Radial basis function approach in nuclear mass predictions

TL;DR

This paper demonstrates that applying the radial basis function (RBF) approach significantly enhances the accuracy of nuclear mass predictions across various models, reducing errors by up to 78% and highlighting the importance of high-precision experimental data.

Contribution

The study introduces a novel application of the RBF approach to nuclear mass predictions, achieving substantial accuracy improvements across multiple models.

Findings

RBF reduces rms deviations by up to 78%.

Strong correlations exist within three units in nuclear mass predictions.

RBF achieves accuracy comparable to atomic mass extrapolation methods.

Abstract

The radial basis function (RBF) approach is applied in predicting nuclear masses for 8 widely used nuclear mass models, ranging from macroscopic-microscopic to microscopic types. A significantly improved accuracy in computing nuclear masses is obtained, and the corresponding rms deviations with respect to the known masses is reduced by up to 78%. Moreover, strong correlations are found between a target nucleus and the reference nuclei within about three unit in distance, which play critical roles in improving nuclear mass predictions. Based on the latest Weizs\"{a}cker-Skyrme mass model, the RBF approach can achieve an accuracy comparable with the extrapolation method used in atomic mass evaluation. In addition, the necessity of new high-precision experimental data to improve the mass predictions with the RBF approach is emphasized as well.