The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV 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 36.1fb-1 for the tt and +jet and 36.7-1 for the dijet event topologies.

Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC / Aaboud, M., Aad, G., Abbott, B., Abdinov, O., Abeloos, B., Abhayasinghe, D.K., Abidi, S.H., Abouzeid, O.S., Abraham, N.L., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B.S., Adachi, S., Adamczyk, L., Adelman, J., Adersberger, M., Adiguzel, A., Adye, T., Affolder, A.A., et al.. - In: THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6044. - 79:5(2019). [10.1140/epjc/s10052-019-6847-8]

Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC

Artoni, G.;Bagnaia, P.;Betti, A.;Bini, C.;De Cecco, S.;Gentile, S.;Giagu, S.;Kado, M.;Lacava, F.;Luci, C.;Messina, A.;Policicchio, A.;Sebastiani, C. D.;
2019

Abstract

The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV 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 36.1fb-1 for the tt and +jet and 36.7-1 for the dijet event topologies.
2019
LHC; hadronic top decays; taggers
01 Pubblicazione su rivista::01a Articolo in rivista
Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC / Aaboud, M., Aad, G., Abbott, B., Abdinov, O., Abeloos, B., Abhayasinghe, D.K., Abidi, S.H., Abouzeid, O.S., Abraham, N.L., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B.S., Adachi, S., Adamczyk, L., Adelman, J., Adersberger, M., Adiguzel, A., Adye, T., Affolder, A.A., et al.. - In: THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6044. - 79:5(2019). [10.1140/epjc/s10052-019-6847-8]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1306255
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