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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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.