Bibliographic record
Abstract
We revisit the conventional implementation of the determination of $V_{us}$ via flavor-breaking (FB) finite-energy sum rule (FESR) analyses of inclusive hadronic $\tau$ decay data, which is known to produce results $>3\sigma$ low compared to determinations from kaon physics and the expectations of three-family unitarity. We show that this implementation fails self-consistency tests, and that the source of this problem is a breakdown of assumptions concerning the treatment of higher dimension OPE contributions. We then provide an alternate implementation of the FB FESR approach which cures these problems. Lattice data for the relevant flavor-breaking correlator combination is also employed to clarify the treatment of the slowly-converging dimension $2$ OPE contribution to the relevant sum rules and quantify the associated truncation uncertainty. We implement this new approach using ALEPH non-strange data, and a combination of ALEPH, BaBar and Belle strange $\tau$ decay data. Normalizing the exclusive $\tau\rightarrow K^-\pi^0\nu_\tau$ mode component of the inclusive strange decay distribution using the recent preliminary BaBar result for the corresponding branching fraction we find a result, $V_{us}=0.2228(23)_{exp}(6)_{th}$, in excellent agreement with the results of $K_{\ell 3}$-based analyses, and in agreement within errors with three-family-unitarity expectations, thus resolving the long-standing inclusive $\tau$ $V_{us}$ puzzle.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".