Discovery of 16 New z ∼ 5.5 Quasars: Filling in the Redshift Gap of Quasar Color Selection
Bibliographic record
Abstract
Abstract We present initial results from the first systematic survey of luminous z ∼ 5.5 quasars. Quasars at z ∼ 5.5, the post-reionization epoch, are crucial tools to explore the evolution of intergalactic medium, quasar evolution, and the early super-massive black hole growth. However, it has been very challenging to select quasars at redshifts 5.3 ≤ z ≤ 5.7 using conventional color selections, due to their similar optical colors to late-type stars, especially M dwarfs, resulting in a glaring redshift gap in quasar redshift distributions. We develop a new selection technique for z ∼ 5.5 quasars based on optical, near-IR, and mid-IR photometric data from Sloan Digital Sky Survey (SDSS), UKIRT InfraRed Deep Sky Surveys—Large Area Survey (ULAS), VISTA Hemisphere Survey (VHS), and Wide Field Infrared Survey Explorer. From our pilot observations in the SDSS-ULAS/VHS area, we have discovered 15 new quasars at 5.3 ≤ z ≤ 5.7 and 6 new lower redshift quasars, with SDSS z band magnitude brighter than 20.5. Including other two z ∼ 5.5 quasars already published in our previous work, we now construct a uniform quasar sample at 5.3 ≤ z ≤ 5.7, with 17 quasars in a ∼4800 square degree survey area. For further application in a larger survey area, we apply our selection pipeline to do a test selection by using the new wide field J-band photometric data from a preliminary version of the UKIRT Hemisphere Survey (UHS). We successfully discover the first UHS selected z ∼ 5.5 quasar.
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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 | 0.001 | 0.001 |
| 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".