A SURVEY OF LUMINOUS HIGH-REDSHIFT QUASARS WITH SDSS AND WISE. II. THE BRIGHT END OF THE QUASAR LUMINOSITY FUNCTION AT z ∼ 5
Bibliographic record
Abstract
ABSTRACT This is the second paper in a series on a new luminous z ∼ 5 quasar survey using optical and near-infrared colors. Here we present a new determination of the bright end of the quasar luminosity function (QLF) at z ∼ 5. Combining our 45 new quasars with previously known quasars that satisfy our selections, we construct the largest uniform luminous z ∼ 5 quasar sample to date, with 99 quasars in the range of 4.7 ≤ z < 5.4 and −29 < M 1450 ≤ −26.8, within the Sloan Digital Sky Survey (SDSS) footprint. We use a modified 1/V a method including flux limit correction to derive a binned QLF, and we model the parametric QLF using maximum likelihood estimation. With the faint-end slope of the QLF fixed as α = −2.03 from previous deeper samples, the best fit of our QLF gives a flatter bright end slope β = −3.58 ± 0.24 and a fainter break magnitude = −26.98 ± 0.23 than previous studies at similar redshift. Combined with previous work at lower and higher redshifts, our result is consistent with a luminosity evolution and density evolution model. Using the best-fit QLF, the contribution of quasars to the ionizing background at z ∼ 5 is found to be 18%–45% with a clumping factor C of 2–5. Our sample suggests an evolution of radio loud fraction with optical luminosity but no obvious evolution with redshift.
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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 | 0.001 |
| 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 | 0.001 |
| 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".