https://doi.org/10.1140/epjc/s10052-021-09312-4
Regular Article - Experimental Physics
Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques
1
LIP, Av. Prof. Gama Pinto, 2, 1649-003, Lisbon, Portugal
2
Instituto Superior Técnico (IST), Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
3
Computer Architecture and Technology Department, University of Granada, Granada, Spain
Received:
22
January
2021
Accepted:
7
June
2021
Published online:
24
June
2021
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 
for shower with energies 
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 
and a negligible bias are obtained for vertical showers with 
.
© The Author(s) 2021
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Funded by SCOAP3
