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Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
journal contribution
posted on 2023-06-09, 18:55 authored by N L Abraham, Benedict AllbrookeBenedict Allbrooke, Lily AsquithLily Asquith, Alessandro CerriAlessandro Cerri, Samuel Jones, Antonella De SantoAntonella De Santo, Fabrizio SalvatoreFabrizio Salvatore, Kate ShawKate Shaw, Thomas StevensonThomas Stevenson, K Suruliz, Mark SuttonMark Sutton, Fabio Tresoldi, Fabrizio Trovato, Iacopo Vivarelli, E Winkels, The ATLAS Collaboration, othersThe performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at vs=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 ?+jet and 36.7 fb -1 -1 for the dijet event topologies.
History
Publication status
- Published
File Version
- Published version
Journal
European Physical Journal C: Particles and FieldsISSN
1434-6044Publisher
EDP SciencesExternal DOI
Issue
5Volume
79Page range
1-54Article number
a375Department affiliated with
- Physics and Astronomy Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2019-09-20First Open Access (FOA) Date
2019-09-20First Compliant Deposit (FCD) Date
2019-09-03Usage metrics
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