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

Farina E. M.;Fraternali M.;Introzzi G.;Livan M.;Negri A.;Rebuzzi D. M.;Rimoldi A.;Sottocornola S.;
2019-01-01

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
The Physics category includes resources of a broad, general nature that contain materials from all areas of physics, The category also includes resources specifically concerned with the following physics sub-fields: mathematical physics, particle and nuclear physics, physics of fluids and plasmas, quantum physics, and theoretical physics.
Esperti anonimi
Inglese
Internazionale
STAMPA
79
5
http://link.springer-ny.com/link/service/journals/10052/index.htm
2934
info:eu-repo/semantics/article
262
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, ...espandi
1 Contributo su Rivista::1.1 Articolo in rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1287106
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