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

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.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: Journal of instrumentation, 2020, 15, P06005

Editorial: Institute of Physics

 Fecha de publicación: 01/06/2020

Nº de páginas: 88

Tipo de publicación: Artículo de Revista

 DOI: 10.1088/1748-0221/15/06/P06005

ISSN: 1748-0221

Url de la publicación: https://doi.org/10.1088/1748-0221/15/06/P06005