Extended Fayans energy density functional: optimization and analysis

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 th...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Paul-Gerhard, Reinhard, O'Neal, Jared, Wild, Stefan M, Nazarewicz, Witold
Format: Article
Language:English
Subjects:
Calibration
Correlation analysis
Datasets
Density functional theory
Even-even nuclei
Machine learning
Mathematical models
Nuclear properties
Parameter sensitivity
Sensitivity analysis
Sensitivity enhancement
Supervised learning
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Summary: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%. The enhanced degrees of freedom of the 14D model reduce correlations between model parameters and enhance sensitivity.
ISSN:2331-8422