Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Observability of an ultraheavy diquark decaying into vectorlike quarks at the LHC

Daniel C. Costache1,2, Calin Alexa1,*, Anca M. Dinu1,2, Ioan M. Dinu1, Ioana Duminica1,2, Matei S. Filip1,2, and Gabriel C. Majeri2

  • *Contact author: calin.alexa@cern.ch

Phys. Rev. D 113, 115056 – Published 22 June, 2026

DOI: https://doi.org/10.1103/qxhl-4pqz

Abstract

We present a comprehensive analysis of the discovery reach and exclusion limits for an ultraheavy diquark scalar (7–9.5 TeV) decaying into a pair of vectorlike quarks (1.5–2 TeV) at the HL-LHC. Building on an improved signal selection efficiency achieved using machine learning techniques, we extend our previous six-jet final-state study by providing a complete likelihood-based statistical treatment of this search topology. The analysis incorporates theoretical and systematic uncertainties through nuisance parameters within the likelihood framework, enabling a consistent statistical interpretation. The mass regions of interest were determined through scans of the local p-values, CLs, and the upper limits on the model-dependent signal strength μ. The results indicate a promising sensitivity to ultraheavy diquark scalars within the explored mass range, suggesting that the HL-LHC could either discover or set stringent exclusion limits on such particles.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (42)

