PREDICTIONS OF QUASAR CLUSTERING: REDSHIFT, LUMINOSITY, AND SELECTION DEPENDENCE
Bibliographic record
Abstract
We show that current clustering observations of quasars and luminous active galactic nuclei (AGNs) can be explained by a merger model augmented by feedback from outflows. Using numerical simulations large enough to study clustering out to 25 comoving h −1 Mpc, we calculate correlation functions, biases, and correlation lengths as a function of AGN redshift and optical and X-ray luminosity. At optical wavelengths, our results match a wide range of current observations and generate predictions for future data sets. We reproduce the weak luminosity dependence of clustering over the currently well-measured range and predict a much stronger dependence at higher luminosities. The increase in the amplitude of binary quasar clustering observed in the Sloan Digital Sky Survey (SDSS) is also reproduced and is predicted to occur at higher redshift, an effect that is due to the one-halo term in the correlation function. On the other hand, our results do not match the rapid evolution of the correlation length observed in the SDSS at z ≃ 3, a discrepancy that is at least partially due to differences in the scales probed by our simulation versus this survey. In fact, we show that changing the distances sampled from our simulations can produce changes as large as 40% in the fitted correlation lengths. Finally, in the X-ray, our simulations produce correlation lengths similar to that observed in the Chandra Deep Field (CDF) North, but not the significantly larger correlation length observed in the CDF-South.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".