Use of Neural Networks to Measure the Muon Contents of EAS Signals in a Water Cherenkov Detector

Details Use of Neural Networks to Measure the Muon Contents of EAS Signals in a Water Cherenkov Detector Use of Neural Networks to Measure the Muon Contents of EAS Signals in a Water Cherenkov Detector

Jul 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003icrc....1...93v&link_type=abstract

Proceedings of the 28th International Cosmic Ray Conference. July 31-August 7, 2003. Trukuba, Japan. Under the auspices of the I

Physics

Scientific paper

The measurement of the muon contents relative to the EM component of extensive air showers (EAS) is one of the requirements to measure the composition of primary cosmic rays in giant arrays of water Cherenkov detectors (WCD) such as the Pierre Auger Observatory. By using bi-dimensional parameter spaces, such as charge vs amplitude, charge vs rise time or charge vs charge/amplitude, for precisely measured inclusive signals of cosmic rays detected in a single WCD of 1.54 m of diameter filled with water up to a height of 1.2 with one 8" phototube positioned in the center at the water level, we find a clear statistical identification of the different components: isolated electrons, isolated muons, muon interactions with the PMT glass envelope and low energy EAS. We make use of real oscilloscope traces of isolated electrons and isolated muons to compose artificial signals of EAS with known µ/E M ratios. Preliminary results encourage the use of neural networks to successfully estimate the µ/E M ratio of real EAS signals.

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