Bayesian evaluation of hadron-quark phase transition models through neutron star observables in light of nuclear and astrophysics data
Debanjan Guha Roy, Anagh Venneti, Tuhin Malik, Swastik Bhattacharya,, Sarmistha Banik

TL;DR
This study uses Bayesian inference to compare hybrid and nucleonic neutron star models against recent astrophysical observations, finding slight preferences for smooth hybrid models but highlighting data tensions and the importance of flexible equations of state.
Contribution
It introduces a Bayesian framework to evaluate hybrid and nucleonic EOSs against observational data, analyzing the impact of model flexibility and phase transition indicators.
Findings
Smooth hybrid EOSs are slightly favored for mass-radius relations.
Tensions exist between older and recent NICER data.
Hybrid models can incorporate stiffer EOSs, affecting tidal deformability.
Abstract
We investigate the role of hybrid and nucleonic equations of state (EOSs) within neutron star (NS) interiors using Bayesian inference to evaluate their alignment with recent observational data from NICER and LIGO-Virgo (LV) collaborations. We find that smooth hybrid EOSs are slightly favoured in explaining NS mass-radius relations, particularly for pulsars such as PSR J0030+0451 and PSR J0740+6620. However, this preference is not definitive, as gravitational wave (GW) data does not significantly differentiate between our hybrid and nucleonic models. Our analysis also reveals tensions between older NICER data and recent measurements for PSR J0437-4715, highlighting the need for more flexible EOS models. Through two sampling approaches - one fixing the hadronic EOS set and the other without fixing the same, we demonstrate that the hybrid EOS model can incorporate stiffer EOSs, resulting…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Geophysics and Gravity Measurements · Gamma-ray bursts and supernovae
