Benchmark for LHC searches for low-mass custodial fiveplet scalars in the Georgi-Machacek model
Bibliographic record
Abstract
The Georgi-Machacek (GM) model is used to motivate and interpret LHC searches for doubly charged scalars decaying to vector bosons pairs. The doubly charged scalars are part of a degenerate fermiophobic custodial fiveplet with states ${H}_{5}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}$, ${H}_{5}^{\ifmmode\pm\else\textpm\fi{}}$, and ${H}_{5}^{0}$ and common mass ${m}_{5}$. The GM model has been extensively studied at the LHC for ${m}_{5}>200\text{ }\text{ }\mathrm{GeV}$, but there is a largely unprobed region of parameter space from $120\text{ }\text{ }\mathrm{GeV}<{m}_{5}<200\text{ }\text{ }\mathrm{GeV}$ where light doubly charged scalars could exist. This region has been neglected by experimental searches due in part to the lack of a benchmark for ${m}_{5}<200\text{ }\text{ }\mathrm{GeV}$. In this paper we propose a new ``low-${m}_{5}$'' benchmark for the GM model, defined for ${m}_{5}\ensuremath{\in}(50,550)\text{ }\text{ }\mathrm{GeV}$, and characterize its properties. We apply all existing experimental constraints and summarize the phenomenology of the surviving parameter space. We show that the benchmark populates almost the entirety of the relevant allowed parameter plane for ${m}_{5}$ below 200 GeV and satisfies the constraints from the 125 GeV Higgs boson signal strengths. We compute the 125 GeV Higgs boson's couplings to fermion and vector boson pairs and show that they are always enhanced in the benchmark relative to those in the Standard Model. We also compute the most relevant production cross sections for ${H}_{5}$ at the LHC, including Drell-Yan production of ${H}_{5}$ pairs. The process $pp\ensuremath{\rightarrow}H\ensuremath{\rightarrow}{H}_{5}{H}_{5}$ contributes in a small region of parameter space, but is small compared to the Drell-Yan production cross section. Finally we compute the branching ratios of ${H}_{5}^{0}\ensuremath{\rightarrow}\ensuremath{\gamma}\ensuremath{\gamma}$ and ${H}_{5}^{\ifmmode\pm\else\textpm\fi{}}\ensuremath{\rightarrow}{W}^{\ifmmode\pm\else\textpm\fi{}}\ensuremath{\gamma}$. The width-to-mass ratio of each of the ${H}_{5}$ states is below 1% over the entire benchmark, so that the narrow-width approximation is well justified.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".