Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F23%3A00366619" target="_blank" >RIV/68407700:21670/23:00366619 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1088/1748-0221/18/06/P06032" target="_blank" >https://doi.org/10.1088/1748-0221/18/06/P06032</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1748-0221/18/06/P06032" target="_blank" >10.1088/1748-0221/18/06/P06032</a>

Result language
angličtina
Original language name
Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10303 - Particles and field physics

Project
<a href="/en/project/EF16_019%2F0000766" target="_blank" >EF16_019/0000766: Engineering applications of microworld physics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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