A new surface parameter for composition studies at high energies

TL;DR

This paper introduces a new surface parameter for cosmic ray composition analysis at high energies, utilizing surface detector data and array geometry to improve discrimination between different primary particles.

Contribution

It presents a novel family of composition estimators based solely on surface data, with analytical assessment of their reliability, stability, and optimization potential.

Findings

The estimators effectively discriminate between proton and Iron primaries.

The analysis shows the influence of lateral distribution slopes on discrimination power.

The parameter remains stable despite potential muon component underestimations in simulations.

Abstract

A new family of parameters intended for composition studies is presented. They make exclusive use of surface data combining the information from the total signal at each triggered detector and the array geometry. We perform an analytical study of these composition estimators in order to assess their reliability, stability and possible optimization. The influence of the different slopes of the proton and Iron lateral distribution function on the discrimination power of the estimators is also studied. Additionally, the stability of the parameter in face of a possible underestimation of the size of the muon component by the shower simulation codes, as it is suggested by experimental evidence, is also studied.