SOLARNET Conference: The Many Scales of the Magnetic Sun

Solar flares originate from active regions (ARs) hosting complex and strong bipolar magnetic fluxes. Forecasting the probability of an AR to flare and defining reliable precursors of intense flares, i.e., X- or M-class flares, are extremely challenging tasks in the space weather field.

In this work, we focus on two metrics as flare precursors, the unsigned flux R and the novel topological parameter D, representing the complexity of a solar active region. Both metrics are based on the automatic recognition of magnetic polarity inversion lines (PILs) in identified SDO/HMI ARs. We use both a heuristic approach and a supervised machine-learning method to validate the effectiveness of these metrics to predict the occurrence of X- or M-class flares in a given solar AR during the following 24 hr period. Moreover, we revise the statistics of CME-flare relationship on Solar Cycle 24 using the GOES database and a CME database recently released, developed using the Drag-Based Model (DBM) to assess the quality of such a database. In particular, we exploit the R and D parameters to classify flaring regions in different classes, studying the CME-flare relationship for those classes over Solar Cycle 24.