Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
arXiv:2206.14904 · doi:10.1088/1748-0221/18/06/P06032
Abstract
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.
arXiv comment45 pages, 22 figures, 5 tables, to be submitted to Nuclear Instruments and Methods in Physics Research - section A
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