ALMA characterizes the dust temperature of <i>z</i> ∼ 5.5 star-forming galaxies
Bibliographic record
Abstract
ABSTRACT The infrared (IR) spectral energy distributions (SEDs) of main-sequence galaxies in the early Universe (z > 4) is currently unconstrained as IR continuum observations are time-consuming and not feasible for large samples. We present Atacama Large Millimetre Array Band 8 observations of four main-sequence galaxies at z ∼ 5.5 to study their IR SED shape in detail. Our continuum data (rest-frame 110 $\rm \mu m$, close to the peak of IR emission) allows us to constrain luminosity-weighted dust temperatures and total IR luminosities. With data at longer wavelengths, we measure for the first time the emissivity index at these redshifts to provide more robust estimates of molecular gas masses based on dust continuum. The Band 8 observations of three out of four galaxies can only be reconciled with optically thin emission redward of rest-frame $100\, {\rm \mu m}$. The derived dust peak temperatures at z ∼ 5.5 ($30\!-\!43\, {\rm K}$) are elevated compared to average local galaxies, however, $\sim 10\, {\rm K}$ below what would be predicted from an extrapolation of the trend at z < 4. This behaviour can be explained by decreasing dust abundance (or density) towards high redshifts, which would cause the IR SED at the peak to be more optically thin, making hot dust more visible to the external observer. From the $850{\hbox{-}}{\rm \mu m}$ dust continuum, we derive molecular gas masses between 1010 and $10^{11}\, {\rm M_{\odot }}$ and gas fractions (gas over total mass) of $30\!-\!80{{\ \rm per\ cent}}$ (gas depletion times of $100\!-\!220\, {\rm Myr}$). All in all, our results provide a first measured benchmark SED to interpret future millimetre observations of normal, main-sequence galaxies in the early Universe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".