Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques Conceição, R. González, B. S. Guillén Perales, Alberto Pimenta, M. Tomé, B. We would like to thank to A. Bueno for all the support and useful discussions during the development of this work. The authors thank also for the financial support by OE Portugal, FCT, I. P., under project PTDC/FIS-PAR/29158/2017. R. C. is grateful for the financial support by OE-Portugal, FCT, I. P., under DL57/2016/cP1330/cT0002. B.S.G. is grateful for the financial support by Grant LIP/BI-14/2020, under project IC&DT, POCI-01-0145FEDER-029158. 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/ √ B ∼ 4 for shower with energies E0 ∼ 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 Nμ > 10. 2021-09-22T11:08:18Z 2021-09-22T11:08:18Z 2021-06-24 info:eu-repo/semantics/article Conceição, R... [et al.]. Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques. Eur. Phys. J. C 81, 542 (2021). [https://doi.org/10.1140/epjc/s10052-021-09312-4] http://hdl.handle.net/10481/70363 10.1140/epjc/s10052-021-09312-4 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Springer