Jet Flavour Classification Using DeepJet
Emil Bols, Jan Kieseler, Mauro Verzetti, Markus Stoye, Anna Stakia

TL;DR
DeepJet is a novel deep learning architecture that significantly improves jet flavour classification and quark-gluon tagging performance in high-energy physics experiments by overcoming previous input size limitations.
Contribution
The paper introduces DeepJet, a new deep learning model that enhances jet flavour classification and extends to quark-gluon tagging, addressing previous input size constraints.
Findings
Improved heavy flavour classification accuracy.
Extended capabilities to quark-gluon tagging.
Overcame input size limitations of prior models.
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.
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