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Record W2096094069 · doi:10.1086/340551

Galaxy Clusters in Hubble Volume Simulations: Cosmological Constraints from Sky Survey Populations

2002· article· en· W2096094069 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGalaxy clusterDark matterGalaxySkyRedshiftCluster (spacecraft)Hubble's lawIntracluster mediumScaling

Abstract

fetched live from OpenAlex

We use gigaparticle N -body simulations to study galaxy cluster populations in Hubble volumes of ΛCDM (Ω m = 0.3, Ω Λ = 0.7) and τCDM (Ω m = 1) world models. Mapping past light cones of locations in the computational space, we create mock sky surveys of dark matter structure to z ≃ 1.4 over 10,000 deg 2 and to z ≃ 0.5 over two full spheres. Calibrating the Jenkins mass function at z = 0 with samples of ~1.5 million clusters, we show that the fit describes the sky survey counts to ≲20% accuracy over all redshifts for systems more massive than poor galaxy groups (5 × 10 13 h -1 M ☉ ). Fitting the observed local temperature function determines the ratio β of specific thermal energies in dark matter and intracluster gas. We derive a scaling with power spectrum normalization β ∝ σ and find that the ΛCDM model requires σ 8 = 1.04 to match β = 1.17 derived from gasdynamic cluster simulations. We estimate a 10% overall systematic uncertainty in σ 8 , 4% arising from cosmic variance in the local sample and the bulk from uncertainty in the absolute mass scale of clusters. Considering distant clusters, the ΛCDM model matches Extended Medium-Sensitivity Survey and ROSAT Deep Cluster Survey X-ray-selected observations under economical assumptions for intracluster gas evolution. Using transformations of mass-limited cluster samples that mimic σ 8 variation, we explore Sunyaev-Zeldovich (SZ) search expectations for a 10 deg 2 survey complete above 10 14 h -1 M ☉ . Cluster counts are shown to be extremely sensitive to σ 8 uncertainty, while redshift statistics, such as the sample median, are much more stable. Redshift information is crucial to extract the full cosmological diagnostic power of SZ cluster surveys. For ΛCDM, the characteristic temperature at a fixed sky surface density is a weak function of redshift, implying an abundance of hot clusters at z > 1. Assuming constant β, one 8 keV cluster at z > 2 and 10 5 keV clusters at z > 3 are expected in the Sloan Digital Sky Survey area. Too many such clusters can falsify the model; detection of clusters more massive than Coma at z > 1 violates ΛCDM at 95% confidence if their surface density exceeds 0.003 deg -2 , or 120 on the whole sky.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.032
GPT teacher head0.245
Teacher spread0.213 · 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 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

Citations363
Published2002
Admission routes1
Has abstractyes

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