Using Convolutional Neural Networks for Muon detection in WCD tank

Abstract

The aim of this paper is to study the possibility of improving the gamma/hadron discrimination in extensive air showers. For this purpose, the identification of hadronic extensive air showers is carried out by means of the detection of muons in water Cherenkov detectors (WCDs). Machine learning algorithms have proven to be useful in a wide variety of fields, and due to their outstanding performance in problems involving complex data, Convolutional Neural Networks (CNNs) have been used in the analysis of the signals measured by the WCDs. Taking simulated events, different approaches were proposed attending to the balance of the classes in the training stage. The results obtained are promising and show that machine learning algorithms provide a powerful tool for muon detection and gamma/hadron discrimination to be considered in future gamma-rays detectors like The Southern Wide-field Gamma-ray Observatory (SWGO) to be built in South America.