Sirunyan, A. M.Işıldak, Bora2020-11-302020-11-302020-061748-0221http://hdl.handle.net/10679/7149https://doi.org/10.1088/1748-0221/15/06/P06005Machine-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 root S = 13 TeV, 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.engopenAccessarticle15600054535090000510.1088/1748-0221/15/06/P06005Large detector-systems performancePattern recognition, cluster finding, calibration and fitting methods2-s2.0-85088524436