclusion is a straightforward consequence of a general fact of representation theory, see \\cite[Corollary 3.8]{Lang-02}. \nFor the sake of simplicity, we take $\\gamma = \\gamma_1 \\cdot \\gamma_2$, where $\\gamma_{1,2} \\in \\mathcal{H}$ (and both $\\gamma_{1,2}$ cannot be equal to $\\gamma_{\\star}$ at the same time since the word $\\gamma$ is in $\\mathbf{G}^*$) but the generalization to longer words is straightforward as we shall see and words of length $1$ are also handled similarly (one does not even need to concatenate orbits in this case). The empty word (corresponding to the identity element in $\\mathbf{G}^*)$ will also be dealt with separately. This proposition is based on the shadowing Theorem \\ref{theorem:shadowing} and the fact that one can concatenate orbits. But we will have to be careful to produce periodic orbits which are primitive.\n\nWe have by Lemma \\ref{lemma:convergence}:\n\\[\n\\rho_1(\\gamma) = \\rho_1(\\gamma_1) \\rho_1(\\gamma_2) = \\rho_{1 ; n,N}(\\gamma_1)\\rho_{1 ; n,n}(\\gamma_2) + o(1), \\quad n \\to \\infty,\n\\]\nwhere we use the convention $\\rho_{i;a,b}$ to denote the expression in \\eqref{eq:rhomn} with respect to $\\nabla^{\\E_i}$, for $i=1,2$. The term $N = N(n) \\geq n$ will ensure that a certain orbit is primitive as we shall see below. Let $x_{n}^\\pm(i)$ be the points on the orbit $\\gamma_i$ that are exponentially close to $x_{\\star}$, given by \\S\\ref{sssection:homoclinic}. Consider the concatenation of the orbits $S := [x_{k_{N}}^-(1) x_{k_{n}}^+(1)] \\cup [x_{k_n}^-(2) x_{k_n}^+(2)]$. Note that the starting points and endpoints of these segments are at distance at most $\\mathcal{O}(e^{-\\theta k_n})$. Thus by the shadowing Theorem \\ref{theorem:shadowing}, there exists a genuine periodic orbit $\\widetilde{\\gamma_n}$ and a point $y_n \\in \\widetilde{\\gamma_n}$ (of period $T'_n$) which $\\mathcal{O}(e^{-\\theta k_n})$-shadows the concatenation $S$ (here, if we have a longer word of length $k$, it suffices to apply the shadowing Theorem \\ref{theorem:shadowing} with $k$ segments). \n\nWe claim that $\\widetilde{\\gamma_n}$ is primitive for all $N$ large enough. Indeed, observe that $\\widetilde{\\gamma_n}$ can be decomposed into the following six subsegments:\n\n\\begin{center}\n\\begin{figure}[htbp!]\n\\includegraphics{primitive.eps}\n\\caption{The orbit $\\widetilde{\\gamma_n}$ is made of six segments: in the first segment (of length $k_{n}T_{\\star}$), it shadows the first portion $[x_{k_{n}}^-(2) x_{0}^-(2)]$ which wraps around $\\gamma_{\\star}$; in the second (of length $T_{\\gamma_2}$), it shadows the trunk $[x_{0}^-(2) x_{0}^+(2)]$, in the third (of length $k_{n}T_{\\star}$), it shadows the last portion of $[x_{0}^+(2) x_{k_{n}}^+(2)]$ which also wraps around $\\gamma_{\\star}$; then this process is repeated but for the second orbit $\\gamma_1$.}\n\\label{fig:primitive}\n\\end{figure}\n\\end{center} \n\nMoreover, the total length of $\\widetilde{\\gamma_n}$ is\n\\[\nT'_n = T_{\\gamma_2} + 2 k_n T_{\\star} + T_{\\gamma_1} + (k_{N}+k_n) T_{\\star} + \\mathcal{O}(e^{-\\theta k_n}).\n\\]\nTake $x \\in \\gamma_1 \\cup \\gamma_2$ with $x \\not \\in \\gamma_{\\star}$, and consider a small $\\varepsilon > 0$ such that $d(x, \\gamma_{\\star}) > 3\\varepsilon$. Let $n$ large enough so that for all $m \\geq n$ we have the tail $[x_m^-(1) x_n^-(1)] \\subset B(\\gamma_{\\star}, \\varepsilon)$, $\\widetilde{\\gamma_n}$ satisfies $d(\\widetilde{\\gamma_n}, x) < \\varepsilon$ and finally, so that the shadowing factor of Theorem \\ref{theorem:shadowing} satisfies $\\mathcal{O}(e^{-\\theta k_n}) < \\varepsilon$. Pick $N \\geq n$ such that $(k_{N} - k_n) T_{\\star} > T'_n\/2$. We argue by contradiction and assume that $\\widetilde{\\gamma_n} = \\gamma_0^k$ for some $k \\geq 2$ and $\\gamma_0 \\in \\mathcal{G}^\\sharp$, a primitive orbit.\n\nThis implies that there is a copy of $\\gamma_0$ in the central red segment of Figure \\ref{fig:primitive} which $\\mathcal{O}(e^{-\\theta k_n})$-shadows the orbit of $x_N^-(1)$ and this forces $\\widetilde{\\gamma_n} \\subset B(\\gamma_{\\star}, 2\\varepsilon)$. Thus $d(\\widetilde{\\gamma_n}, x) > \\varepsilon$, which is a contradiction.\n\n\nBy the first and second items of Lemma \\ref{lemma:ASgeometry}, we have:\n\\[\n\\rho_{1;n,N}(\\gamma_1)\\rho_{1;n,n}(\\gamma_2) = C_{1,y_n \\rightarrow x_{\\star}} C_1(y_n, T'_n)C_{1,y_n \\rightarrow x_{\\star}}^{-1} + \\mathcal{O}(e^{-\\theta k_n}).\n\\]\nBy assumption, we have $\\Tr(C_1(y_n, T'_n)) = \\Tr(C_2(y_n,T'_n))$. This yields:\n\\[\n\\begin{split}\n\\Tr(\\rho_1(\\gamma)) & = \\Tr(C_{1,y_n \\rightarrow x_{\\star}} C_1(y_n, T'_n)C_{1,y_n \\rightarrow x_{\\star}}^{-1} ) + o(1) \\\\\n& = \\Tr(C_1(y_n, T'_n)) + o(1) \\\\\n& = \\Tr(C_2(y_n, T'_n)) + o(1) = \\Tr(\\rho_2(\\gamma)) + o(1).\n\\end{split}\n\\]\nTaking the limit as $n \\rightarrow \\infty$, we obtain the claimed result about characters for all non-empty words $\\gamma \\in \\mathbf{G}^*$. \n\nIt remains to deal with the empty word. For that, take any $\\gamma \\in \\mathbf{G}^*$, and consider $n_i \\in \\mathbb{N}$, a subsequence such that $\\rho_1(\\gamma)^{n_i} \\rightarrow \\mathbbm{1}_{{\\E_1}_{x_{\\star}}}$ and $\\rho_2(\\gamma)^{n_i} \\rightarrow \\mathbbm{1}_{{\\E_2}_{x_{\\star}}}$. Then:\n\\[\n\\Tr\\left(\\rho_1(\\gamma)^{n_i}\\right) =\\Tr\\left(\\rho_2(\\gamma)^{n_i}\\right),\n\\]\nand taking the limit as $i \\rightarrow \\infty$ gives\n\\[\n\\Tr(\\rho_1(\\mathbf{1}_{\\mathbf{G}^*}))=\\rk(\\E_1)=\\rk(\\E_2) =\\Tr(\\rho_2(\\mathbf{1}_{\\mathbf{G}^*})).\n\\]\nBy the mentioned \\cite[Corollary 3.8]{Lang-02}, there is a $p_{\\star}$ satisfying \\eqref{eq:repconj} for $\\gamma \\in \\mathbf{G}^*$.\n\nIt is now straightforward to show \\eqref{eq:repconj} for all $\\gamma \\in \\mathbf{G}$. Applying \\eqref{eq:repconj} with $\\gamma_{\\star} \\gamma \\in \\mathbf{G}^*$, where $\\gamma \\in \\mathcal{H}\\setminus \\{\\gamma_{\\star}\\}$ is arbitrary, we get that:\n\\[\n\\begin{split}\n\\rho_1(\\gamma_{\\star} \\gamma)& = \\rho_1(\\gamma_{\\star}) \\rho_1(\\gamma) \\\\\n& = p_{\\star} \\rho_2(\\gamma_{\\star} \\gamma) p_{\\star}^{-1} = p_{\\star} \\rho_2(\\gamma_{\\star}) p_{\\star}^{-1} p_{\\star} \\rho_2(\\gamma) p_{\\star}^{-1}.\n\\end{split}\n\\]\nSince $\\rho_1(\\gamma) = p_{\\star} \\rho_2(\\gamma) p_{\\star}^{-1}$ (because $\\gamma \\in \\mathbf{G}^*$), we get that $\\rho_1(\\gamma_{\\star}) = p_{\\star} \\rho_2(\\gamma_{\\star}) p_{\\star}^{-1}$, that is $C_1(x_{\\star},T_{\\star}) = p_{\\star} C_2(x_{\\star},T_{\\star}) p_{\\star}^{-1}$ or equivalently $P(x_{\\star},T_{\\star})p_{\\star} = p_{\\star}$ (where $P$ denotes the parallel transport along the flowlines of $(\\varphi_t)_{t \\in \\mathbb{R}}$ with respect to the mixed connection $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_2},\\nabla^{\\E_1})}_X$, as in \\eqref{equation:tp-mixed}), concluding the proof.\n\\end{proof}\n\n\\begin{remark}\n\\rm\nAlthough the representations $\\rho_{1,2}$ depend on choices (namely on a choice of trunk for each homoclinic orbit $\\gamma \\in \\mathcal{H}$), the map $p_{\\star} \\in \\mathrm{U}(\\E_{x_{\\star}})$ does not. Indeed, taking $\\rho_{1,2}'$ two other representations (for some other choices of trunks), one gets by \\eqref{equation:differs}:\n\\[\n\\begin{split}\n\\rho'_1(\\gamma) & = C_1(x_{\\star},T_{\\star})^{m_L(\\gamma)} \\rho_1(\\gamma) C_1(x_{\\star},T_{\\star})^{m_R(\\gamma)} \\\\\n& = C_1(x_{\\star},T_{\\star})^{m_L(\\gamma)} p_{\\star} \\rho_2(\\gamma) p_{\\star}^{-1} C_1(x_{\\star},T_{\\star})^{m_R(\\gamma)} \\\\\n& = C_1(x_{\\star},T_{\\star})^{m_L(\\gamma)} p_{\\star} C_2(x_{\\star},T_{\\star})^{-m_L(\\gamma)} \\rho'_2(\\gamma) C_2(x_{\\star},T_{\\star})^{-m_R(\\gamma)} p_{\\star}^{-1} C_1(x_{\\star},T_{\\star})^{m_R(\\gamma)} \\\\\n& = \\left(P(x_{\\star},m_L(\\gamma)T_{\\star})p_{\\star}\\right) \\rho'_2(\\gamma) \\left(P(x_{\\star},m_R(\\gamma)T_{\\star})p_{\\star}\\right)^{-1} = p_{\\star} \\rho'_2(\\gamma) p_{\\star}^{-1},\n\\end{split}\n\\]\nsince $P(x_{\\star},T_{\\star})p_{\\star}=p_{\\star}$, that is $p_{\\star}$ also conjugates the representations $\\rho'_{1,2}$. Note that the map $p_{\\star}$ given by \\cite[Corollary 3.8]{Lang-02} is generally not unique. Nevertheless, if the representation is irreducible, it is unique modulo the trivial $\\mathbb{S}^{1}$-action.\n\\end{remark}\n\n\n\\subsubsection{Proof of Theorem \\ref{theorem:weak}}\n\nWe can now complete the proof of Theorem \\ref{theorem:weak}.\n\n\\begin{proof}[Proof of Theorem \\ref{theorem:weak}]\nLet $\\mathcal{W}$ be the set of all points belonging to homoclinic orbits in $\\mathcal{H}$. By Lemma \\ref{lemma:dense}, $\\mathcal{W}$ is dense in $\\mathcal{M}$ and we are going to define the map $p$ (which will conjugate the cocycles) on $\\mathcal{W}$ and then show that $p$ is Lipschitz-continuous on $\\mathcal{W}$ so that it extends naturally to $\\mathcal{M}$. The map $p$ is defined as the parallel transport of $p_{\\star}$ with respect to the mixed connection.\n\nBy assumptions, we have $C_i(x_{\\star},T_{\\star})^{k_n} \\rightarrow \\mathbbm{1}_{\\E_*}$, and thus $P(x_{\\star},T_{\\star})^{k_n} \\rightarrow \\mathbbm{1}_{\\mathrm{Hom}({\\E_2}_{x_{\\star}},{\\E_1}_{x_{\\star}} )}$ (where we use the notation $\\mathbbm{1}_{\\mathrm{Hom}({\\E_2}_{x_{\\star}},{\\E_1}_{x_{\\star}} )}(q) = q$ for $q \\in \\mathrm{Hom}({\\E_2}_{x_{\\star}},{\\E_1}_{x_{\\star}})$). Consider a point $x \\in \\gamma$, where $\\gamma \\in \\mathcal{H}$ is a homoclinic orbit and also consider a parametrization of $\\gamma$ as in \\S\\ref{sssection:homoclinic}. For $n \\in \\mathbb{N}$ large enough, consider the point $x_n^- \\in \\gamma$ (which is exponentially close to $x_{\\star}$) and write $x = \\varphi_{T^-_n}(x_n^-)$ for some $T^-_n > 0$. Define:\n\\[\np^-_n(x) := P(x_{k_n}^-,T^-_{k_n})P_{x_{\\star} \\rightarrow x_{k_n}^-} p_{\\star} \\in \\mathrm{U}({\\E_2}_{x},{\\E_1}_{x}).\n\\]\n\n\\begin{lemma}\n\\label{lemma:p-minus}\nFix $\\gamma \\in \\mathcal{H}$. Then for all $x \\in \\gamma$, there exists $p_-(x) \\in \\mathrm{U}({\\E_2}_{x},{\\E_1}_{x} )$ such that $p^-_n(x) \\rightarrow_{n \\rightarrow \\infty} p_-(x)$. There exists $C > 0$ such that: $|p^-_n(x)-p_-(x)| \\leq C\/n$. Moreover, $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_2},\\nabla^{\\E_1})}_X p_- = 0$ on $\\gamma$. \n\\end{lemma}\n\nIn particular, this shows that $p_-$ is smooth in restriction to $\\gamma$ as $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_2},\\nabla^{\\E_1})}_X$ is elliptic on $\\gamma$.\n\n\\begin{proof}\nBy construction, the differential equation is clearly satisfied if the limit exists. Moreover, we have for some time $T_0$ (independent of $n$, $T^-_{k_n} = T_0 + k_n T_{\\star}$):\n\\[\n\\begin{split}\np^-_n(x) & = P(x_{k_n}^-,T^-_{k_n})P_{x_{\\star} \\rightarrow x_{k_n}^-} p_{\\star}\\\\\n& = P(x_0^-, T_0)P(x_{k_n}^-,k_n T_{\\star}) P_{x_{\\star} \\rightarrow x_{k_n}^-} p_{\\star} \\\\\n& = P(x_0^-, T_0) P_{x_{\\star} \\rightarrow x_0^-} P(x_{\\star},T_{\\star})^{k_n} \\left[P(x_{\\star},T_{\\star})^{-k_n}P_{x_0^- \\rightarrow x_{\\star}} P(x_{k_n}^-,k_n T_{\\star}) P_{x_{\\star} \\rightarrow x_{k_n}^-} p_{\\star}\\right].\n\\end{split}\n\\]\nBy assumption, the term outside the bracket converges as $n \\rightarrow \\infty$ and the term between brackets converges by the spiral Lemma \\ref{lemma:spiral}.\n\\end{proof}\n\nWe now claim the following:\n\n\\begin{lemma}\n\\label{lemma:stable}\nThere exists a uniform constant $C > 0$ such that the following holds. Assume that $x$ and $z$ belong to two homoclinic orbits in $\\mathcal{H}$ and $z \\in W^u_{\\mathrm{loc}}(x)$. Then:\n\\[\n\\|P_{x \\rightarrow z}p_-(x) - p_-(z)\\| \\leq C d(x,z).\n\\]\n\\end{lemma}\n\nBy the previous proofs, the point $x$ is associated to points $x^-_n$ on the homoclinic orbit and we will use the same notations for the point $z$ associated to the points $z^-_n$.