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.

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

Ligabue F.
Membro del Collaboration Group
;
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 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
Settore PHYS-01/A - Fisica sperimentale delle interazioni fondamentali e applicazioni
Large detector-systems performance; Pattern recognition, cluster finding, calibration and fitting methods
   Advanced Multi-Variate Analysis for New Physics Searches at the LHC
   AMVA4NewPhysics
   European Commission
   Horizon 2020 Framework Programme
   675440

   Direct and indirect searches for new physics in events with top quarks using LHC proton-proton collisions at the CMS detector
   LHCTOPVLQ
   European Commission
   Horizon 2020 Framework Programme
   752730

   International Training Network for Statistics in High Energy Physics and Society
   INSIGHTS
   European Commission
   Horizon 2020 Framework Programme
   765710
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/149759
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