AI reveals 800 never-before-seen ‘cosmic anomalies’ in old Hubble images
Scientists analyzed more than 100 million image cutouts from a Hubble Space Telescope archive and found hundreds of previously undiscovered objects
Six previously undiscovered astrophysical objects from an archive of Hubble Space Telescope data.
ESA/Hubble/NASA/D. O’Ryan/P. Gómez/European Space Agency/M. Zamani/ESA/Hubble
The universe is so vast, and the difficulty of discovering all that there is out in the cosmos is so great, that one might as well 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” hidden in archival data from the Hubble Space Telescope.
Researchers at the European Space Agency (ESA) developed an AI tool that sifted through nearly 100 million image cutouts in the Hubble Legacy Archive, a collection of data from as early as 35 years ago. Incredibly, the AI took just two and a half days to run through the entire archive, a task that would have taken a human research team exponentially longer to accomplish.
Merging galaxies from Hubble’s archive.
ESA/Hubble/NASA/D. O’Ryan/P. Gómez/European Space Agency/M. Zamani/ESA/Hubble
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The hunt turned up more than 1,300 “anomalous objects,” including galaxy mergers, jellyfish galaxies (so named for their trailing tentacles of gas) and other unusual features. Among these were scores of possible gravitational lenses—spots 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 oddball objects that defied easy explanation. Of all the found objects, some 800 had never been described before.
A collisional ring galaxy from Hubble’s archive.
ESA/Hubble/NASA/D. O’Ryan/P. Gómez/European Space Agency/M. Zamani/ESA/Hubble
The work was published last month in the journal Astronomy & Astrophysics.
In a statement, ESA data scientist and co-author on the paper Pablo Gómez said the AI approach could offer a model for exploring other space science archives. “It [shows] how useful this tool will be for other large datasets,” he said.
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