The DEEP2 Galaxy Redshift Survey: the red sequence AGN fraction and its environment and redshift dependence
Bibliographic record
Abstract
We measure the dependence of the active galactic nuclei (AGN) fraction on local environment at z∼ 1, using spectroscopic data taken from the DEEP2 Galaxy Redshift Survey, and Chandra X-ray data from the All-Wavelength Extended Groth Strip International Survey (AEGIS). To provide a clean sample of AGN, we restrict our analysis to the red sequence population; this also reduces additional colour–environment correlations. We find evidence that high-redshift LINERs in DEEP2 tend to favour higher density environments relative to the red population from which they are drawn. In contrast, Seyferts and X-ray selected AGN at z∼ 1 show little (or no) environmental dependencies within the same underlying population. We compare these results with a sample of local AGN drawn from the Sloan Digital Sky Survey (SDSS). Contrary to the high-redshift behaviour, we find that both LINERs and Seyferts in the SDSS show a slowly declining red sequence AGN fraction towards high-density environments. Interestingly, at z∼ 1 red sequence Seyferts and LINERs are approximately equally abundant. By z∼ 0, however, the red Seyfert population has declined relative to the LINER population by over a factor of ∼4.5. We speculate on possible interpretations of our results.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".