Article6 Min ReadNASA’s COFFIES Uses AI to Predict Storm-Causing Active Regions on Sun
Ente: NASA - Procurement
Paese: US
Descrizione
6 min read NASA’s COFFIES Uses AI to Predict Storm-Causing Active Regions on Sun Desiree Apodaca NASA's Heliophysics Missions Communications Lead Aug 14, 2026 Article EDITOR’S NOTE: This story w as revised Aug. 27, 2026, to clarify the interrelationship between the COFFIES DRIVE Science Center and SWAO. As humanity looks to the Moon and stars for future exploration, predicting space weather — conditions in space primarily driven by the Sun — is more important than ever. Now, a team of astrophysicists and data scientists with NASA’s COFFIES (Consequence Of Fields and Flows in the Interior and Exterior of the Sun) has developed a novel machine-learning model capable of predicting the emergence of active regions on the Sun up to 12 hours before they appear. The Sun is constantly churning. Intense concentrations of localized magnetic fields can suddenly break through the solar surface, forming sunspots. Space weather forecasters then collectively number and track sunspots since they are visible manifestations of active regions, which serve as the main engines behind severe space weather events such as solar flares and coronal mass ejections. These eruptions send waves of high-energy radiation and charged particles across space, creating storms that can threaten astronauts, disable satellites, and disrupt radio communications on Earth. NASA's Solar Dynamics Observatory captured this image of a solar flare — seen as the bright flash in the upper right — on June 30, 2026. The image shows a subset of extreme ultraviolet light that highlights the extremely hot material in flares and which is colorized in teal. NASA’s Goddard Space Flight Center/SDO By bridging expertise across different scientific institutions, COFFIES, a NASA DRIVE (Diversify, Realize, Integrate, Venture, Educate) Science Center, brought together a team of researchers from New Jersey Institute of Technology (NJIT), Princeton University, and NASA’s Ames Research Center in California’s Silicon Valley. The team turned to advanced artificial intelligence architectures — which dictate how data is processed and used to produce reliable predictions or actions — to capture subtle, time-based pattern changes on the solar surface before an active region took shape. By analyzing data captured by the agency's Solar Dynamics Observatory and using NASA Ames' supercomputing resources , this new approach, published in the Journal of Geophysical Research: Machine Learning and Computation, looks at fluctuations in acoustic waves caused by sunspot regions when the regions form beneath the solar surface and begin the journey upward to emerge on the surface. “We cannot directly see the magnetic structure while it is still rising through the solar interior. Instead, we must look for indirect effects — very small changes in the magnetic field and in the pattern of acoustic waves continually traveling through the Sun,” said Alexander Kosovichev, a COFFIES co-investigator at NJIT. “The developed technique identifies precursors associated with an emerging active region in slight changes of the Sun’s acoustic power — more like a slight change in rhythm within a very noisy orchestra.” To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video This video is an example of what scientists use when analyzing the solar surface. This particular time frame tracks the magnetic field on the Sun’s surface during the emergence of active region AR11158 in February 2011. The blue square grid highlights a target area on the Sun. The squares on the right side translates the data from the target grid area to show opposing magnetic polarities, indicated by the warm and cool-colored tones. The first column of blocks shows targeted areas at original resolution, the middle column displays data as 2D maps, and the right column plots changes in magnetic polarity over time as 1D curves. By watching these blocks, scientists can see signs of active region emergence, such as drops in acoustic waves and rises in magnetic fields. NASA’s COFFIES DRIVE Science Center/Irina Kitiashvili and Spiridon Kasapis To develop current operational forecasts, the National Oceanic and Atmospheric Administration’s Space Weather Prediction Center and the United States Air Force monitor active regions that are already visible on the Sun to analyze the regions’ characteristics and estimate the probability of solar flares. The COFFIES team aims to revolutionize this process. The AI model the team developed uses a specialized early detection system to handle very long sequences of data — called sliding-window transformer architecture — to use observations to find tiny reductions in the Sun’s acoustic activity and magnetic field, signals that scientists struggled to capture until now. These reductions form patterns that the AI model uses to predict active regions several hours before they become visible on the solar surface. Instead of looking at all activity on the solar su
Settori: procurement
Vai al bando originale
Registrati gratis su Bandolo per trovare bandi compatibili con la tua azienda.