[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127116-en":3,"doc-seo-127116-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127116,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Using Machine Learning to Search for Vector Boson Scattering at the CMS Detector During Run 2","This thesis presents the application of multiple machine learning methods to search for vector boson scattering (VBS) events in the semileptonic WV channel. VBS is targeted because it probes electroweak symmetry breaking and the Higgs mechanism, with sensitivity to physics beyond the Standard Model. Boosted decision trees and deep neural networks are trained on Monte Carlo samples and applied to 137 fb−1 of CMS proton–proton collision data from 2016–2018 at √s = 13 TeV, with hyperparameters and inputs systematically varied to identify the best-performing models.","Northern Illinois University  \nHuskie Commons  \n\n| Graduate Research Theses & Dissertations | Graduate Research & Artistry |\n| --- | --- |\n| 2023\u003Cbr>Using Machine Learning to Search for Vector Boson Scattering atthe CMS Detector During Run 2\u003Cbr>Mark Mekosh\u003Cbr>[markmekosh@gmail.com](markmekosh@gmail.com)\u003Cbr>Follow this and additional works at: [https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations](https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations)\u003Cbr> Part of the Physics Commons |  |\n\nRecommended Citation  \nMekosh, Mark, \"Using Machine Learning to Search for Vector Boson Scattering at the CMS Detector During Run 2\" (2023) . Graduate Research Theses & Dissertations. 7164.  \n[https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7164](https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7164)  \nThis Dissertation/Thesis is brought to you for free and open access by the Graduate Research & Artistry at Huskie Commons. It has been accepted for inclusion in Graduate Research Theses & Dissertations by an authorized administrator of Huskie Commons. For more information, please contact [jschumacher@niu.edu](jschumacher@niu.edu).  \nABSTRACT  \nUSING MACHINE LEARNING TO SEARCH FOR VECTOR BOSON SCATTERING AT THE CMS DETECTOR DURING RUN 2  \nMark Mekosh, M.S.  \nDepartment of Physics  \nNorthern Illinois University, 2023  \nMichael Eads, Director  \nThis work reports on the use of different machine learning (ML) techniques in the search for vector boson scattering (VBS) events in the semileptonic WV channel. VBS is an important process for studying electroweak symmetry breaking (EWSB), the Higgs mechanism, as well as for probing beyond the standard model physics. Boosted decision trees as well as deep neural networks were trained on Monte Carlo simulation samples and applied to 137 fb −1 of proton-proton collision data taken from 2016 to 2018 by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) with a center of mass energy √s = 13 TeV. The ML model hyperparameters and inputs were varied to find the best performing combination, and the results of those models are discussed.  \nNORTHERN ILLINOIS UNIVERSITY  \nDE KALB, ILLINOIS  \nMAY 2023  \nUSING MACHINE LEARNING TO SEARCH FOR VECTOR BOSON SCATTERING AT THE CMS DETECTOR  \nDURING RUN 2  \nBY  \nMARK MEKOSH  \n© 2023 Mark Mekosh  \nA THESIS SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE  \nMASTER OF SCIENCE  \nDEPARTMENT OF PHYSICS  \nThesis Director:  \nMichael Eads  \nACKNOWLEDGEMENTS  \nI would like to acknowledge my advisor Dr. Michael Eads for his guidance, and especially for his patience, throughout this research. I would also like to acknowledge Sergey Uzunyan for the code I used for much of the research, it was a tremendous learning opportunity to build onto the code framework he provided. I would also like to thank Ramanpreet Singh for answering my constant questions throughout this whole process. Finally I would like to thank my committee members Dr. Stephen Martin and Dr. Vishnu Zutshi for providing feedback on my thesis.  \nDEDICATION  \nI dedicate this work to my parents who always encouraged me to pursue my education, even when they had no idea what I was studying.  \nTABLE OF CONTENTS  \nPage  \nList [of Tables ................................................... vi](of Tables ................................................... vi)  \n[List of Figures................................................... vii](List of Figures................................................... vii)  \nChapter  \n1 Theory ..................................................... 1  \n1.1 Historical Context .......................................... 1  \n1.2 The standard model ........................................ 2  \n1.2.1 Fermions ........................................... 4  \n1.2.1.1 Quarks ...................................... 6  \n1.2.1.2 Leptons ..................................... 7  \n1.2.2 Bosons ....","cbCaih9AxxLSBSGE","https://ap.wps.com/l/cbCaih9AxxLSBSGE","pdf",7238483,1,110,"English","en",105,"# TABLE OF CONTENTS\n## List of Tables\n## List of Figures\n## Chapter 1 Theory\n### 1.1 Historical Context\n### 1.2 The standard model\n### 1.3 Quantum Field Theories\n## Chapter 2 Vector Boson Scattering\n## Chapter 3 The LHC and the CMS Experiment\n## Chapter 4 Analysis and Methods","[{\"question\":\"What is the primary goal of the thesis?\",\"answer\":\"To search for vector boson scattering events in the semileptonic WV channel using machine learning techniques.\"},{\"question\":\"Which machine learning models are trained and evaluated?\",\"answer\":\"Boosted decision trees and deep neural networks are trained on Monte Carlo simulation samples and evaluated using CMS collision data.\"},{\"question\":\"What dataset and collision conditions are used during Run 2?\",\"answer\":\"The analysis uses 137 fb−1 of proton–proton collision data recorded by the CMS experiment from 2016 to 2018 at a center-of-mass energy of √s = 13 TeV.\"}]","Using Machine Learning to Search for Vector Boson Scattering at the CMS Detector During Run 2 | 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