Deep learning for nuclear masses in deformed relativistic Hartree-Bogoliubov theory in continuum

TL;DR

This paper uses deep learning to extend nuclear mass tables based on relativistic Hartree-Bogoliubov theory, enabling the study of deformation effects on r-process nucleosynthesis, with findings showing significant sensitivity of abundances to deformation in certain mass ranges.

Contribution

It introduces a deep neural network approach to extend deformed relativistic Hartree-Bogoliubov mass tables, including odd-odd and odd-even nuclei, for improved r-process abundance predictions.

Findings

r-process abundances are sensitive to nuclear deformation

Deformation effects are significant in the mass range A=80-120

DNN effectively extends the DRHBc mass table to odd nuclei

Abstract

Most nuclei are deformed, and these deformations play an important role in various nuclear and astrophysical phenomena. Microscopic nuclear mass models have been developed based on covariant density functional theory to explore exotic nuclear properties. Among these, we adopt mass models based on the relativistic continuum Hartree-Bogoliubov theory (RCHB) with spherical symmetry and the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with axial symmetry to study the effects of deformation on the abundances produced during the rapid neutron-capture process (r-process). Since the DRHBc mass table has so far been completed only for even-Z nuclei, we first investigate whether a Deep Neural Network (DNN) can be used to extend the DRHBc mass table by focusing on nuclear binding energies. To incorporate information about odd-odd and odd-even isotopes into the DNN, we also use binding energies from AME2020 as a training set, in addition to those from the DRHBc mass table for even-Z nuclei. After generating an improved mass table through the DNN study, we conduct a sensitivity analysis of r-process abundances to deformation or mass variations using the RCHB$^\star$ and DRHBc$^\star$ mass tables (where $\star$ indicates that the mass table is obtained from the DNN study). For the r-process sensitivity study, we consider magnetohydrodynamic jets and collapsar jets. Our findings indicate that r-process abundances are sensitive to nuclear deformation, particularly within the mass range of $A=80-120$.