TeV-scale vector leptoquark from Pati-Salam unification with vectorlike families
Bibliographic record
Abstract
In this paper, we show an explicit way to realize a TeV-scale vector leptoquark from the Pati-Salam (PS) unification with extra vectorlike families. The leptoquark mass is constrained to be heavier than PeV-scale by the measurement of a flavor violating kaon decay, $K_L \to \mu e$, in conventional models. This strong constraint can be avoided by introducing vectorlike families consistently with the quark and lepton masses and CKM and PMNS matrices. The other flavor violating processes are also suppressed. In this model, the vector leptoquark can be sufficiently light to explain the recent $b\to s\mu\mu$ anomaly, while the $b\to c\tau\nu$ anomaly is difficult to be explained due to the strong constraints from the $Z^\prime$ boson and vectorlike quark searches at the LHC. When the $b\to s\mu\mu$ anomaly is explained, we show that $\mathcal{O}({0.2})$ % tuning is required in the fermion matrix, the future experiments in $\mu\to e\gamma$ and $\mu$-$e$ conversions will cover the most available parameter space, and sizable neutral meson mixings, induced by the extra Higgs doublets, are unavoidable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".