Skip to main content

Probing stop pair production at the LHC with graph neural networks

A preprint version of the article is available at arXiv.

Abstract

Top-squarks (stops) play a crucial role for the naturalness of supersymmetry (SUSY). However, searching for the stops is a tough task at the LHC. To dig the stops out of the huge LHC data, various expert-constructed kinematic variables or cutting-edge analysis techniques have been invented. In this paper, we propose to represent collision events as event graphs and use the message passing neutral network (MPNN) to analyze the events. As a proof-of-concept, we use our method in the search of the stop pair production at the LHC, and find that our MPNN can efficiently discriminate the signal and back-ground events. In comparison with other machine learning methods (e.g. DNN), MPNN can enhance the mass reach of stop mass by several tens of GeV to over a hundred GeV.

References

  1. 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].

  2. A. Barr, C. Lester and P. Stephens, m(T2): The truth behind the glamour, J. Phys.G 29 (2003) 2343 [hep-ph/0304226] [INSPIRE].

  3. Y. Bai, H.-C. Cheng, J. Gallicchio and J. Gu, Stop the Top Background of the Stop Search, JHEP07 (2012) 110 [arXiv:1203.4813] [INSPIRE].

  4. J. Cao, C. Han, L. Wu, J.M. Yang and Y. Zhang, Probing Natural SUSY from Stop Pair Production at the LHC, JHEP11 (2012) 039 [arXiv:1206.3865] [INSPIRE].

    Article  ADS  Google Scholar 

  5. C. Kilic and B. Tweedie, Cornering Light Stops with Dileptonic mT2, JHEP04 (2013) 110 [arXiv:1211.6106] [INSPIRE].

  6. K. Hagiwara and T. Yamada, Equal-velocity scenario for hiding dark matter at the LHC, Phys. Rev.D 91 (2015) 094007 [arXiv:1307.1553] [INSPIRE].

    ADS  Google Scholar 

  7. H. An and L.-T. Wang, Opening up the compressed region of top squark searches at 13 TeV LHC, Phys. Rev. Lett.115 (2015) 181602 [arXiv:1506.00653] [INSPIRE].

    Article  ADS  Google Scholar 

  8. S. Macaluso, M. Park, D. Shih and B. Tweedie, Revealing Compressed Stops Using High-Momentum Recoils, JHEP03 (2016) 151 [arXiv:1506.07885] [INSPIRE].

  9. M. Czakon, A. Mitov, M. Papucci, J.T. Ruderman and A. Weiler, Closing the stop gap, Phys. Rev. Lett.113 (2014) 201803 [arXiv:1407.1043] [INSPIRE].

    Article  ADS  Google Scholar 

  10. Z. Han, A. Katz, D. Krohn and M. Reece, (Light) Stop Signs, JHEP08 (2012) 083 [arXiv:1205.5808] [INSPIRE].

  11. A. Djouadi and Y. Mambrini, Three body decays of top and bottom squarks, Phys. Rev.D 63 (2001) 115005 [hep-ph/0011364] [INSPIRE].

  12. T. Han, K.-i. Hikasa, J.M. Yang and X.-m. Zhang, The FCNC top squark decay as a probe of squark mixing, Phys. Rev.D 70 (2004) 055001 [hep-ph/0312129] [INSPIRE].

  13. M. Muhlleitner and E. Popenda, Light Stop Decay in the MSSM with Minimal Flavour Violation, JHEP04 (2011) 095 [arXiv:1102.5712] [INSPIRE].

    Article  ADS  Google Scholar 

  14. J. Aebischer, A. Crivellin and C. Greub, One-loop SQCD corrections to the decay of top squarks to charm and neutralino in the generic MSSM, Phys. Rev.D 91 (2015) 035010 [arXiv:1410.8459] [INSPIRE].

    ADS  Google Scholar 

  15. C. Boehm, A. Djouadi and Y. Mambrini, Decays of the lightest top squark, Phys. Rev.D 61 (2000) 095006 [hep-ph/9907428] [INSPIRE].

  16. M.A. Ajaib, T. Li and Q. Shafi, Stop-Neutralino Coannihilation in the Light of LHC, Phys. Rev.D 85 (2012) 055021 [arXiv:1111.4467] [INSPIRE].

