Journal
SYMMETRY-BASEL
Volume 14, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/sym14061078
Keywords
nuclear mass; machine learning; kernel ridge regression; relativistic density functional theory; r-process
Categories
Funding
- National Key R&D Program of China [2018YFA0404400, 2017YFE0116700]
- National Natural Science Foundation of China [11875075, 11935003, 11975031, 12141501, 12070131001]
- China Postdoctoral Science Foundation [2021M700256]
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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.
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.
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