Deep learning techniques for imaging air Cherenkov telescopes

Abstract

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-the-Standard-Model (BSM) 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 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 machine learning 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 multicategory classification using convolutional neural networks, and we also pioneer the use of autoencoders for anomaly detection at VHE gamma-ray experiments. The latter technique has been studied previously in the context of collider physics, to tag anomalous BSM candidates in a model-independent way. In this study, for the first time, we demonstrate the efficacy of these techniques in the domain of VHE gamma-ray experiments. As a case study, we apply our techniques to the High Energy Stereoscopic System 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—or can even be suitably adapted to CCR experiments.