AI discovers 800 cosmic anomalies in Hubble Space Telescope archives, some defy classification

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.

Scientists let AI loose on Hubble's archives

A pair of astronomers at the European Space Agency (ESA) discovered more than 800 previously undocumented "astrophysical anomalies" hiding in Hubble's archives. To do so, researchers David O'Ryan and Pablo Gómez trained an AI model to comb through Hubble's 35-year dataset, hunting for strange objects and flagging them for manual review. It's "a treasure trove of data in which astrophysical anomalies might be found," O'Ryan said in a statement. Studying space is hard. There's lots of it, it's noisy, and the flood of data generated by tools like the Hubble Space Telescope can overwhelm even large research teams. And sometimes space is weird. Very weird. Enter AI, which is great at sifting through massive amounts of information to spot patterns -- flagging the oddities astronomers might otherwise miss. The model used by the astronomers, dubbed AnomalyMatch, scanned nearly 100 million image cutouts from the Hubble Legacy Archive, the first time the dataset has been systematically searched for anomalies. Think weirdly shaped galaxies, light warped by the gravity of massive objects, or planet-forming discs seen edge-on. AnomalyMatch took just two and a half days to go through the dataset, far faster than if a human research team had attempted the task. The findings, published in the journal Astronomy & Astrophysics, revealed nearly 1,400 "anomalous objects," most of which were galaxies merging or interacting. Other anomalies included gravitational lenses (light warped into circles or arcs by massive objects in front of them), jellyfish galaxies (which have dangling "tentacles" of gas), and galaxies with large clumps of stars. "Perhaps most intriguing of all, there were several dozen objects that defied classification altogether," said ESA in a blog post. "This is a fantastic use of AI to maximise the scientific output of the Hubble archive," said Gómez. "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 finds hundreds of never-before-seen 'cosmic anomalies' in old Hubble Telescope images

(Image credit: ESA/Hubble & NASA, D. O'Ryan, P. Gómez (European Space Agency), M. Zamani (ESA/Hubble)) The Hubble Space Telescope takes a lot of pictures. In fact, NASA estimates Hubble has snapped 1.7 million images since it launched in 1990. But this poses a unique issue: It's almost impossible for scientists to examine all of the images. With this in mind, a pair of researchers at the European Space Agency (ESA) built an AI model called AnomalyMatch to comb through the vast Hubble Telescope dataset, and the AI managed to discover 1,300 anomalies, or objects with odd appearances. Hundreds of these anomalies have never been documented before. "This is a powerful demonstration of how AI can enhance the scientific return of archival datasets," Pablo Gómez, one of the ESA researchers who built the model, said in a statement. Many of these new discovered objects of note actually defy classification, NASA explains. Most showed distant galaxies in flux as they merge and interact in strange ways and scientists specifically point out "galaxies with massive star-forming clumps, jellyfish-looking galaxies with gaseous 'tentacles,' and edge-on planet-forming disks in our own galaxy resembling hamburgers." The images Hubble gathers represent the largest volume of observational data in the history of astronomy that we can analyze, yet this dizzying amount of information presents a hurdle for human observers to examine. There's just not enough time. That's why it's promising for NASA to say it took less than three days for the team to sift through nearly 100 million image cutouts using AnomalyMatch. As for how it works? The researchers trained the AI model to detect weird objects through pattern recognition. AnomalyMatch was essentially built to analyze the images in a similar way to how we process visual information inside our brains. NASA calls this project a significant advancement. It's the first time a systematic search for astrophysical anomalies has been conducted on the entire Hubble Legacy Archive, which spans decades of deep space observation. "Archival observations from the Hubble Space Telescope now stretch back 35 years, providing a treasure trove of data in which astrophysical anomalies might be found," David O'Ryan, lead author of the research paper, said in another statement. "The discovery of so many previously undocumented anomalies in Hubble data underscores the tool's potential for future surveys," Gómez said.

AI tool reveals hundreds of 'anomalies' in Hubble telescope archives -- and some defy classification

