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GWCloud: a searchable repository for the creation and curation of gravitational-wave inference results

GWCloud: a searchable repository for the creation and curation of gravitational-wave inference results
GWCloud: a searchable repository for the creation and curation of gravitational-wave inference results
There are at present O ( 100 ) gravitational-wave candidates from compact binary mergers reported in the astronomical literature. As detector sensitivities are improved, the catalog will swell in size: first to O ( 1000 ) events in the A+ era and then to O ( 10 6 ) events in the era of third-generation observatories like Cosmic Explorer and the Einstein Telescope. Each event is analyzed using Bayesian inference to determine properties of the source including component masses, spins, tidal parameters, and the distance to the source. These inference products are the fodder for some of the most exciting gravitational-wave science, enabling us to measure the expansion of the universe with standard sirens, to characterize the neutron-star equation of state, and to unveil how and where gravitational-wave sources are assembled. In order to maximize the science from the coming deluge of detections, we introduce GWCloud, a searchable repository for the creation and curation of gravitational-wave inference products. It is designed with five pillars in mind: uniformity of results, reproducibility of results, stability of results, access to the astronomical community, and efficient use of computing resources. We describe how to use GWCloud with examples, which readers can replicate using the companion code to this paper. We describe our long-term vision for GWCloud.
0067-0049
Baker, A. Makai
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Lasky, Paul D.
21c4d51d-89db-4dc1-b5f9-cd9835d54fad
Thrane, Eric
2bafe758-0f64-458f-9f9a-fede9abc343c
Ashton, Gregory
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Cantos, Jesmigel
5d7d0fc3-f821-4bc5-b782-92baf9e2ce73
Lakerink, Lewis
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Leslie, Asher
85a3dee7-89fd-4768-a28b-1220268dcfb5
Poole, Gregory B.
1b2d62cb-ff32-4142-9cf3-02890c6be28b
Reichardt, Thomas
77e4afb7-07dd-4c28-8b02-f9655c28fa83
Baker, A. Makai
6a0253b1-389c-4843-afb8-1e67dd7119f2
Lasky, Paul D.
21c4d51d-89db-4dc1-b5f9-cd9835d54fad
Thrane, Eric
2bafe758-0f64-458f-9f9a-fede9abc343c
Ashton, Gregory
a8cec4b1-3c98-4b28-af2a-1e37cb3b9f2a
Cantos, Jesmigel
5d7d0fc3-f821-4bc5-b782-92baf9e2ce73
Lakerink, Lewis
9525c1c3-3614-444f-90d3-ba464af3c1c4
Leslie, Asher
85a3dee7-89fd-4768-a28b-1220268dcfb5
Poole, Gregory B.
1b2d62cb-ff32-4142-9cf3-02890c6be28b
Reichardt, Thomas
77e4afb7-07dd-4c28-8b02-f9655c28fa83

Baker, A. Makai, Lasky, Paul D., Thrane, Eric, Ashton, Gregory, Cantos, Jesmigel, Lakerink, Lewis, Leslie, Asher, Poole, Gregory B. and Reichardt, Thomas (2023) GWCloud: a searchable repository for the creation and curation of gravitational-wave inference results. Astrophysical Journal, Supplement Series, 266 (2), [33]. (doi:10.3847/1538-4365/acc938).

Record type: Article

Abstract

There are at present O ( 100 ) gravitational-wave candidates from compact binary mergers reported in the astronomical literature. As detector sensitivities are improved, the catalog will swell in size: first to O ( 1000 ) events in the A+ era and then to O ( 10 6 ) events in the era of third-generation observatories like Cosmic Explorer and the Einstein Telescope. Each event is analyzed using Bayesian inference to determine properties of the source including component masses, spins, tidal parameters, and the distance to the source. These inference products are the fodder for some of the most exciting gravitational-wave science, enabling us to measure the expansion of the universe with standard sirens, to characterize the neutron-star equation of state, and to unveil how and where gravitational-wave sources are assembled. In order to maximize the science from the coming deluge of detections, we introduce GWCloud, a searchable repository for the creation and curation of gravitational-wave inference products. It is designed with five pillars in mind: uniformity of results, reproducibility of results, stability of results, access to the astronomical community, and efficient use of computing resources. We describe how to use GWCloud with examples, which readers can replicate using the companion code to this paper. We describe our long-term vision for GWCloud.

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Accepted/In Press date: 3 March 2023
Published date: 7 June 2023

Identifiers

Local EPrints ID: 508293
URI: http://eprints.soton.ac.uk/id/eprint/508293
ISSN: 0067-0049
PURE UUID: e0c1b4ec-f899-4cdb-8354-1fdb9ac00166
ORCID for Gregory Ashton: ORCID iD orcid.org/0000-0001-7288-2231

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Date deposited: 16 Jan 2026 17:34
Last modified: 17 Jan 2026 03:47

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Contributors

Author: A. Makai Baker
Author: Paul D. Lasky
Author: Eric Thrane
Author: Gregory Ashton ORCID iD
Author: Jesmigel Cantos
Author: Lewis Lakerink
Author: Asher Leslie
Author: Gregory B. Poole
Author: Thomas Reichardt

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