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arXiv · 2608.04967

Bayesian Inference of fine-features of dense matter EOS from future high-precision data of neutron star radii

Bao-An Li·Xavier Grundler·Wen-Jie Xie·Nai-Bo Zhang
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Abstract

Future high-precision X-ray and gravitational wave observatories are expected to measure the radii of neutron stars (NSs) with an accuracy better than about 0.1 km. However, it remains unclear what particular aspects of the Equation of State (EOS) and to what precision they will be better constrained. Within a Bayesian framework using a meta-model EOS and mock high-precision NS data, the posterior probability distribution functions (PDFs) of NS matter EOS parameters for both hadronic and quark phases and the transition between them were recently studied. We report here a few highlights of these studies.

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Bao-An Li, Xavier Grundler, Wen-Jie Xie, Nai-Bo Zhang. 2026-08-05. Bayesian Inference of fine-features of dense matter EOS from future high-precision data of neutron star radii. https://doi.org/10.1051/epjconf%2F202637806009

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