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Record W3195666020 · doi:10.48550/arxiv.2108.10950

Di-Higgs and tri-Higgs boson signals of muon $g-2$ at a muon collider

2021· preprint· en· W3195666020 on OpenAlexaff
Radovan Dermíšek, Keith Hermanek, Navin McGinnis

Bibliographic record

VenuearXiv (Cornell University) · 2021
Typepreprint
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsTRIUMF
Fundersnot available
KeywordsPhysicsParticle physicsLeptonHiggs bosonMuonBosonPhysics beyond the Standard ModelStandard Model (mathematical formulation)Nuclear physicsMuon colliderTwo-Higgs-doublet modelElectronParticle accelerator

Abstract

fetched live from OpenAlex

We show that new physics explanations of the muon $g-2$ anomaly by the contributions of new leptons mediated by the standard model Higgs boson necessarily lead to large rates for $μ^+ μ^- \to hh$ and $μ^+ μ^- \to hhh$ irrespectively of details of the model or the scale of new physics. For new leptons with the same quantum numbers as the standard model leptons, cross sections are expected to be about 240 ab for $μ^+ μ^- \to hh$ independently of the center of mass energy, $\sqrt{s}$, and about 2.7 ab for $μ^+ μ^- \to hhh$ for $\sqrt{s} = 1$ TeV and growing quadratically with $\sqrt{s}$. Predictions for models featuring new leptons with different quantum numbers and for a type-II two Higgs doublet model, where additional Higgs bosons can contribute to muon $g-2$, are also presented.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.057
GPT teacher head0.203
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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