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(Seminar) Predictions of nuclear masses and half-lives with Bayesian neural network approach
2019-07-10  【 】【打印】【关闭

CAS Key Laboratory of Theoretical Physics

Institute of Theoretical Physics

Chinese Academy of Sciences

Seminar

Title

题目

Predictions of nuclear masses and half-lives with Bayesian neural network approach

Speaker

报告人

Zhongming Niu (牛中明)

Affiliation

所在单位

Anhui University

Date

日期

15:00, Jul 10 (Wednesday), 2019

Venue

地点

ITP South Building 6420

Abstract

摘要

Bayesian neural network (BNN) approach is employed to improve the predictions of nuclear masses and half-lives. It is found that the noise error in the likelihood function plays an important role in the predictive performance for the BNN approach. By including a distribution for the noise error, theoretical predictions can be improved remarkably. In addition to the proton and mass numbers, we further include two quantities related to nuclear pairing and shell effects into the input layer for nuclear mass predictions, and two quantities related to nuclear pairing and reaction energies into the input layer for nuclear half-life predictions. As a result, the theoretical accuracies are significantly improved for both nuclear masses and nuclear half-lives. This manifests that better predictive performance can be achieved if more physical features are included into the BNN approach.

Contact Person

所内联系人

Shan-Gui Zhou (周善贵)
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