Though being seemingly disparate and with relatively new intersection, high energy nuclear physics and machine learning have already begun to merge and yield interesting results during the last few years. It's worthy to raise the profile of utilizing this novel mindset from machine learning in high energy nuclear physics, to help more interested readers see the breadth of activities around this intersection. The aim of this mini-review is to introduce to the community the current status and report an overview of applying machine learning for high energy nuclear physics, to present from different aspects and examples how scientific questions involved in high energy nuclear physics can be tackled using machine learning.
Extraction of ground-state nuclear deformations from ultra-relativistic heavy-ion collisions: Nuclear structure physics context
University of York（约克大学）；University of Warsaw（华沙大学）；Michigan State University（密歇根州立大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Duke University（杜克大学）
QCD and nuclear physics. How to explain the coincidence between the action radius of nuclear forces and the characteristic scale of the neutron-neutron electrostatic interaction?
Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery
School of Nuclear Science and Technology, University of South China（南华大学核科学技术学院）；INFN — Istituto Nazionale di Fisica Nucleare — Sezione di Bari（意大利国家核物理研究所巴里分部）；Key Laboratory of Quark and Lepton Physics (MOE) & Institute of Particle Physics, Central China Normal University（华中师范大学夸克与轻子物理教育部重点实验室及粒子物理研究所）；Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences（中国科学院近代物理研究所南方核科学理论中心）；School of Physics, East China Normal University（华东师范大学物理系）；Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University（复旦大学核物理与离子束应用教育部重点实验室及近代物理研究所）；Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University（国家自然科学基金委与复旦大学上海理论核物理研究中心）；School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理学院）；School of Artificial Intelligence, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）人工智能学院）
GAIA meets LZ: a high velocity tail-tale sign for dark matter
Saha Institute of Nuclear Physics（萨哈核物理研究所）；Homi Bhabha National Institute（霍米·巴巴国立学院）；Tata Institute of Fundamental Research（塔塔基础研究所）；Indian Institute of Technology Bombay（孟买印度理工学院）
The Hong Kong University of Science and Technology（香港科技大学）；Université catholique de Louvain（鲁汶天主教大学）；University of California Santa Barbara（加州大学圣巴巴拉分校）
Max Planck Institute for Intelligent Systems Tübingen（马克斯·普朗克智能系统研究所图宾根）；Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（爱因斯坦研究所））；Technical University of Munich（慕尼黑技术大学）；Isomorphic Labs（同构实验室）；University of Cambridge（剑桥大学）；University of Bochum（博德姆大学）
