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 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.
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
Azzi, P.;Bisello, D.;Boletti, A.;Bragagnolo, A.;Carlin, R.;Checchia, P.;Gasparini, F.;Gasparini, U.;Gozzelino, A.;Hoh, S. Y.;Lujan, P.;Margoni, M.;Meneguzzo, A. T.;Pazzini, J.;Ronchese, P.;Rossin, R.;Simonetto, F.;Tosi, M.;Zanetti, M.;Zotto, P.;Zumerle, G.;Presilla, M.;Triossi, A.;Zucchetta, A.;
2020
Abstract
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 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.Pubblicazioni consigliate
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