Quark-Gluon Tagging: Machine Learning vs Detector
High Energy Physics - Phenomenology
2019-06-19 v2
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.
Cite
@article{arxiv.1812.09223,
title = {Quark-Gluon Tagging: Machine Learning vs Detector},
author = {Gregor Kasieczka and Nicholas Kiefer and Tilman Plehn and Jennifer M. Thompson},
journal= {arXiv preprint arXiv:1812.09223},
year = {2019}
}