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Tagging ultraboosted jets at the FCC-hh using machine learning techniques

Sanchari Bhattacharyya1,*, Biplob Bhattacherjee1,†, Camellia Bose1,‡, Debtosh Chowdhury2,§, and Swagata Mukherjee2,∥

  • *Contact author: sanchari1192@gmail.com
  • †Contact author: biplob@iisc.ac.in
  • ‡Contact author: camelliabose@iisc.ac.in
  • §Contact author: debtoshc@iitk.ac.in
  • ∥Contact author: swagata@iitk.ac.in
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Phys. Rev. D 112, 095003 – Published 5 November, 2025

DOI: https://doi.org/10.1103/nl33-vx8t

Abstract

The Future Circular Hadron Collider (FCC-hh) will probe unprecedented energy regimes, enabling direct searches for new elementary particles at a scale of tens of TeV. FCC-hh is currently in the planning stage, and one of its primary physics goals is to search for physics beyond the Standard Model by exploring a previously inaccessible kinematic domain. While venturing into uncharted high-energy territories promises excitement, reconstructing objects with enormous transverse momenta will require overcoming major experimental challenges. This work investigates the identification of boosted W bosons and boosted top quarks in the context of three beyond the Standard Model scenarios: heavy vectorlike quark (B′), heavy neutral gauge boson (Z′), and heavy neutral Higgs boson (H). We employ machine learning techniques, including extreme gradient boosting (XGBoost) and convolutional neural networks (CNN), to identify these ultraboosted objects in the collider from their SM background counterpart. We evaluate the performance of these techniques in distinguishing W jets and top jets from quantum chromodynamics (QCD) jets at extremely high transverse momenta (pT) values, demonstrating their potential for future FCC-hh analyses.

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References (195)

  1. G. Aad et al. (ATLAS Collaboration), Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC, Phys. Lett. B 716, 1 (2012).
  2. S. Chatrchyan et al. (CMS Collaboration), Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC, Phys. Lett. B 716, 30 (2012).
  3. G. Heinrich, Collider physics at the precision frontier, Phys. Rep. 922, 1 (2021).
  4. A. Tumasyan et al. (CMS Collaboration), Measurement of the Higgs boson width and evidence of its off-shell contributions to ZZ production, Nat. Phys. 18, 1329 (2022).
  5. ATLAS Collaboration, Combined measurement of the Higgs boson mass from the H→γγ and H→ZZ*→4ℓ decay channels with the ATLAS detector using s=7, 8, and 13 TeV pp collision data, Phys. Rev. Lett. 131, 251802 (2023).
  6. G. Aad et al. (ATLAS Collaboration), Measurement of the H→γγ and H→ZZ*→4ℓ cross-sections in pp collisions at s=13.6  TeV with the ATLAS detector, Eur. Phys. J. C 84, 78 (2024).
  7. S. Navas et al. (Particle Data Group), Review of particle physics, Phys. Rev. D 110, 030001 (2024).
  8. P. Langacker, The physics of heavy Z′ gauge bosons, Rev. Mod. Phys. 81, 1199 (2009).
  9. L. Basso, A. Belyaev, S. Moretti, and C. H. Shepherd-Themistocleous, Phenomenology of the minimal B-L extension of the standard model: Z′ and neutrinos, Phys. Rev. D 80, 055030 (2009).
  10. S. Amrith, J. M. Butterworth, F. F. Deppisch, W. Liu, A. Varma, and D. Yallup, LHC constraints on a B−L gauge model using contur, J. High Energy Phys. 05 (2019) 154.
  11. T. D. Lee, A theory of spontaneous T violation, Phys. Rev. D 8, 1226 (1973).
  12. G. C. Branco, P. M. Ferreira, L. Lavoura, M. N. Rebelo, M. Sher, and J. P. Silva, Theory and phenomenology of two-Higgs-doublet models, Phys. Rep. 516, 1 (2012).
  13. D. Chowdhury and O. Eberhardt, Global fits of the two-loop renormalized Two-Higgs-Doublet model with soft Z2 breaking, J. High Energy Phys. 11 (2015) 052.
  14. D. Chowdhury and O. Eberhardt, Update of global two-Higgs-doublet model fits, J. High Energy Phys. 05 (2018) 161.
  15. F. Kling, H. Li, A. Pyarelal, H. Song, and S. Su, Exotic Higgs decays in type-II 2HDMs at the LHC and future 100 TeV hadron colliders, J. High Energy Phys. 06 (2019) 031.
  16. F. Kling, S. Su, and W. Su, 2HDM neutral scalars under the LHC, J. High Energy Phys. 06 (2020) 163.
  17. P. Draper, A. Ekstedt, and H. E. Haber, A natural mechanism for approximate Higgs alignment in the 2HDM, J. High Energy Phys. 05 (2021) 235.
  18. L. Wang, J. M. Yang, and Y. Zhang, Two-Higgs-doublet models in light of current experiments: A brief review, Commun. Theor. Phys. 74, 097202 (2022).
