ITP OpenIR  > SCI期刊论文
Guo, J1; Li, JM; Li, TJ; Xu, FZ; Zhang, WX
Deep learning for R-parity violating supersymmetry searches at the LHC
Source PublicationPHYSICAL REVIEW D
Language英语
KeywordJET SUBSTRUCTURE NEURAL-NETWORKS PHYSICS
AbstractSupersymmetry 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
Volume98Issue:7Pages:76017
Subject AreaAstronomy & Astrophysics ; Physics
MOST Discipline CatalogueAstronomy & Astrophysics ; Physics, Particles & Fields
DOI10.1103/PhysRevD.98.076017
Indexed BySCIE
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Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/22794
CollectionSCI期刊论文
Affiliation1.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
Recommended Citation
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.
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