Presentation #111.03 in the session Solar Flare Prediction.
The solar X-ray irradiance is significantly heightened during the course of a solar flare, which can cause radio blackouts due to ionization of the atoms in the ionosphere. As the duration of a solar flare is not related to the size of that flare, it is not directly clear how long those blackouts can persist. Using a random forest regression model trained on data taken from X-ray light curves, we have developed a direct forecasting method that predicts how long the event will remain above background levels. We test this on a large collection of flares observed with GOES-15, and show that it generally outperforms simple linear regression, giving a median error of less than 2 min for the approximate end time of a flare. This random forest model is computationally light enough to be performed in real time, allowing for the prediction to be made during the course of a flare.