AI reveals 800 never-before-seen ‘cosmic anomalies’ in old Hubble images
Scientists analyzed more than 100 million image cutouts from the Hubble Space Telescope collection and found hundreds of previously unseen objects.
Six previously unseen astronomical objects from a collection of Hubble Space Telescope data.
ESA/Hubble/NASA/D. O’Ryan/P. Gomez/European Space Agency/M. Zamani/ESA/Hubble
The universe is so vast, and the difficulty of finding everything there is in the universe is so great that one could even count all the grains of sand in the Sahara. But now, with the help of artificial intelligence, astronomers have revealed more than 800 previously unknown “cosmic anomalies” archival data From the Hubble Space Telescope.
Researchers at the European Space Agency (ESA) have developed an AI tool that sifts through nearly 100 million image cutouts in the Hubble Legacy Archive, a collection of data dating back 35 years. Incredibly, it took the AI only two and a half days to run through the entire collection, a task that would have taken a human research team much longer to complete.
Merging galaxies from the Hubble collection.
ESA/Hubble/NASA/D. O’Ryan/P. Gomez/European Space Agency/M. Zamani/ESA/Hubble
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The hunt revealed more than 1,300 “odd objects”, including merging galaxies, jellyfish galaxies (so named because of their trailing tentacles of gas) and other unusual features. Among these were several possible gravitational lenses – places where a massive object, such as a galaxy, bends the light of a given source, such as another galaxy – as well as dozens of other strange objects that defy easy explanation. Of all the objects found, about 800 had never been described before.
A colliding ring galaxy from the Hubble collection.
ESA/Hubble/NASA/D. O’Ryan/P. Gomez/European Space Agency/M. Zamani/ESA/Hubble
had work published Last month in the journal astronomy and astrophysics.
In a statement, ESA data scientist and co-author of the paper Pablo Gomez said the AI approach could offer a model for exploring other space science archives. “This (shows) how useful this tool will be for other large datasets,” he said.
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