Six of the hundreds of 'anomalies' discovered in the Hubble telescope archives, including three unusual galaxies and three gravitationally lensed objects. (Image credit: ESA/Hubble & NASA, D. O'Ryan, P. Gómez (European Space Agency), M. Zamani (ESA/Hubble)) An artificial intelligence (AI) tool has uncovered more than 1,000 strange cosmic objects in the Hubble Space Telescope's image archive, including some that cannot be explained by science. After searching with the tool for just two days, researchers found 1,300 oddball objects, including chaotic merging galaxies, stars trailing gas, and even some objects that haven't been classified yet. Of these, 800 had never been spotted before, European Space Agency (ESA) officials said in a Jan. 27 statement. The findings were published Dec. 16, 2025, in the journal Astronomy & Astrophysics. "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," NASA officials said in a separate statement. "Remarkably, several dozen objects defied existing classification schemes entirely." Space jellies and sky burgers For the new study, ESA research fellows David O'Ryan and Pablo Gómez developed an AI tool to examine 100 million image cutouts from the Hubble Legacy Archive, which covers the telescope's observations following its 1990 launch. Each of the images was only a few dozen pixels per side, representing a narrow slice of sky barely a thousandth of a degree wide. "Archival observations from the Hubble Space Telescope now stretch back 35 years, providing a treasure trove of data in which astrophysical anomalies might be found," O'Ryan wrote in the paper. In addition to the "jellyfish galaxies" and cosmic "hamburgers," the search uncovered a range of other phenomena. "Most of the anomalies were galaxies undergoing mergers or interactions, which exhibit unusual morphologies or trailing, elongated streams of stars and gas," according to the NASA statement. "Others were gravitational lenses, where the gravity of a foreground galaxy distorts spacetime and bends light from a background galaxy into arcs or rings." The researchers' AI tool, called AnomalyMatch, picked up these features after learning patterns from a training dataset. Using tools like this speeds up the traditional means of discovering strange things in the sky, which usually requires manual inspection or a lucky observation. "While expert astronomers excel at identifying unusual features, the sheer volume of Hubble data makes comprehensive manual review impractical," NASA officials said in a statement. "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." "This is a powerful demonstration of how AI can enhance the scientific return of archival datasets," Gómez added. "The discovery of so many previously undocumented anomalies in Hubble data underscores the tool's potential for future surveys." More datasets where AI may be useful include those from the Euclid space telescope, which is surveying billions of galaxies to create the largest 3D map of the universe ever, and the forthcoming Nancy Grace Roman Telescope and the Vera C. Rubin Observatory, which will hunt for exoplanets and moving objects across vast stretches of the night sky. AI could help researchers sort through the "data deluge" from these large surveys, perhaps allowing for faster pickups of new objects than ever before, according to the NASA 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.

Six strange galaxies found hiding in Hubble's vast archive

Today's Image of the Day from the European Space Agency shows six strange galaxies that look nothing like the calm, orderly spirals many people picture when they hear the word "galaxy." Bent arcs of light, smeared shapes, broken rings, and objects that resist easy labels all appear in one frame. Each one is real. Each one was hiding in plain sight. For decades, images like these have sat quietly inside a massive archive built by one of humanity's most productive space observatories. The challenge has never been a lack of data. It has been knowing where to look. As telescopes grow more powerful, they generate more images than any group of scientists could ever examine by hand. Somewhere in that ocean of pixels are rare events that can reshape what astronomers know about how galaxies grow, collide, and bend space itself. Archives from space telescopes Most astronomical discoveries follow familiar paths. Researchers look for objects that fit known patterns. They confirm theories and fill in gaps, but some of the most valuable finds are the odd ones. These are the systems that behave badly, refuse neat categories, or break expectations. Finding them has traditionally relied on patience, luck, and trained eyes scrolling through endless images. Archives from space telescopes now contain tens of millions of individual image cutouts. Manually checking them would take lifetimes. Even large citizen science projects, where volunteers help classify galaxies, can only cover so much ground. The rarest objects are still easy to miss. This is where a new approach quietly changed the game. Researchers at the European Space Agency turned to artificial intelligence to do something humans cannot do alone: scan nearly 100 million astronomical images in days, not decades, and flag the ones that look different. Teaching a machine to notice strange objects The team developed a neural network called AnomalyMatch and set it loose on the Hubble Legacy Archive, which spans more than three decades of observations. Rather than being told exactly what to find, the system learned to recognize patterns and highlight objects that deviated from the norm. "Archival observations from the Hubble Space Telescope now stretch back 35 years, providing a treasure trove of data in which astrophysical anomalies might be found," said David O'Ryan, lead author of the research. The results came fast. In just two and a half days, the system sifted through nearly 100 million image cutouts and returned a shortlist of likely oddities. Experts stepped back in to confirm what the algorithm flagged. More than 1,300 of those candidates turned out to be genuine anomalies. Over 800 had never been documented in scientific literature. Galaxies caught in the act Many of the newly identified objects are galaxies in mid-transformation. Some are merging, their stars stretched into long tidal tails by gravity. Others are interacting closely, distorting each other into lopsided shapes. These systems offer rare snapshots of how galaxies grow through collisions, a process that has shaped the universe for billions of years. Gravitational lenses make up another major group. In these cases, the gravity of a foreground galaxy bends spacetime and warps the light from a more distant galaxy behind it. The result is a glowing arc or ring, often called an Einstein ring. These lenses are not just visually striking. They act as natural magnifying glasses, allowing astronomers to study extremely distant galaxies that would otherwise be too faint to see. The archive also revealed jellyfish galaxies trailing streams of gas, clumpy galaxies packed with massive star-forming regions, and planet-forming disks viewed edge-on. Some of these disks resemble butterflies or stacked buns because of the way dust blocks their light. Most intriguing were dozens of objects that refused classification entirely. Finding anomalies in Hubble data "These are a fantastic use of AI to maximize the scientific output of the Hubble archive," said study co-author Pablo Gómez. "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." Unusual objects often point to physical processes that are poorly understood or rarely observed. Each one becomes a test case. Why did it form this way? What conditions produced it? What does it reveal about gravity, dark matter, or star formation? Answers to those questions ripple outward, improving models used across astronomy. The study is published in the journal Astronomy & Astrophysics. Image Credit: ESA/Hubble & NASA, D. O'Ryan, P. Gómez (European Space Agency), M. Zamani (ESA/Hubble) -- - Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

