Updated Search for the Flavor-Changing Neutral-Current Decay $D^0 \to \mu^+ \mu^-$
Bibliographic record
Abstract
We report on a search for the flavor-changing neutral-current decay D{sup 0} {yields} {mu}{sup +}{mu}{sup -} in p{bar p} collisions at {radical}s = 1.96TeV using 360 pb{sup -1} of integrated luminosity collected by the CDF II detector at the Fermilab Tevatron collider. A displaced vertex trigger selects long-lived D{sup 0} candidates in the {mu}{sup +}{mu}{sup -}, {pi}{sup +}{pi}{sup -}, and K{sup -}{pi}{sup +} decay modes. We use the Cabibbo-favored D{sup 0} {yields} K{sup -}{pi}{sup +} channel to optimize the selection criteria in an unbiased manner, and the kinematically similar D{sup 0} {yields} {pi}{sup +}{pi}{sup -} channel for normalization. We set an upper limit on the branching fraction {Beta}(D{sup 0} {yields} {mu}{sup +}{mu}{sup -}) < 2.1 x 10{sup -7} (3.0 x 10{sup -7}) at the 90% (95%) confidence level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".