Bayesian Inference of Dense-Matter Equations of State from Small-Radius Compact Stars with Twin-Star Scenarios
Xieyuan Dong, Hong Shen, Jinniu Hu, and Ying Zhang

TL;DR
This paper uses Bayesian methods to analyze dense-matter equations of state, exploring how small-radius neutron stars and twin-star scenarios can reveal phase transitions in dense matter through observational signatures.
Contribution
It introduces a Bayesian framework incorporating recent observations to constrain phase transition parameters and the EOS, highlighting the potential of twin-star signatures in neutron-star measurements.
Findings
Preferred phase transition density around 2.7--2.8 times nuclear saturation density
Energy-density jump of 600--700 MeV at the transition
Disconnection of hybrid star branch with radii 6--7 km at 1.2--1.4 solar masses
Abstract
We investigate dense-matter equations of state (EOSs) within a Bayesian framework, with particular emphasis on whether recent small-radius compact-star candidates can be accommodated in a twin-star scenario. For the hadronic sector, we adopt a meta-modeling EOS constrained by the NICER mass--radius measurements of PSR J00300451, PSR J04374715, PSR J06143329, and the massive pulsar PSR J07406620. The hadronic inference indicates that PSR J06143329 favors a somewhat softer EOS than the other two \(\sim1.4\,M_\odot\) pulsars, while the \(\sim2\,M_\odot\) constraint prevents the EOS from becoming too soft. We then introduce a strong first-order phase transition through a constant-speed-of-sound quark-matter segment. Using HESS J1731347 and XTE J1814338 to constrain the phase-transition parameters, we find a preferred transition density of…
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