In this age of neural net “AI”, even the most skeptical of Butlerians have to agree that these machine learning models can be very, very good at pattern recognition if nothing else. NASA is on the same page, and to take advantage of that pattern recognition, they’ve built a machine learning module called COFFIES, which stands for Consequence Of Fields and Flows in the Interior and Exterior of the Sun, because at NASA everything is an acronym, or at least a backronym. Like most such names, this one is at least vaguely descriptive: the model is trying to predict what’s going on in the material flows and magnetic fields deep within our local star, and using those inferences is able to predict active regions– that’s sunspots to us chickens — up to 12 hours before they visibly form.
The measurements used here are indirect — we can’t chart the magnetohydrodynamic snarls deep inside a star directly, but we can measure the magnetic field and acoustic waves at and above the surface. You could say the model “hears” sunspots forming. Like all such models, it’s a bit of a black box, but heliophysicists may be able to use its predictions to help them understand their own, organic understanding of the big ball of plasma to which we all owe our lives.
This model is thus one of the better things to come out of the “AI” revolution– nobody is going to give over their thinking to the machine and stop trying to understand the Sun, and the few hours of extra warning COFFIES might potentially provide before the next Carrington Event-class geomagnetic storm could prove invaluable, especially since a flare-blocking Storm wall remains a theoretical exercise at best. If you are interested in the sun, COFFIES has an interesting YouTube channel and, as you can see in a recent video, they are doing a lot with AI.