Calibration of nuclear charge density distribution by back-propagation neural networks

Abstract

Based on the back-propagation neural networks and density functional theory, a supervised learning is performed firstly to generate the nuclear charge density distributions. The charge density is further calibrated to the experimental charge radii by a composite loss function. It is found that, when the parity, pairing, and shell effects are taken into account, about $96\%$ of the nuclei in the validation set fall within two standard deviations of the predicted charge radii. The calibrated charge density is then mapped to the matter density, and further mapped to the binding energies according to the Hohenberg-Kohn theorem. It provides an improved description of some nuclei in both binding energies and charge radii. Moreover, the anomalous overbinding in ${}^{48}$Ca implies the existence of an indispensable beyond-mean-field effect.

References in corpus (18)

• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Importance Truncation for Large-Scale Configuration Interaction Approaches
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• A proton density bubble in the doubly magic ${}^{34}$Si nucleus
• Evidence for Symplectic Symmetry in Ab Initio No-Core Shell Model Results for Light Nuclei
• Neutron skin thickness of heavy nuclei with $\alpha$-particle correlations and the slope of the nuclear symmetry energy
• Laser spectroscopy of neutron-rich ${}^{207,208}$Hg isotopes: Illuminating the kink and odd-even staggering in charge radii across the $N=126$ shell closure
• Novel Bayesian neural network based approach for nuclear charge radii
• Charge radii in covariant density functional theory: a global view
• Bayesian evaluation of charge yields of fission fragments of 239U
• Application Of Support Vector Machines To Global Prediction Of Nuclear Properties
• Comparative study on charge radii and their kinks at magic numbers
• Neutron skins as laboratory constraints on properties of neutron stars and on what we can learn from heavy ion fragmentation reactions
• Influence of pairing correlations on the radius of neutron-rich nuclei
• Probing nuclear bubble configuration by proton induced reaction
• Comment on "Ab Initio study of 40-Ca with an importance-truncated no-core shell model"
• A Kohn-Sham Scheme Based Neural Network for Nuclear Systems
• Charge radii of Ca isotopes and correlations

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