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  • Performance of top-quark an...
    Aaboud, M.; Abbott, B.; Adamczyk, L.; Allaire, C.; Alonso, A.; Álvarez Piqueras, D.; Anthony, M. T.; Araujo Ferraz, V.; Barisits, M.-S.; Berger, N.; Bessner, M.; Bi, R.; Biebel, O.; Black, K. M.; Bozson, A. J.; Brendlinger, K.; Burdin, S.; Burmeister, I.; Buttar, C. M.; Camarri, P.; Carrillo-Montoya, G. D.; Ceradini, F.; Chow, Y. S.; Chwastowski, J. J.; Chytka, L.; Citterio, M.; Dahbi, S.; Dai, T.; De, K.; Dehghanian, N.; Derue, F.; Di Petrillo, K. F.; Dolejsi, J.; Dunford, M.; Duvnjak, D.; Dyndal, M.; Farbin, A.; Freundlich, E. M.; Galea, C.; Grivaz, J.-F.; Hassani, S.; Hays, J. M.; Heinrich, J. J.; Iuppa, R.; Jeong, J.; Karpov, S. N.; Kay, E. F.; Khoo, T. J.; Kitali, V.; Kobel, M.; Kodys, P.; Korn, A.; Krasznahorkay, A.; Kuechler, J. T.; Kvita, J.; LeCompte, T.; Lehmann Miotto, G.; Li, M.; Lou, X.; Maekawa, K.; Makida, Y.; Manousos, A.; Mantoani, M.; Millar, D. A.; Mogg, P.; Morii, M.; Muškinja, M.; Nagano, K.; Nuti, F.; Ocariz, J.; Peng, C.; Pohl, D.; Puzo, P.; Readioff, N. P.; Russell, H. L.; Sander, C. O.; Schouwenberg, J. F. P.; Sforza, F.; Snesarev, A. A.; Sommer, P.; Sowden, B. C.; Stanitzki, M. M.; Starovoitov, P.; Styles, N. A.; Tikhonov, Yu. A.; Tipton, P.; Trovato, F.; Tsiareshka, P. V.; Ueda, I.; Valls Ferrer, J. A.; Vlachos, S.; Wang, H.; Wiik-Fuchs, L. A. M.; Wolff, R.; Zalieckas, J.; Zhu, C. G.; Zhu, J.; Zobernig, G.; Zoch, K.; Zur Nedden, M.

    The European physical journal. C, Particles and fields, 2019, Letnik: 79, Številka: 5
    Journal Article

    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 t t ¯ and γ + jet and 36.7 fb - 1 for the dijet event topologies.