\n\n\\begin{proof}\nThere is here a slight subtlety coming from the fact that the parametrizations of the homoclinic orbits $\\gamma$ were chosen in a non-canonical way (via a choice of $A_\\pm$). In particular, it is not true that the flowlines of $z_{k_n}^-$ and $x_{k_n}^-$ shadow each other; in other words, we might not have $T^-_{k_n}(z) = T^-_{k_n}(x)$ but we rather have $T^-_{k_n}(z) = T^-_{k_n}(x) + mT_{\\star}$ for some integer $m \\in \\mathbb{Z}$ depending on both $x$ and $z$.\n\nWe have:\n\\[\n\\begin{split}\n& \\|P_{x \\rightarrow z}p_-(x) - p_-(z)\\| \\\\\n& = \\|P_{x \\rightarrow z} p^-_n(x) - p^-_n(z)\\| + o(1) \\\\\n& = \\|P_{x \\rightarrow z} P(x_{k_n}^-,T^-_{k_n}(x))P_{x_{\\star} \\rightarrow x_{k_n}^-} p_{\\star} - P(z_{k_n}^-,T^-_{k_n}(z))P_{x_{\\star} \\rightarrow z_{k_n}^-} p_{\\star}\\| + o(1) \\\\\n& \\leq C \\| P_{z_{k_n}^- \\rightarrow x_{\\star}}P(z_{k_n}^-,T^-_{k_n}(z))^{-1} P_{x \\rightarrow z} P(x_{k_n}^-,T^-_{k_n}(x))P_{x_{\\star} \\rightarrow x_{k_n}^-}p_{\\star} - p_{\\star}\\| + o(1) \\\\\n& \\leq C \\| P_{z_{k_n}^- \\rightarrow x_{\\star}}P(z_{k_n},mT_{\\star})^{-1}P_{x_{\\star} \\rightarrow z_{k_n-m}^- } \\\\\n& \\hspace{1.2cm} \\times \\left[P_{z_{k_n-m}^- \\rightarrow x_{\\star}}P(z_{k_n-m}^-,T^-_{k_n}(z)-mT_{\\star})^{-1} P_{x \\rightarrow z} P(x_{k_n}^-,T^-_{k_n}(x))P_{x_{\\star} \\rightarrow x_{k_n}^-}\\right]p_{\\star} - p_{\\star}\\| + o(1).\n\\end{split}\n\\]\nApplying the first item of Lemma \\ref{lemma:ASgeometry}, we have that:\n\\[\n\\|P_{z_{k_n-m}^- \\rightarrow x_{\\star}}P(z_{k_n-m}^-,T^-_{k_n}(z)-mT_{\\star})^{-1} P_{x \\rightarrow z} P(x_{k_n}^-,T^-_{k_n}(x))P_{x_{\\star} \\rightarrow x_{k_n}^-} - \\mathbbm{1}_{\\mathrm{End}(\\E_{x_{\\star}})}\\| \\leq Cd(x,z),\n\\]\nwhere the constant $C > 0$ is uniform in $n$. Moreover, observe that\n\\[\n\\lim_{n \\rightarrow \\infty} P_{z_{k_n}^- \\rightarrow x_{\\star}}P(z_{k_n},mT_{\\star})^{-1}P_{x_{\\star} \\rightarrow z_{k_n-m}^- } = P(x_{\\star},mT_{\\star})^{-1}.\n\\]\n\n\nHence:\n\\[\n \\|P_{x \\rightarrow z}p_-(x) - p_-(z)\\| \\leq C\\left(\\| P(x_{\\star},mT_{\\star})^{-1}p_{\\star} - p_{\\star}\\| + d(x,z) + o(1)\\right).\n \\]\nUsing that $P(x_{\\star},T_{\\star})p_{\\star}=p_{\\star}$, we get that the first term on the right-hand side vanishes. Taking the limit as $n \\rightarrow +\\infty$, we obtain the announced result.\n\\end{proof}\n\nNote that we could have done the same construction ``in the future\" by considering instead:\n\\[\np_+(x) = \\lim_{n \\rightarrow \\infty} P(x,T^+_{k_n})^{-1} P_{x_{\\star} \\rightarrow x_{k_n}^+} p_{\\star} \\in \\mathrm{U}({\\E_2}_{x},{\\E_1}_{x}),\n\\]\nwhere $x_n^+ := \\varphi_{T^+_n}(x)$ is exponentially closed to $x_{\\star}$ as in \\S\\ref{sssection:homoclinic}. A similar statement as Lemma \\ref{lemma:stable} holds with the unstable manifold being replaced by the stable one. We have:\n\n\\begin{lemma}\n\\label{lemma:equality}\nFor all $x \\in \\mathcal{W}$, $p_-(x) = p_+(x)$.\n\\end{lemma}\n\n\\begin{proof}\nThis follows from Proposition \\ref{proposition:representation}. Indeed, we have:\n\\[\n\\begin{split}\n\\|p_-(x)-p_+(x)\\| & = \\|P(x_{k_n}^-,T^-_{k_n})P_{x_{\\star} \\rightarrow x_{k_n}^-} p_{\\star} - P(x,T^+_{k_n})^{-1} P_{x_{\\star} \\rightarrow x_{k_n}^+} p_{\\star}\\| + o(1) \\\\\n& \\leq C \\| P_{x_{k_n}^+ \\rightarrow x_{\\star} }P(x,T^+_{k_n})P(x_{k_n}^-,T^-_{k_n})P_{x_{\\star} \\rightarrow x_{k_n}^-}p_{\\star} - p_{\\star}\\| + o(1) \\\\\n& \\leq C \\| P_{x_{k_n}^+ \\rightarrow x_{\\star} }P(x_{k_n}^-,T_{k_n})P_{x_{\\star} \\rightarrow x_{k_n}^-}p_{\\star} - p_{\\star}\\| + o(1),\\\\\n\\end{split}\n\\]\nwhere $T_n := T_n^- + T_n^+$. Observe that:\n\\[\n\\begin{split}\n& P_{x_{k_n}^+ \\rightarrow x_{\\star} }P(x_{k_n}^-,T_{k_n})P_{x_{\\star} \\rightarrow x_{k_n}^-}p_{\\star} \\\\\n& \\hspace{2cm}= C_{1,x_{k_n}^+ \\rightarrow x_{\\star}} C_1(x_{k_n}^-,T_{k_n}) C_{1,x_{\\star} \\rightarrow x_{k_n}^-} p_{\\star} \\left(C_{2,x_{k_n}^+ \\rightarrow x_{\\star}} C_2(x_{k_n}^-,T_{k_n}) C_{2,x_{\\star} \\rightarrow x_{k_n}^-} \\right)^{-1} \\\\\n& \\hspace{2cm} = \\rho_{1,n}(\\gamma) p_{\\star} \\rho_{2,n}(\\gamma)^{-1} = \\rho_1(\\gamma)p_{\\star} \\rho_2(\\gamma)^{-1} + o(1) = p_{\\star} + o(1),\n\\end{split}\n\\]\nby Proposition \\ref{proposition:representation}. Hence $\\|p_-(x)-p_+(x)\\| = o(1)$, that is $p_-(x)=p_+(x)$.\n\\end{proof}\n\nWe can now prove the following lemma:\n\n\\begin{lemma}\n\\label{lemma:lipschitz}\nThe map $p_-$ is Lipschitz-continuous.\n\\end{lemma}\n\n\\begin{proof}\nConsider $x, y \\in \\mathcal{W}$ which are close enough. Let $z := [x,y] = W^{wu}_{\\mathrm{loc}}(x) \\cap W^{s}_{\\mathrm{loc}}(y)$ and define $\\tau$ such that $\\varphi_\\tau(z) \\in W^u_{\\mathrm{loc}}(x)$. Note that $|\\tau| \\leq Cd(x,y)$ for some uniform constant $C > 0$; also observe that the point $z$ is homoclinic to the periodic orbit $x_{\\star}$. We have:\n\\[\n\\begin{split}\n& \\|p_-(x)-P_{y \\rightarrow x}p_-(y)\\| \\\\\n& \\leq \\|p_-(x)-P_{z \\rightarrow x}p_-(z)\\| + \\|p_-(z)-p_+(z)\\| + \\|P_{z \\rightarrow x}p_+(z)-P_{y \\rightarrow x}p_+(y)\\| + \\|p_+(y)-p_-(y)\\| \\\\\n& \\leq \\|p_-(x)-P_{z \\rightarrow x}p_-(z)\\| + \\|P_{x \\rightarrow y}P_{z \\rightarrow x}p_+(z)-p_+(y)\\| \\\\\n& \\leq \\|p_-(x)-P_{\\varphi_{\\tau}(z) \\rightarrow x} p_-(\\varphi_{\\tau}(z))\\| + \\|P_{\\varphi_{\\tau}(z) \\rightarrow x} p_-(\\varphi_{\\tau}(z))- P_{z \\rightarrow x}p_-(z)\\| + \\|P_{x \\rightarrow y}P_{z \\rightarrow x}p_+(z)-p_+(y)\\| \n\\end{split}\n\\]\nwhere the terms disappear between the second and third line by Lemma \\ref{lemma:equality}. By Lemma \\ref{lemma:stable}, the first term is controlled by:\n\\[\n\\|p_-(x)-P_{\\varphi_{\\tau}(z) \\rightarrow x} p_-(\\varphi_{\\tau}(z))\\| \\leq Cd(x,\\varphi_{\\tau}(z)) \\leq Cd(x,y).\n\\]\nAs to the second term, using the second item of Lemma \\ref{lemma:ASgeometry}, we have:\n\\[\n\\|P_{\\varphi_{\\tau}(z) \\rightarrow x} p_-(\\varphi_{\\tau}(z))- P_{z \\rightarrow x}p_-(z)\\| = \\|P_{x \\rightarrow z} P_{\\varphi_{\\tau}(z) \\rightarrow x} P(z,\\tau)p_-(z)- p_-(z)\\| \\leq Cd(x,y).\n\\]\nEventually, the last term $\\|P_{x \\rightarrow y}P_{z \\rightarrow x}p_+(z)-p_+(y)\\|$ is controlled similarly to the first term by applying Lemma \\ref{lemma:stable} (but with the stable manifold instead of unstable).\n\\end{proof}\n\nAs $\\mathcal{W}$ is dense, $p_-$ extends to a Lipschitz-continuous map on $\\mathcal{M}$ which satisfies the equation $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_2},\\nabla^{\\E_1})}_X p_- = 0$ and by \\cite[Theorem 4.1]{Bonthonneau-Lefeuvre-20}, this implies that $p_-$ is smooth. This concludes the proof of the Theorem.\n\n\\end{proof}\n\n\n\n\\subsubsection{Proof of the geometric properties}\n\nWe now prove Proposition \\ref{proposition:opaque}.\n\n\\begin{proof}[Proof of Proposition \\ref{proposition:opaque}]\nThe equivalence between (1) and (2) can be found in \\cite[Section 5]{Cekic-Lefeuvre-20}. If $\\mathcal{F} \\subset \\mathcal{E}$ is a non-trivial subbundle that is invariant by parallel transport along the flowlines of $(\\varphi_t)_{t \\in \\mathbb{R}}$, it is clear that $\\rho$ will leave the space $\\mathcal{F}_{x_{\\star}}$ invariant and thus is not irreducible. Conversely, if $\\rho$ is not irreducible, then there exists a non-trivial $\\mathcal{F}_{x_{\\star}} \\subset \\E_{x_{\\star}}$ preserved by $\\rho$. Let $\\pi_{\\star} : \\mathcal{E}_{x_{\\star}} \\rightarrow \\mathcal{F}_{x_{\\star}}$ be the orthogonal projection. For $x$ on a homoclinic orbit, define $\\pi(x) : \\E_x \\rightarrow \\E_x$ similarly to $p_-$ in Lemma \\ref{lemma:p-minus} by parallel transport of the section $\\pi_{\\star}$ with respect to the connection $\\nabla^{\\mathrm{End}(\\E)}$. Following the previous proofs (we only use $\\rho \\pi_\\star = \\pi_\\star \\rho$), one shows that $\\pi$ extends to a Lipschitz-continuous section on homoclinic orbits which satisfies $\\pi^2 = \\pi$ and $\\nabla^{\\mathrm{End}}_X \\pi = 0$. By \\cite[Theorem 4.1]{Bonthonneau-Lefeuvre-20}, $\\pi$ extends to a smooth section i.e. $\\pi \\in C^\\infty(\\mathcal{M},\\mathrm{End}(\\E))$. Moreover, $\\pi(x_{\\star})=\\pi_{\\star}$, hence $\\pi$ is the projection onto a non-trivial subbundle $\\mathcal{F} \\subset \\mathcal{E}$.\n\\end{proof}\n\n\n\nWe now prove Theorem \\ref{theorem:iso}.\n\n\\begin{proof}[Proof of Theorem \\ref{theorem:iso}]\nThe linear map $\\Phi : \\mathbf{R}' \\rightarrow \\ker \\nabla^{\\mathrm{End}(\\E)}_X|_{C^\\infty(\\mathcal{M},\\mathrm{End}(\\E))}$ is defined in the following way. Consider $u_{\\star} \\in \\mathbf{R}'$ and define, as in Lemma \\ref{lemma:p-minus}, for $x$ on a homoclinic orbit, $u_-(x)$ as the parallel transport of $u_{\\star}$ from $x_{\\star}$ to $x$ along the orbit (with respect to the endomorphism connection $\\nabla^{\\mathrm{End}(\\E)}$). Similarly, one can define $u_+(x)$ by parallel transport from the future. The fact that $u_{\\star} \\in \\mathbf{R}'$ is then used in the following observation (see Lemma \\ref{lemma:equality}):\n\\[\n\\|u_-(x)-u_+(x)\\| = \\|\\rho(\\gamma)u_{\\star} \\rho(\\gamma)^{-1} - u_\\star\\| = 0.\n\\]\n(Note that $\\rho(\\gamma)u_{\\star} \\rho(\\gamma)^{-1}$ corresponds formally to the parallel transport of $u_{\\star}$ with respect to $\\nabla^{\\mathrm{End}(\\E)}$ from $x_{\\star}$ to $x_{\\star}$ along the homoclinic orbit $\\gamma$.) Hence, following Lemma \\ref{lemma:lipschitz}, we get that $u_-$ is Lipschitz-continuous and satisfies $\\nabla^{\\mathrm{End}(\\E)}_X u_- = 0$. By \\cite[Theorem 4.1]{Bonthonneau-Lefeuvre-20}, it is smooth and we set $u_- := \\Phi(u_{\\star}) \\in \\ker \\nabla^{\\mathrm{End}(\\E)}_X|_{C^\\infty(\\mathcal{M},\\mathrm{End}(\\E))}$.\n\nAlso observe that this construction is done by using parallel transport with respect to the unitary connection $\\nabla^{\\mathrm{End}(\\E)}$. As a consequence, if $u_\\star, u_\\star' \\in \\mathbf{R}$ are orthogonal (i.e. $\\Tr(u_\\star^* u_\\star') = 0$), then $\\Phi(u_{\\star})$ and $\\Phi(u'_\\star)$ are also pointwise orthogonal. This proves that $\\Phi$ is injective.\n\nIt now remains to show the surjectivity of $\\Phi$. Let $u \\in\\ker \\nabla^{\\mathrm{End}(\\E)}_X|_{C^\\infty(\\mathcal{M},\\mathrm{End}(\\E))}$. Following \\cite[Section 5]{Cekic-Lefeuvre-20}, we can write $u = u_R + i u_I$, where $u_R^*=u_R,u_I^*=u_I$ and $\\nabla^{\\mathrm{End}(\\E)}_X u_R = \\nabla^{\\mathrm{End}(\\E)}_X u_I = 0$. By \\cite[Lemma 5.6]{Cekic-Lefeuvre-20}, we can then further decompose $u_R = \\sum_{i=1}^p \\lambda_i \\pi_{\\mathcal{F}_i}$ (and same for $u_I$), where $\\lambda_i \\in \\mathbb{R}$, $p \\in \\mathbb{N}$ and $\\mathcal{F}_i \\subset \\E$ is a maximally invariant subbundle of $\\E$ (i.e. it does not contain any non-trivial subbundle that is invariant under parallel transport along the flowlines of $(\\varphi_t)_{t \\in \\mathbb{R}}$ with respect to $\\nabla^{\\E}$), and $\\pi_{\\mathcal{F}_i}$ is the orthogonal projection onto $\\mathcal{F}_i$. Setting $(\\pi_{\\mathcal{F}_i})_\\star := \\pi_{\\mathcal{F}_i}(x_\\star)$, invariance of $\\mathcal{F}_i$ by parallel transport implies that $\\rho(\\gamma)(\\pi_{\\mathcal{F}_i})_\\star = (\\pi_{\\mathcal{F}_i})_\\star\\rho(\\gamma)$, for all $\\gamma \\in \\mathbf{G}$, that is $(\\pi_{\\mathcal{F}_i})_\\star \\in \\mathbf{R}'$. Moreover, we have $\\Phi((\\pi_{\\mathcal{F}_i})_\\star) = \\pi_{\\mathcal{F}_i}$. This proves that both $u_R$ and $u_I$ are in $\\mathrm{ran}(\\Phi)$. This concludes the proof.\n\\end{proof}\n\nIt remains to prove the results concerning invariant sections:\n\n\n\\begin{proof}[Proof of Lemma \\ref{lemma:invariant-section}]\nUniqueness is immediate since $\\nabla^{\\E}_X u = 0$ implies that\n\\[\nX |u|^2 = \\langle \\nabla^{\\E}_X u, u \\rangle = \\langle u , \\nabla^{\\E}_X u \\rangle = 0,\n\\]\nthat is $|u|$ is constant. Now, given $u_\\star$ which is $\\mathbf{G}$-invariant, we can define $u_-(x)$ for $x$ on a homoclinic orbit $\\gamma$ by parallel transport of $u_\\star$ from $x_\\star$ to $x$ along $\\gamma$ with respect to $\\nabla^{\\E}$, similarly to Lemma \\ref{lemma:p-minus} and to the proof of Theorem \\ref{theorem:iso}. We can also define $u_+(x)$ in the same fashion (by parallel transport in the other direction). Then one gets that $\\|u_-(x)-u_+(x)\\| = \\|u_\\star - \\rho(\\gamma)u_\\star\\|=0$ and the same arguments as before show that $u_-$ extends to a smooth function in the kernel of $\\nabla^{\\E}_X$.