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Record W2498983987 · doi:10.1103/physrevd.94.075023

Dilatonlike Higgs boson with scalar singlet dark matter

2016· article· en· W2498983987 on OpenAlexafffund
Robyn Campbell, Stephen Godfrey, Alejandro de la Puente

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHiggs bosonParticle physicsDark matterPhysicsScalar (mathematics)Scalar bosonSinglet stateBosonScalar field dark matterQuantum electrodynamicsNuclear physicsAstrophysicsDark energyCosmologyMathematics

Abstract

fetched live from OpenAlex

We study a model with a Higgs-like dilaton and a standard model gauge-singlet scalar dark matter candidate. We begin by updating the status of identifying the observed 125 GeV Higgs-like boson with the pseudo Nambu-Goldstone boson that arises from the spontaneous breaking of scale invariance using recent Higgs boson signal strength measurements by the ATLAS and CMS collaborations. We then constrain the extended model with recent constraints on the Higgs invisible width, the observed dark matter relic abundance and the latest dark matter direct detection limits. We found that the magnitude of the dilaton-$\ensuremath{\gamma}\ensuremath{\gamma}$ and dilaton-glue-glue coupling is constrained to be close to the standard model values. The mass of the dark matter candidate is constrained to be greater than half the dilaton mass by relic abundance limits and Higgs invisible width limits. Dark matter direct detection limits allow only small mass regions which will be further constrained by upcoming Dark Matter Experiment using Argon Pulse-shape measurements.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.366
Teacher spread0.356 · 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 designTheoretical or conceptual
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

Citations3
Published2016
Admission routes2
Has abstractyes

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