The ALMA REBELS survey: the dust content of <i>z</i> ∼ 7 Lyman break galaxies
Bibliographic record
Abstract
ABSTRACT We include a fully coupled treatment of metal and dust enrichment into the Delphi semi-analytic model of galaxy formation to explain the dust content of 13 Lyman break galaxies (LBGs) detected by the Atacama Large millimetre Array (ALMA) REBELS Large Program at z ≃ 7. We find that the galaxy dust mass, Md, is regulated by the combination of Type II supernova dust production, astration, shock destruction, and ejection in outflows; grain growth (with a standard time-scale τ0 = 30 Myr) plays a negligible role. The model predicts a dust-to-stellar mass ratio of $\sim 0.07\!-\!0.1{{\ \rm per\ cent}}$ and a UV-to-total star formation rate relation such that log(ψUV) = −0.05 [log(ψ)]2 + 0.86 log(ψ) − 0.05 (implying that 55–80 per cent of the star formation is obscured) for REBELS galaxies with stellar mass $M_* = 10^{9}\!-\!10^{10} \rm M_\odot$. This relation reconciles the intrinsic UV luminosity of LBGs with their observed luminosity function at z = 7. However, 2 out of the 13 systems show dust-to-stellar mass ratios ($\sim 0.94\!-\!1.1{{\ \rm per\ cent}}$) that are up to 18 times larger than expected from the fiducial relation. Due to the physical coupling between dust and metal enrichment, even decreasing τ0 to very low values (0.3 Myr) only increases the dust-to-stellar mass ratio by a factor of ∼2. Given that grain growth is not a viable explanation for such high observed ratios of the dust-to-stellar mass, we propose alternative solutions.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 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".