A SURVEY OF LUMINOUS HIGH-REDSHIFT QUASARS WITH SDSS AND WISE. I. TARGET SELECTION AND OPTICAL SPECTROSCOPY
Bibliographic record
Abstract
ABSTRACT High-redshift quasars are important tracers of structure and evolution in the early universe. However, they are very rare and difficult to find when using color selection because of contamination from late-type dwarfs. High-redshift quasar surveys based on only optical colors suffer from incompleteness and low identification efficiency, especially at . We have developed a new method to select quasars with both high efficiency and completeness by combining optical and mid-IR Wide-field Infrared Survey Explorer (WISE) photometric data, and are conducting a luminous quasar survey in the whole Sloan Digital Sky Survey (SDSS) footprint. We have spectroscopically observed 99 out of 110 candidates with z-band magnitudes brighter than 19.5, and 64 (64.6%) of them are quasars with redshifts of and absolute magnitudes of . In addition, we also observed 14 fainter candidates selected with the same criteria and identified 8 (57.1%) of them as quasars with . Among 72 newly identified quasars, 12 of them are at , which leads to an increase of ∼36% of the number of known quasars at this redshift range. More importantly, our identifications doubled the number of quasars with at , which will set strong constraints on the bright end of the quasar luminosity function. We also expand our method to select quasars at z ≳ 5.7. In this paper we report the discovery of four new luminous z ≳ 5.7 quasars based on SDSS–WISE selection.
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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 | 0.003 | 0.002 |
| 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) | 0.001 | 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".