Bayesian Inference of Nucleus Resonance and Neutron Skin
Bayesian Inference of Nucleus Resonance and Neutron Skin
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摘要: In this proceeding, 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 have been presented. Typically, the anticorrelation and positive correlations between the slope parameter and the value of the symmetry energy at the saturation density under the constraint of the neutron-skin thickness and the isovector giant dipole resonance have been discussed respectively. It’s shown that the Bayesian analysis can help to find a compromise for the “PREXII puzzle” and the “soft Tin puzzle”. 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 have been further illustrated. 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.Abstract: In this proceeding, 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 have been presented. Typically, the anticorrelation and positive correlations between the slope parameter and the value of the symmetry energy at the saturation density under the constraint of the neutron-skin thickness and the isovector giant dipole resonance have been discussed respectively. It’s shown that the Bayesian analysis can help to find a compromise for the “PREXII puzzle” and the “soft Tin puzzle”. 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 have been further illustrated. 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.