  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.
