CNN with residual learning extensions in neutrino high energy physics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F21%3A00353091" target="_blank" >RIV/68407700:21340/21:00353091 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1088/1742-6596/1730/1/012133" target="_blank" >https://doi.org/10.1088/1742-6596/1730/1/012133</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/1730/1/012133" target="_blank" >10.1088/1742-6596/1730/1/012133</a>

Result language

angličtina

Original language name

CNN with residual learning extensions in neutrino high energy physics

Original language description

As many reconstruction steps in neutrino high energy physics (HEP) are similar to image pattern recognition tasks, we explore the potential of Convolutional Neural Networks (CNN) combined with residual machine learning algorithm. Characteristic features from neutrino track image pixelmaps are extracted at different scales and these features are used for classification of the type of neutrino interaction. In this contribution, we sumarize observed performance of the residual neural networks (ResNet) for neutrino charged current (CC) interaction detections using image-like Monte Carlo simulated data for muon and electron neutrinos. The two topologies depicted at the neutrino detectors differ, muon neutrino CC interaction is dominated by a slowly ionizing muon, while electron neutrino CC interaction is usually recorded as a wide shower. For the ResNet performance evaluation, we use area under ROC curve (AUC) as the evaluation metric. We observe an improvement while using residual learning compared to general CNN architecture, which is caused by a more stable training with lesser vulnerability to the vanishing gradient of the ResNets. Moreover, stacking other hidden layers within our ResNet model greatly increased the AUC value on the test neutrino dataset without the signs of unstable training or overfitting.

Czech name

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Czech description

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Type

J_ost - 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

Name of the periodical

Journal of Physics Conference Series

ISSN

1742-6588

e-ISSN

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Volume of the periodical

1730

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

6

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85101573067