  19. M. Mangano, P. Azzi, M. Benedikt, A. Blondel, D. A. Britzger, A. Dainese et al., FCC physics opportunities: Future circular collider conceptual design report volume 1. Future circular collider, Technical Report No. 6, CERN, Geneva, 2019, 10.1140/epjc/s10052-019-6904-3.
  20. M. Benedikt, M. Capeans Garrido, F. Cerutti, B. Goddard, J. Gutleber, J. M. Jimenez et al., FCC-hh: The hadron collider: Future circular collider conceptual design report volume 3. Future circular collider, Technical Report, CERN, Geneva, 2019, 10.1140/epjst/e2019-900087-0.
  21. A. J. Larkoski, F. Maltoni, and M. Selvaggi, Tracking down hyper-boosted top quarks, J. High Energy Phys. 06 (2015) 032.
  22. J. Bonilla et al., Jets and jet substructure at future colliders, Front. Phys. 10, 897719 (2022).
  23. T. Bandyopadhyay, S. Mukherjee, S. Raychaudhuri, and R. Vinze, Kinematics and particle identification at very high energy colliders, Springer Proc. Phys. 304, 1251 (2024).
  24. C. Yeh, S. Chekanov, A. Kotwal, J. Proudfoot, S. Sen, N. Tran, Studies of granularity of a hadronic calorimeter for tens-of-TeV jets at a 100 TeV pp collider, J. Instrum. 14, P05008 (2019).
  25. J. Thaler and K. Van Tilburg, Identifying boosted objects with N-subjettiness, J. High Energy Phys. 03 (2011) 015.
  26. A. J. Larkoski, G. P. Salam, and J. Thaler, Energy correlation functions for jet substructure, J. High Energy Phys. 06 (2013) 108.
  27. A. J. Larkoski, I. Moult, and D. Neill, Power counting to better jet observables, J. High Energy Phys. 12 (2014) 009.
  28. A. J. Larkoski, I. Moult, and D. Neill, Building a better boosted top tagger, Phys. Rev. D 91, 034035 (2015).
  29. A. J. Larkoski, I. Moult, and D. Neill, Analytic boosted boson discrimination, J. High Energy Phys. 05 (2016) 117.
  30. I. Moult, L. Necib, and J. Thaler, New angles on energy correlation functions, J. High Energy Phys. 12 (2016) 153.
  31. J. Erdmann, S. Guindon, K. Kroeninger, B. Lemmer, O. Nackenhorst, A. Quadt, and P. Stolte, A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework, Nucl. Instrum. Methods Phys. Res., Sect. A 748, 18 (2014).
  32. L. G. Almeida, M. Backović, M. Cliche, S. J. Lee, and M. Perelstein, Playing tag with ANN: Boosted top identification with pattern recognition, J. High Energy Phys. 07 (2015) 086.
  33. G. Kasieczka, T. Plehn, M. Russell, and T. Schell, Deep-learning top taggers or the end of QCD?, J. High Energy Phys. 05 (2017) 006.
  34. M. Erdmann, B. Fischer, and M. Rieger, Jet-parton assignment in tt¯H events using deep learning, J. Instrum. 12, P08020 (2017).
  35. A. Butter, G. Kasieczka, T. Plehn, and M. Russell, Deep-learned top tagging with a Lorentz layer, SciPost Phys. 5, 028 (2018).
  36. S. Macaluso and D. Shih, Pulling out all the tops with computer vision and deep learning, J. High Energy Phys. 10 (2018) 121.
  37. L. Moore, K. Nordström, S. Varma, and M. Fairbairn, Reports of my demise are greatly exaggerated: N-subjettiness taggers take on jet images, SciPost Phys. 7, 036 (2019).
  38. H. Qu and L. Gouskos, ParticleNet: Jet tagging via particle clouds, Phys. Rev. D 101, 056019 (2020).
  39. A. Butter et al., The machine learning landscape of top taggers, SciPost Phys. 7, 014 (2019).
  40. T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoders, arXiv:1903.02032.
  41. S. Diefenbacher, H. Frost, G. Kasieczka, T. Plehn, and J. M. Thompson, CapsNets continuing the convolutional quest, SciPost Phys. 8, 023 (2020).
  42. A. Chakraborty, S. H. Lim, M. M. Nojiri, and M. Takeuchi, Neural network-based top tagger with two-point energy correlations and geometry of soft emissions, J. High Energy Phys. 07 (2020) 111.
  43. S. Bhattacharya, M. Guchait, and A. H. Vijay, Boosted top quark tagging and polarization measurement using machine learning, Phys. Rev. D 105, 042005 (2022).
  44. A. Shmakov, M. J. Fenton, T.-W. Ho, S.-C. Hsu, D. Whiteson, and P. Baldi, SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention, SciPost Phys. 12, 178 (2022).
