Application of Convolutional Neural Networks in Neutrino Physics

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of Convolutional Neural Networks in Neutrino Physics

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Application of Convolutional Neural Networks in Neutrino Physics

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Physics Conference Series

ISSN

1742-6588

e-ISSN

—

Svazek periodika

1730

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

6

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85101494799