Találati lista | Tudóstér

001-es BibID:	BIBFORM119455
035-os BibID:	(WoS)000867442500009 (Scopus)85135918744
Első szerző:	CMS Collaboration
Cím:	Identification of hadronic tau lepton decays using a deep neural network / CMS Collaboration
Dátum:	2022
ISSN:	1748-0221
Megjegyzések:	A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau(h)) that originate from genuine tau leptons in the CMS detector against tau(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(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(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(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(h) reconstruction method are validated with LHC proton-proton collision data at root s = 13 TeV.
Tárgyszavak:	Természettudományok Fizikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Large detector systems for particle and astroparticle physics Particle identification methods Pattern recognition, cluster finding, calibration and fitting methods
Megjelenés:	Journal of Instrumentation. - 17 : 7 (2022), p. 1-52. -
További szerzők:	Bartók Márton (fizikus) Karancsi János (1987-) (fizikus) Raics Péter (1944-) (fizikus) Trócsányi Zoltán (1961-) (fizikus) Ujvári Balázs (1977-) (fizikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: