Advertisement

Journal of High Energy Physics

, 2018:59 | Cite as

An operational definition of quark and gluon jets

  • Patrick T. Komiske
  • Eric M. Metodiev
  • Jesse Thaler
Open Access
Regular Article - Theoretical Physics
  • 31 Downloads

Abstract

While “quark” and “gluon” jets are often treated as separate, well-defined objects in both theoretical and experimental contexts, no precise, practical, and hadron-level definition of jet flavor presently exists. To remedy this issue, we develop and advocate for a data-driven, operational definition of quark and gluon jets that is readily applicable at colliders. Rather than specifying a per-jet flavor label, we aggregately define quark and gluon jets at the distribution level in terms of measured hadronic cross sections. Intuitively, quark and gluon jets emerge as the two maximally separable categories within two jet samples in data. Benefiting from recent work on data-driven classifiers and topic modeling for jets, we show that the practical tools needed to implement our definition already exist for experimental applications. As an informative example, we demonstrate the power of our operational definition using Z+jet and dijet samples, illustrating that pure quark and gluon distributions and fractions can be successfully extracted in a fully well-defined manner.

Keywords

Jets 

Notes

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.

References

  1. [1]
    M.H. Seymour, Tagging a heavy Higgs boson, in the proceedings of the Large Hadron Collider Workshop, October 4-9, Aachen, Germany (1990).Google Scholar
  2. [2]
    M.H. Seymour, Searches for new particles using cone and cluster jet algorithms: a comparative study, Z. Phys. C 62 (1994) 127 [INSPIRE].
  3. [3]
    J.M. Butterworth, B.E. Cox and J.R. Forshaw, W W scattering at the CERN LHC, Phys. Rev. D 65 (2002) 096014 [hep-ph/0201098] [INSPIRE].
  4. [4]
    J.M. Butterworth, J.R. Ellis and A.R. Raklev, Reconstructing sparticle mass spectra using hadronic decays, JHEP 05 (2007) 033 [hep-ph/0702150] [INSPIRE].
  5. [5]
    J.M. Butterworth, A.R. Davison, M. Rubin and G.P. Salam, Jet substructure as a new Higgs search channel at the LHC, Phys. Rev. Lett. 100 (2008) 242001 [arXiv:0802.2470] [INSPIRE].ADSCrossRefGoogle Scholar
  6. [6]
    A. Abdesselam et al., Boosted objects: a probe of beyond the standard model physics, Eur. Phys. J. C 71 (2011) 1661 [arXiv:1012.5412] [INSPIRE].
  7. [7]
    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].
  8. [8]
    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].
  9. [9]
    D. Adams et al., Towards an understanding of the correlations in jet substructure, Eur. Phys. J. C 75 (2015) 409 [arXiv:1504.00679] [INSPIRE].
  10. [10]
    A.J. Larkoski, I. Moult and B. Nachman, Jet substructure at the Large Hadron Collider: a review of recent advances in theory and machine learning, arXiv:1709.04464 [INSPIRE].
  11. [11]
    L. Asquith et al., Jet substructure at the Large Hadron Collider: experimental review, arXiv:1803.06991 [INSPIRE].
  12. [12]
    C.F. Berger, T. Kucs and G.F. Sterman, Event shape/energy flow correlations, Phys. Rev. D 68 (2003) 014012 [hep-ph/0303051] [INSPIRE].
  13. [13]
    L.G. Almeida et al., Substructure of high-p T jets at the LHC, Phys. Rev. D 79 (2009) 074017 [arXiv:0807.0234] [INSPIRE].
  14. [14]
    S.D. Ellis et al., Jet shapes and jet algorithms in SCET, JHEP 11 (2010) 101 [arXiv:1001.0014] [INSPIRE].ADSCrossRefGoogle Scholar
  15. [15]
