Bayesian inference of nucleus resonance and neutron skin

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.

High-energy nuclear physics meets machine learning

“…The neutron skin is used as a constraint in the calculation of the positive and negative correlations between the symmetry energy and the slope parameter at the saturation density. With this constraint, the Bayesian analysis achieves a compromise between the "conflicting" data that lead to the famous "PREXII puzzle" and the "soft Tin puzzle" [57,58].…”