Yetkin, Elif Aslı2020-12-082020-12-082020-061748-0221https://hdl.handle.net/11411/2781https://doi.org/10.1088/1748-0221/15/06/P0600588 pagesMachine-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/openAccessPattern recognition, cluster finding, calibration and fitting methodsLarge detector-systems performanceArticle10.1088/1748-0221/15/06/P06005Q3WOS:000545350900005