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

TL;DR

This paper demonstrates that machine learning can effectively identify muons in a compact water Cherenkov detector, enabling gamma/hadron discrimination and muon counting with high accuracy in gamma-ray observatories.

Contribution

It introduces a novel ML-based analysis for muon tagging in a simplified water Cherenkov detector, improving gamma/hadron discrimination and muon estimation.

Findings

Achieved gamma/hadron discrimination with S/√B ~ 4 at 1 TeV.

Obtained ~20% resolution in muon number estimation.

Demonstrated effective muon tagging with reduced detector volume.

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 $E_0 \sim1\,$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 $N_{\mu} > 10$.