Measurements of differential cross-sections of $WbWb$ production in the dilepton channel in $pp$ collisions at $\sqrt{s}$ = 13 TeV using the ATLAS detector
Bibliographic record
Abstract
A bstract At the Large Hadron Collider, the WbWb final state is expected to be dominated by $$ t\overline{t} $$ t t ¯ production with a contribution from single-top processes. Differential cross-sections for WbWb production in the dilepton decay channel are measured at the particle level as a function of various kinematic variables. The analysis is based on data from proton-proton collisions at a centre-of-mass energy of $$ \sqrt{s}=13 $$ s = 13 TeV, recorded by the ATLAS detector at the Large Hadron Collider over the period from 2015 to 2018, corresponding to an integrated luminosity of 140 fb − 1 . Measurements are performed within the fiducial phase-space defined by the presence of two b -jets and one electron and one muon of opposite charges. The differential cross-sections are corrected for detector effects and unfolded to the particle level. Results are compared with predictions from Monte Carlo event generators at next-to-leading order in perturbative quantum chromodynamics; overall the measurements are in reasonable agreement with several generator setups, although no single prediction is able to describe all measured distributions simultaneously. These measurements provide valuable constraints on the modelling of WbWb production and the interference between doubly resonant and singly resonant WbWb production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".