Clusters of Galaxies at 1 < z < 2 : The Spitzer Adaptation of the Red-Sequence Cluster Survey
Bibliographic record
Abstract
As the densest galaxy environments in the universe, clusters are vital to our understanding of the role that environment plays in galaxy formation and evolution. Unfortunately, the evolution of high-redshift cluster galaxies is poorly understood because of the ``cluster desert'' that exists at 1 < z < 2. The SpARCS collaboration is currently carrying out a 1-passband (z') imaging survey which, when combined with the pre-existing 50 square degree 3.6 micron Spitzer SWIRE Legacy Survey data, will efficiently detect hundreds of clusters in the cluster desert using an infrared application of the well-proven cluster red-sequence technique. We have already tested this 1-color (z' - [3.6]) approach using a 6 square degree ``pilot patch'' and shown it to be extremely successful at detecting clusters at 1 < z < 2. The clusters discovered in this project will be the first large sample of ``nascent'' galaxy clusters which connect the star-forming proto-cluster regions at z > 2 to the quiescent population at z < 1. The existing seven-passband Spitzer data (3.6, 4.5, 5.8, 8.0, 24, 70, 160 micron) will allow us to make the first measurements of the evolution of the cluster red-sequence, IR luminosity function, and the mid-IR dust-obscured star-formation rate for 1 < z < 2 clusters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".