Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks With 9.3 Years of Data in IceCube DeepCore

Document Type

Article

Publication Date

3-2025

Publisher

American Physical Society

Source Publication

Physical Review Letters

Source ISSN

0031-9007

Original Item ID

DOI: 10.1103/PhysRevLett.134.091801

Abstract

The DeepCore subdetector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5 GeV. Data taken between 2012 and 2021 (3387 days) are utilized for an atmospheric 𝜈𝜇 disappearance analysis that studied 150 257 neutrino-candidate events with reconstructed energies between 5 and 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⁢m232=2.40+0.05−0.04×10−3  eV2 and sin2⁡𝜃23=0.54+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.

Comments

Physical Review Letters, Vol. 134, No. 9 (March 2025). DOI.

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