THE EVOLUTION OF DUSTY STAR FORMATION IN GALAXY CLUSTERS TO<i>z</i>= 1:<i>SPITZER</i>INFRARED OBSERVATIONS OF THE FIRST RED-SEQUENCE CLUSTER SURVEY
Bibliographic record
Abstract
We present an IR study of high-redshift galaxy clusters with the MIPS camera on the Spitzer Space Telescope. Employing a sample of 42 clusters from the RCS-1 over the redshift range 0.3 < z < 1.0 and spanning an approximate range in mass of 10^{14-15} Msun, we show the number of IR-luminous galaxies in clusters above a fixed IR luminosity of 2x10^{11} Msun per unit cluster mass evolves as (1+z)^{5.1+/-1.9}. These results assume a single star forming galaxy template; the presence of AGN, and an evolution in their relative contribution to the mid-IR galaxy emission, will alter the overall number counts per cluster and their rate of evolution. We infer the total SFR per unit cluster mass and find T_SFR/M_c ~ (1+z)^{5.4+/-1.9}. This evolution can be attributed entirely to the change in the in-falling field galaxy population. The T_SFR/M_c (binned over all redshift) decreases with increasing cluster mass with a slope (T_SFR/M_c ~ M_c^{-1.5+/-0.4}) consistent with the dependence of the stellar-to-total mass per unit cluster mass seen locally. The inferred star formation seen here could produce ~5-10% of the total stellar mass in massive clusters at z = 0. Finally, we show a clear decrease in the number of IR-bright galaxies per unit optical galaxy in the cluster cores, confirming star formation continues to avoid the highest density regions of the universe at z ~ 0.75 (the average redshift of the high-redshift clusters). While several previous studies appear to show enhanced star formation in high-redshift clusters relative to the field we note that these papers have not accounted for the overall increase in galaxy or dark matter density at the location of clusters. Once this is done, clusters at z ~ 0.75 have the same or less star formation per unit mass or galaxy as the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".