{"title":"Artificial Neural Networks in High Energy Physics Data Processing (Succinct Survey) and Probable Future Developments","authors":"A. Shevel","doi":"10.1134/S1063779624030742","DOIUrl":null,"url":null,"abstract":"<p>The rising the role of artificial neural networks (ANN) as part of machine learning/deep learning (ML/DL) in high energy physics (HEP) and related areas can be seen last decade. Several reasons for rising the role of ANN were observed. It is paid attention to specific topics: learning transfer, distributed learning, ensemble of ANN. A lot of new experimental data will come from existing and new complex data taking systems in coming years, which will require advanced analysis with ANN running on appropriate computing hardware. Finally, the idea of future ANN development directions for HEP and related areas has been supposed.</p>","PeriodicalId":729,"journal":{"name":"Physics of Particles and Nuclei","volume":"55 3","pages":"309 - 312"},"PeriodicalIF":0.5000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics of Particles and Nuclei","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1134/S1063779624030742","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, PARTICLES & FIELDS","Score":null,"Total":0}
引用次数: 0
Abstract
The rising the role of artificial neural networks (ANN) as part of machine learning/deep learning (ML/DL) in high energy physics (HEP) and related areas can be seen last decade. Several reasons for rising the role of ANN were observed. It is paid attention to specific topics: learning transfer, distributed learning, ensemble of ANN. A lot of new experimental data will come from existing and new complex data taking systems in coming years, which will require advanced analysis with ANN running on appropriate computing hardware. Finally, the idea of future ANN development directions for HEP and related areas has been supposed.
作为机器学习/深度学习（ML/DL）的一部分，人工神经网络（ANN）在高能物理（HEP）及相关领域的作用在过去十年中不断提升。人工神经网络的作用不断提升有几个原因。其中，学习转移、分布式学习、ANN 的集合等特定主题受到了关注。未来几年，大量新的实验数据将来自于现有的和新的复杂数据采集系统，这就需要在适当的计算硬件上运行 ANN 进行高级分析。最后，我们提出了针对 HEP 和相关领域的未来 ANN 发展方向。
期刊介绍：
The journal Fizika Elementarnykh Chastits i Atomnogo Yadr of the Joint Institute for Nuclear Research (JINR, Dubna) was founded by Academician N.N. Bogolyubov in August 1969. The Editors-in-chief of the journal were Academician N.N. Bogolyubov (1970–1992) and Academician A.M. Baldin (1992–2001). Its English translation, Physics of Particles and Nuclei, appears simultaneously with the original Russian-language edition. Published by leading physicists from the JINR member states, as well as by scientists from other countries, review articles in this journal examine problems of elementary particle physics, nuclear physics, condensed matter physics, experimental data processing, accelerators and related instrumentation ecology and radiology.
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