Abstract
Solar Energy Particles (SEPs) can be associated with solar flares and
coronal mass ejections (CMEs) and offer energy spectra ranging from few
KeVs to many GeVs. These events can occur without any notable indication
and alter the radiation environment of the inner solar systems, which
can potentially lead to precarious conditions for humans in space,
affect the interior of spacecraft’s sensitive electronics, and trigger
radio blackouts. Identifying the most critical physical parameters of
the Solar Dynamic Observatory (SDO) to detect SEPs can allow for a swift
response against its adverse effects. With the profusion of high-quality
time series data from the SDO, which accounts for the modulating
background of magnetic activity and the inherently dynamic phenomenon of
pre-flares and post-flare phases; antithetical to non-representative
data with the point-in-time measurements employed earlier, selection of
vital parameters for solar flare classification using machine learning
algorithms appears to be a well-fitted problem in this realm. The
primary issue of dealing with multivariate time series data (mvts) is
the large number of physical parameters operating at a rapid frequency,
making the data dimensionality very high and thus causing the learning
process to curb. Moreover, manually selecting vital parameters is a
tedious and costly task on which experts may not always agree on the
results. In response, we examined feature subset selection using
multiple algorithms on both mvts data and the statistical features
derived from mvts segments (vectorized data). We used the SWAN-SF (Space
Weather Analytics for Solar Flares) benchmark dataset collected from May
2010 - September 2018 to conduct our experiments. The comprehensive
study gives a stable scheme to recognize the critical physical
parameters, which boosts the learning process and can be used as a
blueprint to foretell future solar flare episodes.