Direct Top-Quark Width Measurement at CDF
Bibliographic record
Abstract
We present a measurement of the top-quark width in the $\mathrm{\text{lepton}}+\mathrm{\text{jets}}$ decay channel of $t\overline{t}$ events produced in $p\overline{p}$ collisions at Fermilab's Tevatron collider and collected by the CDF II detector. From a data sample corresponding to $4.3\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ of integrated luminosity, we identify 756 candidate events. The top-quark mass and the mass of the hadronically decaying $W$ boson that comes from the top-quark decay are reconstructed for each event and compared with templates of different top-quark widths (${\ensuremath{\Gamma}}_{t}$) and deviations from nominal jet energy scale (${\ensuremath{\Delta}}_{\mathrm{JES}}$) to perform a simultaneous fit for both parameters, where ${\ensuremath{\Delta}}_{\mathrm{JES}}$ is used for the in situ calibration of the jet energy scale. By applying a Feldman-Cousins approach, we establish an upper limit at 95% confidence level (CL) of ${\ensuremath{\Gamma}}_{t}<7.6\text{ }\text{ }\mathrm{GeV}$ and a two-sided 68% CL interval of $0.3\text{ }\text{ }\mathrm{GeV}<{\ensuremath{\Gamma}}_{t}<4.4\text{ }\text{ }\mathrm{GeV}$ for a top-quark mass of $172.5\text{ }\text{ }\mathrm{GeV}/{c}^{2}$, which are consistent with the standard model prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".