engleski

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

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 $\sqrt{; ; ; s}; ; ; =$ 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.

High energy physics ; Experimental particle physics ; LHC ; CMS ; Particle Physics Experiments ; Physics ; Vector boson scattering ; Hadron-Hadron scattering (experiments) ; Supersymmetry ; Higgs physics ; Particle and resonance production ; B physics ; Particle correlations and fluctuations ; Quarkonium ; Elementary Particles and Fields ; Beyond Standard Model ; Jets ; QCD ; Top physics ; Diboson ; Electroweak ; CKM matrix ; Top quark ; Large detector-systems performance ; Pattern recognition

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