Performance of top-quark and WW-boson tagging with ATLAS in Run 2 of the LHC
Aaboud, Morad; Aad, Georges; Abbott, Brad; Abdinov, Ovsat Bahram oglu; Abeloos, Baptiste; Abhayasinghe, Deshan Kavishka; Abidi, Syed Haider; AbouZeid, Hass; Abraham, Nadine L.; Abramowicz, Halina; Buanes, Trygve; Djuvsland, Julia Isabell; Eigen, Gerald; Fomin, Nikolai; Lipniacka, Anna; Martin dit Latour, Bertrand; Mæland, Steffen; Stugu, Bjarne; Yang, Zongchang; Zalieckas, Justas; Bugge, Magnar Kopangen; Cameron, David Gordon; Catmore, James Richard; Feigl, Simon; Franconi, Laura; Garonne, Vincent; Gramstad, Eirik; Hellesund, Simen; Morisbak, Vanja; Oppen, Henrik; Ould-Saada, Farid; Pedersen, Maiken; Read, Alexander Lincoln; Røhne, Ole Myren; Sandaker, Heidi; Serfon, Cédric; Stapnes, Steinar; Vadla, Knut Oddvar Høie; Abreu, Henso; Abulaiti, Yiming; Acharya, Bobby S.; Adachi, Shunsuke; Adam, Lennart; Adamczyk, Leszek; Adelman, Jareed; Adersberger, Michael; Adigüzel, Aytül; Adye, Tim; Affolder, Anthony Allen; Afik, Yoav; ATLAS, Collaboration
Peer reviewed, Journal article
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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.