diff --git "a/-dE3T4oBgHgl3EQfrgpm/content/tmp_files/load_file.txt" "b/-dE3T4oBgHgl3EQfrgpm/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/-dE3T4oBgHgl3EQfrgpm/content/tmp_files/load_file.txt" @@ -0,0 +1,1057 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf,len=1056 +page_content='SciPost Physics Submission Anomalies, Representations, and Self-Supervision Barry M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman Plehn Institut für Theoretische Physik, Universität Heidelberg, Germany January 13, 2023 Abstract We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The Anomaly- CLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It uses a permutation-invariant Trans- former Encoder architecture to map the objects measured in a collider event to the represen- tation space, where the data augmentations define a representation space which is sensitive to potential anomalous features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' An AutoEncoder trained on background representations then computes anomaly scores for a variety of signals in the representation space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' With AnomalyCLR we find significant improvements on performance metrics for all signals when compared to the raw data baseline.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Contents 1 Introduction 2 2 Dataset 4 3 AnomalyCLR 5 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1 Contrastive learning 5 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 CLR for anomaly detection 6 4 Application to event-level anomalies 8 5 Anomaly scores 10 6 Results 11 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1 Comparison of methods 11 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 The effect of anomaly-augmentations 12 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3 The effect of representation dimension 13 7 Summary & conclusions 14 References 15 1 arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='04660v1 [hep-ph] 11 Jan 2023 SciPost Physics Submission 1 Introduction Model-agnostic new physics searches are one of the most interesting analysis prospects for the LHC and other colliders.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Over the past decade the LHC has searched for new physics based on model-specific hypothesis testing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Despite these efforts there has been no strong evidence of new physics found.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It is possible that new physics does exist at the scales probed by the LHC, and has not been uncovered due to the particular signal not being covered by previous analysis hypotheses.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The ATLAS and CMS collaborations have both implemented model-agnostic new physics searches to deal with this [1, 2], however these methods suffer some drawbacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For example scanning high-dimensional parameter spaces can lead to large look-elsewhere effects, or methods can lack the ability to make full use of the high-granularity low-level information collected in the experi- ments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Recent progress in machine learning based high-energy physics tools are making significant advances in solving many problems of such classical methods [3].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The main machine learning tools to date for data-driven model-agnostic searches are based either on density-related scores, or on classification scores using a background-dominated control sample.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The latter, typically known as CWoLa methods (Classification Without Labels) [4–6] have been shown to be very successful in applications such as bump hunting [7–14] and semi-visible jet searches [15], providing both anomaly scores and background estimates.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' However they run into difficulty when the dimension of the input space or number of observables becomes large, and so the question of whether or not they can be used on low level data is still uncertain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' CWoLa tools have already been adopted by the ATLAS collaboration [16].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Density-based methods use machine learning to estimate the density in the phase space, and then identify anomalies as those laying in the low density regions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' These tools typically work on high-dimensional inputs and so can be used on low-level data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The first density-based meth- ods were the AutoEncoder studies [17, 18], where the network is optimised to compress and reconstruct the kinematics of a jet or event.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' While this is not strictly density estimation, the optimisation is highly aligned with learning a density, since regions of the phase space which are most populated are those which should be reconstructed the best and thus have the low- est anomaly score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' There has been significant progress with the AutoEncoder tools and other density-based anomaly detection methods in recent years [19–33], with studies covering inter- pretability of AutoEncoders [34, 35], topic modelling [36, 37], null hypothesis tests for anomaly detection [38], ABCD methods [39], the Normalised AutoEncoder (NAE) [40], and normalising flow techniques [41–44].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For a comprehensive summary of many different anomaly detection methods we refer the reader to the community challenge papers in Refs [45,46].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' One issue with the density-based approaches [44,47] is that the score is not invariant under simple transformations in the phase space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This means that a simple re-mapping of the momenta or coordinates fundamentally changes what the anomaly score is.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This poses the question of how to choose a representation of the data for use in density-based anomaly detection tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It is also worth noting that despite the great progress that more sophisticated neural network archi- tectures and the implementation of symmetries in networks has brought to supervised classifi- cation [48–51], they have not yet led to the same progress in anomaly detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In this work we develop a new approach to density-based anomaly detection using self-supervision, which de- fines the representation of the data in a model-agnostic way using the power of highly expressive networks such as transformers or graph networks to boost anomaly detection performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Supervised machine learning methods use the idea of a truth-label to optimise the neural net- 2 SciPost Physics Submission works, usually to classify between data with different truth labels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Unsupervised methods are those which do not require truth labels, instead optimising a network using a reconstruction loss or a negative log likelihood, for example.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Self-supervision on the other hand uses ‘pseudo-labels’, labels generated from the data without knowledge of a truth label, to optimise the networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In contrastive learning [52], these labels correspond to a link between an original event and an aug- mented event.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We define the augmentation as some physical modification of the event kinematics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Contrastive learning uses the pseudo-labels to devise an auxiliary task for the network optimisation through the contrastive loss function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Now the network learns how to process high-dimensional correlations in the data, and thus the representations learned by these networks can be very useful for downstream tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We introduced the self-supervised JetCLR method in [53] and demonstrated its ability to construct highly expressive representations for classification tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In [54] this same technique was used to construct representations for CWoLa-based anomaly detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In addition to these works, other self-supervised / representation learning techniques have been applied in particle physics [55,56] and in other scientific disciplines such as astrophysics [57–60].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In [53,54] the augmentations corresponded to transformations of the event to which the underlying physics should be invariant to rotations or translations, but also soft-collinear parton splittings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We introduce AnomalyCLR, a new method based on the idea of ‘anomaly-augmentations’.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' These anomaly-augmentations are modifications of the original event to which the underlying physics is not invariant.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In fact these augmentations are chosen to mimic very general features that anomalous events might have, such as high multiplicity, large MET, or large pT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Despite choos- ing explicitly the augmentations, the approach does not target any specific new physics model, and we will see from the results that the approach is model agnostic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' AnomalyCLR projects the kine- matics of each event to a representation vector, which we then use to train an AutoEncoder and define the anomaly scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It enriches the representation space using known invariances in the data, such as invariance to azimuthal rotations, and known generic features of anomalies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Self- supervised anomaly detection methods have gained prominence in the machine learning literature recently [61–64], and while the approaches are necessarily domain specific, we have drawn on these methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The anomaly score can be computed in different ways, and we opt for the Au- toEncoder approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' So the workflow is as follows;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' train AnomalyCLR to obtain a representation vector for each event in the dataset, then train an AutoEncoder on these representations to obtain the anomaly scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This is in contrast to the typical approach of training the AutoEncoder directly on the raw kinematical data from the events.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We test AnomalyCLR on the CMS Anomaly Detection Challenge dataset [65], and, compared to the raw data baseline, we find significant improvements on all signals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In Section 2 we will discuss the dataset and the different backgrounds and signals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In Section 3 we will then introduce the AnomalyCLR idea, first discussing contrastive learning and then how this can be modified for use in anomaly detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The specifics of the application to event-level collider data such as the CMS ADC dataset is given in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The discussion on how we estimate anomaly scores is given in Section 5, where the architecture and optimisation of the AutoEncoder we use is discussed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The results are presented in Section 6, along with an analysis of how different anomaly-augmentations and different representation dimensions affect the results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We conclude in Section 7 with a discussion of the results and future directions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 3 SciPost Physics Submission 2 Dataset To test the performance of the AnomalyCLR representations compared to raw data in an anomaly detection task we use the CMS anomaly detection challenge dataset [65], which contains simu- lated proton-proton collisions with a 13 TeV centre-of-mass energy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The events are selected to have at least one e or µ with transverse momenta pT >23.