Cluster assembly times as a cosmological test
Bibliographic record
Abstract
ABSTRACT The abundance of galaxy clusters in the low-redshift Universe provides an important cosmological test, constraining a product of the initial amplitude of fluctuations and the amount by which they have grown since early times. The degeneracy of the test with respect to these two factors remains a limitation of abundance studies. Clusters will have different mean assembly times, however, depending on the relative importance of initial fluctuation amplitude and subsequent growth. Thus, structural probes of cluster age such as concentration, shape, or substructure may provide a new cosmological test that breaks the main degeneracy in number counts. We review analytical predictions for how mean assembly time should depend on cosmological parameters, and test these predictions using cosmological simulations. Given the overall sensitivity expected, we estimate the cosmological parameter constraints that could be derived from the cluster catalogues of forthcoming surveys such as Euclid, the Nancy Grace Roman Space Telescope, eROSITA, or CMB-S4. We show that by considering the structural properties of their cluster samples, such surveys could easily achieve errors of Δσ8 = 0.01 or better.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".