A3COSMOS: the dust attenuation of star-forming galaxies at <i>z</i> = 2.5–4.0 from the COSMOS-ALMA archive
Bibliographic record
Abstract
ABSTRACT We present an analysis of the dust attenuation of star-forming galaxies at z = 2.5–4.0 through the relationship between the UV spectral slope (β), stellar mass (M*), and the infrared excess (IRX = LIR/LUV) based on far-infrared continuum observations from the Atacama Large Millimeter/sub-millimeter Array (ALMA). Our study exploits the full ALMA archive over the COSMOS field processed by the A3COSMOS team, which includes an unprecedented sample of ∼1500 galaxies at z ∼ 3 as primary or secondary targets in ALMA band 6 or 7 observations with a median continuum sensitivity of 126 $\rm {\mu Jy\, beam}^{-1}$ (1σ). The detection rate is highly mass dependent, decreasing drastically below log (M*/M⊙) = 10.5. The detected galaxies show that the IRX–β relationship of massive (log M*/M⊙ > 10) main-sequence galaxies at z = 2.5–4.0 is consistent with that of local galaxies, while starbursts are generally offset by $\sim 0.5\, {\rm dex}$ to larger IRX values. At the low-mass end, we derive upper limits on the infrared luminosities through stacking of the ALMA data. The combined IRX–M* relation at $\rm {log\, ({\it M}_{\ast }/\mathrm{M}_{\odot })\gt 9}$ exhibits a significantly steeper slope than reported in previous studies at similar redshifts, implying little dust obscuration at log M*/M⊙ < 10. However, our results are consistent with earlier measurements at z ∼ 5.5, indicating a potential redshift evolution between z ∼ 2 and z ∼ 6. Deeper observations targeting low-mass galaxies will be required to confirm this finding.
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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 | 0.002 |
| 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) | 0.003 | 0.001 |
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".