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The pseudo-rapidity (|η|) is required to be <3 and <2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1 respectively for e and µ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Further, the events are allowed to have up to 10 jets with pT > 15 GeV and |η| < 4, up to 4 muons pT > 3 GeV and |η| < 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1, up to 4 electrons pT > 3 GeV and |η|<3 and missing transverse energy (MET).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The dataset is generated with Pythia 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='240 generator [66] with a fast detector simulation using Delphes 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 [67] with the Phase-II CMS detector card.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The jets are reconstructed using anti-kt algorithm [68].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In the provided dataset each event is formatted such that the first entry is assigned for MET, next eight are assigned for electrons and muons respectively and, the final 10 entries are for jets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For each particle object the data set contains information of pT, η, φ and particle id such that the shape of an event in the data frame is [N,19,4] where N is the total number of events.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Note that if an event has less than the maximum allowed of a type of object, the remaining entries in that case are zero padded.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The background dataset consists of a number of Standard Model processes and to determine the performance of the anomaly detection algorithm four light BSM scenarios are considered.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Backgrounds For the SM background a collection of events are generated from production channels with at least a single lepton in the final state.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The fraction of events to be included in the SM for each process is fixed by its trigger efficiency and the LO cross section.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Thus, four leading processes are considered: W and Z inclusive productions, QCD multijet contributions, and t¯t production.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The proportions between the four processes are given in [69] as: pp → W ± + jets → ℓ±νℓ + jets (59.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2%) pp → Z + jets → ℓ+ℓ− + jets (6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='7%) pp → t¯t + jets (0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3%) pp → jets (33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='8%) .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' (1) with ℓ = e,µ,τ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The QCD multijet production is by far the largest production process at the LHC.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Although leptons in QCD multijet backgrounds are rarely present and mainly originate from decays of unstable hadrons, the sheer volume of QCD multijet production makes it one of the largest processes in the data stream for the challenge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' New physics signals The signal datasets provided by the challenge consist of events simulated from the following signal models: Leptoquark (LQ): A 80 GeV LQ decaying in to a b and τ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Neutral scalar boson A: A 50 GeV neutral scalar boson A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The production mechanism pp → A+X → Z∗Z∗+X (with X is inclusive activity) followed by both Z∗ decaying into charged leptons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Scalar boson h0: A scalar boson 60 GeV h0 with pp → h0 + X → τ+τ− + X production.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 4 SciPost Physics Submission A charged scalar h±: Charged scalar with 60 GeV mass and pp → h±+X → τν+X production.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The most distinguishing high-level features of these signals when compared with the background processes are the electron, muon, and jet multiplicities and the pT and MET distributions † .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 3 AnomalyCLR In this section we describe the AnomalyCLR method ‡.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Contrastive learning of representations (CLR) [52] is a technique used to construct highly-expressive representations of data for use in downstream tasks, in our case this task is anomaly detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It is self-supervised in that the technique does not require any ‘truth’ labels for the training data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The advantage of this from the collider physics perspective is that the technique could be run directly on experimental data rather than on simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Due to the ability of deep learning methods to learn non-trivial correlations in data that is not expected to be well-modelled by simulation, this is an important aspect of CLR for anomaly detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1 Contrastive learning The basic idea is that some function f (·) (typically a neural network) is used to map from the data space D to a representation space R, with the function being optimised to solve some auxiliary task which does not require truth labels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This auxiliary task is framed as an optimisation problem using ‘pseudo-labels’.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In the anomaly detection scenario addressed in this work, the function that performs the mapping from D to R is optimised only on background data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Given that the collider events or objects such as jets typically consist of unordered sets of particles reconstructed by the experiment, we opt for a permutation-invariant function to perform the mapping from D to R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Specifically, we use a transformer encoder neural network, there are more details on this later in the section.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The auxiliary task that our function is optimised to solve uses augmentations of the collider data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In the traditional contrastive learning approach these augmentations are used to define two types of pseudo-labels: 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Positive-pair labels These labels match each data point in the sample to an augmented version of itself.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Negative-pair labels These labels match each data point in the sample to every other data point which is not itself or an augmented/transformed version of itself.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The function f (·) is then trained to map from the raw data to the representation space such that positive-pairs are close together in R and negative-pairs are far apart in R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' These two optimi- sation goals are referred to as alignment (of positive-pairs) and uniformity (of negative-pairs), respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The augmentations are chosen to be modifications of the data that should leave the underlying physics unchanged, for example a symmetry in the physical system or an augmentation that could mimic a detector resolution effect.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' †We note that since the publication of previous papers using this dataset, a bug fix in the simulation has resulted in a new dataset, and so it is difficult to make direct comparisons between new and old results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' ‡The code will be made available at https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='com/bmdillon/AnomalyCLR.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 5 SciPost Physics Submission Each data point is described by an array of data xi with the subscript labelling the specific data point.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We denote an augmentation of a data point as x′ i, with the positive-pairs and negative-pairs being defined as the sets {(xi, x′ i)} and {(xi, x j)}∪{(xi, x′ j)} for i ̸= j, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The contrastive loss function that the network is trained to minimise then is LCLR = −log es(zi,z′ i)/τ � j̸=i∈batch � es(zi,zj)/τ + es(zi,z′ j)/τ� , (2) where zi = f (xi) and z′ i = f (x′ i) are the outputs of the mapping function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The cosine similar- ity measure s(·,·) is used to compare events and measure distances between them in the new representation space, s(zi,zj) = zi · zj |zi||zj| = cosθi j .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' (3) In this way, s(·,·) projects each vector zi to the surface of a unit hypersphere and computes the cosine distance between each pair.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' As it stands, s(·,·) is not a proper distance metric, however we could form one by taking di j = θi j/π as the distance between each event in the representation space, although we do not explore this here.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The numerator of the contrastive loss in Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' (2) accounts for the positive-pair and alignment, where distances between events and their augmented counter-parts enter.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' While the denominator accounts for the negative-pairs and uniformity, where distances between completely different events are accounted for.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The degree to which we trade off between the different tasks is determined by the temperature hyper-parameter τ in the loss function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 CLR for anomaly detection While contrastive learning has been shown to be very useful in generating representations for downstream classification tasks [53], there is a potential issue when using this approach for down- stream anomaly detection tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For the classification task, for example in [53], the function f (·) is optimised on data from both the background and signal classes, despite not using their truth- labels explicitly in the optimisation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Through the contrastive learning this allows the function to encode non-trivial features of both the background and signal data in the representations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' When using contrastive learning for a downstream anomaly detection task however, the function f (·) is optimised on just the background data (or at least a significantly background-dominated dataset).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This means that the representation learned by the function f (·) will focus solely on features rele- vant for the background data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This could mean that anomalous data is not out-of-distribution and so may not lead to competitive performance in downstream anomaly detection tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This will become evident when we look at the results in Section 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' To remedy this we introduce Anoma- lyCLR, a modified approach to contrastive learning for anomaly detection in particle physics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' At the core of this approach is the introduction of ‘anomaly-augmentations’, such that we now have two categories for augmentations: 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Physical augmentations These are augmentations of the data that we would like the mapping to be invariant to.