This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Long before dark sunspots appear on the sun's surface, a new active region—where powerful solar eruptions can originate—begins showing subtle signs of its formation. Now, researchers say a new artificial intelligence model can detect those early signals and forecast the emergence of solar active regions nearly nine hours in advance on average.
In a study published in the Journal of Geophysical Research: Machine Learning and Computation, a research team led by New Jersey Institute of Technology (NJIT) reports that an artificial intelligence model called EarlyDetect can identify precursor signals of active region emergence in the sun's acoustic activity and magnetic field. Scientists have struggled to capture such signals until now. NJIT undergraduate researcher Jonas Tirona, the study's corresponding author, developed the approach with NJIT computer scientists and solar physicists, along with collaborators at Princeton University and NASA's Ames Research Center, using observations from NASA's Solar Dynamics Observatory (SDO).
"The most valuable thing this work shows is that we can use machine learning to predict when solar active regions will emerge in advance," said Tirona, an incoming senior computer science major and Albert Dorman Honors College scholar. "That early warning could allow satellite communications companies or power grid companies to prepare and potentially mitigate damage from solar storms." Active regions—magnetically intense areas where sunspots form—begin emerging over several hours, while their full development can take one to several days. As magnetic fields rise toward the sun's surface, they leave faint signatures in acoustic waves that scientists can detect through helioseismology, the study of solar vibrations.
To identify those signatures, the team's EarlyDetect model analyzes hourly acoustic power maps and magnetic field measurements from NASA's Solar Dynamics Observatory. The acoustic maps are derived from sound-wave observations recorded every 45 seconds by the Helioseismic and Magnetic Imager (HMI) aboard NASA's SDO. "The main difficulty is that an active region begins developing beneath the sun's visible surface, where we cannot directly observe the magnetic structure," said Alexander Kosovichev, distinguished professor of physics at NJIT and co-principal investigator of the project.
"Instead, we're looking for very small changes in the magnetic field and in the pattern of acoustic waves continually traveling through the sun. It's more like detecting a slight change in rhythm within a very noisy orchestra." The team's model uses a Transformer architecture—the same type of AI technology behind large language models such as ChatGPT. While those systems learn patterns in text, EarlyDetect learns patterns in solar observations to predict future changes in the sun's activity.
After joining the project last year, Tirona and the team discovered that a filtering technique they had applied to help the AI model identify important patterns in solar data was actually making its forecasts worse. "That surprised us most," Kosovichev said. "We initially expected it to help isolate useful short-timescale patterns.
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