The composite nature of Dust-Obscured Galaxies (DOGs) at<i>z</i> ∼ 2–3 in the COSMOS field – I. A far-infrared view
Bibliographic record
Abstract
Dust-Obscured Galaxies (DOGs) are bright 24 μm-selected sources with extreme obscuration at optical wavelengths. They are typically characterized by a rising power-law continuum of hot dust (TD ∼ 200–1000 K) in the near-IR indicating that their mid-IR luminosity is dominated by an active galactic nucleus (AGN). DOGs with a fainter 24 μm flux display a stellar bump in the near-IR and their mid-IR luminosity appears to be mainly powered by dusty star formation. Alternatively, it may be that the mid-IR emission arising from AGN activity is dominant but the torus is sufficiently opaque to make the near-IR emission from the AGN negligible with respect to the emission from the host component. In an effort to characterize the astrophysical nature of the processes responsible for the IR emission in DOGs, this paper exploits Herschel data (PACS + SPIRE) on a sample of 95 DOGs within the COSMOS field. We derive a wealth of far-IR properties (e.g. total IR luminosities; mid-to-far-IR colours; dust temperatures and masses) based on spectral energy distribution fitting. Of particular interest are the 24 μm-bright DOGs (F24 μm > 1 mJy). They present bluer far-IR/mid-IR colours than the rest of the sample, unveiling the potential presence of an AGN. The AGN contribution to the total 8–1000 μm flux increases as a function of the rest-frame 8 μm-luminosity irrespective of the redshift. This confirms that faint DOGs (L8 μm < 1012 L⊙) are dominated by star formation while brighter DOGs show a larger contribution from an AGN.
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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 | 0.001 |
| 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) | 0.002 | 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".