Extended Fayans energy density functional: optimization and analysis
arxiv(2024)
摘要
The Fayans energy density functional (EDF) has been very successful in
describing global nuclear properties (binding energies, charge radii, and
especially differences of radii) within nuclear density functional theory. In a
recent study, supervised machine learning methods were used to calibrate the
Fayans EDF. Building on this experience, in this work we explore the effect of
adding isovector pairing terms, which are responsible for different proton and
neutron pairing fields, by comparing a 13D model without the isovector pairing
term against the extended 14D model. At the heart of the calibration is a
carefully selected heterogeneous dataset of experimental observables
representing ground-state properties of spherical even-even nuclei. To quantify
the impact of the calibration dataset on model parameters and the importance of
the new terms, we carry out advanced sensitivity and correlation analysis on
both models. The extension to 14D improves the overall quality of the model by
about 30
between model parameters and enhance sensitivity.
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