Machine-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). Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques [journal article - articolo]. In JOURNAL OF INSTRUMENTATION. Retrieved from http://hdl.handle.net/10446/163542

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Re V.;Vai I.;