Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network

CORC > 高能物理研究所 > 中国科学院高能物理研究所 > 粒子天体物理中心

	Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network
	CMS Collaboration
刊名	PHYSICAL REVIEW D
	2013
卷号	87 期号:7
关键词	STANDARD MODEL PP COLLISIONS ROOT-S=7 TEV PHYSICS JETS
通讯作者	Chatrchyan, S (reprint author), Yerevan Phys Inst, Yerevan 375036, Armenia.
英文摘要	In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network is employed to discriminate possible SUSY signals from a standard model background. The analysis uses a data sample collected with the CMS detector during the 2011 LHC run, corresponding to an integrated luminosity of 4: 98 fb(-1) of proton-proton collisions at the center-of-mass energy of 7 TeV. Compared to other CMS analyses, this one uses relaxed criteria on missing transverse energy (E-T > 40 GeV) and total hadronic transverse energy (HT > 120 GeV), thus probing different regions of parameter space. Agreement is found between standard model expectation and observations, yielding limits in the context of the constrained minimal supersymmetric standard model and on a set of simplified models. DOI: 10.1103/PhysRevD.87.072001
学科主题	Astronomy & Astrophysics; Physics
类目[WOS]	Astronomy & Astrophysics ; Physics, Particles & Fields
收录类别	SCI
语种	英语
WOS记录号	WOS:000316954200001
公开日期	2016-02-25
内容类型	期刊论文
源URL	[http://ir.ihep.ac.cn/handle/311005/213497]
专题	高能物理研究所_粒子天体物理中心
作者单位	中国科学院高能物理研究所
推荐引用方式 GB/T 7714	CMS Collaboration. Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network[J]. PHYSICAL REVIEW D,2013,87(7).
APA	CMS Collaboration.(2013).Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network.PHYSICAL REVIEW D,87(7).
MLA	CMS Collaboration."Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network".PHYSICAL REVIEW D 87.7(2013).