A next-generation gamma-ray observatory powered by machine learning techniques

Abstract:
The Southern Wide-field Gamma-ray Observatory (SWGO) is the next-generation ground-based gamma-ray observatory to survey the Southern hemisphere sky. The experiment, currently in an R&D phase, is expected to have a large array of a few square kilometers composed of water Cherenkov detectors (WCDs) placed at a high altitude (4.4 km a.s.l. or higher) in South America.

Such an ambitious project requires the design of a high-performance and cost-effective WCD to cope with the observatory needs, particularly the capability to identify shower muons, which is essential to ensure excellent gamma/hadron discrimination.

In this work, it is shown that efficient muon tagging (and counting) can be achieved using water Cherenkov detectors with a reduced water volume and multiple PMTs, provided that the PMT signal spatial and time patterns are interpreted by an analysis based on Machine Learning (ML).
The proposed WCDs would highly boost the physics capabilities of SWGO, enabling it to cover, with a wide field of view, a wide energy range, from low energies ($\sim$ 100 GeV) up to the PeV region.