Speaker
Description
The IceCube Neutrino Observatory consists of 5,160 digital optical modules, which are deployed over a cubic kilometer deep within the South Pole ice. In the lower center of the array, the DeepCore subdetector is more densely configured, improving the reconstruction performance of neutrinos at the GeV-scale, where atmospheric neutrino oscillations can be studied. Neutrino oscillation probabilities are functions of neutrino travel distance (L) and energy (E). The DeepCore detector sees atmospheric neutrinos in a wide range of L and E, which is complementary to long baseline accelerator experiments and also makes the oscillation analysis both interesting and challenging. Convolutional neural networks (CNN) have proved to be a valid and robust reconstruction technique in many modern neutrino experiments. In this poster, I will present a preliminary study of atmospheric muon neutrino disappearance, which is established on the new reconstructions using CNNs, and compare this to the expected sensitivity obtained using likelihood-based reconstructions and to the recent IceCube results.
| Collaboration | IceCube collaboration |
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