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Electron and Muon Anomalous Magnetic Moment in the $\mathbb{Z}_3$-NMSSM

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arxiv 2306.06854 v1 pith:U6LGNHVY submitted 2023-06-12 hep-ph

classification hep-ph
keywords susytimeselectronanomalousaroundcasesconcludeddark
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Inspired by the recent measurements of the muon and electron anomalous magnetic moments, the rapid progress of the LHC search for supersymmetry, and the significantly improved sensitivities of dark matter direct detection experiments, we studied the supersymmetric contribution to the electron \texorpdfstring{$g-2$}{}, $a_e^{\rm SUSY}$, in the Next-to-Minimal Supersymmetric Standard Model with a discrete $\mathbb{Z}_3$ symmetry. We concluded that $a_e^{\rm SUSY}$ was mainly correlated with $a_\mu^{\rm SUSY}$ by the formula $a_e^{\rm SUSY}/m_e^2 \simeq a_\mu^{\rm SUSY}/m_\mu^2$, and significant violations of this correlation might occur only in rare cases. As a result, $a_e^{\rm SUSY}$ was typically around $5 \times 10^{-14}$ when $a_\mu^{\rm SUSY} \simeq 2.5 \times 10^{-9}$. We also concluded that the dark matter direct detection and LHC experiments played crucial roles in determining the maximum reach of $a_e^{\rm SUSY}$. Concretely, $a_e^{\rm SUSY}$ might be around $3 \times 10^{-13}$ in the optimum cases if one used the XENON-1T experiment to limit the supersymmetry parameter space. This prediction, however, was reduced to $1.5 \times 10^{-13}$ after implementing the LZ restrictions and $1.0 \times 10^{-13}$ when further considering the LHC restrictions.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Deciphering compressed electroweakino excesses with MadAnalysis 5

    hep-ph 2025-07 conditional novelty 6.0 of 10

    MadAnalysis 5 v1.11 adds validated recasting of two ATLAS compressed-electroweakino searches and shows that a proposed NMSSM scenario fits the soft-lepton and monojet excesses less well than simpler models.

  2. Shedding Light on Dark Matter at the LHC with Machine Learning

    hep-ph 2025-09 conditional novelty 5.0 of 10

    A machine-learned LHC analysis projects 5-sigma sensitivity to singlino-dominated NMSSM dark matter via radiative higgsino decays to photons, covering higgsino masses up to 225 GeV.

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