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Record W1965166130 · doi:10.1088/0004-637x/726/2/64

PARAMETERIZING AND MEASURING DARK ENERGY TRAJECTORIES FROM LATE INFLATONS

2010· article· en· W1965166130 on OpenAlexaff
Zhiqi Huang, J. Richard Bond, Lev Kofman

Bibliographic record

VenueThe Astrophysical Journal · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsPhysicsQuintessenceDark energyRedshiftCosmic microwave backgroundAstrophysicsParameter spaceCosmologyGalaxyQuantum mechanicsStatistics

Abstract

fetched live from OpenAlex

Bulk dark energy (DE) properties are determined by the redshift evolution of its pressure-to-density ratio, w de ( z ). An experimental goal is to decide if the DE is dynamical, as in the quintessence (and phantom) models treated here. We show that a three-parameter approximation w de ( z ; ε s , ε ϕ ∞ , ζ s ) fits well the ensemble of trajectories for a wide class of late-inflaton potentials V (ϕ). Markov Chain Monte Carlo probability calculations are used to confront our w de ( z ) trajectories with current observational information on Type Ia supernova, cosmic microwave background, galaxy power spectra, weak lensing, and the Lyα forest. We find that the best-constrained parameter is a low-redshift slope parameter, ε s ∝ (∂ln V /∂ϕ) 2 when the DE and matter have equal energy densities. A tracking parameter ε ϕ ∞ defining the high-redshift attractor of 1 + w de is marginally constrained. ζ s is poorly determined, which characterizes the evolution of ε s , and is a measure of ∂ 2 ln V /∂ϕ 2 . The constraints we find already rule out some popular quintessence and phantom models, or restrict their potential parameters. We also forecast how the next generation of cosmological observations improve the constraints: by a factor of about five on ε s and ε ϕ ∞ , but with ζ s remaining unconstrained (unless the true model significantly deviates from ΛCDM). Thus, potential reconstruction beyond an overall height and a gradient is not feasible for the large space of late-inflaton models considered here.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.217
Teacher spread0.209 · 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

Citations21
Published2010
Admission routes1
Has abstractyes

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