America’s Leading Alien Hunters Depend On AI To Speed Their Search

Harvard’s Galileo Project has brought high-end academic research to a once-fringe pursuit, and the Pentagon is watching. America’s Leading Alien Hunters Depend On AI To Speed Their Search

When Laura Dominé was getting her Ph.D. in physics at Stanford University, her research was on neutrinos: elementary particles, minuscule even to physicists, that to laypeople sound made-up.

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Neutrinos are almost massless and electrically neutral, and therefore they pass through matter as if it were air. Trillions of the so-called ghost particles are zipping through you right now unnoticed, continuing journeys that began, for many of them, a single second after the Big Bang.

The detectors built to find evidence of neutrinos are themselves fantastical things. These cavernous chambers, deep in the Earth, are filled with heavy water or liquid argon and lined with exquisitely sensitive photosensors or grids of delicate wiring.

Every once in a while, thanks to something called the weak force, a neutrino will react with a subatomic particle inside the vat and be rendered detectable by human instruments.

Dominé worked on a machine learning algorithm that could spot these reactions, helping physicists continue to piece together their portrait of the neutrino and, through it, the workings of our strange universe.

After earning her doctorate in 2023, Dominé didn’t join classmates who were going on to traditional astrophysics research programs or tech companies or algorithmic finance firms.

Instead she took a postdoctoral fellowship in Cambridge, Massachusetts, at the Harvard & Smithsonian Center for Astrophysics and its Galileo Project, which is building a different kind of detector.

On a patch of blacktop in the woods a half-hour outside of Boston, the Galileo Project’s array of equipment includes acoustic sensors, a radio-frequency spectrum analyzer, a charged-particle counter, a weather station with a magnetometer, plus several cameras, including eight infrared ones housed in a 20-inch-wide dome reminiscent of R2-D2’s head.

This unprepossessing thicket of instruments is pointed at the sky, looking for what are known, in the small research community dedicated to studying them, as unidentified anomalous phenomena, or UAP. (The rest of us call them UFOs.)

Dominé and her fellow researchers are willing to consider all explanations for the anomalies they might uncover. And to them that means being open to the possibility that what they’ll find are signs of intelligent extraterrestrial life—not in some distant galaxy but in our own terrestrial airspace.

Determined to apply mainstream scientific rigor to their unorthodox quest, the Galileo team has committed to sifting an entire sky’s worth of data, 24 hours a day. It’s something they’re only able to do because of recent advances in artificial intelligence. “This is the only way to solve this,” Dominé says.

America’s Leading Alien Hunters Depend On AI To Speed Their Search

The availability of such tools has coincided with a cultural shift, one fed by a cascade of US Department of Defense disclosures about its own research into ufology. Although the community of respectable alien-hunting academics like Dominé is still small, it’s no longer confined to the fringes.

Research programs similar to Harvard’s have sprouted at Wellesley College, Germany’s University of Würzburg and the Nordic Institute for Theoretical Physics, along with the Pentagon.

These programs are often run and staffed by people with blue-chip résumés such as Dominé’s, but algorithms are proving to be powerful partners.

Rapid improvements in AI software, and the computer servers and other hardware needed to run it, have made it possible to process huge amounts of data in real time from multiple sources, including cameras and other instruments deployed in the field.

These systems are advancing beyond their human training, teaching themselves to spot entirely new kinds of objects in the sky.

Nobel laureate Enrico Fermi summed up the conundrum of extraterrestrial life most succinctly back in 1950. “Where is everybody?” he asked. That is, with the universe so vast and so rich in planets not unlike Earth, why haven’t we found evidence that we’re not alone?

To that end, astronomers use spectroscopes to peer at planets light years away, assessing whether their atmospheres might contain the same chemical signatures as ours—evidence, potentially, of oceans full of primitive, algaelike organisms. A recent paper claims to have found just that.

Looking for evidence that aliens are sending some sort of advanced technology to our planet, however, is something very different. It’s historically been a matter of examining grainy photographs or nodding solemnly along to witness accounts of sloe-eyed humanoids bathed in light, and it’s long been plagued by hoaxes and an openness to occult thinking.

The public perception of ufology, however, changed on Dec. 16, 2017, when a New York Times story revealed that the Defense Department had itself been secretly studying UAP.

The online version of the story included videos taken from US Navy fighter jets showing oval blobs that seemed to fly in physics-defying ways. The revelations, after decades of denials from the US government about its interest in the topic, prompted Congress to order the Defense Department and the Office of the Director of National Intelligence to jointly prepare an annual public report outlining their activities in the area.

