Evaluating the price of tiny kinetic mixing
Bibliographic record
Abstract
We consider both ``bottom-up'' and ``top-down'' approaches to the origin of gauge kinetic mixing. We focus on the possibilities for obtaining kinetic mixings $\ensuremath{\epsilon}$ which are consistent with experimental constraints and are much smaller than the naive estimates ($\ensuremath{\epsilon}\ensuremath{\sim}{10}^{\ensuremath{-}2}--{10}^{\ensuremath{-}1}$) at the one-loop level. In the bottom-up approach, we consider the possible suppression from multiloop processes. Indeed we argue that kinetic mixing through gravity alone, requires at least six loops and could be as large as $\ensuremath{\sim}{10}^{\ensuremath{-}13}$. In the top-down approach we consider embedding the Standard Model and a $U(1{)}_{X}$ in a single grand-unified gauge group as well as the mixing between Abelian and non-Abelian gauge sectors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".