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Lemma \\ref{lemma:rank2}]\nThis is based on the following:\n\n\\begin{lemma}\nAssume that for all periodic orbits $\\gamma \\in \\mathcal{G}$, there exists $u_\\gamma \\in C^\\infty(\\gamma,\\E|_{\\gamma})$ such that $\\nabla^{\\E}_X u_{\\gamma} = 0$. Then for all $g \\in \\mathbf{G}$, there exists $u_g \\in \\E_{x_\\star}$ such that $\\rho(g)u_g = u_g$. \n\\end{lemma}\n\n\\begin{proof}\nRecall that by the construction of Proposition \\ref{proposition:representation}, each element $\\rho(g) \\in \\mathrm{U}(\\E_{x_\\star})$ can be approximated by the holonomy $C_{y_n \\to x_\\star}C(y_n,T'_n)C_{x_\\star \\to y_n}$ along a sequence of periodic orbits of points $y_n$ converging to $x_\\star$. Now, each $C(y_n,T'_n)$ has $1$ as eigenvalue by assumption and taking the limit as $n \\rightarrow \\infty$, we deduce that $1$ is an eigenvalue of $\\rho(g)$.\n\\end{proof}\n\nAs a consequence, we can write for all $g \\in \\mathbf{G}$, in a fixed orthonormal basis of $\\E_{x_\\star}$\t:\n\\[\n\\rho(g) = \\alpha_g \\begin{pmatrix} 1 & 0 \\\\ 0 & s(g) \\end{pmatrix} \\alpha_g^{-1},\n\\]\nfor some $\\alpha_g \\in \\mathrm{U}(\\E_{x_\\star})$ and $s(g)$ is an $(r-1) \\times (r-1)$ matrix. For $\\rk(\\E)=1$, the Lemma is then a straightforward consequence of Lemma \\ref{lemma:invariant-section} since the conjugacy $\\alpha_g$ does not appear. For $\\rk(\\E)=2$, one has the remarkable property that $s(g)$ is \\emph{still} a representation of $ \\mathbf{G}$ since $\\det \\rho(g) = s(g) \\in \\mathrm{U}(1)$. As a consequence, $\\rho : \\mathbf{G} \\rightarrow \\mathrm{U}(\\E_{x_\\star})$ has the same character as $\\rho' : \\mathbf{G} \\rightarrow \\mathrm{U}(\\E_{x_\\star})$ defined by:\n\\[\n\\rho'(g) := \\begin{pmatrix} 1 & 0 \\\\ 0 & s(g) \\end{pmatrix}.\n\\]\nBy \\cite[Corollary 3.8]{Lang-02}, we then conclude that these representations are isomorphic, that is there exists $p_\\star \\in \\mathrm{U}(\\E_{x_\\star})$ such that $\\rho(g) = p_\\star \\rho'(g) p_\\star^{-1}$. If $u_\\star' \\in \\E_{x_\\star}$ denotes the vector fixed by $\\rho'(\\mathbf{G})$, then $u_\\star := p_\\star u'_\\star$ is fixed by $\\rho(\\mathbf{G})$. We then conclude by Lemma \\ref{lemma:invariant-section}.\n\\end{proof}\n\n\n\n\n\n\n\n\n\n\\section{Pollicott-Ruelle resonances and local geometry on the moduli space of connections}\n\n\\label{section:geometry}\n\nThis section is devoted to the study of the moduli space of connections, with the point of view of Pollicott-Ruelle resonances. We will first deal with the opaque case and then outline the main distinctions with the non-opaque case. We consider a Hermitian vector bundle $(\\mathcal{E},\\nabla^{\\mathcal{E}})$ endowed with a unitary connection over the Anosov Riemannian manifold $(M,g)$. Recall the notation of \\S \\ref{section:twisted}: we write $\\mathbf{X} = (\\pi^*\\nabla^{\\E})_X$, $\\RR_\\pm(z) = (\\pm \\mathbf{X} + z)^{-1}$ for its resolvent and $\\RR^\\pm_0$, $\\Pi_0^\\pm$ for the holomorphic parts and the spectral projector at zero, respectively.\n\n\\subsection{The Coulomb gauge}\n\nWe study the geometry of the space of connections (and of the moduli space of gauge-equivalent connections) in a neighborhood of a given unitary connection $\\nabla^{\\mathcal{E}}$ of regularity $C^s_*$ (for ${\\color{red}1 < }s < \\infty$\\footnote{It is very likely that the case $s=\\infty$ still works. This would require to use the Nash-Moser Theorem.}) such that $\\ker(\\nabla^{\\mathrm{End}(\\E)}) = \\mathbb{C} \\cdot \\mathbbm{1}_{\\E}$. For the standard differential topology of Banach manifolds, we refer the reader to \\cite{Lang-99}. We denote by\n\\[\n\\mathcal{O}_s(\\nabla^{\\mathcal{E}}) := \\left\\{ \\nabla^{\\mathcal{E}} + p^{-1} \\nabla^{\\mathrm{End}(\\E)}p ~|~ p \\in C_*^{s+1}(M,\\mathrm{U}(\\E)),\\,\\, \\|p - \\mathbbm{1}\\|_{C^{s+1}_*} < \n\\delta\\right\\}\n\\]\nthe orbit of gauge-equivalent connections of $C^{s}_*$ regularity, where $\\delta > 0$ is small enough so that $\\mathcal{O}_s(\\nabla^{\\E})$ is a smooth Banach submanifold. We also define the slice at $\\nabla^{\\E}$ by\n\\[\n\\mathcal{S}_s(\\nabla^{\\E}) := \\nabla^{\\E} + \\ker (\\nabla^{\\mathrm{End}(\\E)})^* \\cap \\big\\{A \\in C^{s}(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E)): \\|A\\|_{C_*^s} < \\delta\\big\\}.\n\\]\nNote that $\\mathbb{S}^1$ acts by multiplication freely and properly on $C_*^s(M, \\mathrm{U}(\\E))$ and hence we may form the quotient Banach manifold, denoted by $C_*^s(M, \\mathrm{U}(\\E))\/\\mathbb{S}^1$, which in particular satisfies\n\\begin{equation}\n\tT_{\\mathbbm{1}_{\\E}} \\Big(C_*^s(M, \\mathrm{U}(\\E))\/\\mathbb{S}^1\\Big) = C_*^s(M, \\mathrm{End}_{\\mathrm{sk}}(\\E))\/\\big(\\mathbb{R}\\cdot (i\\mathbbm{1}_{\\E})\\big), \n\\end{equation}\nwhere we used the identification of tangent spaces given by the exponential map.\n\tNext, observe that the map $O: p \\mapsto p^*\\nabla^{\\E}$ is injective modulo the multiplication action of $\\mathbb{S}^1$ on $C^{s + 1}_*(M, \\mathrm{U}(\\E))$ and that it is an immersion at $p = \\mathbbm{1}$ with $\\dd_{\\mathbbm{1}} O (\\Gamma) = \\nabla^{\\mathrm{End}(\\E)} \\Gamma$. Therefore by equation \\eqref{eq:decomposition-tt}, $\\mathcal{O}_s(\\nabla^{\\E})$ and $\\mathcal{S}_s$ are smooth transverse Banach manifolds with:\n\t\\[T_{\\nabla^{\\E}} \\mathcal{O}(\\nabla^{\\mathrm{End}(\\E)}) = \\ran(\\nabla^{\\mathrm{End}(\\E)}), \\quad T_{\\nabla^{\\mathrm{End}(\\E)}} \\mathcal{S}_s = \\ker(\\nabla^{\\mathrm{End}(\\E)})^*.\\]\n\t\n\n\nWe will say that a connection $\\nabla_2^{\\E}$ is in the \\emph{Coulomb gauge} with respect to $\\nabla_1^{\\E}$ if $(\\nabla_1^{\\mathrm{End}(\\E)})^*(\\nabla_2^{\\E} - \\nabla_1^{\\E}) =0$. The following lemma shows that, near $\\nabla^{\\E}$, we may always put a pair of connections in the Coulomb gauge with respect to each other. It is a slight generalisation of the usual claim (see \\cite[Proposition 2.3.4]{Donaldson-Kronheimer-90}).\n\n\\begin{lemma}[Coulomb gauge]\n\\label{lemma:coulomb}\nLet $s > 1$. There exists $\\varepsilon = \\varepsilon(s, \\nabla^{\\E}) > 0$ and a neighbourhood $\\mathbbm{1}_{\\E} \\in \\mathcal{U} \\subset C_*^{s + 1}(M, \\mathrm{U}(\\E))\/\\mathbb{S}^1$ such that for any $A_i \\in C^{s}(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$ with $\\|A_i\\|_{C^s_*} < \\varepsilon$, after setting $\\nabla_i^{\\E} = \\nabla^{\\E} + A_i$ for $i = 1, 2$, there exists a unique $p_{A_1, A_2} \\in \\mathcal{U}$ such that $p_{A_1, A_2}^*\\nabla_2^{\\E} - \\nabla_1^{\\E} \\in \\ker (\\nabla_1^{\\mathrm{End}(\\E)})^*$. Furthermore, if $A_i$ are smooth, then $p_{A_1, A_2}$ is smooth.\nMoreover, the map \n\\[\n\\big(C^{s}(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))\\big)^2 \\ni (A_1, A_2) \\mapsto \\phi(A_1, A_2) := p_{A_1, A_2}^*\\nabla_2^{\\E} \\in \\mathcal{S}_s(\\nabla_1^{\\E}),\n\\]\nis smooth. Setting $\\phi(A) := \\phi(0, A)$, we have:\n \\[\n \\dd \\phi|_{A = 0} = \\pi_{\\ker (\\nabla^{\\mathrm{End}(\\E)})^*}.\n \\]\n\\end{lemma}\n\\begin{proof}\nNote that the exponential map $\\exp: C^{s + 1}_*(M, \\mathrm{End}_{\\mathrm{sk}}(\\E)) \\cap \\{i \\mathbbm{1}_{\\E}\\}^{\\perp_{L^2}} \\to C^{s + 1}_*(M, \\mathrm{U}(\\E))\/\\mathbb{S}^1$ is well-defined and a local diffeomorphism at zero, so we reduce the claim to finding a neighbourhood $0 \\in \\mathcal{V} \\subset C^{s + 1}_*(M, \\mathrm{End}_{\\mathrm{sk}}(\\E)) \\cap \\{i\\mathbbm{1}_{\\E}\\}^{\\perp_{L^2}}$ and setting $p = p_{A_1, A_2} = \\exp(\\chi_{A_1, A_2})$ for $\\chi = \\chi_{A_1, A_2} \\in \\mathcal{V}$, that is $\\mathcal{U} = \\exp(\\mathcal{V})$. Define the functional\n\t\\begin{align*}\n\t\t&F: \\big(C^s_*(M, T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))\\big)^2 \\times C^{s + 1}_*(M, \\mathrm{End}_{\\mathrm{sk}}(\\E)) \\cap \\{i\\mathbbm{1}_{\\E}\\}^{\\perp_{L^2}}\\to C^{s - 1}_*(M, \\mathrm{End}_{\\mathrm{sk}}(\\E))\\\\\n\t\t&\\cap \\{i\\mathbbm{1}_{\\E}\\}^{\\perp_{L^2}}, F(A_1, A_2, \\chi) :=\n\t\t(\\nabla_1^{\\mathrm{End}(\\E)})^*\\Big(\\exp(-\\chi) \\nabla^{\\mathrm{End}(\\E)} \\exp(\\chi) + \\exp(-\\chi) A_2 \\exp(\\chi) - A_1\\Big).\n\t\\end{align*}\n\tWe have $F$ well-defined, i.e. with values in skew-Hermitian endomorphisms, since $\\nabla^{\\E}$ is unitary, and integrating by parts $\\langle{F(A_1, A_2, \\chi), \\mathbbm{1}_{\\E}}\\rangle_{L^2} = 0$; note that $F$ is smooth in its entries. Next, we compute the partial derivative with respect to the $\\chi$ variable at $A_1 = A_2 = 0$ and $\\chi = 0$:\n\t\\[\\dd_{\\chi}F(0, 0, 0) (\\Gamma) = \\partial_t|_{t = 0} F(0, 0, t\\Gamma) = (\\nabla^{\\mathrm{End}(\\E)})^* \\nabla^{\\mathrm{End}(\\E)} \\Gamma.\\]\n\tThis derivative is an isomorphism on \n\t\\[(\\nabla^{\\mathrm{End}(\\E)})^* \\nabla^{\\mathrm{End}(\\E)}: C^{s + 1}_*(M, \\mathrm{End}_{\\mathrm{sk}}(\\E)) \\cap \\{i \\mathbbm{1}_{\\E}\\}^{\\perp_{L^2}} \\to C^{s - 1}_*(M, \\mathrm{End}_{\\mathrm{sk}}(\\E)) \\cap \\{i\\mathbbm{1}_{\\E}\\}^{\\perp_{L^2}},\\]\n\tby the Fredholm property of $(\\nabla^{\\mathrm{End}(\\E)})^* \\nabla^{\\mathrm{End}(\\E)}$ and since $\\ker (\\nabla^{\\mathrm{End}(\\E)}) = \\mathbb{C} \\cdot \\mathbbm{1}_{\\E}$ by assumption. The first claim then follows by an application of the implicit function theorem for Banach spaces. \n\t\n\tThe fact that $p$ is smooth if $(A_1, A_2)$ is, is a consequence of elliptic regularity and the fact that $C_*^s$ is an algebra, along with the Coulomb property:\n\t\\[(\\nabla_1^{\\mathrm{End}(\\E)})^* \\nabla_1^{\\mathrm{End}(\\E)} p = (\\nabla_1^{\\mathrm{End}(\\E)}p) p^{-1} \\bullet \\nabla_1^{\\mathrm{End}(\\E)} p + p(\\nabla_1^{\\mathrm{End}(\\E)})^* \\big(p^{-1} (A_1 - A_2) p\\big) \\in C_*^{s}\\]\n\timplies $p \\in C_*^{s+2}$. Bootstrapping we obtain $p_{A_1, A_2} \\in C^\\infty$. Here $\\bullet$ denotes the operation of taking the inner product on the differential form side and multiplication on the endomorphism side.\n\t\n\tEventually, we compute the derivative of $\\phi(A)$. Write $p_A := p_{0, A}$ and $\\chi_A := \\chi_{0, A}$, where $\\chi_A$ is orthogonal to $i \\mathbbm{1}_{\\E}$ with respect to the $L^2$-scalar product, so that by definition\n\t\\begin{equation}\n\t\\label{equation:phi}\n\t\\phi(A) = \\nabla^{\\E} + p_A^{-1} \\nabla^{\\mathrm{End}(\\E)} p_A + p_A^{-1} A p_A.\n\t\\end{equation}\n\tBy differentiating the relation $F(A, \\chi_A) := F(0, A,\\chi_A)=0$ at $A=0$, we obtain for every $\\Gamma \\in C^s_*(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$:\n\t\\begin{align*}\n\t0 &= \\dd_A F|_{A=0,\\chi=0}(\\Gamma) + \\dd_\\chi F|_{A=0,\\chi=0}(\\dd \\chi_A|_{A=0}(\\Gamma))\\\\\n\t&= (\\nabla^{\\mathrm{End}(\\E)})^*\\Gamma + (\\nabla^{\\mathrm{End}(\\E)})^*\\nabla^{\\mathrm{End}(\\E)} \\dd \\chi_A|_{A=0}(\\Gamma),\n\t\\end{align*}\n\tthat is $\\dd \\chi_A|_{A=0}(\\Gamma) = - [(\\nabla^{\\mathrm{End}(\\E)})^*\\nabla^{\\mathrm{End}(\\E)}]^{-1} (\\nabla^{\\mathrm{End}(\\E)})^*\\Gamma$. \t\n\t Observe that $\\dd p_A|_{A=0} = \\dd \\chi_A|_{A=0}$ via the exponential map and by \\eqref{equation:phi}, we obtain:\n\t\\[\n\t\\dd \\phi|_{A=0}(\\Gamma) = \\nabla^{\\mathrm{End}(\\E)} \\dd \\chi_A|_{A=0}(\\Gamma) + \\Gamma = \\Gamma - \\nabla^{\\mathrm{End}(\\E)}[(\\nabla^{\\mathrm{End}(\\E)})^*\\nabla^{\\mathrm{End}(\\E)}]^{-1} (\\nabla^{\\mathrm{End}(\\E)})^*\\Gamma.\n\t\\]\n\tWe then conclude by \\eqref{equation:projection}\n\\end{proof}\n\nIn particular, the proof also gives that the map\n\\[\nC^{s}_*(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E)) \\ni A \\mapsto \\phi(A) \\in \\mathcal{S}_s := \\mathcal{S}_{s}(\\nabla^{\\E}),\n\\]\nis constant along orbits of gauge-equivalent connections (by construction)\n\n\n\n\n\\subsection{Resonances at $z=0$: finer remarks}\n\\label{ssection:finer}\n\n\n\\begin{lemma}\\label{lemma:symmetricspectrum}\nThe Pollicott-Ruelle resonance spectrum of $\\mathbf{X}$ is symmetric with respect to the real axis.