Flexibility and predictive power of CDF-based Bayesian inference under extreme mass/radius constraints

Determine whether the Bayesian inference framework based on covariant density functionals with density-dependent meson–nucleon couplings retains sufficient flexibility and predictive power when constraints from astrophysical objects with potentially extreme high masses (e.g., PSR J0952-0607) or ultra-small radii (e.g., HESS J1731-347) are incorporated, by assessing its ability to reproduce their measured mass–radius properties and associated multimessenger observables.

Background

Previous Bayesian inference studies using covariant density functionals (CDFs) have not incorporated the most extreme astrophysical constraints, such as very high-mass pulsars or ultra-compact low-mass objects. This omission leaves open whether the parameterizations and density-dependent couplings in these models can accommodate such data without losing consistency with nuclear physics and other astrophysical observations.

This work extends earlier analyses to include NICER measurements, gravitational-wave constraints, and specifically the high-mass PSR J0952-0607 and the ultra-compact HESS J1731-347. The authors aim to test if the CDF-based framework can remain predictive across these extremes and quantify any necessary model extensions (e.g., more complex isovector density dependence or low-density deconfinement transitions) indicated by the data.