The Z b b ¯ $$ Zb\overline{b} $$ couplings at future e + e − colliders
Bibliographic record
Abstract
Many new physics models predict sizable modifications to the SM $$ Zb\overline{b} $$ couplings, while the corresponding measurements at LEP and SLC exhibit some discrepancy with the SM predictions. After updating the current results on the $$ Zb\overline{b} $$ coupling constraints from global fits, we list the observables that are most important for improving the $$ Zb\overline{b} $$ coupling constraints and estimate the expected precision reach of three proposed future e + e − colliders, CEPC, ILC and FCC-ee. We consider both the case that the results are SM-like and the one that the $$ Zb\overline{b} $$ couplings deviate significantly from the SM predictions. We show that, if we assume the value of the $$ Zb\overline{b} $$ couplings to be within 68% CL of the current measurements, any one of the three colliders will be able to rule out the SM with more than 99.9999% CL (5σ). We study the implications of the improved $$ Zb\overline{b} $$ coupling constraints on new physics models, and point out their complementarity with the constraints from the direct search of new physics particles at the LHC, as well as with Higgs precision measurements. Our results provide a further motivation for the construction of future e + e − colliders.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".