Congress also mandated that the Pentagon stand up a new, more public UAP research program, the All-domain Anomaly Resolution Office. Sean Kirkpatrick, a materials physicist and longtime defense and intelligence official, was AARO’s first director, and he acknowledges some of the cultural challenges.

“If NASA and the scientific community are talking about finding extraterrestrial life, as long as that conversation is confined to the far reaches of the galaxy and the universe, that’s a very scientific debate,” he says. “But as that conversation gets closer and closer to Earth, somewhere around Mars, it starts moving into a more conspiratorial bent. Once you cross the stratosphere, it’s completely out of control.”

Dominé’s own obsession with intelligent extraterrestrial life dates to her childhood. She grew up in a small apartment in an upscale western suburb of Paris. Her father, a self-taught IT consultant, immigrated from Vietnam as a teenager, and her French mother, blessed with an ear for languages, worked as a hotel receptionist in Paris before having Dominé and her two siblings.

Dominé was an indefatigable reader as a child and a voracious learner of languages—not only French and English, but also German and Japanese.

She also studied Swahili. (She’s since added others, with varying degrees of proficiency: her father’s native Vietnamese, the African Twi language, Cherokee and the endangered Yup’ik Eskimo language.) “I was confident that some day I would be able to speak most languages that existed in the world,” she recalls.

She wasn’t so much interested in conversing fluently, though, as unpacking the patterns of their syntax. They were, to her, a series of puzzles. By the time she started high school, two years early, she had taught herself various computer languages, and chunks of physics, too.

Dominé particularly loved the books of Jacques Vallée. The French astronomer is known in Silicon Valley for doing some of the foundational research that led to the creation of the internet. Everywhere else, though, his prominence comes from a parallel career as a globe-trotting investigator of UFO sightings. The ufologist played by François Truffaut in Close Encounters of the Third Kind is based on him.

Dominé devoured Vallée’s books about his investigations as well as Passport to Magonia, in which he explores the ways that accounts of modern extraterrestrial encounters mirror the supernatural elements of much folklore and mythology.

Dominé’s parents shared her deep enthusiasm for the topic. Her dad had the family computer set up to participate in SETI@home, a global crowdsourced computing project to analyze radio telescope data in search of messages from deep space.

Dominé became convinced that the questions Vallée was asking comprised “the largest gaping hole in our understanding of reality” and that studying astrophysics was the best bet to tackle them.

But as she progressed through France’s prestigious École Polytechnique, then through graduate school at Stanford, where she focused on particle physics, it was hard to see a way to apply her growing expertise to the subject.

In early 2022, Dominé was a year away from getting her doctorate, after which she planned to leave academia; she was thinking of working for a climate change startup, or developing AI tools to help preserve endangered languages, or taking a year off to become a master woodworker. That April, however, she read an article about the Galileo Project and its founder, Avi Loeb.

Loeb, a theoretical astrophysicist, Harvard professor and onetime chair of the university’s astronomy department, made his name with bold ideas about black holes, the Big Bang and the birth of stars.

Today, though, his career is in its second act. In 2018, Loeb and a junior researcher published a paper suggesting that ‘Oumuamua—an unusually shaped space object whose trajectory through our solar system had mystified astronomers—might in fact be an alien space probe.

That provocative hypothesis, which Loeb expanded into his bestselling book Extraterrestrial: The First Sign of Intelligent Life Beyond Earth, made him a celebrity and led to conversations with a collection of wealthy men who shared his interest.

With their money, he founded the Galileo Project in 2021. In 2023, Loeb led an expedition to comb the seafloor near Papua New Guinea for what he’s publicly suggested might be the remnants of another space probe that had burned up in the atmosphere. He took along a film crew and documented his progress in a series of daily Medium posts and media interviews.

“The universe is a very unusual place,” he says, in an interview at his home in Lexington, Massachusetts. “You can’t be dogmatic when most of the stuff you discuss is not understood.”

Loeb is a deeply controversial figure to his fellow astrophysicists, who argue that his eagerness to publicly hypothesize about extraterrestrial technologies goes along with a refusal to consider less splashy explanations for the various phenomena in question (comets, for example, or mundane meteoroids).

But the concept of the Galileo Project as a place where there were resources and money for serious UAP research exhilarated Dominé when she read about it. She was particularly intrigued by the concept of the observatory.

You don’t have to believe in alien space probes to see its value: Finding something would be extraordinary, but not finding anything would be clarifying too. That’s how science works. Dominé immediately applied for a postdoc position. A week and a half later, Loeb called to offer her the job.

When she got to Cambridge in April of 2023, Dominé met Richard Cloete, the first postdoc Loeb had hired for the project. Since then, the two have worked together closely. Like Dominé, Cloete is the first in his immediate family to get a college degree, though his path was more winding.

