Subaru High-z Exploration of Low-luminosity Quasars (SHELLQs). XVI. 69 New Quasars at 5.8 < z < 7.0
Bibliographic record
Abstract
Abstract We present the spectroscopic discovery of 69 quasars at 5.8 < z < 7.0, drawn from the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) imaging survey data. This is the 16th publication from the Subaru High-z Exploration of Low-Luminosity Quasars (SHELLQs) project, and it completes identification of all but the faintest candidates (i.e., i-band dropouts with z AB < 24 and y-band detections, and z-band dropouts with y AB < 24) with Bayesian quasar probability P Q B > 0.1 in the HSC-SSP third public data release (PDR3). The sample reported here also includes three quasars with P Q B < 0.1 at z ∼ 6.6, which we selected in an effort to completely cover the reddest point sources with simple color cuts. The number of high-z quasars discovered in SHELLQs has now grown to 162, including 23 type II quasar candidates. This paper also presents identification of seven galaxies at 5.6 < z < 6.7, an [O iii] emitter at z = 0.954, and 31 Galactic cool stars and brown dwarfs. High-z quasars and galaxies compose 75% and 16%, respectively, of all the spectroscopic SHELLQs objects that pass our latest selection algorithm with the PDR3 photometry. That is, a total of 91% of the objects lie at z > 5.6. This demonstrates that the algorithm has very high efficiency, even though we are probing an unprecedentedly low luminosity population down to M 1450 ∼ −21 mag.
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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 | 0.001 |
| 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) | 0.003 | 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".