Search for the production of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Z</mml:mi><mml:mi>W</mml:mi></mml:math>and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Z</mml:mi><mml:mi>Z</mml:mi></mml:math>boson pairs decaying into charged leptons and jets 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 mathvariant="bold">=</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
We present a measurement of the production cross section for $ZW$ and $ZZ$ boson pairs in final states with a pair of charged leptons, from the decay of a $Z$ boson, and at least two jets, from the decay of a $W$ or $Z$ boson, using the full sample of proton-antiproton collisions recorded with the CDF II detector at the Tevatron, corresponding to $8.9\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ of integrated luminosity. We increase the sensitivity to vector-boson decays into pairs of quarks using a neural-network discriminant that exploits the differences between the spatial spread of energy depositions and charged-particle momenta contained within the jet of particles originating from quarks and gluons. Additionally, we employ new jet energy corrections to Monte Carlo simulations that account for differences in the observed energy scales for quark and gluon jets. The number of signal events is extracted through a simultaneous fit to the dijet mass spectrum in three classes of events: events likely to contain jets with a heavy-quark decay, events likely to contain jets originating from light quarks, and events that fail these identification criteria. We determine the production cross section to be ${\ensuremath{\sigma}}_{ZW+ZZ}={2.5}_{\ensuremath{-}1.0}^{+2.0}\text{ }\text{ }\mathrm{pb}$ ($<6.1\text{ }\text{ }\mathrm{pb}$ at the 95% confidence level), consistent with the standard model prediction of 5.1 pb.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.021 |
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".