Bayesian constraints on quark stars from multi-messenger observations
Wen-Jie Xie, Cheng-Jun Xia, Chen Zhang, Renxin Xu

TL;DR
This paper uses Bayesian analysis to explore quark star models under current observational constraints, highlighting their ability to explain ultra-low mass objects and the impact of priors on high-mass interpretations.
Contribution
It introduces a comprehensive Bayesian framework for quark star equations of state, incorporating color-superconducting phases and perturbative QCD corrections, and assesses observational constraints.
Findings
Quark star models can naturally explain ultra-low mass objects like HESS J1731-347.
High-mass object interpretations are highly prior-dependent, affecting the inferred stiffness of the equation of state.
Current data tightly constrain the effective bag constant and stiffness but cannot distinguish between different color-superconducting phases.
Abstract
We perform a systematic Bayesian analysis of quark star equations of state under current multimessenger constraints, investigating the impact of prior assumptions and extreme-mass observations. Quark matter is modeled within an interacting MIT bag framework that consistently accommodates color-superconducting phases (2SC, 2SC+s, and CFL) and perturbative QCD corrections. We find that quark star models exhibit a distinct advantage in naturally accommodating the ultra-low mass object HESS J1731-347, a configuration that is challenging for standard neutron star models. In the high-mass regime, the interpretation of the secondary component of GW190814 is shown to be strongly prior-dependent: only broad priors allow for the substantial stiffness required to support such a massive object (2.6 M), while more restrictive priors favor a softer equation of state consistent with…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · High-Energy Particle Collisions Research · Statistical Mechanics and Entropy
