<div class="csl-bib-body">
<div class="csl-entry">Tumasyn, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Dragicevic, M., Del Valle, A. E., Frühwirth, R., Jeitler, M., Krammer, N., Lechner, L., Liko, D., Mikulec, I., Paulitsch, P., Pitters, F. M., Schieck, J., Schöfbeck, R., Schwarz, D., & Vetens, W. (2022). Identification of hadronic tau lepton decays using a deep neural network. <i>Journal of Instrumentation</i>, <i>17</i>, Article P07023. https://doi.org/10.1088/1748-0221/17/07/P07023</div>
</div>
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dc.identifier.issn
1748-0221
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/142260
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dc.description.abstract
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τhτh) that originate from genuine tau leptons in the CMS detector against τhτh candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τhτh candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine τhτh to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τhτh reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved τhτh reconstruction method are validated with LHC proton-proton collision data at s=s
= 13 TeV.
Note:Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/TAU-20-001 (CMS Public Pages)
en
dc.language.iso
en
-
dc.publisher
IOP PUBLISHING LTD
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dc.relation.ispartof
Journal of Instrumentation
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dc.subject
LHC proton
en
dc.title
Identification of hadronic tau lepton decays using a deep neural network
en
dc.type
Article
en
dc.type
Artikel
de
dc.contributor.affiliation
National Academy of Sciences of Armenia, Armenia
-
dc.contributor.affiliation
Institute of High Energy Physics, Austria
-
dc.contributor.affiliation
Institute of High Energy Physics, Austria
-
dc.contributor.affiliation
Institute of High Energy Physics, Austria
-
dc.contributor.affiliation
Institute of High Energy Physics, Austria
-
dc.type.category
Original Research Article
-
tuw.container.volume
17
-
tuw.journal.peerreviewed
true
-
tuw.peerreviewed
true
-
tuw.researchTopic.id
M5
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tuw.researchTopic.name
Composite Materials
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tuw.researchTopic.value
100
-
dcterms.isPartOf.title
Journal of Instrumentation
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tuw.publication.orgunit
E141-03 - Forschungsbereich Nuclear and Particle Physics
-
tuw.publisher.doi
10.1088/1748-0221/17/07/P07023
-
dc.identifier.articleid
P07023
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dc.identifier.eissn
1748-0221
-
tuw.author.orcid
0000-0002-0054-3369
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tuw.author.orcid
0000-0002-1058-8093
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tuw.author.orcid
0000-0002-2332-8784
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wb.sci
true
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wb.sciencebranch
Physik, Astronomie
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wb.sciencebranch.oefos
1030
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wb.sciencebranch.value
100
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item.fulltext
no Fulltext
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item.openairetype
research article
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item.languageiso639-1
en
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item.grantfulltext
none
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item.openairecristype
http://purl.org/coar/resource_type/c_2df8fbb1
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item.cerifentitytype
Publications
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crisitem.author.dept
National Academy of Sciences of Armenia
-
crisitem.author.dept
Institute of High Energy Physics
-
crisitem.author.dept
E141 - Atominstitut
-
crisitem.author.dept
Institute of High Energy Physics
-
crisitem.author.dept
Institute of High Energy Physics
-
crisitem.author.dept
E105 - Institut für Stochastik und Wirtschaftsmathematik
-
crisitem.author.dept
E141 - Atominstitut
-
crisitem.author.dept
TU Wien
-
crisitem.author.dept
Institute of High Energy Physics
-
crisitem.author.dept
TU Wien
-
crisitem.author.dept
Institute of High Energy Physics
-
crisitem.author.dept
E141-03 - Forschungsbereich Nuclear and Particle Physics