Machine learning is giving scientists an unusual new way to listen to the Sun, detecting subtle changes in its acoustic vibrations before emerging magnetic regions become visible and begin developing into sunspots. The technique, developed by researchers working with NASA’s COFFIES solar-science programme, uses patterns hidden in the Sun’s oscillations to forecast the emergence of large active regions hours before conventional observations can clearly reveal them. The breakthrough does not mean a computer is literally hearing the Sun like a human would; rather, it is learning to recognize changes in solar vibrations that can signal magnetic activity developing beneath the surface. (New Jersey Institute of Technology)
Machine: Scientists Teach Computers to Detect Solar Trouble Before Humans Can See It
The research centres on machine-learning models known as Long Short-Term Memory networks, or LSTMs, which are designed to identify patterns in time-series data. Researchers trained the system using observations from the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager, examining acoustic oscillations, magnetic flux and changes in the Sun’s visible-light intensity. The study used observations of 40 emerging active regions for training and five additional events for testing. The results showed that the system could identify the signatures associated with large active regions several hours before their initial visible signal appeared. (New Jersey Institute of Technology)
That matters because active regions are closely associated with sunspots and can become sources of solar flares and other forms of space weather. The researchers reported that the machine-learning analysis could recognize the emergence process while the magnetic signal was still only a small fraction of its eventual strength. In some earlier testing, the approach detected emerging activity between about five and 48 hours before the time reported by NOAA, although the more recent peer-reviewed study emphasizes a several-hour predictive window and makes clear that this is a developing forecasting technique rather than a finished operational warning system. (NASA Technical Reports Server)
COFFIES Turns Solar Vibrations Into Forecasts
The work fits into the broader mission of NASA’s COFFIES — the Consequences Of Fields and Flows in the Interior and Exterior of the Sun — a NASA-funded DRIVE Science Center based at Stanford University. COFFIES seeks to understand how flows and magnetic fields inside the Sun interact to produce the solar activity cycle. Its researchers use observations, physical modelling and increasingly sophisticated computational techniques to investigate processes that cannot simply be watched directly beneath the solar surface. (Coffies)
The timing is particularly significant because COFFIES researchers are continuing to develop machine-learning approaches for solar forecasting. The centre’s current research listings include work on machine-learning predictions of emerging active regions, while newer 2026 research efforts include datasets and transformer-based forecasting methods intended to improve prediction capabilities. In other words, the computer has not replaced the solar physicist; it has simply been given a very complicated job description: study millions of pieces of solar data and notice something suspicious before the Sun makes the announcement itself. (Coffies)
The potential payoff extends well beyond scientific curiosity. Better advance warning of solar activity could eventually help researchers improve forecasts of space-weather disturbances that can affect satellites, communications, navigation systems and other technologies. For now, however, the findings should be treated as an important research advance rather than a promise that every future sunspot will receive an accurate appointment notification. As scientists refine the models and test them on larger datasets, OGM News will continue watching this remarkable attempt to make the Sun’s hidden signals speak a little more clearly.
[…] […]