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Anomaly-augmentations These are unphysical augmentations of the data that are supposed to mimic potential anoma- lies, we want the representations to be highly discriminative towards these augmentations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 6 SciPost Physics Submission We add a third pseudo-label: 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Anomaly-pair labels These labels match each data point in the sample to an anomaly-augmented version of itself.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The advantage of anomaly-augmentations is that we can increase the sensitivity of the anomaly detection tools to anomalies using just the background data, potentially the data directly measured at colliders.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This keeps the approach in line with the original data-driven CLR idea.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We can then define the anomaly-augmented contrastive loss function as LAnomCLR = −log e[s(zi,z′ i)−s(zi,z∗ i )]/τ � j̸=i∈batch � es(zi,zj)/τ + es(zi,z′ j)/τ� , (4) where we denote the representations of the anomaly-augmented events by z∗, and so the anomaly- pair is defined as {(xi, x∗ i )}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Note that the anomaly-augmentations only enter in the numerator of Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' (4), and without these the loss function becomes the regular contrastive loss function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Introducing the anomaly-pairs we expose the network to data features that are outside of the background distribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The CLR portion of the loss function still optimises for alignment and uniformity, however this uniformity is now disrupted by the anomaly-pair term.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' As a result the background data will not be uniformly distributed in the representation space, with some regions encoding features of the anomaly-augmented data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This means that anomalous data with features similar to those generated by the anomaly-augmentations should be out-of-distribution in this representation space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We did some minor testing on alternative forms of this loss function, for example including the anomaly-augmentations in the denominator of the loss function with the negative-pairs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' How- ever since the anomaly-augmentations compute distances between a data point and its augmented counter-part, and not between other data points (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' i ̸= j), it is more intuitive to include this term in the numerator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The denominator in Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' (4) is used to encode features in the represen- tation space that discriminate between the different data points used during training, which for anomaly detection is the background data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This is not necessary for anomaly detection, and the anomaly-pairs should provide the representations with all the discriminative power they need, so we experimented with removing the denominator in Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' (4) altogether, and found that this is sufficient.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In this case the loss function is written as L+ AnomCLR = −log e[s(zi,z′ i)−s(zi,z∗ i )]/τ = s(zi,z∗ i ) − s(zi,z′ i) τ , (5) where the plus sign in L+ AnomCLR indicates that only positive-pairs are used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This results in a much less computationally expensive loss function, since we no longer need to compute pair-wise cor- relations between each entry in a batch the complexity scales as Nbatch rather than N 2 batch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We also remove the dependence on τ in L+ AnomCLR, since there is no longer a trade-off between positive- and negative-pairs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We could of course introduce a term to control the trade-off between the physical and anomaly-augmentation terms, but we do not explore that here.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In our results we will compare the performance of both loss functions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 7 SciPost Physics Submission 4 Application to event-level anomalies The application of AnomalyCLR to different physical scenarios requires an understanding of the data and the physics in order to construct the physical and anomaly-augmentations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For the event- level dataset discussed in Section 2 we consider three physical augmentations the data: 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Azimuthal rotations The whole final state is rotated by an angle φ randomly sampled from [0,2π].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' η − φ smearing The (η,φ) coordinate of every object in the event is resampled according from a Normal distribution centred on the original coordinate and with a variance inversely proportional to the pT, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' η′ ∼ N (η,σ(pT)) and φ′ ∼ N (φ,σ(pT)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Energy smearing The pT of every object in the event is re-sampled according to p′ T ∼ N (pT, f (pT)) with f (pT) determining the strength of the smearing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' These augmentations reflect both the symmetries in the data and the experimental resolution of the detector.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Detectors are imperfect, especially in measuring jet energies, and we encode this in the representations of the data through the energy-smearing augmentation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Here we re-sample the jet pT’s as p′ T ∼ N (pT, f (pT)), where f (pT) = � 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='052p2 T + 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='502pT is the energy smearing applied by Delphes (the pT’s are normalised by 1GeV).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' If not explicitly mentioned, we always assume units of GeV for energy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For the anomaly-augmentation we consider some very simple scenarios: 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Multiplicity shift, x′ i = m(xi) For each event m(·) adds a random number of electrons, muons, and jets to the event.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The number is chosen randomly within the limits (ne,4− ne), (nµ,4− nµ), and (nj,10− nj) for electrons, muons, and jet, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The azimuthal angle and pseudo-rapidities are also chosen randomly within the limits allowed, and the pT for each object is chosen as a random fraction of the maximum pT in the event.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Once the objects have been added, the MET of the event is recalculated and updated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Multiplicity shift, keeping MET and the total pT constant, , x′ i = m(xi) This is similar to the above augmentation, but now m(·) generates the extra objects by splitting the existing objects and smearing the η−φ coordinates using the function used in the physical augmentations above.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' pT and MET shifts, x′ i = spT (xi) Here spT (·) shifts the pT’s in the event by the same random factor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We randomly choose whether we shift just the MET, just the reconstructed object pT’s, or both.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' And we ensure that the the trigger selection is not spoiled by these shifts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' With the physical augmentations we apply all of them simultaneously, whereas for the anomaly- augmentations we apply just one augmentation to each event.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The augmentation that is applied is selected randomly and uniformly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We do not apply both a physical augmentation and an anomaly- augmentation to the events in s(zi,z∗ i ), since this would conflict with the optimisation goal of the s(zi,z′ i) term.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It would also be possible to have an anomaly-augmentation that removes objects from the event, however this effect is already captured by the augmentation that adds objects to the event.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Many of the events in the background have the minimal multiplicity allowed by the applied cuts, so the effect of an anomaly-pair with a low multiplicity background event and the same event augmented to have more objects is the exact same as the effect of an anomaly-pair 8 SciPost Physics Submission with a high-multiplicity background event augmented to have less objects.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This is because of the symmetry in the distance function s(zi,z∗ i ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' So the anomaly-augmentations here are as general as can be, and do not target any specific new physics scenario, therefore the technique should be model-agnostic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Architecture and training The collider event data being used has a well-defined structure: MET: one entry with (pT,η,φ) Electrons: four entries, each with (pT,η,φ) Muons: four entries, each with (pT,η,φ) Jets: ten entries, each with (pT,η,φ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This amounts to a 19 × 3 array, with the electrons, muons, and jets being ordered by pT and hav- ing zero-padded entries where there is less than the maximum allowed number of reconstructed objects.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The multiplicity is typically much less than the maximum allowed, so the data for a single collider event can have many zeros.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The transformer allows us to avoid this by having a permutation-invariant and variable length input format.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Because the data is now processed in a permutation-invariant way, the information on which entry corresponds to which object (MET, electron, muon, or jet) is lost.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We reinstate this information by adding a one-hot encoded ID vec- tor to (pT,η,φ), with a 1 indicating the correct ID.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This means that each reconstructed object is now represented by a 7D vector.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Before passing the kinematic data to the transformer we do some very minor preprocessing to make sure that the numbers the networks see are O(1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Specifically, we divide all MET and pT values by the average pT of all objects (electrons, muons, jets) in the background dataset, we do not shift the values to be centred on zero because the distribution is highly peaked at zero and we want the preprocessed data to have the same sparsity as the original data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We then divide all η and φ values by 4 and π, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' When training the AutoEncoder networks discussed in the next section we use the same preprocessing of the data, this ensures that any difference in the results can be attributed to AnomalyCLR.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The transformer starts by projecting each object to a larger vector whose dimension is deter- mined by the embedding dimension.