  We evaluate machine learning methods for event classification in the
Active-Target Time Projection Chamber detector at the National Superconducting
Cyclotron Laboratory (NSCL) at Michigan State University. An automated method
to single out the desired reaction product would result in more accurate
physics results as well as a faster analysis process. Binary and multi-class
classification methods were tested on data produced by the $^{46}$Ar(p,p)
experiment run at the NSCL in September 2015. We found a Convolutional Neural
Network to be the most successful classifier of proton scattering events for
transfer learning. Results from this investigation and recommendations for
event classification in future experiments are presented.
