Sirunyan, A. M.Tumasyan, A.Adam, W.Ambrogi, F.Bergauer, T.Brandstetter, J.Dragicevic, M.2025-03-232025-03-2320201748-0221https://doi.org/10.1088/1748-0221/15/06/P06005https://hdl.handle.net/11486/5676Machine-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.eninfo:eu-repo/semantics/openAccessLarge detector-systems performancePattern recognition, cluster finding, calibration and fitting methodsArticle15610.1088/1748-0221/15/06/P060052-s2.0-85088524436Q2WOS:000545350900005Q3