# Bayesian Analysis for Extracting Properties of the Nuclear Equation of   State from Observational Data including Tidal Deformability from GW170817

**Authors:** A. Ayriyan, D. Alvarez-Castillo, D. Blaschke, H. Grigorian

arXiv: 1812.10796 · 2019-02-19

## TL;DR

This paper introduces a Bayesian method to analyze observational data, including tidal deformability from GW170817, to constrain the properties of the nuclear equation of state and hybrid star models.

## Contribution

It develops a novel Bayesian analysis approach applied to hybrid equations of state, incorporating recent gravitational wave data to estimate model parameters.

## Key findings

- Probabilistic constraints on hybrid star parameters.
- Compatibility of models with GW170817 data.
- Insights into the nuclear matter equation of state.

## Abstract

We develop a Bayesian analysis method for selecting the most probable equation of state under a set of constraints from compact star physics, which now include the tidal deformability from GW170817. We apply this method for the first time to a two-parameter family of hybrid equations of state that is based on realistic models for the hadronic phase (KVORcut02) and the quark matter phase (SFM$\alpha$) which produce a third family of hybrid stars in the mass-radius diagram. One parameter ($\alpha$) characterizes the screening of the string tension in the string-flip model of quark matter while the other ($\Delta_P$) belongs to the mixed phase construction that mimics the thermodynamics of pasta phases and includes the Maxwell construction as a limiting case for $\Delta_P=0$. We present the corresponding results for compact star properties like mass, radius and tidal deformabilities and use empirical data for them in the newly developed Bayesian analysis method to obtain the probabilities for the model parameters within their considered range.

## Full text

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## Figures

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## References

39 references — full list in the complete paper: https://tomesphere.com/paper/1812.10796/full.md

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Source: https://tomesphere.com/paper/1812.10796