Gauging lepton flavor SU(3) for the muon g − 2
Bibliographic record
Abstract
Abstract Gauging a specific difference of lepton numbers such as Lμ− Lτ is a popular model-building option, which gives rise to economical explanations for the muon anomalous magnetic moment. However, this choice of gauge group seems rather arbitrary, and additional physics is required to reproduce the observed neutrino masses and mixings. We address these shortcomings by embedding Lμ− Lτ in the vectorial SU(3) gauge symmetry of lepton flavor. The vacuum expectation values (VEVs) of scalar fields in the fundamental, six-dimensional and adjoint representations allow for phenomenologically viable lepton and gauge boson masses. The octet scalar gives rise to charged lepton masses, and together with the triplet scalar generates masses for all the leptophilic gauge bosons except for the Lμ− Lτ one. The latter gets its smaller mass from the sextet VEVs, which also generate the neutrino masses, and are determined up to an overall scaling by the observed masses and mixings. The model predicts three heavy neutral leptons at the GeV-TeV scale as well as vectorlike charged lepton partners; it requires the mass of the lightest active neutrino to exceed 10−4 eV, and it naturally provides a resolution of the Cabibbo angle anomaly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".