Probing stop pair production at the LHC with graph neural networks
文献类型：期刊论文
| 作者 | Abdughani, Murat2,3,4; Ren, Jie3,4; Wu, Lei2; Yang, Jin Min1,3,4 |
| 刊名 | JOURNAL OF HIGH ENERGY PHYSICS
 |
| 出版日期 | 2019 |
| 期号 | 8页码:55 |
| ISSN号 | 1029-8479 |
| 关键词 | MEASURING MASSES HADRON |
| DOI | 10.1007/JHEP08(2019)055 |
| 英文摘要 | Top-squarks (stops) play a crucial role for the naturalness of supersymmetry (SUSY). However, searching for the stops is a tough task at the LHC. To dig the stops out of the huge LHC data, various expert-constructed kinematic variables or cutting-edge analysis techniques have been invented. In this paper, we propose to represent collision events as event graphs and use the message passing neutral network (MPNN) to analyze the events. As a proof-of-concept, we use our method in the search of the stop pair production at the LHC, and find that our MPNN can efficiently discriminate the signal and back-ground events. In comparison with other machine learning methods (e.g. DNN), MPNN can enhance the mass reach of stop mass by several tens of GeV to over a hundred GeV. |
| 学科主题 | Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/27066]  |
| 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.Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China 2.Nanjing Normal Univ, Dept Phys, Nanjing 210023, Jiangsu, Peoples R China 3.Nanjing Normal Univ, Inst Theoret Phys, Nanjing 210023, Jiangsu, Peoples R China 4.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 5.Tohoku Univ, Dept Phys, Sendai, Miyagi 9808578, Japan |
| 推荐引用方式 GB/T 7714 | Abdughani, Murat,Ren, Jie,Wu, Lei,et al. Probing stop pair production at the LHC with graph neural networks[J]. JOURNAL OF HIGH ENERGY PHYSICS,2019(8):55. |
| APA | Abdughani, Murat,Ren, Jie,Wu, Lei,&Yang, Jin Min.(2019).Probing stop pair production at the LHC with graph neural networks.JOURNAL OF HIGH ENERGY PHYSICS(8),55. |
| MLA | Abdughani, Murat,et al."Probing stop pair production at the LHC with graph neural networks".JOURNAL OF HIGH ENERGY PHYSICS .8(2019):55. |
入库方式： OAI收割
来源：理论物理研究所
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