The Evolution of Dusty Star Formation and Stellar Mass Assembly in Clusters: Results from the IRAC 3.6, 4.5, 5.8, and 8.0 μm Cluster Luminosity Functions
Bibliographic record
Abstract
We present a catalog of 99 candidate clusters and groups of galaxies in the redshift range 0.1 < z phot < 1.3 discovered in the Spitzer FLS. The clusters are selected by their R c − 3.6 μ m galaxy color-magnitude relation using the cluster red-sequence algorithm. Using this cluster sample, we compute the 3.6, 4.5, 5.8, and 8.0 μ m cluster LFs. Similar to previous studies, we find that for the bands that trace stellar mass at these redshifts (3.6 and 4.5 μ m ) the evolution in M * is consistent with a passively evolving population of galaxies with a high formation redshift ( z f > 1.5). Using the 3.6 μ m LF as a proxy for stellar luminosity, we remove this component from the MIR (5.8 and 8.0 μ m ) cluster LFs and measure the LF of dusty star formation/AGNs in clusters. We find that at z < 0.4 the bright end of the cluster 8.0 μ m LF is well described by a composite population of quiescent galaxies and regular star-forming galaxies with a mix consistent with typical cluster blue fractions; however, at z > 0.4, an additional population of dusty starburst galaxies is required to properly model the 8.0 μ m LFs. Comparison to field studies at similar redshifts shows a strong differential evolution in the field and cluster 8.0 μ m LFs with redshift. At z ∼ 0.65 8.0 μ m -detected galaxies are more abundant in clusters compared to the field, but thereafter the number of 8.0 μ m sources in clusters declines with decreasing redshift, and by z ∼ 0.15, clusters are underdense relative to the field by a factor of ~5. The rapid differential evolution between the cluster and field LFs is qualitatively consistent with recent field galaxy studies that show that the star formation rates of galaxies in high-density environments are larger than those in low-density environments at higher redshift.
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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 | 0.002 |
| 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 | 0.001 | 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".