Subaru High-<i>z</i> Exploration of Low-Luminosity Quasars (SHELLQs). IX. Identification of two red quasars at <i>z</i> &gt; 5.6
Bibliographic record
Abstract
Abstract We present the first discovery of dust-reddened quasars (red quasars) in the high-z universe (z &gt; 5.6). This is a result from the Subaru High-z Exploration of Low-Luminosity Quasars (SHELLQs) project, which is based on the sensitive multi-band optical imaging data produced by the Hyper Suprime-Cam (HSC) Subaru Strategic Program survey. We identified four red quasar candidates from the 93 spectroscopically confirmed high-z quasars in the SHELLQs sample, based on detections in the Wide-field Infrared Survey Explorer (WISE) data at 3.4 and 4.6 μm (rest-frame ∼5000–6500 Å). The amount of dust reddening was estimated with spectral energy distribution (SED) fits over optical and mid-infrared wavelengths. Two of the four candidates were found to be red quasars with dust reddening of E(B − V) &gt; 0.1. The remaining SHELLQs quasars without individual WISE detections are significantly fainter in the WISE bands and bluer than the red quasars, although we did detect them in the W1 band in a stacked image. We also conducted the same SED fits for high-z optically-luminous quasars, but no red quasar was found. This demonstrates the power of Subaru HSC to discover high-z red quasars, which are fainter than the limiting magnitudes of past surveys in the rest-frame ultraviolet, due to dust extinction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".