Scalable Dark Siren Cosmology with gwcosmo: GPU Acceleration, Validation and Systematics
Abstract
As the number of confident gravitational-wave detections grows, population-level hierarchical analyses face increasing computational costs. Dark-siren cosmological inference integrates over the localisation volume of each gravitational-wave source. To remain feasible without discarding information from the quieter but more numerous sources in the catalogue, significant efficiency improvements are vital for analysis pipelines. In this work, we present an upgraded version of the cosmological inference pipeline gwcosmo, which leverages vectorisation on graphics processing units to process the entire gravitational-wave catalogue in parallel with each iteration. This new implementation achieves a speed-up of 1000 times over the previous version, facilitating analyses of O5-like numbers of GW events on wall-clock timescales of hours. Our results demonstrate the scalability of the gwcosmo pipeline, specifically its ability to handle the increasing computational load of expanding event catalogues, positioning it as a vital tool for future advances in dark-siren cosmology.
Versions
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BibTeX
@article{papadopoulos2026scalable,
title = {{Scalable Dark Siren Cosmology with gwcosmo: GPU Acceleration, Validation and Systematics}},
author = {Alexander Papadopoulos and Christian E.A Chapman-Bird and Rachel Gray and Christopher Messenger and Tom Bertheas},
journal = {Physical Review D},
year = {2026},
month = sep,
doi = {10.1103/53c5-mtk5},
eprint = {2605.23538},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05642751v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Scalable Dark Siren Cosmology with gwcosmo: GPU Acceleration, Validation and Systematics
AU - Alexander Papadopoulos
AU - Christian E.A Chapman-Bird
AU - Rachel Gray
AU - Christopher Messenger
AU - Tom Bertheas
PY - 2026
DA - 2026/09/15
JO - Physical Review D
DO - 10.1103/53c5-mtk5
UR - https://hal.science/hal-05642751v1
AB - As the number of confident gravitational-wave detections grows, population-level hierarchical analyses face increasing computational costs. Dark-siren cosmological inference integrates over the localisation volume of each gravitational-wave source. To remain feasible without discarding information from the quieter but more numerous sources in the catalogue, significant efficiency improvements are vital for analysis pipelines. In this work, we present an upgraded version of the cosmological inference pipeline gwcosmo, which leverages vectorisation on graphics processing units to process the entire gravitational-wave catalogue in parallel with each iteration. This new implementation achieves a speed-up of 1000 times over the previous version, facilitating analyses of O5-like numbers of GW events on wall-clock timescales of hours. Our results demonstrate the scalability of the gwcosmo pipeline, specifically its ability to handle the increasing computational load of expanding event catalogues, positioning it as a vital tool for future advances in dark-siren cosmology.
SN - 2470-0010
SN - 2470-0029
ER -