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. Aset 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 t ¯t and γ + jet and 36.7 fb−1 for the dijet event topologies.
aboud M., A.G. (2019). Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS, 79(5), 1-54 [10.1140/epjc/s10052-019-6847-8].
Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC
Alberghi G. L.;Bellagamba L.;Bindi M.;Boscherini D.;Cabras G.;Caforio D.;De Castro S.;Fabbri F.;Fabbri L.;Franchini M.;Gabrielli A.;Giacobbe B.;Manghi F.;Macchiolo A.;Massa L.;Mengarelli A.;Monzani S.;Polini A.;Rinaldi L.;Romano M.;Sbarra C.;Sbrizzi A.;Semprini-Cesari N.;Sioli M.;Todome K.;Ucchielli G.;Valentinetti S.;Villa M.;Vittori C.;Vivarelli I.;Zoccoli A.;
2019
Abstract
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. Aset 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 t ¯t and γ + jet and 36.7 fb−1 for the dijet event topologies.File | Dimensione | Formato | |
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