Machine learning in nuclear physics at low and intermediate energies

被引:63
|
作者
He, Wanbing [1 ,2 ]
Li, Qingfeng [3 ,4 ]
Ma, Yugang [1 ,2 ]
Niu, Zhongming [5 ]
Pei, Junchen [6 ,7 ]
Zhang, Yingxun [8 ,9 ]
机构
[1] Fudan Univ, Inst Modern Phys, Key Lab Nucl Phys & Ion Beam Applicat MOE, Shanghai 200433, Peoples R China
[2] NSFC & Fudan Univ, Shanghai Res Ctr Theoret Nucl Phys, Shanghai 200438, Peoples R China
[3] Huzhou Univ, Sch Sci, Huzhou 313000, Peoples R China
[4] Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China
[5] Anhui Univ, Sch Phys & Optoelect Engn, Hefei 230601, Peoples R China
[6] Peking Univ, Sch Phys, State Key Lab Nucl Phys & Technol, Beijing 100871, Peoples R China
[7] Chinese Acad Sci, Inst Modern Phys, Southern Ctr Nucl Sci Theory SCNT, Huizhou 516000, Peoples R China
[8] China Inst Atom Energy, Dept Nucl Phys, Beijing 102413, Peoples R China
[9] Guangxi Normal Univ, Guangxi Key Lab Nucl Phys & Technol, Guilin 541004, Peoples R China
基金
中国国家自然科学基金;
关键词
machine learning; nuclear physics; low and intermediate energies; KERNEL RIDGE-REGRESSION; NEUTRON SKIN THICKNESS; HEAVY-ION COLLISIONS; DECAY HALF-LIVES; SYMMETRY ENERGY; NEURAL-NETWORKS; BAYESIAN-INFERENCE; MASS PREDICTIONS; GAMMA DISCRIMINATION; MONTE-CARLO;
D O I
10.1007/s11433-023-2116-0
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Machine learning (ML) is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing tasks. In this review, we first briefly introduce the different methodologies used in ML algorithms and techniques. As a snapshot of many applications by ML, some selected applications are presented, especially for low- and intermediate-energy nuclear physics, which include topics on theoretical applications in nuclear structure, nuclear reactions, properties of nuclear matter, and experimental applications in event identification/reconstruction, complex system control, and firmware performance. Finally, we present a summary and outlook on the possible directions of ML use in low-intermediate energy nuclear physics and possible improvements in ML algorithms.
引用
收藏
页数:19
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