[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122341-en":3,"doc-seo-122341-105":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},122341,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","LOOKING FOR NEW PHYSICS: FROM DARK MATTER TO MACHINE LEARNING","A dissertation explores extensions beyond the Standard Model through two complementary avenues: phenomenology and data analysis. From the model-building side, dark matter is treated as a robust indicator of new physics, motivating new dark-matter models with studies of their properties and experimental probing strategies. From the machine-learning side, the work develops improvements to “Classification Without Labels” and “Anomaly Detection with Density Estimation,” addressing anomaly discovery using modern models and techniques.","LOOKING FOR NEW PHYSICS: FROM DARK MATTER TO MACHINE LEARNING  \nA Dissertation  \nPresented to the Faculty of the Graduate School of Cornell University  \nin Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy  \nby  \nYik Chuen, San  \nDecember 2024  \n© 2024 Yik Chuen, San  \nALL RIGHTS RESERVED  \nLOOKING FOR NEW PHYSICS: FROM DARK MATTER TO MACHINE  \nLEARNING  \nYik Chuen, San, Ph.D.  \nCornell University 2024  \nEver since the Standard Model (SM) has been written down, countless efforts have been made to complement/extend it with new inputs from both theoretical and experimental sides. In this work, we provide such possible extensions from two perspectives: phenomenology (model-building) and data analysis.  \nFrom the model-building perspective, dark matter has proven to be one of the most robust signs we have about new physics. As such, we propose new models involving dark matter, along with analyses of their properties and methods of probing them in experiments.  \nAn alternative approach of discovering new physics involves the use of more sophisticated data analysis methods based on modern machine learning models and techniques. On this front, we present works regarding improvements towards two existing proposals- ‘Classification Without Labels’and ‘Anomaly Detection with Density Estimation’.  \nBIOGRAPHICAL SKETCH  \nYik Chuen San obtained his Bachelor of Science degree from the Hong Kong University of Science and Technology in August 2018 . Afterwards, he started pursuing his doctoral degree at Cornell University under the supervision of Prof. Maxim Perelstein, with a focus on theoretical particle physics. Outside of physics, he also enjoys playing the piano and cooking.  \nThis work is dedicated to my parents and my bygone self, without whom I  \nwould not be here today.  \nACKNOWLEDGEMENTS  \nMy journey for the past six years has been one full of swerves-from living by myself for the first time in a foreign land, to experiencing a global pandemic, to suffering from depression, and now to me concluding this journey with anewfound understanding of what I am. Throughout this journey, I have met, both directly and indirectly, many people who, in one way or another, have helped shape me into the human I am today.  \nFirst and foremost, I thank my mother and father for giving me the treasure of life, and a chance to see a world I could not have fathomed. My journey would not have been possible without their support.  \nFor the people I met at Cornell, I thank all my friends: Andrew and Mijo, for the countless banter we have had about both the academic and the nonacademic; Ameen, for being not just an admirable colleague, but also the perfect roommate; Fernanda, Steven, and Margarita, for taking the initiatives to invite me out for fun (even though I don’t always end up joining); Han and Avinash, for listening to my rants about life; and Namitha, for sharing various teaching duties and giving me help whenever I needed it.  \nI also thank professors in the particle phenomenology group: Csaba, for his inspiring lectures which reshaped my understanding of physics; and Yuval, for being always so cheerful and approachable; and of course, Maxim, my supervisor, for taking a chance on me and teaching me how to think about physics and do research properly, for giving me guidance when I am lost, and for letting me feel that I belong here.  \nFinally, I would like to express gratitude to my past self, for having a dream and deciding to pursue this path. You might be disappointed if you could see me today, but I want you to know that while this is not the destination you had  \nenvisioned, there is no regret along this path that we have taken.  \nIt is almost certain that I have missed many people who I should thank, and for that, I apologize-alas, my memory is not what it used to be six years ago!  \nYik Chuen San  \nIthaca, July 2024  \nTABLE OF CONTENTS  \nBiographical Sketch .............................. iii  \nDedication .........","cbCaipsdtYeGywHY","https://ap.wps.com/l/cbCaipsdtYeGywHY","pdf",3179624,1,116,"English","en",105,"# Table of Contents\n## Biographical Sketch\n## Dedication\n## Acknowledgements\n## Table of Contents\n## 1 Introduction\n## 1.1 Dark Matter\n## 1.1.1 Evidence of Dark Matter\n## 1.1.2 Searches of Dark Matter\n## 1.1.3 Types of Dark Matter Models\n## 1.2 Machine Learning in Particle Physics\n## 1.2.1 Supervised Learning\n## 1.2.2 Unsupervised Learning\n## 2 Dark Matter as a Solution to Muonic Puzzles\n## 2.1 Introduction\n## 2.2 Model\n## 2.3 Results\n## 2.4 Conclusions\n## 3 Dark Z at the International Linear Collider\n## 3.1 Introduction\n## 3.2 Three-Parameter Dark Z Model\n## 3.3 Experimental Constraints and the ILC Reach\n## 3.4 Precision Measurements at the Dark Z Pole\n## 3.5 Conclusions\n## 4 Anomaly Detection in the Presence of Irrelevant Features\n## 4.1 Introduction\n## 4.2 Dataset\n## 4.3 CWoLa on a Tree: Classifier BDTs","[{\"question\":\"What two perspectives does the dissertation use to extend the Standard Model?\",\"answer\":\"It uses phenomenology (model-building) and data analysis. The first builds new dark-matter-related models and studies how to probe them experimentally, while the second applies modern machine-learning approaches to analyze particle-physics data for new signals.\"},{\"question\":\"How does the work approach dark matter from the perspective of new physics?\",\"answer\":\"It treats dark matter as one of the most robust signs of physics beyond the Standard Model. It then proposes dark-matter models, analyzes their properties, and discusses methods to probe them in experiments.\"},{\"question\":\"Which machine-learning proposals are improved in this dissertation?\",\"answer\":\"The work presents improvements related to two proposals: “Classification Without Labels” and “Anomaly Detection with Density Estimation.” It focuses on using these methods to detect anomalies and advance data-driven discovery.\"}]","LOOKING FOR NEW PHYSICS: FROM DARK MATTER TO MACHINE LEARNING | PDF",1785810109,292,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"looking-for-new-physics-from-dark-matter-to-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/looking-for-new-physics-from-dark-matter-to-machine-learning/122341/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What two perspectives does the dissertation use to extend the Standard Model?","Question",{"text":75,"@type":76},"It uses phenomenology (model-building) and data analysis. The first builds new dark-matter-related models and studies how to probe them experimentally, while the second applies modern machine-learning approaches to analyze particle-physics data for new signals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the work approach dark matter from the perspective of new physics?",{"text":80,"@type":76},"It treats dark matter as one of the most robust signs of physics beyond the Standard Model. It then proposes dark-matter models, analyzes their properties, and discusses methods to probe them in experiments.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning proposals are improved in this dissertation?",{"text":84,"@type":76},"The work presents improvements related to two proposals: “Classification Without Labels” and “Anomaly Detection with Density Estimation.” It focuses on using these methods to detect anomalies and advance data-driven discovery.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]