  45. S. H. Lim and M. M. Nojiri, Morphology for jet classification, Phys. Rev. D 105, 014004 (2022).
  46. F. A. Dreyer and H. Qu, Jet tagging in the Lund plane with graph networks, J. High Energy Phys. 03 (2021) 052.
  47. J. A. Aguilar-Saavedra, Pulling the Higgs and top needles from the jet stack with feature extended supervised tagging, Eur. Phys. J. C 81, 734 (2021).
  48. M. Andrews et al., End-to-end jet classification of boosted top quarks with the CMS open data, EPJ Web Conf. 251, 04030 (2021).
  49. H. Qu, C. Li, and S. Qian, Particle transformer for jet tagging, arXiv:2202.03772.
  50. F. A. Dreyer, R. Grabarczyk, and P. F. Monni, Leveraging universality of jet taggers through transfer learning, Eur. Phys. J. C 82, 564 (2022).
  51. I. Ahmed, A. Zada, M. Waqas, and M. U. Ashraf, Application of deep learning in top pair and single top quark production at the LHC, Eur. Phys. J. Plus 138, 795 (2023).
  52. J. M. Munoz, I. Batatia, and C. Ortner, Boost invariant polynomials for efficient jet tagging, Mach. Learn. Sci. Tech. 3, 04LT05 (2022).
  53. S. K. Choi, J. Li, C. Zhang, and R. Zhang, Automatic detection of boosted Higgs boson and top quark jets in an event image, Phys. Rev. D 108, 116002 (2023).
  54. M. He and D. Wang, Quark/gluon discrimination and top tagging with dual attention transformer, Eur. Phys. J. C 83, 1116 (2023).
  55. A. Bogatskiy, T. Hoffman, D. W. Miller, J. T. Offermann, and X. Liu, Explainable equivariant neural networks for particle physics: PELICAN, J. High Energy Phys. 03 (2024) 113.
  56. W. Shen, D. Wang, and J. M. Yang, Hierarchical high-point energy flow network for jet tagging, J. High Energy Phys. 09 (2023) 135.
  57. B. Iş𝚤ldak, A. Hayreter, M. Hüdaverdi, F. Ilg𝚤n, S. Salva, E. Şimşek et al., Investigating the violation of charge-parity symmetry through top-quark chromoelectric dipole moments by using machine learning techniques, Acta Phys. Pol. B 54, 5 (2023).
  58. R. Sahu and K. Ghosh, ML-based top taggers: Performance, uncertainty and impact of tower & tracker data integration, SciPost Phys. 17, 166 (2024).
  59. P. Baroň, J. Kvita, R. Přívara, J. Tomeček, and R. Vodák, Application of machine learning based top quark and W jet tagging to hadronic four-top final states induced by SM as well as BSM processes, in 16th International Workshop on Top Quark Physics (2023), arXiv:2310.13009.
  60. A. Bogatskiy, T. Hoffman, and J. T. Offermann, 19 parameters is all you need: Tiny neural networks for particle physics, in 37th Conference on Neural Information Processing Systems (2023), arXiv:2310.16121.
  61. R. Liu, A. Gandrakota, J. Ngadiuba, M. Spiropulu, and J.-R. Vlimant, Efficient and robust jet tagging at the LHC with knowledge distillation, in 37th Conference on Neural Information Processing Systems (2023), arXiv:2311.14160.
  62. J. Batson and Y. F. Kahn, Scaling laws in jet classification, SciPost Phys. Core 8, 034 (2025).
  63. A. Furuichi, S. H. Lim, and M. M. Nojiri, Jet classification using high-level features from anatomy of top jets, J. High Energy Phys. 07 (2024) 146.
  64. V. S. Ngairangbam and M. Spannowsky, Interpretable deep learning models for the inference and classification of LHC data, J. High Energy Phys. 05 (2024) 004.
  65. G. Aad et al. (ATLAS Collaboration), Search for tt¯ resonances in fully hadronic final states in pp collisions at s=13  TeV with the ATLAS detector, J. High Energy Phys. 10 (2020) 061.
  66. ATLAS Collaboration, Boosted hadronic vector boson and top quark tagging with ATLAS using Run 2 data, Technical Report No. ATL-PHYS-PUB-2020-017, CERN, Geneva, 2020.
  67. ATLAS Collaboration, Constituent-based top-quark tagging with the ATLAS detector, Technical Report No. ATL-PHYS-PUB-2022-039, CERN, Geneva, 2022.
  68. S. Chatrchyan et al. (CMS Collaboration), Search for anomalous tt¯ production in the highly boosted all-hadronic final state, J. High Energy Phys. 09 (2012) 029.
  69. A. M. Sirunyan et al. (CMS Collaboration), Search for tt¯ resonances in highly boosted lepton+jets and fully hadronic final states in proton-proton collisions at s=13  TeV, J. High Energy Phys. 07 (2017) 001.
  70. A. M. Sirunyan et al. (CMS Collaboration), Search for top squark production in fully-hadronic final states in proton-proton collisions at s=13  TeV, Phys. Rev. D 104, 052001 (2021).
  71. P. Keicher, Machine learning in top physics in the ATLAS and CMS Collaborations, in 15th International Workshop on Top Quark Physics (2023), arXiv:2301.09534.
