AI combed Hubble’s archive, saw hundreds of cosmic anomalies

• AI sifted through nearly 100 million Hubble image cutouts, identifying over 1,300 unusual objects in just a few days — more than 800 of them never before documented in scientific literature.
• Most of the anomalies are rare or odd cosmic phenomena — including merging galaxies, gravitational lenses, ring galaxies, and galaxies with unusual shapes or features.
• A new AI tool called AnomalyMatch made the discoveries possible, allowing astronomers to rapidly comb the vast Hubble archive and highlight objects that would be extremely time-consuming to find manually.

NASA originally published this article on January 27, 2026. Read the original here. Edits by EarthSky.

AI combed Hubble’s archive

A team of astronomers has employed a cutting-edge, artificial intelligence-assisted technique to uncover rare astronomical phenomena within archived data from the NASA-ESA Hubble Space Telescope. The 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. They identified more than 1,300 objects with an odd appearance in just two-and-a-half days — more than 800 of which had never been documented in scientific literature.

Most of the 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!

The formidable challenge of identifying types of galaxies

Identifying such a diverse array of rare objects within the vast and growing repository of Hubble and other telescope data presents a formidable challenge. Never in the history of astronomy has such a volume of observational data been available for analysis.

To address this challenge, researchers David O’Ryan and Pablo Gómez of ESA (the European Space Agency) developed an AI tool capable of inspecting millions of astronomical images in a fraction of the time required by human experts. Their neural network, named AnomalyMatch, was trained to detect rare and unusual objects by recognizing patterns in data — mimicking the way the human brain processes visual information. David O’Ryan, lead author of the study published in Astronomy & Astrophysics, said:

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.

The 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 commented:

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.

Bottom line: Guided by astronomers, AI combed Hubble’s archive – some 100 million Hubble image cutouts – and identified more than 1,300 unusual objects.

Source: Identifying astrophysical anomalies in 99.6 million source cutouts from the Hubble legacy archive using AnomalyMatch

Via NASA

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