ITP OpenIR  > SCI期刊论文
Ren, J1,2,3; Wu, L1; Yang, JM; Zhao, J2,3
Exploring supersymmetry with machine learning
Source PublicationNUCLEAR PHYSICS B
Language英语
KeywordDARK-MATTER PROGRAM EFFICIENT
AbstractInvestigation of well-motivated parameter space in the theories of Beyond the Standard Model (BSM) plays an important role in new physics discoveries. However, a large-scale exploration of models with multi-parameter or equivalent solutions with a finite separation, such as supersymmetric models, is typically a time-consuming and challenging task. In this paper, we propose a self-exploration method, named Machine Learning Scan (MLS), to achieve an efficient test of models. As a proof-of-concept, we apply MLS to investigate the subspace of MSSM and CMSSM and find that such a method can reduce the computational cost and may be helpful for accelerating the exploration of supersymmetry. (C) 2019 The Author(s). Published by Elsevier B.V.
2019
ISSN0550-3213
Volume943Pages:114613
Subject AreaPhysics
MOST Discipline CataloguePhysics, Particles & Fields
DOI10.1016/j.nuclphysb.2019.114613
Indexed BySCIE
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Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/23411
CollectionSCI期刊论文
Affiliation1.Nanjing Normal Univ, Dept Phys, Nanjing 210023, Jiangsu, Peoples R China
2.Nanjing Normal Univ, Inst Theoret Phys, Nanjing 210023, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China
5.Tohoku Univ, Dept Phys, Sendai, Miyagi 9808578, Japan
Recommended Citation
GB/T 7714
Ren, J,Wu, L,Yang, JM,et al. Exploring supersymmetry with machine learning[J]. NUCLEAR PHYSICS B,2019,943:114613.
APA Ren, J,Wu, L,Yang, JM,&Zhao, J.(2019).Exploring supersymmetry with machine learning.NUCLEAR PHYSICS B,943,114613.
MLA Ren, J,et al."Exploring supersymmetry with machine learning".NUCLEAR PHYSICS B 943(2019):114613.
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