Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects

Abstract

Bayesian neural network (BNN) approach is employed to improve the nuclear mass predictions of various models. It is found that the noise error in the likelihood function plays an important role in the predictive performance of the BNN approach. By including a distribution for the noise error, an appropriate value can be found automatically in the sampling process, which optimizes the nuclear mass predictions. Furthermore, two quantities related to nuclear pairing and shell effects are added to the input layer in addition to the proton and mass numbers. As a result, the theoretical accuracies are significantly improved not only for nuclear masses but also for single-nucleon separation energies. Due to the inclusion of the shell effect, in the unknown region, the BNN approach predicts a similar shell-correction structure to that in the known region, e.g., the predictions of underestimation of nuclear mass around the magic numbers in the relativistic mean-field model. This manifests that better predictive performance can be achieved if more physical features are included in the BNN approach.

15 pages, 4 figures, and 3 tables

References in corpus (8)

• Relativistic Continuum Hartree Bogoliubov Theory for Ground State Properties of Exotic Nuclei
• Surface diffuseness correction in global mass formula
• Spin-isospin resonances: A self-consistent covariant description
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Isospin corrections for superallowed Fermi beta decay in self-consistent relativistic random phase approximation approaches
• Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^- Halflives
• Reexamining the temperature and neutron density conditions for r-process nucleosynthesis with augmented nuclear mass models
• Improved radial basis function approach with the odd-even corrections

Cited by in corpus (11)

• Nuclear mass predictions with machine learning reaching the accuracy required by $r$-process studies
• Nuclear masses in extended kernel ridge regression with odd-even effects
• Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
• Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei
• Bayesian evaluation of charge yields of fission fragments of 239U
• Prediction of Nuclear Charge Density Distribution with Feedback Neural Network
• Sensitivity study of \emph{r}-process abundances to nuclear masses
• The description of giant dipole resonance key parameters with multitask neural networks
• Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging
• Report from the A.I. For Nuclear Physics Workshop
• Impact of the nuclear mass uncertainties on the r process