Nuclear Mass Predictions of the Relativistic Density Functional Theory with the Kernel Ridge Regression and the Application to r-Process Simulations

The kernel ridge regression (KRR) and its updated version taking into account the odd-even effects (KRRoe) are employed to improve the mass predictions of the relativistic density functional theory. Both the KRR and KRRoe approaches can improve the mass predictions to a large extent. In particular, the KRRoe approach can significantly improve the predictions of the one-nucleon separation energies. The extrapolation performances of the KRR and KRRoe approaches to neutron-rich nuclei are examined, and the impacts of the KRRoe mass corrections on the r-process simulations are studied. It is found that the KRRoe mass corrections for the nuclei in the r-process path are remarkable in the light mass region, e.g., A < 150, and this could influence the corresponding r-process abundances.

Automated summary

The kernel ridge regression (KRR) and its updated version with odd-even effects (KRRoe) are used to improve mass predictions in relativistic density functional theory. Both techniques show significant improvements, particularly in predicting one-nucleon separation energies. The impact of KRRoe mass corrections on r-process simulations is studied, revealing its significant influence on nuclei in the light mass region, potentially affecting r-process abundances.