  72. B. Bhattacherjee, C. Bose, A. Chakraborty, and R. Sengupta, Boosted top tagging and its interpretation using Shapley values, Eur. Phys. J. Plus 139, 1131 (2024).
  73. C. Bose, A. Chakraborty, S. Chowdhury, and S. Dutta, Interplay of traditional methods and machine learning algorithms for tagging boosted objects, Eur. Phys. J. Spec. Top. 233, 2531 (2024).
  74. R. Baruah, A. Choudhury, K. Ghosh, S. Mondal, and R. Sahu, Probing sub-TeV Higgsinos aided by a machine-learning-based top tagger in the context of trilinear R-parity violating SUSY, Phys. Rev. D 111, 095004 (2025).
  75. R. K. Barman and S. Biswas, Top-philic machine learning, Eur. Phys. J. Spec. Top. 233, 2497 (2024).
  76. K. Fraser, Probing undiscovered particles with theory and data-driven tools, Ph.D. thesis, Harvard University (main), Harvard University, 2024.
  77. S. Mondal and L. Mastrolorenzo, Machine learning in high energy physics: A review of heavy-flavor jet tagging at the LHC, Eur. Phys. J. Spec. Top. 233, 2657 (2024).
  78. S. Chowdhury, A. Chakraborty, and S. Dutta, Boosted top tagging through flavour-violating interactions at the LHC, Eur. Phys. J. C 85, 231 (2025).
  79. A. Choudhury, A. Mondal, S. Mondal, and S. Sarkar, Improving sensitivity of trilinear R-parity violating SUSY searches using machine learning at the LHC, Phys. Rev. D 109, 035001 (2024).
  80. A. Chakraborty, A. De, R. M. Godbole, and M. Guchait, Tagging a boosted top quark with a τ final state, Phys. Rev. D 108, 035011 (2023).
  81. A. Chakraborty, S. Dasmahapatra, H. Day-Hall, B. Ford, S. Jain, and S. Moretti, Fat b-jet analyses using old and new clustering algorithms in new Higgs boson searches at the LHC, Eur. Phys. J. C 83, 347 (2023).
  82. E. Alvarez, M. Szewc, A. Szynkman, S. A. Tanco, and T. Tarutina, Exploring unsupervised top tagging using Bayesian inference, SciPost Phys. Core 6, 046 (2023).
  83. D. Alvestad, N. Fomin, J. Kersten, S. Maeland, and I. Strümke, Beyond cuts in small signal scenarios: Enhanced sneutrino detectability using machine learning, Eur. Phys. J. C 83, 379 (2023).
  84. T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, CoRR abs/1603.02754 (2016).
  85. K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biol. Cybern. 36, 193 (1980).
  86. K. Fukushima, Artificial vision by multi-layered neural networks: Neocognitron and its advances, Neural Netw. 37, 103 (2013).
  87. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE 86, 2278 (1998).
  88. E. Coleman, M. Freytsis, A. Hinzmann, M. Narain, J. Thaler, N. Tran, and C. Vernieri, The importance of calorimetry for highly-boosted jet substructure, J. Instrum. 13, T01003 (2018).
  89. M. Selvaggi, A delphes parameterisation of the FCC-hh detector, Technical Report No. CERN-FCC-PHYS-2020-0003, CERN, Geneva, 2020.
  90. B. Auerbach, S. Chekanov, J. Love, J. Proudfoot, and A. V. Kotwal, Sensitivity to new high-mass states decaying to tt¯ at a 100 TeV collider, Phys. Rev. D 91, 034014 (2015).
  91. C. Helsens, D. Jamin, M. L. Mangano, T. G. Rizzo, and M. Selvaggi, Heavy resonances at energy-frontier hadron colliders, Eur. Phys. J. C 79, 569 (2019).
  92. D. Bertolini, P. Harris, M. Low, and N. Tran, Pileup per particle identification, J. High Energy Phys. 10 (2014) 059.
  93. B. Bhattacherjee, N. Khan, and A. Patra, Anatomy of heavy gauge bosons in a left-right supersymmetric model, Phys. Rev. D 100, 075010 (2019).
  94. Z. Lu, J. Qin, H. Li, Z.-L. Han, F. Huang, C.-Y. Li, X.-H. Yang, and Z.-J. Yang, Phenomenological study of heavy neutral gauge bosons in the left-right symmetric model at future muon collider, J. High Energy Phys. 08 (2025) 207.
  95. S. Bhattacharyya and A. Datta, Phenomenology of an E6 inspired extension of the Standard Model: Higgs sector, Phys. Rev. D 105, 075021 (2022).
  96. Q.-H. Cao and D.-M. Zhang, Collider phenomenology of the 3-3-1 model, arXiv:1611.09337.
  97. L. Di Luzio, J. Fuentes-Martin, A. Greljo, M. Nardecchia, and S. Renner, Maximal flavour violation: A Cabibbo mechanism for leptoquarks, J. High Energy Phys. 11 (2018) 081.
