Pileup and underlying event mitigation with iterative constituent subtraction

  • P. BertaEmail author
  • L. Masetti
  • D.W. Miller
  • M. Spousta
Open Access
Regular Article - Experimental Physics


The hard-scatter processes in hadronic collisions are often largely contaminated with soft background coming from pileup in proton-proton collisions, or underlying event in heavy-ion collisions. This paper presents a new background subtraction method for jets and event observables (such as missing transverse energy) which is based on the previously published Constituent Subtraction algorithm. The new subtraction method, called Iterative Constituent Subtraction, applies event-wide implementation of Constituent Subtraction iteratively in order to fully equilibrate the background subtraction across the entire event. Besides documenting the new method, we provide guidelines for setting the free parameters of the subtraction algorithm. Using particle-level simulation, we provide a comparison of Iterative Constituent Subtraction with several existing methods from which we conclude that the new method has a significant potential to improve the background mitigation in both proton-proton and heavy-ion collisions.


Jet substructure Hadron-Hadron scattering (experiments) Hard scattering Jets Minimum bias 


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]
    ATLAS luminosity public results from run 2, (2018).
  2. [2]
    CMS luminosity public results from run 2, (2018).
  3. [3]
    ATLAS and CMS collaborations, Report on the Physics at the HL-LHC and Perspectives for the HE-LHC, in HL/HE-LHC Physics Workshop: final jamboree, CERN, Geneva Switzerland (2019).Google Scholar
  4. [4]
    CMS collaboration, Observation and studies of jet quenching in PbPb collisions at nucleon-nucleon center-of-mass energy = 2.76 TeV, Phys. Rev.C 84 (2011) 024906 [arXiv:1102.1957] [INSPIRE].
  5. [5]
    M. Cacciari and G.P. Salam, Pileup subtraction using jet areas, Phys. Lett.B 659 (2008) 119 [arXiv:0707.1378] [INSPIRE].
  6. [6]
    M. Cacciari, G.P. Salam and G. Soyez, The Catchment Area of Jets, JHEP04 (2008) 005 [arXiv:0802.1188] [INSPIRE].
  7. [7]
    G. Soyez, G.P. Salam, J. Kim, S. Dutta and M. Cacciari, Pileup subtraction for jet shapes, Phys. Rev. Lett.110 (2013) 162001 [arXiv:1211.2811] [INSPIRE].ADSCrossRefGoogle Scholar
  8. [8]
    P. Berta, M. Spousta, D.W. Miller and R. Leitner, Particle-level pileup subtraction for jets and jet shapes, JHEP06 (2014) 092 [arXiv:1403.3108] [INSPIRE].
  9. [9]
    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].
  10. [10]
    D. Bertolini, P. Harris, M. Low and N. Tran, Pileup Per Particle Identification, JHEP10 (2014) 059 [arXiv:1407.6013] [INSPIRE].ADSCrossRefGoogle Scholar
  11. [11]
    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].
  12. [12]
    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
  13. [13]
    D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP02 (2010) 084 [arXiv:0912.1342] [INSPIRE].ADSCrossRefGoogle Scholar
  14. [14]
    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].
  15. [15]
    A.J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop, JHEP05 (2014) 146 [arXiv:1402.2657] [INSPIRE].ADSCrossRefGoogle Scholar
  16. [16]
    M. Dasgupta, A. Fregoso, S. Marzani and G.P. Salam, Towards an understanding of jet substructure, JHEP09 (2013) 029 [arXiv:1307.0007] [INSPIRE].ADSCrossRefGoogle Scholar
  17. [17]
    F.A. Dreyer, L. Necib, G. Soyez and J. Thaler, Recursive Soft Drop, JHEP06 (2018) 093 [arXiv:1804.03657] [INSPIRE].ADSCrossRefGoogle Scholar
  18. [18]
    Y.L. Dokshitzer, G.D. Leder, S. Moretti and B.R. Webber, Better jet clustering algorithms, JHEP08 (1997) 001 [hep-ph/9707323] [INSPIRE].
