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Record W2154748230 · doi:10.1086/513700

Three‐Year<i>Wilkinson Microwave Anisotropy Probe</i>(<i>WMAP</i>) Observations: Implications for Cosmology

2007· article· en· W2154748230 on OpenAlexaff
David N. Spergel, Rachel Bean, Olivier Doré, M. R. Nolta, C. L. Bennett, J. Dunkley, G. Hinshaw, N. Jarosik, Eiichiro Komatsu, Lyman A. Page, Hiranya V. Peiris, Licia Verde, M. Halpern, Ryley Hill, A. Kogut, M. Limon, S. S. Meyer, N. Odegard, Gregory S. Tucker, J. L. Weiland, Edward J. Wollack, E. L. Wright

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsCMB cold spotPhysicsCosmic microwave backgroundAstrophysicsSpectral indexCosmologyHubble's lawSpectral densityDark energyCosmic background radiationCold dark matterObservational cosmologyMatter power spectrumAnisotropyAstronomySpectral lineQuantum mechanicsStatistics

Abstract

fetched live from OpenAlex

A simple cosmological model with only six parameters (matter density, Ω m h 2 , baryon density, Ω b h 2 , Hubble constant, H 0 , amplitude of fluctuations, σ 8 , optical depth, τ, and a slope for the scalar perturbation spectrum, n s ) fits not only the 3 year WMAP temperature and polarization data, but also small-scale CMB data, light element abundances, large-scale structure observations, and the supernova luminosity/distance relationship. Using WMAP data only, the best-fit values for cosmological parameters for the power-law flat Λ cold dark matter (ΛCDM) model are (Ω m h 2 ,Ω b h 2 , h , n s ,τ,σ 8 ) = (0.1277 ,0.02229 ± 0.00073,0.732 ,0.958 ± 0.016,0.089 ± 0.030,0.761 ). The 3 year data dramatically shrink the allowed volume in this six-dimensional parameter space. Assuming that the primordial fluctuations are adiabatic with a power-law spectrum, the WMAP data alone require dark matter and favor a spectral index that is significantly less than the Harrison-Zel'dovich-Peebles scale-invariant spectrum ( n s = 1, r = 0). Adding additional data sets improves the constraints on these components and the spectral slope. For power-law models, WMAP data alone puts an improved upper limit on the tensor-to-scalar ratio, r 0.002 < 0.65 (95% CL) and the combination of WMAP and the lensing-normalized SDSS galaxy survey implies r 0.002 < 0.30 (95% CL). Models that suppress large-scale power through a running spectral index or a large-scale cutoff in the power spectrum are a better fit to the WMAP and small-scale CMB data than the power-law ΛCDM model; however, the improvement in the fit to the WMAP data is only Δχ 2 = 3 for 1 extra degree of freedom. Models with a running-spectral index are consistent with a higher amplitude of gravity waves. In a flat universe, the combination of WMAP and the Supernova Legacy Survey (SNLS) data yields a significant constraint on the equation of state of the dark energy, w = -0.967 . If we assume w = -1, then the deviations from the critical density, Ω K , are small: the combination of WMAP and the SNLS data implies Ω k = -0.011 ± 0.012. The combination of WMAP 3 year data plus the HST Key Project constraint on H 0 implies Ω k = -0.014 ± 0.017 and Ω Λ = 0.716 ± 0.055. Even if we do not include the prior that the universe is flat, by combining WMAP , large-scale structure, and supernova data, we can still put a strong constraint on the dark energy equation of state, w = -1.08 ± 0.12. For a flat universe, the combination of WMAP and other astronomical data yield a constraint on the sum of the neutrino masses, m ν < 0.66 eV (95%CL). Consistent with the predictions of simple inflationary theories, we detect no significant deviations from Gaussianity in the CMB maps using Minkowski functionals, the bispectrum, trispectrum, and a new statistic designed to detect large-scale anisotropies in the fluctuations.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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 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

Citations6,653
Published2007
Admission routes1
Has abstractyes

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