Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos
The IceCube Neutrino Observatory, located under 1.4 km of Antarctic ice, instruments a cubic kilometer of ice with 5,160 optical modules that detect Cherenkov radiation originating from neutrino interactions. The more densely instrumented center, DeepCore, aims to detect atmospheric neutrinos at 10-...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2109.08152 https://arxiv.org/abs/2109.08152 |
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ftdatacite:10.48550/arxiv.2109.08152 2023-05-15T14:01:28+02:00 Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos Micallef, Jessie 2021 https://dx.doi.org/10.48550/arxiv.2109.08152 https://arxiv.org/abs/2109.08152 unknown arXiv https://dx.doi.org/10.1088/1748-0221/16/09/c09019 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ High Energy Astrophysical Phenomena astro-ph.HE Instrumentation and Methods for Astrophysics astro-ph.IM High Energy Physics - Experiment hep-ex FOS Physical sciences article-journal Article ScholarlyArticle Text 2021 ftdatacite https://doi.org/10.48550/arxiv.2109.08152 https://doi.org/10.1088/1748-0221/16/09/c09019 2022-03-10T13:50:04Z The IceCube Neutrino Observatory, located under 1.4 km of Antarctic ice, instruments a cubic kilometer of ice with 5,160 optical modules that detect Cherenkov radiation originating from neutrino interactions. The more densely instrumented center, DeepCore, aims to detect atmospheric neutrinos at 10-GeV scales to improve important measurements of fundamental neutrino properties such as the oscillation parameters and to search for non-standard interactions. Sensitivity to oscillation parameters, dependent on the distance traveled over the neutrino energy (L/E), is limited in IceCube by the resolution of the arrival angle (which determines L) and energy (E). Event reconstruction improvements can therefore directly lead to advancements in oscillation results. This work uses a Convolutional Neural Network (CNN) to reconstruct the energy of 10-GeV scale neutrino events in IceCube, providing results with competitive resolutions and faster runtimes than previous likelihood-based methods. : 5 pages, 3 figures, for Very Large Volume Neutrino Telescope Workshop (VLVnT-2021), proceedings submitted to JINST Article in Journal/Newspaper Antarc* Antarctic DataCite Metadata Store (German National Library of Science and Technology) Antarctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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High Energy Astrophysical Phenomena astro-ph.HE Instrumentation and Methods for Astrophysics astro-ph.IM High Energy Physics - Experiment hep-ex FOS Physical sciences |
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High Energy Astrophysical Phenomena astro-ph.HE Instrumentation and Methods for Astrophysics astro-ph.IM High Energy Physics - Experiment hep-ex FOS Physical sciences Micallef, Jessie Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos |
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High Energy Astrophysical Phenomena astro-ph.HE Instrumentation and Methods for Astrophysics astro-ph.IM High Energy Physics - Experiment hep-ex FOS Physical sciences |
description |
The IceCube Neutrino Observatory, located under 1.4 km of Antarctic ice, instruments a cubic kilometer of ice with 5,160 optical modules that detect Cherenkov radiation originating from neutrino interactions. The more densely instrumented center, DeepCore, aims to detect atmospheric neutrinos at 10-GeV scales to improve important measurements of fundamental neutrino properties such as the oscillation parameters and to search for non-standard interactions. Sensitivity to oscillation parameters, dependent on the distance traveled over the neutrino energy (L/E), is limited in IceCube by the resolution of the arrival angle (which determines L) and energy (E). Event reconstruction improvements can therefore directly lead to advancements in oscillation results. This work uses a Convolutional Neural Network (CNN) to reconstruct the energy of 10-GeV scale neutrino events in IceCube, providing results with competitive resolutions and faster runtimes than previous likelihood-based methods. : 5 pages, 3 figures, for Very Large Volume Neutrino Telescope Workshop (VLVnT-2021), proceedings submitted to JINST |
format |
Article in Journal/Newspaper |
author |
Micallef, Jessie |
author_facet |
Micallef, Jessie |
author_sort |
Micallef, Jessie |
title |
Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos |
title_short |
Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos |
title_full |
Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos |
title_fullStr |
Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos |
title_full_unstemmed |
Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos |
title_sort |
using convolutional neural networks to reconstruct energy of gev scale icecube neutrinos |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2109.08152 https://arxiv.org/abs/2109.08152 |
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Antarctic |
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Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_relation |
https://dx.doi.org/10.1088/1748-0221/16/09/c09019 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2109.08152 https://doi.org/10.1088/1748-0221/16/09/c09019 |
_version_ |
1766271298749595648 |