The Sun’s magnetic field controls its flare activity and, consequently, magnetic storms on Earth. However, it has a very complex structure. Recently, scientists have used artificial intelligence to map it.
Features of measuring the Sun’s magnetic fields
Researchers at the University of Hawaii’s Institute for Astronomy (IfA) are helping to change the way scientists study the Sun. A team led by the University of Hawaii has developed a new artificial intelligence (AI) tool that can map the Sun’s magnetic field in three dimensions with unprecedented accuracy, supporting research related to the Daniel K. Inouye Solar Telescope built and managed by the NSF National Solar Observatory (NSO) on Haleakalā.
“The Sun is the most powerful source of space weather that can affect everyday life on Earth, especially now that we rely so heavily on technology,” said Kai Yang, a postdoctoral researcher at IfA who led the work. “The Sun’s magnetic field causes explosive phenomena such as solar flares and coronal mass ejections. This new technique helps us understand what causes these phenomena and improves space weather forecasts, allowing us to warn of dangers earlier in order to protect the systems we use every day.”
The Sun’s magnetic field controls eruptions that can disrupt satellites, power grids, and communications on Earth. However, this field is difficult to measure, which complicates the creation of accurate maps. Instruments can show how the field tilts, but not whether it is directed toward or away from us, like when you look at a rope from the side and don’t know which end is closer.
Another problem is height. When scientists look at the Sun, they see several layers at once, making it difficult to determine how high each magnetic structure is. Sunspots complicate this task because their strong magnetic fields bend the surface downward, creating a depression.
Discovery based on artificial intelligence and a 3D map of the Sun’s magnetic field
IfA researchers, in collaboration with the National Solar Observatory and the NSF National Center for Atmospheric Research High Altitude Observatory, have created a new machine learning system that combines real data with fundamental laws of physics. Their Haleakalā Disambiguation Decoder algorithm is based on a simple rule: magnetic fields form loops and do not begin or end. Based on this, artificial intelligence can determine the true direction of the field and estimate the correct height of each layer.
This method works well on detailed computer models of the Sun, including quiet regions, bright active regions, and sunspots. Its accuracy is particularly useful for understanding high-resolution images obtained with the Daniel K. Inovie Solar Telescope.
Thanks to this new machine learning tool, the Daniel K. Inouye Solar Telescope can help scientists create a more accurate 3D map of the Sun’s magnetic field. It also reveals related features, such as vector electric currents in the solar atmosphere, which were previously very difficult to measure. Together, this gives us a clearer picture of what causes powerful solar flares.
Thanks to these achievements, researchers can see the Sun’s magnetic landscape more accurately and improve predictions of solar activity, which affects life on Earth.
According to phys.org
