Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
- NázevTitle
- Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectorsNeutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
- Druh výsledkuResult type
- Článek v časopiseJournal article
- AutořiAuthors
- J. Tingey, S. Bash, J. Cesar, T. Dodwell, S. Germani, P. Koojiman, P. Mánek, M. Ozkaynak, A. Perch, J. Thomas, L. Whitehead
- DOIDOI
- 10.1088/1748-0221/18/06/P06032
- Časopis / citaceJournal / citation
- Journal of Instrumentation. 2023, 2023 (18)(06), P06032. ISSN 1748-0221.
- RokYear
- 2023
- JazykLanguage
- eng
- WoSWoS
- 001084428300001
- ScopusScopus
- 2-s2.0-85164222597
- RIVRIV
- RIV/68407700:21670/23:00366619!RIV24-MSM-21670___
- ProjektProject
- Institucionální podpora na rozvoj výzkumné org.Institucionální podpora na rozvoj výzkumné org.; Inženýrské aplikace fyziky mikrosvětaEngineering applications of microworld physics
AbstraktAbstract
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.
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.