Search for $$W' \rightarrow tb \rightarrow qqbb$$ W ′ → t b → q q b b decays in $$pp$$ p p collisions at $$\sqrt{s}$$ s = 8 TeV with the ATLAS detector
Bibliographic record
Abstract
A search for a massive [Formula: see text] gauge boson decaying to a top quark and a bottom quark is performed with the ATLAS detector in [Formula: see text] collisions at the LHC. The dataset was taken at a centre-of-mass energy of [Formula: see text] and corresponds to [Formula: see text] of integrated luminosity. This analysis is done in the hadronic decay mode of the top quark, where novel jet substructure techniques are used to identify jets from high-momentum top quarks. This allows for a search for high-mass [Formula: see text] bosons in the range 1.5-3.0 [Formula: see text]. [Formula: see text]-tagging is used to identify jets originating from [Formula: see text]-quarks. The data are consistent with Standard Model background-only expectations, and upper limits at 95 % confidence level are set on the [Formula: see text] cross section times branching ratio ranging from [Formula: see text] to [Formula: see text] for left-handed [Formula: see text] bosons, and ranging from [Formula: see text] to [Formula: see text] for [Formula: see text] bosons with purely right-handed couplings. Upper limits at 95 % confidence level are set on the [Formula: see text]-boson coupling to [Formula: see text] as a function of the [Formula: see text] mass using an effective field theory approach, which is independent of details of particular models predicting a [Formula: see text] boson.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".