Application of machine learning in the determination of impact parameter in the Sn-132 + Sn-124 system

doi:10.1103/PhysRevC.104.034608

	Application of machine learning in the determination of impact parameter in the Sn-132 + Sn-124 system
	Li, Fupeng 1,7; Wang, Yongjia 1; Gao, Zepeng 1,6; Li, Pengcheng 1,8; Lu, Hongliang 5; Li, Qingfeng 1,4; Tsang, C. Y.2,3; Tsang, M. B.2,3
刊名	PHYSICAL REVIEW C
	2021-09-07
卷号	104 期号:3 页码:8
ISSN号	2469-9985
DOI	10.1103/PhysRevC.104.034608
通讯作者	Wang, Yongjia(wangyongjia@zjhu.edu.cn) ; Li, Qingfeng(liqf@zjhu.edu.cn)
英文摘要	Background: Sn-132 + Sn-124 collisions at a beam energy of 270 MeV/nucleon were performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the nuclear equation of state. Reconstructing the impact parameter is one of the important tasks in the experiment as it relates to many observables. Purpose: In this work, we employ three commonly used algorithms in machine learning, the artificial neural network (ANN), the convolutional neural network (CNN), and the light gradient boosting machine (LightGBM), to determine the impact parameter by analyzing either the charged particle spectra or several features simulated with events from the ultrarelativistic quantum molecular dynamics (UrQMD) model. Method: To closely imitate experimental data and investigate the generalizability of the trained machine learning algorithms, incompressibility of nuclear equation of state and the in-medium nucleon-nucleon cross sections are varied in the UrQMD model to generate the training data. Results: The mean absolute error Delta b between the true and the predicted impact parameter is smaller than 0.45 fm if training and testing sets are sampled from the UrQMD model with the same parameter set. However, if training and testing sets are sampled with different parameter sets, Delta b would increase to 0.8 fm. Conclusion: The generalizability of the trained machine learning algorithms suggests that these machine learning algorithms can be used reliably to reconstruct the impact parameter in experiments.
资助项目	National Natural Science Foundation of China[U2032145] ; National Natural Science Foundation of China[11875125] ; National Natural Science Foundation of China[12047568] ; National Key Research and Development Program of China[2020YFE0202002] ; Ten Thousand Talent Program of Zhejiang province[2018R52017] ; U.S. Department of Energy[DE-SC0021235] ; U.S. Department of Energy[DE-NA0003908] ; U.S. National Science Foundation[PHY-1565546]
WOS关键词	QUANTUM MOLECULAR-DYNAMICS ; HEAVY-ION COLLISIONS ; EQUATION-OF-STATE ; NEURAL-NETWORKS ; ENERGY ; FLOW
WOS研究方向	Physics
语种	英语
出版者	AMER PHYSICAL SOC
WOS记录号	WOS:000693633700007
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China ; Ten Thousand Talent Program of Zhejiang province ; U.S. Department of Energy ; U.S. National Science Foundation
内容类型	期刊论文
源URL	[http://119.78.100.186/handle/113462/136448]
专题	中国科学院近代物理研究所
通讯作者	Wang, Yongjia; Li, Qingfeng
作者单位	1.Huzhou Univ, Sch Sci, Huzhou 313000, Peoples R China 2.Michigan State Univ, Dept Phys & Astron, E Lansing, MI 48824 USA 3.Michigan State Univ, Natl Superconducting Cyclotron Lab, E Lansing, MI 48824 USA 4.Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China 5.Huawei Technol Co Ltd, HiSilicon Res Dept, Shenzhen 518000, Peoples R China 6.Shenyang Normal Univ, Coll Phys Sci & Technol, Shenyang 110034, Peoples R China 7.Zhejiang Univ Technol, Coll Sci, Hangzhou 310014, Peoples R China 8.Lanzhou Univ, Sch Nucl Sci & Technol, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Li, Fupeng,Wang, Yongjia,Gao, Zepeng,et al. Application of machine learning in the determination of impact parameter in the Sn-132 + Sn-124 system[J]. PHYSICAL REVIEW C,2021,104(3):8.
APA	Li, Fupeng.,Wang, Yongjia.,Gao, Zepeng.,Li, Pengcheng.,Lu, Hongliang.,...&Tsang, M. B..(2021).Application of machine learning in the determination of impact parameter in the Sn-132 + Sn-124 system.PHYSICAL REVIEW C,104(3),8.
MLA	Li, Fupeng,et al."Application of machine learning in the determination of impact parameter in the Sn-132 + Sn-124 system".PHYSICAL REVIEW C 104.3(2021):8.