info:eu-repo/semantics/article
Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC
Fecha
2018-08Registro en:
Aaboud, M.; Aad, G.; Abbott, B.; Abdinov, O.; Abeloos, B.; et al.; Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC; Springer; European Physical Journal C: Particles and Fields; C79; 375; 8-2018; 1-79
1434-6044
CONICET Digital
CONICET
Autor
Aaboud, M.
Aad, G.
Abbott, B.
Abdinov, O.
Abeloos, B.
Alconada Verzini, María Josefina
Alonso, Francisco
Arduh, Francisco Anuar
Dova, Maria Teresa
Hoya, Joaquín
Monticelli, Fernando Gabriel
Wahlberg, Hernan Pablo
Bossio Sola, Jonathan David
Daneri, María Florencia
Devesa, Maria Roberta
Marceca, Gino
Otero y Garzon, Gustavo Javier
Piegaia, Ricardo Nestor
Sacerdoti, Sabrina
Zinonos, Z.
Zinser, M.
Ziolkowski, M.
Živković, L.
Zobernig, G.
Zoccoli, A.
Zoch, K.
Nedden, M. zur
Zorbas, T. G.
Zou, R.
Zwalinski, L.
The ATLAS Collaboration
Resumen
The performance of identification algorithms ("taggers") for hadronically decaying top quarks and W bosons in pp collisions at s√ = 13 TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1 fb−1 for the tt¯ and γ+jet and 36.7 fb−1 for the dijet event topologies.