    ADS  Google Scholar 

  17. M. Drees, M. Hanussek and J.S. Kim, Light Stop Searches at the LHC with Monojet Events, Phys. Rev.D 86 (2012) 035024 [arXiv:1201.5714] [INSPIRE].

    ADS  Google Scholar 

  18. Z.-H. Yu, X.-J. Bi, Q.-S. Yan and P.-F. Yin, Detecting light stop pairs in coannihilation scenarios at the LHC, Phys. Rev.D 87 (2013) 055007 [arXiv:1211.2997] [INSPIRE].

    ADS  Google Scholar 

  19. M. Perelstein and A. Weiler, Polarized Tops from Stop Decays at the LHC, JHEP03 (2009) 141 [arXiv:0811.1024] [INSPIRE].

    Article  ADS  Google Scholar 

  20. T. Plehn, M. Spannowsky and M. Takeuchi, Stop searches in 2012, JHEP08 (2012) 091 [arXiv:1205.2696] [INSPIRE].

  21. C. Han, K.-i. Hikasa, L. Wu, J.M. Yang and Y. Zhang, Current experimental bounds on stop mass in natural SUSY, JHEP10 (2013) 216 [arXiv:1308.5307] [INSPIRE].

    Article  ADS  Google Scholar 

  22. M.R. Buckley, T. Plehn and M.J. Ramsey-Musolf, Top squark with mass close to the top quark, Phys. Rev.D 90 (2014) 014046 [arXiv:1403.2726] [INSPIRE].

    ADS  Google Scholar 

  23. D. Goncalves, D. Lopez-Val, K. Mawatari and T. Plehn, Automated third generation squark production to next-to-leading order, Phys. Rev.D 90 (2014) 075007 [arXiv:1407.4302] [INSPIRE].

    ADS  Google Scholar 

  24. B. Fuks, P. Richardson and A. Wilcock, Studying the sensitivity of monotop probes to compressed supersymmetric scenarios at the LHC, Eur. Phys. J.C 75 (2015) 308 [arXiv:1408.3634] [INSPIRE].

    Article  ADS  Google Scholar 

  25. T. Eifert and B. Nachman, Sneaky light stop, Phys. Lett.B 743 (2015) 218 [arXiv:1410.7025] [INSPIRE].

    Article  ADS  Google Scholar 

  26. A. Kobakhidze, N. Liu, L. Wu and J.M. Yang, ATLAS Z-peaked excess in the MSSM with a light sbottom or stop, Phys. Rev.D 92 (2015) 075008 [arXiv:1504.04390] [INSPIRE].

    ADS  Google Scholar 

  27. K.-i. Hikasa, J. Li, L. Wu and J.M. Yang, Single top squark production as a probe of natural supersymmetry at the LHC, Phys. Rev.D 93 (2016) 035003 [arXiv:1505.06006] [INSPIRE].

    ADS  Google Scholar 

  28. A. Kobakhidze, N. Liu, L. Wu, J.M. Yang and M. Zhang, Closing up a light stop window in natural SUSY at LHC, Phys. Lett.B 755 (2016) 76 [arXiv:1511.02371] [INSPIRE].

    Article  ADS  Google Scholar 

  29. H.-C. Cheng, C. Gao, L. Li and N.A. Neill, Stop Search in the Compressed Region via Semileptonic Decays, JHEP05 (2016) 036 [arXiv:1604.00007] [INSPIRE].

    Article  ADS  Google Scholar 

  30. C. Han, J. Ren, L. Wu, J.M. Yang and M. Zhang, Top-squark in natural SUSY under current LHC run-2 data, Eur. Phys. J.C 77 (2017) 93 [arXiv:1609.02361] [INSPIRE].

  31. G.H. Duan, K.-i. Hikasa, L. Wu, J.M. Yang and M. Zhang, Leptonic mono-top from single stop production at the LHC, JHEP03 (2017) 091 [arXiv:1611.05211] [INSPIRE].

    Article  ADS  Google Scholar 

  32. P. Jackson, C. Rogan and M. Santoni, Sparticles in motion: Analyzing compressed SUSY scenarios with a new method of event reconstruction, Phys. Rev.D 95 (2017) 035031 [arXiv:1607.08307] [INSPIRE].

