Galaxy Clusters in Hubble Volume Simulations: Cosmological Constraints from Sky Survey Populations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".