# Equation of state sensitivities when inferring neutron star and dense matter properties

## TL;DR

This paper investigates how different equation of state parametrizations and prior assumptions influence neutron star property inferences from astrophysical data, highlighting significant sensitivities in the constraints derived.

## Contribution

It compares various Bayesian inference methods and parametrizations to understand their impact on neutron star equation of state constraints.

## Key findings

- Choice of parametrization affects mass-radius inferences
- Prior assumptions significantly influence constraints
- Results are relevant for gravitational wave data analysis

## Abstract

Understanding the dense matter equation of state at extreme conditions is an important open problem. Astrophysical observations of neutron stars promise to solve this, with NICER poised to make precision measurements of mass and radius for several stars using the waveform modelling technique. What has been less clear, however, is how these mass-radius measurements might translate into equation of state constraints and what are the associated equation of state sensitivities. We use Bayesian inference to explore and contrast the constraints that would result from different choices for the equation of state parametrization; comparing the well-established piecewise polytropic parametrization to one based on physically motivated assumptions for the speed of sound in dense matter. We also compare the constraints resulting from Bayesian inference to those from simple compatibility cuts. We find that the choice of equation of state parametrization and particularly its prior assumptions can have a significant effect on the inferred global mass-radius relation and the equation of state constraints. Our results point to important sensitivities when inferring neutron star and dense matter properties. This applies also to inferences from gravitational wave observations.