Strange quark mass from flavor breaking in hadronic<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>τ</mml:mi></mml:math>decays
Bibliographic record
Abstract
The strange quark mass is extracted from a finite energy sum rule (FESR) analysis of the flavor-breaking difference of light-light and light-strange quark vector-plus-axial-vector correlators, using spectral functions determined from hadronic $\ensuremath{\tau}$ decay data. We point out problems for existing FESR treatments associated with potentially slow convergence of the perturbative series for the mass-dependent terms in the OPE over certain parts of the FESR contour, and show how to construct alternate weight choices which not only cure this problem, but also (1) considerably improve the convergence of the integrated perturbative series, (2) strongly suppress contributions from the region of s values where the errors on the strange current spectral function are still large and (3) essentially completely remove uncertainties associated with the subtraction of longitudinal contributions to the experimental decay distributions. The result is an extraction of ${m}_{s}$ with statistical errors comparable to those associated with the current experimental uncertainties in the determination of the CKM angle, ${V}_{\mathrm{us}}.$ We find ${m}_{s}(1$ $\mathrm{GeV})=158.6\ifmmode\pm\else\textpm\fi{}18.7\ifmmode\pm\else\textpm\fi{}16.3\ifmmode\pm\else\textpm\fi{}13.3$ MeV (where the first error is statistical, the second due to that on ${V}_{\mathrm{us}},$ and the third theoretical).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".