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 1𝜎 errors are measured to be Δm232=2.40+0.05−0.04×10−3 eV2 and sin2𝜃23=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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Andeen, Karen, "Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks With 9.3 Years of Data in IceCube DeepCore" (2025). Psychology Faculty Research and Publications. 673.
https://epublications.marquette.edu/psych_fac/673
Comments
Published version. Physical Review Letters, Vol. 134, No. 9 (March 2025). DOI. © American Physical Society. Used with permission.
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.