High-energy nuclear physics meets machine learning  

作　　者：Wan-Bing He Yu-Gang Ma Long-Gang Pang Hui-Chao Song Kai Zhou 

机构地区：[1]Key Laboratory of Nuclear Physics and Ion-beam Application(MOE),Institute of Modern Physics,Fudan University,Shanghai,200433,China [2]Shanghai Research Center for Theoretical Nuclear Physics,NSFC and Fudan University,Shanghai,200438,China [3]Institute of Particle Physics and Key Laboratory of Quark and Lepton Physics(MOE),Central China Normal University,Wuhan,430079,China [4]School of Physics and Center for High Energy Physics,Peking University,Beijing,100871,China [5]Frankfurt Institute for Advanced Studies(FIAS),60438,Frankfurt am Main,Germany

出　　处：《Nuclear Science and Techniques》2023年第6期9-41,共33页核技术（英文）

基　　金：supported in part by the National Natural Science Foundation of China under contract Nos.11890714,12147101(Ma),12075098(Pang),12247107,12075007(Song);the Germany BMBF under the ErUM-Data project(Zhou);the Guangdong Major Project of Basic and Applied Basic Research No.2020B0301030008(Ma).

摘　　要：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.

关 键 词：Heavy-ion collisions Machine learning Initial state Bulk properties Medium effects Hard probes OBSERVABLES 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O571[自动化与计算机技术—控制科学与工程]