Abstract
Cherenkov images of air showers can also be classified using multifractal and wavelet parameters, as compared to the conventional Hillas image parameters. This technique was applied to the images recorded by the cameras of the stereoscopic imaging air Cherenkov-telescopes operated by the HEGRA collaboration. With respect to the identification of particles, the performance of multifractal and wavelet parameters was examined using a data sample from the observation of the active galaxy Mkn 501 that showed a high gamma-ray flux. The multifractal parameters were also combined with the Hillas parameters using a neural network approach in order to further improve the gamma/hadron-separation.
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