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- A nd will likely remain- A predominantly probabilistic challenge.
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|Titolo:||Feature Ranking of Active Region Source Properties in Solar Flare Forecasting and the Uncompromised Stochasticity of Flare Occurrence|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||01.01 - Articolo su rivista|