Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Identification of hadronic tau lepton decays using a deep neural network. / The CMS collaboration.
в: Journal of Instrumentation, Том 17, № 7, P07023, 01.07.2022.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Identification of hadronic tau lepton decays using a deep neural network
AU - The CMS collaboration
AU - Tumasyan, A.
AU - Adam, W.
AU - Andrejkovic, J. W.
AU - Bergauer, T.
AU - Chatterjee, S.
AU - Dragicevic, M.
AU - Escalante Del Valle, A.
AU - Frühwirth, R.
AU - Jeitler, M.
AU - Krammer, N.
AU - Lechner, L.
AU - Liko, D.
AU - Mikulec, I.
AU - Paulitsch, P.
AU - Pitters, F. M.
AU - Schieck, J.
AU - Schöfbeck, R.
AU - Schwarz, D.
AU - Templ, S.
AU - Waltenberger, W.
AU - Wulz, C. E.
AU - Chekhovsky, V.
AU - Litomin, A.
AU - Makarenko, V.
AU - Darwish, M. R.
AU - De Wolf, E. A.
AU - Janssen, T.
AU - Kello, T.
AU - Lelek, A.
AU - Rejeb Sfar, H.
AU - Van Mechelen, P.
AU - Van Putte, S.
AU - Van Remortel, N.
AU - Blekman, F.
AU - Bols, E. S.
AU - D'Hondt, J.
AU - Delcourt, M.
AU - El Faham, H.
AU - Lowette, S.
AU - Moortgat, S.
AU - Morton, A.
AU - Müller, D.
AU - Sahasransu, A. R.
AU - Tavernier, S.
AU - Blinov, V.
AU - Dimova, T.
AU - Kardapoltsev, L.
AU - Kozyrev, A.
AU - Ovtin, I.
AU - Skovpen, Y.
N1 - Publisher Copyright: © 2022 CERN.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τ h) that originate from genuine tau leptons in the CMS detector against τ h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τ h 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 τ h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τ h 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 τ h reconstruction method are validated with LHC proton-proton collision data at s = 13 TeV.
AB - A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τ h) that originate from genuine tau leptons in the CMS detector against τ h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τ h 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 τ h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τ h 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 τ h reconstruction method are validated with LHC proton-proton collision data at s = 13 TeV.
KW - calibration and fitting methods
KW - cluster finding
KW - Large detector systems for particle and astroparticle physics
KW - Particle identification methods
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85135918744&partnerID=8YFLogxK
U2 - 10.1088/1748-0221/17/07/P07023
DO - 10.1088/1748-0221/17/07/P07023
M3 - Article
AN - SCOPUS:85135918744
VL - 17
JO - Journal of Instrumentation
JF - Journal of Instrumentation
SN - 1748-0221
IS - 7
M1 - P07023
ER -
ID: 36932517