Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using Machine Learning techniques

Abstract

The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray experiments. In this work, it is shown that a good muon tagging (and counting) can be achieved using a water Cherenkov detector with a reduced water volume and 4 PMTs, provided that the PMT signal spatial and time patterns are interpreted by an analysis based on Machine Learning. The developed analysis has been tested for different shower and array configurations. The output of the ML analysis, the probability of having a muon in the WCD station, has been used to notably discriminate between gamma and hadron induced showers with S/ √ B ∼ 4 for shower with energies E 0 ∼ 1 TeV.