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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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Skynet’s New Observing Mode: The Campaign Manager
Abstract Built in 2004, the Skynet robotic telescope network originally consisted of six 0.4 m telescopes located at the Cerro-Tololo Inter-American Observatory in the Chilean Andes. The network was designed to carry out simultaneous multi-wavelength observations of gamma-ray bursts (GRBs) when they are only tens of seconds old. To date, the network has been expanded to ≈20 telescopes, including a 20 m radio telescope, that span four continents and five countries. The Campaign Manager (CM) is a new observing mode that has been developed for Skynet. Available to all Skynet observers, the CM semi-autonomously and indefinitely scales and schedules exposures on the observer’s behalf while allowing for modification to scaling parameters in real time. The CM is useful for follow up to various transient phenomena including gravitational-wave events, GRB localizations, young supernovae, and eventually, sufficiently bright Argus Optical Array and Large Synoptic Survey Telescope events.
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- Award ID(s):
- 2007853
- PAR ID:
- 10374193
- Date Published:
- Journal Name:
- Publications of the Astronomical Society of the Pacific
- Volume:
- 134
- Issue:
- 1031
- ISSN:
- 0004-6280
- Page Range / eLocation ID:
- 015001
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1538-3873/ac3f7c
