NASA and IBM have released a new open-source machine learning model to help scientists better understand and predict the physics and weather patterns of the sun. Surya, trained on over a decade’s worth of NASA solar data, should help give scientists an early warning when a dangerous solar flare is likely to hit Earth.

Solar storms occur when the sun erupts energy and particles into space. They can produce solar flares and slower-moving coronal mass ejections that can disrupt radio signals, flip computer bits onboard satellites, and endanger astronauts with bursts of radiation. 

There’s no way to prevent these sorts of effects, but being able to predict when a large solar flare will occur could let people work around them. However, as Louise Harra, an astrophysicist at ETH Zurich, puts it, “when it erupts is always the sticking point.”

Scientists can easily tell from an image of the sun if there will be a solar flare in the near future, says Harra, who did not work on Surya. But knowing the exact timing and strength of a flare is much harder, she says. That’s a problem because a flare’s size can make the difference between small regional radio blackouts every few weeks (which can still be disruptive) or a devastating solar superstorm that would cause satellites to fall out of orbit and electrical grids to fail. Some solar scientists believe we are overdue for a solar superstorm of this magnitude.

While machine learning has been used to study solar weather events before, the researchers behind Surya hope the quality and sheer scale of their data will help it predict a wider range of events more accurately. 

The model’s training data came from NASA’s Solar Dynamics Observatory, which collects pictures of the sun at many different wavelengths of light simultaneously. That made for a dataset of over 250 terabytes in total.

Early testing of Surya showed it could predict some solar flares two hours in advance. “It can predict the solar flare’s shape, the position in the sun, the intensity,” says Juan Bernabe-Moreno, an AI researcher at IBM who led the Surya project. Two hours may not be enough to protect against all the impacts a strong flare could have, but every moment counts. IBM claims in a blog post that this can as much as double the warning time currently possible with state-of-the-art methods, though exact reported lead times vary. It’s possible this predictive power could be improved through, for example, fine-tuning or by adding other data, as well. 

According to Harra, the hidden patterns underlying events like solar flares are hard to understand from Earth. She says that while astrophysicists know the conditions that make these events happen, they still do not understand why they occur when they do. “It’s just those tiny destabilizations that we know happen, but we don’t know when,” says Harra. The promise of Surya lies in whether it can find the patterns underlying those destabilizations faster than any existing methods, buying us extra time.

However, Bernabe-Moreno is excited for the potential beyond predicting solar flares. He hopes to use Surya alongside previous models he worked on for IBM and NASA that predict weather here on Earth to better understand how solar storms and Earth weather are connected. “There is some evidence about solar weather influencing lightning, for example,” he says. “What are the cross effects, and where and how do you map the influence from one type of weather to the other?”

Because Surya is a foundation model, trained without a specialized job, NASA and IBM hope that it can find many patterns in the sun’s physics, much as general-purpose large language models like ChatGPT can take on many different tasks. They believe Surya could even enable new understandings about how other celestial bodies work. 

“Understanding the sun is a proxy for understanding many other stars,” Bernabe-Moreno says. “We look at the sun as a laboratory.”