Prediction of Solar Eruptions Using Filament Metadata

Abstract

Abstract We perform a statistical analysis of erupting and non-erupting solar filaments to determine the properties related to the eruption potential. In order to perform this study, we correlate filament eruptions documented in the Heliophysics Event Knowledgebase (HEK) with HEK filaments that have been grouped together using a spatiotemporal tracking algorithm. The HEK provides metadata about each filament instance, including values for length, area, tilt, and chirality. We add additional metadata properties such as the distance from the nearest active region and the magnetic field decay index. We compare trends in the metadata from erupting and non-erupting filament tracks to discover which properties present signs of an eruption. We find that a change in filament length over time is the most important factor in discriminating between erupting and non-erupting filament tracks, with erupting tracks being more likely to have decreasing length. We attempt to find an ensemble of predictive filament metadata using a Random Forest Classifier approach, but find the probability of correctly predicting an eruption with the current metadata is only slightly better than chance.