  1. A. Tumasyan et al. (CMS Collaboration), Search for resonant and nonresonant production of pairs of dijet resonances in proton-proton collisions at s=13  TeV, J. High Energy Phys. 07 (2023) 161.
  2. G. Aad et al. (ATLAS Collaboration), Pursuit of paired dijet resonances in the run 2 dataset with ATLAS, Phys. Rev. D 108, 112005 (2023).
  3. A. Hayrapetyan et al. (CMS Collaboration), Search for resonant production of pairs of dijet resonances through broad mediators in proton-proton collisions at s=13  TeV, arXiv:2507.17884.
  4. A. Crivellin, C. A. Manzari, B. Mellado, and S.-E. Dahbi, Consistency and interpretation of the LHC dijet excesses, Phys. Rev. D 107, 054045 (2023).
  5. ATLAS phase-II upgrade scoping document, Technical Report, CERN, Geneva, 2015, 10.17181/CERN.7CRX.AJHP.
  6. J. Butler, D. Contardo, M. Klute, J. Mans, and L. Silvestris (CMS Collaboration), CMS phase II upgrade scope document, Technical Report, CERN, Geneva, 2015, https://cds.cern.ch/record/2055167.
  7. B. A. Dobrescu, LHC probes of the 10 TeV scale, arXiv:1912.13155.
  8. G. Aad et al. (ATLAS Collaboration), Search for pair-production of vector-like quarks in lepton+jets final states containing at least one b-tagged jet using the run 2 data from the ATLAS experiment, Phys. Lett. B 854, 138743 (2024).
  9. 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.
  10. A. Hayrapetyan et al. (CMS Collaboration), Searches for pair-produced multijet resonances using data scouting in proton-proton collisions at s=13  TeV, Phys. Rev. Lett. 133, 201803 (2024).
  11. B. A. Dobrescu, R. M. Harris, and J. Isaacson, Ultraheavy resonances at the LHC: Beyond the QCD background, arXiv:1810.09429.
  12. B. A. Dobrescu, TeV-scale particles and LHC events with dijet pairs, J. High Energy Phys. 06 (2025) 113.
  13. I. Duminica, C. Alexa, I. M. Dinu, B. A. Dobrescu, and M.-S. Filip, Ultraheavy diquark decaying into vectorlike quarks at the LHC, Phys. Rev. D 111, 1105025 (2025).
  14. I. Zurbano Fernandez et al., High-luminosity large hadron collider (HL-LHC): Technical design report, CERN, Geneva, 2020, Vol. 10, https://cds.cern.ch/record/2749422?ln=en.
  15. M. S. Filip, C. Alexa, D. C. Costache, I. M. Dinu, I. Duminica, and G. C. Majeri, Hl-lhc sensitivity to an ultraheavy suu diquark in the uχ channel, arXiv:2601.11181.
  16. Searches for dijet resonances in pp collisions at s=13  TeV using the 2016 and 2017 datasets, Technical Report, CERN, Geneva, 2018, https://cds.cern.ch/record/2637847.
  17. J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, J. High Energy Phys. 07 (2014) 079.
  18. I. Duminica, I.-M. Dinu, A. Jinaru, B. Dobrescu, and C. Alexa, Resonant production of vectorlike quarks at the HL-LHC, Proc. Sci., ICHEP2024 (2025) 308.
  19. C. Bierlich et al., A comprehensive guide to the physics and usage of pythia 8.3, SciPost Phys. Codebases 2022, 8 (2022).
  20. R. D. Ball et al. (NNPDF Collaboration), Parton distributions for the LHC Run II, J. High Energy Phys. 04 (2015) 040.
  21. M. Cacciari, G. P. Salam, and G. Soyez, The anti-kt jet clustering algorithm, J. High Energy Phys. 04 (2008) 063.
  22. M. Cacciari, G. P. Salam, and G. Soyez, fastjet user manual, Eur. Phys. J. C 72, 1896 (2012).
  23. 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.
  24. I.-M. Dinu, Diquark Simulation, GitHub (2023). https://github.com/imdinu/diquark-simulation/tree/main/hadronic/cards.
  25. F. Pedregosa et al., Scikit-learn: Machine learning in python, J. Mach. Learn. Res. 12, 2825 (2011), https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf.
  26. I.-M. Dinu, Diquark analysis, GitHub (2023). https://github.com/imdinu/diquark-analysis.
  27. T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, How many trees in a random forest?, in Machine Learning and Data Mining in Pattern Recognition, edited by P. Perner (Springer, Berlin, Heidelberg, 2012), pp. 154–168, 10.1007/978-3-642-31537-4_13.
  28. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd ed., Springer Series in Statistics (Springer, New York Inc., 2009), 10.1007/978-0-387-84858-7.
  29. P. Probst, M. N. Wright, and A.-L. Boulesteix, Hyperparameters and tuning strategies for random forest, WIREs Data Min. Knowl. Discovery 9, e1301 (2019).
  30. A. Ghosh, B. Nachman, T. Plehn, L. Shire, T. M. P. Tait, and D. Whiteson, Statistical patterns of theory uncertainties, SciPost Phys. Core 6, 045 (2023).
  31. B. Giacobbe (ATLAS LUCID Group Collaboration), LUCID-3: The upgrade of the ATLAS luminosity detector for high-luminosity LHC, J. Instrum. 19, C03053 (2024).
  32. Procedure for the LHC Higgs boson search combination in Summer 2011, Technical Report, CERN, Geneva, 2011, https://cds.cern.ch/record/1379837.
  33. A. L. Read, Presentation of search results: The CLS technique, J. Phys. G 28, 2693 (2002).
  34. W. Verkerke and D. Kirkby, The RooFit toolkit for data modeling, in Statistical Problems in Particle Physics, Astrophysics and Cosmology, pp. 186–189, 10.1142/9781860948985_0039.
  35. I. Antcheva, M. Ballintijn, B. Bellenot, M. Biskup, R. Brun, N. Buncic, P. Canal et al., root—A C++ framework for petabyte data storage, statistical analysis and visualization, Comput. Phys. Commun. 180, 2499 (2009).
  36. L. Moneta, K. Belasco, K. Cranmer, S. Kreiss, A. Lazzaro, D. Piparo, G. Schott, W. Verkerke, and M. Wolf, The RooStats Project, Proc. Sci., ACAT2010 (2010) 057 [arXiv:1009.1003].
  37. D.-C. Costache, Diquark statistics, GitHub (2025). https://github.com/IFIN-DFPE/diquark-statistics.
  38. 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).
  39. J. e. a. Butterworth, PDF4LHC recommendations for LHC run II, J. Phys. G 43, 023001 (2016).
  40. R. A. Khalek, S. Bailey, J. Gao, L. Harland-Lang, and J. Rojo, Towards ultimate parton distributions at the high-luminosity LHC, Eur. Phys. J. C 78 (2018).
  41. Search for resonant production of pairs of dijet resonances in proton-proton collisions at s=13.6  TeV, Technical Report, CERN, Geneva, 2025, https://cds.cern.ch/record/2947130.
  42. I. Zisopoulos (CMS Collaboration), Searches for new resonances in CMS, Technical Report, CERN, Geneva, 2025, 10.22323/1.476.0249.

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation