Clustering of Low-Redshift (z≤2.2) Quasars from the Sloan Digital Sky Survey
Bibliographic record
Abstract
We present measurements of the Quasar Two‐Point Correlation Function, ζQ, over the redshift range 0.3⩽z⩽2.2 based upon a homogeneous sample of 38,208 quasars with spectroscopic redshifts from the Data Release 5 Quasar Catalogue. Over this redshift range, we find that the redshift‐space correlation function, ζ(s), is well‐fit by a single power‐law, with s0 = 5.95±0.45 h−1 Mpc and γs = 1.16−0.16+0.11 when fit over 1.0⩽s⩽25.0 h−1 Mpc. Dividing the sample into redshift slices, we find no evidence for evolution of quasar clustering, with the correlation length staying roughly constant at s0∼6–7 h−1 Mpc at z≲2.2. Comparing our clustering measurements to those reported for X‐ray selected AGN at z∼0.5–1, we find reasonable agreement in some cases but significantly lower correlation lengths in others. Assuming a standard ΛCDM cosmology, we find the linear bias evolves from b∼1.4 at z = 0.5 to b∼3 at z = 2.2, with b(z = 1.27) = 2.06±0.03 for the full sample. We compare our data to analytical models and infer that quasars inhabit dark matter haloes of constant mass Mhalo∼1–2×1012 h−1 M⊙ from redshifts z∼2.5 (the peak of quasar activity) to z∼0. The measured evolution of the clustering amplitude is in reasonable agreement with recent theoretical models, although measurements to fainter limits will be needed to distinguish different scenarios for quasar feeding and black hole growth.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".