Study of Deuteron Separation Energy Based on Bayesian Neural Network Approach

Abstract

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.

Keywords

• bayesian neural network
• deuteron separation energy