  This work presents a novel approach to water Cherenkov neutrino detector
event reconstruction and classification. Three forms of a Convolutional Neural
Network have been trained to reject cosmic muon events, classify beam events,
and estimate neutrino energies, using only a slightly modified version of the
raw detector event as input. When evaluated on a realistic selection of
simulated CHIPS-5kton prototype detector events, this new approach
significantly increases performance over the standard likelihood-based
reconstruction and simple neural network classification.
