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arXiv 2106.09609hep-ex
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用$\sqrt{s} = 13$ ATLAS质子-质子碰撞数据研究含轻子和多喷注末态的R宇称破缺超对称性

Search for R-parity violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using $\sqrt{s} = 13$ TeV proton-proton collision data

ATLAS Collaboration

摘要:

在具有高喷注多重性、至少一个孤立轻子和零或至少三个$b$标记喷注的末态中寻找R宇称破坏超对称性。该搜索使用大型强子对撞机第2次运行期间ATLAS实验收集的139 fb $\sqrt{s} = 13$ TeV质子-质子碰撞数据的$^{-1}$。结果被解释的R-宇称违反超对称模型的背景下,具有胶子生产,顶夸克生产,或电弱生产。背景的主要来源估计使用数据驱动的模型,在中等射流多重性的基础上,预测$b$标记的射流多重性分布在搜索中使用的更高的射流多重性。机器学习技术用于达到对电弱产生的灵敏度,将数据驱动的背景估计扩展到机器学习判别式的形状。没有明显超过标准模型的预期,并在95%的置信水平的排除极限提取,达到高达2.4 TeV的胶子质量,1.35 TeV的顶夸克质量,和320(365)GeV的higgsino(wino)质量。

英文摘要:

A search for R-parity violating supersymmetry in final states characterised by high jet multiplicity, at least one isolated light lepton and either zero or at least three $b$-tagged jets is presented. The search uses 139 fb$^{-1}$ of $\sqrt{s} = 13$ TeV proton-proton collision data collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature gluino production, top-squark production, or electroweakino production. The dominant sources of background are estimated using a data-driven model, based on observables at medium jet multiplicity, to predict the $b$-tagged jet multiplicity distribution at the higher jet multiplicities used in the search. Machine learning techniques are used to reach sensitivity to electroweakino production, extending the data-driven background estimation to the shape of the machine learning discriminant. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence-level are extracted, reaching as high as 2.4 TeV in gluino mass, 1.35 TeV in top-squark mass, and 320 (365) GeV in higgsino (wino) mass.

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