Cosmic ray mass composition at the knee using azimuthal fluctuations of air shower particles detected at ground by the KASCADE experiment

Abstract

The presence of hadronic sub-showers causes azimuthal non-uniformity in the particle distributions on the ground in vertical air showers. The $LCm$ parameter, which quantifies the non-uniformity of the signal recorded in detectors located at a given distance on a ring around the shower axis, has been successfully introduced as a gamma/hadron discriminator at PeV energies \cite{Conceicao:2022lkc}. In this work, we demonstrate that the $LCm$ parameter can effectively serve as a mass composition discriminator in experiments that employ a compact array of detectors, like KASCADE. We reconstruct the $LCm$ parameter distributions in the energy range $\lg (E/\mathrm{eV})=[15.0\text{ - }16.0]$ using measurements from the KASCADE experiment, with intervals of $\lg (E/\mathrm{eV})=0.2$, which are then fitted with MC templates for five primary nuclei species p, He, C, Si, and Fe considering three hadronic interaction models: QGSjet-II-04, EPOS-LHC and SIBYLL 2.3d. We find that the $LCm$ parameter exhibits minimal dependence on the specific hadronic interaction model considered. The reconstructed fractions of individual species demonstrate a linear decrease in the abundance of protons and He nuclei with increasing energy, while the heavier components become prevalent above the \textit{knee} as predicted by all three hadronic interaction models. Our findings indicate that the abundance of particle types as a function of energy aligns with different astrophysical models that link the \textit{knee} to the acceleration and propagation of cosmic rays within the Galaxy. Furthermore, they also demonstrate excellent agreement with three more recent data-driven astrophysical models. These findings suggest that the $LCm$ parameter could be a valuable tool for forthcoming measurements of the LHAASO experiment to enhance our knowledge about the origin and acceleration mechanisms of cosmic rays.

arXiv commentAccepted for publication in this form in JCAP

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Cited by in corpus (2)

• Energy spectra of elemental groups of cosmic rays with the KASCADE experiment data and machine learning
• Cosmic-Ray Mass Composition around the Knee via Principal Component Analysis