Physics
Scientific paper
Apr 1997
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997jphg...23..487t&link_type=abstract
Journal of Physics G, Vol. 23, No. 4, p. 487 - 506
Physics
Cosmic Rays: Classification, Cosmic Rays: Families
Scientific paper
A feed-forward neural network trained using backpropagation is applied to discriminate between proton-induced cosmic ray families and heavy nucleus-induced ones. Fifteen input variables which characterize three-dimensional behaviour of the families are chosen. The network successfully classify the events with classification efficiency ≡85%. The trained neural network classifier is applied to the cosmic ray families observed in the Pamir chambers. The fraction of heavy nucleus-induced events is estimated, from the network-output distribution, to be at most ≡3% of the observed families.
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