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Record W3103997786

Cosmological Parameters from the Comparison of the 2MASS Gravity Field with Peculiar Velocity Surveys

2005· article· en· W3103997786 on OpenAlexaff

Bibliographic record

VenueCERN Document Server (European Organization for Nuclear Research) · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhysicsAstrophysicsOmegaPeculiar velocityRedshiftPhotometry (optics)GalaxySupernovaSigmaSurface brightnessRedshift surveyDark matterAstronomy
DOInot available

Abstract

fetched live from OpenAlex

We compare the peculiar velocity field within 65 $h^{-1}$ Mpc predicted from 2MASS photometry and public redshift data to three independent peculiar velocity surveys based on type Ia supernovae, surface brightness fluctuations in ellipticals, and Tully-Fisher distances to spirals. The three peculiar velocity samples are each in good agreement with the predicted velocities and produce consistent results for $\\beta_{K}=\\Omega\\sbr{m}^{0.6}/b_{K}$. Taken together the best fit $\\beta_{K} = 0.49 \\pm 0.04$. We explore the effects of morphology on the determination of $\\beta$ by splitting the 2MASS sample into E+S0 and S+Irr density fields and find both samples are equally good tracers of the underlying dark matter distribution, but that early-types are more clustered by a relative factor $b\\sbr{E}/b\\sbr{S} \\sim 1.6$. The density fluctuations of 2MASS galaxies in $8 h^{-1}$ Mpc spheres in the local volume is found to be $\\sigma\\sbr{8,K} = 0.9$. From this result and our value of $\\beta_{K}$, we find $\\sigma_8 (\\Omega\\sbr{m}/0.3)^{0.6} = 0.91\\pm0.12$. This is in excellent agreement with results from the IRAS redshift surveys, as well as other cosmological probes. Combining the 2MASS and IRAS peculiar velocity results yields $\\sigma_8 (\\Omega\\sbr{m}^/0.3)^{0.6} = 0.85\\pm0.05$.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.257
Teacher spread0.231 · 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.

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

Citations57
Published2005
Admission routes1
Has abstractyes

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