Prospects for deep-learning-based mass reconstruction of ultra-high-energy cosmic rays using simulated air-shower profiles

作者：Z. Wang, Eric William Mayotte, Sonja Mayotte, Nathan T. Woo, Julia Burton-Heibges, Nicolas San Martin, C. Smith · 发表于：Journal of Cosmology and Astroparticle Physics · 年份：2026 · DOI：10.1088/1475-7516/2026/06/069 · 研究领域：Astrophysics and Cosmic Phenomena、Gamma-ray bursts and supernovae、Dark Matter and Cosmic Phenomena

Abstract Knowledge of the mass composition of ultra-high-energy cosmic rays is crucial to understanding their origins; however, current approaches have limited event-by-event resolution. With fluorescence telescope measurements of the longitudinal shower profile, there are opportunities to improve this situation by applying Machine Learning (ML) to leverage more information beyond X max alone. To our knowledge, we present the first study of a deep-learning neural-network approach to predict a primary's mass (ln A ) directly from the longitudinal energy-deposit profile of simulated extensive air showers. We train and validate our model on simulated showers, generated with CONEX and EPOS-LHC, covering nuclei from A = 1 to 61, sampled uniformly in ln A . After rescaling, our network achieves a maximum bias better than 0.4 in lnA with a resolution between 1.5 for protons and 1 for iron, corresponding to a proton-iron Merit Factor of 2.19 (AUC = 0.976). We benchmark this against simpler ML models trained on profile-shape parameters ( X max , E cal , R , and L ) extracted from the same data. We find that even simple models can substantially exceed published benchmarks for combinations of these observables, demonstrating that ML methods applied even to standard profile-shape parameters can significantly improve available mass sensitivity. The CNN outperforms this strong baseline, and this performance is only mildly degraded when cross-predicting on simulations made with the Sibyll-2...