  This article presents novel applications of unsupervised machine learning
methods to the problem of event separation in an active target detector, the
Active-Target Time Projection Chamber (AT-TPC). The overarching goal is to
group similar events in the early stages of the data analysis, thereby
improving efficiency by limiting the computationally expensive processing of
unnecessary events. The application of unsupervised clustering algorithms to
the analysis of two-dimensional projections of particle tracks from a resonant
proton scattering experiment on $^{46}$Ar is introduced. We explore the
performance of autoencoder neural networks and a pre-trained VGG16
convolutional neural network. We study clustering performance on both data from
a simulated $^{46}$Ar experiment, and real events from the AT-TPC detector. We
find that a $k$-means algorithm applied to simulated data in the VGG16 latent
space forms almost perfect clusters. Additionally, the VGG16+$k$-means approach
finds high purity clusters of proton events for real experimental data. We also
explore the application of clustering the latent space of autoencoder neural
networks for event separation. While these networks show strong performance,
they suffer from high variability in their results.
