Application of Graph Networks to a wide-field Water-Cherenkov-based Gamma-Ray Observatory
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917962665951232 |
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| author | Glombitza, Jonas Schneider, Martin Leitl, Franziska Funk, Stefan van Eldik, Christopher |
| author_facet | Glombitza, Jonas Schneider, Martin Leitl, Franziska Funk, Stefan van Eldik, Christopher |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16565 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Graph Networks to a wide-field Water-Cherenkov-based Gamma-Ray Observatory Glombitza, Jonas Schneider, Martin Leitl, Franziska Funk, Stefan van Eldik, Christopher Instrumentation and Methods for Astrophysics 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. |
| title | Application of Graph Networks to a wide-field Water-Cherenkov-based Gamma-Ray Observatory |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2411.16565 |