Studies of the Strange Hadronic Tau Decay Tau- to K0(S) Pi- Nu-Tau Using the BaBar Detector
Bibliographic record
Abstract
A study of the decay {tau}{sup -} {yields} K{sub S}{sup 0}{pi}{sup -} {nu}{sub {tau}} (K{sub S}{sup 0} {yields} {pi}{sup +}{pi}{sup -}) using the BABAR detector is presented. Using 124.4 fb{sup -1} of data we measure {Beta}({tau}{sup -} {yields} {bar K}{sup 0}{pi}{sup -}{nu}{sub {tau}}) = (0.830 {+-} 0.005(stat) {+-} 0.042(syst))%, which is the world's most precise measurement to date of this branching ratio, and is consistent with the current world average. This preliminary result, unlike most of the {Beta}({tau}{sup -} {yields} {bar K}{sup 0}{pi}{sup -}{nu}{sub {tau}}) measurements already published, is systematics dominated and so the biggest future improvement to this number should come from reducing the systematic uncertainties in the analysis. A study of the K{pi} mass spectrum, from which the strange (K{pi}) spectral function can be measured, reveals excess contributions above the K*(892) tail at higher K{pi} mass. While in the past this has been thought to be due to K*(892) - K*(1410) interference, we find that the K*(1410), whose branching ratio to K{pi} is approximately 7%, seems insufficient to explain the excess mass observed in the data. Instead, we perform a fit using a K*(892) - K*(1680) interference model and find better agreement. The discrepancy that remains could be due to an s-wave contribution to the interference that is not parameterized in the model used, and/or detector smearing that is not accounted for in our fit. We also attempt to find an s-wave contribution to the K{pi} mass spectrum by searching for an sp-interference effect. While we find a hint that such an effect exists, we have neither the confidence in the statistics nor systematics in the higher K{pi} mass region to announce an observation. We conclude that it would be a worthwhile study to pursue.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".