Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue

TL;DR

This paper validates BILBY, a Bayesian inference library, for analyzing gravitational-wave signals from compact binary mergers, demonstrating its reliability and readiness for the expanding GW observational data.

Contribution

The paper demonstrates BILBY’s effectiveness in analyzing real and simulated gravitational-wave signals, confirming its suitability for future GW data analysis.

Findings

BILBY produces reliable parameter estimates for simulated signals.

BILBY accurately reproduces results from the GWTC-1 catalog.

The work provides reproducible analysis configurations for future use.

Abstract

Gravitational waves provide a unique tool for observational astronomy. While the first LIGO--Virgo catalogue of gravitational-wave transients (GWTC-1) contains eleven signals from black hole and neutron star binaries, the number of observations is increasing rapidly as detector sensitivity improves. To extract information from the observed signals, it is imperative to have fast, flexible, and scalable inference techniques. In a previous paper, we introduced BILBY: a modular and user-friendly Bayesian inference library adapted to address the needs of gravitational-wave inference. In this work, we demonstrate that BILBY produces reliable results for simulated gravitational-wave signals from compact binary mergers, and verify that it accurately reproduces results reported for the eleven GWTC-1 signals. Additionally, we provide configuration and output files for all analyses to allow for easy reproduction, modification, and future use. This work establishes that BILBY is primed and ready to analyse the rapidly growing population of compact binary coalescence gravitational-wave signals.