Multiscale image analysis applied to gamma/hadron discrimination for VHE gamma-ray astronomy with ARGO-YBJ

Abstract

Intrinsic differences in the processes involved in the development of electromagnetic and hadronic showers in the atmosphere have been evidenced by means of a careful analysis of the event image given by the ARGO-YBJ detector. The images have been analyzed at different length scales and their multifractal nature has been studied. The use of the multiscale approach together with a properly designed and trained Artificial Neural Network, allowed us to obtain a good gamma/hadron discrimination power. If confirmed by further studies on different event cathegories, this result would allow to nearly double the detector sensitivity to gamma ray sources.

arXiv comment10 pages, 5 figures, Presented at the "Incontro Nazionale di Astrofisica delle Alte Energie", Rome 15-16 May 2003

References in corpus (3)

• First results on characterization of Cerenkov images through combined use of Hillas, fractal and wavelet parameters
• Particle Identification by Multifractal Parameters in Gamma-Astronomy with the HEGRA-Cherenkov-Telescopes
• Conceptual Design of a Next-Generation All-Sky Gamma-Ray Telescope Operating at TeV Energies

Cited by in corpus (1)

• Artificial Neural Network based gamma-hadron segregation methodology for TACTIC telescope