Nuclear charge radii in Bayesian neural networks revisited

Abstract

In this work, a refined Bayesian neural network (BNN) based approach with six inputs including the proton number, mass number, and engineered features associated with the pairing effect, shell effect, isospin effect, and “abnormal” shape staggering effect of 181,183,185Hg, is proposed to accurately describe nuclear charge radii. The new approach is able to well describe the charge radii of atomic nuclei with A≥40 and Z≥20. The standard root-mean-square deviation is 0.014 fm for both the training and validation data. In particular, the predicted charge radii of proton-rich and neutron-rich calcium isotopes are found in good agreement with data. We further demonstrate the reliability of the BNN approach by investigating the variations of the root-mean-square deviations with extrapolation distances, mass numbers, and isospin asymmetries.