Bayesian inference of the dense matter equation of state built upon covariant density functionals

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