The CMS collaboration2020-11-042020-11-042020The CMS collaboration. "Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques." <i>Journal of Instrumentation,</i> 15, (2020) IOP: https://doi.org/10.1088/1748-0221/15/06/P06005.https://hdl.handle.net/1911/109513Machine-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.engOriginal content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Journal articlehttps://doi.org/10.1088/1748-0221/15/06/P06005