arXiv subjects

A. Haungs

Publications and source records attributed to A. Haungs.

129 records · Page 8Linked to original sources

arXiv2001

Image and Non-Image Parameters of Atmospheric Cherenkov Events: a comparative study of their gamma-ray/hadron classification potential in UHE regime

In this exploratory simulation study, we compare the event-progenitor classification potential of a variety of measurable parameters of atmospheric Cherenkov pulses which are produced by ultra-high energy (UHE) gamma-ray and hadron progenitors and are likely to be recorded by the TACTIC array of atmospheric Cherenkov telescopes. The parameters derived from Cherenkov images include Hillas, fractal and wavelet moments, while those obtained from non-image Cherenkov data consist of pulse profile rise-time and base width and the relative ultraviolet to visible light content of the Cherenkov event. It is shown by a neural-net approach that these parameters, when used in suitable combinations, can bring about a proper segregation of the two event types, even with modest sized data samples of progenitor particles.

astro-ph↗

arXiv2000

First results of the air shower experiment KASCADE

The main goals of the KASCADE (KArlsruhe Shower Core and Array DEtector) experiment are the determination of the energy spectrum and elemental composition of the charged cosmic rays in the energy range around the knee at ca. 5 PeV. Due to the large number of measured observables per single shower a variety of different approaches are applied to the data, preferably on an event-by-event basis. First results are presented and the influence of the high-energy interaction models underlying the analyses is discussed.

astro-ph↗

arXiv1999

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

Based on Monte Carlo simulations using the CORSIKA code, it is shown that Cerenkov 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 more efficient primary characterization scheme than the one based on the use of Hillas or fractal parameters alone.

astro-ph↗