  98. N. Greiner, K. Kong, J.-C. Park, S. C. Park, and J.-C. Winter, Model-independent production of a top-philic resonance at the LHC, J. High Energy Phys. 04 (2015) 029.
  99. J. H. Kim, K. Kong, S. J. Lee, and G. Mohlabeng, Probing TeV scale top-philic resonances with boosted top-tagging at the high luminosity LHC, Phys. Rev. D 94, 035023 (2016).
  100. M. Aaboud et al. (ATLAS Collaboration), Search for heavy particles decaying into top-quark pairs using lepton-plus-jets events in proton–proton collisions at s=13  TeV with the ATLAS detector, Eur. Phys. J. C 78, 565 (2018).
  101. M. Aaboud et al. (ATLAS Collaboration), Search for heavy particles decaying into a top-quark pair in the fully hadronic final state in pp collisions at s=13  TeV with the ATLAS detector, Phys. Rev. D 99, 092004 (2019).
  102. G. Aad et al. (ATLAS Collaboration), Search for tt¯ resonances in fully hadronic final states in pp collisions at s=13  TeV with the ATLAS detector, J. High Energy Phys. 10 (2020) 061.
  103. G. Aad et al. (ATLAS Collaboration), Search for top-philic heavy resonances in pp collisions at s=13  TeV with the ATLAS detector, Eur. Phys. J. C 84, 157 (2024).
  104. H. E. Haber and L. Stephenson Haskins, Supersymmetric theory and models, in Theoretical Advanced Study Institute in Elementary Particle Physics: Anticipating the Next Discoveries in Particle Physics (World Scientific Press, Singapore, 2018), pp. 355–499, 10.1142/9789813233348_0006.
  105. R. K. Barman, B. Bhattacherjee, A. Choudhury, D. Chowdhury, J. Lahiri, and S. Ray, Current status of MSSM Higgs sector with LHC 13 TeV data, Eur. Phys. J. Plus 134, 150 (2019).
  106. H. Bahl, S. Liebler, and T. Stefaniak, MSSM Higgs benchmark scenarios for Run 2 and beyond: the low tanβ region, Eur. Phys. J. C 79, 279 (2019).
  107. R. K. Barman, B. Bhattacherjee, I. Chakraborty, A. Choudhury, and N. Khan, Electroweakino searches at the HL-LHC in the baryon number violating MSSM, Phys. Rev. D 103, 015003 (2021).
  108. A. Adhikary, R. K. Barman, B. Bhattacherjee, A. De, R. M. Godbole, and S. Kulkarni, Long-lived NLSP in the NMSSM, Phys. Rev. D 108, 035020 (2023).
  109. R. K. Barman, G. Belanger, B. Bhattacherjee, R. Godbole, G. Mendiratta, and D. Sengupta, Invisible decay of the Higgs boson in the context of a thermal and nonthermal relic in MSSM, Phys. Rev. D 95, 095018 (2017).
  110. S. P. Martin, A supersymmetry primer, Adv. Ser. Dir. High Energy Phys. 18, 1 (1998).
  111. S. Bhattacharyya, Heavy Higgs boson searches at the LHC in light of a left-right symmetric model, Phys. Rev. D 109, 015034 (2024).
  112. T. Appelquist, H.-C. Cheng, and B. A. Dobrescu, Bounds on universal extra dimensions, Phys. Rev. D 64, 035002 (2001).
  113. H.-C. Cheng, Introduction to extra dimensions, in Theoretical Advanced Study Institute in Elementary Particle Physics: Physics of the Large and the Small (2011), pp. 125–162, 10.1142/9789814327183_0003.
  114. Q. Shafi, E6 as a unifying gauge symmetry, Phys. Lett. B 79, 301 (1978).
  115. T. Fukuyama, SO(10) GUT in four and five dimensions: A review, Int. J. Mod. Phys. A 28, 1330008 (2013).
  116. V. Keus, S. F. King, and S. Moretti, Three-Higgs-doublet models: Symmetries, potentials and Higgs boson masses, J. High Energy Phys. 01 (2014) 052.
  117. I. P. Ivanov, Building and testing models with extended Higgs sectors, Prog. Part. Nucl. Phys. 95, 160 (2017).
  118. J. Shao, I. P. Ivanov, and M. Korhonen, Symmetries for the 4HDM: II. Extensions by rephasing groups, J. Phys. A 57, 385401 (2024).
  119. M. P. Bento, J. C. Romão, and J. a. P. Silva, Unitarity bounds for all symmetry-constrained 3HDMs, J. High Energy Phys. 08 (2022) 273.
  120. R. Boto, J. C. Romão, and J. a. P. Silva, Current bounds on the type-Z Z3 three-Higgs-doublet model, Phys. Rev. D 104, 095006 (2021).
  121. S. Carrolo, J. C. Romão, J. a. P. Silva, and F. Vazão, Symmetry and decoupling in multi-Higgs boson models, Phys. Rev. D 103, 075026 (2021).