    ADS  Google Scholar 

  33. D. Goncalves, K. Sakurai and M. Takeuchi, Tagging a monotop signature in natural SUSY, Phys. Rev.D 95 (2017) 015030 [arXiv:1610.06179] [INSPIRE].

    ADS  Google Scholar 

  34. 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].

    Article  ADS  Google Scholar 

  35. Z. Kang, J. Li and M. Zhang, Uncover Compressed Supersymmetry via Boosted Bosons from the Heavier Stop/Sbottom, Eur. Phys. J.C 77 (2017) 371 [arXiv:1703.08911] [INSPIRE].

    Article  ADS  Google Scholar 

  36. G.H. Duan, L. Wu and R. Zheng, Resonant Higgs pair production as a probe of stop at the LHC, JHEP09 (2017) 037 [arXiv:1706.07562] [INSPIRE].

    Article  ADS  Google Scholar 

  37. H. Baer, V. Barger, J.S. Gainer, H. Serce and X. Tata, Reach of the high-energy LHC for gluinos and top squarks in SUSY models with light Higgsinos, Phys. Rev.D 96 (2017) 115008 [arXiv:1708.09054] [INSPIRE].

    ADS  Google Scholar 

  38. ATLAS collaboration, Search for supersymmetry in final states with charm jets and missing transverse momentum in 13 TeV pp collisions with the ATLAS detector, JHEP09 (2018) 050 [arXiv:1805.01649] [INSPIRE].

  39. ATLAS collaboration, Search for direct top squark pair production in final states with two leptons in \( \sqrt{s} \)= 13 TeV pp collisions with the ATLAS detector, Eur. Phys. J. C 77 (2017) 898 [arXiv:1708.03247] [INSPIRE].

  40. ATLAS collaboration, Search for a scalar partner of the top quark in the jets plus missing transverse momentum final state at \( \sqrt{s} \)= 13 TeV with the ATLAS detector, JHEP12 (2017) 085 [arXiv:1709.04183] [INSPIRE].

    ADS  Google Scholar 

  41. CMS collaboration, Search for new phenomena with the M T2variable in the all-hadronic final state produced in proton-proton collisions at \( \sqrt{s} \)= 13 TeV, Eur. Phys. J.C 77 (2017) 710 [arXiv:1705.04650] [INSPIRE].

  42. CMS collaboration, Search for top squark pair production in pp collisions at \( \sqrt{s} \)= 13 TeV using single lepton events, JHEP10 (2017) 019 [arXiv:1706.04402] [INSPIRE].

  43. CMS collaboration, Search for top squarks decaying via four-body or chargino-mediated modes in single-lepton final states in proton-proton collisions at \( \sqrt{s} \)= 13 TeV, JHEP09 (2018) 065 [arXiv:1805.05784] [INSPIRE].

  44. P.C. Bhat, Multivariate Analysis Methods in Particle Physics, Ann. Rev. Nucl. Part. Sci.61 (2011) 281 [INSPIRE].

    Article  ADS  Google Scholar 

  45. B.P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu and G. McGregor, Boosted decision trees, an alternative to artificial neural networks, Nucl. Instrum. Meth.A 543 (2005) 577 [physics/0408124] [INSPIRE].

    Article  ADS  Google Scholar 

  46. P. Baldi, P. Sadowski and D. Whiteson, Searching for Exotic Particles in High-Energy Physics with Deep Learning, Nature Commun.5 (2014) 4308 [arXiv:1402.4735] [INSPIRE].

    Article  ADS  Google Scholar 

  47. P. Baldi, P. Sadowski and D. Whiteson, Enhanced Higgs Boson to τ +τ Search with Deep Learning, Phys. Rev. Lett.114 (2015) 111801 [arXiv:1410.3469] [INSPIRE].

    Article  ADS  Google Scholar 

  48. M. Bridges, K. Cranmer, F. Feroz, M. Hobson, R. Ruiz de Austri and R. Trotta, A Coverage Study of the CMSSM Based on ATLAS Sensitivity Using Fast Neural Networks Techniques, JHEP03 (2011) 012 [arXiv:1011.4306] [INSPIRE].

    Article  ADS  MATH  Google Scholar 

  49. A. Buckley, A. Shilton and M.J. White, Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques, Comput. Phys. Commun.183 (2012) 960 [arXiv:1106.4613] [INSPIRE].

