[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117499-en":3,"doc-seo-117499-105":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},117499,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","MACHINE LEARNING APPLICATIONS FOR IMPROVING ACCELERATOR OPERATIONS - Dissertation","This dissertation advances particle-accelerator operations by applying machine learning to multiple core tasks, including beam orbit control, beam quality measurement, and beam optimization. Large-scale accelerators often have partially known relationships between control parameters and performance data, limiting purely physics-driven modeling. The work develops and demonstrates two major machine-learning approaches on several Cornell University and Brookhaven National Laboratory accelerators: neural networks for learning accurate parameter-to-performance mappings and automated optimization algorithms for determining optimal operating settings. Results support integrating ML into accelerator control systems and provide groundwork for the Electron Ion Collider development at Brookhaven National Laboratory.","MACHINE LEARNING APPLICATIONS FOR IMPROVING ACCELERATOR OPERATIONS  \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  \nWeijian (Lucy) Lin  \nDecember 2024  \n© 2024 Weijian Lin  \nALL RIGHTS RESERVED  \nMACHINE LEARNING APPLICATIONS FOR IMPROVING ACCELERATOR  \nOPERATIONS  \nWeijian (Lucy) Lin, Ph.D.  \nCornell University 2024  \nIn this dissertation, we attempt to improve operation at particle accelerator facilities by applying various machine learning techniques to several key areas of acceleration operation for a particle accelerator, such as beam orbit control, beam quality measurement, and beam optimization.  \nFor complicated large scale machines such as accelerators, the inner mappings between control parameters and performance data are often only partially known, but they can be learned and simulated using machine learning methods without extensive physics understandings.  \nIn this dissertation, two major types of machine learning techniques were developed for and demonstrated on several accelerators located at Cornell University and Brookhaven National Laboratory: neural network design for constructing accurate mappings between control parameters and performance data, and optimization algorithm development for finding optimal operation parameters automatically. These successful applications show the benefits of integrating machine learning algorithms with accelerator control system, and build the foundation for including similar techniques in the ongoing development and construction of the Electron Ion Collider at Brookhaven National Laboratory.  \nBIOGRAPHICAL SKETCH  \nWeijian (Lucy) Lin was born and raised in Shenzhen, China. She first came to the United States in 2012 to attend high school at Poughkeepsie Day School in Poughkeepsie, New York. Then she attended Smith College in Northampton, Massachusetts from 2015-2019 where she obtained a Bachelor of Arts in Physics. During this time, she worked with Professor William Williams and performed high-precision spectroscopy on the 2s3d1D2 state in neutral Beryllium-9 . In 2018, she participated in Smith’s study abroad program in Hamburg, Germany. During this time, she worked as a summer research intern at the German Electron Synchrotron (DESY) and ran 3D simulation studies of the Enhanced Lateral Drift (ELAD) sensor for the Compact Muon Solenoid (CMS) experiment at CERN. This experience at DESY sparked her interest in particle accelerators. In the fall of 2019, she came to Cornell University to pursue a Ph.D. in physics, working with Professor Georg Hoffstaetter de Torquat in experimental accelerator physics. In her spare time, Lucy likes to explore new restaurants and try new food with her friends. She also likes to play video games, watch anime and true crime documentaries, and sing karaoke. She also enjoys watching soccer and is a long-time supporter of Real Madrid C.F.  \nDedicated to my supportive parents, to my funny research group, and to my loving friends, both online and in reality, especially to my best best friend Sarah Elghazoly, without whom I probably would have lost my mind before  \neven graduating college.  \nACKNOWLEDGEMENTS  \nFirst and foremost, I would like to thank my advisor Georg Hoffstaetter de Torquat for taking me on as his student. Emailing Georg about potential PhD project was one of the best life decisions I made. He is the most passionate, supportive, and resourceful advisor I have ever had, and words can’t describe how grateful I am to him for all the help during my PhD journey.  \nThank you to the other two members of my committee-Matthias Liepe and Erich Mueller, for their helpful advice and genuine interest in my work.  \nThank you to the Center for Bright Beams (CBB) for funding the majority of my research. The CBB community has been really supportive and helpful for my PhD journey. Especially the annual meetings gave me a gr","cbCaipdNhkej2vII","https://ap.wps.com/l/cbCaipdNhkej2vII","pdf",18351729,1,159,"English","en",105,"# MACHINE LEARNING APPLICATIONS FOR IMPROVING ACCELERATOR OPERATIONS\n## Overview of the dissertation\n## Biographical sketch\n## Acknowledgements","[{\"question\":\"What accelerator operation areas does this dissertation target?\",\"answer\":\"It targets beam orbit control, beam quality measurement, and beam optimization to improve accelerator performance during operation.\"},{\"question\":\"How does the dissertation handle partially known mappings in accelerator systems?\",\"answer\":\"It uses machine learning to learn and simulate the relationships between control parameters and performance data without requiring extensive physics modeling details.\"},{\"question\":\"What two main machine learning techniques are developed and demonstrated?\",\"answer\":\"The dissertation develops neural network designs to construct accurate control-parameter-to-performance mappings and optimization algorithms to automatically find optimal operation parameters.\"}]","MACHINE LEARNING APPLICATIONS FOR IMPROVING ACCELERATOR OPERATIONS - 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