Speaker
Description
Bayesian inference offers a powerful framework for constraining the nuclear equation of state (EoS) across a wide range of densities by combining information from astrophysical observations, ab-initio nuclear theory, and heavy-ion collisions. In this work, we refine a unified meta-modeling framework for the EoS by incorporating low-density corrections based on energy density functionals constrained by ab-initio neutron matter calculations. This refinement improves consistency with nuclear physics in the dilute regime, which is critical for accurately modeling neutron star crustal properties.
We explore how these improvements impact predictions of the crust-core transition density and pressure, crustal composition, and the moment of inertia fraction. Our results emphasize the importance of combining theoretical and experimental constraints across densities to robustly model the EoS, paving the way for future high-precision multimessenger studies.
