This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.22323/1.414.1114 in citations.
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-04822 in citations.
Reconstruction of atmospheric neutrino events at JUNO
Reconstruction of atmospheric neutrino events at JUNO
The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kt liquid scintillation detector, which will be completed in 2023 as the largest of its kind. JUNO aims to determine the neutrino mass ordering by observing the energy dependent oscillation probabilities of reactor anti-neutrinos.JUNOs lar...
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Personal Name(s): | Wirth, Rosmarie |
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Rifai, Mariam / Molla Colomer, Marta | |
Contributing Institute: |
Experimentelle Hadrondynamik; IKP-2 |
Imprint: |
Sissa Medialab Trieste, Italy
2022
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Physical Description: |
Volume 414 |
DOI: |
10.22323/1.414.1114 |
DOI: |
10.34734/FZJ-2023-04822 |
Conference: | 41st International Conference on High Energy physics, Bologna (Italy), 2022-07-06 - 2022-07-13 |
Document Type: |
Contribution to a conference proceedings |
Research Program: |
Cosmic Matter in the Laboratory |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-04822 in citations.
The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kt liquid scintillation detector, which will be completed in 2023 as the largest of its kind. JUNO aims to determine the neutrino mass ordering by observing the energy dependent oscillation probabilities of reactor anti-neutrinos.JUNOs large volume provides the opportunity to detect atmospheric neutrino events with lower energies than today’s large Cherenkov experiments. As atmospheric neutrinos reach the detector from all directions, partially experiencing the matter effect, they are especially interesting for observing the neutrino mass ordering via the matter effects on their oscillation probabilities. This article presents the preliminary performance of direction and energy reconstruction methods for atmospheric neutrino events at JUNO. The former uses a traditional approach, based on the reconstruction of the photon emission topology in the JUNO detector. For the energy reconstruction, a traditional approach as well as a machine learning based using Graph Convolutional Networks, are shown. |