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


Sirunyan A. M. , Tumasyan A., Adam W., Ambrogi F., Bergauer T., Brandstetter J., ...Daha Fazla

JOURNAL OF INSTRUMENTATION, cilt.15, 2020 (SCI İndekslerine Giren Dergi) identifier identifier

Özet

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.