Abstract
The CHIPS R&D programme aims to demonstrate significant cost reduction in the building of large neutrino detectors, whilst contributing to the global knowledge on the CP-violating phase in neutrino oscillations. A series of event reconstruction algorithms have been developed considering the charge and time information from all of the photomultiplier tubes to reconstruct charged particle tracks. A particle identification algorithm was developed using two artificial neural networks to select charged-current νe interactions from νµ backgrounds. It has been demonstrated that using a 6% photocathode coverage of 3” photomultiplier tubes can provide the required performance from the particle identification to achieve the goals of the experiment.
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