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.

Identification of hadronic tau lepton decays using a deep neural network / Tumasyan A., Adam W., Andrejkovic J.W., Bergauer T., Chatterjee S., Dragicevic M., Escalante Del Valle A., Fruhwirth R., Jeitler M., Krammer N., Lechner L., Liko D., Mikulec I., Paulitsch P., Pitters F.M., Schieck J., Schofbeck R., Schwarz D., Templ S., Waltenberger W., et al.. - In: JOURNAL OF INSTRUMENTATION. - ISSN 1748-0221. - ELETTRONICO. - 17:(2022), pp. 0-0. [10.1088/1748-0221/17/07/P07023]

Identification of hadronic tau lepton decays using a deep neural network

Ceccarelli R.;Ciulli V.;D'Alessandro R.;Focardi E.;Latino G.;Lenzi P.;Lizzo M.;Seidita R.;
2022

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.
2022
17
0
0
Tumasyan A.; Adam W.; Andrejkovic J.W.; Bergauer T.; Chatterjee S.; Dragicevic M.; Escalante Del Valle A.; Fruhwirth R.; Jeitler M.; Krammer N.; Lechn...espandi
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1286812
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 65
  • ???jsp.display-item.citation.isi??? 61
social impact