Gamma–hadron discrimination in extensive air showers using a neural network

Abstract A neural algorithm was developed to separate electromagnetic and hadronic showers detected with an air shower array. The requirements on the detector performance are very general, so that the results of the calculation can be applied to a wide set of detectors, actually operating or planned for the future. More then 700 000 showers were generated using the Corsika package and were propagated through an ideal pixel-like detector. The peculiarities of each class of showers are presented in detail and it is shown how the neural net architecture is structured around them. The neural net performances were studied for different sets of simulated data. The physics relevance of the gamma–hadron separation is also discussed.

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