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
- Klíčová slovaKeywords
- Cherenkov detector, neutrino detector, Particle identification methods
- DOIDOI
- 10.1088/1748-0221/18/06/P06032
- Časopis / citaceJournal / citation
- Journal of Instrumentation 18(06), P06032 (2023) · ISSN 1748-0221
- RokYear
- 2023
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- Institucionální podpora na rozvoj výzkumné org.Institucionální podpora na rozvoj výzkumné org.; Inženýrské aplikace fyziky mikrosvětaEngineering applications of microworld physics
- CitovánoCited by
- 9 (INSPIRE-HEP)
- Plný text (open access)Full text (open access)
- https://iopscience.iop.org/article/10.1088/1748-0221/18/06/P06032/pdf
- Citace ke staženíDownload citation
- TXT · BibTeX
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.