Search for standard model Higgs boson production in association with a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>W</mml:mi></mml:math>boson at CDF
Bibliographic record
Abstract
We present a search for the standard model Higgs boson production in association with a $W$ boson in proton-antiproton collisions ($p\overline{p}\ensuremath{\rightarrow}{W}^{\ifmmode\pm\else\textpm\fi{}}H\ensuremath{\rightarrow}\ensuremath{\ell}\ensuremath{\nu}b\overline{b}$) at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector which correspond to an integrated luminosity of approximately $2.7\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$. We recorded this data with two kinds of triggers. The first kind required high-${\mathrm{p}}_{T}$ charged leptons and the second required both missing transverse energy and jets. The search selects events consistent with a signature of a single lepton (${e}^{\ifmmode\pm\else\textpm\fi{}}/{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}$), missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method and a jet probability tagging method. Kinematic information is fed in an artificial neural network to improve discrimination between signal and background. The search finds that both the observed number of events and the neural network output distributions are consistent with the standard model background expectations, and sets 95% confidence level upper limits on the production cross section times branching ratio. The limits are expressed as a ratio to the standard model production rate. The limits range from 3.6 (4.3 expected) to 61.1 (43.2 expected) for Higgs masses from 100 to $150\text{ }\text{ }\mathrm{GeV}/{c}^{2}$, respectively.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".