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

Abstract 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-strahlun...

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Autores principales:	Jie Ren, Daohan Wang, Lei Wu, Jin Min Yang, Mengchao Zhang
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Jets Phenomenological Models Nuclear and particle physics. Atomic energy. Radioactivity QC770-798
Acceso en línea:	https://doaj.org/article/a8f6e07f085d4b81bfa38d7fbf5ca49a
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spelling	oai:doaj.org-article:a8f6e07f085d4b81bfa38d7fbf5ca49a2021-11-21T12:41:39ZDetecting an axion-like particle with machine learning at the LHC10.1007/JHEP11(2021)1381029-8479https://doaj.org/article/a8f6e07f085d4b81bfa38d7fbf5ca49a2021-11-01T00:00:00Zhttps://doi.org/10.1007/JHEP11(2021)138https://doaj.org/toc/1029-8479Abstract 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.Jie RenDaohan WangLei WuJin Min YangMengchao ZhangSpringerOpenarticleJetsPhenomenological ModelsNuclear and particle physics. Atomic energy. RadioactivityQC770-798ENJournal of High Energy Physics, Vol 2021, Iss 11, Pp 1-26 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Jets Phenomenological Models Nuclear and particle physics. Atomic energy. Radioactivity QC770-798
spellingShingle	Jets Phenomenological Models Nuclear and particle physics. Atomic energy. Radioactivity QC770-798 Jie Ren Daohan Wang Lei Wu Jin Min Yang Mengchao Zhang Detecting an axion-like particle with machine learning at the LHC
description	Abstract 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.
format	article
author	Jie Ren Daohan Wang Lei Wu Jin Min Yang Mengchao Zhang
author_facet	Jie Ren Daohan Wang Lei Wu Jin Min Yang Mengchao Zhang
author_sort	Jie Ren
title	Detecting an axion-like particle with machine learning at the LHC
title_short	Detecting an axion-like particle with machine learning at the LHC
title_full	Detecting an axion-like particle with machine learning at the LHC
title_fullStr	Detecting an axion-like particle with machine learning at the LHC
title_full_unstemmed	Detecting an axion-like particle with machine learning at the LHC
title_sort	detecting an axion-like particle with machine learning at the lhc
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/a8f6e07f085d4b81bfa38d7fbf5ca49a
work_keys_str_mv	AT jieren detectinganaxionlikeparticlewithmachinelearningatthelhc AT daohanwang detectinganaxionlikeparticlewithmachinelearningatthelhc AT leiwu detectinganaxionlikeparticlewithmachinelearningatthelhc AT jinminyang detectinganaxionlikeparticlewithmachinelearningatthelhc AT mengchaozhang detectinganaxionlikeparticlewithmachinelearningatthelhc
_version_	1718418844959113216

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

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