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arXiv · 2206.14904

Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors

Josh Tingey·Simeon Bash·John Cesar·Thomas Dodwell·Stefano Germani·Paul Kooijman·Petr Mánek·Mustafa Ozkaynak·Andy Perch·Jennifer Thomas·Leigh Whitehead
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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.

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BibTeXRIS

Josh Tingey, Simeon Bash, John Cesar, Thomas Dodwell, Stefano Germani, Paul Kooijman, Petr Mánek, Mustafa Ozkaynak, Andy Perch, Jennifer Thomas, Leigh Whitehead. 2022-06-29. Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors. https://doi.org/10.1088/1748-0221/18/06/p06032

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