Quark-Gluon Jet Discrimination Using Convolutional Neural Networks

Jason Sang Hun Lee, Inkyu Park, Ian James Watson, Seungjin Yang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Currently, newly developed artificial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classification of particle physics collider data. One such challenging task is to distinguish quark-initiated jets from gluon-initiated jets. Following previous work, we treat the jet as an image by pixelizing track information and calorimeter deposits as reconstructed by the detector. We test the deep learning paradigm by training several recently developed, state-of-the-art convolutional neural networks on the quark-gluon discrimination task. We compare the results obtained using various network architectures trained for quark-gluon discrimination and also a boosted decision tree (BDT) trained on summary variables.

Original languageEnglish
Pages (from-to)219-223
Number of pages5
JournalJournal of the Korean Physical Society
Issue number3
StatePublished - 1 Feb 2019


  • Fragmentation
  • Jet
  • Jet tagging
  • Machine learning
  • QCD


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