| Issue |
A&A
Volume 704, December 2025
|
|
|---|---|---|
| Article Number | A227 | |
| Number of page(s) | 15 | |
| Section | Catalogs and data | |
| DOI | https://doi.org/10.1051/0004-6361/202555512 | |
| Published online | 16 December 2025 | |
Identifying astrophysical anomalies in 99.6 million source cutouts from the Hubble legacy archive using AnomalyMatch
European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n,
28692
Villaneuva de la Cañada,
Madrid
★ Corresponding author: david.oryan@esa.int
Received:
14
May
2025
Accepted:
13
October
2025
Aims. Astronomical archives contain vast quantities of unexplored data that potentially harbour rare and scientifically valuable cosmic phenomena. We leverage new semi-supervised methods to extract such objects from the Hubble Legacy Archive.
Methods. We have systematically searched approximately 100 million image cutouts from the entire Hubble Legacy Archive using the recently developed AnomalyMatch method, which combines semi-supervised and active learning techniques for the efficient detection of astrophysical anomalies. This comprehensive search rapidly uncovered a multitude of astrophysical anomalies presented here that significantly expand the inventory of known rare objects.
Results. Among our discoveries are 86 new candidate gravitational lenses, 18 jellyfish galaxies, and 417 mergers or interacting galaxies. The efficiency and accuracy of our iterative detection strategy allows us to trawl the complete archive within just 2–3 days, highlighting its potential for large-scale astronomical surveys.
Conclusions. We present a detailed overview of these newly identified objects, discuss their astrophysical significance, and demonstrate the considerable potential of AnomalyMatch to efficiently explore extensive astronomical datasets, including, for example, the upcoming Euclid data releases.
Key words: methods: data analysis / catalogs / galaxies: general / galaxies: interactions / galaxies: peculiar
© The Authors 2025
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.