Bayesian Inference of the Dense Matter Equation of State built upon Covariant Density Functionals
Mikhail V. Beznogov, Adriana R. Raduta

TL;DR
This paper employs Bayesian analysis with a covariant density functional model to constrain the dense matter equation of state, integrating nuclear physics data and neutron star observations to explore implications for neutron star properties and dense matter behavior.
Contribution
It introduces a Bayesian framework using a modified covariant density functional model to analyze dense matter EOS with multiple physical constraints and explores their correlations and astrophysical implications.
Findings
Pressure and energy per particle of PNM constrain isovector behavior.
Tension with Dirac effective mass from spin-orbit splitting.
Potential for neutron stars up to 2.7 solar masses.
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…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Gamma-ray bursts and supernovae · Geological and Geophysical Studies
