@misc{10481/70363,
year = {2021},
month = {6},
url = {http://hdl.handle.net/10481/70363},
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/
√
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.},
organization = {OE Portugal},
organization = {FCT, I. P.	PTDC/FIS-PAR/29158/2017	
DL57/2016/cP1330/cT0002},
organization = {ICDT	LIP/BI-14/2020	
POCI-01-0145FEDER-029158},
publisher = {Springer},
title = {Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques},
doi = {10.1140/epjc/s10052-021-09312-4},
author = {Conceição, R. and González, B. S. and Guillén Perales, Alberto and Pimenta, M. and Tomé, B.},
}