A dark seesaw solution to low energy anomalies: MiniBooNE, the muon (g − 2), and BaBar
Bibliographic record
Abstract
A recent update from MiniBooNE has strengthened the observed 4.8σ excess of e-like events. Motivated by this and other notable deviations from standard model predictions, such as the muon (g−2), we propose a solution to low energy anomalies through a dark neutrino sector. The model is renormalizable and can also explain light neutrino masses with an anomaly-free and dark U(1)′ gauge symmetry broken at the GeV scale. Large kinetic mixing leads to s-channel production of heavy neutral leptons at e+e− colliders, where we point out and explain a ≳2σ excess observed in the BaBar monophoton data. Our model is also compatible with anomalous e-like events seen at old accelerator experiments, as well as with an excess of double vertex signatures observed at CCFR.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".