\n\\end{lemma}\n\n\\begin{proof}\nIf $z_0$ is a resonance associated to $-\\mathbf{X}$ i.e. a pole of $z \\mapsto \\RR_+(z)$ then $\\overline{z}_0$ is a resonance associated to $+\\mathbf{X}$ i.e. a pole of $z \\mapsto \\RR_-(z)$ by \\eqref{equation:adjoint}. Let $u \\in \\mathcal{H}_+^s$ be a non-zero resonant state such that $-\\mathbf{X} u = z_0 u$, for some $s > 0$. Let $R : (x,v) \\mapsto (x,-v)$ be the antipodal map. By inspecting the construction of the anisotropic Sobolev space in \\cite{Faure-Sjostrand-11}, we see that we may assume $R^* : \\mathcal{H}_\\pm^s \\rightarrow \\mathcal{H}_\\mp^s$ is an isomorphism.\\footnote{Simply replace the degree function $m$ in the construction by $\\frac{m - R^*m}{2}$, which then implies that $R^*m = -m$.}\nThen $-R^* \\mathbf{X} u = \\mathbf{X} R^* u = z_0 R^* u$ and $R^*u \\in \\mathcal{H}_-^s$. Thus $R^*u$ is a resonant state associated to the resonance $z_0$. So both $z_0$ and $\\overline{z}_0$ are resonances for $+\\mathbf{X}$ and the same holds for $-\\mathbf{X}$.\n\\end{proof}\n\nConsider a contour $\\gamma \\subset \\mathbb{C}$ such that $-\\mathbf{X}$ has no resonances other than zero inside or on $\\gamma$. By continuity of resonances (see \\cite{Bonthonneau-19}), there is an $\\varepsilon > 0$ such that for all skew-Hermitian $1$-forms $A$ with $\\|A\\|_{C_*^s} < \\varepsilon$ the operator $-\\mathbf{X}_A := (\\pi^*(\\nabla^{\\E} + A))_X$ has no resonances on $\\gamma$. Here we need to take $s$ large enough (depending on the dimension), so that the framework of microlocal analysis applies.\n\nIn the specific case where $\\dim \\ker(\\mathbf{X}|_{\\mathcal{H}_+}) = 1$, we denote by $\\lambda_A$ the unique resonance of $-\\mathbf{X}_A$ within $\\gamma$. Note that the map $A \\mapsto \\lambda_A$ is $C^3$-regular for $\\varepsilon > 0$ small enough.\n\n\n\\begin{lemma}\n\\label{lemma:diff-2}\nAssume that $\\dim \\ker(\\mathbf{X}|_{\\mathcal{H}_+}) = 1$. Then $\\lambda_A \\in \\mathbb{R}$ and for $\\Gamma \\in C^\\infty(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$:\n\\[\n\\dd \\lambda_A|_{A=0} = 0, ~~ \\dd^2 \\lambda_A|_{A=0}(\\Gamma,\\Gamma) = - \\langle \\Pi \\pi_1^*\\Gamma u_0, \\pi_1^*\\Gamma u_0 \\rangle_{L^2},\n\\]\nwhere $u_0$ is a resonant state associated to $A=0$ and $\\|u_0\\|_{L^2}=1$.\n\\end{lemma}\n\n\\begin{proof}\nBy the symmetry property of Lemma \\ref{lemma:symmetricspectrum} and continuity of resonances we know $\\lambda_A \\in \\mathbb{R}$. Also, observe that $u_0$ is either pure odd or pure even with respect to $v$ (i.e. $R^*u_0 = u_0$ or $R^*u_0 = -u_0$) because $R^*$ keeps $\\ker (\\mathbf{X})$ fixed and $\\ker (\\mathbf{X})$ is assumed to be one dimensional.\n\nFor the second claim, it is sufficient to start with the equality $-\\mathbf{X}_{\\tau \\Gamma} u_{\\tau \\Gamma} = \\lambda_{\\tau \\Gamma} u_{\\tau \\Gamma}$, where $\\Gamma \\in C^\\infty(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$, $\\tau \\in (-\\delta, \\delta)$ is small enough so that $\\tau \\mapsto \\lambda_{\\tau \\Gamma}$ and $\\tau \\mapsto u_{\\tau \\Gamma} \\in \\mathcal{H}_+$ are $C^3$, and to compute the derivatives at $\\tau=0$. Observe that $\\dot{\\mathbf{X}}_0 = \\pi_1^*\\Gamma,\\, \\ddot{\\mathbf{X}}_0 = 0$. We obtain: $-\\dot{\\mathbf{X}}_0 u_0 -\\mathbf{X}_0 \\dot{u}_0 = \\dot{\\lambda}_0 u_0$ and taking the $L^2$ scalar product with $u_0$, we find $\\dot{\\lambda}_0 = 0$, using that $u_0$ is either pure odd or pure even. Thus $\\dot{u}_0- \\Pi_0^+ \\dot{u}_0 = -\\mathbf{R}_0^+ \\pi_1^*\\Gamma u_0$. Then, taking the second derivative at $\\tau = 0$, we get: $-2 \\pi_1^*\\Gamma \\dot{u}_0 - \\mathbf{X}_0 \\ddot{u}_0 = \\ddot{\\lambda}_0 u_0$ and taking once again the scalar product with $u_0$, we find $\\ddot{\\lambda}_0 = - 2 \\langle \\mathbf{R}_0^+ \\pi_1^*\\Gamma u_0, \\pi_1^*\\Gamma u_0 \\rangle_{L^2}$. It is then sufficient to observe that by symmetry (using $(\\mathbf{R}_0^+)^* = \\mathbf{R}_0^-$ and $\\ddot{\\lambda}_0 \\in \\mathbb{R}$)\n\\[\n\\langle \\mathbf{R}_0^+ \\pi_1^*\\Gamma u_0, \\pi_1^*\\Gamma u_0 \\rangle_{L^2} = \\langle \\mathbf{R}_0^- \\pi_1^*\\Gamma u_0, \\pi_1^*\\Gamma u_0 \\rangle_{L^2}.\n\\]\nThis proves the result.\n\\end{proof}\n\n\n\\subsection{P-R resonance at $0$ of the mixed connection: opaque case}\n\n\\label{ssection:pollicott-ruelle-dea}\n\nWe now further assume that $\\mathbf{X} := (\\pi^* \\nabla^{\\mathrm{End}(\\E)})_X$ has the resonant space at $0$ spanned by $\\mathbbm{1}_{\\E}$. This condition is known as the \\emph{opacity} of the connection $\\pi^*\\nabla^{\\E}$. When $(M,g)$ is Anosov, this is known to be a generic condition, see \\cite[Theorem 1.6]{Cekic-Lefeuvre-20}.\n\n\nAs in \\S\\ref{sssection:connection-induced}, we assume that $s \\gg 1$ (so that standard microlocal analysis applies) and we introduce the mixed connection induced by $\\nabla_1^{\\E} = \\nabla^{\\E} + A_1$ and $\\nabla_2^{\\E} = \\nabla^{\\E} + A_2$, namely\n\\[\n\\nabla^{\\Hom(\\nabla_1^{\\E}, \\nabla_2^{\\E})} u = \\nabla^{\\mathrm{End}(\\E)} u + A_2u - u A_1,\n\\]\nand we set $\\mathbf{X}_{A_1, A_2} := (\\pi^* \\nabla^{\\Hom(\\nabla_1^{\\E}, \\nabla_2^{\\E})})_X$ and $z \\mapsto \\RR_{\\pm}(z, A_i)$ for the resolvents. The operator $\\mathbf{X} := \\mathbf{X}_{0, 0}$ has the resonant space at $z=0$ spanned by $\\mathbbm{1}_{\\E}$ \nand we denote by $\\lambda_{A_1, A_2}$ the unique resonance in $\\left\\{\\Re(z) \\leq 0\\right\\}$ close to $0$, namely the unique pole of $\\RR_{\\pm}(z,A_i)$ close to $0$. For $\\|A_1\\|_{C_*^s}, \\|A_2\\|_{C_*^s}$ small enough we know that the map $(A_1, A_2) \\mapsto \\lambda_{A_1, A_2}$ is $C^3$ (see \\S \\ref{ssection:finer}) and by Lemma \\ref{lemma:diff-2} that $\\lambda_{A_1, A_2} \\in \\mathbb{R}$. In fact $\\lambda_{A_1, A_2}$ descends to the moduli space: if $p_i^*(\\nabla^{\\E} + A_i') = \\nabla^{\\E} + A_i$ for $\\|A_i'\\|_{C_*^s}$ small enough, then using \\eqref{equation:lien} we get\n\\begin{equation}\\label{eq:mixedunitaryequiv}\n\t\\mathbf{X}_{A_1', A_2'}u = (p_2)^{-1} \\cdot \\mathbf{X}_{A_1, A_2}(p_2 u (p_1)^{-1}) \\cdot p_1, \\quad u \\in \\mathcal{H}_+.\n\\end{equation}\nHere we used that $\\mathcal{H}_+$ is stable under multiplication by $C_*^s$ for $s$ large enough; hence $\\mathbf{X}_{A_1, A_2}$ and $\\mathbf{X}_{A_1', A_2'}$ have equal P-R spectra and so $\\lambda_{A_1, A_2} = \\lambda_{A_1', A_2'}$.\n\n\n\nNext, we need a uniform estimate for the $\\Pi_1^{\\mathrm{End}(\\E)}$ operator in a neighbourhood of $\\nabla^{\\E}$:\n\n\\begin{lemma}\\label{lemma:Pi_1uniform}\n\tAssume $\\Pi_1^{\\mathrm{End}(\\E)}$ is $s$-injective. There are constants $\\varepsilon, C > 0$ depending only on $\\nabla^{\\E}$ such that for all skew-Hermitian $1$-forms with $\\|A\\|_{C_*^s} < \\varepsilon$:\n\t\\[\n\\forall f \\in H^{-1\/2}(M, T^*M \\otimes \\mathrm{End}(\\mathcal{E})), ~~~ \\langle \\Pi_1^{\\mathrm{End}(\\nabla^{\\E} + A)} f, f \\rangle_{L^2} \\geq C \\|\\pi_{\\ker (\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A})^*}f\\|^2_{H^{-1\/2}}.\n\\]\n\\end{lemma}\n\\begin{proof}\n\n\tObserve firstly that the left hand side of the inequality vanishes on potential tensors by \\eqref{eq:Pi_mproperties} and hence it suffices to consider $f \\in \\ker (\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A)})^*$. Then we obtain:\n\t\\begin{align*}\n\t\t\t\\langle \\Pi_1^{\\mathrm{End}(\\nabla^{\\E} + A)} &f, f \\rangle_{L^2} = \\langle \\Pi_1^{\\mathrm{End}(\\nabla^{\\E})} f, f \\rangle_{L^2} + \\langle (\\Pi_1^{\\mathrm{End}(\\nabla^{\\E} + A)} - \\Pi_1^{\\mathrm{End}(\\nabla^{\\E})}) f, f \\rangle_{L^2}\\\\\n\t\t\t&\\geq C_0 \\|\\pi_{\\ker(\\nabla^{\\mathrm{End}(\\E)})^*}f\\|_{H^{-1\/2}}^2 - \\|\\Pi_1^{\\mathrm{End}(\\nabla^{\\E} + A)} - \\Pi_1^{\\mathrm{End}(\\nabla^{\\E})}\\|_{H^{-1\/2} \\to H^{1\/2}} \\|f\\|_{H^{-1\/2}}^2\\\\\n\t\t\t&\\geq \\frac{1}{4} C_0 \\|f\\|_{H^{-1\/2}}^2 - \\frac{1}{2} C_0 \\|\\pi_{\\ker(\\nabla^{\\mathrm{End}(\\nabla^{\\E})})^*} - \\pi_{\\ker (\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A})^*}\\|_{H^{-1\/2} \\to H^{-1\/2}} \\|f\\|_{H^{-1\/2}}^2\\\\\n\t\t\t&\\geq \\frac{1}{8}C_0 \\|f\\|_{H^{-1\/2}}^2.\n\t\\end{align*}\n\tIn the second line, we used Lemma \\ref{lemma:generalized-xray} (item 3) with a constant $C_0$. In the next line we used that the map $A \\mapsto \\Pi_1^{\\mathrm{End}(\\nabla^{\\E} + A)} \\in \\Psi^{-1}$ is continuous; the proof of this fact is analogous to the proof of \\cite[Proposition 4.1]{Guillarmou-Knieper-Lefeuvre-19} and we omit it. Thus for $\\varepsilon > 0$ small enough $\\|\\Pi_1^{\\mathrm{End}(\\nabla^{\\E} + A)} - \\Pi_1^{\\mathrm{End}(\\nabla^{\\E})}\\|_{H^{-1\/2} \\to H^{1\/2}} \\leq C_0\/4$. Similarly in the last line we used that $A \\mapsto \\pi_{\\ker(\\nabla^{\\mathrm{End}(\\E) + A})^*} \\in \\Psi^0$ is continuous which follows from standard microlocal analysis from \\eqref{equation:projection}, so again we choose $\\varepsilon > 0$ small enough so that $\\|\\pi_{\\ker(\\nabla^{\\mathrm{End}(\\nabla^{\\E})})^*} - \\pi_{\\ker (\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A)})^*}\\|_{H^{-1\/2} \\to H^{-1\/2}} \\leq C_0\/4$. The claim follows by setting $C = C_0\/8$.\n\\end{proof}\n\nRecall that $\\lambda_{A_1, A_2} \\leq 0$ in the following:\n\n\\begin{lemma}\n\\label{lemma:borne-lambda-a-1}\nAssume that the generalized X-ray transform $\\Pi^{\\mathrm{End}(\\E)}_1$ defined with respect to the connection $\\nabla^{\\mathrm{End}(\\E)}$ is $s$-injective. For $s \\gg 1$ large enough, there exist constants $\\varepsilon, C > 0$ such that for all $A_i \\in C^s(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$ with $\\|A_i\\|_{C^s_*} < \\varepsilon$ for $i = 1, 2$, we have:\n\\[\n0 \\leq \\|\\phi(A_1, A_2) - \\nabla_1^{\\E}\\|^2_{H^{-1\/2}(M,T^*M\\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))} \\leq C |\\lambda_{A_1, A_2}|.\n\\]\n\\end{lemma}\n\n\n\n\n\\begin{proof}\nWe introduce two functionals in the vicinity of $\\nabla^{\\E}$, for small enough $\\varepsilon > 0$: \n\\begin{align*}\n\tF_1, F_2 &: \\big(C^{s_0}_*(M, \\mathrm{End}_{\\mathrm{sk}}(\\E)) \\cap \\{\\|A\\|_{C_*^{s_0}} < \\varepsilon\\}\\big)^2 \\to \\mathbb{R},\\\\\n\t F_1(A_1, A_2) &:=\\lambda_{A_1, A_2}, \\quad F_2(A_1, A_2) := -\\|\\phi(A_1, A_2)-\\nabla_1^{\\E}\\|^2_{H^{-1\/2}}.\n\\end{align*}\nThey are well-defined and restrict as $C^{3}$-regular maps on $\\mathcal{S}_{s_0}$ for some $s_0 \\gg 1$ large enough by Lemma \\ref{lemma:coulomb} and the discussion above. Moreover, using Lemma \\ref{lemma:diff-2}, we have for all $A$:\n\\[F_1(A, A) = F_2(A, A) = 0 \\quad \\mathrm{and}\\quad \\dd F_1|_{(A, A)} = \\dd F_2|_{(A, A)} = 0.\\] \nWe will compare the second partial derivatives in the variable $A_2$ at a point $(A, A)$. Given $\\Gamma \\in T_{\\nabla^{\\E}} \\mathcal{S}_{s_0} \\simeq \\ker (\\nabla^{\\mathrm{End}(\\E)})^*$, we have by Lemma \\ref{lemma:coulomb}:\n\\begin{equation}\\label{eq:F_2}\n\\dd^2_{A_2} F_2|_{(A, A)}(\\Gamma,\\Gamma) = -2\\|\\pi_{\\ker (\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A)})^*} \\Gamma\\|^2_{H^{-1\/2}}\n\\end{equation}\nBy Lemma \\ref{lemma:diff-2}, we have\n\\[\n\\dd^2_{A_2} F_1|_{(A, A)}(\\Gamma,\\Gamma) = -c^2 \\langle \\Pi^{\\mathrm{End}(\\nabla^{\\E} + A)} \\pi_1^* \\Gamma \\mathbbm{1}_{\\E}, \\pi_1^* \\Gamma \\mathbbm{1}_{\\E} \\rangle_{L^2} = - c^2 \\langle \\Pi^{\\mathrm{End}(\\nabla^{\\E} + A)}_1 \\Gamma, \\Gamma \\rangle_{L^2},\n\\]\nfor some constant $c > 0$, where $\\Pi^{\\mathrm{End}(\\nabla^{\\E} +A)}$ denotes the $\\Pi$ operator with respect to the endomorphism connection induced by $\\nabla^{\\E} + A$. We used here that the orthogonal projection to the resonant space $\\mathbb{C} \\mathbbm{1}_{\\E}$ of $\\mathbf{X}_{A, A}$ at zero vanishes, because $\\langle{\\pi_1^*\\Gamma, \\mathbbm{1}_{\\E}}\\rangle_{L^2} = 0$ as $\\pi_1^*\\Gamma$ is odd. For $\\varepsilon > 0$ small enough, by Lemma \\ref{lemma:Pi_1uniform} we know $\\Pi^{\\mathrm{End}(\\nabla^{\\E} + A)}_1$ is $s$-injective and there is a constant $C' = C'(\\nabla^{\\E}) > 0$ such that:\n\\begin{equation}\\label{eq:F_1}\n\\dd^2_{A_2} F_1|_{(A, A)}(\\Gamma,\\Gamma) \\leq - C' \\|\\pi_{\\ker(\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A)})^*}\\Gamma\\|^2_{H^{-1\/2}} = C'\/2 \\dd^2_{A_2} F_2|_{(A, A)}(\\Gamma,\\Gamma).