[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121482-en":3,"doc-seo-121482-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121482,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine learning application for particle accelerator optimization-a review","Particle accelerators require efficient tuning and reliable diagnostics due to the complexity of tightly coupled hardware subsystems and unpredictable interactions. Machine learning methods—such as artificial neural networks, random forest, reinforcement learning, genetic algorithms, and Bayesian optimization—are applied to improve accelerator performance and efficiency. The review synthesizes how ML supports particle beam design, operation, and control, emphasizing its role in addressing nonlinear behavior, reducing costly monitoring, and advancing accelerator science and technology.","Machine learning application for particle accelerator  \noptimization-a review  \nIsti Dian Rachmawati1,2, Nazrul Effendy1, Taufik2  \n1Intelligent and Embedded System Research Group, Department of Nuclear Engineering and Engineering Physics, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia  \n2Research Center for Accelerator Technology, Research Organization for Nuclear Energy, National Research and Innovation Agency ,  \nSouth Tangerang, Indonesia  \n\n| Article history:\u003Cbr>Received Oct 25, 2024 Revised Jun 12, 2025 Accepted Jul 10, 2025 |\n| --- |\n| Keywords:\u003Cbr>Accelerator Machine learning Neural networks Optimization Particle Random forest |\n\nCorresponding Author:  \nParticle accelerators receive significant attention from researchers. This machine consists of various interdependent elements, so it is complex. Efficient system tuning and diagnostics are essential for utilizing accelerator technology. In addition, machine learning (ML) has been applied in several applications. ML methods such as artificial neural networks, random forest, reinforcement learning, genetic algorithm, and Bayesian optimization have been used for accelerator optimization. The optimization of particle accelerators covers their performance and efficiency. This paper reviews the application of ML techniques in optimizing particle accelerators, highlighting their importance in addressing the complexity inherent in accelerator systems and advancing accelerator science and technology.  \nThis is an open access article under the CC BY-SA license.  \nNazrul Effendy  \nIntelligent and Embedded System Research Group, Department of Nuclear Engineering and Engineering Physics Faculty of Engineering, Universitas Gadjah Mada  \nSt. Grafika 2, Yogyakarta, Indonesia  \n[Email: nazrul@ugm.ac.id](Email: nazrul@ugm.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nParticle accelerators accelerate charged particles at atomic and subatomic sizes [1] . Particle accelerators play crucial role in industrial applications, scientific research, and healthcare, including production of radioisotopes [2], nuclear forensics [3], genetic mutation [4], [5], accelerator-driven systems [6]–[8], nuclear laboratories, materials research [9]–[12], and boron neutron capture therapy. Protons and electrons, which are charged with atomic particles, comprise most of the particle stream. Generally, particle accelerators are developed according to their specific purposes, and the type of application depends on accelerator's energy.  \nSome particle accelerators have complex experimental installations and produce directed beams of high-energy particles toward targets. The main components of an accelerator consist of the charged particle beam source or injector, acceleration system, vacuum tube system, optic system, target system, and instrumentation and control system. The interrelationships among the systems result in high complexity. Considering the complexity of each subsystem and the unpredictability of interactions among them, it is pretty challenging to avoid failures and operational errors [13] . Navigating the nonlinear functions of the components and dynamic machine settings in accelerator optimization is a significant challenge affecting particle beam design, operation, and control [14] .  \nParticle accelerators are nonlinear systems, and further research is necessary due to their complexity [15] . There are many intrinsic nonlinear interactions between its system components. It is challenging to navigate through the nonlinear functions of thousands of components and dynamic machine settings in  \nparticle accelerator optimization [16] . These factors affect particle beam design, operation, and control. Conventional methods have not been successful in this domain, leading to constant and costly system monitoring by human operators. Artificial intelligence (AI) itself has been widely applied in several applications [17]–[19] . AI algorithms are essential for control, tuning ","cbCaikaM6nSZYNfW","https://ap.wps.com/l/cbCaikaM6nSZYNfW","pdf",530317,1,"English","en",105,"# Introduction\n## Challenges of accelerator optimization\n## Role of AI and machine learning\n# Method\n## Research questions and literature search","[{\"question\":\"Why is optimization in particle accelerators considered challenging?\",\"answer\":\"Particle accelerators are nonlinear systems with complex interactions among many interdependent components and dynamic settings, making failures and operational errors difficult to avoid.\"},{\"question\":\"Which machine learning methods are commonly used for accelerator optimization?\",\"answer\":\"The review highlights artificial neural networks, random forest, reinforcement learning, genetic algorithms, and Bayesian optimization for improving accelerator settings and outcomes.\"},{\"question\":\"What practical goals does machine learning support in accelerator systems?\",\"answer\":\"ML supports improving performance and efficiency, enhancing tuning and diagnostics, and enabling stability and control through analysis of sensor data and predictive modeling.\"}]","Machine learning application for particle accelerator optimization-a review | PDF",1785735853,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-application-for-particle-accelerator-optimization-a-review","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-application-for-particle-accelerator-optimization-a-review/121482/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is optimization in particle accelerators considered challenging?","Question",{"text":74,"@type":75},"Particle accelerators are nonlinear systems with complex interactions among many interdependent components and dynamic settings, making failures and operational errors difficult to avoid.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning methods are commonly used for accelerator optimization?",{"text":79,"@type":75},"The review highlights artificial neural networks, random forest, reinforcement learning, genetic algorithms, and Bayesian optimization for improving accelerator settings and outcomes.",{"name":81,"@type":72,"acceptedAnswer":82},"What practical goals does machine learning support in accelerator systems?",{"text":83,"@type":75},"ML supports improving performance and efficiency, enhancing tuning and diagnostics, and enabling stability and control through analysis of sensor data and predictive modeling.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]