Jet Flavour Classification Using DeepJet
This addresses a critical problem for high-energy physics experiments at the LHC, but it appears incremental as it builds on existing deep learning approaches with specific architectural improvements.
The paper tackles jet flavour classification in high-energy physics by proposing DeepJet, a novel deep learning architecture that overcomes input size limitations of previous methods, resulting in improved heavy flavour classification performance and extension to quark-gluon tagging.
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.