Study of Deuteron Separation Energy Based on Bayesian Neural Network Approach  

作　　者：XING Kang LIANG Yan SUN Xiaojun 

机构地区：[1]College of Physics and Technology,Guangxi Normal University,Guilin 541004,China [2]Guangxi Key Laboratory of Nuclear Physics and Technology,Guangxi Normal University,Guilin 541004,China

出　　处：《原子能科学技术》2023年第4期721-728,共8页Atomic Energy Science and Technology

基　　金：Supported by National Natural Science Foundation of China (12065003);Central Government Guidance Funds for Local Scientific and Technological Development of China (Guike ZY22096024);Natural Science Foundation of Guangxi (2019GXNSFDA185011);Scientific Research and Technology Development Project of Guilin (20210104-2)。

摘　　要：Deuteron separation energy is not only the basis for validating the nuclear mass models and nucleon-nucleon interaction potential,but also can determine the stability of a nuclide to certain extent.Bayesian neural network(BNN)approach,which has strong predictive power and can naturally give theoretical errors of predicted values,had been successfully applied to study the different kinds of separations except the deuteron separation.In this paper,several typical nuclear mass models,such as macroscopic model BW2,macroscopic-microscopic model WS4,and microscopic model HFB-31,are chosen to study the deuteron separation energy combining BNN approach.The root-mean-square deviations of these models are partly reduced.In addition,the inclusion of physical parameters related to the pair and shell effects in the input layer can further improve the theoretical accuracy for the deuteron separation energy.The results show that the theoretical predictions are more reliable as more physical features of BNN approach are included.

关 键 词：Bayesian neural network deuteron separation energy pair and shell effects 

分 类 号：O571[理学—粒子物理与原子核物理]