Structure of Quark Star: A Comparative Analysis of Bayesian Inference and Neural Network Based Modeling

Abstract

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.

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• Translating neutron star observations to nuclear symmetry energy via artificial neural networks
• Bayesian inference of quark star equation of state using the NICER PSR J0030+0451 data
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• Detecting Hyperons in neutron stars -- a machine learning approach
• Equation of State of Decompressed Quark Matter, and Observational Signatures of Quark-Star Mergers