Does SUSY have friends? A new approach for LHC event analysis

Abstract

We present a novel technique for the analysis of proton-proton collision events from the ATLAS and CMS experiments at the Large Hadron Collider. For a given final state and choice of kinematic variables, we build a graph network in which the individual events appear as weighted nodes, with edges between events defined by their distance in kinematic space. We then show that it is possible to calculate local metrics of the network that serve as event-by-event variables for separating signal and background processes, and we evaluate these for a number of different networks that are derived from different distance metrics. Using a supersymmetric electroweakino and stop production as examples, we construct prototype analyses that take account of the fact that the number of simulated Monte Carlo events used in an LHC analysis may differ from the number of events expected in the LHC dataset, allowing an accurate background estimate for a particle search at the LHC to be derived. For the electroweakino example, we show that the use of network variables outperforms both cut-and-count analyses that use the original variables and a boosted decision tree trained on the original variables. The stop example, deliberately chosen to be difficult to exclude due its kinematic similarity with the top background, demonstrates that network variables are not automatically sensitive to BSM physics. Nevertheless, we identify local network metrics that show promise if their robustness under certain assumptions of node-weighted networks can be confirmed.

A preprint version of the article is available at ArXiv.

References

  1. [1]

    GAMBIT collaboration, Combined collider constraints on neutralinos and charginos, Eur. Phys. J. C 79 (2019) 395 [arXiv:1809.02097] [INSPIRE].

  2. [2]

    J. Alwall, P. Schuster and N. Toro, Simplified models for a first characterization of new physics at the LHC, Phys. Rev. D 79 (2009) 075020 [arXiv:0810.3921] [INSPIRE].

    ADS  Article  Google Scholar 

  3. [3]

    GAMBIT collaboration, A global fit of the MSSM with GAMBIT, Eur. Phys. J. C 77 (2017) 879 [arXiv:1705.07917] [INSPIRE].

  4. [4]

    GAMBIT collaboration, GAMBIT: the global and modular beyond-the-standard-model inference tool, Eur. Phys. J. C 77 (2017) 784 [Addendum ibid. 78 (2018) 98] [arXiv:1705.07908] [INSPIRE].

  5. [5]

    GAMBIT collaboration, Global fits of GUT-scale SUSY models with GAMBIT, Eur. Phys. J. C 77 (2017) 824 [arXiv:1705.07935] [INSPIRE].

  6. [6]

    E. Bagnaschi et al., Likelihood analysis of the pMSSM11 in light of LHC 13 TeV data, Eur. Phys. J. C 78 (2018) 256 [arXiv:1710.11091] [INSPIRE].

    ADS  Article  Google Scholar 

  7. [7]

    J. C. Costa et al., Likelihood analysis of the sub-GUT MSSM in light of LHC 13 TeV data, Eur. Phys. J. C 78 (2018) 158 [arXiv:1711.00458] [INSPIRE].

    ADS  Article  Google Scholar 

  8. [8]

    S. Hong et al., Discriminating topology in galaxy distributions using network analysis, Mon. Not. Roy. Astron. Soc. 459 (2016) 2690 [arXiv:1603.02285] [INSPIRE].

    ADS  Article  Google Scholar 

  9. [9]

    E. A. Moreno et al., Interaction networks for the identification of boosted H → \( b\overline{b} \) decays, Phys. Rev. D 102 (2020) 012010 [arXiv:1909.12285] [INSPIRE].

    ADS  Article  Google Scholar 

  10. [10]

    E. A. Moreno et al., JEDI-net: a jet identification algorithm based on interaction networks, Eur. Phys. J. C 80 (2020) 58 [arXiv:1908.05318] [INSPIRE].

    ADS  Article  Google Scholar 

  11. [11]

    H. Qu and L. Gouskos, ParticleNet: jet tagging via particle clouds, Phys. Rev. D 101 (2020) 056019 [arXiv:1902.08570] [INSPIRE].

    ADS  Article  Google Scholar 

  12. [12]

    I. Henrion et al., Neural message passing for jet physics (2017).

  13. [13]

    M. Abdughani, J. Ren, L. Wu and J. M. Yang, Probing stop pair production at the LHC with graph neural networks, JHEP 08 (2019) 055 [arXiv:1807.09088] [INSPIRE].

