[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124109-en":3,"doc-seo-124109-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124109,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","The Search for Dark Matter at the ATLAS Detector using Machine Learning","Presents a comprehensive doctoral-level study of dark matter searches using the ATLAS detector, combining particle physics foundations with modern machine-learning techniques for high-energy physics event classification. Covers the Standard Model and extensions, collider and reconstruction methods in ATLAS, Monte Carlo simulation workflows, and dark-matter phenomenology including key experimental evidence and candidate models. Then details ML algorithms and optimisation strategies, and evaluates benchmark datasets using model-independent approaches such as CNN-VAE and related variants to distinguish signal from background.","The Search for Dark Matter at the ATLAS Detector using Machine Learning  \nJoe Michael Melquiades Davies  \nCandidate ID Number: 140096767  \nSubmitted in partial ful􀀌lment of the requirements of the Degree of Doctor of Philosophy  \nSchool of Physical and Chemical Sciences  \nQueen Mary University of London 22-09-2023  \nContents  \n1 Introduction 6  \n2 The Standard Model of Particle Physics and Beyond 9  \n2.1 The Standard Model ......................... 9  \n2.1.1 Electroweak Theory ..................... 12  \n2.1.2 Electroweak Uni􀀌cation ................... 14  \n2.2 Quantum Chronodynamics ..................... 16  \n2.2.1 The Yang-Mills Lagrangian ................. 16  \n2.2.2 Asymptotic Freedom and Quark Con􀀌nement ....... 17  \n2.3 Collider Physics ............................ 18  \n2.3.1 Particle Factorisation .................... 18  \n3 Monte Carlo Simulation and Reconstructed Object in ATLAS 20  \n3.1 Monte Carlo Simulation ....................... 21  \n3.1.1 MC Production ........................ 22  \n3.1.2 Simulation Tools ....................... 23  \n3.2 Reconstruction of Physics Objects ................. 24  \n3.2.1 Hadronic Tau Decays .................... 24  \n3.2.2 Missing Transverse Energy ................. 25  \n3.2.3 Muons ............................. 28  \n3.3 Hadronic Jet Reconstruction .................... 29  \n3.3.1 Jet Input ........................... 30  \n3.3.2 Jet Clustering Algorithms .................. 31  \n3.3.3 Jet Grooming ......................... 33  \n3.3.4 Jet Substructure ....................... 34  \n3.4 Jet Calibration ............................ 35  \n3.5 Jet Tagging .............................. 39  \n4 The Large Hadron Collider and the ATLAS Detector 45  \n4.1 The Large Hadron Collider ..................... 45  \n4.1.1 ATLAS ............................ 50  \n5 Dark Matter 63  \n5.1 Evidence ................................ 63  \n5.1.1 Velocity Dispersion Curves ................. 63  \n5.1.2 The Bullet Cluster ...................... 65  \n5.2 Dark Matter Candidates ....................... 66  \n5.2.1 Weakly Interacting Massive Particles ............ 67  \n5.2.2 Issues with the WIMP Paradigm .............. 78  \n5.3 The Dark Sector ........................... 79  \n5.3.1 Dark Portal Processes .................... 80  \n5.4 Dark Jets ............................... 82  \n6 Machine Learning and Applications to HEP Analysis 85  \n6.1 Background .............................. 85  \n6.2 Machine Learning Libraries ..................... 86  \n6.2.1 Scikit-Learn .......................... 87  \n6.2.2 Tensor􀀍ow .......................... 87  \n6.2.3 Keras ............................. 88  \n6.2.4 PyTorch ............................ 88  \n6.3 Decision Trees ............................. 88  \n6.4 Arti􀀌cial Neural Networks ...................... 92  \n6.4.1 Convolutional Neural Networks ............... 96  \n6.4.2 Autoencoders ......................... 99  \n6.5 Naive Bayes' Classi􀀌er ........................ 101  \n6.6 Optimisation ............................. 103  \n6.6.1 Grid/Random Searches ................... 103  \n6.6.2 Bayesian Optimization .................... 104  \n6.7 Machine Learning Dark Matter Searches at ATLAS ........ 105  \n7 Benchmark Data and Model Independent Event Classi􀀌cation for the LHC 110  \n7.1 The Data ............................... 114  \n7.2 Figures of Merit ........................... 119  \n7.3 CNN-VAE ............................... 119  \n7.4 CNN-􀀌VAE .............................. 122  \n7.5 Results and Discussion ........................ 123  \n7.5.1 CNN-VAE Results ...................... 123  \n7.5.2 CNN-􀀌VAE Results ..................... 125  \n7.5.3 Results from Other Methods ................ 128  \n7.6 Conclusion .............................. 137  \n8 The Search for Resonant Production of Dark Quarks in the Dijet Final State with the ATLAS Detector 140  \n8.1 Data and Simulation ......................... 142  \n8.2 Object and Event Selections ..................... 143  \n8.3 Background Estimation ..................","cbCait71FAEk2mSi","https://ap.wps.com/l/cbCait71FAEk2mSi","pdf",15747032,1,257,"English","en",105,"# Contents\n## Introduction\n## The Standard Model of Particle Physics and Beyond\n## Monte Carlo Simulation and Reconstructed Object in ATLAS\n## The Large Hadron Collider and the ATLAS Detector\n## Dark Matter\n## Machine Learning and Applications to HEP Analysis\n## Benchmark Data and Model Independent Event Classification for the LHC\n## The Search for Resonant Production of Dark Quarks in the Dijet Final State with the ATLAS Detector\n## Possible Improvements on the Search for Dark Quarks through an ML Approach\n## Conclusion\n## Appendices","[{\"question\":\"What detector and experimental context does the work use for dark matter searches?\",\"answer\":\"The study focuses on the ATLAS detector at the Large Hadron Collider and the associated physics objects reconstructed from collider events.\"},{\"question\":\"Which machine-learning methods are applied to HEP analysis in this thesis?\",\"answer\":\"It reviews and applies methods including decision trees, artificial neural networks, convolutional neural networks, autoencoders, Naive Bayes, and optimisation strategies such as grid/random and Bayesian optimisation, with dedicated dark-matter search implementations.\"},{\"question\":\"How does the thesis evaluate model-independent event classification at the LHC?\",\"answer\":\"It introduces benchmark data and compares figures of merit while using approaches such as CNN-VAE and CNN-VAE variants, followed by results and discussion and comparisons to other methods.\"}]","The Search for Dark Matter at the ATLAS Detector using Machine Learning | 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detector and experimental context does the work use for dark matter searches?","Question",{"text":75,"@type":76},"The study focuses on the ATLAS detector at the Large Hadron Collider and the associated physics objects reconstructed from collider events.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning methods are applied to HEP analysis in this thesis?",{"text":80,"@type":76},"It reviews and applies methods including decision trees, artificial neural networks, convolutional neural networks, autoencoders, Naive Bayes, and optimisation strategies such as grid/random and Bayesian optimisation, with dedicated dark-matter search implementations.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis evaluate model-independent event classification at the LHC?",{"text":84,"@type":76},"It introduces benchmark data and compares figures of merit while using approaches such as CNN-VAE and CNN-VAE variants, followed by results and discussion and comparisons to 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