New physics in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>b</mml:mi><mml:mo stretchy="false">→</mml:mo><mml:mi>s</mml:mi><mml:msup><mml:mi>e</mml:mi><mml:mo>+</mml:mo></mml:msup><mml:msup><mml:mi>e</mml:mi><mml:mo>−</mml:mo></mml:msup></mml:math>?
Bibliographic record
Abstract
At present, the measurements of some observables in $B\ensuremath{\rightarrow}{K}^{*}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}$ and ${B}_{s}^{0}\ensuremath{\rightarrow}\ensuremath{\phi}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}$ decays, and of ${R}_{{K}^{(*)}}\ensuremath{\equiv}\mathcal{B}(B\ensuremath{\rightarrow}{K}^{(*)}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}})/\mathcal{B}(B\ensuremath{\rightarrow}{K}^{(*)}{e}^{+}{e}^{\ensuremath{-}})$, are in disagreement with the predictions of the standard model. While most of these discrepancies can be removed with the addition of new physics (NP) in $b\ensuremath{\rightarrow}s{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}$, a difference of $\ensuremath{\gtrsim}1.7\ensuremath{\sigma}$ still remains in the measurement of ${R}_{{K}^{*}}$ at small values of ${q}^{2}$, the dilepton invariant mass squared. In the context of a global fit, this is not a problem. However, it does raise the question: if the true value of ${R}_{{K}^{*}}^{\mathrm{low}}$ is near its measured value, what is required to explain it? In this paper, we show that, if one includes NP in $b\ensuremath{\rightarrow}s{e}^{+}{e}^{\ensuremath{-}}$, one can generate values for ${R}_{{K}^{*}}^{\mathrm{low}}$ that are within $\ensuremath{\sim}1\ensuremath{\sigma}$ of its measured value. Using a model-independent, effective-field-theory approach, we construct many different possible NP scenarios. We also examine specific models containing leptoquarks or a ${Z}^{\ensuremath{'}}$ gauge boson. Here, additional constraints from lepton-flavor-violating observables, ${B}_{s}^{0}\ensuremath{-}{\overline{B}}_{s}^{0}$ mixing, and neutrino trident production must be taken into account, but we still find a number of viable NP scenarios. For the various scenarios, we examine the predictions for ${R}_{{K}^{(*)}}$ in other ${q}^{2}$ bins, as well as for the observable ${Q}_{5}\ensuremath{\equiv}{P}_{5}^{\ensuremath{'}\ensuremath{\mu}\ensuremath{\mu}}\ensuremath{-}{P}_{5}^{\ensuremath{'}ee}$.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.744 | 0.569 |
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".