Measurement of the top quark mass in final states with two leptons
Bibliographic record
Abstract
We present measurements of the top quark mass (${m}_{t}$) in $t\overline{t}$ candidate events with two final state leptons using $1\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ of data collected by the D0 experiment. Our data sample is selected by requiring two fully identified leptons or by relaxing one lepton requirement to an isolated track if at least one jet is tagged as a $b$ jet. The top quark mass is extracted after reconstructing the event kinematics under the $t\overline{t}$ hypothesis using two methods. In the first method, we integrate over expected neutrino rapidity distributions, and in the second we calculate a weight for the possible top quark masses based on the observed particle momenta and the known parton distribution functions. We analyze 83 candidate events in the data and obtain ${m}_{t}=176.2\ifmmode\pm\else\textpm\fi{}4.8(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}2.1(\mathrm{sys})\text{ }\text{ }\mathrm{GeV}$ and ${m}_{t}=173.2\ifmmode\pm\else\textpm\fi{}4.9(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}2.0(\mathrm{sys})\text{ }\text{ }\mathrm{GeV}$ for the two methods, respectively. Accounting for correlations between the two methods, we combine the measurements to obtain ${m}_{t}=174.7\ifmmode\pm\else\textpm\fi{}4.4(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}2.0(\mathrm{sys})\text{ }\text{ }\mathrm{GeV}$.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.000 |
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".