Researchers at the University of Hawaiʻi Institute for Astronomy (IfA) are helping reshape how scientists study the Sun. The UH-led team has developed a new artificial intelligence (AI) tool that can map the Sun's magnetic field in three dimensions with unprecedented accuracy, supporting research tied to the U.S. National Science Foundation (NSF) Daniel K. Inouye Solar Telescope built and managed by the NSF National Solar Observatory (NSO) on Haleakalā. The team's findings were published in the Astrophysical Journal .
"The Sun is the strongest space weather source that can affect everyday life here on Earth, especially now that we rely so much on technology," said Kai Yang, an IfA postdoctoral researcher who led the work. "The Sun's magnetic field drives explosive events like solar flares and coronal mass ejections. This new technique helps us understand what triggers these events and strengthens space weather forecasts, giving us earlier warnings to protect the systems we use every day."
The Sun's magnetic field controls eruptions that can disrupt satellites, power systems and communications on Earth. However, the field is tough to measure, making it difficult to create accurate maps. Instruments can show the way the field tilts, but not whether it points toward us or away from us, like looking at a rope from the side and not knowing which end is closer. Another problem is height. When scientists look at the Sun, they see several layers at the same time, so it's difficult to tell how high each magnetic structure actually is. Sunspots make this even trickier because their strong magnetic fields bend the surface downward, creating a dip.
AI-powered insights
IfA researchers partnered with the National Solar Observatory and the High Altitude Observatory of the NSF National Center for Atmospheric Research to build a new machine-learning system that blends real data with the basic laws of physics. Their algorithm, the Haleakalā Disambiguation Decoder, relies on a simple rule: magnetic fields form loops and don't start or end. From there, the AI can figure out the true direction of the field and estimate the correct height of each layer.
The method has worked well on detailed computer models of the Sun, including calm areas, bright active regions and sunspots. Its accuracy is especially helpful for making sense of the high-resolution images from the Daniel K. Inouye Solar Telescope.
"With this new machine-learning tool, the Daniel K. Inouye Solar Telescope can help scientists build a more accurate 3D map of the Sun's magnetic field," said Yang. "It also reveals related features, like vector electric currents in the solar atmosphere that were previously very hard to measure. Together, this gives us a clearer picture of what drives powerful solar eruptions."
Clearer Sun insights
With these advances, researchers can see the Sun's magnetic landscape more accurately and improve predictions of the solar activity that impacts life on Earth.
The post UH scientists help unlock the Sun's magnetic secrets with AI first appeared on University of Hawaiʻi System News .
