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Record W3082509679 · doi:10.1088/1475-7516/2021/04/043

Gravitational waves from dark sectors, oscillating inflatons, and mass boosted dark matter

2021· article· en· W3082509679 on OpenAlexaff

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsPerimeter InstituteArthur B. McDonald-Canadian Astroparticle Physics Research InstituteQueen's University
Fundersnot available
KeywordsGravitational waveDark matterGravitational wave backgroundDark energyInflatonScalar field dark matterPrimordial black holePlanckScalar field

Abstract

fetched live from OpenAlex

Abstract Gravitational wave signatures from dynamical scalar field configurations provide a compelling observational window on the early universe. Here we identify intriguing connections between dark matter and scalars fields that emit gravitational waves, either through a first order phase transition or oscillating after inflation. To study gravitational waves from first order phase transitions, we investigate a simplified model consisting of a heavy scalar coupled to a vector and fermion field. We then compute gravitational wave spectra sourced by inflaton field configurations oscillating after E-Model and T-Model inflation. Some of these gravitational wave signatures can be uncovered by the future Big Bang Observatory, although in general we find that MHz-GHz frequency gravitational wave sensitivity will be critical for discovering the heaviest dark sectors. Intriguingly, we find that scalars undergoing phase transitions, along with E-Model and T-Model potentials, can impel a late-time dark matter mass boost and generate up to Planck mass dark matter. For phase transitions and oscillating inflatons, the largest dark matter mass boosts correspond to higher amplitude stochastic gravitational wave backgrounds.

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 score0.678
Threshold uncertainty score0.461

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 head0.241
Teacher spread0.233 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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