GWPopulation: Hardware agnostic population inference for compact binaries and beyond

TL;DR

GWPopulation offers a versatile, robust, and user-friendly framework for hierarchical Bayesian inference in gravitational-wave astronomy, enabling efficient analysis of large catalogs of compact binary observations and beyond.

Contribution

It introduces a hardware-agnostic, extensible tool for hierarchical inference that has been widely adopted in gravitational-wave astrophysics, facilitating analysis of growing observational data.

Findings

Successfully applied to LIGO-Virgo-KAGRA data

Produced flagship results for gravitational-wave populations

Demonstrated extensibility to various inference problems

Abstract

Since the first direct detection of gravitational waves by the LIGO--Virgo collaboration in 2015, the size of the gravitational-wave transient catalog has grown to nearly 100 events, with more than as many observed during the ongoing fourth observing run. Extracting astrophysical/cosmological information from these observations is a hierarchical Bayesian inference problem. GWPopulation is designed to provide simple-to-use, robust, and extensible tools for hierarchical inference in gravitational-wave astronomy/cosmology. It has been widely adopted for gravitational-wave astronomy, including producing flagship results for the LIGO-Virgo-KAGRA collaborations. While designed to work with observations of compact binary coalescences, GWPopulation may be available to a wider range of hierarchical Bayesian inference problems.