ITP OpenIR  > SCI期刊论文
Abdughani, Murat; Ren, Jie; Wu, Lei2,3; Yang, Jin-Min; Zhao, Jun1
Supervised Deep Learning in High Energy Phenomenology: a Mini Review
Source PublicationCOMMUNICATIONS IN THEORETICAL PHYSICS
Language英语
KeywordNEURAL-NETWORKS DARK-MATTER PROGRAM MODEL ASYMMETRY GAME
AbstractDeep 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.
2019
ISSN0253-6102
Volume71Issue:8Pages:955-990
Cooperation Status国际
Subject AreaPhysics
MOST Discipline CataloguePhysics, Multidisciplinary
DOI10.1088/0253-6102/71/8/955
Indexed BySCIE
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Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/27100
CollectionSCI期刊论文
Affiliation1.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China
3.Nanjing Normal Univ, Dept Phys, Nanjing 210023, Jiangsu, Peoples R China
4.Nanjing Normal Univ, Inst Theoret Phys, Nanjing 210023, Jiangsu, Peoples R China
5.Tohoku Univ, Dept Phys, Sendai, Miyagi 9808578, Japan
Recommended Citation
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.
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