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Bayesian inferences on covariant density functionals from multimessenger astrophysical data. I. Nucleonic models by Li, Jia-Jie; Tian, Yu; Sedrakian, Armen is a scholarly article available to read on EtoBox.

What is Bayesian inferences on covariant density functionals from multimessenger astrophysical data. I. Nucleonic models about?

[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 measured mass to date for the ``black widow" pulsar PSR J0952-0607 and the mass-radius estimates for the ultra-compact, low-mass object HESS J1731-347. [Results] Our systematic Bayesian analysis indicates that our CDF models can support higher maximum masses for CSs, reaching up to $2.4$-$2.5\,M_{\odot

Author

Li, Jia-Jie; Tian, Yu; Sedrakian, Armen

Published

2025

Language

EN