Scientists analyzed more than 100 million image slices from the Hubble Space Telescope archive and discovered hundreds of previously unknown objects.
By Jackie Flynn Mogensen edited by Claire Cameron
Six never-before-seen astrophysical objects from a Hubble Space Telescope data archive.
ESA/Hubble/NASA/D. O’Ryan/P. Gómez/European Space Agency/Mr. Zamani/ESA/Hubble
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The universe is so vast and the difficulty of discovering everything in the cosmos is so great that we might as well count all the grains of sand in the Sahara. But today, thanks to artificial intelligence, astronomers have revealed more than 800 previously unknown “cosmic anomalies” hidden in the atmosphere. archive data of 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 earlier eras. 35 years ago. Incredibly, the AI took just two and a half days to search through the entire archive, a task that would have taken a human research team much longer.
Merging galaxies from the Hubble archives.
ESA/Hubble/NASA/D. O’Ryan/P. Gómez/European Space Agency/Mr. Zamani/ESA/Hubble
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The hunt revealed more than 1,300 “anomalous objects,” including galaxy mergers, jellyfish galaxies (so named because of their gas tentacles), and other unusual features. Among these were dozens of possible gravitational lenses – places where a massive object, like a galaxy, bends light from a given source, like another galaxy – as well as dozens of other strange objects that defied easy explanation. Of all the objects found, around 800 had never been described before.
A collisional ring galaxy from the Hubble archives.
ESA/Hubble/NASA/D. O’Ryan/P. Gómez/European Space Agency/Mr. Zamani/ESA/Hubble
The work was published last month in the newspaper Astronomy and astrophysics.
In a statement, Pablo Gómez, ESA data scientist and co-author of the paper, said the AI approach could offer a model for exploring other space science archives. “He [shows] how useful this tool will be for other large datasets,” he said.
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