Predictions of nuclear charge radii based on the convolutional neural network

作     者：Ying-Yu Cao Jian-You Guo Bo Zhou Ying-Yu Cao;Jian-You Guo;Bo Zhou

作者机构：Key Laboratory of Nuclear Physics and Ion‑beam Application(MOE)Institute of Modern PhysicsFudan UniversityShanghai 200433China School of Physics and Optoelectronics EngineeringAnhui UniversityHefei 230601China Shanghai Research Center for Theoretical Nuclear PhysicsNSFC and Fudan UniversityShanghai 200438China 

出 版 物：《Nuclear Science and Techniques》 (核技术（英文）)

年 卷 期：2023年第34卷第10期

页      面：83-90页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0827[工学-核科学与技术] 082701[工学-核能科学与工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by Shanghai “Science and Technology Innovation Action Plan” Project (No. 21ZR140950) 

主　　题：Nuclear charge radii Machine learning Neural network 

摘      要：In this study, we developed a neural network that incorporates a fully connected layer with a convolutional layer to predict the nuclear charge radii based on the relationships between four local nuclear charge radii. The convolutional neural network(CNN) combines the isospin and pairing effects to describe the charge radii of nuclei with A ≥ 39 and Z ≥ 20. The developed neural network achieved a root mean square(RMS) deviation of 0.0195 fm for a dataset with 928 nuclei. Specifically, the CNN reproduced the trend of the inverted parabolic behavior and odd–even staggering observed in the calcium isotopic chain, demonstrating reliable predictive capability.