Tingey, Josh;
Bash, Simeon;
Cesar, John;
Dodwell, Thomas;
Germani, Stefano;
Kooijman, Paul;
Mánek, Petr;
... Whitehead, Leigh; + view all
(2023)
Neutrino characterisation using convolutional neural networks in CHIPS water Cherenkov detectors.
Journal of Instrumentation
, 18
, Article P06032. 10.1088/1748-0221/18/06/p06032.
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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.
| Type: | Article |
|---|---|
| Title: | Neutrino characterisation using convolutional neural networks in CHIPS water Cherenkov detectors |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1748-0221/18/06/p06032 |
| Publisher version: | https://doi.org/10.1088/1748-0221/18/06/P06032 |
| Language: | English |
| Additional information: | © The Author(s). Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | Cherenkov detectors; Neutrino detectors; Particle identification methods |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10173131 |
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