Journal of High Energy Physics

, 2017:51 | Cite as

Pileup Mitigation with Machine Learning (PUMML)

  • Patrick T. Komiske
  • Eric M. Metodiev
  • Benjamin Nachman
  • Matthew D. Schwartz
Open Access
Regular Article - Theoretical Physics


Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.




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]
    CMS collaboration, Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV, 2017 JINST 12 P02014 [arXiv:1607.03663] [INSPIRE].
  2. [2]
    ATLAS collaboration, Jet energy scale measurements and their systematic uncertainties in proton-proton collisions at \( \sqrt{s}=13 \) TeV with the ATLAS detector, Phys. Rev. D 96 (2017) 072002 [arXiv:1703.09665] [INSPIRE].
  3. [3]
    CMS collaboration, Description and performance of track and primary-vertex reconstruction with the CMS tracker, 2014 JINST 9 P10009 [arXiv:1405.6569] [INSPIRE].
  4. [4]
    ATLAS collaboration, Characterization of Interaction-Point Beam Parameters Using the pp Event-Vertex Distribution Reconstructed in the ATLAS Detector at the LHC, ATLAS-CONF-2010-027 (2010) [INSPIRE].
  5. [5]
    ATLAS collaboration, Performance of primary vertex reconstruction in proton-proton collisions at \( \sqrt{s}=7 \) TeV in the ATLAS experiment, ATLAS-CONF-2010-069 (2010) [INSPIRE].
  6. [6]
    ATLAS collaboration, Performance of pile-up mitigation techniques for jets in pp collisions at \( \sqrt{s}=8 \) TeV using the ATLAS detector, Eur. Phys. J. C 76 (2016) 581 [arXiv:1510.03823] [INSPIRE].
  7. [7]
    ATLAS collaboration, Identification and rejection of pile-up jets at high pseudorapidity with the ATLAS detector, Eur. Phys. J. C 77 (2017) 580 [arXiv:1705.02211] [INSPIRE].
  8. [8]
    CMS collaboration, Pileup Jet Identification, CMS-PAS-JME-13-005 (2013) [INSPIRE].
  9. [9]
    M. Cacciari and G.P. Salam, Pileup subtraction using jet areas, Phys. Lett. B 659 (2008) 119 [arXiv:0707.1378] [INSPIRE].ADSCrossRefGoogle Scholar
  10. [10]
    CMS collaboration, Determination of Jet Energy Calibration and Transverse Momentum Resolution in CMS, 2011 JINST 6 P11002 [arXiv:1107.4277] [INSPIRE].
  11. [11]
    ATLAS collaboration, Jet energy measurement with the ATLAS detector in proton-proton collisions at \( \sqrt{s}=7 \) TeV, Eur. Phys. J. C 73 (2013) 2304 [arXiv:1112.6426] [INSPIRE].
  12. [12]
    ATLAS collaboration, Monte Carlo Calibration and Combination of In-situ Measurements of Jet Energy Scale, Jet Energy Resolution and Jet Mass in ATLAS, ATLAS-CONF-2015-037 (2015) [INSPIRE].
  13. [13]
    CMS collaboration, Jet energy scale and resolution performances with 13 TeV data, CMS-DP-2016-020 (2016).
  14. [14]
    ATLAS collaboration, Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1, Eur. Phys. J. C 77 (2017) 490 [arXiv:1603.02934] [INSPIRE].
  15. [15]
    CMS collaboration, Particle-flow reconstruction and global event description with the CMS detector, 2017 JINST 12 P10003 [arXiv:1706.04965] [INSPIRE].
  16. [16]
    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
  17. [17]
    D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE].ADSCrossRefGoogle Scholar
  18. [18]
    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].ADSGoogle Scholar
  19. [19]
    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].ADSGoogle Scholar
  20. [20]
    A.J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop, JHEP 05 (2014) 146 [arXiv:1402.2657] [INSPIRE].ADSCrossRefGoogle Scholar
  21. [21]
    M. Cacciari, G.P. Salam and G. Soyez, SoftKiller, a particle-level pileup removal method, Eur. Phys. J. C 75 (2015) 59 [arXiv:1407.0408] [INSPIRE].ADSCrossRefGoogle Scholar
  22. [22]
    D. Krohn, M.D. Schwartz, M. Low and L.-T. Wang, Jet Cleansing: Pileup Removal at High Luminosity, Phys. Rev. D 90 (2014) 065020 [arXiv:1309.4777] [INSPIRE].ADSGoogle Scholar
  23. [23]