    J. Thaler and K. Van Tilburg, Identifying boosted objects with N-subjettiness, JHEP 03 (2011) 015 [arXiv:1011.2268] [INSPIRE].ADSCrossRefGoogle Scholar
  16. [16]
    J. Thaler and K. Van Tilburg, Maximizing boosted top identification by minimizing N-subjettiness, JHEP 02 (2012) 093 [arXiv:1108.2701] [INSPIRE].ADSCrossRefGoogle Scholar
  17. [17]
    D. Krohn, M.D. Schwartz, T. Lin and W.J. Waalewijn, Jet charge at the LHC, Phys. Rev. Lett. 110 (2013) 212001 [arXiv:1209.2421] [INSPIRE].ADSCrossRefGoogle Scholar
  18. [18]
    A.J. Larkoski, G.P. Salam and J. Thaler, Energy correlation functions for jet substructure, JHEP 06 (2013) 108 [arXiv:1305.0007] [INSPIRE].ADSMathSciNetCrossRefzbMATHGoogle Scholar
  19. [19]
    A.J. Larkoski, D. Neill and J. Thaler, Jet shapes with the broadening axis, JHEP 04 (2014) 017 [arXiv:1401.2158] [INSPIRE].ADSCrossRefGoogle Scholar
  20. [20]
    A.J. Larkoski, J. Thaler and W.J. Waalewijn, Gaining (mutual) information about quark/gluon discrimination, JHEP 11 (2014) 129 [arXiv:1408.3122] [INSPIRE].ADSCrossRefGoogle Scholar
  21. [21]
    I. Moult, L. Necib and J. Thaler, New angles on energy correlation functions, JHEP 12 (2016) 153 [arXiv:1609.07483] [INSPIRE].ADSCrossRefGoogle Scholar
  22. [22]
    P.T. Komiske, E.M. Metodiev and J. Thaler, Energy flow polynomials: A complete linear basis for jet substructure, JHEP 04 (2018) 013 [arXiv:1712.07124] [INSPIRE].CrossRefGoogle Scholar
  23. [23]
    D. Krohn, J. Thaler and L.-T. Wang, Jet trimming, JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE].
  24. [24]
    S.D. Ellis, C.K. Vermilion and J.R. Walsh, Recombination algorithms and jet substructure: pruning as a tool for heavy particle searches, Phys. Rev. D 81 (2010) 094023 [arXiv:0912.0033] [INSPIRE].
  25. [25]
    S.D. Ellis, C.K. Vermilion and J.R. Walsh, Techniques for improved heavy particle searches with jet substructure, Phys. Rev. D 80 (2009) 051501 [arXiv:0903.5081] [INSPIRE].
  26. [26]
    M. Dasgupta, A. Fregoso, S. Marzani and G.P. Salam, Towards an understanding of jet substructure, JHEP 09 (2013) 029 [arXiv:1307.0007] [INSPIRE].ADSCrossRefGoogle Scholar
  27. [27]
    A.J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft drop, JHEP 05 (2014) 146 [arXiv:1402.2657] [INSPIRE].
  28. [28]
    H.P. Nilles and K.H. Streng, Quark-gluon separation in three jet events, Phys. Rev. D 23 (1981) 1944 [INSPIRE].
  29. [29]
    L.M. Jones, Tests for determining the parton ancestor of a hadron jet, Phys. Rev. D 39 (1989) 2550 [INSPIRE].
  30. [30]
    Z. Fodor, How to see the differences between quark and gluon jets, Phys. Rev. D 41 (1990) 1726 [INSPIRE].
  31. [31]
    L. Jones, Towards a systematic jet classification, Phys. Rev. D 42 (1990) 811 [INSPIRE].
  32. [32]
    L. Lönnblad, C. Peterson and T. Rognvaldsson, Using neural networks to identify jets, Nucl. Phys. B 349 (1991) 675 [INSPIRE].
  33. [33]
    J. Pumplin, How to tell quark jets from gluon jets, Phys. Rev. D 44 (1991) 2025 [INSPIRE].
  34. [34]
    J. Gallicchio and M.D. Schwartz, Quark and gluon tagging at the LHC, Phys. Rev. Lett. 107 (2011) 172001 [arXiv:1106.3076] [INSPIRE].
  35. [35]
    J. Gallicchio and M.D. Schwartz, Quark and gluon jet substructure, JHEP 04 (2013) 090 [arXiv:1211.7038] [INSPIRE].ADSCrossRefGoogle Scholar
  36. [36]
    B. Bhattacherjee et al., Associated jet and subjet rates in light-quark and gluon jet discrimination, JHEP 04 (2015) 131 [arXiv:1501.04794] [INSPIRE].ADSCrossRefGoogle Scholar
  37. [37]
