Bayesian Inferences on Analytical Equations of State Approximations of Neutron Stars

Abstract

Equations of state (EoS) for dense matter are commonly provided in tabulated pressure-energy density relations, complicating numerical implementation and potentially compromising thermodynamic consistency. In this work, we propose a universal, piecewise-continuous functional form with a common parametrization capable of representing a broad class of dense matter EoSs. We validate this parametrization against tabulated EoSs from the CompOSE and LALSimulation repositories. Following the initial deterministic fitting, we employed two distinct Bayesian approaches: one based on synthetic EoSs generated by adding noise to the fitted EoS and another based on multimessenger measurements of neutron-star masses and tidal deformabilities. In both approaches, the initial best-fit parameters are used as reference values. For the considered EoSs, spanning from very soft to very stiff, the resulting fits reproduce the defined tabulated EoSs and the associated macroscopic neutron-star observables in the repositories with sufficient degree of accuracy. The inferred parameters exhibit strong correlations arising from the continuity conditions imposed at the segment boundaries. These correlations persist in the low- and intermediate-density segments under multimessenger inference but become weak in the core, reflecting the limited constraining power of current observations at high densities. The ability of a single functional form to describe EoSs derived from different microscopic frameworks, together with the recurrence of similar parameter correlations, suggests that diverse dense matter EoSs may share a common underlying structure.

Comments: 27 pages, 7 captioned figures