The Nature of Faint<i>Spitzer</i>‐selected Dust‐obscured Galaxies
Bibliographic record
Abstract
We use deep far-IR, submillimeter, radio, and X-ray imaging and mid-IR spectroscopy to explore the nature of a sample of Spitzer -selected dust-obscured galaxies (DOGs) in GOODS-N. A sample of 79 galaxies satisfy the criteria R − [ 24] > 14 (Vega) down to S 24 > 100 μJy (median flux density S 24 = 180 μJy). Twelve of these galaxies have IRS spectra available, which we use to measure redshifts and classify these objects as being dominated by star formation or active galactic nucleus (AGN) activity in the mid-IR. The IRS spectra and Spitzer photometric redshifts confirm that the DOGs lie in a tight redshift distribution around z ∼ 2. Based on mid-IR colors, 80% of DOGs are likely dominated by star formation; the stacked X-ray emission from this subsample of DOGs is also consistent with star formation. Since only a small number of DOGs are individually detected at far-IR and submillimeter wavelengths, we use a stacking analysis to determine the average flux from these objects and plot a composite IR (8-1000 μm) spectral energy distribution (SED). The average luminosity of these star-forming DOGs is L IR ∼ 1 × 10 12 L ☉ . We compare the average star-forming DOG to the average bright ( S 850 > 5 mJy) submillimeter galaxy (SMG); the S 24 > 100 μJy DOGs are 3 times more numerous but 8 times less luminous in the IR. The far-IR SED shape of DOGs is similar to that of SMGs (average dust temperature of around 30 K), but DOGs have a higher mid-IR-to-far-IR flux ratio. The average star formation-dominated DOG has a star formation rate of 200 M ☉ yr −1 , which, given their space density, amounts to a contribution of 0.01 M ☉ yr −1 Mpc −3 (or 5%-10%) to the star formation rate density at z ∼ 2.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".