    D. Bertolini, P. Harris, M. Low and N. Tran, Pileup Per Particle Identification, JHEP 10 (2014) 059 [arXiv:1407.6013] [INSPIRE].
  24. [24]
    P. Berta, M. Spousta, D.W. Miller and R. Leitner, Particle-level pileup subtraction for jets and jet shapes, JHEP 06 (2014) 092 [arXiv:1403.3108] [INSPIRE].ADSCrossRefGoogle Scholar
  25. [25]
    ATLAS collaboration, Constituent-level pile-up mitigation techniques in ATLAS, ATLAS-CONF-2017-065 (2017) [INSPIRE].
  26. [26]
    D. Bertolini, T. Chan and J. Thaler, Jet Observables Without Jet Algorithms, JHEP 04 (2014) 013 [arXiv:1310.7584] [INSPIRE].ADSCrossRefGoogle Scholar
  27. [27]
    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
  28. [28]
    L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images — deep learning edition, JHEP 07 (2016) 069 [arXiv:1511.05190] [INSPIRE].CrossRefGoogle Scholar
  29. [29]
    P. Baldi, K. Bauer, C. Eng, P. Sadowski and D. Whiteson, Jet Substructure Classification in High-Energy Physics with Deep Neural Networks, Phys. Rev. D 93 (2016) 094034 [arXiv:1603.09349] [INSPIRE].ADSGoogle Scholar
  30. [30]
    J. Barnard, E.N. Dawe, M.J. Dolan and N. Rajcic, Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks, Phys. Rev. D 95 (2017) 014018 [arXiv:1609.00607] [INSPIRE].ADSGoogle Scholar
  31. [31]
    G. Kasieczka, T. Plehn, M. Russell and T. Schell, Deep-learning Top Taggers or The End of QCD?, JHEP 05 (2017) 006 [arXiv:1701.08784] [INSPIRE].ADSCrossRefGoogle Scholar
  32. [32]
    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
  33. [33]
    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].CrossRefGoogle Scholar
  34. [34]
    M. Paganini, L. de Oliveira and B. Nachman, CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks, arXiv:1705.02355 [INSPIRE].
  35. [35]
    ATLAS collaboration, ATLAS Phase-II Upgrade Scoping Document, CERN-LHCC-2015-020 (2015).
  36. [36]
    CMS collaboration, CMS Phase II Upgrade Scope Document, CERN-LHCC-2015-019 (2015).
  37. [37]
    CMS collaboration, Technical Proposal for the Phase-II Upgrade of the CMS Detector, CERN-LHCC-2015-010 (2015) [INSPIRE].
  38. [38]
    F. Chollet, Keras, (2015)
  39. [39]
    J. Bergstra et al., Theano: A cpu and gpu math compiler in python, in proceedings of the 9th Python in Science Conference, Austin, Texas, U.S.A., 28 June-3 July 2010, pp. 1-7.Google Scholar
  40. [40]
    K. He, X. Zhang, S. Ren and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7-13 December 2015, pp. 1026-1034 [ 10.1109/ICCV.2015.123] [arXiv:1502.01852].
  41. [41]
    D. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv:1412.6980.
  42. [42]
    T. Sjöstrand et al., An Introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012] [INSPIRE].
  43. [43]
    M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].ADSCrossRefGoogle Scholar
  44. [44]
    M. Cacciari, G.P. Salam and G. Soyez, The Anti-k t jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].ADSCrossRefzbMATHGoogle Scholar
  45. [45]
    J. Pumplin, How to tell quark jets from gluon jets, Phys. Rev. D 44 (1991) 2025 [INSPIRE].ADSGoogle Scholar
  46. [46]
    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
  47. [47]
    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
  48. [48]
    ATLAS collaboration, Simulation of Pile-up in the ATLAS Experiment, J. Phys. Conf. Ser. 513 (2014) 022024 [INSPIRE].
  49. [49]
    ATLAS collaboration, ATLAS Simulation using Real Data: Embedding and Overlay, J. Phys. Conf. Ser. 898 (2017) 042004 [INSPIRE].

Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Patrick T. Komiske
    • 1
  • Eric M. Metodiev
    • 1
  • Benjamin Nachman
    • 2
  • Matthew D. Schwartz
    • 3
  1. 1.Center for Theoretical Physics, Massachusetts Institute of TechnologyCambridgeU.S.A.
  2. 2.Physics DivisionLawrence Berkeley National LaboratoryBerkeleyU.S.A.
  3. 3.Department of PhysicsHarvard UniversityCambridgeU.S.A.

Personalised recommendations