\n\\end{equation}\nAs a consequence, writing $G(A_2) := F_1(A, A_2) - C'\/4 \\times F_2(A, A_2)$, we have $G(A)=0, \\dd G|_{A_2 = A} = 0$ and by \\eqref{eq:F_1}, \\eqref{eq:F_2}\n\\[\\dd^2 G|_{A_2 = A}(\\Gamma,\\Gamma) \\leq C'\/4 \\dd^2_{A_2} F_2|_{(A, A)}(\\Gamma,\\Gamma) = -C'\/2 \\|\\pi_{\\ker(\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A)})^*} \\Gamma\\|^2_{H^{-1\/2}}.\\]\nIf we now Taylor expand the $C^3$-map $\\mathcal{S}_{s_0} \\ni A_2 \\mapsto G(A_2)$ at $A_2 = A$, we obtain:\n\\begin{align*}\nG(A + \\Gamma) &= \\dfrac{1}{2} \\dd^2 G|_{A_2=A}(\\Gamma, \\Gamma) + \\mathcal{O}(\\|\\Gamma\\|_{C_*^{s_0}}^3)\\\\\n &\\leq -C'\/4 \\|\\Gamma\\|_{H^{-1\/2}}^2 + C'\/4 \\|(\\pi_{\\ker(\\nabla^{\\mathrm{End}(\\E)})^*} - \\pi_{\\ker(\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A)})^*})\\Gamma\\|_{H^{-1\/2}}^2 + C''\\|\\Gamma\\|^3_{C_*^{s_0}}\\\\\n &\\leq -C'\/8\\|\\Gamma\\|_{H^{-1\/2}}^2 + C''\\|\\Gamma\\|_{C_*^{s_0}}^3.\n\\end{align*}\nIn the second line we introduced a uniform constant $C'' = C''(\\nabla^{\\E}) > 0$ using the $C^3$-regular property and $\\pi_{\\ker(\\nabla^{\\mathrm{End}(\\E)})^*} \\Gamma = \\Gamma$. For the last line, we observed that $A \\mapsto \\pi_{\\ker(\\nabla^{\\mathrm{End}(\\nabla^{\\E} + A)})^*} \\in \\Psi^0$ is a continuous map by equation \\eqref{equation:projection} and hence by standard microlocal analysis the $H^{-1\/2} \\to H^{-1\/2}$ estimate is arbitrarily small for $\\varepsilon$ small enough. This estimate holds for all $\\|A\\|_{C_*^{s_0}}, \\|\\Gamma\\|_{C_*^{s_0}} < \\varepsilon\/2$.\n\nChoosing $s \\gg s_0$ and assuming that $A \\in C_*^s(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$ with $\\|A\\|_{C^s_*} < \\varepsilon$, there is a $C'''(\\nabla^{\\E}) > 0$, such that for $\\varepsilon > 0$ with $C''' \\varepsilon \\leq C'\/16$, we then obtain by interpolation:\n\\[\nG(A + \\Gamma) \\leq -C'\/8 \\|\\Gamma\\|^2_{H^{-1\/2}} + \\underbrace{C''' \\|\\Gamma\\|_{C^s_*}}_{\\leq C'\/16} \\|\\Gamma\\|_{H^{-1\/2}}^2 \\leq - C'\/16 \\|\\Gamma\\|_{H^{-1\/2}}^2 \\leq 0.\n\\]\nAfter taking $\\varepsilon > 0$ small enough, the statement holds with $C=C'\/2$.\n\\end{proof}\n\n\n\n\n\n\n\n\\begin{remark}\n\\rm\nIt was proved in \\cite{Guillarmou-Knieper-Lefeuvre-19} that there exists a metric $G$ on the moduli space of isometry classes (of metrics with negative sectional curvature) which generalizes the usual Weil-Petersson metric on Teichm\\\"uller space in the sense that, in the case of a surface, the restriction of $G$ to Teichm\\\"uller space is equal to the Weil-Petersson metric. We point out that the operator $\\Pi_1$ also allows to define a metric $G$ at a generic point $\\mathfrak{a}_0 \\in \\mathbb{A}_{\\E}$, similarly to \\cite{Guillarmou-Knieper-Lefeuvre-19}. Indeed, if $\\mathfrak{a}_0 \\in \\mathbb{A}_{\\E}$, taking a representative $\\nabla^{\\E} \\in \\mathfrak{a}_0$, one has $T_{\\mathfrak{a}_0}\\mathbb{A}_{\\E} \\simeq \\ker (\\nabla^{\\mathrm{End}})^*$ and thus, given $\\Gamma \\in \\ker (\\nabla^{\\mathrm{End}})^*$, one can consider:\n\\[\nG_{\\mathfrak{a}_0}(\\Gamma,\\Gamma) := \\langle \\Pi_1 \\Gamma, \\Gamma \\rangle_{L^2(M,T^*M\\otimes\\mathrm{End}(\\E))} \\geq c \\|\\Gamma\\|^2_{H^{-1\/2}},\n\\]\nfor some constant $c>0$. Lemma \\ref{lemma:Pi_1uniform} shows that the constant $c$ is locally uniform with respect to $\\mathfrak{a}_0$.\n\\end{remark}\n\n\n\\subsection{P-R resonance at $0$ of the mixed connection: non-opaque case}\n\\label{ssection:non-opaque}\n\n\nThe aim of this paragraph is to deal with neighbourhoods of connections that are not necessarily opaque, and only assume $\\Pi_1^{\\mathrm{End}(\\E)}$ is injective. In other words, we do not want to assume the resonant space of $-(\\pi^*\\nabla^{\\mathrm{End}(\\E)})_X$ at zero is spanned by $\\mathbbm{1}_{\\E}$ necessarily.\n\nNext, as in \\S \\ref{ssection:pollicott-ruelle-dea}, we introduce the mixed connection with respect to $\\nabla^{\\E} + A$ and $\\nabla^{\\E}$, denoted by $\\nabla^{\\Hom(\\nabla^{\\E} + A, \\nabla^{\\E})}$, and set $\\mathbf{X}_A :=(\\pi^*\\nabla^{\\Hom(\\nabla^{\\E} + A, \\nabla^{\\E})})_X$. We assume $s \\gg 1$. As before, consider a contour $\\gamma \\subset \\mathbb{C}$ around zero such that $\\mathbf{X} := \\mathbf{X}_0$ has only the resonance zero within $\\gamma$ and $\\varepsilon > 0$ such that $-\\mathbf{X}_A$ has no resonances on $\\gamma$ for all $\\|A\\|_{C_*^s} < \\varepsilon$. We introduce\n\\[\\Pi_A^+ := \\frac{1}{2\\pi i} \\int_\\gamma (z + \\mathbf{X}_A)^{-1} dz, \\quad \\lambda_A := \\Tr(-\\mathbf{X}_A\\Pi_A^+).\\]\nThis generalises the quantity studied in \\S \\ref{ssection:finer}, where it was assumed that the multiplicity of $\\mathbf{X}$ at zero is equal to one. By \\cite{Bonthonneau-19} it follows that $A \\mapsto \\Pi_A^+$ is $C^3$-regular and hence $A \\mapsto \\lambda_A$ is also $C^3$-regular. As in \\eqref{eq:mixedunitaryequiv}, we observe that the operators $-\\mathbf{X}_A$ and $-\\mathbf{X}_{A'}$ are unitarily equivalent on $\\mathcal{H}_+$ whenever $\\nabla^{\\E} + A$ and $\\nabla^{\\E} + A'$ are gauge equivalent; hence $\\lambda_A = \\lambda_{A'}$ and so $\\lambda_A$ descends to the local moduli space\n\n\n\nNote also that $\\re(\\lambda_A) \\leq 0$, since all resonances of $-\\mathbf{X}_A$ lie in the half-plane $\\{\\re z \\leq 0\\}$ and this gives us hope that $\\re(\\lambda_A)$ controls the distance between the connections. Assume that the resonant space of $-\\mathbf{X}$ at zero is spanned by smooth $L^2$-orthonormal resonant states $\\{u_i\\}_{i = 1}^p$. We have the following generalisation of Lemma \\ref{lemma:diff-2}:\n\n\\begin{lemma}\n\\label{lemma:variations}\nFor $A \\in C^\\infty(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$ with $\\|A\\|_{C_*^s} < \\varepsilon$, we have $\\lambda_A \\in \\mathbb{R}$ and the following perturbation formulas hold:\n\\[\n\\dd \\lambda_A|_{A=0} = 0, ~~ \\dd^2 \\lambda_A|_{A=0}(\\Gamma,\\Gamma) = - \\sum_{i = 1}^p \\langle \\Pi u_i \\pi_1^*\\Gamma, u_i \\pi_1^*\\Gamma\\rangle_{L^2}.\n\\]\n\\end{lemma}\n\\begin{proof}\n\tThe fact that $\\lambda_A$ is real follows from the symmetry of the spectrum of $-\\mathbf{X}_A$ shown in Lemma \\ref{lemma:symmetricspectrum}. The first derivative formula is obvious as $\\lambda_A \\leq 0$; the second one follows from minor adaptations of \\cite[Lemma 5.9]{Cekic-Lefeuvre-20}, where the analogous case of endomorphisms was considered.\n\\end{proof}\n\nNext, by a straightforward adaptation of the Lemma \\ref{lemma:coulomb}, we obtain the existence of $\\varepsilon > 0$, such that for all $A$ with $\\|A\\|_{C_*^s} < \\varepsilon$, there is a smooth map $A \\mapsto \\phi(A) \\in \\mathcal{S}_s$ that sends $\\nabla^{\\E} + A$ to Coulomb gauge with respect to $\\nabla^{\\E}$, that is it satisfies $\\phi(A) - \\nabla^{\\E} \\in \\ker (\\nabla^{\\mathrm{End}(\\E)})^*$.\n\\begin{remark}\\rm\n\tWe cannot get the analogous statement to Lemma \\ref{lemma:coulomb} for parameters $(A_1, A_2)$, because the range of $F(A_1, A_2, \\chi)$ equals $\\ker(\\nabla_1^{\\mathrm{End}(\\E)})^\\perp$ and this is not uniform in $A_1$ (i.e. $\\ker \\nabla_1^{\\mathrm{End}(\\E)}$ changes as we move $A_1$); the space $\\mathbb{A}_{\\E}$ is not a smooth manifold at reducible connections.\n\\end{remark}\n\n\n\n\n\n\nIn the following lemma, we will need to assume that $\\pi_{1*} \\Pi_0^+ = 0$. Equivalently, this means that the resonant states $u_i \\in \\ker(\\mathbf{X}|_{\\mathcal{H}_+})$ satisfy $\\pi_{1*} u_i = 0$, for $i = 1, \\dotso, p$, i.e. the degree $1$ Fourier mode of all the $u_i$ vanishes.\n\n\\begin{lemma}\n\\label{lemma:maininequalitygeneral}\nAssume that the generalized X-ray transform $\\Pi^{\\mathrm{End}(\\E)}_1$ defined with respect to the connection $\\nabla^{\\mathrm{End}(\\E)}$ is $s$-injective and additionally that $\\pi_{1*} \\Pi_0^+ = 0$. For $s \\gg 1$ large enough, there exist constants $\\varepsilon, C > 0$ such that for all $A \\in C^s(M,T^*M \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$ with $\\|A\\|_{C^s_*} < \\varepsilon$:\n\\[\n0 \\leq \\|\\phi(A)-\\nabla^{\\E}\\|^2_{H^{-1\/2}(M,T^*M\\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))} \\leq C |\\lambda_A|.\n\\]\n\\end{lemma}\n\\begin{proof}\nThis is straightforward from the proof of Lemma \\ref{lemma:borne-lambda-a-1}. With the same functionals $F_1(A) = \\lambda_A$ and $F_2(A) = -\\|\\phi(A) - \\nabla^{\\E}\\|^2_{H^{-1\/2}}$, the only slight difference is the computation of $\\dd^2 F_1$. Pick an $L^2$-orthonormal basis $u_1, \\dotso, u_p$ of the resonant space of $-\\mathbf{X}$ at zero such that $u_1 = c\\mathbbm{1}_{\\E}$, where $c$ is a fixed constant. By Lemmas \\ref{lemma:variations} and \\ref{lemma:relations-resolvent}, we have\n\\[\n\\dd^2 F_1|_{A=0}(\\Gamma,\\Gamma) = -\\sum_{i = 1}^p \\langle \\Pi u_i \\pi_1^* \\Gamma, u_i \\pi_1^* \\Gamma \\rangle_{L^2} \\leq -c^2 \\langle \\Pi_1^{\\mathrm{End}(\\E)} \\Gamma, \\Gamma \\rangle_{L^2}.\n\\]\nNote importantly that we have used $\\Pi_0^+ \\pi_1^*\\Gamma = 0$. This follows from the expression for the projector $\\Pi_0^+ = \\sum_{i = 1}^p \\langle{\\bullet, u_i}\\rangle_{L^2} u_i$ and $\\pi_{1*}u_i = 0$ for all $i$. This suffices to run the proof in the same manner. \n\\end{proof}\n\n\n\n\n\\section{Injectivity of the primitive trace map}\n\nWe can now prove the main results stated in the introduction.\n\n\n\n\\label{section:proofs}\n\n\n\n\n\\subsection{The local injectivity result}\n\n\\label{ssection:proof-injectivity}\n\nWe now prove the injectivity result of Theorem \\ref{theorem:injectivity}.\n\n\\begin{proof}[Proof of Theorem \\ref{theorem:injectivity}]\nWe fix a regularity exponent $N \\gg 1$ large enough so that the results of \\S\\ref{section:geometry} apply. We fix $\\nabla^{\\mathcal{E}}$, a smooth unitary connection on $\\E$ and assume that it is \\emph{generic} (see page \\pageref{def:generic}). \nBy mere continuity, the same properties hold for every connection $\\nabla^{\\E} + A$ such that $\\|A\\|_{C^N_*} < \\varepsilon$, where $\\varepsilon > 0$ is small enough depending on $\\nabla^{\\E}$.\n\nConsider two smooth unitary connections $\\nabla_i^{\\E} = \\nabla^{\\E} + A_i$ such that $\\|A_i\\|_{C^N_*} < \\varepsilon$ for $i = 1, 2$. Assume that $\\mathcal{T}^\\sharp(\\nabla_1^{\\E})=\\mathcal{T}^\\sharp(\\nabla_2^{\\E})$. The exact Liv\\v{s}ic cocycle Theorem \\ref{theorem:weak} yields the existence of a smooth map $p \\in C^\\infty(SM,\\mathrm{U}(\\E))$ such that:\n\\[\n\\pi^* \\nabla^{\\Hom(\\nabla_1, \\nabla_2)}_X u = 0\n\\]\nthat is $u$ is a resonant state for the operator $\\mathbf{X}_{A_1, A_2}$ associated to the eigenvalue $0$. Assumptions \\textbf{(A)} and \\textbf{(B)} allow us to apply Lemma \\ref{lemma:borne-lambda-a-1}. We therefore obtain:\n\\[\n\\lambda_{A_1, A_2} = 0 \\leq - C \\|\\phi(A_1, A_2) - \\nabla_1^{\\E}\\|^2_{H^{-1\/2}(M,T^*M \\otimes \\mathrm{End}(\\E))} \\leq 0,\n\\]\nwhere $C = C(\\nabla^{\\E}) > 0$ only depends on $\\nabla^{\\E}$. Hence $\\phi(A_1, A_2) = p_{A_1, A_2}^*\\nabla_2^{\\E} = \\nabla_1^{\\E}$. In other words, the connections are gauge-equivalent.\n\\end{proof}\t\n\nNext, we discuss a version of local injectivity in a neighbourhood of a connection which is non-opaque. We will say a map $f: X \\to Y$ of topological spaces is \\emph{weakly locally injective at $x_0 \\in X$} if there exists a neighbourhood $U \\ni x$ such that $f(x) = f(x_0)$ for $x \\in U$ implies $x = x_0$. This notion appears in non-linear inverse problems where the linearisation is not continuous, see \\cite[Section 2]{Stefanov-Uhlmann-08}. We have:\n\n\\begin{theorem}\\label{thm:weaklocal}\n\tIf $N \\gg 1$ and $[\\nabla^{\\E}] \\in \\mathbb{A}_{\\E}$ is such that the generalised $X$-ray transform $\\Pi_1^{\\mathrm{End}(\\E)}$ is $s$-injective, as well as $\\pi_{1*} \\ker(\\pi^*\\nabla^{\\mathrm{End}(\\E)}_X|_{C^\\infty}) = 0$, then the primitive trace map $\\mathcal{T}^\\sharp$ is weakly locally injective at $[\\nabla^{\\E}]$ in the $C^N$-quotient topology.