The son of a supermarket manager father and a bookkeeper mother, he grew up in South Africa in various suburbs of Cape Town and spent his childhood making radios, Tesla coils, plasma globes, microwave guns and other electronic gadgets.

He moved to England in 2004, when he was 20, and tended bar for five years before going to the University of Westminster, where he studied computer science and engineering. He was only persuaded to get his doctorate by his now-wife, herself a scientist. Before Galileo, he had a postdoc position in data science at the University of Cambridge.

Cloete also has an old fascination with aliens, though it’s mixed with scorn for what passes for research into the matter. “There’s so much nonsense out there,” he says. “Every time some new information would come out, you’d realize it’s bunk and someone’s trying to make money.”

Even well-intentioned UAP research is a small-data affair. Ufologists such as Vallée are detectives rather than scientists, examining fragmentary reports from witnesses who are themselves often mystified by what they’ve seen.

The Galileo Project observatory is meant to rectify that, radically expanding the amount of data about potential UAP and establishing a scientific baseline for studying them.

The observatory’s instruments gather information from the sky around the clock, but the vast majority of the time—just as in a neutrino detector—nothing interesting happens. The plan is for software to do the onerous work of filtering out all the easily identifiable aerial phenomena and focusing in on the anomalies, sending them along for further human analysis by Galileo Project members and the public.

Dominé and Cloete are using open-source computer vision programs of the sort found in self-driving cars. But the Galileo project presents distinct difficulties. The traditional machine learning approach is to teach a computer to recognize a cat by showing it lots and lots of cats.

But unlike a cat, a UAP is not a thing defined by particular traits; it may turn out to be a thing no one has ever seen before.

“We don’t know what we’re looking for,” Cloete says. “We don’t know anything about its movements. That’s the whole point of the project—to define what a UAP is.”

As a result, Dominé and Cloete and the rest of their team (three other full-time observatory staffers, plus a dozen or so frequent volunteers) are training their software to recognize all the normal stuff in the skies, the better to recognize when it’s seeing something strange.

In effect, their software models will learn what a cat is by learning all the things a cat is not.

To that end, Cloete is leading an effort to build massive databases of real and computer-generated images of all the known airborne objects: “a census of the skies,” he calls it.

The trove draws on aerial images already captured by the observatory. To supplement that, Cloete uses an open-source animation program called Blender (the software behind the Oscar-winning animated feature Flow) to generate hundreds of thousands of synthetic images of planes, birds, drones, balloons, blimps and other objects.

With planes, for example, or birds, that meant different models and different species at different altitudes in different orientations and “morphologies”: a plane with its landing gear up and then down, a bird at various points in its wing stroke.

Cloete crammed all the images together in surreal, crowded skyscapes to feed into the computer vision model. The training sessions took place in 12- to 24-hour blocks on Harvard’s Cannon compute cluster, a collection of hundreds of networked servers and AI-friendly graphics processing units spread over three data centers in the area.

Despite the power of the technologies at their disposal, it can be slow work. But the Galileo team has been fortunate in the number of volunteers who’ve asked to help out. Ankit Biswas is a high school junior from Charlotte, North Carolina, who contacted Loeb out of the blue in March of last year.

“I never knew there was serious research going on into this,” he says. “I had always associated it with this fringe subculture. It’s this realm where science blends into this pseudoscientific speculation.”

For Biswas, who’d won all the state science fairs he could find, the project was an outlet commensurate with his ability and energy level. His role on the team was to process location data from airplane transponders to cross-reference with the observatory’s cameras. He worked at it at night and on weekends, when his schoolwork allowed.

In general, airplanes have been one of the easier objects to teach the software to identify, because of the regularity of their speed, trajectories and turns, as well as the transponder data Biswas worked with. Birds, on the other hand, are difficult, and bugs—fast, erratic flyers often close to the camera lens—are a “nightmare,” Cloete says.

The object-detection software his team is using, called You Only Look Once, or YOLO, also has trouble with clouds, dust, fluttering leaves near the horizon and glare from the sun, which YOLO sometimes mistakes for the flying objects it’s supposed to focus on.

(Our own visual processing can be susceptible to the same mistakes, of course, which might explain some of the anomalies human observers have reported over the years.)

The goal is to develop similar software for all of the instruments, then synchronize them, so the entire observatory reacts in real time to what it senses. That would mean that when one of the sensors—the all-sky camera or the infrared array or perhaps the microphone—picks up something interesting, a special zoom camera will swivel toward that patch of sky, and the rest of the observatory will begin saving the data it’s recording.

