Probing stop pair production at the LHC with graph neural networks

被引：51

作者：

Abdughani, Murat ^{[1
,2
,3
,4
]}

Ren, Jie ^{[3
,4
]}

Wu, Lei ^{[1
,2
]}

Yang, Jin Min ^{[3
,4
,5
]}

机构：

[1] Nanjing Normal Univ, Dept Phys, Nanjing 210023, Jiangsu, Peoples R China

[2] Nanjing Normal Univ, Inst Theoret Phys, Nanjing 210023, Jiangsu, Peoples R China

[3] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China

[4] Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China

[5] Tohoku Univ, Dept Phys, Sendai, Miyagi 9808578, Japan

来源：

JOURNAL OF HIGH ENERGY PHYSICS | 2019年 / 2019卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Supersymmetry Phenomenology; MEASURING MASSES; HADRON;

D O I：

10.1007/JHEP08(2019)055

中图分类号：

O412 [相对论、场论]; O572.2 [粒子物理学];

学科分类号：

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 back-ground 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.

引用

页数：14

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