Muon <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>g</mml:mi><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> in two-Higgs-doublet models with vectorlike leptons
Bibliographic record
Abstract
We calculate contributions to the anomalous magnetic moment of the muon from heavy neutral and charged Higgs bosons and new leptons in two-Higgs-doublet models extended by vectorlike leptons. We present detailed predictions of two models with type-II couplings to standard model fermions, motivated by a ${Z}_{2}$ symmetry and supersymmetry. In addition, we compare the results with the standard model extended by vectorlike leptons. We find that the model motivated by a ${Z}_{2}$ symmetry can generate much larger contributions to the magnetic moment compared to the standard model, even by two orders of magnitude due to ${\mathrm{tan}}^{2}\ensuremath{\beta}$ enhancement, while satisfying current constraints. As a consequence, the standard model explanation of the anomaly requires much larger corrections to muon couplings making this model easier to probe at future precision machines. Additionally, we find that the model with couplings motivated by supersymmetry typically leads to much smaller contributions to the magnetic moment as a result of cancellations. We also identify interesting scenarios where contributions from the charged Higgs boson can fully explain the anomaly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".