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

The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray observatories. In this work, it is shown that an efficient 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 (ML). 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/root B similar to 4 for shower with energies E-0 similar to 1 TeV. Finally, for proton-induced showers, an estimator of the number of muons was built by means of the sum of the probabilities of having a muon in the stations. Resolutions about 20% and a negligible bias are obtained for vertical showers with N-mu > 10.

Automated summary

This study demonstrates the efficient use of a water Cherenkov detector combined with machine learning techniques for muon tagging and counting to distinguish between gamma and hadron-induced showers. The ML analysis output of muon probabilities can notably discriminate between different types of showers. An estimator of the number of muons was built by summing the probabilities of having a muon in the stations for vertical showers.