Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
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Dosyalar
Tarih
2020-06
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IOP Publishing Ltd.
Erişim Hakkı
info:eu-repo/semantics/openAccess
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.
Açıklama
88 pages
Anahtar Kelimeler
Pattern recognition, cluster finding, calibration and fitting methods, Large detector-systems performance
Kaynak
Journal of Instrumentation
WoS Q Değeri
Q3
