First results on characterization of Cerenkov images through combined use of Hillas, fractal and wavelet parameters

Abstract

Based on Monte Carlo simulations using the CORSIKA code, it is shown that Cerenkov images produced by ultrahigh energy $\gamma$-rays and cosmic ray nuclei (proton, Neon and Iron) are fractal in nature. The resulting multifractal and wavelet moments when employed in association with the conventional Hillas parameters as inputs to a properly-trained artificial neural network are found to provide more efficient primary characterization scheme than the one based on the use of Hillas or fractal parameters alone.