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dc.contributor.authorTumasyan, Armenpt_BR
dc.contributor.authorSilveira, Gustavo Gil dapt_BR
dc.contributor.authorBernardes, César Augustopt_BR
dc.contributor.authorCMS Collaborationpt_BR
dc.date.accessioned2023-02-10T04:55:06Zpt_BR
dc.date.issued2022pt_BR
dc.identifier.issn1748-0221pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/254560pt_BR
dc.description.abstractA new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τh) that originate from genuine tau leptons in the CMS detector against τh candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τh 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 τh to pass the discriminator against jets increases by 10–30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τh 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 τh reconstruction method are validated with LHC proton-proton collision data at √𝑠 = 13 TeV.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofJournal of Instrumentation. Bristol. Vol. 17, no. 7 (July 2022), P07023, 53 p.pt_BR
dc.rightsOpen Accessen
dc.subjectLarge detector systems for particle and astroparticle physicsen
dc.subjectAceleradores de partículaspt_BR
dc.subjectParticle identification methodsen
dc.subjectColisões proton-protonpt_BR
dc.subjectLeptonspt_BR
dc.subjectPattern recognition, cluster finding, calibration and fitting methodsen
dc.titleIdentification of hadronic tau lepton decays using a deep neural networkpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001154606pt_BR
dc.type.originEstrangeiropt_BR


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