\n\\end{theorem}\n\\begin{proof}\n\tThe proof is analogous to the proof of Theorem \\ref{theorem:injectivity}, by using the results of \\S \\ref{ssection:non-opaque}. We omit the details.\n\\end{proof}\n\nWe shall see below (see Lemma \\ref{lemma:Pi_1inj}) that flat connections have an injective generalised $X$-ray transform $\\Pi_1^{\\mathrm{End}(\\E)}$ and satisfy the additional condition that $\\ker (\\pi^*\\nabla^{\\E})_X|_{C^\\infty}$ consists of elements of degree zero (but might not be opaque). The previous Theorem therefore shows that the primitive trace map is weakly locally injective near such connections. Let us state this as a corollary for the trivial connection, as it partially answers an open question of Paternain \\cite[p33, Question (3)]{Paternain-13}.\n\n\n\n\\begin{corollary}\n\tLet $\\E = M \\times \\mathbb{C}^r$ be the trivial Hermitian vector bundle equipped with the trivial flat connection $d$. Then there exists a neighbourhood $\\mathcal{U} \\ni [d]$ in $\\mathbb{A}_{\\E}$ with $C^N$-quotient topology such that $[d]$ is the unique gauge class of transparent connections in $\\mathcal{U}$.\n\\end{corollary}\t\n\n\\subsection{Global injectivity results}\n\nWe now detail some cases in which Theorem \\ref{theorem:injectivity} can be upgraded.\n\n\n\\subsubsection{Line bundles}\n\n\\label{sssection:line}\n\nWe let $\\mathcal{T}^\\sharp_1$ be the restriction of the total primitive trace map \\eqref{equation:trace-total} to line bundles. The moduli space of all connections on line bundles $\\mathbb{A}_1$ carries a natural Abelian group structure using the tensor product.\nWhen restricted to line bundles, the primitive trace map $\\mathcal{T}^\\sharp_1$ takes value in $\\ell(\\mathcal{C}^\\sharp,\\mathrm{U}(1))$, namely the set of sequences indexed by primitive free homotopy classes. We have:\n\n\\begin{lemma}\nThe map $\\mathcal{T}^\\sharp_1 : \\mathbb{A}_1 \\rightarrow \\ell^\\infty(\\mathcal{C}^\\sharp,\\mathrm{U}(1))$ is a multiplicative group homomorphism.\n\\end{lemma}\n\\begin{proof}\n\tLeft as an exercise to the reader.\n\\end{proof}\n\n\n\\begin{remark}\n\\rm\nThere also exists a group homomorphism for higher rank vector bundles by taking the determinant instead of the trace. More precisely, writing $\\mathbb{A}_r$ for the set of all unitary connections on all possible Hermitian vector bundles of rank $r$ (up to isomorphisms), one can set\n\\begin{equation}\n\\label{equation:det}\n\\det{}^\\sharp : \\mathbb{A} \\rightarrow \\ell^\\infty(\\mathcal{C}^\\sharp,\\mathrm{U}(1)),\n\\end{equation}\nby taking the determinant of the holonomy along each closed primitive geodesic. This map is also a group homomorphism (where the group structure on $\\bigsqcup_{r \\geq 0} \\mathbb{A}_r$) is also obtained by tensor product. Nevertheless, the determinant map \\eqref{equation:det} cannot be injective as all trivial bundles (of different ranks) have same image.\n\\end{remark}\n\n\n\nWe have the following result, mainly due to Paternain \\cite{Paternain-09}:\n\n\n\\begin{prop}[Paternain]\n\\label{proposition:line}\nLet $(M,g)$ be a smooth Anosov $n$-manifold. If $n \\geq 3$, then the restriction of the primitive trace map to line bundles\n\\begin{equation}\n\\label{equation:trace-line}\n\\mathcal{T}_1^\\sharp : \\mathbb{A}_1 \\longrightarrow \\ell^\\infty(\\mathcal{C}^\\sharp),\n\\end{equation}\nis globally injective. Moreover, if $n = 2$ then:\n\\[\n\\ker \\mathcal{T}^\\sharp_1 = \\left\\{ ([\\kappa^{\\otimes n}], [{\\nabla^{\\mathrm{LC}}}^{\\otimes n}]), n \\in \\mathbb{Z}\\right\\},\n\\]\nwhere $\\kappa \\rightarrow M$ denotes the canonical line bundle and $\\nabla^{\\mathrm{LC}}$ is connection induced on $\\kappa$ by the Levi-Civita connection.\n\\end{prop}\n\n\n\nObserve that on surfaces, the trivial line bundle $\\mathbb{C} \\times M \\rightarrow M$ (with trivial connection) and the canonical line bundle $\\kappa \\rightarrow M$ (with the Levi-Civita connection) both have trivial holonomy but are not isomorphic. This explains the existence of a non-trivial kernel for $n=2$. We will need this preliminary lemma:\n\n\\begin{lemma}\n\\label{lemma:iso}\nLet $(M,g)$ be a smooth closed Riemannian manifold of dimension $\\geq 3$ and let $\\pi : SM \\rightarrow M$ be the projection. Let $\\mathcal{L}_1 \\rightarrow M$ and $\\mathcal{L}_2 \\rightarrow M$ be two Hermitian line bundles. If $\\pi^*\\mathcal{L}_1 \\simeq \\pi^*\\mathcal{L}_2$ are isomorphic, then $\\mathcal{L}_1 \\simeq \\mathcal{L}_2$ are isomorphic.\n\\end{lemma}\n\n\\begin{proof}\nThe topology of line bundles is determined by their first Chern class. As a consequence, it suffices to show that $c_1(\\mathcal{L}_1) = c_1(\\mathcal{L}_2)$. By assumption, we have $c_1(\\pi^*\\mathcal{L}_1) = \\pi^* c_1(\\mathcal{L}_1) = c_1(\\pi^*\\mathcal{L}_2) = \\pi^* c_1(\\mathcal{L}_2)$ and thus it suffices to show that $\\pi^* : H^2(M,\\mathbb{Z}) \\rightarrow H^2(SM,\\mathbb{Z})$ is injective when $\\dim(M) \\geq 3$. But this is then a mere consequence of the Gysin exact sequence \\cite[Proposition 14.33]{Bott-Tu-82}. \n\\end{proof}\n\n\nWe can now prove Proposition \\ref{proposition:line}:\n\n\\begin{proof}[Proof of Proposition \\ref{proposition:line}]\nAssume that $\\mathcal{T}^\\sharp_1(\\mathfrak{a}_1) = \\mathcal{T}^\\sharp_1(\\mathfrak{a}_2)$, where $\\mathfrak{a}_1 \\in \\mathbb{A}_{\\mathcal{L}_1}$ and $\\mathfrak{a}_2 \\in \\mathbb{A}_{\\mathcal{L}_2}$ are two classes of connections defined on two (classes of) line bundles. By Theorem \\ref{theorem:weak}, we obtain that the pullback bundles $\\pi^*\\mathcal{L}_1$ and $\\pi^*\\mathcal{L}_2$ are isomorphic, hence $\\mathcal{L}_1 \\simeq \\mathcal{L}_2$ are isomorphic by Lemma \\ref{lemma:iso}. Up to composing by a first bundle (unitary) isomorphism, we can therefore assume that $\\mathcal{L}_1 = \\mathcal{L}_2 =: \\mathcal{L}$. Let $\\nabla^\\mathcal{L}_1 \\in \\mathfrak{a}_1$ and $\\nabla^\\mathcal{L}_2 \\in \\mathfrak{a}_2$ be two representatives of these classes. They satisfy $\\mathcal{T}^\\sharp(\\nabla^{\\mathcal{L}}_1) = \\mathcal{T}^\\sharp(\\nabla^{\\mathcal{L}}_2)$. Combing Theorem \\ref{theorem:weak} with \\cite[Theorem 3.2]{Paternain-09}, the primitive trace map $\\mathcal{T}^\\sharp_{\\mathcal{L}}$ is known to be globally injective for connections on the same fixed bundle. Hence $\\nabla^{\\mathcal{L}}_1$ and $\\nabla^{\\mathcal{L}}_2$ are gauge-equivalent.\n\nFor the second claim, $\\mathrm{x} = ([\\mathcal{L}],\\mathfrak{a})$. If $\\mathcal{T}^\\sharp_1(\\mathrm{x}) = (1,1,...)$ (i.e. the connection is transparent), then by Theorem \\ref{theorem:weak}, one has that $\\pi^*\\mathcal{L} \\rightarrow SM$ is trivial. By the Gysin sequence \\cite[Proposition 14.33]{Bott-Tu-82}, this implies that $c_1(\\mathcal{L})$ is divisible by $2g-2$, where $g$ is the genus of $M$ (see \\cite[Theorem 3.1]{Paternain-09}), hence $[\\mathcal{L}] = [\\kappa^{\\otimes n}]$ for some $n \\in \\mathbb{Z}$. Moreover, the Levi-Civita connection on $\\kappa^{\\otimes n}$ is transparent and by uniqueness (see \\cite[Theorem 3.2]{Paternain-09}), this implies that $\\mathfrak{a} = [{\\nabla^{\\mathrm{LC}}}^{\\otimes n}]$.\n\\end{proof}\n\n\\begin{remark}\n\\rm\nThe target space in \\eqref{equation:trace-line} is actually $\\ell^\\infty(\\mathcal{C}^\\sharp,\\mathrm{U}(1))$ (sequences indexed by $\\mathcal{C}^\\sharp$ and taking values in $\\mathrm{U}(1)$) which can be seen as a subset of $\\mathrm{U}(\\ell^\\infty(\\mathcal{C}^\\sharp))$, the group of unitary operators of the Banach space $\\ell^\\infty(\\mathcal{C}^\\sharp)$ (equipped with the sup norm). Then $\\mathcal{T}_1^{\\sharp}$ is a group homomorphism and Proposition \\ref{proposition:line} asserts that\n\\[\n\\mathcal{T}_1^{\\sharp} : \\mathbb{A}_1 \\rightarrow \\mathrm{U}(\\ell^\\infty(\\mathcal{C}^\\sharp))\n\\]\nis a faithful unitary representation of the Abelian group $\\mathbb{A}_1$.\n\\end{remark}\n\n\nWe end this paragraph with a generalization of Proposition \\ref{proposition:line}. There is a natural submonoid $\\mathbb{A}' \\subset \\mathbb{A}$ which is obtained by considering sums of lines bundles equipped with unitary connections, that is:\n\\[\n\\mathbb{A}' := \\left\\{ \\mathrm{x}_1 \\oplus ... \\oplus \\mathrm{x}_k ~\\mid~ k \\in \\mathbb{N}, \\mathrm{x}_i \\in \\mathbb{A}_1\\right\\}.\n\\]\nWe then have the following:\n\n\\begin{theorem}\n\\label{theorem:sum}\nLet $(M,g)$ be a smooth Anosov Riemannian manifold of dimension $\\geq 3$. Then the restriction of the primitive trace map to $\\mathbb{A}'$:\n\\[\n\\mathcal{T}^{\\sharp} : \\mathbb{A}' \\longrightarrow \\ell^\\infty(\\mathcal{C}^\\sharp)\n\\]\nis globally injective.\n\\end{theorem}\n\n\n\\begin{proof}\nWe consider $\\mathcal{L} := \\mathcal{L}_1 \\oplus ... \\oplus \\mathcal{L}_k$ and $\\mathcal{J} = \\mathcal{J}_1 \\oplus ... \\oplus \\mathcal{J}_{k'}$, two Hermitian vector bundles over $M$, equipped with the respective connections $\\nabla^{\\mathcal{L}_1} \\oplus ... \\oplus \\nabla^{\\mathcal{L}_k}$ and $\\nabla^{\\mathcal{J}_1} \\oplus ... \\oplus \\nabla^{\\mathcal{J}_{k'}}$ and we assume that they have same image by the primitive trace map. Fixing a periodic point $(x_\\star,v_\\star)$ and applying Proposition \\ref{proposition:representation}, we obtain that $k=k'$ and the existence of two isomorphic representations $\\rho_{\\mathcal{L}} : \\mathbf{G} \\to \\mathrm{U}(\\pi^*\\mathcal{L}_{(x_\\star,v_\\star)})$ and $\\rho_{\\mathcal{J}} : \\mathbf{G} \\to \\mathrm{U}(\\pi^*\\mathcal{J}_{(x_\\star,v_\\star)})$, where $\\mathbf{G}$ denotes Parry's free monoid at $(x_\\star, v_\\star)$. Since these representations are sums of $1$-dimensional representations, there is a unitary isomorphism $p_\\star : \\pi^*\\mathcal{L}_{(x_\\star,v_\\star)} \\to \\pi^*\\mathcal{J}_{(x_\\star,v_\\star)}$ such that for each $i \\in \\left\\{1,...,k\\right\\}$, there exists $\\sigma(i) \\in \\left\\{1,...,k\\right\\}$ with $p^{(i)}_\\star := p_\\star|_{\\pi^*\\mathcal{L}_{i, (x_\\star, v_\\star)}}$ is a representation isomorphism $p^{(i)}_\\star: \\pi^* \\mathcal{L}_{i, (x_\\star,v_\\star)} \\to \\pi^* \\mathcal{J}_{\\sigma(i), (x_\\star,v_\\star)}$.\n\nNow, following the arguments of Lemma \\ref{lemma:p-minus}, we parallel-transport $p^{(i)}_\\star$ along the homoclinic orbits with respect to the pullback of the mixed connection $\\pi^*\\nabla^{\\mathrm{Hom}(\\nabla^{\\mathcal{L}_i}, \\nabla^{\\mathcal{J}_{\\sigma(i)}})}_X$ (induced by the connections $\\nabla^{\\mathcal{L}_i}$ on $\\mathcal{L}_i$ and $\\nabla^{\\mathcal{J}_{\\sigma(i)}}$ on $\\mathcal{J}_{\\sigma(i)}$); the Lipschitz-regularity of the obtained section follows, as in Lemma \\ref{lemma:lipschitz}, from the fact that $p_\\star^{(i)} \\rho_{\\mathcal{L}_i}(g) = \\rho_{\\mathcal{J}_{\\sigma(i)}}(g) p_\\star^{(i)}$ for all $g \\in \\mathbf{G}$.\nUsing the regularity result of \\cite{Bonthonneau-Lefeuvre-20}, we thus obtain a unitary section\n\\[\np^{(i)} \\in C^\\infty(SM,\\pi^*\\mathrm{Hom}(\\mathcal{L}_i, \\mathcal{J}_{\\sigma(i)}))\n\\]\nconjugating the parallel transports along geodesic flowlines with respect to the connections $\\pi^* \\nabla^{\\mathcal{L}_i}$ and $\\pi^* \\nabla^{\\mathcal{J}_{\\sigma(i)}}$. In particular, the existence of such $p^{(i)}$ ensures that $\\mathcal{T}^{\\sharp}_1(\\mathcal{L}_i,\\nabla^{\\mathcal{L}_i}) = \\mathcal{T}^{\\sharp}_1(\\mathcal{J}_{\\sigma(i)},\\nabla^{\\mathcal{J}_{\\sigma(i)}})$. We then conclude by Proposition \\ref{proposition:line}, showing that each pair $(\\mathcal{L}_i, \\nabla^{\\mathcal{L}_i})$ is isomorphic to $(\\mathcal{J}_{\\sigma(i)}, \\nabla^{\\mathcal{J}_{\\sigma(i)}})$, for $i = 1, \\dotso, k$.