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arXiv 1807.09088hep-phhep-ex
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基于图神经网络的LHC生产线停车对生成研究

Probing stop pair production at the LHC with graph neural networks

Murat Abdughani, Jie Ren, Lei Wu, Jin Min Yang

摘要:

顶方(停止)对超对称性(SUSY)的自然性起着至关重要的作用。然而,在LHC上寻找停止点是一项艰巨的任务。为了从大型强子对撞机的巨大数据中挖掘出停止点,人们发明了各种专家构造的运动学变量或尖端的分析技术。在本文中,我们提出了表示碰撞事件的事件图,并使用消息传递神经网络(MPNN)的事件进行分析。作为一个概念验证,我们使用我们的方法在LHC的停止对生产的搜索,并发现我们的MPNN可以有效地区分信号和背景事件。与其他机器学习方法(例如DNN)相比,MPNN可以将停止质量的质量范围提高几十GeV到一百多GeV。

英文摘要:

Top-squarks (stops) play a crucial role for the naturalness of supersymmetry (SUSY). However, searching for the stops is a tough task at the LHC. To dig the stops out of the huge LHC data, various expert-constructed kinematic variables or cutting-edge analysis techniques have been invented. In this paper, we propose to represent collision events as event graphs and use the message passing neutral network (MPNN) to analyze the events. As a proof-of-concept, we use our method in the search of the stop pair production at the LHC, and find that our MPNN can efficiently discriminate the signal and background events. In comparison with other machine learning methods (e.g. DNN), MPNN can enhance the mass reach of stop mass by several tens of GeV to over a hundred GeV.

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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