REBELS-IFU: dust build-up in massive galaxies at redshift 7
Bibliographic record
Abstract
ABSTRACT In recent years, observations with the James Webb Space Telescope (JWST) have started to map out the rapid metal enrichment of the early Universe, while (sub)millimetre observations have simultaneously begun to reveal the ubiquity of dust beyond $z\gtrsim 6$. However, the pathways that led to the assembly of early dust reservoirs remain poorly quantified, and require pushing our understanding of key scaling relations between dust, gas, and metals into the early Universe. We investigate the dust build-up in twelve $6.5 \lesssim z \lesssim 7.7$ galaxies drawn from the Reionization Era Bright Emission Line Survey (REBELS) that benefit from (i) JWST/NIRSpec strong-line metallicity measurements, (ii) Atacama Large Millimetre/submillimetre Array (ALMA) [C ii]-based redshifts and gas masses, and (iii) dust masses from single- or multi-band ALMA continuum observations. Combining these measurements, we investigate the dust-to-gas (DtG), dust-to-metal (DtM), and dust-to-stellar mass (DtS) ratios of our sample as a function of metallicity. While our analysis is limited by systematic uncertainties related to the [C ii]-to-H$_2$ conversion factor and dust temperature, we explore a wide range of possible values, and carefully assess their impact on our results. Under a fiducial set of assumptions, we find an average $\log (\mathrm{DtG}) = -3.02 \pm 0.23$, only slightly below that of local metal-rich galaxies. On the other hand, at fixed metallicity our average $\log (\mathrm{DtS}) = -2.15 \pm 0.42$ is significantly larger than that of low-redshift galaxies. Finally, through a comparison to various theoretical models of high-redshift dust production, we find that assembling the dust reservoirs in massive galaxies at $z\approx 7$ likely requires the combination of rapid supernova enrichment and efficient interstellar medium dust growth.
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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 | 0.000 |
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".