Application of Convolutional Neural Networks to Reconstruct GeV-Scale IceCube Neutrino Events
The IceCube Neutrino Observatory instruments a cubic kilometer of ice at the South Pole to detect atmospheric and astrophysical neutrinos. IceCube consists of a 3D array of 5160 optical modules which detect light from relativistic charged particles resulting from neutrino interactions in the ice. Th...
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ftdatacite:10.5281/zenodo.4123943 2023-05-15T18:22:12+02:00 Application of Convolutional Neural Networks to Reconstruct GeV-Scale IceCube Neutrino Events Micallef, Jessica 2020 https://dx.doi.org/10.5281/zenodo.4123943 https://zenodo.org/record/4123943 unknown Zenodo https://zenodo.org/communities/neutrino2020-posters https://dx.doi.org/10.5281/zenodo.4123942 https://zenodo.org/communities/neutrino2020-posters Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Text Poster article-journal ScholarlyArticle 2020 ftdatacite https://doi.org/10.5281/zenodo.4123943 https://doi.org/10.5281/zenodo.4123942 2021-11-05T12:55:41Z The IceCube Neutrino Observatory instruments a cubic kilometer of ice at the South Pole to detect atmospheric and astrophysical neutrinos. IceCube consists of a 3D array of 5160 optical modules which detect light from relativistic charged particles resulting from neutrino interactions in the ice. This can be used to reconstruct the neutrino's energy and direction, but becomes particularly challenging at energies less than 100 GeV since IceCube is optimized for TeV-PeV scale neutrinos. These GeV-scale neutrinos are important for studying neutrino properties, such as the oscillation parameters. My work uses convolutional neural networks to reconstruct the energy and direction of these GeV-scale neutrinos in the IceCube detector. Still Image South pole DataCite Metadata Store (German National Library of Science and Technology) South Pole |
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description |
The IceCube Neutrino Observatory instruments a cubic kilometer of ice at the South Pole to detect atmospheric and astrophysical neutrinos. IceCube consists of a 3D array of 5160 optical modules which detect light from relativistic charged particles resulting from neutrino interactions in the ice. This can be used to reconstruct the neutrino's energy and direction, but becomes particularly challenging at energies less than 100 GeV since IceCube is optimized for TeV-PeV scale neutrinos. These GeV-scale neutrinos are important for studying neutrino properties, such as the oscillation parameters. My work uses convolutional neural networks to reconstruct the energy and direction of these GeV-scale neutrinos in the IceCube detector. |
format |
Still Image |
author |
Micallef, Jessica |
spellingShingle |
Micallef, Jessica Application of Convolutional Neural Networks to Reconstruct GeV-Scale IceCube Neutrino Events |
author_facet |
Micallef, Jessica |
author_sort |
Micallef, Jessica |
title |
Application of Convolutional Neural Networks to Reconstruct GeV-Scale IceCube Neutrino Events |
title_short |
Application of Convolutional Neural Networks to Reconstruct GeV-Scale IceCube Neutrino Events |
title_full |
Application of Convolutional Neural Networks to Reconstruct GeV-Scale IceCube Neutrino Events |
title_fullStr |
Application of Convolutional Neural Networks to Reconstruct GeV-Scale IceCube Neutrino Events |
title_full_unstemmed |
Application of Convolutional Neural Networks to Reconstruct GeV-Scale IceCube Neutrino Events |
title_sort |
application of convolutional neural networks to reconstruct gev-scale icecube neutrino events |
publisher |
Zenodo |
publishDate |
2020 |
url |
https://dx.doi.org/10.5281/zenodo.4123943 https://zenodo.org/record/4123943 |
geographic |
South Pole |
geographic_facet |
South Pole |
genre |
South pole |
genre_facet |
South pole |
op_relation |
https://zenodo.org/communities/neutrino2020-posters https://dx.doi.org/10.5281/zenodo.4123942 https://zenodo.org/communities/neutrino2020-posters |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.4123943 https://doi.org/10.5281/zenodo.4123942 |
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1766201566263508992 |