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[2602.23321] Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays
Summary
This paper presents a novel method using deep ensemble graph neural networks to accurately reconstruct the direction and energy of cosmic rays detected by autonomous radio arrays.
Why It Matters
Understanding cosmic rays is crucial for astrophysics and can provide insights into high-energy processes in the universe. This research enhances detection methods, potentially improving data accuracy in cosmic ray studies, which could influence future experiments and technologies in astrophysics.
Key Takeaways
- Introduces a graph neural network approach for cosmic ray detection.
- Achieves high angular resolution (0.092°) and energy reconstruction accuracy (16.4%).
- Incorporates physical knowledge into the GNN architecture to improve predictions.
- Utilizes uncertainty estimation to enhance reliability of outputs.
- Explores model robustness under real-world variations.
Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2602.23321 (astro-ph) [Submitted on 26 Feb 2026] Title:Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays Authors:Arsène Ferrière, Aurélien Benoit-Lévy, Olivier Martineau-Huynh, Matías Tueros View a PDF of the paper titled Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays, by Ars\`ene Ferri\`ere and Aur\'elien Benoit-L\'evy and Olivier Martineau-Huynh and Mat\'ias Tueros View PDF HTML (experimental) Abstract:Using advanced machine learning techniques, we developed a method for reconstructing precisely the arrival direction and energy of ultra-high-energy cosmic rays from the voltage traces they induced on ground-based radio detector arrays. In our approach, triggered antennas are represented as a graph structure, which serves as input for a graph neural network (GNN). By incorporating physical knowledge into both the GNN architecture and the input data, we improve the precision and reduce the required size of the training set with respect to a fully data-driven approach. This method achieves an angular resolution of 0.092° and an electromagnetic energy reconstruction resolution of 16.4% on simulated data with realistic noise conditions. We also employ uncertainty estimation methods to enhance the reliability of our predictions, quantifying the confidence o...
