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We use publicly available data to perform a search for correlations of high energy neutrino candidate events detected by IceCube and high-energy photons seen by the HAWC Collaboration. Our search is focused on unveiling such correlations outside of the Galactic plane. This search is sensitive to correlations in the neutrino candidate and photon skymaps which would arise from a population of unidentified point sources. We find no evidence for such a correlation, but suggest strategies for improvements with new datasets. Published by the American Physical Society2024
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This content will become publicly available on February 5, 2026
Enhancing events in neutrino telescopes through deep-learning-driven superresolution
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 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 superresolution 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. Published by the American Physical Society2025
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- Award ID(s):
- 2019786
- PAR ID:
- 10592307
- Publisher / Repository:
- American Physical Society
- Date Published:
- Journal Name:
- Physical Review D
- Volume:
- 111
- Issue:
- 4
- ISSN:
- 2470-0010
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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