Hierarchical Inference of Binary Neutron Star Mass Distribution and Equation of State with Gravitational Waves

Jacob Golomb,Colm Talbot

doi:10.3847/1538-4357/ac43bc

Abstract

Abstract Gravitational-wave observations of binary neutron star mergers provide valuable information about neutron star structure and the equation of state of dense nuclear matter. Numerous methods have been proposed to analyze the population of observed neutron stars, and previous work has demonstrated the necessity of jointly fitting the astrophysical distribution and the equation of state in order to accurately constrain the equation of state. In this work, we introduce a new framework to simultaneously infer the distribution of binary neutron star masses and the nuclear equation of state using Gaussian mixture model density estimates, which mitigates some of the limitations previously used methods suffer from. Using our method, we reproduce previous projections for the expected precision of our joint mass distribution and equation-of-state inference with tens of observations. We also show that mismodeling the equation of state can bias our inference of the neutron star mass distribution. While we focus on neutron star masses and matter effects, our method is widely applicable to population inference problems.

Highlights

Over the past six years, the LIGO-Virgo gravitational wave detectors (LIGO Scientific Collaboration et al 2015; Acernese et al 2015) have made > 50 observations of black hole and neutron star binary mergers (Abbott et al 2021a), providing a new way to study some of the most energetic events in the universe
We introduce a new framework to simultaneously infer the distribution of binary neutron star masses and the nuclear equation of state using Gaussian mixture model density estimates which mitigates some of the limitations previously-used methods suffer from
We demonstrate a new method of hierarchically combining posterior distributions from binary neutron star (BNS) merger events and inferring mass distribution and equation of state (EOS) parameters simultaneously

Summary

Introduction

Over the past six years, the LIGO-Virgo gravitational wave detectors (LIGO Scientific Collaboration et al 2015; Acernese et al 2015) have made > 50 observations of black hole and neutron star binary mergers (Abbott et al 2021a), providing a new way to study some of the most energetic events in the universe. Population inference from gravitational wave observations is performed by comparing catalogs of observed events to models of the astrophysical distribution These astrophysical models include strongly physically motivated models (e.g., Zevin et al (2021); Wong et al (2021)), phenomenological models inspired by theoretical predictions and prior observations (e.g., Farrow et al (2019); Wysocki et al (2020)), or data-driven models (e.g., Tiwari & Fairhurst (2021)). With central densities reaching several times nuclear saturation density, neutron stars —observed via kilonovae spectra and light curves, X-ray pulsar measurements, and gravitational waves —provide a probe of nuclear physics at supersaturation densities (Bogdanov et al 2019; Miller et al.2019; Miller et al 2021; Silva et al 2021; Metzger 2019; Coughlin et al 2018)

Methods

Results

Conclusion