Exploring the low $$\tan \beta $$ region of two Higgs doublet models at the LHC
Bibliographic record
Abstract
Abstract Current interpretations of the LHC results on two Higgs doublet models (2HDM) underestimate the sensitivity due to neglecting higher order effects. In this work, we revisit the impact of these effects using the current cross-section times branching ratio limits of the $$A\rightarrow hZ, H \rightarrow VV$$ A → h Z , H → V V and $$H\rightarrow hh$$ H → h h channels. With a degenerate heavy Higgs mass $$m_\varPhi $$ m Φ , we find that the LHC searches gain sensitivity to the small $$\tan \beta $$ tan β region after including loop corrections, even close to $$\cos (\beta -\alpha )=0$$ cos ( β - α ) = 0 which is not reachable at tree level for all types of 2HDM. For a benchmark point with $$m_\varPhi =300$$ m Φ = 300 GeV, $$\tan \beta <1.8(1.2)$$ tan β < 1.8 ( 1.2 ) can be probed for the Type-I(II) 2HDM model for $$\cos (\beta -\alpha )=0$$ cos ( β - α ) = 0 . When the deviation from $$\cos (\beta -\alpha )=0$$ cos ( β - α ) = 0 is larger, the region for which current searches have exclusion potential becomes larger.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".