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 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 $S/\sqrt{B}\sim 4$ for shower with energies $E_0\sim 1$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 $N_{\mu }>10$.

arXiv comment11 pages, 9 figures; to appear in EPJC

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• Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
• Discriminating sub-TeV gamma and hadron-induced showers through their footprints
• The Potential of Water-Cherenkov Air Shower Arrays for detecting transient sources of high-energy astrophysical neutrinos
• Application of Graph Networks to a wide-field Water-Cherenkov-based Gamma-Ray Observatory
• Evaluation of the potential of a gamma-ray observatory to detect astrophysical neutrinos through inclined showers
• Future developments in ground-based gamma-ray astronomy
• Enhancing Neutrino Reconstruction in Water-Cherenkov Air Shower Arrays Using Multi-Photosensors