Quark-gluon tagging: Machine learning vs detector

Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn, Jennifer M. Thompson

SciPost Phys. 6, 069 (2019) · published 18 June 2019

Abstract

Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists.


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Gluons Large Hadron Collider (LHC) Machine learning (ML) Tagging

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