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The embeddings for each object are then passed through the transformer, with a feed-forward network between each transformer layer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The output from the transformer has a dimension of (n× model dimension) with n being the number of objects in the event.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The last steps are to sum over the n vectors in this output, which enforces the permutation- invariance, and to pass this vector through a fully-connected head network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The output of this head network is what is passed to the loss function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For more details on the architecture we re- fer the reader to [53], here we only list the hyper-parameters used in training the network in Table 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The representation used in the anomaly detection task is taken from the output of the transformer network, before being passed through the head network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It is well documented in the machine learning literature that these intermediate representations from self-supervised networks generally contain more discriminating features, for example in [52].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 9 SciPost Physics Submission hyper-parameter model (embedding) dimension 160 feed-forward hidden dimension 160 output dimension 160 # self-attention heads 4 # transformer layers (N) 4 # layers 2 dropout rate 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1 hyper-parameter optimiser Adam(β1=0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='9, β2=0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='999) learning rate 5 × 10−5 batch size 128 # epochs 500 Table 1: Default setup of the transformer-encoder network and the AnomalyCLR train- ing, unless noted explicitly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 5 Anomaly scores The basic flow in an AutoEncoder involves two steps;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' (i) mapping high-dimensional input data to a compressed latent space using a neural network called an encoder, and (ii) mapping the compressed latent space representation to a reconstructed version of the input data using a neural network called a decoder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We refer to the encoder network as e(·) and the decoder network as d(·).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' With input data of dimension D, and a bottleneck of dimension B, the encoder maps e : �D → �B, while the decoder maps d : �B → �D, with the AutoEncoder defined as h = e ◦ d : �D → �D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Acting on a single input ⃗x, the AutoEncoder returns ⃗x′ = h(⃗x), and is optimised to minimise the mean-squared-error (MSE) loss function between the input and reconstructed input, L(⃗x,θ) = � ⃗x − ⃗x′�2 , (6) where θ represents the learnable parameters of the AutoEncoder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In the limit where the AutoEn- coder is able to reconstruct inputs perfectly, which is guaranteed to be possible when D = B, the function hθ(·) is simply the identity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' But with B < D the AutoEncoder may not be able to perfectly reconstruct all features in the data, and therefore it should learn to reconstruct only the most common or prominent features in the data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This means that events containing rare or anomalous features should have a larger ‘reconstruction loss’, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' L(⃗x,θ), and this can then be used as the anomaly score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The encoder and decoder networks have 5 feed forward layers each with 256, 128, 64, 32, and 16 neurons, connected by a 5-dimensional bottleneck.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The activation function between layers is a LeakyReLU with default slope.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The decoder is a mirrored version of the encoder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We don’t apply regularization techniques during training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The training is performed using Adam optimiser with learning rate 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='001 for 100 epochs, the batch size is 4096, and the number of SM events used is 106.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Note that we have not optimised the AutoEncoder architecture, simply choosing the same architecture used in [39].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Instead we have only ensured that they are trained to convergence and that the training is stable.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The AutoEncoder is trained on both the representations obtained from contrastive learning and the raw data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In the case of the raw data we apply the same preprocessing to the data as is applied to the data in the contrastive learning network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In this way we ensure that any differences in the anomaly detection performance can be attributed to the contrastive learning methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 10 SciPost Physics Submission 6 Results In this section we present some results using the different techniques discussed in the preceding sections.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The results here are three-fold;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' we first compare the different methods based on anomaly detection performance, we then study the effects of the different anomaly-augmentations on the AnomalyCLR performance, and lastly we look at the effect of the representation dimension on the performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1 Comparison of methods We compare the methods using the ROC (Receiver Operating Characteristic) curves, the SI (Signif- icance Improvement) curves, and the AUC (Area Under the ROC Curve).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The baseline we compare to is the AutoEncoder trained on raw kinematic data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We present results using the CLR method without anomaly-augmentations (LCLR), and the CLR method with anomaly-augmentations (both LAnomCLR and L+ AnomCLR).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' So we have 4 methods in total to compare.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For all results on the raw data we have trained 5 AutoEncoder networks and taken the central value and the error estimation from the mean and standard deviation of the results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For the CLR methods we also aggregate over 5 different runs, where in each run we train a different transformer network and a different Au- toEncoder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The CLR representations have a dimension of 160 and where anomaly-augmentations are used we have used them all as outlined in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 1 we present AnomalyCLR results using L+ AnomCLR and see that it leads to significant improvements over the raw data representations, not only in the AUC but also at all signal efficiencies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In the Significance Improvement (SI) curves we also see large improvements, with the SI being between ∼ 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='5−4 for A → 4l and h+.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We can see from Table 2 that the L+ AnomCLR loss function is clearly advantageous over LAnomCLR, beating it on all signals with the exception of A → 4l, where LAnomCLR achieves better performance at εs =0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A point of interest here is that the AutoEncoder on raw data outperforms the AutoEncoder on the CLR representations in most cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This is likely due to the fact that traditional CLR optimises for uniformity, and since it is trained on background only, the mapping is not optimised to sepa- rate SM-like background events from any event which may look different to that.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The benefit of Signal AE-Raw CLR AnomCLR AnomCLR+ AUC A 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='885(2) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='880(7) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='907(6) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='909(3) h0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='755(2) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='740(5) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='765(4) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='776(2) h+ 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='900(4) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='87(1) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='913(2) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='930(1) LQ 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='856(2) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='841(9) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='854(3) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='880(1) ε−1 b (εs =0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3) A 47(2) 80(22) 156(34) 139(20) h0 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='9(7) 11(1) 18(1) 23(1) h+ 60(10) 28(6) 98(9) 171(7) LQ 24.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='4(6) 18(2) 28(3) 39(1) SI(εs =0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3) A 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='05(5) 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='7(4) 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='7(4) 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='5(2) h0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='16(3) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='99(4) 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='26(4) 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='44(3) h+ 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3(2) 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6(2) 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0(1) 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='9(1) LQ 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='48(2) 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3(1) 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6(1) 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='88(2) Table 2: Comparison of the different CLR loss functions, with and without anomaly- augmentations, and the AE trained on raw data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 11 SciPost Physics Submission 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='8 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 ϵs 100 101 102 103 ϵ−1 b AE-Raw A, AUC=0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='885(2) h0, AUC=0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='755(2) h+, AUC=0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='900(4) LQ, AUC=0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='856(2) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='8 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 ϵs 100 101 102 103 ϵ−1 b AnomCLR+ A, AUC:0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='909(3) h0, AUC:0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='776(2) h+, AUC:0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='930(1) LQ, AUC:0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='880(1) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='8 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 ϵs 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 SI AE-Raw A h0 h+ LQ 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='8 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 ϵs 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='0 SI AnomCLR+ A h0 h+ LQ Figure 1: Comparison between the AE on raw data and the AE on the CLR representa- tions trained with the L+ AnomCLR loss function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' anomaly-augmentations here is strikingly clear.