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

Journal of High Energy Physics

Daohan Wang¹  Jin Min Yang¹  Lei Wu²  Mengchao Zhang³  Jie Ren⁴ 

[1] CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, 100190, Beijing, China;School of Physical Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China;Department of Physics and Institute of Theoretical Physics, Nanjing Normal University, 210023, Nanjing, China;Department of Physics and Siyuan Laboratory, Jinan University, 510632, Guangzhou, China;School of Aerospace Engineering, Beijing Institute of Technology, 100081, Beijing, China;

关键词: Jets;    Phenomenological Models;   

DOI  :  10.1007/JHEP11(2021)138

来源: Springer

【 摘 要 】

Axion-like particles (ALPs) appear in various new physics models with spon- taneous 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 a → γγ 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.

【 授权许可】

CC BY