Search for FCNC single top-quark production at root s=7 TeV with the ATLAS detector
Bibliographic record
Abstract
A search for the production of single top-quarks via flavour-changing neutral-currents is presented. Data collected with the ATLAS detector at a centre-of-mass energy of root s = 7 TeV, corresponding to an integrated luminosity of 2.05 fb(-1), are used. Candidate events with a semileptonic top-quark decay signature are classified as signal- or background-like events by using several kinematic variables as input to a neural network. No signal is observed in the neural network output distribution and a Bayesian upper limit is placed on the production cross-section. The observed upper limit at 95% confidence level on the cross-section multiplied by the t -> Wb branching fraction is measured to be sigma(qg -> t) x B(t -> Wb) < 3.9 pb. This upper limit is converted using a model-independent approach into upper limits on the coupling strengths kappa(ugt)/Lambda < 6.9.10(-3) TeV-1 and kappa(cgt)/Lambda < 1.6.10(-2) TeV-1, where A is the new physics scale, and on the branching fractions B(t -> ug) < 5.7.10(-5) and B(t -> cg) < 2.7.10(-4).
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".