Jet flavour classification using DeepJet
E. S. Bols𝑏 CERN, Espl. des Particules 1, 1211 Meyrin, SwitzerlandJ. Kieseler𝑐 Imperial College London, South Kensington Campus, London SW7 2AZ, U.K. 𝑑 Reexen Technology 𝑒 National Centre for Scientific Research 'Demokritos', Patr. Gregoriou E & 27 Neapoleos Str, Athens, GreeceM. Verzetti𝑎 Vrije Universiteit Brussels, Pleinlaan 2, 1050 Brussels, BelgiumM. Stoye𝑏 CERN, Espl. des Particules 1, 1211 Meyrin, SwitzerlandA. Stakia
2020en
ABI
Annotatsiya
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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