Bayesian inference of nucleus resonance and neutron skin

TL;DR

This paper uses Bayesian methods within the Skyrme-Hartree-Fock framework to constrain the nuclear matter equation of state by analyzing nucleus resonance and neutron-skin data, addressing existing puzzles and exploring parameter correlations.

Contribution

It demonstrates the application of Bayesian analysis to nuclear EOS constraints, incorporating higher-order parameters and resolving specific experimental puzzles.

Findings

Bayesian analysis helps resolve the PREXII and soft Tin puzzles.

It reveals correlations between symmetry energy and slope parameters.

Inclusion of higher-order EOS parameters alters constraint results.

Abstract

In this proceeding, we have presented some highlight results on the constraints of the nuclear matter equation of state (EOS) from the data of nucleus resonance and neutron-skin thickness using the Bayesian approach based on the Skyrme-Hartree-Fock model and its extension. Typically, we have discussed the anti-correlation and positive correlation between the slope parameter and the value of the symmetry energy at the saturation density under the constraint of, respectively, the neutron-skin thickness and the isovector giant dipole resonance. We have shown that the Bayesian analysis can help to find a compromise for the ``PREXII puzzle'' and the ``soft Tin puzzle". We have further illustrated the possible modifications on the constraints of lower-order EOS parameters as well as the relevant correlation when higher-order EOS parameters are incorporated as independent variables. For a given model and parameter space, the Bayesian approach serves as a good analysis tool suitable for multi-messengers versus multi-variables, and is helpful for constraining quantitatively the model parameters as well as their correlations.