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Impact of vacuum stability, perturbativity and XENON1T on global fits of $$\mathbb {Z}_2$$ and $$\mathbb {Z}_3$$ scalar singlet dark matter

2018· article· en· W2810444431 on OpenAlexafffund

Bibliographic record

VenueThe European Physical Journal C · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsMcGill University
FundersAustralian Research CouncilScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaEngineering and Physical Sciences Research CouncilDeutsche ForschungsgemeinschaftPartnership for Advanced Computing in Europe AISBLH2020 European Research CouncilImperial College London
KeywordsElectroweak interactionDark matterPlanckScalar (mathematics)Scalar field dark matterHiggs bosonWIMPCoupling (piping)

Abstract

fetched live from OpenAlex

Scalar singlet dark matter is one of the simplest and most predictive realisations of the WIMP (weakly-interacting massive particle) idea. Although the model is constrained from all directions by the latest experimental data, it still has viable regions of parameter space. Another compelling aspect of scalar singlets is their ability to stabilise the electroweak vacuum. Indeed, models of scalar dark matter are not low-energy effective theories, but can be valid all the way to the Planck scale. Using the GAMBIT framework, we present the first global fit to include both the low-energy experimental constraints and the theoretical constraints from UV physics, considering models with a scalar singlet charged under either a $$\mathbb {Z}_2$$ or a $$\mathbb {Z}_3$$ symmetry. We show that if the model is to satisfy all experimental constraints, completely stabilise the electroweak vacuum up to high scales, and also remain perturbative to those scales, one is driven to a relatively small region of parameter space. This region has a Higgs-portal coupling slightly less than 1, a dark matter mass of 1–2 TeV and a spin-independent nuclear scattering cross-section around $$10^{-45}\,\hbox {cm}^2$$ .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.260
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations80
Published2018
Admission routes2
Has abstractyes

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