Binary Quasars in the Sloan Digital Sky Survey: Evidence for Excess Clustering on Small Scales
Bibliographic record
Abstract
We present a sample of 221 new quasar pairs with proper transverse separations R prop < 1 h -1 Mpc over the redshift range 0.5 < z < 3.0, discovered from an extensive follow-up campaign to find companions around the Sloan Digital Sky Survey and 2dF QSO Redshift Survey quasars. This sample includes 26 new binary quasars with separations R prop < 50 h -1 kpc (θ < 10''), more than doubling the number of such systems known. We define a statistical sample of binaries selected with homogeneous criteria and compute its selection function, taking into account sources of incompleteness. The first measurement of the quasar correlation function on scales 10 h -1 kpc < R prop < 400 h -1 kpc is presented. For R prop ≲ 40 h -1 kpc, we detect an order of magnitude excess clustering over the expectation from the large-scale ( R prop ≳ 3 h -1 Mpc) quasar correlation function, extrapolated down as a power law (γ = 1.53) to the separations probed by our binaries. The excess grows to ∼30 at R prop ∼ 10 h -1 kpc and provides compelling evidence that the quasar autocorrelation function gets progressively steeper on submegaparsec scales. This small-scale excess can likely be attributed to dissipative interaction events that trigger quasar activity in rich environments. Recent small-scale measurements of galaxy clustering and quasar-galaxy clustering are reviewed and discussed in relation to our measurement of small-scale quasar clustering.
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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 | 0.002 | 0.001 |
| 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".