Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore
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 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 errors are measured to be and . 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. Published by the American Physical Society 2025
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- US Department of Energy; USDOE; USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 2529515
- Journal Information:
- Physical Review Letters, Journal Name: Physical Review Letters Journal Issue: 9 Vol. 134; ISSN 0031-9007; ISSN PRLTAO
- Publisher:
- American Physical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Precision measurement of the baryon lifetime
Improved Measurement of Violation Parameters in Decays in the Vicinity of the Resonance