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Taming nucleon density distributions with deep neural network

文献类型：期刊论文

作者Yang, Zu-Xing1,2,3; Fan, Xiao-Hua4; Yin, Peng1,5; Zuo, Wei1,2
刊名PHYSICS LETTERS B
出版日期2021-12-10
卷号823页码:7
ISSN号0370-2693
DOI10.1016/j.physletb.2021.136650
通讯作者Yang, Zu-Xing(yangzuxing16@impcas.ac.cn) ; Fan, Xiao-Hua(fanxiaohua@swu.edu.cn) ; Zuo, Wei(zuowei@impcas.ac.cn)
英文摘要With the datasets of the density distributions calculated by Skyrme density functional theories, we elaborated deep neural networks to generate the density profile and provide a table of related hyperparameters set for similar applications of other structural models. In the process of machine learning with the objective/target functions that normalized mean square error and Kullback-Leibler divergence (cross entropy), there is a turning point showing the transition from the Fermi-like distribution to the realistic Skyrme distribution, while this property is transcended when Pearson chi(2) divergence is employed. A training program of about 35 minutes with only about 5% - 10% nuclei (200 - 300) is sufficient to describe the nucleon density distributions of all the nuclear chart within 2% relative error. We obtain similar results employing different datasets calculated by different Skyrme density functional theories. We further investigate the extrapolation properties, which show that an addition of 15 nucleons is acceptable. Based on the results, we propose a mixed dataset approach and a retraining approach in order to go beyond a single physical structure model. (C) 2021 The Authors. Published by Elsevier B.V.
WOS关键词ELECTRON-SCATTERING ; MODEL
资助项目Key Research Program of the Chinese Academy of Sciences[XDPB15] ; National Natural Science Foundation of China[11975282] ; National Natural Science Foundation of China[11705240] ; National Natural Science Foundation of China[12005175] ; National Natural Science Foundation of China[11435014] ; 973 Program of China[2013CB834405] ; CUSTIPEN (China-U.S. Theory Institute for Physics with Exotic Nuclei) - U.S. Department of Energy, office of Science[DE-SC0009971] ; Fundamental Research Funds for the Central Universities[SWU119076]
WOS研究方向Astronomy & Astrophysics ; Physics
语种英语
WOS记录号WOS:000710220700008
出版者ELSEVIER
资助机构Key Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; 973 Program of China ; CUSTIPEN (China-U.S. Theory Institute for Physics with Exotic Nuclei) - U.S. Department of Energy, office of Science ; Fundamental Research Funds for the Central Universities
源URL[http://119.78.100.186/handle/113462/136091]  
专题中国科学院近代物理研究所
通讯作者Yang, Zu-Xing; Fan, Xiao-Hua; Zuo, Wei
作者单位1.Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China
2.Univ Chinese Acad Sci, Sch Nucl Sci & Technol, Beijing 100049, Peoples R China
3.RIKEN Nishina Ctr, Wako, Saitama 3510198, Japan
4.Southwest Univ, Sch Phys Sci & Technol, Chongqing 400715, Peoples R China
5.Iowa State Univ, Dept Phys & Astron, Ames, IA 50011 USA
推荐引用方式
GB/T 7714		 Yang, Zu-Xing,Fan, Xiao-Hua,Yin, Peng,et al. Taming nucleon density distributions with deep neural network[J]. PHYSICS LETTERS B,2021,823:7.
APA Yang, Zu-Xing,Fan, Xiao-Hua,Yin, Peng,&Zuo, Wei.(2021).Taming nucleon density distributions with deep neural network.PHYSICS LETTERS B,823,7.
MLA Yang, Zu-Xing,et al."Taming nucleon density distributions with deep neural network".PHYSICS LETTERS B 823(2021):7.

入库方式： OAI收割

来源：近代物理研究所

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