  122. H. Georgi and M. Machacek, Doubly charged higgs bosons, Nucl. Phys. B262, 463 (1985).
  123. M. S. Chanowitz and M. Golden, Higgs boson triplets with MW=MZcosθW, Phys. Lett. 165B, 105 (1985).
  124. H. E. Logan and M. B. Reimer, Characterizing a benchmark scenario for heavy Higgs boson searches in the Georgi-Machacek model, Phys. Rev. D 96, 095029 (2017).
  125. G. Bélanger, J. Dutta, R. M. Godbole, S. Kraml, M. Mitra, R. Padhan, and A. Roy, Revisiting the decoupling limit of the Georgi-Machacek model with a scalar singlet, J. High Energy Phys. 10 (2024) 058.
  126. Z. Bairi and A. Ahriche, More constraints on the Georgi-Machacek model, Phys. Rev. D 108, 055028 (2023).
  127. D. Chowdhury, P. Mondal, and S. Samanta, Next-to-leading order unitarity fits in the extended Georgi-Machacek model, arXiv:2404.18996.
  128. A. Kundu, P. Mondal, and P. B. Pal, Custodial symmetry, the Georgi-Machacek model, and other scalar extensions, Phys. Rev. D 105, 115026 (2022).
  129. A. M. Sirunyan et al. (CMS Collaboration), Search for a heavy Higgs boson decaying to a pair of W bosons in proton-proton collisions at s=13  TeV, J. High Energy Phys. 03 (2020) 034.
  130. A. Tumasyan et al. (CMS Collaboration), Search for a heavy Higgs boson decaying into two lighter Higgs bosons in the ττbb final state at 13 TeV, J. High Energy Phys. 11 (2021) 057.
  131. A. Tumasyan et al. (CMS Collaboration), Search for a massive scalar resonance decaying to a light scalar and a Higgs boson in the four b quarks final state with boosted topology, Phys. Lett. B 842, 137392 (2023).
  132. A. M. Sirunyan et al. (CMS Collaboration), Search for charged Higgs bosons decaying into a top and a bottom quark in the all-jet final state of pp collisions at s=13  TeV, J. High Energy Phys. 07 (2020) 126.
  133. A. Tumasyan et al. (CMS Collaboration), Search for a charged Higgs boson decaying into a heavy neutral Higgs boson and a W boson in proton-proton collisions at s=13  TeV, J. High Energy Phys. 09 (2023) 032.
  134. G. Aad et al. (ATLAS Collaboration), Search for heavy neutral Higgs bosons decaying into a top quark pair in 140  fb−1 of proton-proton collision data at s=13  TeV with the ATLAS detector, J. High Energy Phys. 08 (2024) 013.
  135. G. Aad et al. (ATLAS Collaboration), Search for the decay of the Higgs boson to a Z boson and a light pseudoscalar particle decaying to two photons, Phys. Lett. B 850, 138536 (2024).
  136. G. Aad et al. (ATLAS Collaboration), Search for heavy Higgs bosons with flavour-violating couplings in multi-lepton plus b-jets final states in pp collisions at 13 TeV with the ATLAS detector, J. High Energy Phys. 12 (2023) 081.
  137. G. Aad et al. (ATLAS Collaboration), Search for charged Higgs bosons decaying into a top quark and a bottom quark at s=13  TeV with the ATLAS detector, J. High Energy Phys. 06 (2021) 145.
  138. G. Aad et al. (ATLAS Collaboration), Search for a heavy charged Higgs boson decaying into a W boson and a Higgs boson in final states with leptons and b-jets in s=13  TeV pp collisions with the ATLAS detector, J. High Energy Phys. 02 (2025) 143.
  139. ATLAS Collaboration, Search for heavy resonances in the decay channel W+W−→eνμν in pp Collisions at s=13  TeV using 139  fb−1 of data with the ATLAS detector, Technical Report No. ATLAS-CONF-2022-066, CERN, Geneva, 2022, https://cds.cern.ch/record/2842518.
  140. CMS Collaboration, Identifying hadronically decaying vector bosons merged into a single jet, Technical Report No. CMS-PAS-JME-13-006, CERN, 2013.
  141. S. Gopalakrishna, T. Mandal, S. Mitra, and R. Tibrewala, LHC Signatures of a Vector-like b’, Phys. Rev. D 84, 055001 (2011).
  142. S. Gopalakrishna, T. Mandal, S. Mitra, and G. Moreau, LHC signatures of warped-space vectorlike quarks, J. High Energy Phys. 08 (2014) 079.
  143. A. Azatov, D. Chowdhury, D. Ghosh, and T. S. Ray, Same sign di-lepton candles of the composite gluons, J. High Energy Phys. 08 (2015) 140.
  144. R. Benbrik et al., Signatures of vector-like top partners decaying into new neutral scalar or pseudoscalar bosons, J. High Energy Phys. 05 (2020) 028.
  145. A. Banerjee et al., Phenomenological aspects of composite Higgs scenarios: Exotic scalars and vector-like quarks, arXiv:2203.07270.
