Bayesian uncertainty quantification for nuclear matter incompressibility

TL;DR

This paper applies Bayesian methods to infer nuclear matter incompressibility and related parameters from experimental data, providing quantified uncertainties and demonstrating consistency across different nuclei.

Contribution

It introduces a Bayesian framework to estimate nuclear matter properties with uncertainty quantification using experimental resonance and neutron-skin data.

Findings

Estimated K_0 = 223_{-8}^{+7} MeV at 68% confidence level.

Consistent posterior distributions for different nuclei despite slight differences in MAP values.

Quantified uncertainties improve understanding of nuclear matter incompressibility.

Abstract

Within a Bayesian statistical framework using the standard Skyrme-Hartree-Fock model, the maximum {\it a posteriori} (MAP) values and uncertainties of nuclear matter incompressibility and isovector interaction parameters are inferred from the experimental data of giant resonances and neutron-skin thicknesses of typical heavy nuclei. With the uncertainties of the isovector interaction parameters constrained by the data of the isovector giant dipole resonance and the neutron-skin thickness, we have obtained $K_0 = 223_{-8}^{+7}$ MeV at 68% confidence level using the data of the isoscalar giant monopole resonance in $^{208}$Pb measured at the Research Center for Nuclear Physics (RCNP), Japan, and at the Texas A&M University (TAMU), USA. Although the corresponding $^{120}$Sn data gives a MAP value for $K_0$ about 5 MeV smaller than the $^{208}$Pb data, there are significant overlaps in their posterior probability distribution functions.