    ADS  Article  Google Scholar 

  14. [14]

    IceCube collaboration, Graph neural networks for IceCube signal classification, arXiv:1809.06166 [INSPIRE].

  15. [15]

    S. Farrell et al., Novel deep learning methods for track reconstruction, in the proceedings of the 4th International Workshop Connecting The Dots 2018 (CTD2018), march 20–22, Seattle, U.S.A. (2018), arXiv:1810.06111 [INSPIRE].

  16. [16]

    J. Arjona Martínez, O. Cerri, M. Pierini, M. Spiropulu and J.-R. Vlimant, Pileup mitigation at the Large Hadron Collider with graph neural networks, Eur. Phys. J. Plus 134 (2019) 333 [arXiv:1810.07988] [INSPIRE].

    Article  Google Scholar 

  17. [17]

    P. T. Komiske, E. M. Metodiev and J. Thaler, Cutting multiparticle correlators down to size, Phys. Rev. D 101 (2020) 036019 [arXiv:1911.04491] [INSPIRE].

    ADS  Article  Google Scholar 

  18. [18]

    S. R. Qasim, J. Kieseler, Y. Iiyama and M. Pierini, Learning representations of irregular particle-detector geometry with distance-weighted graph networks, Eur. Phys. J. C 79 (2019) 608 [arXiv:1902.07987] [INSPIRE].

    ADS  Article  Google Scholar 

  19. [19]

    P. T. Komiske, E. M. Metodiev and J. Thaler, Metric space of collider events, Phys. Rev. Lett. 123 (2019) 041801 [arXiv:1902.02346] [INSPIRE].

    ADS  Article  Google Scholar 

  20. [20]

    P. T. Komiske, R. Mastandrea, E. M. Metodiev, P. Naik and J. Thaler, Exploring the space of jets with CMS open data, Phys. Rev. D 101 (2020) 034009 [arXiv:1908.08542] [INSPIRE].

    ADS  Article  Google Scholar 

  21. [21]

    E. Deza and M. M. Deza, Dictionary of Distances, Elsevier, Amsterdam The Netherlands (2006).

    Google Scholar 

  22. [22]

    J. Heitzig, J. F. Donges, Y. Zou, N. Marwan and J. Kurths, Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes, Eur. Phys. J. B 85 (2012) 38 [arXiv:1101.4757] [INSPIRE].

    ADS  Article  Google Scholar 

  23. [23]

    J. F. Donges et al., Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25 (2015) 113101 [arXiv:1507.01571] [INSPIRE].

    ADS  MathSciNet  MATH  Article  Google Scholar 

  24. [24]

    S. N. Soffer and A. Vázque, Network clustering coefficient without degree-correlation biases, Phys. Rev. E 71 (2005) 057101.

  25. [25]

    L. Moneta et al., The roostats project, PoS(ACAT2010)057 [arXiv:1009.1003].

  26. [26]

    K. Cranmer, Statistical challenges for searches for new physics at the LHC, in Statistical Problems in Particle Physics, Astrophysics and Cosmology, L. Lyons and M. K. Ünel, World Scientific, Singapore (2006) [physics/0511028].

  27. [27]

    R. D. Cousins, J. T. Linnemann and J. Tucker, Evaluation of three methods for calculating statistical significance when incorporating a systematic uncertainty into a test of the background-only hypothesis for a poisson process, Nucl. Instrum. Meth. A 595 (2008) 480.

    ADS  Article  Google Scholar 

  28. [28]

    J. T. Linnemann, Measures of significance in hep and astrophysics, physics/0312059.

  29. [29]

    ATLAS collaboration, Search for chargino-neutralino production with mass splittings near the electroweak scale in three-lepton final states in \( \sqrt{s} \)=13 TeV pp collisions with the ATLAS detector, Phys. Rev. D 101 (2020) 072001 [arXiv:1912.08479] [INSPIRE].

  30. [30]

    ATLAS collaboration, Search for top-squark pair production in final states with one lepton, jets, and missing transverse momentum using 36 fb1 of \( \sqrt{s} \) = 13 TeV pp collision data with the ATLAS detector, JHEP 06 (2018) 108 [arXiv:1711.11520] [INSPIRE].

  31. [31]

    ATLAS collaboration, Search for a scalar partner of the top quark in the all-hadronic \( t\overline{t} \) plus missing transverse momentum final state at \( \sqrt{s} \) = 13 TeV with the ATLAS detector, Eur. Phys. J. C 80 (2020) 737 [arXiv:2004.14060] [INSPIRE].

