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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Original versionAaboud M, Aad G, Abbott B, Abdinov OBo, Abeloos B, Abhayasinghe DK, Abidi SH, AbouZeid H, Abraham NL, Abramowicz H, Buanes T, Djuvsland JI, Eigen G, Fomin N, Lipniacka A, Martin dit Latour B, Mæland S, Stugu B, Yang Z, Zalieckas J, Bugge MK, Cameron DG, Catmore JR, Feigl S, Franconi L, Garonne V, Gramstad E, Hellesund S, Morisbak V, Oppen H, Ould-Saada F, Pedersen M, Read AL, Røhne OM, Sandaker H, Serfon C, Stapnes S, Vadla KOH, Abreu H, Abulaiti Y, Acharya BS, Adachi S, Adam L, Adamczyk L, Adelman J, Adersberger M, Adigüzel A, Adye T, Affolder AA, Afik Y, ATLAS C. Performance of top-quark and WW-boson tagging with ATLAS in Run 2 of the LHC. European Physical Journal C. 2019;79:375 https://doi.org/10.1140/epjc/s10052-019-6847-8
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.