Search for Wt-channel single top quark production in 7 TeV proton-proton collisions using the ATLAS detector
Bibliographic record
Abstract
The production of single top quarks through the weak interaction is of great interest\nin particle physics, as it provides the only means to directly measure the CKM matrix\nelement |Vtb|, one of the few remaining Standard Model parameters which has yet to be\nprecisely constrained by direct experimental measurements. This thesis describes a search\nfor Wt-channel single top quark production using proton-proton collision data collected\nat a center-of-mass energy of 7 TeV by the ATLAS detector at the Large Hadron Collider,\ncorresponding to a total integrated luminosity of 35 pb-1. Collision events having exactly two oppositely-charged electrons or muons, a single high-transverse-momentum jet, and large missing or total transverse energy are selected, resulting in a sample with enhanced single top purity. 38 data events are found to survive the selection, in good agreement with the expected sum from all signal and background predictions of 38.6 ± 6.0. The Wt content of the selected sample is predicted to amount to 4.3 ± 0.3 events. The resulting cross-section measurement is 10 +20-10 (stat) +20-10 (syst) pb. As this level of precision is insufficient to claim evidence or discovery of this process, an upper limit is also derived for the parameter of interest, resulting in σ(pp→Wt) < 70 pb at 95% confidence level.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".