    D. Ferreira de Lima, P. Petrov, D. Soper and M. Spannowsky, Quark-gluon tagging with shower deconstruction: unearthing dark matter and Higgs couplings, Phys. Rev. D 95 (2017) 034001 [arXiv:1607.06031] [INSPIRE].
  38. [38]
    B. Bhattacherjee et al., Quark-gluon discrimination in the search for gluino pair production at the LHC, JHEP 01 (2017) 044 [arXiv:1609.08781] [INSPIRE].ADSCrossRefGoogle Scholar
  39. [39]
    P.T. Komiske, E.M. Metodiev and M.D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination, JHEP 01 (2017) 110 [arXiv:1612.01551] [INSPIRE].ADSCrossRefzbMATHGoogle Scholar
  40. [40]
    J. Davighi and P. Harris, Fractal based observables to probe jet substructure of quarks and gluons, Eur. Phys. J. C 78 (2018) 334 [arXiv:1703.00914] [INSPIRE].
  41. [41]
    T. Cheng, Recursive neural networks in quark/gluon tagging, Comput. Softw. Big Sci. 2 (2018) 3 [arXiv:1711.02633] [INSPIRE].CrossRefGoogle Scholar
  42. [42]
    Y. Sakaki, Quark jet rates and quark/gluon discrimination in multi-jet final states, arXiv:1807.01421 [INSPIRE].
  43. [43]
    G.P. Salam, Towards jetography, Eur. Phys. J. C 67 (2010) 637 [arXiv:0906.1833] [INSPIRE].
  44. [44]
    A. Banfi, G.P. Salam and G. Zanderighi, Infrared safe definition of jet flavor, Eur. Phys. J. C 47 (2006) 113 [hep-ph/0601139] [INSPIRE].
  45. [45]
    A. Buckley and C. Pollard, QCD-aware partonic jet clustering for truth-jet flavour labelling, Eur. Phys. J. C 76 (2016) 71 [arXiv:1507.00508] [INSPIRE].
  46. [46]
    J. Gallicchio and M.D. Schwartz, Pure samples of quark and gluon jets at the LHC, JHEP 10 (2011) 103 [arXiv:1104.1175] [INSPIRE].ADSCrossRefGoogle Scholar
  47. [47]
    C. Frye, A.J. Larkoski, M.D. Schwartz and K. Yan, Precision physics with pile-up insensitive observables, arXiv:1603.06375 [INSPIRE].
  48. [48]
    C. Frye, A.J. Larkoski, M.D. Schwartz and K. Yan, Factorization for groomed jet substructure beyond the next-to-leading logarithm, JHEP 07 (2016) 064 [arXiv:1603.09338] [INSPIRE].ADSCrossRefGoogle Scholar
  49. [49]
    J.R. Andersen et al., Les Houches 2015: physics at TeV colliders standard model working group report, arXiv:1605.04692 [INSPIRE].
  50. [50]
    P. Gras et al., Systematics of quark/gluon tagging, JHEP 07 (2017) 091 [arXiv:1704.03878] [INSPIRE].ADSCrossRefGoogle Scholar
  51. [51]
    CMS collaboration, Performance of quark/gluon discrimination in 8 TeV pp data, CMS-PAS-JME-13-002 (2013).
  52. [52]
    ATLAS collaboration, Light-quark and gluon jet discrimination in pp collisions at \( \sqrt{s} = 7 \) TeV with the ATLAS detector, Eur. Phys. J. C 74 (2014) 3023 [arXiv:1405.6583] [INSPIRE].
  53. [53]
    ATLAS collaboration, Measurement of the charged-particle multiplicity inside jets from \( \sqrt{s} = 8 \) TeV pp collisions with the ATLAS detector, Eur. Phys. J. C 76 (2016) 322 [arXiv:1602.00988] [INSPIRE].
  54. [54]
    CMS collaboration, Performance of quark/gluon discrimination in 13 TeV data, CMS-DP-2016-070 (2016).
  55. [55]
    ATLAS collaboration, Quark versus gluon jet tagging using charged particle multiplicity with the ATLAS detector, ATL-PHYS-PUB-2017-009 (2017).
  56. [56]
    CMS collaboration, Measurement of jet substructure observables in tt events from proton-proton collisions at \( \sqrt{s}=13 \) TeV, arXiv:1808.07340 [INSPIRE].
  57. [57]
    J.H. Collins, K. Howe and B. Nachman, CWoLa hunting: extending the bump hunt with machine learning, arXiv:1805.02664 [INSPIRE].
  58. [58]
