Bibliographic record
Abstract
We study the couplings of a $CP$-even neutral Higgs boson $h$ in a model containing one scalar $\mathrm{SU}(2{)}_{L}$ doublet, one real triplet, and one complex triplet with hypercharge 1. Because the two triplets contribute to the $\ensuremath{\rho}$ parameter with opposite signs, the triplet vacuum expectation values can be sizable. We show that (i) the $hWW$ and $hZZ$ couplings can be larger than the corresponding values in the standard model, and (ii) the ratio of the $WW$ and $ZZ$ couplings of $h$ can be different than the corresponding ratio in the standard model. Neither of these results can occur in models containing only Higgs doublets. We also compute the rates for $gg\ensuremath{\rightarrow}h\ensuremath{\rightarrow}WW$ and $gg\ensuremath{\rightarrow}h\ensuremath{\rightarrow}ZZ$ and find that, for reasonable parameter values and ${M}_{h}\ensuremath{\sim}140--180\text{ }\text{ }\mathrm{GeV}$, the hadron collider rate for $gg\ensuremath{\rightarrow}h\ensuremath{\rightarrow}WW$ ($ZZ$) can be up to 20% (5 times) larger than in the standard model. We discuss implications for Higgs coupling extraction at the LHC.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".