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.
Highlights
Ray bursts (GRBs) –intense and fast shots of gamma radiation – are examples of interesting target sources, both multimessenger astrophysical counterparts of very high-energy (VHE) neutrinos and gravitational wave events [1,2,3]
Even though indirect methods are very effective at the VHE energy region, a significant drawback is that one has to deal with an enormous hadronic background produced by the cosmic-rays continuously reaching the Earth [8]
This work aims to demonstrate that a dedicated design of the water Cherenkov detector (WCD) combined with stateof-the-art machine learning techniques can be used to identify muons and with it provide an effective gamma/hadron discrimination
Summary
The direct detection of gamma-rays is only possible using satellite-borne instruments [6]. Indirect detection techniques take advantage of the secondary shower of particles, known as extensive air showers (EAS), produced by the interaction of the gamma-ray with the Earth’s atmosphere to infer their direction and energy [7]. This work aims to demonstrate that a dedicated design of the water Cherenkov detector (WCD) combined with stateof-the-art machine learning techniques can be used to identify muons and with it provide an effective gamma/hadron discrimination. Such a detector can have a reduced water height. A discussion on the performance for inclined shower events and a sparser detector array is presented in Sect. 7, followed by the conclusions
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