Measurement of the transverse momentum and $$\phi ^*_{\eta }$$ ϕ η ∗ distributions of Drell–Yan lepton pairs in proton–proton collisions at $$\sqrt{s}=8$$ s = 8 TeV with the ATLAS detector
Bibliographic record
Abstract
Distributions of transverse momentum $$p_T^{\ell \ell }$$ and the related angular variable $$\phi ^*_\eta $$ of Drell–Yan lepton pairs are measured in 20.3 fb $$^{-1}$$ of proton–proton collisions at $$\sqrt{s}=8$$ TeV with the ATLAS detector at the LHC. Measurements in electron-pair and muon-pair final states are corrected for detector effects and combined. Compared to previous measurements in proton–proton collisions at $$\sqrt{s}=7$$ TeV, these new measurements benefit from a larger data sample and improved control of systematic uncertainties. Measurements are performed in bins of lepton-pair mass above, around and below the Z-boson mass peak. The data are compared to predictions from perturbative and resummed QCD calculations. For values of $$\phi ^*_\eta < 1$$ the predictions from the Monte Carlo generator ResBos are generally consistent with the data within the theoretical uncertainties. However, at larger values of $$\phi ^*_\eta $$ this is not the case. Monte Carlo generators based on the parton-shower approach are unable to describe the data over the full range of $$p_T^{\ell \ell }$$ while the fixed-order prediction of Dynnlo falls below the data at high values of $$p_T^{\ell \ell }$$ . ResBos and the parton-shower Monte Carlo generators provide a much better description of the evolution of the $$\phi ^*_\eta $$ and $$p_T^{\ell \ell }$$ distributions as a function of lepton-pair mass and rapidity than the basic shape of the data.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".