Measurement of the top quark mass and<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:mo>→</mml:mo><mml:mi>t</mml:mi><mml:mover accent="true"><mml:mi>t</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:math>cross section in the all-hadronic mode with the CDF II detector
Bibliographic record
Abstract
We present a measurement of the top quark mass and of the top-antitop ($t\overline{t}$) pair production cross section using $p\overline{p}$ data collected with the CDF II detector at the Tevatron Collider at the Fermi National Accelerator Laboratory and corresponding to an integrated luminosity of $2.9\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$. We select events with six or more jets satisfying a number of kinematical requirements imposed by means of a neural-network algorithm. At least one of these jets must originate from a $b$ quark, as identified by the reconstruction of a secondary vertex inside the jet. The mass measurement is based on a likelihood fit incorporating reconstructed mass distributions representative of signal and background, where the absolute jet energy scale (JES) is measured simultaneously with the top quark mass. The measurement yields a value of $174.8\ifmmode\pm\else\textpm\fi{}2.4(\mathrm{stat}+\mathrm{JES}{)}_{\ensuremath{-}1.0}^{+1.2}(\mathrm{syst})\text{ }\text{ }\mathrm{GeV}/{c}^{2}$, where the uncertainty from the absolute jet energy scale is evaluated together with the statistical uncertainty. The procedure also measures the amount of signal from which we derive a cross section, ${\ensuremath{\sigma}}_{t\overline{t}}=7.2\ifmmode\pm\else\textpm\fi{}0.5(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.0(\mathrm{syst})\ifmmode\pm\else\textpm\fi{}0.4(\mathrm{lum})\text{ }\text{ }\mathrm{pb}$, for the measured values of top quark mass and JES.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".