Performance of top-quark and W -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 s = 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 γ+ jet and 36.7 fb - 1 for the dijet event topologies.
Duke Scholars
Altmetric Attention Stats
Dimensions Citation Stats
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Related Subject Headings
- Nuclear & Particles Physics
- 5107 Particle and high energy physics
- 5102 Atomic, molecular and optical physics
- 5101 Astronomical sciences
- 0206 Quantum Physics
- 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Related Subject Headings
- Nuclear & Particles Physics
- 5107 Particle and high energy physics
- 5102 Atomic, molecular and optical physics
- 5101 Astronomical sciences
- 0206 Quantum Physics
- 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics