THE ENVIRONMENT OF z > 1 3CR RADIO GALAXIES AND QSOS: FROM PROTO-CLUSTERS TO CLUSTERS OF GALAXIES?
Bibliographic record
Abstract
ABSTRACT We study the cluster environment for a sample of 21 radio loud active galactic nuclei from the 3CR catalog at , 12 radio galaxies (RGs) and nine quasars, with Hubble Space Telescope (HST) images in the optical and IR. We use two different approaches to determine cluster candidates. We identify the early-type galaxies (ETGs) in every field by modeling each of the sources within a 40″ radius of the targets with a Sèrsic profile. Using a simple passive evolution model, we derive the expected location of the ETGs on the red sequence (RS) in the color–magnitude diagram for each of the fields of our sources. For seven targets, the model coincides with the position of the ETGs. A second approach involves a search for over densities. We compare the object densities of the sample as a whole and individually against control fields taken from the GOODS-S region of 3D-HST survey. With this method we determine the fields of ten targets to be cluster candidates. Four cluster candidates are found by both methods. The two methods disagree in some cases, depending on the specific properties of each field. For the most distant RG in the 3CR catalog (3C 257 at z = 2.47), we identify a population of bluer ETGs that lie on the expected location of the RS model for that redshift. This appears to be the general behavior of ETGs in our fields and it is possibly a signature of the evolution of such galaxies. Our results are consistent with half of the z > 1 RGs being located in dense, rapidly evolving environments.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".