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.
@article{arxiv.2409.14143,
title = {GWPopulation: Hardware agnostic population inference for compact binaries and beyond},
author = {Colm Talbot and Amanda Farah and Shanika Galaudage and Jacob Golomb and Hui Tong},
journal= {arXiv preprint arXiv:2409.14143},
year = {2024}
}
Comments
Paper submitted to JOSS describing https://github.com/ColmTalbot/gwpopulation