Identifying and characterizing the most heavily dust-obscured galaxies at 1 ≤ <i>z</i> ≤ 4
Bibliographic record
Abstract
ABSTRACT We present 65 extremely dust-obscured galaxies from the UltraVISTA DR3 survey of the COSMOS field at 1 < z < 4. In contrast to other studies of dusty galaxies, we select our sample based on dust attenuation measured by UV–MIR spectral energy distribution (SED) modelling that allows for extreme attenuation levels. We construct our sample by making cuts at 1 ≤ z ≤ 4, AV ≥ 3, and log(M*/M⊙) ≥ 10.5. This method reliably selects galaxies exhibiting independent indicators of significant dust content, including far-infrared detection rates. We perform panchromatic SED modelling with matched Herschel photometry and find stellar and dust properties that differ from typical submillimetre galaxy (SMG) samples as well as Herschel sources matched in redshift and stellar mass. Our sources have lower star formation rates and higher AV than SMGs, but comparable total infrared luminosities. Most of our sample falls on or near the star-forming main sequence for this redshift range. Finally, we perform a morphological analysis with galfit using the KS-band images and Hubble F814W and F160W imaging when available. Typical axial ratios of ∼0.4 suggest disc-like morphology for the majority of our sources, and we note only three apparent merging systems. Our sample generally agrees with the size–mass relation for star-forming galaxies, with a tail extending to smaller sizes. We conclude that the most heavily obscured galaxies in this redshift range share many characteristics with typical star-forming galaxies, forming a population of dusty galaxies that overlaps, but is not encompassed by, those selected through dust emission.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".