Glombitza J, Joshi V, Bruno B, Funk S (2023)
Publication Type: Journal article
Publication year: 2023
Book Volume: 2023
Article Number: 008
Journal Issue: 11
DOI: 10.1088/1475-7516/2023/11/008
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.
APA:
Glombitza, J., Joshi, V., Bruno, B., & Funk, S. (2023). Application of graph networks to background rejection in Imaging Air Cherenkov Telescopes. Journal of Cosmology and Astroparticle Physics, 2023(11). https://dx.doi.org/10.1088/1475-7516/2023/11/008
MLA:
Glombitza, Jonas, et al. "Application of graph networks to background rejection in Imaging Air Cherenkov Telescopes." Journal of Cosmology and Astroparticle Physics 2023.11 (2023).
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