The Sloan Digital Sky Survey Quasar Survey: Quasar Luminosity Function from Data Release 3
Bibliographic record
Abstract
We determine the number counts and z = 0-5 luminosity function for a well-defined, homogeneous sample of quasars from the Sloan Digital Sky Survey (SDSS). We conservatively define the most uniform statistical sample possible, consisting of 15,343 quasars within an effective area of 1622 deg 2 that was derived from a parent sample of 46,420 spectroscopically confirmed broad-line quasars in the 5282 deg 2 of imaging data from SDSS Data Release 3. The sample extends from i = 15 to 19.1 at z ≲ 3 and to i = 20.2 for z ≳ 3. The number counts and luminosity function agree well with the results of the Two Degree Field QSO Redshift Survey (2QZ) at redshifts and luminosities at which the SDSS and 2QZ quasar samples overlap, but the SDSS data probe to much higher redshifts than does the 2QZ sample. The number density of luminous quasars peaks between redshifts 2 and 3, although uncertainties in the selection function in this range do not allow us to determine the peak redshift more precisely. Our best-fit model has a flatter bright-end slope at high redshift than at low redshift. For z < 2.4 the data are best fit by a redshift-independent slope of β = -3.1 [Φ( L ) ∝ L β ]. Above z = 2.4 the slope flattens with redshift to β ≳ -2.37 at z = 5. This slope change, which is significant at the ≳5 σ level, must be accounted for in models of the evolution of accretion onto supermassive black holes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.006 |
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".