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Record W2139777296 · doi:10.1088/1475-7516/2012/06/004

Runnings in the curvaton

2012· article· en· W2139777296 on OpenAlexaff

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicBlack Holes and Theoretical Physics
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of TorontoPerimeter Institute
Fundersnot available
KeywordsNon-GaussianityCosmic microwave backgroundQuadratic equationInflatonCosmological perturbation theoryInflation (cosmology)Cosmic background radiationPerturbation theory (quantum mechanics)

Abstract

fetched live from OpenAlex

We investigate the scale-dependence, or the runnings, of linear and second order density perturbations generated in various curvaton scenarios. We argue that the second order perturbations, i.e. non-Gaussianity, can strongly depend on the scale, even when the linear perturbations are nearly scale-invariant. We present analytic formulae for the runnings from curvatons with general energy potentials, and clarify the conditions under which fNL becomes strongly scale-dependent. From the point of view of the fNL running, curvaton potentials can be classified into roughly two categories by whether the potential flattens or steepens compared to a quadratic one. As such examples, we study pseudo-Nambu-Goldstone curvatons, and self-interacting curvatons, respectively. The dynamics of non-quadratic curvatons and the behaviors of the resulting density perturbations are clarified by analytical methods. Then we also study models where multiple source can be responsible for density perturbations such as the multi-curvaton, and mixed curvaton and inflaton models where the running of fNL can also be large due to their multi-source nature. We make quantitative analysis for each curvaton scenario and discuss in what cases the scale-dependence, in particular, of fNL can be large enough to be probed with future CMB experiments.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.206

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 head0.011
GPT teacher head0.255
Teacher spread0.243 · 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 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

Citations35
Published2012
Admission routes1
Has abstractyes

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