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Record W2736432061 · doi:10.1088/1361-6382/aaba22

Inflationary magneto-(non)genesis, increasing kinetic couplings, and the strong coupling problem

2018· article· en· W2736432061 on OpenAlexafffund
Hossein Bazrafshan Moghaddam, Evan McDonough, Ryo Namba, Robert Brandenberger

Bibliographic record

VenueClassical and Quantum Gravity · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Science Research and TechnologyCanada Research Chairs
KeywordsInflatonPhysicsKinetic termElectromagnetismModuliCoupling (piping)Minimal couplingBack-reactionAmplitudeQuantum electrodynamicsInflation (cosmology)Magnetic fieldKinetic energyClassical mechanicsTheoretical physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract We study the generation of magnetic fields during inflation making use of a coupling of the inflaton and moduli fields to electromagnetism via the photon kinetic term, and assuming that the coupling is an increasing function of time. We demonstrate that the strong coupling problem of inflationary magnetogenesis can be avoided by incorporating the destabilization of moduli fields after inflation. The magnetic field always dominates over the electric one, and thus the severe constraints on the latter from backreaction, which are the demanding obstacles in the case of a decreasing coupling function, do not apply to the current scenario. However, we show that this loophole to the strong coupling problem comes at a price: the normalization of the amplitude of magnetic fields is determined by this coupling term and is therefore suppressed by a large factor after the moduli destabilization completes. From this we conclude that there is no self-consistent and generic realization of primordial magnetogenesis producing scale-invariant fields in the case of an increasing kinetic coupling.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.491

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 studies0.0000.001
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 head0.232
Teacher spread0.224 · 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 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

Citations22
Published2018
Admission routes2
Has abstractyes

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