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Harnessing the Hubble Space Telescope Archives: A Catalog of 21,926 Interacting Galaxies

Abstract:
Abstract Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems requires ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalog of interacting galaxies from the Hubble Space Telescope science archives; this catalog is larger than previously published catalogs by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST F814W images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalog. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with z < 1. Sixty-five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and “backlit” overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalog publicly available for use by the community. In addition to providing a new catalog of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3847/1538-4357/acc0ff
Publication website:
https://research-information.bris.ac.uk/files/429303847/Thesis_Upload.pdf

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Role:
Author
ORCID:
0000-0002-8555-3012
More by this author
Role:
Author
ORCID:
0000-0001-5882-3323


Publisher:
American Astronomical Society
Journal:
The Astrophysical Journal More from this journal
Volume:
948
Issue:
1
Pages:
40-40
Publication date:
2023-05-04
DOI:
EISSN:
1538-4357
ISSN:
0004-637X


Language:
English
Keywords:
Pubs id:
1344344
Local pid:
pubs:1344344
Source identifiers:
W4376122198
Deposit date:
2026-05-07
ARK identifier:
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