Quasar Clustering at 25<i>h</i><sup>−1</sup>kpc from a Complete Sample of Binaries1
Bibliographic record
Abstract
We present spectroscopy of binary quasar candidates, with component separations of 3'' ⩽ Δ θ < 6'', selected from Data Release 4 of the Sloan Digital Sky Survey (SDSS DR4) using kernel density estimation (KDE). Of our 27 new quasar pair observations, 10 are binary quasars, which doubles the number of known g < 21 binaries with 3'' ⩽ Δ θ < 6'' separations. Several of our observed binaries are wide-separation lens candidates that merit additional higher resolution spectroscopy, as well as deep imaging to search for lensing galaxies. Our candidates are initially selected by UV excess ( u − g < 1), but are otherwise selected irrespective of the relative colors of the quasar pair, and we thus use them to suggest optimal color similarity and photometric redshift approaches for targeting binary quasars or projected quasar pairs. We find that a third or more of all binary quasars have quite dissimilar components on the basis of a typical color similarity criterion (χ 2 color ≲ 20). From a sample that is complete on proper scales of 23.7 h −1 kpc < R prop < 29.9 h −1 kpc , we determine the projected quasar correlation function to be p = 24.0 -10.8 +16.9 , which is 2 σ lower than recent estimates. We argue that our low p estimates may indicate redshift evolution in the quasar correlation function from z ∼ 1.9 to z ∼ 1.4 on scales of R prop ∼ 25 h −1 kpc. The size of this evolution broadly tracks quasar clustering on larger scales, consistent with merger-driven models of quasar origin. Spectroscopy of all of our DR4 KDE binary quasar candidates should be sufficient to detect quasar clustering evolution at R prop ∼ 25 h −1 kpc for z < 2.5 in a single homogeneous sample.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".