The Pan-STARRS1 z > 5.6 Quasar Survey. II. Discovery of 55 Quasars at 5.6 < z < 6.5
Bibliographic record
Abstract
Abstract The identification of bright quasars at z ≳ 6 enables detailed studies of supermassive black holes, massive galaxies, structure formation, and the state of the intergalactic medium within the first billion years after the Big Bang. We present the spectroscopic confirmation of 55 quasars at redshifts 5.6 < z < 6.5 and UV magnitudes −24.5 < M 1450 < −28.5 identified in the optical Pan-STARRS1 and near-IR VIKING surveys (48 and 7, respectively). Five of these quasars have independently been discovered in other studies. The quasar sample shows an extensive range of physical properties, including 17 objects with weak emission lines, 10 broad absorption line quasars, and 5 objects with strong radio emission (radio-loud quasars). There are also a few notable sources in the sample, including a blazar candidate at z = 6.23, a likely gravitationally lensed quasar at z = 6.41, and a z = 5.84 quasar in the outskirts of the nearby (D ∼ 3 Mpc) spiral galaxy M81. The blazar candidate remains undetected in NOEMA observations of the [C ii] and underlying emission, implying a star formation rate <30–70 M ⊙ yr−1. A significant fraction of the quasars presented here lies at the foundation of the first measurement of the z ∼ 6 quasar luminosity function from Pan-STARRS1 (introduced in a companion paper). These quasars will enable further studies of the high-redshift quasar population with current and future facilities.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".