AI Discovers Hundreds of Anomalies in Archive of Hubble Images

"This is a powerful demonstration of how AI can enhance the scientific return of archival datasets." The universe is unfathomably vast, and for the astronomers trying to understand it, that means having to gather a commensurately mind-boggling amount of data. Wouldn't it be nice if there was something that could help speed through looking for patterns in all the trillions of galaxies out there, and their quadrillions of stars? The term "AI" has become a catch-all these days for all kinds of dubious tech of varying degrees of automation and reliability, but certain types have found a very practical and welcome use among astronomers. Using a custom-built AI tool, for instance, a team of scientists at the European Space Agency have identified over a thousand "anomalies" in an archive of Hubble space telescope images that have gone unnoticed for decades, according to a NASA release. Their work, described in a new study published in the journal Astronomy & Astrophysics, is the first systematic search for astrophysical anomalies across the entire archive. "Archival observations from the Hubble Space Telescope now span 35 years, offering a rich dataset in which astrophysical anomalies may be hidden," said lead author David O'Ryan, an ESA astrophysicist, in a NASA statement. To make the discoveries, the researchers used their AI tool, which they're calling AnomalyMatch, on nearly 100 million snippets of Hubble images that were only a few pixels on each side. In less than three days, the neural network identified over 1,300 anomalous objects, more than 800 of which had never been documented in scientific literature. The result is a veritable cosmic freak show. According to NASA, most of the anomalies were from galaxies colliding with each other, in violent and hugely disruptive events called galactic mergers. The AI tool also flagged a distinct type of realm known as a jellyfish galaxy, which are defined by their numerous streams of star-forming gas that appear to dangle from one side of the galaxy's main disk, giving them the appearance of tentacles. Other oddities include edge-on planet-forming disks that look like hamburgers, and so-called gravitational lenses, in which the light of a massive foreground object like a galaxy bends the light behind it to act almost like a magnifying glass. And some of the unearthed objects defined classification altogether, NASA said. "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." The clever automated anomaly hunter comes as NASA faces brutal cuts under the Trump administration, with entire buildings being closed even at its most historic facilities, and mass layoffs. The administration has also eagerly deployed AI across the federal government, including government-tailored versions of OpenAI models, and an AI tool to help accelerate the approval of drugs. That said, astronomers have been toying with AI solutions for a while now. Typically leveraged to interpret large datasets like in this latest work, it's also been used to identify potentially habitable exoplanets and refine images of black holes. While the field's old guard emphasize caution while using these tools, it's clear they have their uses.

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:

AI Identifies More Than 1,300 Unusual Objects in Hubble Space Telescope Images

Discoveries include lenses, mergers, and jellyfish galaxies AI has analysed decades of images from the Hubble Space Telescope and found unusual celestial objects that had gone unnoticed by astronomers. ESA scientists employed a neural network called AnomalyMatch to search 100 million image cutouts from the Hubble Legacy Archive in approximately 2.5 days. They identified more than 1,300 unusual objects, of which about 800 had never been recorded before, including colliding galaxies, gravitational lenses, and jellyfish galaxies. This shows how AI can speed up new discoveries in large astronomical archives. AI searches Hubble's archive According to NASA, Hubble's massive data archive is simply too extensive for astronomers to search through by hand. To address this, ESA scientists David O'Ryan and Pablo Gómez created an AI model named AnomalyMatch, which was trained to identify unusual patterns in images. In only 2-3 days, it searched through about 100 million small 'cutouts' from the Hubble Legacy Archive - a process that would have taken astronomers many years to accomplish. This was the first systematic search of the entire Hubble archive for anomalies. After the AI identified promising candidates, scientists verified about 1,300 anomalies in the archive. Rare cosmic objects discovered 'For example, a lot of the oddities we saw were galaxies merging or interacting with each other. Some were strange gravitational lenses, where one galaxy's gravity bends the light from another behind it. Then there were galaxies with massive areas where stars are forming or those with 'jellyfish' gas tails. Some of the edge-on disks that are forming planets even resembled hamburgers. What's pretty incredible is that several dozen anomalies just don't seem to fit into any known category.