Search for heavy resonances decaying into a <i>Z</i> or <i>W</i> boson and a Higgs boson in final states with leptons and <i>b</i>-jets in 139 <i>fb</i><sup><i>-1 </i></sup>of <i>pp</i> collisions at <i>\\sqrt{s}=13 </i>TeV with the ATLAS detector
Bibliographic record
Abstract
This article presents a search for new resonances decaying into a Z or W boson and a 125 GeV Higgs boson h , and it targets the $$ \\nu \\overline{\\nu}b\\overline{b} $$ ν ν ¯ b b ¯ , $$ {\\ell}^{+}{\\ell}^{-}b\\overline{b} $$ ℓ + ℓ − b b ¯ , or $$ {\\ell}^{\\pm}\\nu b\\overline{b} $$ ℓ ± νb b ¯ final states, where ℓ = e or μ , in proton-proton collisions at $$ \\sqrt{s} $$ s = 13 TeV. The data used correspond to a total integrated luminosity of 139 fb − 1 collected by the ATLAS detector during Run 2 of the LHC at CERN. The search is conducted by examining the reconstructed invariant or transverse mass distributions of Zh or Wh candidates for evidence of a localised excess in the mass range from 220 GeV to 5 TeV. No significant excess is observed and 95% confidence-level upper limits between 1.3 pb and 0.3 fb are placed on the production cross section times branching fraction of neutral and charged spin-1 resonances and CP-odd scalar bosons. These limits are converted into constraints on the parameter space of the Heavy Vector Triplet model and the two-Higgs-doublet model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".