Probing new physics signals with symmetry-restored Yukawa textures
Bibliographic record
Abstract
Abstract We implement a symmetry violation guideline into a two-Higgs-doublet model embedded with three right-handed neutrinos, and exploit the generic Yukawa structures of the model via a hypothetical symmetry restoration of a global $$U_Q(1)^3\times U_L(1)^3$$ UQ(1)3×UL(1)3 symmetry. We then apply a mass-powered parametrization to construct the phenomenologically motivated Yukawa interactions, which enables us to incorporate correlatively the neutrino mass, dark matter, as well as the lepton-flavor universality violations in $$R_{K^{(*)}}$$ RK(∗) and $$R_{D^{(*)}}$$ RD(∗) . Specifically, two atmospheric-scale neutrino masses are generated by a low-scale seesaw mechanism, while the much lighter one, being of $$\mathcal {O}(10^{-6})~{{\mathrm{eV}}}$$ O(10-6)eV , is fixed by a $$7.1~{\mathrm{keV}}$$ 7.1keV sterile neutrino dark matter produced primordially by the freeze-in mechanism. On the other hand, the neutrino and the charged-lepton mass hierarchies encoded in the mass-powered textures can naturally account for the $$R_{K^{(*)}}$$ RK(∗) and the $$R_{D^{(*)}}$$ RD(∗) anomalies, respectively. As a further application, a milder discrepancy of the muon $$g{-}2$$ g-2 is observed, which has also been implied by the recently refined lattice results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".