AI uncovers 800 cosmic anomalies hidden in 35 years of Hubble Space Telescope data

AI reveals 800 never-before-seen 'cosmic anomalies' in old Hubble images

I agree my information will be processed in accordance with the Scientific American and Springer Nature Limited Privacy Policy. We leverage third party services to both verify and deliver email. By providing your email address, you also consent to having the email address shared with third parties for those purposes. The universe is so vast and the difficulty of discovering all that there is out in the cosmos 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 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 more than 100 million image cutouts in the Hubble Legacy Archive, a collection of 35-year-old data. 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. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. 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 the gravity of one galaxy bends the light of another -- as well as dozens of other oddball objects that defied easy explanation. Of all the found objects, some 800 had never been described before. The work was published last year in the journal Astronomy & Astrophysics. 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 in a statement.

Astronomers discover over 800 cosmic anomalies using a new AI tool

Here's a use of AI that appears to do more good than harm. A pair of astronomers at the European Space Agency (ESA) developed a neural network that searches through space images for anomalies. The results were far beyond what human experts could have done. In two and a half days, it sifted through nearly 100 million image cutouts, discovering 1,400 anomalous objects. The creators of the AI model, David O'Ryan and Pablo Gómez, call it AnomalyMatch. The pair trained it on (and applied it to) the Hubble Legacy Archive, which houses tens of thousands of datasets from Hubble's 35-year history. "While trained scientists excel at spotting cosmic anomalies, there's simply too much Hubble data for experts to sort through at the necessary level of fine detail by hand," the ESA wrote in its press release. After less than three days of scanning, AnomalyMatch returned a list of likely anomalies. It still requires human eyes at the end: Gómez and O'Ryan reviewed the candidates to confirm which were truly abnormal. Among the 1,400 anomalous objects the pair confirmed, more than 800 were previously undocumented. Most of the results showed galaxies merging or interacting, which can lead to odd shapes or long tails of stars and gas. Others were gravitational lenses. (That's where the gravity of a foreground galaxy bends spacetime so that the light from a background galaxy is warped into a circle or arc.) Other discoveries included planet-forming disks viewed edge-on, galaxies with huge clumps of stars and jellyfish galaxies. Adding a bit of mystery, there were even "several dozen objects that defied classification altogether." "This is a fantastic use of AI to maximize the scientific output of the Hubble archive," Gómez is quoted as saying in the ESA's announcement. "Finding so many anomalous objects in Hubble data, where you might expect many to have already been found, is a great result. It also shows how useful this tool will be for other large datasets."

AI Sifts Through a Mountain of Hubble Data, Uncovers Hundreds of Cosmic Weirdos

The universe is filled with innumerable astrophysical objects, each one different from the last. But even amid this vast diversity, some stand out as truly bizarre. A pair of astronomers recently discovered hundreds of these cosmic weirdos buried in archival Hubble Space Telescope data. These objects have waited years for researchers to catalog and investigate their unusual characteristics, and thanks to AI, they finally have. "Archival observations from the Hubble Space Telescope now span 35 years, offering a rich dataset in which astrophysical anomalies may be hidden," David O'Ryan, a research fellow at the European Space Agency (ESA) and lead author of the study published in Astronomy & Astrophysics, said in an agency statement. O'Ryan and his colleague, ESA data scientist Pablo Gómez, created an AI-assisted data analysis tool called AnomalyMatch and used it to search for rare astronomical objects in the Hubble Legacy Archive. It took just two and a half days to sift through nearly 100 million image cutouts and identify nearly 1,400 anomalous objects, 800 of which were previously unknown to science. "This is a powerful demonstration of how AI can enhance the scientific return of archival datasets," Gómez said in a NASA statement. "The discovery of so many previously undocumented anomalies in Hubble data underscores the tool's potential for future surveys." Mining Hubble's vast archive Hubble has spent more than three decades continuously surveying the cosmos. To date, the telescope has made more than 1.7 million observations, building a data goldmine that has significantly expanded our understanding of the universe. However, sifting through this mountain of data to find rare and anomalous objects, such as colliding galaxies, gravitational lenses, and ring galaxies, is an onerous task for astronomers. Gómez and O'Ryan developed AnomalyMatch to do the heavy lifting for them. Their AI tool is a neural network -- a machine learning model designed to mimic the way the human brain processes data and recognizes patterns. AnomalyMatch is trained to sniff out cosmic objects that look unusual, compiling a list of targets that astronomers like O'Ryan and Gómez can then examine more closely to confirm and classify. A wealth of weirdos Of the 800-odd oddballs AnomalyMatch and its creators identified, most were galaxies actively merging or interacting with other galaxies, morphing them into unusual shapes or giving them trailing tails of stars and gas. They also found many gravitational lenses -- massive celestial bodies that bend spacetime and warp the light around them, acting as a natural lens -- and other rare objects such as galaxies with huge star clumps, jellyfish galaxies with gaseous "tentacles," and planet-forming disks that resemble hamburgers or butterflies when viewed edge-on. Most intriguing were several dozen objects that defied classification entirely, presenting new opportunities to probe never-before-seen cosmic structures. The findings show that neural networks like AnomalyMatch can maximize the value of data archives like Hubble's. Gómez and O'Ryan hope their tool will unlock new discoveries from forthcoming datasets as well, including that of ESA's Euclid space telescope and the National Science Foundation and U.S. Department of Energy's Vera C. Rubin Observatory. These next-generation surveys will produce a deluge of data, and analyzing that data will require next-generation techniques. Combing through the cosmos with AI could open the door to a whole new world of scientific discovery.

AI Unlocks Hundreds of Cosmic Anomalies in Hubble Archive | Newswise

Six previously undiscovered, weird and fascinating astrophysical objects are displayed in this new image from NASA's Hubble Space Telescope. They include three lenses with arcs distorted by gravity, one galactic merger, one ring galaxy, and one galaxy that defied classification. Newswise -- A team of astronomers has employed a cutting-edge, artificial intelligence-assisted technique to uncover rare astronomical phenomena within archived data from NASA's 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. 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. "Archival observations from the Hubble Space Telescope now span 35 years, offering a rich dataset in which astrophysical anomalies may be hidden," said David O'Ryan, lead author of the study published in Astronomy & Astrophysics. 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. "This is a powerful demonstration of how AI can enhance the scientific return of archival datasets," Gómez said. "The discovery of so many previously undocumented anomalies in Hubble data underscores the tool's potential for future surveys." Hubble is just one of many astronomical archives poised to benefit from AI-driven analysis. Facilities such as NASA's upcoming Nancy Grace Roman Space Telescope, a well as ESA's Euclid and the National Science Foundation and Department of Energy's 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. The Hubble Space Telescope has been operating for over three decades and continues to make ground-breaking discoveries that shape our fundamental understanding of the universe. Hubble is a project of international cooperation between NASA and ESA . NASA's Goddard Space Flight Center in Greenbelt, Maryland, manages the telescope and mission operations. Lockheed Martin Space, based in Denver, also supports mission operations at Goddard. The Space Telescope Science Institute in Baltimore, which is operated by the Association of Universities for Research in Astronomy, conducts Hubble science operations for NASA. To learn more about Hubble, visit: