PaperPanorama

arXiv:2009.13653·v2·Nuclear Theory

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🇺🇸

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 of the hadron-quark transition density and the relative energy density jump there are and at 68\% confidence level, respectively. The corresponding probability distribution of QM fraction in a 1.4 M NS peaks around 0.9 in a 10 km sphere. Strongly correlated to the PDFs of and , the PDF of the QM speed of sound squared peaks at , and the total probability of being less than 1/3 is very small. (2) The correlations between PDFs of hadronic and QM EOS parameters are very weak. [Conclusions:] The available astrophysical data considered together with all known EOS constraints from theories and terrestrial nuclear experiments prefer the formation of a large volume of QM even in canonical NSs.

Comments: Added Fig.5 and the associated discussions/references regarding effects of the Seidov condition for first-order phase transition. Physical Review C in press

Citation historyopen in Citation History ↗