Jet Flavour Classification Using DeepJet
High Energy Physics - Experiment
2020-12-14 v2 Data Analysis, Statistics and Probability
Machine Learning
Abstract
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern deep learning techniques. This new model, called DeepJet, overcomes the limitations in input size that affected previous approaches. As a result, the heavy flavour classification performance improves, and the model is extended to also perform quark-gluon tagging.
Cite
@article{arxiv.2008.10519,
title = {Jet Flavour Classification Using DeepJet},
author = {Emil Bols and Jan Kieseler and Mauro Verzetti and Markus Stoye and Anna Stakia},
journal= {arXiv preprint arXiv:2008.10519},
year = {2020}
}
Comments
14 pages, 9 figures, accepted for publication in JINST