    Article  ADS  MATH  Google Scholar 

  50. N. Bornhauser and M. Drees, Determination of the CMSSM Parameters using Neural Networks, Phys. Rev.D 88 (2013) 075016 [arXiv:1307.3383] [INSPIRE].

    ADS  Google Scholar 

  51. S. Caron, J.S. Kim, K. Rolbiecki, R. Ruiz de Austri and B. Stienen, The BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine learning, Eur. Phys. J.C 77 (2017) 257 [arXiv:1605.02797] [INSPIRE].

    Article  ADS  Google Scholar 

  52. G. Bertone, M.P. Deisenroth, J.S. Kim, S. Liem, R. Ruiz de Austri and M. Welling, Accelerating the BSM interpretation of LHC data with machine learning, Phys. Dark Univ.24 (2019) 100293 [arXiv:1611.02704] [INSPIRE].

    Article  Google Scholar 

  53. J. Gilmer, S.S. Schoenholz, P.F. Riley, O. Vinyals and G.E. Dahl, Neural Message Passing for Quantum Chemistry, arXiv:1704.01212.

  54. M. Gori, G. Monfardini and F. Scarselli, A new model for learning in graph domains, in proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, Que., Canada, 31 July - 4 August 2005, [https://doi.org/10.1109/IJCNN.2005.1555942].

  55. F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner and G. Monfardini, The Graph Neural Network Model, IEEE Trans. Neural Networks20 (2009) 61.

    Article  Google Scholar 

  56. S. Farrell et al., Particle Track Reconstruction with Deep Learning, proceedings of the Deep Learning for Physical Sciences Workshop at NIPS 17, Long Beach, CA, U.S.A., 2017.

  57. D.P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, arXiv:1412.6980 [INSPIRE].

  58. http://pytorch.org/.

  59. ATLAS collaboration, Search for top-squark pair production in final states with one lepton, jets and missing transverse momentum using 36 fb −1of \( \sqrt{s} \)= 13 TeV pp collision data with the ATLAS detector, JHEP06 (2018) 108 [arXiv:1711.11520] [INSPIRE].

  60. J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections and their matching to parton shower simulations, JHEP07 (2014) 079 [arXiv:1405.0301] [INSPIRE].

    Article  ADS  Google Scholar 

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

  62. DELPHES 3 collaboration, DELPHES 3, A modular framework for fast simulation of a generic collider experiment, JHEP02 (2014) 057 [arXiv:1307.6346] [INSPIRE].

    ADS  Google Scholar 

  63. M. Cacciari, G.P. Salam and G. Soyez, The anti-k tjet clustering algorithm, JHEP04 (2008) 063 [arXiv:0802.1189] [INSPIRE].

    Article  ADS  MATH  Google Scholar 

  64. M. Drees, H. Dreiner, D. Schmeier, J. Tattersall and J.S. Kim, CheckMATE: Confronting your Favourite New Physics Model with LHC Data, Comput. Phys. Commun.187 (2015) 227 [arXiv:1312.2591] [INSPIRE].

    Article  ADS  Google Scholar 

  65. W. Beenakker, M. Klasen, M. Krämer, T. Plehn, M. Spira and P.M. Zerwas, The production of charginos/neutralinos and sleptons at hadron colliders, Phys. Rev. Lett.83 (1999) 3780 [Erratum ibid. 100 (2008) 029901] [hep-ph/9906298] [INSPIRE].

    Article  ADS  Google Scholar 

  66. 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].

    Article  ADS  Google Scholar 

  67. R. Boughezal, C. Focke, X. Liu and F. Petriello, W -boson production in association with a jet at next-to-next-to-leading order in perturbative QCD, Phys. Rev. Lett.115 (2015) 062002 [arXiv:1504.02131] [INSPIRE].

    Article  ADS  Google Scholar 

Download references

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Wu.

Additional information

ArXiv ePrint: 1807.09088

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abdughani, M., Ren, J., Wu, L. et al. Probing stop pair production at the LHC with graph neural networks. J. High Energ. Phys. 2019, 55 (2019). https://doi.org/10.1007/JHEP08(2019)055

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/JHEP08(2019)055

Keywords

  • Supersymmetry Phenomenology