Supervised Deep Learning in High Energy Phenomenology: a Mini Review
文献类型：期刊论文
| 作者 | Abdughani, Murat; Ren, Jie; Wu, Lei3,4; Yang, Jin-Min ; Zhao, Jun2
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| 刊名 | COMMUNICATIONS IN THEORETICAL PHYSICS
 |
| 出版日期 | 2019 |
| 卷号 | 71期号:8页码:955-990 |
| ISSN号 | 0253-6102 |
| 关键词 | NEURAL-NETWORKS DARK-MATTER PROGRAM MODEL ASYMMETRY GAME |
| DOI | 10.1088/0253-6102/71/8/955 |
| 英文摘要 | Deep learning, a branch of machine learning, has been recently applied to high energy experimental and phenomenological studies. In this note we give a brief review on those applications using supervised deep learning. We first describe various learning models and then recapitulate their applications to high energy phenomenological studies. Some detailed applications are delineated in details, including the machine learning scan in the analysis of new physics parameter space, the graph neural networks in the search of top-squark production and in the CP measurement of the top-Higgs coupling at the LHC. |
| 学科主题 | Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/27100]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 作者单位 | 1.Tohoku Univ, Dept Phys, Sendai, Miyagi 9808578, Japan 2.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China 4.Nanjing Normal Univ, Dept Phys, Nanjing 210023, Jiangsu, Peoples R China 5.Nanjing Normal Univ, Inst Theoret Phys, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Abdughani, Murat,Ren, Jie,Wu, Lei,et al. Supervised Deep Learning in High Energy Phenomenology: a Mini Review[J]. COMMUNICATIONS IN THEORETICAL PHYSICS,2019,71(8):955-990. |
| APA | Abdughani, Murat,Ren, Jie,Wu, Lei,Yang, Jin-Min,&Zhao, Jun.(2019).Supervised Deep Learning in High Energy Phenomenology: a Mini Review.COMMUNICATIONS IN THEORETICAL PHYSICS,71(8),955-990. |
| MLA | Abdughani, Murat,et al."Supervised Deep Learning in High Energy Phenomenology: a Mini Review".COMMUNICATIONS IN THEORETICAL PHYSICS 71.8(2019):955-990. |
入库方式： OAI收割
来源：理论物理研究所
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