    J.R. Andersen et al., Les Houches 2017: physics at TeV colliders standard model working group report, talk given at the 10th Les Houches Workshop on Physics at TeV Colliders (PhysTeV 2017), June 5-23, Les Houches, France (2018), arXiv:1803.07977 [INSPIRE].
  59. [59]
    D. Reichelt, P. Richardson and A. Siodmok, Improving the simulation of quark and gluon jets with HERWIG 7, Eur. Phys. J. C 77 (2017) 876 [arXiv:1708.01491] [INSPIRE].
  60. [60]
    E.M. Metodiev, B. Nachman and J. Thaler, Classification without labels: learning from mixed samples in high energy physics, JHEP 10 (2017) 174 [arXiv:1708.02949] [INSPIRE].ADSCrossRefGoogle Scholar
  61. [61]
    E.M. Metodiev and J. Thaler, Jet Topics: disentangling quarks and gluons at colliders, Phys. Rev. Lett. 120 (2018) 241602 [arXiv:1802.00008] [INSPIRE].ADSCrossRefGoogle Scholar
  62. [62]
    J. Neyman and E.S. Pearson, On the problem of the most efficient tests of statistical hypotheses, Phil. Trans. Roy. Soc. London A 231 (1933) 289.Google Scholar
  63. [63]
    G. Blanchard et al., Classification with asymmetric label noise: Consistency and maximal denoising, Electron. J. Stat. 10 (2016) 2780.MathSciNetCrossRefzbMATHGoogle Scholar
  64. [64]
    T. Cohen, M. Freytsis and B. Ostdiek, (Machine) learning to do more with less, JHEP 02 (2018) 034 [arXiv:1706.09451] [INSPIRE].
  65. [65]
    P.T. Komiske, E.M. Metodiev, B. Nachman and M.D. Schwartz, Learning to classify from impure samples with high-dimensional data, Phys. Rev. D 98 (2018) 011502 [arXiv:1801.10158] [INSPIRE].
  66. [66]
    L.M. Dery, B. Nachman, F. Rubbo and A. Schwartzman, Weakly supervised classification in high energy physics, JHEP 05 (2017) 145 [arXiv:1702.00414] [INSPIRE].ADSCrossRefzbMATHGoogle Scholar
  67. [67]
    J. Katz-Samuels, G. Blanchard and C. Scott, Decontamination of mutual contamination models, arXiv:1710.01167.
  68. [68]
    S. Arora, R. Ge and A. Moitra, Learning topic models — Going beyond SVD, in the proceedings of the 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science (FOCS’12), October 20-23, New Brunswick, U.S.A. (2012).Google Scholar
  69. [69]
    A. Andreassen, I. Feige, C. Frye and M.D. Schwartz, JUNIPR: a framework for unsupervised machine learning in particle physics, arXiv:1804.09720 [INSPIRE].
  70. [70]
    CMS collaboration, Studies of jet mass in dijet and W/Z + jet events, JHEP 05 (2013) 090 [arXiv:1303.4811] [INSPIRE].
  71. [71]
    T. Sjöstrand et al., An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012] [INSPIRE].
  72. [72]
    M. Cacciari, G.P. Salam and G. Soyez, FastJet user manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].
  73. [73]
    M. Cacciari, G.P. Salam and G. Soyez, The anti-k t jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].
  74. [74]
    P.T. Komiske, E.M. Metodiev, B. Nachman and M.D. Schwartz, Pileup Mitigation with Machine Learning (PUMML), JHEP 12 (2017) 051 [arXiv:1707.08600] [INSPIRE].ADSCrossRefGoogle Scholar
  75. [75]
    S. Chang, T. Cohen and B. Ostdiek, What is the machine learning?, Phys. Rev. D 97 (2018) 056009 [arXiv:1709.10106] [INSPIRE].
  76. [76]
    T. Roxlo and M. Reece, Opening the black box of neural nets: case studies in stop/top discrimination, arXiv:1804.09278 [INSPIRE].
  77. [77]
    L. de Oliveira, M. Paganini and B. Nachman, Learning particle physics by example: location-aware generative adversarial networks for physics synthesis, Comput. Softw. Big Sci. 1 (2017) 4 [arXiv:1701.05927] [INSPIRE].
  78. [78]
