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Cosmic ray data collected by the KASCADE air shower experiment are competitive in terms of quality and statistics with those of modern observatories. We present a novel mass composition analysis based on archival data acquired from 1998 to 2013 provided by the KASCADE Cosmic ray Data Center (KCDC). The analysis is based on modern machine learning techniques trained on simulation data provided by KCDC. We present spectra for individual groups of primary nuclei, the results of a search for anisotropies in the event arrival directions taking mass composition into account, and search for gamma-ray candidates in the PeV energy domain.
KASCADE-Grande and its original array of KASCADE were dedicated to measure individual air showers of cosmic rays with great detail in the primary energy range of 100 TeV up to 1 EeV. The experiment has significantly contributed to investigations of t
The detection of high-energy cosmic rays above a few hundred TeV is realized by the observation of extensive air-showers. By using the multi-detector setup of KASCADE-Grande, energy spectrum, elemental composition, and anisotropies of high-energy cos
Over the past 20 years, KASCADE and its extension KASCADE-Grande were dedicated to measure high-energy cosmic rays with primary energies of 100 TeV to 1 EeV. The data accumulation was fully completed and all experimental components were dismantled, t
A new family of parameters intended for composition studies in cosmic ray surface array detectors is proposed. The application of this technique to different array layout designs has been analyzed. The parameters make exclusive use of surface data co
ALICE, a general purpose experiment designed to investigate nucleus-nucleus collisions at the CERN Large Hadron Collider (LHC), has also been used to detect atmospheric muons produced by cosmic-ray interactions in the atmosphere. In this contributi