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2 The effect of anomaly-augmentations We now want to study how the addition of the individual anomaly-augmentations affects the anomaly detection performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For this we use just L+ AnomCLR , however we expect the results with LAnomCLR to be similar.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We use a representation dimension of 160 and obtain the error estimate from just 2 runs due to the computational cost of the scan.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We can see from Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 2 that the affect of the augmentations together results in the best over- all performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' One thing we noticed is that it can be difficult to determine from the affect of individual augmentations, or subgroups of them, what the performance of all of them together will be.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For example, in most cases if we take just the m(x) augmentation, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' the multiplicity augmentation that simply adds reconstructed objects, we see that it alone decreases performance below baseline for three out of four signals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' However when used in combination with the others it increases the performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This is most clear for the leptoquark signal, where all augmenta- 12 SciPost Physics Submission 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='800 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='850 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='900 A AUC 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='700 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='750 h0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='800 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='850 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='900 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='950 h+ m(x) m(x) spT(x) m(x)/m(x) all anomaly augmentations 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='750 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='800 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='850 LQ Figure 2: Results of a scan on the anomaly-augmentations used with the L+ AnomCLR loss function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The augmentations are defined in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The dashed lines here correspond to the AutoEncoder on raw data baseline performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' tions taken individually result in a performance which is at or below baseline, but when taken together we get a significant boost in the AUC.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We also see the interplay between the m(x) and m(x) augmentations, since individually these augmentations do not seem to help much, but when they are both applied in the same optimisation we see a reduced error and in most cases better performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' When drawing conclusions here we should keep in mind that only two runs for each combination have been used to compute the mean and error estimation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3 The effect of representation dimension With CLR we can project our raw data from D to a representation of any dimension we like.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We would expect that the larger the representation dimension the more information that can be encoded in the space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' However we also expect that this would plateau or even peak at some point, and this what we want to investigate here.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' For this we use just L+ AnomCLR , however we expect the results with LAnomCLR to be similar.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Here we also obtain the error estimate from just 2 runs due to the computational cost of the scan.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' In Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 3 we see that increasing the representation dimension certainly improves the perfor- mance of the anomaly detection, at least up until a certain point.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The A, h0, and LQ signals all appear to achieve peak performance somewhere between dimensions 120 and 200, while the h+ signals performance increases right up until 400.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' There is no fundamental limitation related to the representation size which we would expect to cause a degradation at larger dimensions, however there are two points we should keep in mind here.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The first is simple, these means and variances are calculated with only two runs, so more runs might present a clearer picture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The 13 SciPost Physics Submission 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='850 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='875 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='900 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='925 A AUC 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='740 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='760 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='780 h0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='900 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='920 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='940 h+ 4 8 12 20 40 80 120 160 200 300 400 500 representation dimension 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='840 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='860 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='880 LQ Figure 3: Results of a scan on the representation dimension used with the L+ AnomCLR loss function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The dashed lines here correspond to the AutoEncoder on raw data baseline performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' second point is that we have not optimised the AutoEncoder architecture or hyper-parameters as the representation size increases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' While it is beyond the scope of this paper, it is possible that an independent hyper-parameter optimisation for each representation dimension would improve these results, particularly at larger dimensions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' What these results show is that there is a clear tendancy for the results to improve as we increase from dimensions of ∼ 4 to ∼ 100, as we would naturally expect.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 7 Summary & conclusions In this paper we have introduced AnomalyCLR§, a new method for density-based anomaly detec- tion in high-energy physics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It makes use of anomalous augmentations of collider data to build a representation space from which to construct anomaly scores with a range of methods, for exam- ple using AutoEncoders.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' It is a self-supervised method, based on the contrastive learning idea.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We tested this method on the CMS ADC dataset, and compared to the raw data baselines we find large improvements on all signals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' At a fixed signal efficiency of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3 and a fixed representation dimen- sion of 160 we find significance improvements for the different signals in the range of 14−70%, and a decreased relative error on the significance improvement in each case.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Allowing for varying signal efficiencies and representation dimensions would improve these performance markers even §The AnomalyCLR code, along with the event-level anomaly detection application, will be made available at https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='com/bmdillon/AnomalyCLR.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 14 SciPost Physics Submission further.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Density-based anomaly detection, using AutoEncoders or normalising flows, suffer from the ambiguity that a change in the ‘coordinate system’ or representation of the data results in a fun- damental change in how the anomaly score is defined.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This makes it difficult to choose a suitable representation by hand, for example a simple re-mapping of pT’s along with some re-scaling of numerical inputs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' These simple choices are difficult to motivate from a physics perspective and can drastically change the results of the anomaly detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This change can be for better or for worse, and typically depends on the signal models used to test the algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' AnomalyCLR addresses this by constructing a representation of the data using self-supervised contrastive learning with the addition of anomaly-augmented data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The anomaly-augmented data is constructed from the background data through feature augmentation, designed to emulate a generic anomaly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We have discussed in detail how we do this for the event-level anomalies in the CMS ADC dataset, however this would of course be different in different physics cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We proposed a new loss function which we use to train a deep transformer-based neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This network projects the events to a new representation, in which the anomaly-augmented events are far from their original counterparts, while being close to events which are similar.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' The transformer network then learns a highly discriminative representation of the events which is sensitive to the presence of potential anomalies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We have seen that the choice of these augmentations is quite model agnostic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This model-agnostic nature of the approach can be seen in how the results improve across all four signals considered.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' We have shown the effectiveness of self-supervision and the idea of anomaly-augmentations in significantly enhancing anomaly detection performance in a model-agnostic way.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' This opens the door to further studies, such as improving the density-estimation portion of the method with a more sophisticated hyper-parameter optimisation of the AutoEncoders, using normalising flows, or even using the Normalised AutoEncoder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' More generally, the use of anomaly-augmented data could be explored further in other anomaly detection approaches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Acknowledgements We would like to thank Jernej Kamenik and Ben Nachman for their helpful comments on the manuscript.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' BMD acknowledges funding from the Alexander von Humboldt Foundation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' LF, TM, and TP are funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant 396021762 – TRR 257: Particle Physics Phenomenology after the Higgs Discovery and Germany’s Excellence Strategy EXC 2181/1 - 390900948 (the Heidelberg STRUCTURES Ex- cellence Cluster).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' References [1] A general search for new phenomena with the ATLAS detector in pp collisions at �s = 8 TeV, ATLAS-CONF-2014-006 (ATLAS-CONF-2014-006) (2014), https://cds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='cern.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='ch/record/ 1666536.