Application of Convolutional Neural Networks in Neutrino Physics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F21%3A00353092" target="_blank" >RIV/68407700:21340/21:00353092 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1088/1742-6596/1730/1/012116" target="_blank" >https://doi.org/10.1088/1742-6596/1730/1/012116</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1742-6596/1730/1/012116" target="_blank" >10.1088/1742-6596/1730/1/012116</a>

Result language
angličtina
Original language name
Application of Convolutional Neural Networks in Neutrino Physics
Original language description
Convolutional neural networks (CNNs), as a deep learning algorithm, have successfully been used for analyzing visual image data over the past years. As some of the physical experiments can produce image-like data, it is more than fitting to combine the interdisciplinary knowledge between high energy physics and deep learning. Especially in the domain of neutrino physics, the particle classification problem has played an important role and CNNs have shown exceptional results for the image classification. In this paper, results of application of CNN called SE-ResNET on Monte Carlo simulated image data is presented. These visual images are tailored to fit measured data from future Deep Underground Neutrino Experiment. The image classification focuses primarily on neutrino flavor classification, namely on classification of charged current (CC) electron νe, CC muon νμ, CC tauon ντ and neutral current (NC); and secondarily on other characteristics of the image, such as whether the observed particle is neutrino or antineutrino. The results are important for further physical analysis of the neutrino experiment event, e.g. for study of neutrino oscillation.
Czech name
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Czech description
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Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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