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

David O'Ryan, Bruno Merín, Brooke D Simmons, Antónia Vojteková, Anna Anku, Mike Walmsley, Izzy L Garland, Tobias Géron, William Keel, Sandor Kruk, Chris J. Lintott, Kameswara Bharadwaj Mantha, Karen L Masters, Jan Reerink, Rebecca J. Smethurst, Matthew R. Thorne

Abstract:

Mergers play a complex role in galaxy formation and evolution. Continuing to improve our under-
standing of these systems require ever larger samples, which can be difficult (even impossible) to select
from individual surveys. We use the new platform ESA Datalabs to assemble a catalogue of interacting
galaxies from the \textit{Hubble Space Telescope} science archives; this catalogue is larger than previously pub-
lished catalogues 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 Catalogue. We employ a com-
bination of automated visual representation and visual analysis to identify a clean sample of 21,926
interacting galaxy systems, mostly with z < 1. 65% 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 catalogue publicly available for use by the community. In addition to providing a new
catalogue 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.