Hot Dust-obscured Galaxies with Excess Blue Light
Bibliographic record
Abstract
Abstract Hot dust-obscured galaxies (Hot DOGs) are among the most luminous galaxies in the universe. Powered by highly obscured, possibly Compton-thick, active galactic nuclei (AGNs), Hot DOGs are characterized by spectral energy distributions that are very red in the mid-infrared yet dominated by the host galaxy stellar emission in the UV and optical. An earlier study identified a subsample of Hot DOGs with significantly enhanced UV emission. One target, W0204–0506, was studied in detail and, based on Chandra observations, it was concluded that the enhanced emission was most likely due to either extreme unobscured star formation (star formation rate > 1000 M ⊙ yr −1 ) or to light from the highly obscured AGN scattered by gas or dust into our line of sight. Here, we present a follow-up study of W0204–0506 as well as two more Hot DOGs with excess UV emission. For the two new objects we obtained Chandra/ACIS-S observations, and for all three targets we obtained Hubble Space Telescope/WFC3 F555W and F160W imaging. The analysis of these observations, combined with multiwavelength photometry and UV/optical spectroscopy suggests that UV emission is most likely dominated by light from the central highly obscured, hyperluminous AGN that has been scattered into our line of sight, by either gas or dust. We cannot decisively rule out, however, that star formation or a second AGN in the system may significantly contribute to the UV excess of these targets.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".