Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424038" target="_blank" >RIV/00216208:11320/20:10424038 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KmqdVdbn8L" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KmqdVdbn8L</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1748-0221/15/06/P06005" target="_blank" >10.1088/1748-0221/15/06/P06005</a>
Alternative languages
Result language
angličtina
Original language name
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
Original language description
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at root S = 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1). Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10304 - Nuclear physics
Result continuities
Project
<a href="/en/project/LM2018104" target="_blank" >LM2018104: Research Infrastructure for experiments at CERN</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Instrumentation [online]
ISSN
1748-0221
e-ISSN
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Volume of the periodical
15
Issue of the periodical within the volume
6
Country of publishing house
GB - UNITED KINGDOM
Number of pages
87
Pages from-to
P06005
UT code for WoS article
000545350900005
EID of the result in the Scopus database
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