Jet-images — deep learning edition

  • Luke de Oliveira
  • Michael Kagan
  • Lester Mackey
  • Benjamin Nachman
  • Ariel Schwartzman
Open Access
Regular Article - Experimental Physics


Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physicallymotivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.


Jet substructure Hadron-Hadron scattering (experiments) 


Open Access

This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.


  1. [1]
    A. Altheimer et al., Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks, J. Phys. G 39 (2012) 063001 [arXiv:1201.0008] [INSPIRE].ADSCrossRefGoogle Scholar
  2. [2]
    A. Altheimer et al., Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012, Eur. Phys. J. C 74 (2014) 2792 [arXiv:1311.2708] [INSPIRE].
  3. [3]
    D. Adams et al., Towards an Understanding of the Correlations in Jet Substructure, Eur. Phys. J. C 75 (2015) 409 [arXiv:1504.00679] [INSPIRE].ADSCrossRefGoogle Scholar
  4. [4]
    J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging, JHEP 02 (2015) 118 [arXiv:1407.5675] [INSPIRE].ADSCrossRefGoogle Scholar
  5. [5]
    L.G. Almeida, M. Backović, M. Cliche, S.J. Lee and M. Perelstein, Playing Tag with ANN: Boosted Top Identification with Pattern Recognition, JHEP 07 (2015) 086 [arXiv:1501.05968] [INSPIRE].ADSCrossRefGoogle Scholar
  6. [6]
    K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556.
  7. [7]
    I.J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville and Y. Bengio, Maxout Networks, arXiv:1302.4389.
  8. [8]
    G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, arXiv:1207.0580.
  9. [9]
    CMS collaboration, Identification techniques for highly boosted W bosons that decay into hadrons, JHEP 12 (2014) 017 [arXiv:1410.4227] [INSPIRE].
  10. [10]
    ATLAS collaboration, Identification of boosted, hadronically-decaying W and Z bosons in \( \sqrt{s}=13 \) TeV Monte Carlo Simulations for ATLAS, ATL-PHYS-PUB-2015-033 (2015).
  11. [11]
    ATLAS collaboration, Performance of Boosted W Boson Identification with the ATLAS Detector, ATL-PHYS-PUB-2014-004 (2014).
  12. [12]
    ATLAS collaboration, Search for high-mass diboson resonances with boson-tagged jets in proton-proton collisions at \( \sqrt{s}=8 \) TeV with the ATLAS detector, JHEP 12 (2015) 055 [arXiv:1506.00962] [INSPIRE].
  13. [13]
    CMS collaboration, Search for massive resonances in dijet systems containing jets tagged as W or Z boson decays in pp collisions at \( \sqrt{s}=8 \) TeV, JHEP 08 (2014) 173 [arXiv:1405.1994] [INSPIRE].
  14. [14]
    CMS collaboration, Search for the production of an excited bottom quark decaying to tW in proton-proton collisions at \( \sqrt{s}=8 \) TeV, JHEP 01 (2016) 166 [arXiv:1509.08141] [INSPIRE].
  15. [15]
    CMS collaboration, Search for vector-like charge 2/3 T quarks in proton-proton collisions at \( \sqrt{s} \) = 8 TeV, Phys. Rev. D 93 (2016) 012003 [arXiv:1509.04177] [INSPIRE].
  16. [16]
    CMS collaboration, Search for pair-produced vectorlike B quarks in proton-proton collisions at \( \sqrt{s}=8 \) TeV, Phys. Rev. D 93 (2016) 112009 [arXiv:1507.07129] [INSPIRE].
  17. [17]
    CMS collaboration, Search for a massive resonance decaying into a Higgs boson and a W or Z boson in hadronic final states in proton-proton collisions at \( \sqrt{s}=8 \) TeV, JHEP 02 (2016) 145 [arXiv:1506.01443] [INSPIRE].
  18. [18]
    CMS collaboration, Search for a Higgs Boson in the Mass Range from 145 to 1000 GeV Decaying to a Pair of W or Z Bosons, JHEP 10 (2015) 144 [arXiv:1504.00936] [INSPIRE].
  19. [19]
    CMS collaboration, Search for Narrow High-Mass Resonances in Proton-Proton Collisions at \( \sqrt{s}=8 \) TeV Decaying to a Z and a Higgs Boson, Phys. Lett. B 748 (2015) 255 [arXiv:1502.04994] [INSPIRE].
  20. [20]
    ATLAS collaboration, Search for squarks and gluinos with the ATLAS detector in final states with jets and missing transverse momentum using \( \sqrt{s}=8 \) TeV proton-proton collision data, JHEP 09 (2014) 176 [arXiv:1405.7875] [INSPIRE].
