2020/07/31 by Silvia Traversi, Prasanta Char · 1 citation
Physics and Astronomy · #nucl-th #astro-ph.HE
paper · pdf · doi:10.3847/1538-4357/abbfb4
arxiv created 2020/11/10 · arxiv updated 2020/12/16
In this work, we compare two powerful parameter estimation methods namely Bayesian inference and Neural Network based learning to study the quark matter equation of state with constant speed of sound parametrization and the structure of the quark stars within the two-family scenario. We use the mass and radius estimations from several X-ray sources and also the mass and tidal deformability measurements from gravitational wave events to constrain the parameters of our model. The results found from the two methods are consistent. The predicted speed of sound is compatible with the conformal limit.