Deep learning in color: towards automated quark/gluon jet discrimination

Abstract

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

23 pages, 9 figures, updated to JHEP version, added table of contents, minor typos fixed

References in corpus (26)

• Deep Learning in Neural Networks: An Overview
• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• FastJet user manual
• Herwig++ Physics and Manual
• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• Herwig 7.0 / Herwig++ 3.0 Release Note
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Top-tagging: A Method for Identifying Boosted Hadronic Tops
• Performance of $b$-Jet Identification in the ATLAS Experiment
• Energy Correlation Functions for Jet Substructure
• Jet-Images -- Deep Learning Edition
• A determination of parton distributions with faithful uncertainty estimation
• Gaining (Mutual) Information about Quark/Gluon Discrimination
• Quark and Gluon Tagging at the LHC
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• Quark and Gluon Jet Substructure
• Performance of tau-lepton reconstruction and identification in CMS
• Factorization for groomed jet substructure beyond the next-to-leading logarithm
• Light-quark and gluon jet discrimination in pp collisions at $\sqrt{s}$ = 7 TeV with the ATLAS detector
• Multivariate discrimination and the Higgs + W/Z search
• Enhanced Higgs to ${\tau }^{+}{\tau }^{-}$ Searches with Deep Learning
• A neural network clustering algorithm for the ATLAS silicon pixel detector
• Associated jet and subjet rates in light-quark and gluon jet discrimination
• Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network
• Role of Neural Networks in the Search of the Higgs Boson at LHC

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