Nuclear charge radii in Bayesian neural networks revisited

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. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/). Funded by SCOAP3.

Automated summary

In this study, a refined Bayesian neural network (BNN) based approach with six inputs, including proton number, mass number, and engineered features associated with various effects, is proposed to accurately describe nuclear charge radii. The new approach shows good performance in describing charge radii of atomic nuclei with A > 40 and Z > 20, with a standard root-mean-square deviation of 0.014 fm for the training and validation data. The predicted charge radii of proton-rich and neutron-rich calcium isotopes also agree well with the data, demonstrating the reliability of the BNN approach.