Detecting an axion-like particle with machine learning at the LHC

被引:20
|
作者
Ren, Jie [1 ]
Wang, Daohan [2 ,3 ]
Wu, Lei [4 ,5 ]
Yang, Jin Min [2 ,3 ]
Zhang, Mengchao [6 ,7 ]
机构
[1] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China
[2] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China
[3] Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China
[4] Nanjing Normal Univ, Dept Phys, Nanjing 210023, Peoples R China
[5] Nanjing Normal Univ, Inst Theoret Phys, Nanjing 210023, Peoples R China
[6] Jinan Univ, Dept Phys, Guangzhou 510632, Peoples R China
[7] Jinan Univ, Siyuan Lab, Guangzhou 510632, Peoples R China
基金
中国国家自然科学基金;
关键词
Jets; Phenomenological Models;
D O I
10.1007/JHEP11(2021)138
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
Axion-like particles (ALPs) appear in various new physics models with spontaneous global symmetry breaking. When the ALP mass is in the range of MeV to GeV, the cosmology and astrophysics bounds are so far quite weak. In this work, we investi- gate such light ALPs through the ALP-strahlung production processes pp -> W(+/-)a, Za with the sequential decay alpha -> gamma gamma at the 14 TeV LHC with an integrated luminosity of 3000 fb(-1) (HL-LHC). Building on the concept of jet image which uses calorimeter towers as the pixels of the image and measures a jet as an image, we investigate the potential of machine learning techniques based on convolutional neural network (CNN) to identify the highly boosted ALPs which decay to a pair of highly collimated photons. With the CNN tagging algorithm, we demonstrate that our approach can extend current LHC sensitivity and probe the ALP mass range from 0.3 GeV to 5 GeV. The obtained bounds are stronger than the existing limits on the ALP-photon coupling.
引用
收藏
页数:26
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