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [2] C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Collaboration, MUSiC, a Model Unspecific Search for New Physics, in pp Collisions at �s = 8 TeV (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 15 SciPost Physics Submission [3] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Plehn, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Butter, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Krause, Modern Machine Learning for LHC Physicists (2022), arXiv:2211.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='01421.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [4] E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Metodiev, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Thaler, Classification without labels: Learning from mixed samples in high energy physics, JHEP 10, 174 (2017), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP10(2017)174, arXiv:1708.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02949.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [5] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Shih, Anomaly Detection with Density Estimation, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 101, 075042 (2020), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='075042, arXiv:2001.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='04990.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [6] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hallin, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Isaacson, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kasieczka, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Krause, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Quadfasel, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Schlaffer, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Shih and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sommerhalder, Classifying anomalies through outer density estimation, Phys- ical Review D 106(5) (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/physrevd.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='106.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='055006, https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 1103%2Fphysrevd.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='106.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='055006.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [7] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Collins, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Howe and B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman, Anomaly Detection for Resonant New Physics with Machine Learning, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Lett.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 121(24), 241803 (2018), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevLett.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='121.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='241803, arXiv:1805.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02664.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [8] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Collins, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Howe and B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman, Extending the search for new resonances with ma- chine learning, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D99(1), 014038 (2019), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='014038, arXiv:1902.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02634.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [9] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Andreassen, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Shih, Simulation Assisted Likelihood-free Anomaly Detection, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 101(9), 095004 (2020), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='095004, arXiv:2001.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='05001.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [10] O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Amram and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Suarez, Tag N’ Train: a technique to train improved classifiers on unlabeled data, JHEP 01, 153 (2021), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP01(2021)153, arXiv:2002.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12376.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [11] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Raine, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Klein, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sengupta and T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Golling, CURTAINs for your Sliding Window: Con- structing Unobserved Regions by Transforming Adjacent Intervals (2022), arXiv:2203.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='09470.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [12] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hallin, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kasieczka, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Quadfasel, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Shih and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sommerhalder, Resonant anomaly detection without background sculpting (2022), arXiv:2210.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='14924.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [13] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Chen, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman and F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sala, Resonant Anomaly Detection with Multiple Reference Datasets (2022), arXiv:2212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='10579.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [14] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Golling, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Klein, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mastandrea and B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman, FETA: Flow-Enhanced Transportation for Anomaly Detection (2022), arXiv:2212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='11285.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [15] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Finke, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Krämer, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Lipp and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mück, Boosting mono-jet searches with model-agnostic ma- chine learning, JHEP 08, 015 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP08(2022)015, arXiv:2204.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='11889.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [16] G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Aad et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=', Dijet resonance search with weak supervision using �s = 13 TeV pp collisions in the ATLAS detector, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Lett.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 125(13), 131801 (2020), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevLett.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='125.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='131801, arXiv:2005.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02983.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [17] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Heimel, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kasieczka, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Plehn and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Thompson, QCD or What?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=', SciPost Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 6, 030 (2019), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='21468/SciPostPhys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='030, arXiv:1808.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='08979.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 16 SciPost Physics Submission [18] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Farina, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nakai and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Shih, Searching for New Physics with Deep Autoencoders, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 101, 075021 (2020), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='075021, arXiv:1808.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='08992.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [19] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Blance, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Spannowsky and P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Waite, Adversarially-trained autoencoders for robust un- supervised new physics searches, JHEP 10, 047 (2019), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP10(2019)047, arXiv:1905.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='10384.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [20] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Roy and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Vijay, A robust anomaly finder based on autoencoder (2019), arXiv:1903.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02032.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [21] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Cheng, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='-F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Arguin, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Leissner-Martin, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pilette and T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Golling, Variational Autoencoders for Anomalous Jet Tagging (2020), arXiv:2007.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='01850.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [22] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pol, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Berger, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Cerminara, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Germain and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pierini, Anomaly Detection With Condi- tional Variational Autoencoders, In Eighteenth International Conference on Machine Learning and Applications (2020), arXiv:2010.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='05531.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [23] O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Atkinson, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Bhardwaj, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Englert, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ngairangbam and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Spannowsky, Anomaly detection with convolutional Graph Neural Networks, JHEP 08, 080 (2021), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP08(2021)080, arXiv:2105.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='07988.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [24] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Tsan, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kansal, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Aportela, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Diaz, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Duarte, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Krishna, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mokhtar, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='-R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Vlimant and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pierini, Particle Graph Autoencoders and Differentiable, Learned Energy Mover’s Distance, In 35th Conference on Neural Information Processing Systems (2021), arXiv:2111.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12849.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [25] V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ngairangbam, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Spannowsky and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Takeuchi, Anomaly detection in high- energy physics using a quantum autoencoder, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 105(9), 095004 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='105.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='095004, arXiv:2112.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='04958.