Identification Of Heavy, Energetic, Hadronically Decaying Particles Using Machine-Learning Techniques

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Tarih

2020

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

The CMS Collaboration

Erişim Hakkı

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

Anahtar Kelimeler

Large detector-systems performance, Pattern recognition, Cluster finding, calibration and fitting methods

Kaynak

WoS Q Değeri

Q4

Scopus Q Değeri

Q2

Cilt

Sayı

Künye

Journal Of Instrumentatıon, 15 (6), 2020