  19. [19]
    M. Wobisch and T. Wengler, Hadronization corrections to jet cross-sections in deep inelastic scattering, in Monte Carlo generators for HERA physics. Proceedings of Workshop, Hamburg Germany (1998), pg. 270.Google Scholar
  20. [20]
    S. Catani, Y.L. Dokshitzer, M.H. Seymour and B.R. Webber, Longitudinally invariant K tclustering algorithms for hadron hadron collisions, Nucl. Phys.B 406 (1993) 187 [INSPIRE].
  21. [21]
    S.D. Ellis and D.E. Soper, Successive combination jet algorithm for hadron collisions, Phys. Rev.D 48 (1993) 3160 [hep-ph/9305266] [INSPIRE].
  22. [22]
    M. Cacciari, G.P. Salam and G. Soyez, The anti-k tjet clustering algorithm, JHEP04 (2008) 063 [arXiv:0802.1189] [INSPIRE].ADSCrossRefGoogle Scholar
  23. [23]
    P.T. Komiske, E.M. Metodiev, B. Nachman and M.D. Schwartz, Pileup Mitigation with Machine Learning (PUMML), JHEP12 (2017) 051 [arXiv:1707.08600] [INSPIRE].
  24. [24]
    J. Arjona Mart´ınez, O. Cerri, M. Pierini, M. Spiropulu and J.-R. Vlimant, Pileup mitigation at the Large Hadron Collider with Graph Neural Networks, Eur. Phys. J. Plus134 (2019) 333 [arXiv:1810.07988].CrossRefGoogle Scholar
  25. [25]
    S. Carrazza and F.A. Dreyer, Jet grooming through reinforcement learning, Phys. Rev.D 100 (2019) 014014 [arXiv:1903.09644] [INSPIRE].
  26. [26]
    M. Cacciari, G.P. Salam and G. Soyez, Use of charged-track information to subtract neutral pileup, Phys. Rev.D 92 (2015) 014003 [arXiv:1404.7353] [INSPIRE].
  27. [27]
    Y. Mehtar-Tani, A. Soto-Ontoso and M. Verweij, A background estimator for jet studies in p+p and A+A collisions, arXiv:1904.12815 [INSPIRE].
  28. [28]
    G. Soyez, Pileup mitigation at the LHC: a theorists view, habilitation, IPhT, Saclay France (2018).Google Scholar
  29. [29]
    G. Bellettini, R. Bertani, C. Bradaschia, R. Del Fabbro, A. Scribano and G. Terreni, Hadron calorimeter towers with a high space resolution, Nucl. Instrum. Meth.204 (1982) 73 [INSPIRE].CrossRefGoogle Scholar
  30. [30]
    W. Lampl et al., Calorimeter clustering algorithms: Description and performance, ATL-LARG-PUB-2008-002 (2008).
  31. [31]
    CMS collaboration, Particle-flow reconstruction and global event description with the CMS detector, 2017 JINST12 P10003 [arXiv:1706.04965] [INSPIRE].
  32. [32]
    ALICE collaboration, Medium modification of the shape of small-radius jets in central Pb-Pb collisions at \( \sqrt{{}^s NN} \) = 2.76 TeV, JHEP10 (2018) 139 [arXiv:1807.06854] [INSPIRE].
  33. [33]
    ALICE collaboration, First measurement of jet mass in Pb-Pb and p-Pb collisions at the LHC, Phys. Lett.B 776 (2018) 249 [arXiv:1702.00804] [INSPIRE].
  34. [34]
    CMS collaboration, Measurement of the groomed jet mass in PbPb and pp collisions at \( \sqrt{{}^s NN} \) = 5.02 TeV, JHEP10 (2018) 161 [arXiv:1805.05145] [INSPIRE].
  35. [35]
    CMS collaboration, Measurement of the Splitting Function in pp and Pb-Pb Collisions at \( \sqrt{s_{NN}} \) = 5.02 TeV, Phys. Rev. Lett.120 (2018) 142302 [arXiv:1708.09429] [INSPIRE].
  36. [36]
    CMS collaboration, Updates to Constituent Subtraction in Heavy Ions at CMS, CERN-CMS-DP-2018-024 (2018).
  37. [37]
    CMS collaboration, Pileup Removal Algorithms, CMS-PAS-JME-14-001 (2014).
  38. [38]
    ATLAS collaboration, Impact of Alternative Inputs and Grooming Methods on Large-R Jet Reconstruction in ATLAS, ATL-PHYS-PUB-2017-020 (2017).
  39. [39]
    ATLAS collaboration, Impact of Pile-up on Jet Constituent Multiplicity in ATLAS, ATL-PHYS-PUB-2018-011 (2018).
  40. [40]
    ATLAS collaboration, Constituent-level pile-up mitigation techniques in ATLAS, ATLAS-CONF-2017-065 (2017).
  41. [41]