(Saving all of the data all of the time would quickly overwhelm the team’s storage capacity.) The system has been continuously collecting data since 2023, but it remains in the so-called commissioning stage, where Cloete and Dominé and others are working to ensure that the software models they are creating can do the basic but vital work of separating signal from noise.

In January, Dominé published a paper, co-authored with Cloete, Loeb, Biswas and others, on the data from the observatory’s array of infrared cameras. The YOLO software was able to identify 36% of the planes the cameras picked up. “By the standards of my Ph.D. neutrino work, that’s not great,” Dominé concedes, but she expects it to improve meaningfully.

“It’s a demonstration of the level of rigor that we want to stick with for our studies of UAP,” Dominé says, “and the level we would expect from our colleagues.”

She predicts the Galileo observatory should be able to reliably detect outliers coming from all its sensors within the next year or two, and three more observatories are in the works in Indiana, Nevada and Pennsylvania.

The Pentagon is closely following the work at the Harvard & Smithsonian Center for Astrophysics. Thus far, AARO has examined more than 1,800 reported UAP sightings, most of them from members of the military.

Cross-referencing information from sightings with other data sources within the government, such as weather and flight records, its researchers have determined that hundreds of cases have straightforward explanations—balloons, clouds, drones, etc.

AARO’s current director, Jon Kosloski, is a mathematician and engineer who’s detailed to the office from the National Security Agency, where previously he led research teams in quantum optics and arcane aspects of cryptography.

He comes to the work less from an interest in aliens than a broader fascination with needles and haystacks; among his unclassified research is work on designing a “language-agnostic” search engine.

Kosloski says that a small number of the cases his team has examined, about 50 to 60, are “true anomalies” that have stumped the government scientists and engineers AARO works with.

“There are interesting cases that I, with my physics and engineering background and time in the intelligence community, do not understand,” Kosloski told reporters this past November, “and I don’t know anybody else who understands them, either.”

Information about those cases hasn’t been disclosed, but Kosloski says his office’s aim is to eventually declassify as much as possible about them. As he’s careful to emphasize, the term UAP is by no means just a shorthand for alien spacecraft.

For the Pentagon in particular, the more immediate concern is that one of our rivals might have secretly developed a technology far more advanced than ours.

“[AI] Is The Only Way To Solve This”

The cases that have been published have revealed the unsuitability of military-sensing technology for AARO’s purposes. The now-famous Navy jet footage was taken by the planes’ infrared targeting cameras.

“All these military sensors are designed to detect and track large, fast-moving objects like missiles, aircraft and ships so they can put a weapon on them,” says Kirkpatrick, Kosloski’s predecessor.

They’re not built for scientific observation. That, and the classified nature of their specifications, makes the sort of commissioning work that Cloete and Dominé are doing basically impossible.

Also, Kirkpatrick points out, military sensors have important functions that the Pentagon wouldn’t want to compromise: “You can’t repurpose missile defense radars for chasing drones, because then they’re not doing missile defense.”

To address this, AARO has collaborated with researchers at the Georgia Tech Research Institute to develop its own suite of sensors and software called Gremlin, for Government Radar Multispectral Interrogator.

Most of the details about the project remain secret, but the limited information that’s been released shows that the system consists of equipment very similar to Harvard’s observatory: radar, radio antennas and telescopes gathering visual and infrared images and electromagnetic radiation.

Kosloski says only that Gremlin is installed at a national security site within US borders where there have been multiple UAP sightings.

Like Harvard’s team, AARO is developing custom machine learning software for UAP detection. And though Kosloski suggests that they’re further along in the process, they’ve encountered similar issues.

The undisclosed location where the sensors are being tested is relatively bug-free, Kosloski says, though in previous tests the AARO computer vision algorithm seemed to be confused by grasshoppers.

The Pentagon’s effort is in its infancy, and may not survive long enough to answer any of the big questions. Kosloski, in an interview at the Pentagon, says he’s seen no evidence that his program will be affected by the drastic cuts the Trump administration has begun to somewhat haphazardly impose—as a candidate, Donald Trump promised more government transparency around the issue.

At Harvard, Dominé and Cloete’s paths are diverging. They both plan to continue studying UAP, but while Cloete is extending his postdoc until 2027, Dominé will be going somewhere else.

Over the past few months, she’s watched with alarm as fellow researchers and academics with international backgrounds have been detained and targeted for deportation by US authorities.

At the same time, she’s felt some of the fallout from the Trump administration’s curtailment of federal funding for scientific research. She’d relied on the National Oceanic and Atmospheric Administration for some of her data—information the agency isn’t collecting any more.

It’s become clear to her that, despite America’s growing interest in work like hers, she won’t be able to keep doing it here. All of the research positions she’s applying for are in other countries.

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