Measurement of the mass difference between top and antitop quarks
Bibliographic record
Abstract
We present a measurement of the mass difference between top ($t$) and antitop ($\overline{t}$) quarks using $t\overline{t}$ candidate events reconstructed in the final state with one lepton and multiple jets. We use the full data set of Tevatron $\sqrt{s}=1.96\text{ }\text{ }\mathrm{TeV}$ proton-antiproton collisions recorded by the CDF II detector, corresponding to an integrated luminosity of $8.7\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$. We estimate event by event the mass difference to construct templates for top pair signal events and background events. The resulting mass difference distribution in data compared to signal and background templates using a likelihood fit yields $\ensuremath{\Delta}{M}_{\mathrm{top}}={M}_{t}\ensuremath{-}{M}_{\overline{t}}=\ensuremath{-}1.95\ifmmode\pm\else\textpm\fi{}1.11(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}0.59(\mathrm{syst})\text{ }\text{ }\mathrm{GeV}/{c}^{2}$ and is in agreement with the standard model prediction of no mass difference.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 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".