WIDE FIELD MULTIBAND IMAGING OF LOW REDSHIFT QUASAR ENVIRONMENTS
Bibliographic record
Abstract
We present photometry of the large-scale environments of a sample of 12 broad line active galactic nuclei (AGNs) with 0.06 < z < 0.37 from deep images in the Sloan Digital Sky Survey u , g , r , and i filters taken with the 90Prime prime focus camera on the Steward Observatory Bok Telescope. We measure galaxy clustering around these AGNs using two standard techniques: correlation amplitude ( B gq ) and the two point correlation function. We find average correlation amplitudes for the 10 radio-quiet objects in the sample equal to (9 ± 18, 144 ± 114, −39 ± 56, 295 ± 260) Mpc 1.77 in ( u , g , r , i ), all consistent with the expectation from galaxy clustering. Using a ratio of the galaxy–quasar cross-correlation function to the galaxy autocorrelation function, we calculate the relative bias of galaxies and AGNs, b gq . The bias in the u band, b gq = 3.08 ± 0.51 is larger compared to that calculated in the other bands, but it does not correlate with AGN luminosity, black hole mass, or AGN activity via the luminosity of the [O iii ] emission line. Thus ongoing nuclear accretion activity is not reflected in the large scale environments from ∼10 h −1 kpc to ∼0.5 h −1 Mpc and may indicate a non-merger mode of AGN activity and/or a significant delay between galaxy mergers and nuclear activity in this sample of mostly radio-quiet quasars.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".