    M. Paganini, L. de Oliveira and B. Nachman, Accelerating science with generative adversarial networks: an application to 3D particle showers in multilayer calorimeters, Phys. Rev. Lett. 120 (2018) 042003 [arXiv:1705.02355] [INSPIRE].
  79. [79]
    M. Paganini, L. de Oliveira and B. Nachman, CaloGAN: simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks, Phys. Rev. D 97 (2018) 014021 [arXiv:1712.10321] [INSPIRE].
  80. [80]
    R.T. D’Agnolo and A. Wulzer, Learning new physics from a machine, arXiv:1806.02350 [INSPIRE].
  81. [81]
    K. Fraser and M.D. Schwartz, Jet charge and machine learning, JHEP 10 (2018) 093 [arXiv:1803.08066] [INSPIRE].CrossRefGoogle Scholar
  82. [82]
    C. Frye, A.J. Larkoski, J. Thaler and K. Zhou, Casimir meets Poisson: improved quark/gluon discrimination with counting observables, JHEP 09 (2017) 083 [arXiv:1704.06266] [INSPIRE].ADSCrossRefGoogle Scholar
  83. [83]
  84. [84]
    K. Datta and A. Larkoski, How much information is in a jet?, JHEP 06 (2017) 073 [arXiv:1704.08249] [INSPIRE].ADSCrossRefGoogle Scholar
  85. [85]
    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
  86. [86]
    L. de Oliveira et al., Jet-images — Deep learning edition, JHEP 07 (2016) 069 [arXiv:1511.05190] [INSPIRE].CrossRefGoogle Scholar
  87. [87]
    P. Baldi et al., Jet substructure classification in high-energy physics with deep neural networks, Phys. Rev. D 93 (2016) 094034 [arXiv:1603.09349] [INSPIRE].
  88. [88]
    D. Guest et al., Jet flavor classification in high-energy physics with deep neural networks, Phys. Rev. D 94 (2016) 112002 [arXiv:1607.08633] [INSPIRE].
  89. [89]
  90. [90]
    F. Pedregosa et al., Scikit-learn: machine learning in Python, J. Mach. Learning Res. 12 (2011) 2825.MathSciNetzbMATHGoogle Scholar
  91. [91]
    G. Louppe, K. Cho, C. Becot and K. Cranmer, QCD-aware recursive neural networks for jet physics, arXiv:1702.00748 [INSPIRE].
  92. [92]
    A. Butter, G. Kasieczka, T. Plehn and M. Russell, Deep-learned top tagging with a Lorentz layer, SciPost Phys. 5 (2018) 028 [arXiv:1707.08966] [INSPIRE].ADSCrossRefGoogle Scholar
  93. [93]
    S. Egan et al., Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC, arXiv:1711.09059 [INSPIRE].
  94. [94]
    P.T. Komiske, E.M. Metodiev and J. Thaler, Energy flow networks: deep sets for particle jets, arXiv:1810.05165 [INSPIRE].
  95. [95]
    F. Chollet, Keras, https://github.com/fchollet/keras (2017).
  96. [96]
    M. Abadi et al., Tensorflow: a system for large-scale machine learning, OSDI 16 (2016) 265.Google Scholar
  97. [97]
    V. Nair and G.E. Hinton, Rectified linear units improve restricted Boltzmann machines, in the proceedings of the 27th International Conference on Machine learning (ICML-10), June 21-24, Haifa, Israel (2010).Google Scholar
  98. [98]
    K. He, X. Zhang, S. Ren and J. Sun, Delving deep into rectifiers: surpassing human-level performance on imagenet classification, in the proceedings IEEE International Conference on Computer Vision (ICCV2015), December 11-18, Santiago, Chile (2015).Google Scholar
  99. [99]
    D.P. Kingma and J. Ba, Adam: a method for stochastic optimization, arXiv:1412.6980 [INSPIRE].
  100. [100]
    S. Gao, C.H. Lee and J.H. Lim, An ensemble classifier learning approach to ROC optimization, 18th International Conference on Pattern Recognition (ICPR’06), August 20-24, Hong Kong (2006).Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Center for Theoretical PhysicsMassachusetts Institute of TechnologyCambridgeU.S.A.

Personalised recommendations