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arXiv · 1812.09223

Quark-Gluon Tagging: Machine Learning vs Detector

Gregor Kasieczka·Nicholas Kiefer·Tilman Plehn·Jennifer M. Thompson
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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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Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn, Jennifer M. Thompson. 2018-12-21. Quark-Gluon Tagging: Machine Learning vs Detector. https://doi.org/10.21468/scipostphys.6.6.069

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