Astrophysics
Identifying astrophysical anomalies in 99.6 million source cutouts from the Hubble legacy archive using AnomalyMatch
Researchers used the AI-powered AnomalyMatch tool to scan 99.6 million Hubble Space Telescope images, uncovering 86 new gravitational lens candidates and hundreds of rare interacting galaxies in just days.
Imagine the Hubble Space Telescope has been taking photos for over 30 years, and nobody has had time to look carefully at all of them. There are about 100 million little image stamps sitting in a digital archive, most never closely examined. These researchers built a smart computer system called AnomalyMatch that works a bit like training a dog to sniff out truffles — you show it a few examples of weird, interesting things, and it goes hunting through the entire archive to find more. In just 2 to 3 days, it flagged hundreds of extraordinary cosmic objects: galaxies crashing into each other, galaxies with gas being ripped away so they look like jellyfish, and gravitational lenses where one galaxy bends light from another galaxy behind it like a cosmic magnifying glass. The exciting part is that humans alone would have taken centuries to do this job.
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Systematically searched approximately 100 million image cutouts from the entire Hubble Legacy Archive using the AnomalyMatch method
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Discovered 86 new candidate gravitational lenses within the Hubble Legacy Archive
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Identified 18 jellyfish galaxies and 417 mergers or interacting galaxies
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The iterative detection strategy can trawl the complete archive within just 2–3 days
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Demonstrated the potential of AnomalyMatch for large-scale surveys including upcoming Euclid data releases
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.