GWPopulation: Hardware agnostic population inference for compact binaries and beyond

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.

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BibTeX RIS

BibTeX

@article{talbot2026gwpopulation,
  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 = {J.Open Source Softw.},
  year = {2026},
  month = oct,
  doi = {10.21105/joss.07753},
  eprint = {2409.14143},
  archivePrefix = {arXiv},
  url = {https://hal.science/hal-04742113v1},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - GWPopulation: Hardware agnostic population inference for compact binaries and beyond
AU  - Colm Talbot
AU  - Amanda Farah
AU  - Shanika Galaudage
AU  - Jacob Golomb
AU  - Hui Tong
PY  - 2026
DA  - 2026/10/09
JO  - J.Open Source Softw.
DO  - 10.21105/joss.07753
UR  - https://hal.science/hal-04742113v1
AB  - 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.
ER  -

Abstract

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