Deep learning for R-parity violating supersymmetry searches at the LHC
文献类型：期刊论文
| 作者 | Guo, J1; Li, JM; Li, TJ ; Xu, FZ; Zhang, WX
|
| 刊名 | PHYSICAL REVIEW D
 |
| 出版日期 | 2018 |
| 卷号 | 98期号:7页码:76017 |
| 关键词 | JET SUBSTRUCTURE NEURAL-NETWORKS PHYSICS |
| ISSN号 | 2470-0010 |
| DOI | 10.1103/PhysRevD.98.076017 |
| 英文摘要 | Supersymmetry with hadronic R-parity violation in which the lightest neutralino decays into three quarks is still wealdy constrained. This work aims to further improve the current search for this scenario by the boosted decision tree method with additional information from jet substructure. In particular, we find a deep neural network turns out to perform well in characterizing the neutralino jet substructure. We first construct a convolutional neutral network (CNN) which is capable of tagging the neutralino jet in any signal process by using the idea of jet image. When applied to pure jet samples, such a CNN outperforms the N-subjettiness variable by a factor of a few in tagging efficiency. Moreover, we find the method, which combines the CNN output and jet invariant mass, can perform better and is applicable to a wider range of neutralino mass than the CNN alone. Finally, the ATLAS search for the signal of gluino pair production with subsequent decay (g) over tilde -> qq (chi) over tilde (0)(1)(-> qqq) is recast as an application. In contrast to the pure sample, the heavy contamination among jets in this complex final state renders the discriminating powers of the CNN and N subjettiness similar. By analyzing the jets substructure in events which pass the ATLAS cuts with our CNN method, the exclusion limit on gluino mass can be pushed up by similar to 200 GeV for neutralino mass similar to 100 GeV. |
| 学科主题 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/22794]  |
| 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.Sichuan Univ, Coll Phys Sci & Technol, Ctr Theoret Phys, Chengdu 610064, Sichuan, Peoples R China 2.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 3.Korea Inst Adv Study, Sch Phys, Seoul 130722, South Korea 4.Univ Chinese Acad Sci, Sch Phys Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 5.Tsinghua Univ, Inst Modern Phys, Beijing 100084, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, J,Li, JM,Li, TJ,et al. Deep learning for R-parity violating supersymmetry searches at the LHC[J]. PHYSICAL REVIEW D,2018,98(7):76017. |
| APA | Guo, J,Li, JM,Li, TJ,Xu, FZ,&Zhang, WX.(2018).Deep learning for R-parity violating supersymmetry searches at the LHC.PHYSICAL REVIEW D,98(7),76017. |
| MLA | Guo, J,et al."Deep learning for R-parity violating supersymmetry searches at the LHC".PHYSICAL REVIEW D 98.7(2018):76017. |
入库方式： OAI收割
来源：理论物理研究所
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