  The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to
measure nuclear reaction cross sections relevant for nuclear astrophysics,
fusion studies, and other applications. From the MUSIC data produced in one
experiment scientists carefully extract an order of $10^3$ events of interest
from about $10^{9}$ total events, where each event can be represented by an
18-dimensional vector. However, the standard data classification process is
based on expert-driven, manually intensive data analysis techniques that
require several months to identify patterns and classify the relevant events
from the collected data. To address this issue, we present a method for the
classification of events originating from specific $\alpha$-induced reactions
by combining statistical and machine learning methods that require
significantly less input from the domain scientist, relative to the standard
technique.
  We applied the new method to two experimental data sets and compared our
results with those obtained using traditional methods. With few exceptions, the
number of events classified by our method agrees within $\pm20\%$ with the
results obtained using traditional methods. With the present method, which is
the first of its kind for the MUSIC data, we have established the foundation
for the automated extraction of physical events of interest from experiments
using the MUSIC detector.
