Improved determination of the sample composition of dimuon events produced in ppÌ collisions at âs=1.96 TeV
Bibliographic record
Abstract
We use a new method to estimate with 5% accuracy the contribution of pion and kaon in-flight-decays to the dimuon data set acquired with the CDF detector. Based on this improved estimate, we show that the total number and the properties of the collected dimuon events are not yet accounted for by ordinary sources of dimuons which also include the contributions, as measured in the data, of heavy flavor, Υ{hooked}, and Drell-Yan production in addition to muons mimicked by hadronic punchthrough. The number of unaccounted events corresponds to (12.8±3.2)% of the bb̄ production. We find that (23±6)% of the unaccounted events contain additional muon candidates. For comparison, this fraction is (6.9±0.4)% for events due to bb̄ production. © 2011 The Author(s).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".