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Performance of top-quark and ππ-boson tagging with ATLAS in Run 2 of the LHC
Aaboud, M. Rogan, Christopher ATLAS Collaboration
Aaboud, M.
Rogan, Christopher
ATLAS Collaboration
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Abstract
The performance of identification algorithms (βtaggersβ) for hadronically decaying top quarks and W bosons in pp collisions at π β = 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 fbβ1 for the π‘π‘Β― and πΎ+jet and 36.7 fbβ1 for the dijet event topologies.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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2019-04-30
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SpringerOpen
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Aaboud, M., Aad, G., Abbott, B. et al. Performance of top-quark and ππ-boson tagging with ATLAS in Run 2 of the LHC. Eur. Phys. J. C 79, 375 (2019). https://doi.org/10.1140/epjc/s10052-019-6847-8