  Very High Energy (VHE) gamma rays and charged cosmic rays (CCRs) provide an
observational window into the acceleration mechanisms of extreme astrophysical
environments. One of the major challenges at Imaging Air Cherenkov Telescopes
(IACTs) designed to look for VHE gamma rays, is the separation of air showers
initiated by CCRs which form a background to gamma ray searches. Two other less
well studied problems at IACTs are a) the classification of different primary
nuclei among the CCR events and b) identification of anomalous events initiated
by Beyond Standard Model particles that could give rise to shower signatures
which differ from the standard images of either gamma rays or CCR showers. The
problems of categorizing the primary particle that initiates a shower image, or
the problem of tagging anomalous shower events in a model independent way, are
problems that are well suited to a machine learning (ML) approach. Traditional
studies that have explored gamma ray/CCR separation have used a multivariate
analysis based on derived shower properties, which contains significantly
reduced information about the shower. In our work, we address the problems
outlined above by using ML architectures trained on full simulated shower
images, as opposed to training on just a few derived shower properties. We
illustrate the techniques of binary and multi-category classification using
convolutional neural networks, and we also pioneer the use of autoencoders for
anomaly detection at VHE gamma ray experiments. As a case study, we apply our
techniques to the H.E.S.S. experiment. However, the real strength of the
techniques that we broach here in the context of VHE gamma ray observatories,
is that these methods can be applied broadly to any other IACT, such as the
upcoming Cherenkov Telescope Array (CTA), or can even be suitably adapted to
CCR experiments.
