Digging the population of compact binary mergers out of the noise

TL;DR

This paper introduces a hierarchical inference method to analyze gravitational wave data, effectively characterizing the population of compact binary mergers while accounting for uncertainties, selection effects, and noise contamination.

Contribution

The paper presents a novel hierarchical inference approach that robustly estimates the population parameters of binary mergers, including noise contamination, which was not addressed in previous methods.

Findings

Method successfully accounts for uncertainties in individual event parameters.

It effectively handles mass-dependent selection effects.

The approach is robust against noise contamination in the data.

Abstract

Coalescing compact binaries emitting gravitational wave (GW) signals, as recently detected by the Advanced LIGO-Virgo network, constitute a population over the multi-dimensional space of component masses and spins, redshift, and other parameters. Characterizing this population is a major goal of GW observations and may be approached via parametric models. We demonstrate hierarchical inference for such models with a method that accounts for uncertainties in each binary merger's individual parameters, for mass-dependent selection effects, and also for the presence of a second population of candidate events caused by detector noise. Thus, the method is robust to potential biases from a contaminated sample and allows us to extract information from events that have a relatively small probability of astrophysical origin.