Speaker
Chris Walter
(Duke University)
Description
We report the development of a proton identification method for the \superk 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 $91.7\pm3$\%. We selected 78 $\mu$-like and 47 e-like events in the SK-I and SK-II data set. With this data, a clear zenith angle distortion of the neutrino direction itself is reported in a sub-GeV sample of $\mu$ neutrinos where the lepton angular correlation to the incoming neutrino is weak. Our fit to \mutau oscillations using the neutrino ${L\over E }$ distribution of the CCQE sample alone yields a wide acceptance region compatible with our previous results and excludes the no-oscillation hypothesis at 3 sigma.
Primary author
Chris Walter
(Duke University)
