Bayesian inferences on covariant density functionals from multimessenger astrophysical data: Nucleonic models
Jia-Jie Li (Southwest U., Chongqing), Yu Tian (Southwest U., Chongqing), Armen Sedrakian (FIAS, Frankfurt, U. Wroclaw)

TL;DR
This study applies Bayesian inference to covariant density functional models using multi-messenger astrophysical data, including extreme mass and radius measurements, to better constrain neutron star properties and model parameters.
Contribution
It extends Bayesian analysis of covariant density functional models by incorporating extreme astrophysical constraints, improving parameter constraints and model robustness.
Findings
Supports higher maximum neutron star masses up to 2.4-2.5 solar masses
Challenges in softening the EoS to fit ultra-compact object data
Tighter constraints on model parameters consistent with nuclear and astrophysical data
Abstract
[Background] Bayesian inference frameworks incorporating multi-messenger astrophysical constraints have recently been applied to covariant density functional (CDF) models to constrain their parameters. Among these, frameworks utilizing CDFs with density-dependent meson-nucleon couplings furnishing the equation of state (EoS) of compact star (CS) matter have been explored. [Purpose] The aforementioned inference framework has not yet incorporated astrophysical objects with potentially extreme high masses or ultra-small radii among its constraints, leaving its flexibility and predictive power under such extreme parameters still unknown. [Method] We apply the Bayesian inference framework based on CDFs with density dependent couplings. The astrophysical data is expanded to include not only the latest multi-messenger constraints from NICER and gravitational wave events but also the highest…
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