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Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore
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Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore
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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 $\nu_\mu$ 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$\sigma$ errors are measured to be $\Delta$m$^2_{32}$ = $2.40\substack{+0.05 \\ -0.04} \times 10^{-3} \textrm{ eV}^2$ and sin$^2$$\theta_{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.
Forward citations
Cited by 7 Pith papers
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Atmospheric Neutrino Oscillations: the Full Picture
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Second-order perturbative neutrino oscillation probabilities for generic (non-symmetric) Earth density profiles enable accurate multi-shell evolution for oscillation tomography.
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Constraints on long-range neutrino interactions from a variety of $U(1)^\prime$ symmetries using atmospheric neutrinos at IceCube DeepCore
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Neutrino Constraints on Scalar-Tensor Gravity
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Probing non-unitarity of the PMNS matrix in P2SO and comparison with DUNE
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