Abstract
We have proposed the possibility of a cost-efficient way to improve the detector performance for water Cherenkov detectors, by reflecting the usually lost light falling between photo-detectors onto the other side of the tank with retro-reflectors. Using a detector simulation based on optical measurements of retro-reflectors, we developed a convolutional neural network-based reconstruction algorithm. Here we report on the reconstruction performance for ring events in the energy scale expected for atmospheric and accelerator neutrinos under various candidate detector configurations.
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