Recent neutrino oscillation result with the IceCube experiment
This work addresses neutrino oscillation studies for the physics community, but it appears incremental as it builds on existing methods and data.
The IceCube experiment tackled the problem of studying atmospheric neutrino oscillations by using Convolutional Neural Networks to reconstruct neutrino interactions in its DeepCore subdetector, achieving advances in physics sensitivity and presenting recent results on muon neutrino disappearance.
The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole. IceCube's primary scientific goal is the detection of TeV neutrino emissions from astrophysical sources. At the lower center of the IceCube array, there is a subdetector called DeepCore, which has a denser configuration that makes it possible to lower the energy threshold of IceCube and observe GeV-scale neutrinos, opening the window to atmospheric neutrino oscillations studies. Advances in physics sensitivity have recently been achieved by employing Convolutional Neural Networks to reconstruct neutrino interactions in the DeepCore detector. In this contribution, the recent IceCube result from the atmospheric muon neutrino disappearance analysis using the CNN-reconstructed neutrino sample is presented and compared to the existing worldwide measurements.