Top-quark mass measurement in events with jets and missing transverse energy using the full CDF data set
Bibliographic record
Abstract
We present a measurement of the top-quark mass using 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 $8.7\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$. The analysis uses events with one semileptonic $t$ or $\overline{t}$ decay, but without detection of the electron or muon. We select events with significant missing transverse energy and multiple jets. We veto events containing identified electrons or muons. We obtain distributions of the top-quark masses and the invariant mass of the two jets from $W$-boson decays from data and compare these to templates derived from signal and background samples to extract the top-quark mass and the energy scale of the calorimeter jets with in situ calibration. A likelihood fit of the templates from signal and background events to the data yields the top-quark mass, ${M}_{\mathrm{top}}=173.93\ifmmode\pm\else\textpm\fi{}1.64(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}0.87(\mathrm{syst})\text{ }\text{ }\mathrm{GeV}/{c}^{2}$. This result is the most precise measurement to date of the mass of the top quark in this event topology.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".