GWPopulation: Hardware agnostic population inference for compact binaries and beyond
C. Talbot, A. M. Farah, S. Galaudage, Jacob Golomb, Hui Tong
Abstract
Since the first direct detection of gravitational waves by the LIGO-Virgo collaboration in 2015 (B.P. Abbott et al., 2016), the size of the gravitational-wave transient catalog has grown to nearly 100 events (R. Abbott et al., 2023), with the ongoing fourth observing run more than doubling the total number.Extracting astrophysical or 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 gravitationalwave astronomy or cosmology.It has been widely adopted for gravitational-wave astronomy, including producing flagship results for the LIGO-Virgo-KAGRA collaborations (Abac et al., 2024; R. Abbott et al., 2023) 1 .While designed to work with observations of compact binary coalescences, GWPopulation may be available to a wider range of hierarchical Bayesian inference problems.