\n\\end{proof}\n\n\n\n\\subsubsection{Flat bundles}\n\n\\label{sssection:flat}\n\nWe discuss the particular case of flat vector bundles. It is well-known that the data of a vector bundle equipped with a unitary connection (modulo isomorphism) is equivalent to a unitary representation of the fundamental group (modulo inner automorphisms of the unitary group). More precisely, given $\\rho \\in \\Hom(\\pi_1(M),\\mathrm{U}(r))$, one can associate a Hermitian bundle $\\E \\rightarrow M$ equipped with a flat unitary connection $\\nabla^{\\E}$ by the following process: let $\\widetilde{M}$ be the universal cover of $M$; consider the trivial bundle $\\mathbb{C}^r \\times \\widetilde{M}$ equipped with the flat connection $d$ and define the relation $(x,v) \\sim (x',v')$ if and only if $x' = \\gamma(x), v' = \\rho(\\gamma)v$, for some $\\gamma \\in \\pi_1(M)$; then $(\\E,\\nabla^{\\E})$ is obtained by taking the quotient $\\mathbb{C}^r \\times \\widetilde{M}\/\\sim$. Changing $\\rho$ by an isomorphic representation $\\rho' = p \\cdot \\rho \\cdot p^{-1}$ (for $p \\in \\mathrm{U}(r)$) changes the connection by a gauge-equivalent connection and this process gives a one-to-one correspondence between the moduli spaces. \n\n\nFor $r \\geq 0$, we let\n\\[\n\\mathcal{M}_r := \\mathrm{Hom}(\\pi_1(M),\\mathrm{U}(r))\/\\sim,\n\\]\nbe the moduli space of unitary representations of the fundamental group, where two representations are equivalent $\\sim$ whenever they are isomorphic. The space $\\mathcal{M}_r$ is called the \\emph{character variety}, see \\cite{Labourie-13} for instance. For $r=0$, it is reduced to a point; for $r=1$, it is given by $\\mathcal{M}_1 = \\mathrm{U}(1)^{b_1(M)}$, where $b_1(M)$ denotes the first Betti number of $M$. Given $\\mathrm{x} \\in \\mathcal{M}_r$, we let $\\Psi(\\mathrm{x}) = (\\E_{\\mathrm{x}}, \\nabla^{\\E_{\\mathrm{x}}})$ be the data of a Hermitian vector bundle equipped with a unitary connection (up to gauge-equivalence) described by the above process. The primitive trace map $\\mathcal{T}^\\sharp$ can then be seen as a map:\n\\[\n\\mathcal{T}^\\sharp : \\bigsqcup_{r \\geq 0} \\mathcal{M}_r \\rightarrow \\ell^\\infty(\\mathcal{C}^\\sharp), \\quad \\mathcal{T}^\\sharp(\\mathrm{x}) := \\mathcal{T}^\\sharp(\\nabla^{\\E_{\\mathrm{x}}})\n\\]\nwhere the right-hand side is understood by \\eqref{equation:trace}. We then have the following:\n\n\n\\begin{prop}\n\\label{proposition:flat}\nLet $(M, g)$ be an Anosov manifold of dimension $\\geq 2$. Then the primitive trace map\n\\[\n\\mathcal{T}^\\sharp : \\bigsqcup_{r \\geq 0} \\mathcal{M}_r \\rightarrow \\ell^\\infty(\\mathcal{C}^\\sharp), \n\\]\nis globally injective. Moreover, given $\\mathrm{x}_0 = ([\\E_0],[\\nabla^{\\E}_0]) \\in \\mathcal{M}_r$, the primitive trace map is weakly locally injective (in the sense of Theorem \\ref{thm:weaklocal}) near $\\mathrm{x}_0$ in the space $\\mathbb{A}_{[\\E_0]}$ of all unitary connections on $[\\E_0]$.\n\\end{prop}\n\nThe previous Proposition \\ref{proposition:flat} will be strengthened below when further assuming that $(M,g)$ has negative curvature (see Lemma \\ref{lemma:small-curvature}): we will show that the primitive trace map is globally injective on connections with \\emph{small} curvature.\nThe first part of Proposition \\ref{proposition:flat} could be proved by purely algebraic arguments; nevertheless, we provide a proof with dynamical flavour, which is more in the spirit of the present article. We need a preliminary result:\n\n\\begin{lemma}\\label{lemma:Pi_1inj}\nAssume $(M,g)$ is Anosov and $\\nabla^{\\E}$ is a flat and unitary connection on the Hermitian vector bundle $\\E \\rightarrow M$. If $\\mathbf{X} := (\\pi^*\\nabla^{\\E})_X$, then:\n\\begin{itemize}\n\\item If $\\mathbf{X} u = f$ with $f=f_0 + f_1 \\in C^\\infty(M,(\\Omega_0 \\oplus \\Omega_1) \\otimes \\E)$ and $u \\in C^\\infty(SM,\\pi^*\\E)$, then $f_0 = 0$ and $u$ is of degree $0$. \n\\item In particular, smooth invariant sections $u \\in \\ker \\mathbf{X}|_{C^\\infty(SM,\\pi^*\\E)}$ are of degree $0$.\n\\item The operator $\\Pi_1^{\\E}$ is s-injective.\n\\end{itemize}\n\\end{lemma}\n\n\n\n\n\\begin{proof}\nThe proof is based on the twisted Pestov identity for flat connections. \n\n\\begin{lemma}[Twisted Pestov identity]\nLet $u\\in H^2(SM,\\pi^*\\E)$. Then\n\\[\n\\|\\nabla^{\\E} _{\\mathbb{V}} \\mathbf{X} u \\|_{L^2}^2 = \\|\\mathbf{X} \\nabla^{\\E} _{\\mathbb{V}} u \\|_{L^2}^2 - \\langle R \\nabla^{\\E} _{\\mathbb{V}} u, \\nabla^{\\E} _{\\mathbb{V}} u \\rangle_{L^2} + (n -1) \\| \\mathbf{X} u \\|_{L^2}^2.\n\\]\n\\end{lemma}\n\nFor the notation, see \\S \\ref{sssection:twisted-fourier}; for a proof, we refer to \\cite[Proposition 3.3]{Guillarmou-Paternain-Salo-Uhlmann-16}. An important point is that the following inequality holds for Anosov manifolds:\n\\[\n\\|\\mathbf{X} \\nabla^{\\E} _{\\mathbb{V}} u \\|_{L^2}^2 - \\langle R \\nabla^{\\E} _{\\mathbb{V}} u, \\nabla^{\\E} _{\\mathbb{V}} u \\rangle_{L^2} \\geq C\\|\\nabla^{\\E}_{\\mathbb{V}} u\\|^2_{L^2},\n\\]\nwhere $C > 0$ is independent of $u$, see \\cite[Theorem 7.2]{Paternain-Salo-Uhlmann-15} for the case of the trivial line bundle (the generalization to the twisted case is straightforward). We thus obtain:\n\\begin{equation}\n\\label{equation:inegalite}\n\\|\\nabla^{\\E} _{\\mathbb{V}} \\mathbf{X} u \\|_{L^2}^2 \\geq C\\|\\nabla^{\\E}_{\\mathbb{V}} u\\|^2_{L^2} + (n -1) \\| \\mathbf{X} u \\|_{L^2}^2.\n\\end{equation}\n\nBy assumption, $\\mathbf{X} u = f_0 + f_1 \\in C^\\infty(M, (\\Omega_0 \\oplus \\Omega_1) \\otimes \\E)$. Observe that this equation can be split into odd\/even parts, namely: $\\mathbf{X} u_{\\mathrm{even}} = f_1, \\mathbf{X} u_{\\mathrm{odd}} = f_0$, and $u_{\\mathrm{even},\\mathrm{odd}} \\in C^\\infty(SM,\\pi^*\\E)$ have respective even\/odd Fourier components. Applying \\eqref{equation:inegalite} with $u_{\\mathrm{odd}}$, we obtain $f_0 = 0$, $\\mathbf{X} u_{\\mathrm{odd}} = 0$ and $\\nabla^{\\E}_{\\mathbb{V}} u_{\\mathrm{odd}} = 0$, that is $u_{\\mathrm{odd}}$ is of degree $0$ but $0$ is even so $u_{\\mathrm{odd}} = 0$. As far as $u_{\\mathrm{even}}$ is concerned, observe that $\\nabla^{\\E} _{\\mathbb{V}} \\mathbf{X} u_{\\mathrm{even}} = \\nabla^{\\E} _{\\mathbb{V}} f_1$ and:\n\\[\n\\|\\nabla^{\\E} _{\\mathbb{V}} f_1\\|_{L^2}^2 = \\langle -\\Delta_{\\mathbb{V}}^{\\E} f_1, f_1 \\rangle_{L^2} = (n-1) \\|f_1\\|^2_{L^2}.\n\\]\nHence, applying the twisted Pestov identity with $u_{\\mathrm{even}}$, we obtain:\n\\[\n0 = \\|\\mathbf{X} \\nabla^{\\E} _{\\mathbb{V}} u_{\\mathrm{even}} \\|_{L^2}^2 - \\langle R \\nabla^{\\E} _{\\mathbb{V}} u_{\\mathrm{even}}, \\nabla^{\\E} _{\\mathbb{V}} u_{\\mathrm{even}} \\rangle_{L^2} \\geq C\\|\\nabla^{\\E}_{\\mathbb{V}} u_{\\mathrm{even}}\\|^2_{L^2},\n\\]\nthat is $u_{\\mathrm{even}}$ is of degree $0$. This proves the first point and the second point is a direct consequence of the first point.\n\nFor the last point, consider the equation $\\mathbf{X} u = \\pi_1^*f$. By the first point, $u$ is of degree zero, so $u = \\pi_0^*u'$ for some $u' \\in C^\\infty(M, \\E)$. Hence by \\eqref{eq:pullback} we get $f = \\nabla^{\\E}u'$ and the conclusion follows by Lemma \\ref{lemma:x-ray}.\n\\end{proof}\n\n\nWe can now prove Proposition \\ref{proposition:flat}.\n\n\\begin{proof}\nWe prove the first part of Proposition \\ref{proposition:flat}. We assume that $\\mathcal{T}^\\sharp(\\mathrm{x}_1) =\\mathcal{T}^\\sharp(\\mathrm{x}_2)$ or, equivalently, that $\\mathcal{T}^\\sharp(\\nabla^{\\E_{\\mathrm{x}_1}}) = \\mathcal{T}^\\sharp(\\nabla^{\\E_{\\mathrm{x}_2}})$. The exact Liv\\v{s}ic cocycle Theorem \\ref{theorem:weak} implies that the bundles $\\pi^*\\E_{\\mathrm{x}_1}$ and $\\pi^*\\E_{\\mathrm{x}_2}$ are isomorphic and yields the existence of a section $p \\in C^\\infty(SM,\\mathrm{U}(\\pi^*\\E_{\\mathrm{x}_2}, \\pi^*\\E_{\\mathrm{x}_1}))$ such that: $C_{\\mathrm{x}_1}(x,t) = p(\\varphi_t x)C_{\\mathrm{x}_2}(x,t)p(x)^{-1}$ for all $x \\in \\mathcal{M}, t \\in \\mathbb{R}$, which is equivalent to\n\\[\n\\pi^* \\nabla^{\\mathrm{Hom}(\\nabla^{\\E_{\\mathrm{x}_2}}, \\nabla^{\\E_{\\mathrm{x}_1}})}_X p = 0,\n\\]\nwhere $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_{\\mathrm{x}_2}}, \\nabla^{\\E_{\\mathrm{x}_1}})}$ is the mixed connection induced by $\\nabla^{\\E_{\\mathrm{x}_2}}$ and $\\nabla^{\\E_{\\mathrm{x}_1}}$ on $\\mathrm{Hom}(\\E_{\\mathrm{x}_2}, \\E_{\\mathrm{x}_1})$. Observe that by \\eqref{equation:induced-curvature}, the curvature of $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_{\\mathrm{x}_2}}, \\nabla^{\\E_{\\mathrm{x}_1}})}$ vanishes as both curvatures $F_{\\nabla^{\\E_{\\mathrm{x}_{1,2}}}}$ vanish. Applying Lemma \\ref{lemma:Pi_1inj} with $\\mathbf{X} := \\pi^* \\nabla^{\\mathrm{Hom}(\\nabla^{\\E_{\\mathrm{x}_2}}, \\nabla^{\\E_{\\mathrm{x}_1}})}_X$ acting on the pullback bundle $\\pi^*\\mathrm{Hom}(\\E_{\\mathrm{x}_2}, \\E_{\\mathrm{x}_1})$, we get that $p$ is of degree $0$, which is equivalent to the fact that the connections are gauge-equivalent.\n\nAs to the second part of Proposition \\ref{proposition:flat}, by Theorem \\ref{thm:weaklocal} it is a straightforward consequence of the s-injectivity of $\\Pi_1^{\\mathrm{End}(\\E_0)}$ and the fact that elements of $\\ker(\\pi^*\\nabla^{\\mathrm{End}(\\E_0)})_X|_{C^\\infty})$ are of degree zero, which follows from Lemma \\ref{lemma:Pi_1inj} (items 2 and 3).\n\\end{proof}\n\n\n\\subsubsection{Negative sectional curvature}\n\n\\label{sssection:negative}\n\nWe now assume further that the Riemannian manifold $(M,g)$ has negative sectional curvature. We introduce the following condition:\n\n\\begin{definition}\nWe say that the pair of connections $(\\nabla^{\\E_1},\\nabla^{\\E_2})$ satisfies the \\emph{spectral condition} if the mixed connection $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_1},\\nabla^{\\E_2})}$ has no non-trivial twisted CKTs. \n\\end{definition}\n\nThis condition is symmetric in the pair $(\\nabla^{\\E_1},\\nabla^{\\E_2})$. Observe that by \\eqref{equation:lien}, the previous condition is invariant by changing one of the two connections by $p^* \\nabla^{\\E_i}$, for some vector bundle isomorphism $p$, and thus this condition descends to the moduli space. We then define\n\\begin{equation}\\label{eq:setS}\n\\mathbf{S} \\subset \\mathbb{A} \\times \\mathbb{A},\n\\end{equation}\nthe subspace of all pairs of equivalence classes of connections satisfying the spectral condition. The set $\\mathbf{S}$ is open and dense (for the $C^N_*$-topology, $N \\gg 1$) as shown in Appendix \\ref{appendix:ckts}. Moreover, it also contains all pairs of connections with \\emph{small curvature}, that is if\n\\[\n\\Omega_\\varepsilon := \\left\\{ \\mathrm{x} = ([\\E],[\\nabla^{\\E}]) \\in \\mathbb{A}, ~~~ \\|F_{\\nabla^{\\E}}\\|_{L^\\infty(M,\\Lambda^2 T^*M \\otimes \\mathrm{End}(\\E))} < \\varepsilon \\right\\} \\subset \\mathbb{A},\n\\]\nthen we have the following:\n\n\n\\begin{lemma}\n\\label{lemma:small-curvature}\nLet $(M,g)$ be a negatively-curved Riemannian manifold of dimension $\\geq 2$ and let $-\\kappa < 0$ be an upper bound for the sectional curvature. There exists $\\varepsilon(n,\\kappa) > 0$ such that:\n\\[\n\\Omega_{\\varepsilon(n,\\kappa)} \\times \\Omega_{\\varepsilon(n,\\kappa)} \\subset \\mathbf{S}.\n\\]\nOne can take $\\varepsilon(n,\\kappa) =\\dfrac{\\kappa \\sqrt{n-1}}{4}$.