Mass and Redshift Dependence of Star Formation in Relaxed Galaxy Clusters
Bibliographic record
Abstract
We investigate the star formation properties of dynamically relaxed galaxy clusters as a function of cluster mass for 308 low-redshift clusters drawn from the Sloan Digital Sky Survey (SDSS) C4 cluster catalog. It is important to establish if cluster star formation properties have a mass dependence before comparing clusters at different epochs, and here we use cluster velocity dispersion, σ, as a measure of cluster mass. We select clusters with no significant substructure, a subset of the full C4 sample, so that velocity dispersion is an accurate tracer of cluster mass. We find that the total stellar mass, the number of star-forming galaxies, and total star formation rate scale linearly with the number of member galaxies, with no residual dependence on cluster velocity dispersion. With the mass-dependence of cluster star formation rates established, we compare the SDSS clusters with a sample of z ≃ 0.75 clusters from the literature and find that on average (correcting for the mass growth of clusters between the two redshifts) the total Hα luminosity of the high-redshift clusters is 10 times greater than that of the low-redshift clusters. This can be explained by a decline in the Hα luminosities of individual cluster galaxies by a factor of up to ~10 since z ≃ 0.75. The magnitude of this evolution is comparable to that of field galaxies over a similar redshift interval, and thus the effect of the cluster environment on the evolution of star-forming galaxies is at most modest. Our results suggest that the physical mechanism driving the evolution of cluster star formation rates is independent of cluster mass, at least for clusters with velocity dispersion greater than 450 km s −1 , and operates over a fairly long timescale such that the star formation rates of individual galaxies decline by an order of magnitude over ~7 billion years.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".