  Of all the activity observed on the Sun, two of the most energetic events are
flares and Coronal Mass Ejections (CMEs). Usually, solar active regions that
produce large flares will also produce a CME, but this is not always true
(Yashiro et al., 2005). Despite advances in numerical modeling, it is still
unclear which circumstances will produce a CME (Webb & Howard, 2012).
Therefore, it is worthwhile to empirically determine which features distinguish
flares associated with CMEs from flares that are not. At this time, no
extensive study has used physically meaningful features of active regions to
distinguish between these two populations. As such, we attempt to do so by
using features derived from [1] photospheric vector magnetic field data taken
by the Solar Dynamics Observatory's Helioseismic and Magnetic Imager instrument
and [2] X-ray flux data from the Geostationary Operational Environmental
Satellite's X-ray Flux instrument. We build a catalog of active regions that
either produced both a flare and a CME (the positive class) or simply a flare
(the negative class). We then use machine-learning algorithms to [1] determine
which features distinguish these two populations, and [2] forecast whether an
active region that produces an M- or X-class flare will also produce a CME. We
compute the True Skill Statistic, a forecast verification metric, and find that
it is a relatively high value of approximately 0.8 plus or minus 0.2. We
conclude that a combination of six parameters, which are all intensive in
nature, will capture most of the relevant information contained in the
photospheric magnetic field.
