The Physical Drivers of the Luminosity-weighted Dust Temperatures in High-redshift Galaxies
Bibliographic record
Abstract
Abstract The underlying distribution of galaxies’ dust spectral energy distributions (SEDs) (i.e., their spectra reradiated by dust from rest-frame ∼3 μ m to 3 mm) remains relatively unconstrained owing to a dearth of far-IR/(sub)millimeter data for large samples of galaxies. It has been claimed in the literature that a galaxy’s dust temperature—observed as the wavelength where the dust SED peaks ( λ peak )—is traced most closely by its specific star formation rate (sSFR) or parameterized “distance” to the SFR– M ⋆ relation (the galaxy “main sequence”). We present 024 resolved 870 μ m ALMA dust continuum observations of seven z = 1.4–4.6 dusty star-forming galaxies chosen to have a large range of well-constrained luminosity-weighted dust temperatures. We also draw on similar-resolution dust continuum maps from a sample of ALESS submillimeter galaxies from Hodge et al (2016). We constrain the physical scales over which the dust radiates and compare those measurements to characteristics of the integrated SED. We confirm significant correlations of λ peak with both L IR (or SFR) and Σ IR (∝SFR surface density). We investigate the correlation between log 10 ( λ peak ) and log 10 (Σ IR ) and find the relation to hold as would be expected from the Stefan–Boltzmann law, or the effective size of an equivalent blackbody. The correlations of λ peak with sSFR and distance from the SFR– M ⋆ relation are less significant than those for Σ IR or L IR ; therefore, we conclude that the more fundamental tracer of galaxies’ luminosity-weighted integrated dust temperatures are indeed their star formation surface densities in line with local universe results, which relate closely to the underlying geometry of dust in the interstellar medium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".