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

Meridiani P.;Amapane N.;Argiro S.;Bellan R.;Bellora A.;Cappati A.;Costa M.;Covarelli R.;Kiani B.;Migliore E.;Monaco V.;Monteil E.;Obertino M. M.;Pacher L.;Angioni G. L. P.;Romero A.;Salvatico R.;Sola V.;Solano A.;Soldi D.;Shchelina K.;Rumerio P.;Ravera F.;
2020-01-01

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
15
6
P06005
P06005
Large detector-systems performance; Pattern recognition, cluster finding, calibration and fitting methods
Sirunyan A.M.; Tumasyan A.; Adam W.; Ambrogi F.; Bergauer T.; Dragicevic M.; Ero J.; Del Valle A.E.; Flechl M.; Fruhwirth R.; Jeitler M.; Krammer N.; ...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1787514
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