Neutrino characterisation using convolutional neural networks in CHIPS water Cherenkov detectors
Year: 2023
Type: article
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.
Source: Journal of Instrumentation
Authors: Josh Tingey, Simeon Bash, John Cesar, Thomas Dodwell, Stefano Germani, Paul Kooijman, Petr Mánek, Mustafa Ozkaynak, Andy Perch, Jennifer Thomas, Leigh Whitehead
Language: English
Topic: Neutrino Physics Research
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,045
Award: G3957425120
OpenAlex ID: W4382725174