Two astronomers at the European Space Agency just proved AI can do more than generate text - it can hunt for cosmic weirdness. David O'Ryan and Pablo Gómez trained an AI model called AnomalyMatch to scan through 35 years of Hubble Space Telescope archives, uncovering nearly 1,400 bizarre astrophysical objects that had been hiding in plain sight. The discovery, published in Astronomy & Astrophysics, shows how machine learning can tackle the overwhelming data deluge facing modern astronomy - and it's already reshaping how scientists approach archival research.
The European Space Agency just gave astronomers a powerful new reason to revisit old data. Researchers David O'Ryan and Pablo Gómez trained an AI model to comb through the Hubble Space Telescope's 35-year archive, and what they found validates what many suspected - there's gold hiding in those pixels.
AnomalyMatch, the custom AI model at the heart of the project, scanned nearly 100 million image cutouts from the Hubble Legacy Archive in just two and a half days. That's the first time anyone has systematically searched the entire dataset for anomalies. The result? Nearly 1,400 bizarre objects that don't fit the usual cosmic patterns, most of which had never been documented before.
"It's a treasure trove of data in which astrophysical anomalies might be found," O'Ryan told ESA in a statement. That might be an understatement.
The discoveries span the weird and wonderful. Most of the flagged objects turned out to be galaxies caught in the act of merging or gravitationally tangling with neighbors. But the haul also included gravitational lenses - light bent into circles and arcs by massive objects acting like cosmic magnifying glasses - and jellyfish galaxies, which sport dangling tentacles of gas stripped away as they plow through galaxy clusters.
