Inference of Neutron Star Mass Distributions and the Equation of State from Multi-messenger Observations

TL;DR

This paper combines multiple observational data sources to infer neutron star mass distributions and the equation of state, revealing their sensitivity to prior assumptions and providing new constraints on maximum neutron star mass.

Contribution

It introduces a Bayesian framework integrating diverse data types and explores the impact of different EoS models and priors on neutron star mass inferences.

Findings

Mass distributions vary across neutron star populations.

Using a uniform prior shifts the maximum mass posterior to higher values.

The maximum mass posterior increases from ~2.09 to ~2.15 solar masses.

Abstract

We construct a combined model to incorporate neutron star (NS) mass measurements with electromagnetic mass-radius constraints and gravitational-wave observations using Bayesian inference. We use different mass distributions for three populations depending on the companion stars: double neutron stars, NS - white dwarfs, and low-mass X-ray binaries (LMXB). To observe the effects of different parametrizations, we use two equation of state (EoS) models: a piecewise polytrope and a fixed sound-speed model at high densities in combination with a low-density EoS. Our results show that the mass distributions of these NS populations are distinct and sensitive to the EoS prior choices. In addition, we show for the first time that using a uniform prior on the observable NS maximum mass, rather than a nuisance parameter in the unknown high-density EoS, shifts the posterior maximum mass to larger values. For polytropic EoSs, the maximum mass posterior changes from $M_\mathrm{max}=2.09_{-0.07}^{+0.18} M_\odot$ to $2.15_{-0.10}^{+0.19} M_\odot$ at 90% confidence level. This change in prior also impacts the shape of the mass distribution for NSs in LMXB, shifting the posterior for the population mean from $1.51_{-0.13}^{+0.13} M_\odot$ to $1.62_{-0.12}^{+0.15} M_\odot$ at 68% confidence level.