Miucci, AntonioAntonioMiucciMerlassino, ClaudiaClaudiaMerlassinoHaug, SigveSigveHaug0000-0003-0442-3361Weber, MichaelMichaelWeber0000-0002-2770-9031Anders, John KennethJohn KennethAndersBeck, Hans PeterHans PeterBeck0000-0001-7212-1096Ereditato, AntonioAntonioEreditatoRimoldi, MarcoMarcoRimoldiWeston, Thomas DanielThomas DanielWeston2024-10-052024-10-052019https://boris-portal.unibe.ch/handle/20.500.12422/54765The 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.en500 - Science::530 - Physicsarticle10.7892/boris.14363910.1140/epjc/s10052-019-6847-8