Flexible, extensible, hardware-agnostic gravitational-wave population inference.
It provides:
Implementations of widely used likelihood compatible with Bilby.
A standard format for defining new population models.
A collection of standard population models.
If you’re using this on high-performance computing clusters, you may be interested in the associated pipeline code gwpopulation_pipe.
Attribution#
Please cite Talbot et al. (2025) if you use GWPopulation in your research.
@article{Talbot2025,
author = {Colm Talbot and Amanda Farah and Shanika Galaudage and Jacob Golomb and Hui Tong},
title = {GWPopulation: Hardware agnostic population inference for compact binaries and beyond},
journal = {Journal of Open Source Software},
doi = {10.21105/joss.07753},
url = {https://doi.org/10.21105/joss.07753},
year = {2025},
publisher = {The Open Journal},
volume = {10},
number = {109},
pages = {7753},
archivePrefix = {arXiv},
eprint = {2409.14143},
primaryClass = {astro-ph.IM},
}
The older citation can also be included for the initial proof-of-principle for the application of hardware acceleration Talbot et al. (2019).
@ARTICLE{2019PhRvD.100d3030T,
author = {{Talbot}, Colm and {Smith}, Rory and {Thrane}, Eric and {Poole}, Gregory B.},
title = "{Parallelized inference for gravitational-wave astronomy}",
journal = {\prd},
year = 2019,
month = aug,
volume = {100},
number = {4},
eid = {043030},
pages = {043030},
doi = {10.1103/PhysRevD.100.043030},
archivePrefix = {arXiv},
eprint = {1904.02863},
primaryClass = {astro-ph.IM},
}
Additionally, please consider citing the original references for the implemented models which should be include in docstrings.