  21. [21]
    ATLAS collaboration, Search for a high-mass Higgs boson decaying to a W boson pair in pp collisions at \( \sqrt{s}=8 \) TeV with the ATLAS detector, JHEP 01 (2016) 032 [arXiv:1509.00389] [INSPIRE].
  22. [22]
    ATLAS collaboration, Search for an additional, heavy Higgs boson in the HZZ decay channel at \( \sqrt{s}=8 \) TeV in pp collision data with the ATLAS detector, Eur. Phys. J. C 76 (2016) 45 [arXiv:1507.05930] [INSPIRE].
  23. [23]
    ATLAS collaboration, Search for production of W W/W Z resonances decaying to a lepton, neutrino and jets in pp collisions at \( \sqrt{s}=8 \) TeV with the ATLAS detector, Eur. Phys. J. C 75 (2015)209 [Erratum ibid. C 75 (2015) 370] [arXiv:1503.04677] [INSPIRE].
  24. [24]
    ATLAS collaboration, Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at \( \sqrt{s}=7 \) TeV with the ATLAS detector, New J. Phys. 16 (2014) 113013 [arXiv:1407.0800] [INSPIRE].
  25. [25]
    T. Sjöstrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE].
  26. [26]
    T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE].
  27. [27]
    M. Cacciari, G.P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].ADSCrossRefGoogle Scholar
  28. [28]
    M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].ADSCrossRefGoogle Scholar
  29. [29]
    D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE].ADSCrossRefGoogle Scholar
  30. [30]
    J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness, JHEP 03 (2011) 015 [arXiv:1011.2268] [INSPIRE].ADSCrossRefGoogle Scholar
  31. [31]
    A.J. Larkoski, D. Neill and J. Thaler, Jet Shapes with the Broadening Axis, JHEP 04 (2014) 017 [arXiv:1401.2158] [INSPIRE].ADSCrossRefGoogle Scholar
  32. [32]
    P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P.-A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res. 11 (2010) 3371.MathSciNetMATHGoogle Scholar
  33. [33]
    X. Glorot, A. Bordes and Y. Bengio, Deep sparse rectifier neural networks, J. Mach. Learn. Res. 15 (2011) 315.Google Scholar
  34. [34]
    D. Scherer, A. Muller and S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, in proceedings of The International Conference on Artificial Neural Networks (ICANN), Thessaloniki, Greece, 15-18 September 2010, Springer.Google Scholar
  35. [35]
    K. He, X. Zhang, S. Ren and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, arXiv:1502.01852.
  36. [36]
    F. Chollet, Keras,, (2015).
  37. [37]
    D.P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv:1412.6980.
  38. [38]
    Y. Nesterov, A method of solving a convex programming problem with convergence rate O(1/sqr(k)), Sov. Math. Dokl. 27 (1983) 372.MathSciNetMATHGoogle Scholar
  39. [39]
    J. Gallicchio and M.D. Schwartz, Seeing in Color: Jet Superstructure, Phys. Rev. Lett. 105 (2010) 022001 [arXiv:1001.5027] [INSPIRE].ADSCrossRefGoogle Scholar
  40. [40]
    ATLAS collaboration, Measurement of colour flow with the jet pull angle in \( t\overline{t} \) events using the ATLAS detector at \( \sqrt{s}=8 \) TeV, Phys. Lett. B 750 (2015) 475 [arXiv:1506.05629] [INSPIRE].
  41. [41]
    ATLAS collaboration, Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at \( \sqrt{s}=8 \) TeV, Eur. Phys. J. C 76 (2016) 154 [arXiv:1510.05821] [INSPIRE].
  42. [42]
    ATLAS collaboration, Identification of high transverse momentum top quarks in pp collisions at \( \sqrt{s}=8 \) TeV with the ATLAS detector, arXiv:1603.03127 [INSPIRE].
  43. [43]
    CMS collaboration, Boosted Top Jet Tagging at CMS, CMS-PAS-JME-13-007.

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Luke de Oliveira
    • 1
  • Michael Kagan
    • 2
  • Lester Mackey
    • 3
  • Benjamin Nachman
    • 2
  • Ariel Schwartzman
    • 2
  1. 1.Institute for Computational and Mathematical EngineeringStanford UniversityStanfordU.S.A.
  2. 2.SLAC National Accelerator LaboratoryStanford UniversityMenlo ParkU.S.A.
  3. 3.Department of StatisticsStanford UniversityStanfordU.S.A.

Personalised recommendations