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Identification of heavy, energetic, hadronically decaying particles using machine-learning techniquesSirunyan, A.M.Tumasyan, A.Adam, W.Ambrogi, F.Bergauer, T.Dragicevic, M.Erö, J.Escalante Del Valle, A.Flechl, M.Frühwirth, R.Jeitler, M.Krammer, N.Krätschmer, I.Liko, D.Madlener, T.Mikulec, I.Rad, N.Schieck, J.Schöfbeck, R.Spanring, M.Waltenberger, W.Wulz, C.-E.Zarucki, M.Drugakov, V.Mossolov, V.Suarez Gonzalez, J.Darwish, M.R.De Wolf, E.A.Di Croce, D.Janssen, X.Lelek, A.Pieters, M.Rejeb Sfar, H.Van Haevermaet, H.Van Mechelen, P.Van Putte, S.Van Remortel, N.Blekman, F.Bols, E.S.Chhibra, S.S.D'Hondt, J.De Clercq, J.Lontkovskyi, D.Lowette, S.Marchesini, I.Moortgat, S.Python, Q.Skovpen, K.Tavernier, S.Van Doninck, W.Large detector-systems performancePattern recognition, cluster finding, calibration and fitting methodsMachine learningUncertainty analysisCalibration methodDetector systemsFitting methodHadronic decaysLarge detector-system performanceLarge detectorsMachine learning techniquesPattern recognition, cluster finding, calibration and fitting methodPerformanceSystems performancePattern recognitionMachine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at s = 13TeV, corresponding to an integrated luminosity of 35.9 fb-1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency. © 2020 CERN for the benefit of the CMS collaboration..Institute of Physics2020Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/13691https://doi.org/10.1088/1748-0221/15/06/p060051748-0221enJournal of Instrumentationinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/13691