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [26] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ostdiek, Deep Set Auto Encoders for Anomaly Detection in Particle Physics (2021), arXiv:2109.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='01695.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [27] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Barron, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Curtin, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kasieczka, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Plehn and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Spourdalakis, Unsupervised Hadronic SUEP at the LHC, JHEP 12, 129 (2021), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP12(2021)129, arXiv:2107.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12379.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [28] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kahn, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Gonski, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ochoa, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Williams and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Brooijmans, Anomalous jet identification via sequence modeling, JINST 16(08), P08012 (2021), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1088/1748-0221/16/08/P08012, arXiv:2105.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='09274.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [29] K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Fraser, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Homiller, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mishra, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ostdiek and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Schwartz, Chal- lenges for unsupervised anomaly detection in particle physics, JHEP 03, 066 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP03(2022)066, arXiv:2110.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='06948.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [30] F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Canelli, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' de Cosa, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pottier, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Niedziela, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pedro and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pierini, Autoencoders for semivisible jet detection, JHEP 02, 074 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP02(2022)074, arXiv:2112.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02864.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [31] Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hao, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kansal, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Duarte and N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Chernyavskaya, Lorentz Group Equivariant Autoencoders (2022), arXiv:2212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='07347.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [32] O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Atkinson, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Bhardwaj, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Englert, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Konar, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ngairangbam and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Spannowsky, IRC- Safe Graph Autoencoder for Unsupervised Anomaly Detection, Front.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Artif.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Intell.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 5, 943135 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3389/frai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='943135, arXiv:2204.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12231.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 17 SciPost Physics Submission [33] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Bradshaw, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Chang and B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ostdiek, Creating simple, interpretable anomaly de- tectors for new physics in jet substructure, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 106(3), 035014 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='106.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='035014, arXiv:2203.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='01343.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [34] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Bortolato, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kamenik and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Smolkoviˇc, Bump Hunting in Latent Space (2021), arXiv:2103.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='06595.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [35] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Plehn, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sauer and P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sorrenson, Better Latent Spaces for Better Autoencoders, SciPost Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 11, 061 (2021), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='21468/SciPostPhys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='061, arXiv:2104.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='08291.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [36] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Faroughy and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kamenik, Uncovering latent jet substructure, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 100, 056002 (2019), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='056002, arXiv:1904.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='04200.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [37] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Faroughy, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kamenik and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Szewc, Learning the latent structure of collider events, JHEP 10, 206 (2020), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP10(2020)206, arXiv:2005.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12319.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [38] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kamenik and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Szewc, Null Hypothesis Test for Anomaly Detection (2022), arXiv:2210.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02226.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [39] V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mikuni, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Shih, Online-compatible unsupervised nonresonant anomaly detection, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 105(5), 055006 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='105.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='055006, arXiv:2111.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='06417.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [40] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Favaro, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Plehn, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sorrenson and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Krämer, A Normalized Autoencoder for LHC Triggers (2022), arXiv:2206.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='14225.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [41] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Park, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rankin, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='-M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Udrescu, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Yunus and P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Harris, Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge (2020), arXiv:2011.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='03550.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [42] P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Jawahar, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Aarrestad, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Chernyavskaya, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pierini, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Wozniak, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ngadiuba, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Duarte and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Tsan, Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows, Front.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Big Data 5, 803685 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3389/fdata.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='803685, arXiv:2110.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='08508.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [43] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Caron, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hendriks and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Verheyen, Rare and Different: Anomaly Scores from a combi- nation of likelihood and out-of-distribution models to detect new physics at the LHC, SciPost Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 12(2), 077 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='21468/SciPostPhys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='077, arXiv:2106.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='10164.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [44] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Buss, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Finke, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Krämer, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Morandini, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mück, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Oleksiyuk and T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Plehn, What’s Anomalous in LHC Jets?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' (2022), arXiv:2202.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='00686.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [45] G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kasieczka et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=', The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics (2021), arXiv:2101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='08320.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [46] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Aarrestad et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=', The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider, SciPost Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 12(1), 043 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='21468/SciPostPhys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='043, arXiv:2105.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='14027.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [47] G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kasieczka, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mastandrea, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mikuni, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pettee and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Shih, Anomaly Detec- tion under Coordinate Transformations (2022), arXiv:2209.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='06225.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 18 SciPost Physics Submission [48] G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kasieczka, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Plehn, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Butter, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Cranmer, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Debnath, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Fairbairn, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Faroughy, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Fedorko, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Gay, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Gouskos, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kamenik et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=', The Machine Learning Land- scape of Top Taggers, SciPost Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 7, 014 (2019), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='21468/SciPostPhys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='014, arXiv:1902.