Energy spectra and elemental composition determination on mountain altitudes and sea level

Abstract

The accurate measurement of the elemental composition and energy spectra of primary cosmic rays in the energy range 10 14 – 10 17 eV is vitally important. The information carried by Extensive Air showers (EAS) in this energy range is limited by significant fluctuations of the shower development, insufficient experimental sampling and uncertainties of the detector response. Additional uncertainties arise from different simulation procedures and statistical methods used for inferring results from the measurements. We propose to combine an advanced EAS simulation program like CORSIKA with the ANI multivariate statistical analysis package for an event-by-event analysis and for a standardization of the inference from EAS data. As an example, the possibilities of modern EAS installations at mountain altitudes and sea level are explored. The accuracies of the elemental composition determination based on measured components of EAS are presented. The Neural Networks classification and Bayesian Decision Making approaches are used and compared. The new nonparametric methods of primary energy estimations are discussed.