Physics impact of ILC Higgs coupling measurements: The effect of theory uncertainties
Bibliographic record
Abstract
We study the effect of theoretical and parametric uncertainties on the ability of future Higgs coupling measurements at the International Linear Collider (ILC) to reveal deviations from the standard model (SM). To quantify the impact of these uncertainties we plot $\ensuremath{\Delta}{\ensuremath{\chi}}^{2}=25$ contours for the deviations between the SM Higgs couplings and the light Higgs couplings in the ${m}_{h}^{\mathrm{max}}$ benchmark scenario of the minimal supersymmetric standard model (MSSM). We consider the theoretical uncertainties in the SM Higgs decay partial widths and production cross section and the parametric uncertainties in the bottom and charm masses and the strong coupling ${\ensuremath{\alpha}}_{s}$. We find that the impact of the theoretical and parametric uncertainties is moderate in the first phase of ILC data-taking ($500\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ at 350 GeV center-of-mass energy), reducing the reach in the $CP$-odd MSSM Higgs mass ${M}_{A}$ by about 10% to $\ensuremath{\sim}500\text{ }\text{ }\mathrm{GeV}$, while in the second phase ($1000\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ at 1000 GeV) these uncertainties are larger than the experimental uncertainties and reduce the reach in ${M}_{A}$ by about a factor of 2, from $\ensuremath{\sim}1200$ down to $\ensuremath{\sim}600\text{ }\text{ }\mathrm{GeV}$. The bulk of the effect comes from the parametric uncertainties in ${m}_{b}$ and ${\ensuremath{\alpha}}_{s}$, followed by the theoretical uncertainty in ${\ensuremath{\Gamma}}_{b}$.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".