High-energy nuclear physics meets machine learning

• Published in: Nuclear science and techniques Vol. 34; no. 6; pp. 82 - 114
• Main Authors: He, Wan-Bing, Ma, Yu-Gang, Pang, Long-Gang, Song, Hui-Chao, Zhou, Kai
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2023; Shanghai Research Center for Theoretical Nuclear Physics,NSFC and Fudan University,Shanghai 200438,China%Institute of Particle Physics and Key Laboratory of Quark and Lepton Physics(MOE),Central China Normal University,Wuhan 430079,China%School of Physics and Center for High Energy Physics,Peking University,Beijing 100871,China%Frankfurt Institute for Advanced Studies(FIAS),60438 Frankfurt am Main,Germany; Key Laboratory of Nuclear Physics and Ion-beam Application(MOE),Institute of Modern Physics,Fudan University,Shanghai 200433,China
• Subjects: Beam Physics; Nuclear Energy; Particle Acceleration and Detection; Particle and Nuclear Physics; Physics; Physics and Astronomy; Review Article; Heavy-ion collisions; Medium effects; Initial state; Machine learning; Bulk properties; Observables; Hard probes
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-023-01233-z

Although seemingly disparate, high-energy nuclear physics (HENP) and machine learning (ML) have begun to merge in the last few years, yielding interesting results. It is worthy to raise the profile of utilizing this novel mindset from ML in HENP, to help interested readers see the breadth of activities around this intersection. The aim of this mini-review is to inform the community of the current status and present an overview of the application of ML to HENP. From different aspects and using examples, we examine how scientific questions involving HENP can be answered using ML.