Solar flares release large amounts of energy as intense radiation, but they can also eject billions of tons of solar plasma into space and towards Earth.
Charged particles reaching Earth’s magnetic field can cause damage to satellite electronics, disrupt air traffic communications and navigation, and in the worst case can induce currents in our power grid leading to large scale power outages.

Since 1974, X-ray flares are automatically detected and classified by the National Oceanic and Atmospheric Administration’s (NOAA) GOES satellites in the 1–8 Å wavelength range. Based on peak soft X-ray flux in this range, flares are logarithmically classified as A, B, C, M, and X, from weaker to stronger.

Some of the major challenges the flare-forecasting researchers are up against are rooted in the rarity of the events of interest, the high dimensionality of observational data, and the dynamic behavior of the Sun.

We still do not completely understand all the physical mechanisms driving solar flares. In particular, we do not know if and when an active region will produce a flare or how strong it may be. This is where machine learning & deep learning comes into play.

Using various state-of-the-art ML architectures, my goal is to build a prediction framework as precise as possible. Let’s see how well this goes!