\n\\end{lemma}\n\n\n\n\n\\begin{proof}\nWe start by a preliminary discussion. Given a Hermitian vector bundle $\\E \\to M$ with metric $\\langle \\bullet, \\bullet \\rangle$, a unitary connection $\\nabla^{\\E}$, we introduce, following \\cite[Section 3]{Guillarmou-Paternain-Salo-Uhlmann-16}, an operator $\\mathcal{F}^{\\E} \\in C^\\infty(SM,\\mathcal{N} \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))$ (recall that $\\mathcal{N}$ is the normal bundle, see \\S\\ref{sssection:line-bundle}) defined by the equality:\n\\begin{equation}\n\\label{equation:twisted-curvature}\n \\langle \\mathcal{F}^{\\E}(x,v) e, w \\otimes e' \\rangle := \\langle F_{\\nabla^{\\E}}(v,w)e,e' \\rangle,\n\\end{equation}\nwhere $F_{\\nabla^{\\E}}$ is the connection of $\\nabla^{\\E}$, and $(x,v) \\in SM, e, e' \\in \\E_x, w \\in \\mathcal{N}(x,v)$, and the metric on the left-hand side is the natural extension of the metric $\\langle \\bullet, \\bullet \\rangle$ on $\\E$ to $\\mathcal{N} \\otimes \\E$ by tensoring with the metric $g$. A straightforward computation shows that:\n\\begin{equation}\n\\label{equation:bad-bound}\n\\|\\mathcal{F}^{\\E}\\|_{L^\\infty(SM, \\mathcal{N} \\otimes \\mathrm{End}_{\\mathrm{sk}}(\\E))} \\leq \\|F_{\\nabla^{\\E}}\\|_{L^\\infty(M,\\Lambda^2 T^*M \\otimes \\mathrm{End}(\\E))}. \n\\end{equation}\nNow, let $\\nabla^{\\E_1}$ and $\\nabla^{\\E_2}$ be two unitary connections, $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_1}, \\nabla^{\\E_2})}$ be the mixed connection and $\\mathcal{F}^{\\mathrm{Hom(\\E_1,\\E_2)}}$ be the operator induced by the mixed connection as in \\eqref{equation:twisted-curvature}. Observe that by \\eqref{equation:induced-curvature} and \\eqref{equation:bad-bound}, we get:\n\\begin{equation}\n\\label{equation:bad-bound-2}\n\\begin{split}\n\\|\\mathcal{F}^{\\mathrm{Hom(\\E_1,\\E_2)}}\\|_{L^\\infty} \\leq \\|F_{\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_1}, \\nabla^{\\E_2})}}\\|_{L^\\infty} \\leq \\|F_{\\nabla^{\\E_1}}\\|_{L^\\infty} + \\|F_{\\nabla^{\\E_2}}\\|_{L^\\infty} < 2\\varepsilon(n,\\kappa).\n\\end{split}\n\\end{equation}\nBy \\cite[Theorem 4.5]{Guillarmou-Paternain-Salo-Uhlmann-16}, if $m \\geq 1$ satisfies:\n\\begin{equation}\n\\label{equation:ckts}\nm(m+n-2) \\geq 4 \\dfrac{\\|\\mathcal{F}^{\\mathrm{Hom(\\E_1,\\E_2)}}\\|^2_{L^\\infty}}{\\kappa^2},\n\\end{equation}\nthen there are no twisted CKTs of degree $m$ (for the connection $\\nabla^{\\mathrm{Hom}(\\nabla^{\\E_1}, \\nabla^{\\E_2})}$). Now, the choice of $\\varepsilon(n,\\kappa) > 0$ combined with \\eqref{equation:bad-bound-2} guarantees that \\eqref{equation:ckts} is satisfied for any $m \\geq 1$.\n\\end{proof}\n\n\n\n\nWe then have the following statement:\n\n\\begin{prop}\n\\label{proposition:negative}\nLet $(M,g)$ be a negatively-curved Riemannian manifold of dimension $\\geq 2$. Let $(\\mathfrak{a}, \\mathfrak{a}') \\in \\mathbf{S}$ such that $\\mathcal{T}^\\sharp(\\mathfrak{a}) = \\mathcal{T}^\\sharp(\\mathfrak{a}')$. Then $\\mathfrak{a} = \\mathfrak{a}'$.\n\\end{prop}\n\nIn other words, two connections satisfying the spectral condition and whose images by the primitive trace map are equal, are actually gauge-equivalent.\n\n\\begin{proof}\nConsider two representatives $\\nabla^{\\E_1} \\in \\mathfrak{a}$ and $\\nabla^{\\E_2} \\in \\mathfrak{a}'$. The exact Liv\\v{s}ic cocycle Theorem \\ref{theorem:weak} provides a section $p \\in C^\\infty(SM,\\mathrm{U}(\\pi^*\\E_2,\\pi^*\\E_1))$ such that:\n\\[\n\\pi^* \\nabla^{\\mathrm{Hom}(\\nabla^{\\E_{2}}, \\nabla^{\\E_{1}})}_X p = 0.\n\\]\nBy assumption, $(M,g)$ has negative curvature and thus $p$ has finite Fourier degree by \\cite[Theorem 4.1]{Guillarmou-Paternain-Salo-Uhlmann-16}. Moreover, since $ \\nabla^{\\mathrm{Hom}(\\nabla^{\\E_{2}}, \\nabla^{\\E_{1}})}$ has no non-trivial twisted CKTs, $p$ is of degree $0$ (see \\cite[Theorem 5.1]{Guillarmou-Paternain-Salo-Uhlmann-16}). This shows that the connections are gauge-equivalent.\n\\end{proof}\n\n\n\n\n\n\\subsubsection{Topological results}\n\n\\label{sssection:topology}\n\nIn this section we prove a global \\emph{topological} uniqueness result for the primitive trace map. \n\n\\begin{prop}\\label{proposition:topology}\n\tLet $(M, g)$ be an orientable Anosov manifold. If $\\mathrm{x}_i = ([\\E_i], [\\nabla^{\\E_i}]) \\in \\mathbb{A}$ for $i = 1, 2$, then $\\mathcal{T}^\\sharp(\\mathrm{x}_1) = \\mathcal{T}^\\sharp(\\mathrm{x}_2)$ implies:\n\t\n\t\\begin{itemize}\n\t\t\t\\item If $\\dim M$ is odd or more generally the Euler characteristic $\\chi(M) = 0$ vanishes, then $\\E_1 \\simeq \\E_2$ are isomorphic as vector bundles.\n\t\t\t\\item If $\\dim M = 2d$ for some $d \\in \\mathbb{N}$ and $\\chi(M) \\neq 0$, then\n\t\t\t\t\\begin{itemize}\n\t\t\t\t\t\\item The Chern classes satisfy $c_i(\\E_1) = c_i(\\E_2)$ for $i = 1, \\dotso, d - 1$; also $c_d(\\E_1) - c_d(\\E_2) \\in H^{2d}(M; \\mathbb{Z}) \\cong \\mathbb{Z}$ is a multiple of $\\chi(M)$.\n\t\t\t\t\t\\item If the even cohomology ring $H^{\\mathrm{even}}(M; \\mathbb{Z})$ is torsion-free, and the rank of the bundles is less than $d$ or more generally $c_d(\\E_1) = c_d(\\E_2)$, then $\\E_1$ and $\\E_2$ are stably isomorphic, i.e. there is an $m \\geq 0$ such that $\\E_1 \\oplus \\mathbb{C}^m \\simeq \\E_2 \\oplus \\mathbb{C}^m$\n\t\t\t\t\\end{itemize}\n\t\\end{itemize}\t\n\\end{prop}\n\\begin{proof}\n\tAs a direct consequence of Theorem \\ref{theorem:weak}, from $\\mathcal{T}^\\sharp(\\mathrm{x}_1) = \\mathcal{T}^\\sharp(\\mathrm{x}_2)$ we obtain that $\\pi^*\\E_1 \\simeq \\pi^*\\E_2$ are isomorphic.\n\t\n\tIf $(M, g)$ has a vanishing Euler characteristic, there is a non-vanishing vector field $V \\in C^\\infty(M, TM)$ (see \\cite[Chapter 11]{Bott-Tu-82}), that we normalise to unit norm using the metric $g$ and hence see as a section of $SM$. Then since $\\pi \\circ V = \\id_M$, we get\n\t\\[\\E_1 \\simeq V^*\\pi^*\\E_1 \\simeq V^*\\pi^* \\E_2 \\simeq \\E_2,\\]\n\tcompleting the proof of the first item. \n\t\n\tThe first point of the second item is immediate after an application of the Gysin exact sequence \\cite[Proposition 14.33]{Bott-Tu-82} for the sphere bundle $SM$. The second point follows from the first one and the fact that the Chern character gives an isomorphism between the rational $K$-theory and even rational cohomology, see \\cite[Proposition 4.5]{Hatcher-17}.\n\\end{proof}\n\n\n\nIt is not known to the authors if further results hold as to the injectivity of $\\pi^* : \\mathrm{Vect}(M) \\rightarrow \\mathrm{Vect}(SM)$ in even dimensions ($\\dim M \\geq 4$). \n\n\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzmppy b/data_all_eng_slimpj/shuffled/split2/finalzzmppy new file mode 100644 index 0000000000000000000000000000000000000000..d613a0ab9e17ba5e9e4fb2ce296dc3f65c646428 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzmppy @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nOne of the fundamental problems in the study of complex networks~\\cite{Dorogovtsev_book1,NBW,BBB,Dorogovtsev_book2,NetsCrowdsMarkets} is to identify\nevolution mechanisms that shape the structure and dynamics of large\nreal networks such as the Internet, the world wide web, and various biological and\nsocial networks. In particular, how do complex networks grow so that\nmany of them are scale-free and have strong clustering and non-trivial community structure?\nThe preferential attachment (PA) mechanism~\\cite{PA,Krapivsky,Dorogovtsev2}, where new \\textit{connections} are made\npreferentially to more popular nodes, is widely accepted as the\nplausible explanation for the emergence of the scale-free structures\n(i.e.\\ the power-law degree distributions) in large networks.\nPA has been empirically validated for many real growing\nnetworks~\\cite{Newman,BarabasiJeongNeda,Vazquez,Jeong} using statistical analysis of a sequence of network\nsnapshots, demonstrating that it is indeed a key element of network evolution. Moreover, there is some evidence that the evolution of the community graph --- a graph where nodes represent communities and links refer to members shared by two communities --- is also driven by PA~\\cite{Pollner}.\n\nNevertheless, PA alone cannot explain two other empirically observed universal properties of complex networks: strong clustering~\\cite{WS} and significant community structure~\\cite{GirvanNewman}. Namely, in synthetic networks generated by standard PA, clustering\nis asymptotically zero~\\cite{Bollobas} and there are no communities~\\cite{Fortunato}. To resolve the zero-clustering problem, several modifications of the original PA mechanism have been proposed~\\cite{Dorogovtsev3,Klemm,Vazquez2,Jackson}. To the best of our knowledge, however, none of these models capture all three fundamental properties of complex networks: heavy-tail degree distribution, high clustering, and community structure.\n\n\nIn social networks, the presence of communities, that might represent node clusters based on certain social factors such as economic status or political beliefs, is intuitively expected. A remarkable observation~\\cite{GirvanNewman,www,metabolic,biochemical,financial,metabolic2} is that many other networks, including food webs, the world wide web, metabolic, biochemical, and financial networks, also admit a reasonable division into informative communities. Since that discovery, community detection has become one of the main tools for the analysis and understanding of network data~\\cite{Fortunato,Danon}. \n\nDespite an enormous amount of attention to community detection algorithms and their efficiency, there were very few attempts to answer a more fundamental question: what is the actual mechanism that induces community structure in real networks? For social networks, where there is a strong relationship between a high concentration of triangles and the existence of community structure~\\cite{NP}, triadic closure~\\cite{Rapoport} has been proposed as a plausible mechanism for generating communities~\\cite{Bianconi}. It was also shown by means of a simple agent-based acquaintance model that a large-scale community structure can emerge from the underlying social dynamics~\\cite{Bhat}. There also exist other contributions in this direction, where proposed mechanisms and generative models are specifically tailored for social networks~\\cite{Marian,Toivonen,Lambiotte,Lambiotte2}.\n\n\nHere we show how latent network geometry coupled with preferential attachment of \\textit{nodes} to this geometry induces community structure as well as power-law degree distributions and strong clustering. We prove that these universal properties of complex networks naturally emerge from the new mechanism that we call \\textit{geometric preferential attachment} (GPA), without appealing to the specific nature (e.g.\\ social) of networks. Using the Internet as an example, we demonstrate that GPA generates networks that are in many ways similar to real networks.\n\n\n\\section{Results}\n\\subsection{Geometric Preferential Attachment}\n\n\nIn growing networks the concept of popularity that PA exploits is just one aspect of node attractiveness; another important aspect is similarity~\\cite{PSO}. Namely, if nodes are similar (``birds of feather''), then they have a higher chance of being\nconnected (``flock together''), even if they are not popular. This effect, known as homophily in social sciences~\\cite{McPherson}, has been observed in many real networks of various nature~\\cite{Redner,Watts}.\n\nThe GPA mechanism utilizes the idea that both popularity and similarity are important. We take the node birth time $t=1,2,\\ldots$ as a proxy for node's popularity: all other things being equal, the older the node (i.e.\\ the smaller $t$), the more popular it is. The similarity attribute of node $t$ is modeled by a random variable $\\theta_t$ distributed over a circle $\\mathbb{S}^1$ that abstracts the ``similarity'' space. One can think of the similarity space as an image of a certain projection $p: \\mathcal{A}\\rightarrow\\mathbb{S}^1$ from a space of unknown or not easily measurable attributes $(a^1,\\ldots,a^k)\\in\\mathcal{A}$ of nodes. For social networks, these attributes could be political beliefs, education, and social status, whereas for biological networks, $\\{a^i\\}$ may represent chemical properties of metabolites or geometric properties of protein shapes. While the absolute value of the similarity coordinate $\\theta_t=p(a_t^1,\\ldots,a_t^k)$ does not have any specific meaning, the angular distance $\\theta_{st}=\\pi-|\\pi-|\\theta_s-\\theta_t||$ quantifies the similarity between two nodes. Upon its birth, a new node $t$ connects to an existing node $ss$ it is $r_s(t)=\\beta r_s + (1-\\beta)r_t$. The distance $x_{st}$ between two points in the hyperbolic\nplane of curvature $K=-1$ with polar coordinates\n$(r_s(t),\\theta_s)$ and $(r_t,\\theta_t)$ is approximately~\\cite{Bonahon} $x_{st}=r_s(t)+r_t+2\\ln\\frac{\\theta_{st}}{2}=2\\ln\\left(\\frac{s^\\beta\tt^{2-\\beta}\\theta_{st}}{2}\\right)$. Since for any given $t$, the sets of nodes $s