High-energy nuclear physics meets machine learning

被引：69

作者：

He, Wan-Bing ^{[1
,2
,3
]}

Ma, Yu-Gang ^{[1
,2
,3
]}

Pang, Long-Gang ^{[4
,5
]}

Song, Hui-Chao ^{[6
,7
]}

Zhou, Kai ^{[8
]}

机构：

[1] Fudan Univ, Inst Modern Phys, Key Lab Nucl Phys & Ion Beam Applicat MOE, Shanghai 200433, Peoples R China

[2] Shanghai Res Ctr Theoret Nucl Phys, NSFC, Shanghai 200438, Peoples R China

[3] Fudan Univ, Shanghai 200438, Peoples R China

[4] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China

[5] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China

[6] Peking Univ, Sch Phys, Beijing 100871, Peoples R China

[7] Peking Univ, Ctr High Energy Phys, Beijing 100871, Peoples R China

[8] Frankfurt Inst Adv Studies FIAS, D-60438 Frankfurt, Germany

来源：

NUCLEAR SCIENCE AND TECHNIQUES | 2023年 / 34卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Heavy-ion collisions; Machine learning; Initial state; Bulk properties; Medium effects; Hard probes; Observables; COLLISIONS; INTERPOLATION; VISCOSITY; QUARKS; FLOW;

D O I：

10.1007/s41365-023-01233-z

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

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.

引用

页数：33

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