A model-based clustering approach for mass composition analysis of high energy cosmic rays

We consider multivariate skew-t distributions for modeling composition data of high energy cosmic rays. The model has been validated with simulated data for different primary nuclei and hadronic models focusing on the depth of maximum image and number of muons image observables. Further, we consider mixtures of multivariate skew-t distributions in the framework of model-based clustering for the determination of the cosmic ray mass composition and for event-by-event classification. With respect to other approaches in the field, the method is based on analytical calculations and allows to incorporate different sets of constraints provided by the present hadronic models. We present some applications to simulated data sets generated with different assumptions on the nuclear abundances. As it does not fully rely on the hadronic model predictions, the method is particularly suited to the current experimental scenario in which evidences of discrepancies of the measured data with respect to the models have been reported for some shower observables, such as the number of muons at ground level.