Precise measurement of the $t\bar{t}$ production cross-section and lepton differential distributions in $eμ$ dilepton events from $\sqrt{s}=13$ TeV $pp$ collisions with the ATLAS detector
Bibliographic record
Abstract
The inclusive top quark pair ($t\bar{t}$) cross-section $\sigma _{t\bar{t}}$ has been measured in proton–proton collisions at $\sqrt{s}=13\,\text {TeV}$, using $140\,{\text {fb}^{-1}} $ of data collected by the ATLAS experiment at the Large Hadron Collider. Using events with an opposite-charge $e\mu $ pair and b-tagged jets, the cross-section is measured to be: $\begin{aligned} \sigma _{t\bar{t}} & = 829.3\pm 1.3\,\mathrm {(stat)}\ \pm 8.0\,\mathrm {(syst)}\ \pm 7.3\,\mathrm {(lumi)}\ \\ & \quad \pm 1.9\,\mathrm {(beam)}\,\textrm{pb}, \end{aligned}$where the uncertainties reflect the limited size of the data sample, experimental and theoretical systematic effects, the integrated luminosity, and the proton beam energy, giving a total uncertainty of 1.3%. The result is used to determine the top quark pole mass via the dependence of the predicted cross-section on ${m_{t}^\textrm{pole}}$, giving ${m_{t}^\textrm{pole}}=172.8^{+1.5}_{-1.7}$ $\text {GeV}$. The same event sample is used to measure absolute and normalised differential cross-sections for the $t\bar{t} \rightarrow e\mu \nu \bar{\nu }b\bar{b} $ process as a function of single-lepton and dilepton kinematic variables. Complementary measurements of $e\mu b\bar{b} $ production, treating both $t\bar{t}$ and Wt events as signal, are also provided. Both sets of differential cross-sections are compared to the predictions of various Monte Carlo event generators, demonstrating that the state-of-the-art generators Powheg MiNNLO and Powhegbb4l describe the data better than Powheghvq.
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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".