European Space Agency (ESA) researchers have employed a cutting-edge artificial intelligence-assisted technique to uncover rare astronomical phenomena within archived data from NASA's Hubble Space Telescope. The research team analyzed nearly 100 million image cutouts from the Hubble Legacy Archive, each measuring just a few dozen pixels (7 to 8 arcseconds) on a side. Using a neural network tool called AnomalyMatch, the team identified more than 1,300 objects with an odd appearance in just two and a half days, with over 800 of which had never been documented in scientific literature. The study was published on December 16, 2025, in the journal Astronomy & Astrophysics.
Most of the discovered anomalies were galaxies undergoing mergers or interactions, which exhibit unusual morphologies or trailing, elongated streams of stars and gas. Others were gravitational lenses, where the gravity of a foreground galaxy distorts spacetime and bends light from a background galaxy into arcs or rings. Additional discoveries included galaxies with massive star-forming clumps, jellyfish-looking galaxies with gaseous "tentacles," and edge-on planet-forming disks in our own galaxy resembling hamburgers. Remarkably, several dozen objects defied existing classification schemes entirely, making it impossible for scientists to categorize these objects using current astronomical taxonomies.
Lead author David O'Ryan stated, "Archival observations from the Hubble Space Telescope now span 35 years, offering a rich dataset in which astrophysical anomalies may be hidden." Traditionally, anomalous images are discovered through manual inspection or serendipitous observation. While expert astronomers excel at identifying unusual features, the sheer volume of Hubble data makes comprehensive manual review impractical. Citizen science initiatives have helped expand the scope of data analysis, but even these efforts fall short when faced with archives as extensive as Hubble's or those from wide-field survey telescopes like Euclid, an ESA mission with NASA contributions.
T he work by O'Ryan and Gómez represents a significant advancement. By applying AnomalyMatch to the Hubble Legacy Archive, they conducted the first systematic search for astrophysical anomalies across the entire dataset. After the algorithm flagged likely candidates, the researchers manually reviewed the top-rated sources and confirmed more than 1,300 as true anomalies. Gómez noted, "This is a powerful demonstration of how AI can enhance the scientific return of archival datasets. The discovery of so many previously undocumented anomalies in Hubble data underscores the tool's potential for future surveys." Facilities such as NASA's upcoming Nancy Grace Roman Space Telescope, ESA's Euclid, and the Vera C. Rubin Observatory will generate unprecedented volumes of data. Tools like AnomalyMatch will be essential for navigating this data deluge, enabling astronomers to uncover new and unexpected phenomena—and perhaps even objects never before seen in the universe.