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='09914.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [49] H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Qu and L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 101, 056019 (2020), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='056019, arXiv:1902.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='08570.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [50] H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Qu, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Li and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Qian, Particle Transformer for Jet Tagging (2022), arXiv:2202.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='03772.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [51] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Bogatskiy, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hoffman, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Miller and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Offermann, PELICAN: Permutation Equiv- ariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics (2022), arXiv:2211.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='00454.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [52] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Chen, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kornblith, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Norouzi and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hinton, A Simple Framework for Contrastive Learning of Visual Representations, Proceedings of the 37th International Conference on Machine Learning, PMLR 119, 1597 (2020), https://proceedings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='mlr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='press/v119/chen20j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' html, arXiv:2002.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='05709.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [53] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Kasieczka, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Olischlager, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Plehn, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sorrenson and L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Vo- gel, Symmetries, safety, and self-supervision, SciPost Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 12(6), 188 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='21468/SciPostPhys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='188, arXiv:2108.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='04253.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [54] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Dillon, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mastandrea and B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nachman, Self-supervised anomaly detection for new physics, Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' D 106(5), 056005 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1103/PhysRevD.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='106.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='056005, arXiv:2205.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='10380.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [55] R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Tombs and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Lester, A method to challenge symmetries in data with self-supervised learning, JINST 17(08), P08024 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1088/1748-0221/17/08/P08024, arXiv:2111.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='05442.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [56] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Cheng and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Courville, Invariant representation driven neural classifier for anti-QCD jet tagging, JHEP 10, 152 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP10(2022)152, arXiv:2201.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='07199.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [57] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hayat, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Harrington, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Stein, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Luki´c and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mustafa, Estimating Galactic Distances From Images Using Self-supervised Representation Learning (2021), arXiv:2101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='04293.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [58] G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Stein, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Blaum, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Harrington, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Medan and Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Lukic, Mining for Strong Gravitational Lenses with Self-supervised Learning, Astrophys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 932(2), 107 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3847/1538- 4357/ac6d63, arXiv:2110.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='00023.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [59] G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Stein, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Harrington, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Blaum, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Medan and Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Lukic, Self-supervised similarity search for large scientific datasets, In 35th Conference on Neural Information Processing Systems (2021), arXiv:2110.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='13151.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [60] V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Bonjean, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Tanimura, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Aghanim, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Bonnaire and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Douspis, Self-supervised component separation for the extragalactic submillimeter sky (2022), arXiv:2212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02847.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [61] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Tack, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mo, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Jeong and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Shin, Csi: Novelty detection via contrastive learning on distri- butionally shifted instances, doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='48550/ARXIV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2007.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='08176 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 19 SciPost Physics Submission [62] V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sehwag, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Chiang and P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mittal, Ssd: A unified framework for self-supervised outlier detec- tion, doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='48550/ARXIV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2103.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='12051 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [63] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Reiss and Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hoshen, Mean-shifted contrastive loss for anomaly detection, doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='48550/ARXIV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2106.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='03844 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [64] H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Hojjati, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ho and N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Armanfard, Self-supervised anomaly detection: A survey and outlook, doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='48550/ARXIV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2205.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='05173 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [65] E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Govorkova, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Puljak, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Aarrestad, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pierini, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Wo´zniak and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ngadiuba, LHC physics dataset for unsupervised New Physics detection at 40 MHz, Sci.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Data 9, 118 (2022), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1038/s41597-022-01187-8, arXiv:2107.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='02157.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [66] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Sjöstrand, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ask, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Christiansen, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Corke, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Desai, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Ilten, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mrenna, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Prestel, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Rasmussen and P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Skands, An Introduction to PYTHIA 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2, Comput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Commun.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 191, 159 (2015), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='cpc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='2015.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='01.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='024, arXiv:1410.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='3012.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [67] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' de Favereau, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Delaere, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Demin, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Giammanco, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Lemaître, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Mertens and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Selvaggi, DELPHES 3, A modular framework for fast simulation of a generic collider experiment, JHEP 02, 057 (2014), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP02(2014)057, arXiv:1307.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='6346.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [68] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Cacciari, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Salam and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Soyez, The anti-kt jet clustering algorithm, JHEP 04, 063 (2008), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1088/1126-6708/2008/04/063, arXiv:0802.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1189.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' [69] O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Cerri, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Nguyen, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Pierini, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Spiropulu and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='-R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' Vlimant, Variational Au- toencoders for New Physics Mining at the Large Hadron Collider, JHEP 05, 036 (2019), doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='1007/JHEP05(2019)036, arXiv:1811.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content='10276.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'} +page_content=' 20' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfrgpm/content/2301.04660v1.pdf'}