THE CANADA-FRANCE HIGH-<i>z</i>QUASAR SURVEY: NINE NEW QUASARS AND THE LUMINOSITY FUNCTION AT REDSHIFT 6
Bibliographic record
Abstract
We present discovery imaging and spectroscopy for nine new z ∼ 6 quasars found in the Canada–France High- z Quasar Survey (CFHQS) bringing the total number of CFHQS quasars to 19. By combining the CFHQS with the more luminous Sloan Digital Sky Survey sample, we are able to derive the quasar luminosity function from a sample of 40 quasars at redshifts 5.74 < z < 6.42. Our binned luminosity function shows a slightly lower normalization and flatter slope than found in previous work. The binned data also suggest a break in the luminosity function at M 1450 ≈ −25. A double power-law maximum likelihood fit to the data is consistent with the binned results. The luminosity function is strongly constrained (1σ uncertainty <0.1 dex) over the range −27.5 < M 1450 < −24.7. The best-fit parameters are Φ( M * 1450 ) = 1.14 × 10 −8 Mpc −3 mag −1 , break magnitude M * 1450 = −25.13, and bright end slope β = −2.81. However, the covariance between β and M * 1450 prevents strong constraints being placed on either parameter. For a break magnitude in the range −26 < M * 1450 < −24, we find −3.8 < β < −2.3 at 95% confidence. We calculate the z = 6 quasar intergalactic ionizing flux and show it is between 20 and 100 times lower than that necessary for reionization. Finally, we use the luminosity function to predict how many higher redshift quasars may be discovered in future near-IR imaging surveys.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".