[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118167-en":3,"doc-seo-118167-105":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},118167,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Exploring Patterns in Nuclear Physics Data Through Machine Learning - Thesis","A research thesis investigates how machine learning algorithms can uncover patterns in nuclear physics data and identify relationships among nuclear characteristics. The work demonstrates partial success, especially in revealing correlations between separation energies, shell models, and magic numbers. Major limitations arise from the quality and quantity of available data, which affects predictive accuracy and reliability, including results related to proton and neutron drip lines. The exploratory strategy avoids established physics models, leading to insightful yet sometimes inconclusive findings.","Exploring Patterns in Nuclear Physics Data Through Machine Learning  \nKaren Weider  \nMSc by research  \nUniversity of York  \nSchool of Physics , Engineering and Technology  \nApril 2024  \n1 Abstract  \nThis thesis explores the application of machine learning algorithms to nuclear physics data, aiming to uncover patterns within the data and reveal relationships between various nuclear characteristics. While the research demonstrated some success, particularly in identifying correlations between separation energies, shell models, and magic numbers, it also encountered significant challenges. The most significant among these was the limitation posed by the quality and quantity of available data, which affected the accuracy and reliability of predictions, such as those for proton and neutron drip lines.  \nThe research adopted a broad, exploratory approach, intentionally avoiding the use of established physics models to allow machine learning to independently identify patterns. However, this wide-ranging focus, combined with data limitations , resulted in findings that are insightful but often inconclusive. The experiments conducted, including attempts to relate nuclear deformity to stability and to apply machine learning to a model influenced by the polyspheron model, further underscored the need for better and more targeted data.  \nThis thesis highlights the potential of machine learning in nuclear physics but also emphasises the importance of depth and data quality in future research. The results provide a foundation for more focused studies, where improved datasets and a narrower research scope could yield more definitive insights.  \nContents  \n1 Abstract ......................................................................................................................... 2  \n2 Author’s Declaration....................................................................................................... 9  \n3 Research Objective...................................................................................................... 10  \n4 Chapter 1: Background ................................................................................................ 12  \n4.1 Nuclear Physics ................................................................................................... 12  \n4.1.1 Introduction to Nuclear Physics........................................................................ 12  \n4.1.2 Magic Numbers................................................................................................ 12  \n4.1.3 Major Shell Closures........................................................................................ 12  \n4.1.4 Minor Shell Closures........................................................................................ 12  \n4.1.5 Bosons ............................................................................................................ 13  \n4.1.6 Fermions.......................................................................................................... 13  \n4.1.7 Separation Energy (Sn or Sp) .......................................................................... 13  \n4.1.8 Shell Model ...................................................................................................... 13  \n4.1.9 Spin ................................................................................................................. 14  \n4.1.10 Deformity ..................................................................................................... 14  \n4.1.11 Energy Levels .............................................................................................. 15  \n4.1.12 Energy Levels and Magic Numbers ............................................................. 15  \n4.1.13 Neutron Drip Line ......................................................................................... 15  \n4.1.14 Proton Drip Line ..........................................................................................","cbCaiuKX1Mf1ZBtw","https://ap.wps.com/l/cbCaiuKX1Mf1ZBtw","pdf",2131695,1,85,"English","en",105,"# Abstract\n# Author’s Declaration\n# Research Objective\n# Chapter 1: Background\n## Nuclear Physics\n## Artificial Intelligence and Machine Learning\n# Chapter 2: Methodology\n## Define the Computing Environment\n## Define the Experiment Objective\n## Identify Data Sources and Clean Data\n## Exploratory Data Analysis\n## Problem Definition and Objective Setting\n## Initial Model Selection","[{\"question\":\"What is the main goal of the research on machine learning and nuclear physics data?\",\"answer\":\"To apply machine learning algorithms to nuclear physics data to uncover patterns and reveal relationships between nuclear characteristics.\"},{\"question\":\"Which findings showed notable success in the thesis?\",\"answer\":\"The research identified correlations involving separation energies, shell models, and magic numbers, demonstrating some effectiveness of the approach.\"},{\"question\":\"Why are some predictions limited in accuracy and reliability?\",\"answer\":\"The thesis highlights that limited data quality and data quantity constrain prediction accuracy, affecting outcomes such as those for proton and neutron drip lines.\"}]","Exploring Patterns in Nuclear Physics Data Through Machine Learning - Thesis | PDF",1785681969,214,{"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},"exploring-patterns-in-nuclear-physics-data-through-machine-learning-thesis","",{"@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/exploring-patterns-in-nuclear-physics-data-through-machine-learning-thesis/118167/",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","2026-08-02",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 is the main goal of the research on machine learning and nuclear physics data?","Question",{"text":75,"@type":76},"To apply machine learning algorithms to nuclear physics data to uncover patterns and reveal relationships between nuclear characteristics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which findings showed notable success in the thesis?",{"text":80,"@type":76},"The research identified correlations involving separation energies, shell models, and magic numbers, demonstrating some effectiveness of the approach.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are some predictions limited in accuracy and reliability?",{"text":84,"@type":76},"The thesis highlights that limited data quality and data quantity constrain prediction accuracy, affecting outcomes such as those for proton and neutron drip lines.","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"]