Modeling high energy cosmic rays mass composition data via mixtures of multivariate skew-t distributions

TL;DR

This paper introduces a statistical modeling approach using mixtures of multivariate skew-t distributions to analyze high energy cosmic ray composition data, enabling improved event classification and accommodating model discrepancies.

Contribution

It presents a novel application of skew-t mixture models for cosmic ray data analysis, incorporating constraints from hadronic models and addressing discrepancies in observational data.

Findings

Validated with simulated data for different nuclei and models

Effective in event-by-event classification of cosmic ray composition

Handles discrepancies between measurements and theoretical models

Abstract

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 Xmax and number of muons N{\mu} observables. Further, we consider mixtures of multivariate skew-t distributions for cosmic ray mass composition determination and event-by-event classification. With respect to other approaches in the field, it 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 nuclear abundances assumptions. 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.