Bayesian Inference of the Dense Matter Equation of State built upon Covariant Density Functionals (2212.07168v2)

Abstract: A modified version of the density dependent covariant density functional model proposed in [T. Malik, M. Ferreira, B. K. Agrawal and C. Provid^encia, ApJ 930, 17 (2022)] is employed in a Bayesian analysis to determine the equation of state (EOS) of dense matter with nucleonic degrees of freedom. Various constraints from nuclear physics and microscopic calculations of pure neutron matter (PNM) along with a lower bound on the maximum mass of neutron stars (NSs) are imposed on the EOS models to investigate the effectiveness of progressive incorporation of the constraints, their compatibility as well as correlations among parameters of nuclear matter and properties of NSs. Our results include the different roles played by pressure and energy per particle of PNM in constraining the isovector behavior of nuclear matter; tension with the values of Dirac effective mass extracted from spin-orbit splitting; correlations between the radius of the canonical mass NS and second and third order coefficients in the Taylor expansion of energy per particle as a function of density; correlation between the central pressure of the maximum mass configuration and Dirac effective mass of the nucleon at saturation. For some of our models the tail of the NS maximum mass reaches $2.7~\mathrm{M}_{\odot}$, which means that the secondary object in GW190814 could have been a NS.