Bayesian inference of dense matter EOS encapsulating a first-order hadron-quark phase transition from observables of canonical neutron stars
Wen-Jie Xie, Bao-An Li

TL;DR
This paper uses Bayesian inference with observational data to characterize the dense matter equation of state in neutron stars, revealing a likely first-order hadron-quark phase transition and a significant quark matter fraction in typical neutron stars.
Contribution
It introduces a Bayesian framework combining multiple observational data sets to constrain the dense matter EOS and the properties of the hadron-quark phase transition in neutron stars.
Findings
Most probable transition density is 1.6 times nuclear saturation density.
Quark matter fraction in a 1.4 solar mass neutron star peaks around 90%.
Quark matter speed of sound squared is likely close to the causal limit.
Abstract
[Purpose:] We infer the posterior probability distribution functions (PDFs) and correlations of nine parameters characterizing the EOS of dense neutron-rich matter encapsulating a first-order hadron-quark phase transition from the radius data of canonical NSs reported by LIGO/VIRGO, NICER and Chandra Collaborations. We also infer the quark matter (QM) mass fraction and its radius in a 1.4 M NS and predict their values in more massive NSs. [Method:] Meta-modelings are used to generate both hadronic and QM EOSs in the Markov-Chain Monte Carlo sampling process within the Bayesian statistical framework. An explicitly isospin-dependent parametric EOS for the matter in NSs at equilibrium is connected through the Maxwell construction to the QM EOS described by the constant speed of sound (CSS) model of Alford, Han and Prakash. [Results:] (1) The most probable values…
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