The sensitivity of Imaging Atmospheric Cherenkov Telescopes (IACTs) used to carry out VeryHigh-Energy (VHE; E>100 GeV) gamma-ray astrophysics strongly depends on the ability to
reject cosmic-ray (hadron) background events in favor of gamma rays. Since cosmic-ray initiated
Extensive Air Showers (EAS) dominate those initiated by gamma rays by several orders of
magnitude, the ability to accurately distinguish between gamma-ray or hadron-initiated showers
is a long-standing problem within the IACT community. Motivated by the physical differences in
gamma-ray and hadron EAS, some existing work in this field has focused on implementing deep
learning techniques to solve this classification problem. The predominant deep learning approach
has been to train models in a supervised fashion on simulated EAS data, which has encountered
issues when transitioning from simulation training data to real EAS data.
We take a novel deep learning approach focused on unsupervised learning with real data from
the VERITAS IACT to learn spatial relations and temporal correlations of the EAS. We implemented a Two-Dimensional Convolutional Long-Short Term Memory Autoencoder network
(2DConvLSTM-AE network) given its strong performance in both spatial- and time-relationed
data. The autoencoder architecture enables us to encode a latent space mapping of the generalized
features for a downstream classification. We find that while the 2DConvLSTM-AE is capable of
producing faithful reconstruction of EAS, the ability to differentiate EAS by their origin particle
has not yet been demonstrated but provides a promising avenue for future research.