Precise test of lepton flavour universality in $W$-boson decays into muons and electrons in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
Bibliographic record
Abstract
The ratio of branching ratios of the $W$ boson to muons and electrons, $R^{\mu/e}_W=B(W\rightarrow \mu\nu)/B(W\rightarrow e\nu)$, has been measured using 140 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}=13$ TeV collected with the ATLAS detector at the LHC, probing the universality of lepton couplings. The ratio is obtained from measurements of the $t\bar{t}$ production cross-section in the $ee$, $e\mu$ and $\mu\mu$ dilepton final states. To reduce systematic uncertainties, it is normalised by the square root of the corresponding ratio $R^{\mu\mu/ee}_Z$ for the $Z$ boson measured in inclusive $Z\rightarrow ee$ and $Z\rightarrow\mu\mu$ events. By using the precise value of $R^{\mu\mu/ee}_Z$ determined from $e^+e^-$ colliders, the ratio $R^{\mu/e}_W$ is determined to be $R^{\mu/e}_W = 0.9995 \pm 0.0022 \pm 0.0036 \pm 0.0014$. The three uncertainties correspond to data statistics, experimental systematics and the external measurement of $R^{\mu\mu/ee}_Z$, giving a total uncertainty of 0.0045, and confirming the Standard Model assumption of lepton flavour universality in $W$-boson decays at the 0.5% 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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".