The B → πK puzzle revisited
Bibliographic record
Abstract
For a number of years, there has been a certain inconsistency among the measurements of the branching ratios and CP asymmetries of the four B → πK decays (B+ → π+K0, B+ → π0K+, B 0 → π−K+, B 0 → π0K0). In this paper, we re-examine this B → πK puzzle. We find that the key unknown parameter is |C′/T ′|, the ratio of color-suppressed and color-allowed tree amplitudes. If this ratio is large, |C′/T ′| = 0.5, the SM can explain the data. But if it is small, |C′/T ′| = 0.2, the SM cannot explain the B → πK puzzle —new physics (NP) is needed. The two types of NP that can contribute to B → πK at tree level are Z′ bosons and diquarks. Z′ models can explain the puzzle if the Z′ couples to right-handed uū and/or $$ d\overline{d} $$ , with g ≠ g . Interestingly, half of the many Z′ models proposed to explain the present anomalies in b → sμ+μ− decays have the required Z′ couplings to uū and/or $$ d\overline{d} $$ . Such models could potentially explain both the b → sμ+μ− anomalies and the B → πK puzzle. The addition of a color sextet diquark that couples to ud can also explain the puzzle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".