Identification of the primary mass of inclined cosmic ray showers from depth of maximum and number of muon parameters

In the present work we carry out a study of the high energy cosmic rays mass identification capabilities of a hybrid detector employing both fluorescence telescopes and particle detectors at ground using simulated data. It involves the analysis of extensive showers with zenith angles above 60° making use of the joint distribution of the depth of maximum and muon size at ground level as mass discriminating parameters. The correlation and sensitivity to the primary mass are investigated. Two different techniques – clustering algorithms and neural networks – are adopted to classify the mass identity on an event-by-event basis. Typical results for the achieved performance of identification are reported and discussed. The analysis can be extended in a very straightforward way to vertical showers or can be complemented with additional discriminating observables coming from different types of detectors.