Speaker
Description
The equation of state (EoS) of cold, dense nuclear matter remains one of the central open problems in nuclear astrophysics ,linking microscopic nuclear interactions to macroscopic neutron star observables. In this work, we present a hierarchical Bayesian framework for inferring the dense-matter EoS by combining nuclear theory inputs with multimessenger astrophysical observations .Our approach incorporates constraints from chiral effective field theory (χEFT) at sub-saturation densities through a theory-informed hierarchical prior, while allowing flexible parameterizations at supranuclear densities. The framework is implemented within a Bayesian inference pipeline and benchmarked against standard spectral and piecewise-polytropic EoS representations .We jointly analyze observational constraints from NICER radius measurements, the maximum observed neutron star mass, and gravitational-wave data from binary neutron star mergers, including GW170817. Neutron star structure is computed by solving the Tolman–Oppenheimer–Volkoff equations for each sampled EoS realization ,and parameter inference is performed using Markov Chain Monte Carlo methods .Compared to standard non-hierarchical reconstructions, the hierarchical framework shows improved consistency
across datasets and suggests moderately tighter constraints on neutron star radii. In particular, the inferred radius of a 1.4 solar-mass neutron star is found to lie in the range R_{1.4} \sim 12.0–13.1\,\mathrm{km}, in agreement with current multimessenger constraints. The inclusion of gravitational-wave data further improves constraints on the high-density behavior of the EoS and enhances posterior consistency. Overall, our results indicate that hierarchical ,theory-informed Bayesian modeling provides a flexible and physically motivated framework for multimessenger inference of the dense matter equation of state. This approach strengthens the connection between nuclear theory, neutron star structure, and gravitational-wave observations while remaining consistent with current experimental and observational uncertainties.