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 call be applied to a wide set of detectors, actually operating or planned for the future. More then 700000 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. (C) 2001 Elsevier Science B.V. All rights reserved.
Bussino, S.A.M., Mari, S.M. (2001). Gamma-hadron discrimination in extensive air showers using a neural network. ASTROPARTICLE PHYSICS, 15(1), 65-77 [10.1016/S0927-6505(00)00152-3].
Gamma-hadron discrimination in extensive air showers using a neural network
BUSSINO, Severino Angelo Maria;MARI, Stefano Maria
2001-01-01
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 call be applied to a wide set of detectors, actually operating or planned for the future. More then 700000 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. (C) 2001 Elsevier Science B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
