Towards more holistic Cerenkov event classification schemes

Details Towards more holistic Cerenkov event classification schemes Towards more holistic Cerenkov event classification schemes

Apr 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..558..617r&link_type=abstract

HIGH ENERGY GAMMA-RAY ASTRONOMY: International Symposium. AIP Conference Proceedings, Volume 558, pp. 617-620 (2001).

Mathematics

Logic

X- And Gamma-Ray Telescopes And Instrumentation, Image Processing, Neural Networks, Fuzzy Logic, Artificial Intelligence

Scientific paper

We use an artificial neural network (ANN) to compare the event-progenitor classification potential of various Cerenkov image and non-image parameters. We have identified a family of 9 mutually uncorrelated parameters (2 each of Hillas, fractal, wavelet and temporal parameters and 1 spectral ratio) which can segregate even a moderate-sized sample of gamma-ray and cosmic ray events with a comparatively better efficiency. .

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