Separating gamma- and Hadron-Induced Cosmic Ray Air Showers with Feed-Forward Neural Networks Using the Charged Particle Information

The success of current and future earth-based air shower arrays in detecting point sources of cosmic rays above 10 TeV depends crucially on the possibility of finding efficient methods for separating fl-induced air showers from the overwhelming background of hadron-induced showers. We study the application of computer-simulated feed-forward neural networks in the analysis of cosmic ray data taken with the Geiger towers of the HEGRA air shower array. The combination of these charged particle detectors with the neural net based analysis is characterized by high background rejection and signal efficiency. In contrast to the often-quoted non-transparency of the net technique, the detailed analysis of the net performance gives insight into the physics involved and helps to asses the different information that allow fl/hadron separation. ? This work is supported by the BMBF, FRG, under contract numbers 05 2 WT 164 and 05 2 HH 264. 1 corresponding author: [email protected]..