The ALMA REBELS survey: the dust-obscured cosmic star formation rate density at redshift 7
Bibliographic record
Abstract
ABSTRACT Cosmic dust is an essential component shaping both the evolution of galaxies and their observational signatures. How quickly dust builds up in the early Universe remains an open question that requires deep observations at (sub-)millimetre wavelengths to resolve. Here, we use Atacama Large Millimeter Array observations of 45 galaxies from the Reionization Era Bright Emission Line Survey (REBELS) and its pilot programs, designed to target [C ii] and dust emission in UV-selected galaxies at z ∼ 7, to investigate the dust content of high-redshift galaxies through a stacking analysis. We find that the typical fraction of obscured star formation fobs = SFRIR/SFRUV+IR depends on stellar mass, similar to what is observed at lower redshift, and ranges from fobs ≈ 0.3 − 0.6 for galaxies with log10(M⋆/M⊙) = 9.4–10.4. We further adopt the z ∼ 7 stellar mass function from the literature to extract the obscured cosmic star formation rate density (SFRD) from the REBELS survey. Our results suggest only a modest decrease in the SFRD between 3 ≲ z ≲ 7, with dust-obscured star formation still contributing ${\sim}30{{\ \rm per\ cent}}$ at z ∼ 7. While we extensively discuss potential caveats, our analysis highlights the continued importance of dust-obscured star formation even well into the epoch of reionization.
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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 | 0.001 | 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) | 0.002 | 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".