Speaker
Description
Muon measurements in extensive air showers provide a powerful probe of hadronic interactions at energies beyond the reach of current accelerators. However, their muon content remains one of the key unresolved challenges in ultra-high-energy cosmic ray physics, as current observations show significant discrepancies with model predictions, impacting the interpretation of particle interactions and cosmic-ray composition at the highest energies. The Pierre Auger Observatory, the largest cosmic-ray observatory designed to investigate ultra-high-energy (UHE, E ≳ 10$^{17}$ eV) cosmic rays, has delivered extensive measurements of the muonic component of air showers. These data have enabled detailed studies of the shower development over a wide energy range and revealed systematic differences with respect to model expectations, with important implications for composition studies. In this work, we present an overview of recent results on the muonic component obtained with the Pierre Auger Observatory. Its hybrid detection capabilities provide enhanced sensitivity to air-shower development and to the measurement of the muon content over a broad energy range. The results highlight persistent discrepancies between measurements and predictions, pointing to limitations in current hadronic interaction models. In particular, inconsistencies are observed in the mass interpretation of the muon signal across different observables and shower geometries, and the predicted muon component needs to be increased by about 30–60% to match the data. At the same time, the measured fluctuations of the muon number are consistent with expectations based on composition, suggesting that these discrepancies arise from cumulative effects along the hadronic cascade rather than from the first interaction alone. These results pave the way for the analysis of new data from the upgraded Pierre Auger Observatory, AugerPrime, which will provide enhanced sensitivity to the muonic component and enable more stringent tests of hadronic interaction models.