PT Journal
AU ATLAS Collaboration (Aaboud, Mea
   Alvarez Piqueras, D
   Aparisi Pozo, JA
   Bailey, AJ
   Barranco Navarro, L
   Cabrera Urban, S
   Castillo, FL
   Castillo Gimenez, V
   Cerda Alberich, L
   Costa, MJ
   Escobar, C
   Estrada Pastor, O
   Ferrer, A
   Fiorini, L
   Fullana Torregrosa, E
   Fuster, J
   Garcia, C
   Garcia Navarro, JE
   Gonzalez de la Hoz, S
   Gonzalvo Rodriguez, GR
   Higon-Rodriguez, E
   Jimenez Pena, J
   Lacasta, C
   Lozano Bahilo, JJ
   Madaffari, D
   Mamuzic, J
   Marti-Garcia, S
   Melini, D
   Miñano, M
   Mitsou, VA
   Rodriguez Bosca, S
   Rodriguez Rodriguez, D
   Ruiz-Martinez, A
   Salt, J
   Santra, A
   Soldevila, U
   Sanchez, J
   Valero, A
   Valls Ferrer, JA
   Vos, M
TI Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC
SO European Physical Journal C
JI Eur. Phys. J. C
PY 2019
BP 375
EP 54pp
VL 79
IS 5
DI 10.1140/epjc/s10052-019-6847-8
LA English
AB The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV 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 36.1fb-1 for the tt and +jet and 36.7-1 for the dijet event topologies.
ER