  32. [32]

    T. Sjöstrand et al., An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012] [INSPIRE].

    ADS  MATH  Article  Google Scholar 

  33. [33]

    J. Pumplin, D. R. Stump, J. Huston, H. L. Lai, P. M. Nadolsky and W. K. Tung, New generation of parton distributions with uncertainties from global QCD analysis, JHEP 07 (2002) 012 [hep-ph/0201195] [INSPIRE].

    ADS  Article  Google Scholar 

  34. [34]

    DELPHES 3 collaboration, DELPHES 3, a modular framework for fast simulation of a generic collider experiment, JHEP 02 (2014) 057 [arXiv:1307.6346] [INSPIRE].

  35. [35]

    M. Selvaggi, DELPHES 3: a modular framework for fast-simulation of generic collider experiments, J. Phys. Conf. Ser. 523 (2014) 012033 [INSPIRE].

    Article  Google Scholar 

  36. [36]

    A. Mertens, New features in Delphes 3, J. Phys. Conf. Ser. 608 (2015) 012045.

    Article  Google Scholar 

  37. [37]

    M. Cacciari, G. P. Salam and G. Soyez, The anti-kt jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].

    ADS  MATH  Article  Google Scholar 

  38. [38]

    M. Cacciari, G. P. Salam and G. Soyez, FastJet user manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].

    ADS  MATH  Article  Google Scholar 

  39. [39]

    B. Fuks, M. Klasen, D. R. Lamprea and M. Rothering, Gaugino production in proton-proton collisions at a center-of-mass energy of 8 TeV, JHEP 10 (2012) 081 [arXiv:1207.2159] [INSPIRE].

    ADS  Article  Google Scholar 

  40. [40]

    B. Fuks, M. Klasen, D. R. Lamprea and M. Rothering, Precision predictions for electroweak superpartner production at hadron colliders with Resummino, Eur. Phys. J. C 73 (2013) 2480 [arXiv:1304.0790] [INSPIRE].

    ADS  Article  Google Scholar 

  41. [41]

    W. Beenakker, C. Borschensky, M. Krämer, A. Kulesza and E. Laenen, NNLL-fast: predictions for coloured supersymmetric particle production at the LHC with threshold and Coulomb resummation, JHEP 12 (2016) 133 [arXiv:1607.07741] [INSPIRE].

    ADS  Article  Google Scholar 

  42. [42]

    W. Beenakker, M. Krämer, T. Plehn, M. Spira and P. M. Zerwas, Stop production at hadron colliders, Nucl. Phys. B 515 (1998) 3 [hep-ph/9710451] [INSPIRE].

    ADS  Article  Google Scholar 

  43. [43]

    W. Beenakker, S. Brensing, M. Krämer, A. Kulesza, E. Laenen and I. Niessen, Supersymmetric top and bottom squark production at hadron colliders, JHEP 08 (2010) 098 [arXiv:1006.4771] [INSPIRE].

    ADS  MATH  Article  Google Scholar 

  44. [44]

    W. Beenakker, C. Borschensky, R. Heger, M. Krämer, A. Kulesza and E. Laenen, NNLL resummation for stop pair-production at the LHC, JHEP 05 (2016) 153 [arXiv:1601.02954] [INSPIRE].

    ADS  Article  Google Scholar 

  45. [45]

    M. Grazzini, S. Kallweit, D. Rathlev and M. Wiesemann, W ± Z production at hadron colliders in NNLO QCD, Phys. Lett. B 761 (2016) 179 [arXiv:1604.08576] [INSPIRE].

    ADS  Article  Google Scholar 

  46. [46]

    M. Beneke, P. Falgari, S. Klein and C. Schwinn, Hadronic top-quark pair production with NNLL threshold resummation, Nucl. Phys. B 855 (2012) 695 [arXiv:1109.1536] [INSPIRE].

    ADS  MATH  Article  Google Scholar 

  47. [47]

    M. Cacciari, M. Czakon, M. Mangano, A. Mitov and P. Nason, Top-pair production at hadron colliders with next-to-next-to-leading logarithmic soft-gluon resummation, Phys. Lett. B 710 (2012) 612 [arXiv:1111.5869] [INSPIRE].

