Application of Long Short-Term Memory Deep Learning Networks on Very-High-Energy Gamma-Ray Classification with VERITAS

The sensitivity of Imaging Atmospheric Cherenkov Telescopes (IACTs) used to carry out Very-High-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.