Nuclear mass predictions with a Bayesian neural network

Abstract The Bayesian neural network (BNN) has been widely used to study nuclear physics in recent years. In this study, a BNN was applied to optimize seven theoretical nuclear mass models, namely, six global models and one local model. The accuracy of these models in describing and predicting masses of nuclei with both the proton number and the neutron number greater than or equal to eight was improved effectively for two types of numerical experiments, particularly for the liquid drop model and the relativistic mean-field theory, whose root mean square deviations (RMSDs) for describing (predicting) nuclear masses were reduced by 81.5%−90.6% (66.9%−84.2%). Additionally, the relatively stable RMSDs as nuclei move away from the β -stability line and the good agreement with experimental single-neutron separation energies further confirm the reliability of the BNN.