    ADS  Article  Google Scholar 

  48. [48]

    P. Bärnreuther, M. Czakon and A. Mitov, Percent level precision physics at the Tevatron: first genuine NNLO QCD corrections to \( q\overline{q} \) \( t\overline{t} \) + X , Phys. Rev. Lett. 109 (2012) 132001 [arXiv:1204.5201] [INSPIRE].

    ADS  Article  Google Scholar 

  49. [49]

    M. Czakon and A. Mitov, NNLO corrections to top-pair production at hadron colliders: the all-fermionic scattering channels, JHEP 12 (2012) 054 [arXiv:1207.0236] [INSPIRE].

    ADS  Article  Google Scholar 

  50. [50]

    M. Czakon and A. Mitov, NNLO corrections to top pair production at hadron colliders: the quark-gluon reaction, JHEP 01 (2013) 080 [arXiv:1210.6832] [INSPIRE].

    ADS  Article  Google Scholar 

  51. [51]

    M. Czakon, P. Fiedler and A. Mitov, Total top-quark pair-production cross section at hadron colliders through O(\( {\alpha}_S^4 \)), Phys. Rev. Lett. 110 (2013) 252004 [arXiv:1303.6254] [INSPIRE].

    ADS  Article  Google Scholar 

  52. [52]

    M. Czakon and A. Mitov, Top++: a program for the calculation of the top-pair cross-section at hadron colliders, Comput. Phys. Commun. 185 (2014) 2930 [arXiv:1112.5675] [INSPIRE].

    ADS  Article  Google Scholar 

  53. [53]

    ATLAS collaboration, Search for electroweak production of supersymmetric particles in final states with two or three leptons at \( \sqrt{s} \) = 13 TeV with the ATLAS detector, Eur. Phys. J. C 78 (2018) 995 [arXiv:1803.02762] [INSPIRE].

  54. [54]

    A. J. Barr, B. Gripaios and C. G. Lester, Transverse masses and kinematic constraints: from the boundary to the crease, JHEP 11 (2009) 096 [arXiv:0908.3779] [INSPIRE].

    ADS  Article  Google Scholar 

  55. [55]

    P. Konar, K. Kong, K. T. Matchev and M. Park, Dark matter particle spectroscopy at the LHC: generalizing MT2 to asymmetric event topologies, JHEP 04 (2010) 086 [arXiv:0911.4126] [INSPIRE].

    ADS  MATH  Article  Google Scholar 

  56. [56]

    Y. Bai, H.-C. Cheng, J. Gallicchio and J. Gu, Stop the top background of the stop search, JHEP 07 (2012) 110 [arXiv:1203.4813] [INSPIRE].

    ADS  Article  Google Scholar 

  57. [57]

    C. G. Lester and D. J. Summers, Measuring masses of semiinvisibly decaying particles pair produced at hadron colliders, Phys. Lett. B 463 (1999) 99 [hep-ph/9906349] [INSPIRE].

    ADS  Article  Google Scholar 

  58. [58]

    C. G. Lester and B. Nachman, Bisection-based asymmetric MT2 computation: a higher precision calculator than existing symmetric methods, JHEP 03 (2015) 100 [arXiv:1411.4312] [INSPIRE].

    Article  Google Scholar 

  59. [59]

    A. Hoecker et al., Tmva — Toolkit for multivariate data analysis, (2007).

  60. [60]

    C. Fan et al., Learning to identify high betweenness centrality nodes from scratch: a novel graph neural network approach, arXiv:1905.10418.

  61. [61]

    A. De Simone and T. Jacques, Guiding new physics searches with unsupervised learning, Eur. Phys. J. C 79 (2019) 289 [arXiv:1807.06038] [INSPIRE].

    ADS  Article  Google Scholar 

  62. [62]

    R. T. D’Agnolo and A. Wulzer, Learning new physics from a machine, Phys. Rev. D 99 (2019) 015014 [arXiv:1806.02350] [INSPIRE].

    ADS  Article  Google Scholar 

  63. [63]

    M. Farina, Y. Nakai and D. Shih, Searching for new physics with deep autoencoders, Phys. Rev. D 101 (2020) 075021 [arXiv:1808.08992] [INSPIRE].

    ADS  Article  Google Scholar 

  64. [64]

    T. Heimel, G. Kasieczka, T. Plehn and J. M. Thompson, QCD or what?, SciPost Phys. 6 (2019) 030 [arXiv:1808.08979] [INSPIRE].

