Jet flavour classification using DeepJet

2020/08/31 by Emil Bols, E. S. Bols, Jan Kieseler +7 · 208 citations
Engineering · Mathematics · Physics and Astronomy · #Aerospace engineering #Architecture #Artificial intelligence #Computer science #Engineering #Exploit #Flavour #High-Energy Particle Collisions Research #Jet (fluid) #Large Hadron Collider #Mechanics #Particle Detector Development and Performance #Particle physics #Particle physics theoretical and experimental studies #Physics #Quark #Range (aeronautics) #Systems engineering #Task (project management) #hep-ex #physics.data-an #stat.ML

paper · pdf · doi:10.1088/1748-0221/15/12/p12012

published in Journal of Instrumentation 15(12), P12012 (Institute of Physics) · 14 pages, 9 figures, accepted for publication in JINST

arxiv created 2020/10/27 · openalex publication_date 2020/12/09 · arxiv updated 2020/12/14 · openalex created_date 2020/12/21 · openalex updated_date 2026/08/06

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.

Citations

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Cited by

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• Search for charged-lepton flavour violation in top quark interactions with an up-type quark, a muon, and a τ lepton in proton-proton collisions at √(s) = 13 TeV
• A portrait of the Higgs boson by the CMS experiment ten years after the discovery