Feature Ranking of Active Region Source Properties in Solar Flare Forecasting and the Uncompromised Stochasticity of Flare Occurrence

Abstract

Solar flares originate from magnetically active regions (ARs) but not all solar ARs give rise to a flare. Therefore, the challenge of solar flare prediction benefits from an intelligent computational analysis of physics-based properties extracted from AR observables, most commonly line-of-sight or vector magnetograms of the active region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive AR properties, mainly inferred by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory ( SDO /HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine-learning methods that allow feature ranking as a function of predictive capability, we show that (i) an objective training and testing process is paramount for the performance of every supervised machine-learning method; (ii) most properties include overlapping information and are therefore highly redundant for flare prediction; (iii) solar flare prediction is still—and will likely remain—a predominantly probabilistic challenge.