Optical Properties of Infrared-bright Dust-obscured Galaxies Viewed with Subaru Hyper Suprime-Cam
Bibliographic record
Abstract
Abstract We report on the optical properties of infrared (IR)-bright dust-obscured galaxies (DOGs) that are defined as . Because supermassive black holes (SMBHs) in IR-bright DOGs are expected to be rapidly growing in the major-merger scenario, they provide useful clues for understanding the coevolution of SMBHs and their host galaxies. However, the optical properties of IR-bright DOGs remain unclear because the optical emission of a DOG is very faint. By combining ∼105 deg2 images of the optical, near-IR, and mid-IR data obtained from the Subaru Hyper Suprime-Cam (HSC) survey, the VISTA VIKING survey, and the Wide-field Infrared Survey Explorer all-sky survey, respectively, 571 IR-bright DOGs were selected. We found that IR-bright DOGs show a redder (g − z)AB color than do other populations of dusty galaxies, such as ultraluminous IR galaxies (ULIRGs) at a similar redshift, with a significantly large dispersion. Among the selected DOGs, star formation (SF)-dominated DOGs show a relatively red color, while active galactic nucleus (AGN)-dominated DOGs show a rather blue color in optical. This result is consistent with the idea that the relative AGN contribution in the optical emission becomes more significant at a later stage in the major-merger scenario. We discovered eight IR-bright DOGs showing a significant blue excess in blue HSC bands. This blue excess can be interpreted as a leaked AGN emission that is either a directly leaking or a scattered AGN emission, as proposed for some blue-excess Hot DOGs in earlier studies.
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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 | 0.000 | 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 | 0.000 |
| 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".