4.7 Article

Revisiting a generalized two-Higgs-doublet model in light of the muon anomaly and lepton flavor violating decays at the HL-LHC

Journal

PHYSICAL REVIEW D
Volume 103, Issue 5, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.103.055009

Keywords

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Funding

  1. Department of Atomic Energy, Government of India

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Researchers have explored muon anomaly and lepton flavor violation using a perturbation of a two-Higgs-doublet model, considering constraints from various sources. They found that this model can explain the muon anomaly and predict interesting signs of lepton flavor violation. Additionally, they showed that part of the parameter space can be probed with a significance > 5 sigma using a cut-based analysis and an artificial neural network.
One of the main motivations to look beyond the Standard Model is the discrepancy between the theoretical prediction and observation of the anomalous magnetic moment of the muon. Alleviating this tension between theory and experiment and simultaneously satisfying the bounds from lepton flavor violation data is a challenging task. In this paper, we consider a generalized two-Higgs-doublet model with a Yukawa structure as a perturbation of a type X two-Higgs-doublet model. In view of this model, we explore the muon anomaly and lepton flavor violation along with constraints coming from B physics, theoretical constraints, electroweak observables, and collider data which can restrict the model parameter space significantly. We find that within the framework of this model it is possible to obtain regions allowed by all constraints that can provide an explanation for the observed muon anomaly and at the same time predict interesting signatures of lepton flavor violation. Furthermore, we consider the flavor-violating decay of a low-mass CP-odd scalar to probe the allowed parameter space at future runs of the LHC. With a simple cut-based analysis, we show that part of this parameter space can be probed with a significance > 5 sigma. We also provide an artificial neural network analysis that significantly improves our cut-based results.

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