A multidimensional analysis of the Cherenkov images of air showers induced by very high energy γ-rays and protons

Abstract

A multidimensional analysis based on Bayes decision rules and nonparametric multivariate density estimation is proposed for classification of the Cherenkov light images of air showers registered by an air Cherenkov detector (ACD) with the multichannel light receiver. The differences in the angular size of the image, its orientation and position in the focal plane of the ACD and spectral composition of the Cherenkov light are used in the analysis to distinguish the showers induced by primary γ-rays from showers induced by background cosmic rays (CR). It is shown that the usage of several image parameters together with their correlations can lead to a reduction of the CR background rejection down to a few tenths of a percent while retaining about 50% of useful (γ-ray-induced) events.