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.
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.
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.
Editor: Andrea Gianopoulos
Location: NASA Goddard Space Flight Center
Contact Media:
Claire Andreoli
NASA’s Goddard Space Flight Center
Greenbelt, Maryland
claire.andreoli@nasa.gov
Bethany Downer
ESA/Hubble
Baltimore, Maryland
Ann Jenkins and Christine Pulliam
Space Telescope Science Institute
Baltimore, Maryland
Related Links and Documents
- Science Paper: Identifying astrophysical anomalies in 99.6 million source cutouts from the Hubble legacy archive using AnomalyMatch, PDF (47.32 MB)
- Release on ESA/Hubble Website
