Abstract
Based on Monte Carlo simulations using the CORSIKA code, it is shown that Cherenkov images produced by ultrahigh energy γ-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 a more efficient primary characterization scheme than the one based on the use of Hillas or fractal parameters alone.
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