TY - JOUR
T1 - Performance of top-quark and $W$-boson tagging with ATLAS in Run 2 of the LHC
AU - Clark, Philip James
AU - Farrington, Sinead
AU - Faucci Giannelli, Michele
AU - Gao, Yanyan
AU - Hasib, Ahmed
AU - Leonidopoulos, Christos
AU - Martin, Victoria Jane
AU - Mijovic, Liza
AU - Wynne, Benjamin
AU - Collaboration, Atlas
PY - 2019/4/30
Y1 - 2019/4/30
N2 - The performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at $\sqrt{s}$ = 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 $t\bar{t}$ and $\gamma +\text {jet}$ and 36.7 fb$^{-1}$ for the dijet event topologies.
AB - The performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at $\sqrt{s}$ = 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 $t\bar{t}$ and $\gamma +\text {jet}$ and 36.7 fb$^{-1}$ for the dijet event topologies.
U2 - 10.1140/epjc/s10052-019-6847-8
DO - 10.1140/epjc/s10052-019-6847-8
M3 - Article
SN - 1434-6044
VL - C79
SP - 375
JO - The European Physical Journal C (EPJ C)
JF - The European Physical Journal C (EPJ C)
IS - 5
M1 - Aaboud:2018psm
ER -