    ADS  Article  Google Scholar 

  65. [65]

    J. Hajer, Y.-Y. Li, T. Liu and H. Wang, Novelty detection meets collider physics, Phys. Rev. D 101 (2020) 076015 [arXiv:1807.10261] [INSPIRE].

    ADS  Article  Google Scholar 

  66. [66]

    M. Kuusela, T. Vatanen, E. Malmi, T. Raiko, T. Aaltonen and Y. Nagai, Semi-supervised anomaly detection — Towards model-independent searches of new physics, J. Phys. Conf. Ser. 368 (2012) 012032 [arXiv:1112.3329] [INSPIRE].

    Article  Google Scholar 

  67. [67]

    CDF collaboration, Model-independent and quasi-model-independent search for new physics at CDF, Phys. Rev. D 78 (2008) 012002 [arXiv:0712.1311] [INSPIRE].

  68. [68]

    CMS collaboration, MUSIC — An automated scan for deviations between data and Monte Carlo simulation, AIP Conf. Proc. 1200 (2010) 293.

  69. [69]

    ATLAS collaboration, A model independent general search for new phenomena with the ATLAS detector at \( \sqrt{s} \) = 13, ATLAS-CONF-2017-001 (2017).

  70. [70]

    G. Choudalakis, On hypothesis testing, trials factor, hypertests and the BumpHunter, in PHYSTAT 2011, 1, 2011 [arXiv:1101.0390] [INSPIRE].

  71. [71]

    D0 collaboration, Search for new physics in eμX data at D0 using SLEUTH: a quasi-model-independent search strategy for new physics, Phys. Rev. D 62 (2000) 092004 [hep-ex/0006011] [INSPIRE].

  72. [72]

    H1 collaboration, A general search for new phenomena in ep scattering at HERA, Phys. Lett. B 602 (2004) 14 [hep-ex/0408044] [INSPIRE].

  73. [73]

    H1 collaboration, A general search for new phenomena at HERA, Phys. Lett. B 674 (2009) 257 [arXiv:0901.0507] [INSPIRE].

  74. [74]

    P. Asadi, M. R. Buckley, A. DiFranzo, A. Monteux and D. Shih, Digging deeper for new physics in the LHC data, JHEP 11 (2017) 194 [arXiv:1707.05783] [INSPIRE].

    ADS  Article  Google Scholar 

  75. [75]

    CDF collaboration, Global search for new physics with 2.0 fb1 at CDF, Phys. Rev. D 79 (2009) 011101 [arXiv:0809.3781] [INSPIRE].

  76. [76]

    CMS collaboration, Model unspecific search for new physics in pp collisions at \( \sqrt{s} \) = 7 TeV, CMS-PAS-EXO-10-021 (2011).

  77. [77]

    O. Cerri, T.Q. Nguyen, M. Pierini, M. Spiropulu and J.-R. Vlimant, Variational autoencoders for new physics mining at the Large Hadron Collider, JHEP 05 (2019) 036 [arXiv:1811.10276] [INSPIRE].

    ADS  Article  Google Scholar 

  78. [78]

    A. Blance, M. Spannowsky and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches, JHEP 10 (2019) 047 [arXiv:1905.10384] [INSPIRE].

    ADS  Article  Google Scholar 

  79. [79]

    T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoders, arXiv:1903.02032 [INSPIRE].

  80. [80]

    J. H. Collins, K. Howe and B. Nachman, Anomaly detection for resonant new physics with machine learning, Phys. Rev. Lett. 121 (2018) 241803 [arXiv:1805.02664] [INSPIRE].

    ADS  Article  Google Scholar 

  81. [81]

    J. H. Collins, K. Howe and B. Nachman, Extending the search for new resonances with machine learning, Phys. Rev. D 99 (2019) 014038 [arXiv:1902.02634] [INSPIRE].

    ADS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Martin White.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

ArXiv ePrint: 1912.10625

Rights and permissions

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.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mullin, A., Nicholls, S., Pacey, H. et al. Does SUSY have friends? A new approach for LHC event analysis. J. High Energ. Phys. 2021, 160 (2021). https://doi.org/10.1007/JHEP02(2021)160

Download citation

Keywords

  • Supersymmetry Phenomenology