Image and non-image parameters of atmospheric Cherenkov events: a comparative study of their γ-ray/hadron classification potential in ultrahigh energy regime

• Published in: Astroparticle physics Vol. 17; no. 4; pp. 497 - 508
• Main Authors: Razdan, A., Haungs, A., Rebel, H., Bhat, C.L.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2002
• Subjects: Artificial neural networks; Atmospheric Cherenkov telescopes; Extensive air showers; γ-Rays; 96.40.−z; 98.70.Sa; Extensive air showers; Atmospheric Cherenkov telescopes; γ-Rays; Artificial neural networks
• ISSN: 0927-6505; 1873-2852
• DOI: 10.1016/S0927-6505(01)00178-5

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 ultrahigh energy γ-ray and hadron progenitors and are likely to be recorded by the TACTIC (TeV atmospheric Cherenkov telescope with imaging camera) 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.