Kinematic reconstruction of atmospheric neutrino events in a large water Cherenkov detector with proton identification
Super-Kamiokande Collaboration

TL;DR
This paper introduces a proton identification method in the Super-Kamiokande detector, enabling precise atmospheric neutrino event reconstruction and supporting sterile neutrino and oscillation studies with improved data analysis techniques.
Contribution
A novel proton identification technique using neural networks for water Cherenkov detectors, enabling high-purity neutral current and CCQE event samples for neutrino oscillation research.
Findings
Observed 38 neutral current candidate events with high purity.
Achieved a 55% CCQE fraction in the reconstructed neutrino sample.
Excluded the no-oscillation hypothesis at 3 sigma confidence.
Abstract
We report the development of a proton identification method for the Super-Kamiokande detector. This new tool is applied to the search for events with a single proton track, a high purity neutral current sample of interest for sterile neutrino searches. After selection using a neural network, we observe 38 events in the combined SK-I and SK-II data corresponding to 2285.1 days of exposure, with an estimated signal to background ratio of 1.6 to 1. Proton identification was also applied to a direct search for charged-current quasi-elastic (CCQE) events, obtaining a high precision sample of fully kinematically reconstructed atmospheric neutrinos, which has not been previously reported in water Cherenkov detectors. The CCQE fraction of this sample is 55%, and its neutrino (as opposed to anti-neutrino) fraction is 91.7+/-3%. We selected 78 mu-like and 47 e-like events in the SK-I and SK-II…
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