Identification of the primary mass of inclined cosmic ray showers from depth of maximum and number of muons parameters

TL;DR

This study evaluates the ability of a hybrid detector system to identify the primary mass of inclined cosmic ray showers using depth of maximum and muon count data, employing clustering and neural network techniques.

Contribution

It introduces a combined analysis of fluorescence and ground detectors for inclined showers, applying machine learning methods for improved mass discrimination.

Findings

High accuracy in primary mass identification for inclined showers

Effective use of joint depth of maximum and muon data as discriminators

Method adaptable to vertical showers and additional observables

Abstract

In the present work we carry out a study of the high energy cosmic rays mass identification capabilities of a hybrid detector employing both fluorescence telescopes and particle detectors at ground using simulated data. It involves the analysis of extensive showers with zenith angles above 60 degrees making use of the joint distribution of the depth of maximum and muon size at ground level as mass discriminating parameters. The correlation and sensitivity to the primary mass are investigated. Two different techniques - clustering algorithms and neural networks - are adopted to classify the mass identity on an event-by-event basis. Typical results for the achieved performance of identification are reported and discussed. The analysis can be extended in a very straightforward way to vertical showers or can be complemented with additional discriminating observables coming from different types of detectors.