Measurement of event shapes in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi><mml:mover accent="true"><mml:mi>p</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:math>collisions at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt><mml:mo>=</mml:mo><mml:mn>1.96</mml:mn><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi>TeV</mml:mi></mml:math>
Bibliographic record
Abstract
A study of event-shape observables in proton-antiproton collisions at $\sqrt{s}=1.96\text{ }\text{ }\mathrm{TeV}$ is presented. The data for this analysis were recorded by the CDF II Detector at the Tevatron Collider. The variables studied are the transverse thrust and thrust minor, both defined in the plane perpendicular to the beam direction. The observables are measured using energies from unclustered calorimeter cells. In addition to studies of the differential distributions, we present the dependence of event-shape mean values on the leading-jet transverse energy. Data are compared with pythia Tune A and to resummed parton-level predictions that were matched to fixed-order results at next-to-leading-order (NLO) accuracy ($\mathrm{NLO}+\mathrm{NLL}$). Predictions from pythia Tune A agree fairly well with the data. However, the underlying event contributes significantly to these observables, making it difficult to make direct comparisons to the $\mathrm{NLO}+\mathrm{NLL}$ predictions, which do not account for the underlying event. To overcome this difficulty, we introduce a new observable, a weighted difference of the mean values of the thrust and thrust minor, which is less sensitive to the underlying event, allowing for a comparison with $\mathrm{NLO}+\mathrm{NLL}$. Both pythia Tune A and the $\mathrm{NLO}+\mathrm{NLL}$ calculations agree well within the $20%$ theoretical uncertainty with the data for this observable, indicating that perturbative QCD successfully describes shapes of the hadronic final states.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".