- Open Access
Exploring top-quark signatures of heavy flavor-violating scalars at the LHC
Phys. Rev. D 112, 075038 – Published 21 October, 2025
DOI: https://doi.org/10.1103/8kcm-wfpt
Abstract
In this work, we study flavor-violating scalars (flavons) in a range of large masses that have not been explored previously. We model the interactions with an effective field theory formulation where the flavon is heavier than the top quark. In addition, we assume that the flavon only couples to fermions of the standard model in a flavor-changing way, making it easier to escape experimental constraints. As the flavon couples strongly to top quarks, same-sign and opposite-sign top-quark pair signals can be explored in the search for those particles. We investigate the possibility of probing flavons through their interactions with a top quark plus up and charm quarks for effective couplings of order and masses in the range in the 14 TeV LHC in scenarios with up to .
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References (33)
- C. D. Froggatt and H. B. Nielsen, Nucl. Phys. B147, 277 (1979).
- L. Di Luzio, M. Giannotti, E. Nardi, and L. Visinelli, Phys. Rep. 870, 1 (2020).
- J. Jaeckel and A. Ringwald, Annu. Rev. Nucl. Part. Sci. 60, 405 (2010).
- Y. Ema, K. Hamaguchi, T. Moroi, and K. Nakayama, J. High Energy Phys. 01 (2017) 096.
- F. Björkeroth, E. J. Chun, and S. F. King, J. High Energy Phys. 08 (2018) 117.
- M. Bauer, M. Neubert, S. Renner, M. Schnubel, and A. Thamm, J. High Energy Phys. 09 (2022) 056.
- Y. Jho, S. Knapen, and D. Redigolo, J. High Energy Phys. 10 (2022) 029.
- A. G. Dias, J. Leite, and D. S. V. Gonçalves, Phys. Rev. D 104, 075014 (2021).
- L. Di Luzio, F. Mescia, and E. Nardi, Phys. Rev. D 96, 075003 (2017).
- M. Bauer, M. Neubert, and A. Thamm, J. High Energy Phys. 12 (2017) 044.
- A. Hayrapetyan et al. (CMS Collaboration), Phys. Lett. B 850, 138478 (2024).
- W.-S. Hou and M. Kikuchi, Europhys. Lett. 123, 11001 (2018).
- G. Aad et al. (ATLAS Collaboration), J. High Energy Phys. 12 (2023) 081.
- S. Wahdan (ATLAS Collaboration), arXiv:2411.14026.
- P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, Eur. Phys. J. C 76, 235 (2016).
- L. Anzalone, T. Diotalevi, and D. Bonacorsi, Mach. Learn. Sci. Technol. 3, 035017 (2022).
- W. Esmail, A. Hammad, A. Jueid, and S. Moretti, arXiv:2405.16499.
- A. Alves, A. G. Dias, E. da Silva Almeida, and D. S. V. Gonçalves, Phys. Rev. D 109, 055013 (2024).
- C. Cornella, P. Paradisi, and O. Sumensari, J. High Energy Phys. 01 (2020) 158.
- J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, J. High Energy Phys. 07 (2014) 079.
- R. Frederix, S. Frixione, V. Hirschi, D. Pagani, H. S. Shao, and M. Zaro, J. High Energy Phys. 07 (2018) 185; 11 (2021) 85.
- C. Degrande, C. Duhr, B. Fuks, D. Grellscheid, O. Mattelaer, and T. Reiter, Comput. Phys. Commun. 183, 1201 (2012).
- A. Alloul, N. D. Christensen, C. Degrande, C. Duhr, and B. Fuks, Comput. Phys. Commun. 185, 2250 (2014).
- C. Bierlich et al., SciPost Phys. Codebases 2022, 8 (2022).
- J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3 Collaboration), J. High Energy Phys. 02 (2014) 057.
- G. Cowan, K. Cranmer, E. Gross, and O. Vitells, Eur. Phys. J. C 71, 1554 (2011).
- A. Schwartz and M. Gersaback, Global fit for mixing (2023), https://hflav-eos.web.cern.ch/hflav-eos/charm/CKM23/results_mix_cpv.html.
- E. Golowich, J. Hewett, S. Pakvasa, and A. A. Petrov, Phys. Rev. D 76, 095009 (2007).
- A. Crivellin, A. Kokulu, and C. Greub, Phys. Rev. D 87, 094031 (2013).
- J. Bergstra, D. Yamins, and D. D. Cox, in Proceedings of the 30th International Conference on Machine Learning (PMLR, Georgia, USA, 2013), http://proceedings.mlr.press/v28/bergstra13.pdf.
- D. P. Kingma and J. Ba, arXiv:1412.6980.
- https://doi.org/10.5281/zenodo.16745102
- T. P. Cheng and M. Sher, Phys. Rev. D 35, 3484 (1987).