  Imaging Air Cherenkov Telescopes (IACTs) are essential to ground-based
observations of gamma rays in the GeV to TeV regime. One particular challenge
of ground-based gamma-ray astronomy is an effective rejection of the hadronic
background. We propose a new deep-learning-based algorithm for classifying
images measured using single or multiple Imaging Air Cherenkov Telescopes. We
interpret the detected images as a collection of triggered sensors that can be
represented by graphs and analyzed by graph convolutional networks. For images
cleaned of the light from the night sky, this allows for an efficient algorithm
design that bypasses the challenge of sparse images in deep learning approaches
based on computer vision techniques such as convolutional neural networks. We
investigate different graph network architectures and find a promising
performance with improvements to previous machine-learning and
deep-learning-based methods.
