Predictions of charge density distributions for nuclei with $Z \geq 8$
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, respectivel...
| Authors | Yun Dong Wang, Tian Shuai Shang, Hui Hui Xie et al. |
|---|---|
| Primary category | nucl-th |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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