A CNN predicts ln A from longitudinal shower profiles with bias under 0.4, resolution 1-1.5, and proton-iron merit factor 2.19, outperforming simpler ML models on shape parameters and remaining robust to hadronic model changes.
Mayotte, Measurement and Interpretation of UHECR Mass Composition at the Pierre Auger Observatory, in: 39th International Cosmic Ray Conference
2 Pith papers cite this work. Polarity classification is still indexing.
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The work describes a system-level radio detector design that integrates sky-noise modeling and EMC mitigation to achieve operation close to the galactic-noise limit.
citing papers explorer
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Prospects for Deep-Learning-Based Mass Reconstruction of Ultra-High-Energy Cosmic Rays using Simulated Air-Shower Profiles
A CNN predicts ln A from longitudinal shower profiles with bias under 0.4, resolution 1-1.5, and proton-iron merit factor 2.19, outperforming simpler ML models on shape parameters and remaining robust to hadronic model changes.
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Noise Suppression and Radio Frequency Interference Rejection for Self-Triggered Radio Detectors of Extensive Air Showers
The work describes a system-level radio detector design that integrates sky-noise modeling and EMC mitigation to achieve operation close to the galactic-noise limit.