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Performance of top-quark and [Formula omitted]-boson tagging with ATLAS in Run 2 of the LHC
The performance of identification algorithms ("taggers") for hadronically decaying top quarks and W bosons in pp collisions at [Formula omitted] = 13 TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studi...
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Published in: | The European physical journal. C, Particles and fields Particles and fields, 2019-04, Vol.79 (5), p.1-54 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | The performance of identification algorithms ("taggers") for hadronically decaying top quarks and W bosons in pp collisions at [Formula omitted] = 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 [Formula omitted] for the [Formula omitted] and [Formula omitted] and 36.7 fb [Formula omitted] for the dijet event topologies. |
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ISSN: | 1434-6044 1434-6052 |
DOI: | 10.1140/epjc/s10052-019-6847-8 |