[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121597-en":3,"doc-seo-121597-105":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},121597,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning-Based Energy Estimation for Sterile Neutrino Searches in the NOvA Experiment","This dissertation presents a search for sterile neutrinos using Monte Carlo and experimental data from the NOvA experiment. It develops novel machine learning approaches for energy reconstruction in neutral current events, including a deep learning-based energy estimator integrated into the analysis framework. With this estimator, the sensitivity to sterile-neutrino-induced oscillations is evaluated and reported. The work also examines strategies for further improving the analysis performance and robustness.","Machine Learning-Based Energy Estimation for Sterile Neutrino Searches in the NO􀀗A  \nExperiment  \nA DISSERTATION  \nSUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA  \nBY  \nShaowei Wu  \nIN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  \nDOCTOR OF PHILOSOPHY  \nAdvisor: Professor Gregory Pawloski  \nAugust, 2025  \n© Shaowei Wu 2025  \nALL RIGHTS RESERVED  \nAcknowledgements  \nI would like to express my deepest gratitude to my advisor, Professor Pawloski, for his invaluable academic guidance and support throughout my Ph.D. journey. His patient mentorship has been essential to my development as a researcher.  \nI would like to thank the members of my thesis committee—Professor Coughlin, Professor Heller, and Professor Kapusta—for generously dedicating their time to evaluate my work.  \nI am deeply thankful to my family, especially my wife, Yuqin Xiao, for her unwavering support and companionship throughout my years as a student.  \nI am also grateful to my fellow graduate students, especially Ting Gao, for insightful discussions and theoretical guidance during my doctoral studies.  \nMany thanks to my collaborators in the NOvA collaboration for their support on various aspects of my research. In particular, I would like to acknowledge Dmitrii Torbunov, Joshua Barrow, Shivam, V Hewes, Haejun Oh, Adam Lister, Jeremy Wolcott, Alejandro Yankelevich, Alexander Booth, Adam Aurisano, and Brian Rebel for their contributions.  \nSpecial thanks to my friend Liyi Chen for providing technical support in machine learning, which greatly benefited my research.  \nAbstract  \nThis dissertation presents a search for sterile neutrinos using Monte Carlo datasets and experimental data from the NO􀀗A experiment. This work introduces novel machine learning techniques for energy reconstruction in neutral current events. A deep learning-based energy estimator was developed and integrated into the analysis framework. Using this new energy estimator, the sensitivity to sterile neutrino-induced oscillations is evaluated and presented. Furthermore, the dissertation explores potential methods for further improvement of the analysis.  \nContents  \nAcknowledgements i  \nAbstract ii  \nContents iii  \nList of Tables viii  \nList of Figures x  \n1 Introduction 1  \n2 Physics of Neutrinos 3  \n2.1 The Discovery of Neutrinos ..................... 3  \n2.2 Neutrinos in the standard model . . . . . . . . . . . . . . . . . . 6  \n2.3 Neutrino Oscillation . . . . . . . . . . . . . . . . . . . . . . . . . 9  \n2.3. 1 The History of Neutrino Oscillation Theory   10  \n2.3.2 Neutrino Experiments in History . . . . . . . . . . . . . . 11  \n2.3.3 Neutrino Oscillation in Vacuum . . . . . . . . . . . . . . . 14  \n2.3.4 Two Flavor Neutrino Oscillation in Vacuum . . . . . . . . 19  \n2.3.5 Three Flavor Neutrino Oscillation in Vacuum . . . . . . . 20  \n2.3.6 Matter Potential . . . . . . . . . . . . . . . . . . . . . . . 23  \n2.3.7 Neutrino Oscillation in Matter . . . . . . . . . . . . . . . 26  \n2.4 Sterile Neutrinos . . . . . . . . . . . . . . . . . . . . . . . . . . . 29  \n3 The NO􀀗A Experiment 33  \n3. 1 The NuMI Beam . . . . . . . . . . . . . . . . . . . . . . . . . . . 34  \n3.2 Off-Axis Design ............................ 39  \n3.3 The Liquid Scintillator ........................ 42  \n3.4 The NO􀀗A Detector ......................... 46  \n3.4. 1 The Far Detector . . . . . . . . . . . . . . . . . . . . . . . 46  \n3.4.2 The Near Detector . . . . . . . . . . . . . . . . . . . . . . 48  \n3.5 Data Acquisition System ....................... 49  \n4 Simulation and Calibration 51  \n4. 1 Beam simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 52  \n4.2 Neutrino Event Generation . . . . . . . . . . . . . . . . . . . . . 52  \n4.2.1 Neutrino Event Generation by GENIE ........... 53  \n4.2.2 Neutrino Event Generation by CRY ............ 54  \n4.3 Detector Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 55  \n4.3.1 Detector Geometry .....................","cbCaie0HMF8ZXTki","https://ap.wps.com/l/cbCaie0HMF8ZXTki","pdf",7994874,1,188,"English","en",105,"# Acknowledgements\n# Abstract\n# Contents\n# List of Tables\n# List of Figures\n# 1 Introduction\n# 2 Physics of Neutrinos\n## 2.1 The Discovery of Neutrinos\n## 2.2 Neutrinos in the standard model\n## 2.3 Neutrino Oscillation\n## 2.4 Sterile Neutrinos\n# 3 The NOvA Experiment\n## 3.1 The NuMI Beam\n## 3.2 Off-Axis Design\n## 3.3 The Liquid Scintillator\n## 3.4 The NOvA Detector\n## 3.5 Data Acquisition System\n# 4 Simulation and Calibration\n## 4.1 Beam simulation\n## 4.2 Neutrino Event Generation\n## 4.3 Detector Simulation\n## 4.4 Energy Calibration\n## 4.5 Timing Calibration\n# 5 Event Reconstruction\n## 5.1 Raw digits to cell hits\n## 5.2 Slicer\n## 5.3 Hough Transform\n## 5.4 Vertex Reconstruction\n## 5.5 Prong Reconstruction\n## 5.6 Break Point Fitter\n## 5.7 Kalman Track\n## 5.8 Event Classification\n## 5.9 Particle Classification\n## 5.10 Energy Reconstruction\n# 6 Sterile Analysis\n## 6.1 Event Selection","[{\"question\":\"What is the main goal of this dissertation?\",\"answer\":\"The dissertation aims to search for sterile neutrinos using NOvA data by improving energy reconstruction in neutral current events with machine learning.\"},{\"question\":\"How is machine learning used in the analysis?\",\"answer\":\"A deep learning-based energy estimator is developed and integrated into the analysis framework to reconstruct event energy for the sterile neutrino search.\"},{\"question\":\"How is the sensitivity to sterile neutrino oscillations obtained?\",\"answer\":\"Using the new energy estimator, the sensitivity to sterile-neutrino-induced oscillations is evaluated and presented in the dissertation.\"}]","Machine Learning-Based Energy Estimation for Sterile Neutrino Searches in the NOvA Experiment | 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