Residual Neural Networks in High Energy Physics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00335962" target="_blank" >RIV/68407700:21340/19:00335962 - isvavai.cz</a>
Result on the web
<a href="http://gams.fjfi.cvut.cz/spms2019" target="_blank" >http://gams.fjfi.cvut.cz/spms2019</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Residual Neural Networks in High Energy Physics
Original language description
Classification is a crucial step in high energy physics data analysis. As many reconstruction steps in high energy physics are similar to image pattern recognition tasks, we explore the potential of appropriate deep learning techniques in high energy physics (HEP). In particular, convolutional neural networks (CNN) can be used to extract characteristic features from image pixelmaps at different scales and use these features for particle identification. That is why the CNN techniques can be used for interaction classification in neutrino experiments. In this paper, we summarize the results of our research assignment [1]. We present several classification models with different CNN architectures and show the results for particle classification task on a provided HEP dataset.
Czech name
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Czech description
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Project
<a href="/en/project/LM2015068" target="_blank" >LM2015068: Research Infrastructure for Fermilab Experiments</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of SPMS 2019 - Stochastic and Physical Monitoring Systems
ISBN
978-80-01-06659-1
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
65-71
Publisher name
Česká technika - nakladatelství ČVUT
Place of publication
Praha
Event location
Dobřichovice
Event date
Jun 20, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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