Glombitza J, Schneider M, Leitl F, Funk S, van Eldik C (2025)
Publication Type: Journal article
Publication year: 2025
Book Volume: 2025
Article Number: 066
Journal Issue: 2
DOI: 10.1088/1475-7516/2025/02/066
With their wide field of view and high duty cycle, water-Cherenkov-based observatories are integral to studying the very high-energy gamma-ray sky. For gamma-ray observations, precise event reconstruction and highly effective background rejection are crucial and have been continuously improving in recent years. In this work, we investigate the application of graph neural networks (GNNs) to background rejection and energy reconstruction and benchmark their performance against state-of-the-art methods. In our simulation study, we find that GNNs outperform hand-designed classification algorithms and observables in background rejection and find an improved energy resolution compared to template-based methods.
APA:
Glombitza, J., Schneider, M., Leitl, F., Funk, S., & van Eldik, C. (2025). Application of graph networks to a wide-field water-Cherenkov-based Gamma-ray Observatory. Journal of Cosmology and Astroparticle Physics, 2025(2). https://doi.org/10.1088/1475-7516/2025/02/066
MLA:
Glombitza, Jonas, et al. "Application of graph networks to a wide-field water-Cherenkov-based Gamma-ray Observatory." Journal of Cosmology and Astroparticle Physics 2025.2 (2025).
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