Predictions of charge density distributions for nuclei with $Z \geq 8$

TL;DR

A deep neural network model predicts nuclear charge density distributions for nuclei with Z ≥ 8, significantly outperforming traditional methods and providing high-precision data for various physics applications.

Contribution

The paper introduces a novel DNN approach that incorporates nuclear structure features to accurately predict charge densities, surpassing conventional methods in precision.

Findings

Root-mean-square deviations of 0.0123 fm and 0.0198 fm for charge radii on training and validation sets.

Model significantly improves predictive accuracy over traditional methods.

Provides high-precision charge density data for nuclear physics applications.

Abstract

A deep neural network (DNN) has been developed to accurately predict nuclear charge density distributions for nuclei with proton numbers $Z \geq 8$. By incorporating essential nuclear structure features, the model achieves a significant improvement in predictive accuracy over conventional methods. The charge density distributions are analyzed using a Fourier-Bessel (FB) series expansion, and the DNN is trained on a comprehensive dataset derived from relativistic continuum Hartree-Bogoliubov (RCHB) theory calculations. The model demonstrates exceptional performance, with root-mean-square deviations of 0.0123 fm and 0.0198 fm for charge radii on the training and validation sets, respectively, remarkably surpassing the precision of the original RCHB calculations. Beyond advancing nuclear physics research, this high-precision model provides critical data for applications in atomic physics, nuclear astrophysics, and related fields.