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Record W2110618888 · doi:10.1088/1475-7516/2014/03/020

Gravitational waves from preheating in M-flation

2014· article· en· W2110618888 on OpenAlexaff

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsMcGill UniversityPerimeter InstituteUniversity of Waterloo
FundersScience and Technology Facilities Council
KeywordsInflatonCosmic microwave backgroundInflation (cosmology)Gravitational wavePlanckGravitationSpectral densityCosmic stringField (mathematics)

Abstract

fetched live from OpenAlex

Matrix inflation, or M-flation, is a string theory motivated inflationary model with three scalar field matrices and gauge fields in the adjoint representation of the $\mathbf{U}(N)$ gauge group. One of these $3N^2$ scalars appears as the effective inflaton while the rest of the fields (scalar and gauge fields) can play the role of isocurvature fields during inflation and preheat fields afterwards. There is a region in parameter space and initial field values, "the hilltop region," where predictions of the model are quite compatible with the recent Planck data. We show that in this hilltop region, if the inflaton ends up in the supersymmetric vacuum, the model can have an embedded preheating mechanism. Couplings of the preheat modes are related to the inflaton self-couplings and therefore are known from the CMB data. Through lattice simulations performed using a symplectic integrator, we numerically compute the power spectra of gravitational waves produced during the preheating stage following M-flation. The preliminary numerical simulation of the spectrum from multi-preheat fields peaks in the GHz band with an amplitude $\Omega_{\mathrm{gw}}h^{2} \propto 10^{-16}$, suggesting that the model has concrete predictions for the ultra-high frequency gravity-wave probes. This signature could be used to distinguish the model from rival inflationary models

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.256

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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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