Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution

Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory, relied extensively on machine learning (ML) tools to infer physical quantities from the raw photon hits detected. Neutrino telescope reconstruction algorithms are limited by the sparse sampling of photons by the opt...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Yu, Felix J, Kamp, Nicholas, Argüelles, Carlos A
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Deep learning
Machine learning
Modules
Muons
Neutrinos
Photons
Reconstruction
Telescopes
Online Access:Get full text
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Summary:Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory, relied extensively on machine learning (ML) tools to infer physical quantities from the raw photon hits detected. Neutrino telescope reconstruction algorithms are limited by the sparse sampling of photons by the optical modules due to the relatively large spacing (\(10-100\,{\rm m})\) between them. In this letter, we propose a novel technique that learns photon transport through the detector medium through the use of deep learning-driven super-resolution of data events. These ``improved'' events can then be reconstructed using traditional or ML techniques, resulting in improved resolution. Our strategy arranges additional ``virtual'' optical modules within an existing detector geometry and trains a convolutional neural network to predict the hits on these virtual optical modules. We show that this technique improves the angular reconstruction of muons in a generic ice-based neutrino telescope. Our results readily extend to water-based neutrino telescopes and other event morphologies.
ISSN:2331-8422