  146. A. Bhardwaj, T. Mandal, S. Mitra, and C. Neeraj, Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar, Phys. Rev. D 106, 095014 (2022).
  147. A. Bhardwaj, K. Bhide, T. Mandal, S. Mitra, and C. Neeraj, Discovery prospects of a vectorlike top partner decaying to a singlet boson, Phys. Rev. D 106, 075024 (2022).
  148. J. Bardhan, T. Mandal, S. Mitra, and C. Neeraj, Machine learning-enhanced search for a vectorlike singlet B quark decaying to a singlet scalar or pseudoscalar, Phys. Rev. D 107, 115001 (2023).
  149. J. a. M. Alves, G. C. Branco, A. L. Cherchiglia, C. C. Nishi, J. T. Penedo, P. M. F. Pereira, M. N. Rebelo, and J. I. Silva-Marcos, Vector-like singlet quarks: A roadmap, Phys. Rep. 1057, 1 (2024).
  150. A. Banerjee, V. Ellajosyula, and L. Panizzi, Heavy vector-like quarks decaying to exotic scalars: A case study with triplets, J. High Energy Phys. 01 (2024) 187.
  151. Y.-B. Liu, B. Hu, and C.-Z. Li, Single production of vectorlike quarks with charge 5/3 at the 14 TeV LHC, Nucl. Phys. B1007, 116667 (2024).
  152. A. Banerjee, E. Bergeaas Kuutmann, V. Ellajosyula, R. Enberg, G. Ferretti, and L. Panizzi, Vector-like quarks: Status and new directions at the LHC, SciPost Phys. Core 7, 079 (2024).
  153. Y.-J. Zhang, Y.-T. Zhu, L. Han, Y.-P. Bi, and T.-G. Liu, Searching for the vector-like X-quark at the future electron–proton colliders, Eur. Phys. J. C 84, 1184 (2024).
  154. A. Hayrapetyan et al. (CMS Collaboration), Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton–proton collisions at s=13  TeV at the CMS experiment, Phys. Rep. 1115, 570 (2025).
  155. ATLAS Collaboration, Search for singly produced vectorlike top partners in multilepton final states with 139  fb−1 of pp collision data at s=13  TeV with the ATLAS detector, Phys. Rev. D 109, 112012 (2024).
  156. ATLAS Collaboration, Search for single production of a vectorlike T quark decaying into a Higgs boson and top quark with fully hadronic final states using the ATLAS detector, Phys. Rev. D 105, 092012 (2022).
  157. ATLAS Collaboration, Search for pair-production of vector-like quarks in pp collision events at s=13  TeV with at least one leptonically decaying Z boson and a third-generation quark with the ATLAS detector, Phys. Lett. B 843, 138019 (2023).
  158. ATLAS Collaboration, Search for singly produced vectorlike top partners in multilepton final states with 139  fb−1 of pp collision data at s=13  TeV with the atlas detector, Phys. Rev. D 109, 112012 (2024).
  159. A. Tumasyan et al. (CMS Collaboration), Search for single production of a vector-like T quark decaying to a top quark and a Z boson in the final state with jets and missing transverse momentum at s=13  TeV, J. High Energy Phys. 05 (2022) 093.
  160. A. Tumasyan et al. (CMS Collaboration), Search for a W′ boson decaying to a vector-like quark and a top or bottom quark in the all-jets final state at s=13  TeV, J. High Energy Phys. 09 (2022) 088.
  161. A. Tumasyan et al. (CMS Collaboration), Search for pair production of vector-like quarks in leptonic final states in proton-proton collisions at s=13  TeV, J. High Energy Phys. 07 (2023) 020.
  162. A. Tumasyan et al. (CMS Collaboration), Search for a vector-like quark T′→tH via the diphoton decay mode of the Higgs boson in proton-proton collisions at s=13  TeV, J. High Energy Phys. 09 (2023) 057.
  163. M. Buchkremer, G. Cacciapaglia, A. Deandrea, and L. Panizzi, Model independent framework for searches of top partners, Nucl. Phys. B876, 376 (2013).
  164. J.-Z. Han, J. Yang, S. Xu, and H.-K. Wang, Search for single production of vector-like B quark via bZ channel at the HL-LHC, Nucl. Phys. B975, 115672 (2022).
  165. B. Yang, Z. Li, X. Jia, S. Moretti, and L. Shang, Search for single vector-like B quark production in hadronic final states at the LHC, Eur. Phys. J. C 84, 1124 (2024).
  166. A. M. Sirunyan et al. (CMS Collaboration), Search for single production of vector-like quarks decaying to a top quark and a W boson in proton-proton collisions at s=13  TeV, Eur. Phys. J. C 79, 90 (2019).
  167. G. Aad et al. (ATLAS Collaboration), Optimisation of large-radius jet reconstruction for the ATLAS detector in 13 TeV proton–proton collisions, Eur. Phys. J. C 81, 334 (2021).
  168. G. Aad et al. (ATLAS Collaboration), Search for single production of a vectorlike T quark decaying into a Higgs boson and top quark with fully hadronic final states using the ATLAS detector, Phys. Rev. D 105, 092012 (2022).
