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.
| Comments: | 56 pages, 4 tables, 3 figures |
| Subjects: | Nuclear Theory (nucl-th); Machine Learning (cs.LG); Atomic Physics (physics.atom-ph) |
| Cite as: | arXiv:2604.05312 [nucl-th] |
| (or arXiv:2604.05312v1 [nucl-th] for this version) | |
| https://doi.org/10.48550/arXiv.2604.05312 arXiv-issued DOI via DataCite |
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| Journal reference: | NUCL SCI TECH 37, 93 (2026) |
| Related DOI: | https://doi.org/10.1007/s41365-026-01905-6
DOI(s) linking to related resources |
From: Yundong Wang [view email]
[v1]
Tue, 7 Apr 2026 01:32:53 UTC (913 KB)
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