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Astronomers are enlisting AI to prepare for a data downpour

Tailored algorithms will help filter a coming flood of astronomical observations, helping scientists make new discoveries about the universe.

A computer-simulated image shows a supermassive black hole at the core of a galaxy.
NASA, ESA, and D. Coe, J. Anderson, and R. van der Marel (STScI)

In deserts across Australia and South Africa, astronomers are planting forests of metallic detectors that will together scour the cosmos for radio signals. When it boots up in five years or so, the Square Kilometer Array Observatory will look for new information about the universe’s first stars and the different stages of galactic evolution. 

But after synching hundreds of thousands of dishes and antennas, astronomers will quickly face a new challenge: combing through some 300 petabytes of cosmological data a year—enough to fill a million laptops. 

It’s a problem that will be repeated in other places over the coming decade. As astronomers construct giant cameras to image the entire sky and launch infrared telescopes to hunt for distant planets, they will collect data on unprecedented scales. 

“We really are not ready for that, and we should all be freaking out,” says Cecilia Garraffo, a computational astrophysicist at the Harvard-Smithsonian Center for Astrophysics. “When you have too much data and you don’t have the technology to process it, it’s like having no data.”

In preparation for the information deluge, astronomers are turning to AI for assistance, optimizing algorithms to pick out patterns in large and notoriously finicky data sets. Some are now working to establish institutes dedicated to marrying the fields of computer science and astronomy—and grappling with the terms of the new partnership.

In November 2022, Garraffo set up AstroAI as a pilot program at the Center for Astrophysics. Since then, she has put together an interdisciplinary team of over 50 members that has planned dozens of projects focusing on deep questions like how the universe began and whether we’re alone in it. Over the past few years, several similar coalitions have followed Garraffo’s lead and are now vying for funding to scale up to large institutions.

Garraffo recognized the potential utility of AI models while bouncing between career stints in astronomy, physics, and computer science. Along the way, she also picked up on a major stumbling block for past collaboration efforts: the language barrier. Often, astronomers and computer scientists struggle to join forces because they use different words to describe similar concepts. Garraffo is no stranger to translation issues, having struggled to navigate an English-only school growing up in Argentina. Drawing from that experience, she has worked to put people from both communities under one roof so they can identify common goals and find a way to communicate. 

Astronomers had already been using AI models for years, mainly to classify known objects such as supernovas in telescope data. This kind of image recognition will become increasingly vital when the Vera C. Rubin Observatory opens its eyes next year and the number of annual supernova detections quickly jumps from hundreds to millions. But the new wave of AI applications extends far beyond matching games. Algorithms have recently been optimized to perform “unsupervised clustering,” in which they pick out patterns in data without being told what specifically to look for. This opens the doors for models pointing astronomers toward effects and relationships they aren’t currently aware of. For the first time, these computational tools offer astronomers the faculty of “systematically searching for the unknown,” Garraffo says. In January, AstroAI researchers used this method to catalogue over 14,000 detections from x-ray sources, which are otherwise difficult to categorize.

Another way AI is proving fruitful is by sniffing out the chemical composition of the skies on alien planets. Astronomers use telescopes to analyze the starlight that passes through planets’ atmospheres and gets soaked up at certain wavelengths by different molecules. To make sense of the leftover light spectrum, astronomers typically compare it with fake spectra they generate based on a handful of molecules they’re interested in finding—things like water and carbon dioxide. Exoplanet researchers dream of expanding their search to hundreds or thousands of compounds that could indicate life on the planet below, but it currently takes a few weeks to look for just four or five compounds. This bottleneck will become progressively more troublesome as the number of exoplanet detections rises from dozens to thousands, as is expected to happen thanks to the newly deployed James Webb Space Telescope and the European Space Agency’s Ariel Space Telescope, slated to launch in 2029. 

Processing all those observations is “going to take us forever,” says Mercedes López-Morales, an astronomer at the Center for Astrophysics who studies exoplanet atmospheres. “Things like AstroAI are showing up at the right time, just before these faucets of data are coming toward us.”

Last year López-Morales teamed up with Mayeul Aubin, then an undergraduate intern at AstroAI, to build a machine-learning model that could more efficiently extract molecular composition from spectral data. In two months, their team built a model that could scour thousands of exoplanet spectra for the signatures of five different molecules in 31 seconds, a feat that won them the top prize in the European Space Agency’s Ariel Data Challenge. The researchers hope to train a model to look for hundreds of additional molecules, boosting their odds of finding signs of life on faraway planets. 

AstroAI collaborations have also given rise to realistic simulations of black holes and maps of how dark matter is distributed throughout the universe. Garraffo aims to eventually build a large language model similar to ChatGPT that’s trained on astronomy data and can answer questions about observations and parse the literature for supporting evidence. 

“There’s this huge new playground to explore,” says Daniela Huppenkothen, an astronomer and data scientist at the Netherlands Institute for Space Research. “We can use [AI] to tackle problems we couldn’t tackle before because they’re too computationally expensive.” 

However, incorporating AI into the astronomy workflow comes with its own host of trade-offs, as Huppenkothen outlined in a recent preprint. The AI models, while efficient, often operate in ways scientists don’t fully understand. This opacity makes them complicated to debug and difficult to identify how they may be introducing biases. Like all forms of generative AI, these models are prone to hallucinating relationships that don’t exist, and they report their conclusions with an unfounded air of confidence. 

“It’s important to critically look at what these models do and where they fail,” Huppenkothen says. “Otherwise, we’ll say something about how the universe works and it’s not actually true.”

Researchers are working to incorporate error bars into algorithm responses to account for the new uncertainties. Some suggest that the tools could warrant an added layer of vetting to the current publication and peer-review processes. “As humans, we’re sort of naturally inclined to believe the machine,” says Viviana Acquaviva, an astrophysicist and data scientist at the City University of New York who recently published a textbook on machine-learning applications in astronomy. “We need to be very clear in presenting results that are often not clearly explicable while being very honest in how we represent capabilities.”

Researchers are cognizant of the ethical ramifications of introducing AI, even in as seemingly harmless a context as astronomy. For instance, these new AI tools may perpetuate existing inequalities in the field if only select institutions have access to the computational resources to run them. And if astronomers recycle existing AI models that companies have trained for other purposes, they also “inherit a lot of the ethical and environmental issues inherent in those models already,” Huppenkothen says.

Garraffo is working to get ahead of these concerns. AstroAI models are all open source and freely available, and the group offers to help adapt them to different astronomy applications. She has also partnered with Harvard’s Berkman Klein Center for Internet & Society to formally train the team in AI ethics and learn best practices for avoiding biases. 

Scientists are still unpacking all the ways the arrival of AI may affect the field of astronomy. If AI models manage to come up with fundamentally new ideas and point scientists toward new avenues of study, it will forever change the role of the astronomer in deciphering the universe. But even if it remains only an optimization tool, AI is set to become a mainstay in the arsenal of cosmic inquiry. 

“It’s going to change the game,” Garraffo says. “We can’t do this on our own anymore.” 

Zack Savitsky is a freelance science journalist who covers physics and astronomy. 

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