    M. Boronat, J. Fuster, I. Garcia, P. Roloff, R. Simoniello and M. Vos, Jet reconstruction at high-energy electron-positron colliders, Eur. Phys. J.C 78 (2018) 144 [arXiv:1607.05039] [INSPIRE].
  42. [42]
    T. Golling et al., Physics at a 100 TeV pp collider: beyond the Standard Model phenomena, CERN Yellow Rep.3 (2017) 441.Google Scholar
  43. [43]
    STAR collaboration, Measurements of the jet internal sub-structure and its relevance to parton shower evolution in p+p and Au+Au collisions at STAR, PoS(HardProbes2018) 090 [arXiv:1903.12115].
  44. [44]
    J.K. Behr, D. Bortoletto, J.A. Frost, N.P. Hartland, C. Issever and J. Rojo, Boosting Higgs pair production in the b \( \overline{b} \)b \( \overline{b} \)final state with multivariate techniques, Eur. Phys. J.C 76 (2016) 386 [arXiv:1512.08928] [INSPIRE].
  45. [45]
    G.P. Salam, L. Schunk and G. Soyez, Dichroic subjettiness ratios to distinguish colour flows in boosted boson tagging, JHEP03 (2017) 022 [arXiv:1612.03917] [INSPIRE].ADSCrossRefGoogle Scholar
  46. [46]
    A.J. Larkoski, F. Maltoni and M. Selvaggi, Tracking down hyper-boosted top quarks, JHEP06 (2015) 032 [arXiv:1503.03347] [INSPIRE].ADSCrossRefGoogle Scholar
  47. [47]
    A. Berlin, T. Lin, M. Low and L.-T. Wang, Neutralinos in Vector Boson Fusion at High Energy Colliders, Phys. Rev.D 91 (2015) 115002 [arXiv:1502.05044] [INSPIRE].
  48. [48]
    N. Craig, H.K. Lou, M. McCullough and A. Thalapillil, The Higgs Portal Above Threshold, JHEP02 (2016) 127 [arXiv:1412.0258] [INSPIRE].
  49. [49]
    C. Brust, P. Maksimovic, A. Sady, P. Saraswat, M.T. Walters and Y. Xin, Identifying boosted new physics with non-isolated leptons, JHEP04 (2015) 079 [arXiv:1410.0362] [INSPIRE].ADSCrossRefGoogle Scholar
  50. [50]
    M. Low and L.-T. Wang, Neutralino dark matter at 14 TeV and 100 TeV, JHEP08 (2014) 161 [arXiv:1404.0682] [INSPIRE].
  51. [51]
    F.A. Dreyer, G.P. Salam and G. Soyez, The Lund Jet Plane, JHEP12 (2018) 064 [arXiv:1807.04758] [INSPIRE].ADSCrossRefGoogle Scholar
  52. [52]
    M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual, Eur. Phys. J.C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].
  53. [53]
    FastJet Contrib, February 2019.Google Scholar
  54. [54]
    M. Cacciari and G.P. Salam, Dispelling the N 3myth for the k tjet-finder, Phys. Lett.B 641 (2006) 57 [hep-ph/0512210] [INSPIRE].
  55. [55]
    OPAL collaboration, QCD studies using a cone based jet finding algorithm for e +e collisions at LEP, Z. Phys.C 63 (1994) 197 [INSPIRE].
  56. [56]
    D0 collaboration, Transverse energy distributions within jets in p \( \overline{p} \)collisions at \( \sqrt{s} \) = 1.8TeV, Phys. Lett.B 357(1995) 500 [INSPIRE].
  57. [57]
    H1 collaboration, Measurement of internal jet structure in dijet production in deep inelastic scattering at HERA, Nucl. Phys.B 545 (1999) 3 [hep-ex/9901010] [INSPIRE].
  58. [58]
    ZEUS collaboration, Substructure dependence of jet cross sections at HERA and determination of αs, Nucl. Phys.B 700 (2004) 3 [hep-ex/0405065] [INSPIRE].
  59. [59]
    CDF collaboration, Study of jet shapes in inclusive jet production in p \( \overline{p} \)collisions at \( \sqrt{s} \) = 1.96 TeV, Phys. Rev.D 71 (2005) 112002 [hep-ex/0505013] [INSPIRE].
  60. [60]
    ATLAS collaboration, Study of Jet Shapes in Inclusive Jet Production in pp Collisions at \( \sqrt{s} \) = 7 TeV using the ATLAS Detector, Phys. Rev.D 83 (2011) 052003 [arXiv:1101.0070] [INSPIRE].
  61. [61]
    CMS collaboration, Shape, Transverse Size and Charged Hadron Multiplicity of Jets in pp Collisions at 7 TeV, JHEP06 (2012) 160 [arXiv:1204.3170] [INSPIRE].
  62. [62]
    D0 collaboration, Measurement of color flow in t\( \overline{\mathrm{t}} \)events from p\( \overline{\mathrm{p}} \)collisions at \( \sqrt{s} \) = 1.96 TeV, Phys. Rev.D 83 (2011) 092002 [arXiv:1101.0648] [INSPIRE].
