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Guo, J1; Li, JM; Li, TJ; Xu, FZ; Zhang, WX
Deep learning for R-parity violating supersymmetry searches at the LHC
发表期刊PHYSICAL REVIEW D
语种英语
关键词JET SUBSTRUCTURE NEURAL-NETWORKS PHYSICS
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.
2018
ISSN2470-0010
卷号98期号:7页码:76017
学科领域Astronomy & Astrophysics ; Physics
学科门类Astronomy & Astrophysics ; Physics, Particles & Fields
DOI10.1103/PhysRevD.98.076017
收录类别SCIE
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.itp.ac.cn/handle/311006/22794
专题理论物理所科研产出_SCI论文
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
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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.
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