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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 изданияхстатьяРецензирование

Harvard

The CMS collaboration 2022, 'Identification of hadronic tau lepton decays using a deep neural network', Journal of Instrumentation, Том. 17, № 7, P07023. https://doi.org/10.1088/1748-0221/17/07/P07023

APA

Vancouver

The CMS collaboration. Identification of hadronic tau lepton decays using a deep neural network. Journal of Instrumentation. 2022 июль 1;17(7):P07023. doi: 10.1088/1748-0221/17/07/P07023

Author

The CMS collaboration. / Identification of hadronic tau lepton decays using a deep neural network. в: Journal of Instrumentation. 2022 ; Том 17, № 7.

BibTeX

@article{3d7c2675a429474aaa41d8c13142404c,
title = "Identification of hadronic tau lepton decays using a deep neural network",
abstract = "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. ",
keywords = "calibration and fitting methods, cluster finding, Large detector systems for particle and astroparticle physics, Particle identification methods, Pattern recognition",
author = "{The CMS collaboration} and A. Tumasyan and W. Adam and Andrejkovic, {J. W.} and T. Bergauer and S. Chatterjee and M. Dragicevic and {Escalante Del Valle}, A. and R. Fr{\"u}hwirth and M. Jeitler and N. Krammer and L. Lechner and D. Liko and I. Mikulec and P. Paulitsch and Pitters, {F. M.} and J. Schieck and R. Sch{\"o}fbeck and D. Schwarz and S. Templ and W. Waltenberger and Wulz, {C. E.} and V. Chekhovsky and A. Litomin and V. Makarenko and Darwish, {M. R.} and {De Wolf}, {E. A.} and T. Janssen and T. Kello and A. Lelek and {Rejeb Sfar}, H. and {Van Mechelen}, P. and {Van Putte}, S. and {Van Remortel}, N. and F. Blekman and Bols, {E. S.} and J. D'Hondt and M. Delcourt and {El Faham}, H. and S. Lowette and S. Moortgat and A. Morton and D. M{\"u}ller and Sahasransu, {A. R.} and S. Tavernier and V. Blinov and T. Dimova and L. Kardapoltsev and A. Kozyrev and I. Ovtin and Y. Skovpen",
note = "Publisher Copyright: {\textcopyright} 2022 CERN.",
year = "2022",
month = jul,
day = "1",
doi = "10.1088/1748-0221/17/07/P07023",
language = "English",
volume = "17",
journal = "Journal of Instrumentation",
issn = "1748-0221",
publisher = "IOP Publishing Ltd.",
number = "7",

}

RIS

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