Bibliographic record
Abstract
We show that coupling the standard model to a Lorentz symmetry-violating sector may coexist with viable phenomenology provided that the interaction between the two is mediated by higher-dimensional operators. In particular, if the new sector acquires anisotropic-scaling behavior above a ``Ho\ifmmode \check{r}\else \v{r}\fi{}ava-Lifshitz'' energy scale ${\ensuremath{\Lambda}}_{\mathrm{HL}}$ and couples to the standard model through interactions suppressed by ${M}_{\mathrm{pl}}$, the transmission of the Lorentz violation into the standard model is protected by the ratio ${\ensuremath{\Lambda}}_{\mathrm{HL}}^{2}/{M}_{\mathrm{pl}}^{2}$. A wide-scale separation ${\ensuremath{\Lambda}}_{\mathrm{HL}}\ensuremath{\ll}{M}_{\mathrm{pl}}$ can then make Lorentz-violating terms in the standard model sector within experimental bounds without fine-tuning. We first illustrate our point with a toy example of Lifshitz-type neutral fermion coupled to photon via the magnetic moment operator, and then implement similar proposal for the Ho\ifmmode \check{r}\else \v{r}\fi{}ava-Lifshitz gravity coupled to conventional Lorentz-symmetric matter fields. We find that most radiatively induced Lorentz violation can be controlled by a large-scale separation, but the existence of instantaneously propagating non-Lifshitz modes in gravity can cause a certain class of diagrams to remain quadratically divergent above ${\ensuremath{\Lambda}}_{\mathrm{HL}}$. Such problematic quadratic divergence however can be removed by extending the action with terms of higher Lifshitz-dimension, resulting in a completely consistent setup that can cope with the stringent tests of Lorentz invariance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".