A multivariate study of mass composition for simulated showers at the Auger South Observatory

TL;DR

This study uses multivariate analysis techniques on simulated air shower data from the Auger South Observatory to develop new methods for determining cosmic ray primary composition.

Contribution

It introduces a multivariate analysis approach, including PCA and neural networks, for primary composition diagnostics in cosmic ray air showers.

Findings

Effective discrimination of primary particles achieved

New diagnostic methods outperform traditional techniques

Comprehensive simulation data supports robust analysis

Abstract

The output parameters from the ground array of the Auger South observatory, were simulated for the typical instrumental and environmental conditions at its Malarg\"ue site using the code sample-sim. Extensive air showers started by photons, protons and iron nuclei at the top of the atmosphere were used as triggers. The study utilized the air shower simulation code Aires with both QGSJet and Sibyll hadronic interaction models. A total of 1850 showers were used to produce more than 35,000 different ground events. We report here on the results of a multivariate analysis approach, including principal component analysis and neural networks, to the development of new primary composition diagnostics.