Deep Learning in High Energy Physics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F18%3A00325063" target="_blank" >RIV/68407700:21340/18:00325063 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Deep Learning in High Energy Physics
Original language description
Data analysis in high energy physics (HEP) includes solving complex classification tasks. That is why specific machine learning approaches such as artificial neural networks (ANN) [1] are often utilized today. We present our ANN implementations for Higgs boson occurrence events separation in Monte Carlo simulated data. Our results demonstrate the benefits of deep learning approaches in HEP data analysis and show the great performance for classification of the particle decays.
Czech name
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Czech description
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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
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
SPMS 2018 - Stochastic and Physical Monitoring Systems, Proceedings of the international conference
ISBN
978-80-01-06501-3
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
83-87
Publisher name
Česká technika - nakladatelství ČVUT
Place of publication
Praha
Event location
Dobřichovice
Event date
Jun 18, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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