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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.02163 |
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| _version_ | 1866913340224176128 |
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| author | IceCube Collaboration |
| author_facet | IceCube Collaboration |
| contents | The DeepCore sub-detector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5 GeV. Data taken between 2012-2021 (3,387 days) are utilized for an atmospheric $ν_μ$ disappearance analysis that studied 150,257 neutrino-candidate events with reconstructed energies between 5-100 GeV. An advanced reconstruction based on a convolutional neural network is applied, providing increased signal efficiency and background suppression, resulting in a measurement with both significantly increased statistics compared to previous DeepCore oscillation results and high neutrino purity. For the normal neutrino mass ordering, the atmospheric neutrino oscillation parameters and their 1$σ$ errors are measured to be $Δ$m$^2_{32}$ = $2.40\substack{+0.05 \\ -0.04} \times 10^{-3} \textrm{ eV}^2$ and sin$^2$$θ_{23}$=$0.54\substack{+0.04 \\ -0.03}$. The results are the most precise to date using atmospheric neutrinos, and are compatible with measurements from other neutrino detectors including long-baseline accelerator experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02163 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore IceCube Collaboration High Energy Physics - Experiment The DeepCore sub-detector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5 GeV. Data taken between 2012-2021 (3,387 days) are utilized for an atmospheric $ν_μ$ disappearance analysis that studied 150,257 neutrino-candidate events with reconstructed energies between 5-100 GeV. An advanced reconstruction based on a convolutional neural network is applied, providing increased signal efficiency and background suppression, resulting in a measurement with both significantly increased statistics compared to previous DeepCore oscillation results and high neutrino purity. For the normal neutrino mass ordering, the atmospheric neutrino oscillation parameters and their 1$σ$ errors are measured to be $Δ$m$^2_{32}$ = $2.40\substack{+0.05 \\ -0.04} \times 10^{-3} \textrm{ eV}^2$ and sin$^2$$θ_{23}$=$0.54\substack{+0.04 \\ -0.03}$. The results are the most precise to date using atmospheric neutrinos, and are compatible with measurements from other neutrino detectors including long-baseline accelerator experiments. |
| title | Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2405.02163 |