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arXiv 1301.0916hep-ex
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用人工神经网络寻找具有相反符号双轻子和缺失横能事例的超对称性

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

CMS Collaboration

摘要:

本文提出了在末态有两个相反符号孤立轻子的事件中寻找超对称性(SUSY),并伴有强子喷注和缺失横能量。采用人工神经网络从标准模型背景中识别可能的超对称信号。该分析使用了2011年LHC运行期间CMS探测器收集的数据样本,对应于7 TeV质心能量下质子-质子碰撞的积分亮度为4.98毫微微电子能垒。与其他CMS分析相比,这一分析对缺失横能(缺失ET > 40 GeV)和总强子横能(HT > 120 GeV)使用了宽松的标准,从而探索了参数空间的不同区域。协议之间的标准模型的期望和观察,产生限制的上下文中的约束最小超对称标准模型和一组简化模型。

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 inverse femtobarns 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 (missing ET > 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 mininal supersymmetric standard model and on a set of simplified models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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