Enhancing Neutrino Reconstruction in Water-Cherenkov Air Shower Arrays Using Multi-Photosensors

Abstract

In this article, the potential of water Cherenkov detectors equipped with multi-PMT modules for background-free upward-going neutrino detection and improved direction reconstruction is demonstrated. By analyzing signal time traces with transformer-based models, significant improvements in angular resolution are achieved compared to previous designs with larger PMTs. These detectors enable the reconstruction of neutrino directions with resolutions of approximately $10^{\circ }$ in azimuth and $7^{\circ }$ in zenith for high-signal events, corresponding to an overall opening angle of approximately $10^{\circ }$. This design reduces saturation effects and enhances directional sensitivity, particularly for high-energy neutrinos. The results highlight the potential of WCD arrays as complementary tools for neutrino astronomy, particularly in the context of multimessenger observations of transient astrophysical sources. The nearly continuous operation and wide field of view of these detectors further enhance their suitability for real-time monitoring and alert generation.

arXiv comment11 pages, 7 figures

References in corpus (18)

• HERWIG 6.5: an event generator for Hadron Emission Reactions With Interfering Gluons (including supersymmetric processes)
• Evidence for High-Energy Extraterrestrial Neutrinos at the IceCube Detector
• High Energy Neutrinos from Cosmological Gamma-Ray Burst Fireballs
• Observation of the Crab Nebula with the HAWC Gamma-Ray Observatory
• Observation of high-energy neutrinos from the Galactic plane
• Sensitivity of the KM3NeT/ARCA neutrino telescope to point-like neutrino sources
• A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers
• TeV-PeV Neutrinos from Giant Flares of Magnetars and the Case of SGR 1806-20
• High-Energy Extragalactic Neutrino Astrophysics
• Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using Machine Learning techniques
• Deep learning techniques applied to the physics of extensive air showers
• Neutrino analysis of the September 2010 Crab Nebula flare and time-integrated constraints on neutrino emission from the Crab using IceCube
• The Mercedes water Cherenkov detector
• Application of Graph Networks to background rejection in Imaging Air Cherenkov Telescopes
• Use of Machine Learning for gamma/hadron separation with HAWC
• $P_{\gamma h}^{\alpha }$: a new variable for $\gamma /\mathrm{h}$ discrimination in large gamma-ray ground arrays
• Discriminating sub-TeV gamma and hadron-induced showers through their footprints
• The Potential of Water-Cherenkov Air Shower Arrays for detecting transient sources of high-energy astrophysical neutrinos