Speaker
Description
Abstract
Bayesian analysis methodologies provide a robust framework for systematically integrating prior knowledge to refine the understanding of parameter spaces in theoretical models. Consequently, this approach has emerged as a pivotal tool in research domains such as nuclear physics and nuclear astrophysics. Leveraging experimental data from finite nuclei, we employ Bayesian analysis to constrain the coupling constants and additional parameters within the Relativistic Mean Field (RMF) model. These constraints are subsequently extrapolated to uniform nuclear matter and integrated with bounds derived from complementary theoretical approaches, including chiral effective field theory, heavy-ion collision experiments, and astronomical observations. This synthesis yields a relatively comprehensive constraint on the nuclear matter equation of state. By self-consistently integrating experimental data on binding energy, charge radii, and neutron skin thickness through Bayesian likelihood estimation, the methodology generates probability distributions for model parameters and elucidates inter-parameter correlations. These results furnish critical theoretical benchmarks for advancing research in nuclear physics and nuclear astrophysics.