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Pregled bibliografske jedinice broj: 1258140

Identification of hadronic tau lepton decays using a deep neural network


(CMS Collaboration) Tumasyan, Armen; ...; Antunović, Željko; Brigljević, Vuko; Ferenček, Dinko; Giljanović, Duje; Godinović, Nikola; Kovač, Marko; Lelas, Damir; Majumder, Devdatta et al.
Identification of hadronic tau lepton decays using a deep neural network // Journal of Instrumentation, 17 (2022), P07023, 52 doi:10.1088/1748-0221/17/07/P07023 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1258140 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Identification of hadronic tau lepton decays using a deep neural network

Autori
Tumasyan, Armen ; ... ; Antunović, Željko ; Brigljević, Vuko ; Ferenček, Dinko ; Giljanović, Duje ; Godinović, Nikola ; Kovač, Marko ; Lelas, Damir ; Majumder, Devdatta ; Puljak, Ivica ; Roguljić, Matej ; Starodumov, Andrey ; Đurić, Senka ; Šuša, Tatjana ; Šćulac, Toni ; ... ; Vetens, Wren

Kolaboracija
CMS Collaboration

Izvornik
Journal of Instrumentation (1748-0221) 17 (2022); P07023, 52

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
High energy physics ; Experimental particle physics ; LHC ; CMS ; p p: scattering ; p p: colliding beams ; B: decay ; tau: hadronic decay ; interaction: gauge ; interaction: model ; transverse momentum: missing-energy ; new physics: search for ; mass spectrum: transverse ; black hole: quantum ; vector boson: mass ; W': leptonic decay ; sensitivity ; leptoquark: coupling ; CERN LHC Coll ; leptoquark: mass: lower limit ; anomaly ; channel cross section: upper limit ; effective field theory ; Higgs

Sažetak
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons ($\tau_\mathrm{; ; h}; ; $) that originate from genuine tau leptons in the CMS detector against $\tau_\mathrm{; ; 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 $\tau_\mathrm{; ; 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 $\tau_\mathrm{; ; h}; ; $ to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient $\tau_\mathrm{; ; 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 $\tau_\mathrm{; ; h}; ; $ reconstruction method are validated with LHC proton-proton collision data at $\sqrt{; ; s}; ; =$ 13 TeV.

Izvorni jezik
Engleski

Znanstvena područja
Fizika



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split,
Institut "Ruđer Bošković", Zagreb,
Prirodoslovno-matematički fakultet, Split

Citiraj ovu publikaciju:

(CMS Collaboration) Tumasyan, Armen; ...; Antunović, Željko; Brigljević, Vuko; Ferenček, Dinko; Giljanović, Duje; Godinović, Nikola; Kovač, Marko; Lelas, Damir; Majumder, Devdatta et al.
Identification of hadronic tau lepton decays using a deep neural network // Journal of Instrumentation, 17 (2022), P07023, 52 doi:10.1088/1748-0221/17/07/P07023 (međunarodna recenzija, članak, znanstveni)
(CMS Collaboration) (CMS Collaboration) Tumasyan, A., ..., Antunović, Ž., Brigljević, V., Ferenček, D., Giljanović, D., Godinović, N., Kovač, M., Lelas, D. & Majumder, D. (2022) Identification of hadronic tau lepton decays using a deep neural network. Journal of Instrumentation, 17, P07023, 52 doi:10.1088/1748-0221/17/07/P07023.
@article{article, author = {Tumasyan, Armen and Antunovi\'{c}, \v{Z}eljko and Brigljevi\'{c}, Vuko and Feren\v{c}ek, Dinko and Giljanovi\'{c}, Duje and Godinovi\'{c}, Nikola and Kova\v{c}, Marko and Lelas, Damir and Majumder, Devdatta and Puljak, Ivica and Rogulji\'{c}, Matej and Starodumov, Andrey and \DJuri\'{c}, Senka and \v{S}u\v{s}a, Tatjana and \v{S}\'{c}ulac, Toni and Vetens, Wren}, year = {2022}, pages = {52}, DOI = {10.1088/1748-0221/17/07/P07023}, chapter = {P07023}, keywords = {High energy physics, Experimental particle physics, LHC, CMS, p p: scattering, p p: colliding beams, B: decay, tau: hadronic decay, interaction: gauge, interaction: model, transverse momentum: missing-energy, new physics: search for, mass spectrum: transverse, black hole: quantum, vector boson: mass, W': leptonic decay, sensitivity, leptoquark: coupling, CERN LHC Coll, leptoquark: mass: lower limit, anomaly, channel cross section: upper limit, effective field theory, Higgs}, journal = {Journal of Instrumentation}, doi = {10.1088/1748-0221/17/07/P07023}, volume = {17}, issn = {1748-0221}, title = {Identification of hadronic tau lepton decays using a deep neural network}, keyword = {High energy physics, Experimental particle physics, LHC, CMS, p p: scattering, p p: colliding beams, B: decay, tau: hadronic decay, interaction: gauge, interaction: model, transverse momentum: missing-energy, new physics: search for, mass spectrum: transverse, black hole: quantum, vector boson: mass, W': leptonic decay, sensitivity, leptoquark: coupling, CERN LHC Coll, leptoquark: mass: lower limit, anomaly, channel cross section: upper limit, effective field theory, Higgs}, chapternumber = {P07023} }
@article{article, author = {Tumasyan, Armen and Antunovi\'{c}, \v{Z}eljko and Brigljevi\'{c}, Vuko and Feren\v{c}ek, Dinko and Giljanovi\'{c}, Duje and Godinovi\'{c}, Nikola and Kova\v{c}, Marko and Lelas, Damir and Majumder, Devdatta and Puljak, Ivica and Rogulji\'{c}, Matej and Starodumov, Andrey and \DJuri\'{c}, Senka and \v{S}u\v{s}a, Tatjana and \v{S}\'{c}ulac, Toni and Vetens, Wren}, year = {2022}, pages = {52}, DOI = {10.1088/1748-0221/17/07/P07023}, chapter = {P07023}, keywords = {High energy physics, Experimental particle physics, LHC, CMS, p p: scattering, p p: colliding beams, B: decay, tau: hadronic decay, interaction: gauge, interaction: model, transverse momentum: missing-energy, new physics: search for, mass spectrum: transverse, black hole: quantum, vector boson: mass, W': leptonic decay, sensitivity, leptoquark: coupling, CERN LHC Coll, leptoquark: mass: lower limit, anomaly, channel cross section: upper limit, effective field theory, Higgs}, journal = {Journal of Instrumentation}, doi = {10.1088/1748-0221/17/07/P07023}, volume = {17}, issn = {1748-0221}, title = {Identification of hadronic tau lepton decays using a deep neural network}, keyword = {High energy physics, Experimental particle physics, LHC, CMS, p p: scattering, p p: colliding beams, B: decay, tau: hadronic decay, interaction: gauge, interaction: model, transverse momentum: missing-energy, new physics: search for, mass spectrum: transverse, black hole: quantum, vector boson: mass, W': leptonic decay, sensitivity, leptoquark: coupling, CERN LHC Coll, leptoquark: mass: lower limit, anomaly, channel cross section: upper limit, effective field theory, Higgs}, chapternumber = {P07023} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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