Bibliographic record
Abstract
The most common decay used for measuring 2βs, the phase of [Formula: see text] mixing, is [Formula: see text]. This decay is dominated by the color-suppressed tree diagram, but there are other contributions due to gluonic and electroweak penguin diagrams. These are often referred to as "penguin pollution" (PP) because their inclusion in the amplitude leads to a theoretical error in the extraction of 2βsfrom the data. In the standard model (SM), it is estimated that the PP is negligible, but there is some uncertainty as to its exact size. Now, [Formula: see text] (the measured value of 2βs) is small, in agreement with the SM, but still has significant experimental errors. When these are reduced, if one hopes to be able to see clear evidence of new physics (NP), it is crucial to have the theoretical error under control. In this paper, we show that, using a modification of the angular analysis currently used to measure [Formula: see text] in [Formula: see text], one can reduce the theoretical error due to PP. Theoretical input is still required, but it is much more modest than entirely neglecting the PP. If [Formula: see text] differs from the SM prediction, this points to NP in the mixing. There is also enough information to test for NP in the decay. This method can be applied to all [Formula: see text] decays.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".