Exploring supersymmetry with machine learning
文献类型：期刊论文
| 作者 | Ren, J1,2,3; Wu, L1; Yang, JM ; Zhao, J2,3
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| 刊名 | NUCLEAR PHYSICS B
 |
| 出版日期 | 2019 |
| 卷号 | 943页码:114613 |
| ISSN号 | 0550-3213 |
| 关键词 | DARK-MATTER PROGRAM EFFICIENT |
| DOI | 10.1016/j.nuclphysb.2019.114613 |
| 英文摘要 | Investigation 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. |
| 学科主题 | Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/23411]  |
| 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.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：理论物理研究所
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