Composition determination of cosmic rays from the muon content of the showers

TL;DR

This paper presents a maximum likelihood method to determine cosmic ray composition based on muon measurements, accounting for detector effects and model uncertainties, with implications for understanding cosmic ray origins.

Contribution

It introduces a novel maximum likelihood approach for cosmic ray composition analysis using muon data, incorporating detector simulations and model variations.

Findings

Differences in muon predictions significantly affect composition results.

The method effectively distinguishes primary cosmic ray types.

Model uncertainties impact composition estimates by several percent.

Abstract

The origin and nature of ultra high energy cosmic rays remains being a mystery. However, great progress has been made in recent years due to the observations performed by the Pierre Auger Observatory and Telescope Array. In particular, it is believed that the composition information of the cosmic rays as a function of the energy can play a fundamental role for the understanding of their origin. The best indicators for primary mass composition are the muon content of extensive air shower and the atmospheric depth of the shower maximum. In this work we consider a maximum likelihood method to perform mass composition analyses based on the number of muons measured by underground muon detectors. The analyses are based on numerical simulations of the showers. The effects introduced by the detectors and the methods used to reconstruct the experimental data are also taken into account through a dedicated simulation that uses as input the information of the simulated showers. In order to illustrate the use of the method, we consider AMIGA (Auger Muons and Infill for the Ground Array), the low energy extension of the Pierre Auger Observatory that directly measures the muonic content of extensive air showers. We also study in detail the impact of the use of different high energy hadronic interaction models in the composition analyses performed. It is found that differences of a few percent between the predicted number of muons have a significant impact on composition determination.