Abstract
We use machine learning methods to predict whether an active region (AR)
which produces flares will lead to a solar energetic particle (SEP)
event using Space-Weather Michelson Doppler Imager (MDI) Active Region
Patches (SMARPs). This new data product is derived from maps of the
solar surface magnetic field taken by the Michelson Doppler Imager (MDI)
aboard the Solar and Heliospheric Observatory (SOHO). We survey the
SMARP active regions associated with flares that appear on the solar
disk between June 5, 1996 and August 14, 2010, label those that produced
SEPs as positive and the rest as negative. The AR SMARP features that
correspond to each flare are used to train two different types of
machine learning methods, the support vector machines (SVMs) and the
regression models. The results show that the SMARP data can predict
whether a flare will lead to an SEP with accuracy (ACC) {less than or
equal to}0.72{plus minus}0.12 while allowing for a competitive
leading time of 55.3{plus minus}28.6 minutes for forecasting the SEP
events.