  63. [63]
    CDF collaboration, Study of Substructure of High Transverse Momentum Jets Produced in Proton-Antiproton Collisions at \( \sqrt{s} \) = 1.96 TeV, Phys. Rev.D 85 (2012) 091101 [arXiv:1106.5952] [INSPIRE].
  64. [64]
    CMS collaboration, Identification techniques for highly boosted W bosons that decay into hadrons, JHEP12 (2014) 017 [arXiv:1410.4227] [INSPIRE].
  65. [65]
    ATLAS collaboration, Performance of jet substructure techniques for large-R jets in proton-proton collisions at \( \sqrt{s} \) = 7 TeV using the ATLAS detector, JHEP09 (2013) 076 [arXiv:1306.4945] [INSPIRE].
  66. [66]
    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].
  67. [67]
    J. Gallicchio and M.D. Schwartz, Quark and Gluon Tagging at the LHC, Phys. Rev. Lett.107 (2011) 172001 [arXiv:1106.3076] [INSPIRE].ADSCrossRefGoogle Scholar
  68. [68]
    J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness, JHEP03 (2011) 015 [arXiv:1011.2268] [INSPIRE].
  69. [69]
    J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness, JHEP02 (2012) 093 [arXiv:1108.2701] [INSPIRE].
  70. [70]
    CMS collaboration, Determination of Jet Energy Calibration and Transverse Momentum Resolution in CMS, 2011 JINST6 P11002 [arXiv:1107.4277] [INSPIRE].
  71. [71]
    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].
  72. [72]
    ATLAS collaboration, Measurement of the nuclear modification factor for inclusive jets in Pb+Pb collisions at \( \sqrt{{}^s NN} \) = 5.02 TeV with the ATLAS detector, Phys. Lett.B 790 (2019) 108 [arXiv:1805.05635] [INSPIRE].
  73. [73]
    C.W. Fabjan and F. Gianotti, Calorimetry for particle physics, Rev. Mod. Phys.75 (2003) 1243 [INSPIRE].ADSCrossRefGoogle Scholar
  74. [74]
    ATLAS collaboration, Jet energy resolution in proton-proton collisions at \( \sqrt{s} \) = 7 TeV recorded in 2010 with the ATLAS detector, Eur. Phys. J.C 73 (2013) 2306 [arXiv:1210.6210] [INSPIRE].
  75. [75]
    CMS collaboration, Particle-Flow Event Reconstruction in CMS and Performance for Jets, Taus and MET, CMS-PAS-PFT-09-001 (2009).
  76. [76]
    CMS collaboration, Pileup Removal Algorithms, CMS-PAS-JME-14-001 (2014).
  77. [77]
    ATLAS collaboration, Tagging and suppression of pileup jets, ATL-PHYS-PUB-2014-001 (2014).
  78. [78]
    D0 collaboration, Jet energy scale determination in the D0 experiment, Nucl. Instrum. Meth.A 763 (2014) 442 [arXiv:1312.6873] [INSPIRE].
  79. [79]
    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].
  80. [80]
    T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP05 (2006) 026 [hep-ph/0603175] [INSPIRE].
  81. [81]
    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].ADSCrossRefGoogle Scholar
  82. [82]
    CTEQ collaboration, Global QCD analysis of parton structure of the nucleon: CTEQ5 parton distributions, Eur. Phys. J.C 12 (2000) 375 [hep-ph/9903282] [INSPIRE].
  83. [83]
    Shared software for the Workshop on Mitigation of Pileup Effects at the LHC, CERN, Geneva Switzerland (2014),
  84. [84]
    Workshop on Mitigation of Pileup Effects at the LHC, CERN, Geneva Switzerland (2014),
  85. [85]
    Shared event files for the Workshop on Mitigation of Pileup Effects at the LHC, CERN, Geneva Switzerland (2014),

Copyright information

© The Author(s) 2019

Authors and Affiliations

  1. 1.PRISMA+ Cluster of Excellence and Institute of PhysicsJohannes Gutenberg University MainzMainzGermany
  2. 2.The Enrico Fermi Institute and the Department of PhysicsUniversity of ChicagoChicagoU.S.A.
  3. 3.Institute of Particle and Nuclear Physics, Faculty of Mathematics and PhysicsCharles UniversityPrague 8Czech Republic

Personalised recommendations