Simple and less hadronic-model-dependent method for cosmic-ray composition determination

Abstract

The origin and nature of ultrahigh-energy cosmic rays remains a mystery despite the great progress that has been made in recent years mainly due to the observations performed by the Pierre Auger Observatory and Telescope Array. In particular, the composition information of the cosmic rays as a function of the energy appears to be crucial for the understanding of their origin. The best observables for primary mass composition are the muon content of extensive air showers and the atmospheric depth of the shower maximum development. In this work we present a novel method which allows us to use any type of classification method to perform mass composition analyses based on the number of muons. The method also provides the information of the mean values of this observable corresponding to different primary particles. The analyses are based on numerical simulations of the showers but including experimental uncertainties in the reconstruction of the energy and in the measurement of the number of muons. We also study the impact of the use of different high-energy hadronic interaction models in the composition analyses performed. The biases introduced by the use of different high-energy hadronic interaction models to analyze the data obtained with the presented method are considerably reduced compared with those obtained with classic statistical methods that use a single physical observable.