Examination of machine learning for assessing physical effects:Learning the relativistic continuum mass table with kernel ridge regression

作     者：杜晓凯 郭鹏 吴鑫辉 张双全 Xiao-Kai Du;Peng Guo;Xin-Hui Wu;Shuang-Quan Zhang

作者机构：State Key Laboratory of Nuclear Physics and TechnologySchool of PhysicsPeking UniversityBeijing 100871China 

出 版 物：《Chinese Physics C》 (中国物理C（英文版）)

年 卷 期：2023年第47卷第7期

页      面：138-150页

核心收录：

学科分类：07[理学] 0702[理学-物理学] 

基　　金：Supported by the National Natural Science Foundation of China(11875075,11935003,11975031,12141501,12070131001) the China Postdoctoral Science Foundation under(2021M700256) the State Key Laboratory of Nuclear Physics and Technology,Peking University(NPT2023ZX01,NPT2023KFY02) the President’s Undergraduate Research Fellowship(PURF)of Peking University 

主　　题：machine learning kernel ridge regression relativistic continuum Hartree-Bogoliubov theory nuclear mass table 

摘      要：The kernel ridge regression(KRR)method and its extension with odd-even effects(KRRoe)are used to learn the nuclear mass table obtained by the relativistic continuum Hartree-Bogoliubov theory.With respect to the binding energies of 9035 nuclei,the KRR method achieves a root-mean-square deviation of 0.96 MeV,and the KRRoe method remarkably reduces the deviation to 0.17 MeV.By investigating the shell effects,one-nucleon and twonucleon separation energies,odd-even mass differences,and empirical proton-neutron interactions extracted from the learned binding energies,the ability of the machine learning tool to grasp the known physics is discussed.It is found that the shell effects,evolutions of nucleon separation energies,and empirical proton-neutron interactions are well reproduced by both the KRR and KRRoe methods,although the odd-even mass differences can only be reproduced by the KRRoe method.