Multi-Higgs boson signals of a modified muon Yukawa coupling at a muon collider
Bibliographic record
Abstract
We study di-Higgs and tri-Higgs boson productions at a muon collider as functions of the modification of the muon Yukawa coupling resulting from new physics parametrized by the dimension-six mass operator. We show that the di-Higgs signal can be used to observe a deviation in the muon Yukawa coupling at the 10% level for s=10 TeV and at the 3.5% level for s=30 TeV . The tri-Higgs signal improves the sensitivity dramatically with increasing s , reaching 0.8% at s=30 TeV . We also study all processes involving Goldstone bosons originating from the same operator, discuss possible model dependence resulting from other operators of dimension-six and higher, and identify μ+μ−→hh , μ+μ−→hhh , and μ+μ−→hZLZL as golden channels. We further extend the study to an effective field theory including two Higgs doublets with type-II couplings and show that di-Higgs and tri-Higgs signals involving heavy Higgs bosons can be enhanced in the alignment limit by a factor of (tanβ)4 and (tanβ)6 , respectively, which results in the potential sensitivity to a modified muon Yukawa coupling at the 10−6 level already at a s=10 TeV muon collider. The results can easily be customized for other extensions of the Higgs sector. Published by the American Physical Society 2024
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".