Speaker
Description
Discriminating gamma-ray–induced air showers from the overwhelming hadronic background remains a main challenge in ground-based gamma-ray astronomy. While traditional separation methods rely on muon counting—requiring complex, costly dedicated detector systems—recent studies demonstrate that ground-level observables provide competitive, cost-effective alternatives. Moreover, the variables Pαtail, quantifying signal outliers, and LCm, characterizing azimuthal asymmetries in the shower footprint, have emerged as promising gamma–hadron discrimination parameters.
In this work, we investigate the robustness of these observables against hadronic interaction modeling, a key source of systematic uncertainty in air-shower simulations. We performed CORSIKA simulations of proton-induced air showers for water-Cherenkov detector arrays, initially focusing on vertical showers at 100 TeV and subsequently extending the analysis to a broader energy range and multiple combinations of high- and low-energy interaction models.
A systematic analysis was conducted to test the variables' resilience to different models, or, alternatively, identify significant deviations. Both Pαtail and LCm exhibit strong correlations with muonic content across the explored energies, though low-energy model variations reveal significant differences. This contribution also includes studies of inclined showers and various primary particles to assess the general applicability of these observables and their potential to constrain hadronic interaction models using data from next-generation gamma-ray observatories such as SWGO.
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