  169. G. Aad et al. (ATLAS Collaboration), Search for pair-production of vector-like quarks in pp collision events at s=13  TeV with at least one leptonically decaying Z boson and a third-generation quark with the ATLAS detector, Phys. Lett. B 843, 138019 (2023).
  170. G. Aad et al. (ATLAS Collaboration), Search for singly produced vectorlike top partners in multilepton final states with 139  fb−1 of pp collision data at s=13  TeV with the ATLAS detector, Phys. Rev. D 109, 112012 (2024).
  171. G. Aad et al. (ATLAS Collaboration), Search for new particles in final states with a boosted top quark and missing transverse momentum in proton-proton collisions at s=13  TeV with the ATLAS detector, J. High Energy Phys. 05 (2024) 263.
  172. J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer, and T. Stelzer, MadGraph 5: Going beyond, J. High Energy Phys. 06 (2011) 128.
  173. Mathieu Buchkremer, Giacomo Cacciapaglia, Aldo Deandrea, and Luca Panizzi, VLQ Model, http://feynrules.irmp.ucl.ac.be/wiki/VLQ_bsingletvl (2014).
  174. J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3 Collaboration), delphes 3, A modular framework for fast simulation of a generic collider experiment, J. High Energy Phys. 02 (2014) 057.
  175. T. Sjostrand, S. Mrenna, and P. Z. Skands, A brief introduction to pythia 8.1, Comput. Phys. Commun. 178, 852 (2008).
  176. R. D. Ball et al. (NNPDF Collaboration), Parton distributions for the LHC Run II, J. High Energy Phys. 04 (2015) 040.
  177. M. Cacciari and G. P. Salam, Dispelling the N3 myth for the kt jet-finder, Phys. Lett. B 641, 57 (2006).
  178. M. Cacciari, G. P. Salam, and G. Soyez, FastJet user manual, Eur. Phys. J. C 72, 1896 (2012).
  179. S. Ellis, J. Huston, K. Hatakeyama, P. Loch, and M. Tönnesmann, Jets in hadron–hadron collisions, Prog. Part. Nucl. Phys. 60, 484 (2008).
  180. G. P. Salam, Towards jetography, Eur. Phys. J. C 67, 637 (2010).
  181. A. J. Larkoski, F. Maltoni, and M. Selvaggi, Tracking down hyper-boosted top quarks, J. High Energy Phys. 06 (2015) 032.
  182. A. J. Larkoski and J. Thaler, Aspects of jets at 100 TeV, Phys. Rev. D 90, 034010 (2014).
  183. B. Mukhopadhyaya, T. Samui, and R. K. Singh, Dynamic radius jet clustering algorithm, J. High Energy Phys. 04 (2023) 019.
  184. S. Jain, Revisiting new and old jet clustering algorithms for beyond the standard model Higgs searches in the final states with b-jets, Ph.D. thesis, University of Southampton, Southampton University, 2023.
  185. T. Lapsien, R. Kogler, and J. Haller, A new tagger for hadronically decaying heavy particles at the LHC, Eur. Phys. J. C 76, 600 (2016).
  186. A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, Soft drop, J. High Energy Phys. 05 (2014) 146.
  187. M. Arjovsky, S. Chintala, and L. Bottou, Wasserstein generative adversarial networks, in International Conference on Machine Learning (PMLR, Sydney, NSW, Australia, 2017), pp. 214–223.
  188. https://github.com/gwding/draw_convnet
  189. ATLAS Collaboration, Constituent-based top-quark tagging with the ATLAS detector, Technical Report No. ATL-PHYS-PUB-2022-039, CERN, Geneva, 2022.
  190. G. Cowan, K. Cranmer, E. Gross, and O. Vitells, Asymptotic formulae for likelihood-based tests of new physics, Eur. Phys. J. C 71, 1554 (2011).
  191. L. Borgonovi et al. (FCC Collaboration), Higgs and Electro-weak symmetry breaking at the FCC-hh, Report No. CERN-ACC-2018-0045, 2019.
  192. A. M. Sirunyan et al. (CMS Collaboration), Search for resonant tt¯ production in proton-proton collisions at s=13  TeV, J. High Energy Phys. 04 (2019) 031.
  193. E. Perez Codina and P. G. Roloff, Initial study of the reconstruction of boosted B-hadrons and τ-leptons at FCC-hh, Technical Report No. CERN-ACC-2018-0035, LCD-NOTE-2018-003, CERN, Geneva, 2018.
  194. E. Perez Codina and P. G. Roloff, Hit multiplicity approach to b-tagging in FCC-hh, Technical Report No. CERN-ACC-2018-0023, LCD-Note-2018-001, CERN, Geneva, 2018.
  195. V. D. Barger, Collider Physics (CRC Press, Boca Raton, 1987), ISBN [Amazon][WorldCat].

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Phys. Rev. D 112, 095003– Published 5 November, 2025
  • Received 29 January 2025
  • Accepted 14 October 2025
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