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,...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 14; no. 6; p. 1078
Main Authors Guo, Lihan, Wu, Xinhui, Zhao, Pengwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2022
Subjects
Density functional theory
kernel ridge regression
Kernels
machine learning
Neural networks
nuclear mass
Nuclear physics
Nuclei
r-process
relativistic density functional theory
Relativistic effects
Relativistic theory
Simulation
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Summary: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.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14061078