Measurement of differential cross-sections in tt¯ and tt¯+jets production in the lepton+jets final state in pp collisions at s = 13 TeV using 140 fb<sup>−1</sup> of ATLAS data
Bibliographic record
Abstract
Abstract Differential cross-sections for top-quark pair production, inclusively and in association with jets, are measured in pp collisions at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC using an integrated luminosity of 140 fb−1. The events are selected with one charged lepton (electron or muon) and at least four jets. The differential cross-sections are presented at particle level as functions of several jet observables, including angular correlations, jet transverse momenta and invariant masses of the jets in the final state, which characterise the kinematics and dynamics of the top-antitop system and the hard QCD radiation in the system with associated jets. The typical precision is 5%–15% for the absolute differential cross-sections and 2%–4% for the normalised differential cross-sections. Next-to-leading-order and next-to-next-to-leading-order QCD predictions are found to provide an adequate description of the rate and shape of the jet-angular observables. The description of the transverse momentum and invariant mass observables is improved when next-to-next-to-leading-order QCD corrections are included.
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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 | 0.002 |
| 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".