Dust attenuation, dust content, and geometry of star-forming galaxies
Bibliographic record
Abstract
ABSTRACT We analyse the joint distribution of dust attenuation and projected axis ratios, together with galaxy size and surface brightness profile information, to infer lessons on the dust content and star/dust geometry within star-forming galaxies at 0 < z < 2.5. To do so, we make use of large observational data sets from KiDS + VIKING + HSC-SSP and extend the analysis out to redshift z = 2.5 using the HST surveys CANDELS and 3D-DASH. We construct suites of SKIRT radiative transfer models for idealized galaxies observed under random viewing angles with the aim of reproducing the aforementioned distributions, including the level and inclination dependence of dust attenuation. We find that attenuation-based dust mass estimates are at odds with constraints from far-infrared observations, especially at higher redshifts, when assuming smooth star and dust geometries of equal extent. We demonstrate that UV-to-near-IR and far-infrared constraints can be reconciled by invoking clumpier dust geometries for galaxies at higher redshifts and/or very compact dust cores. We discuss implications for the significant wavelength- and redshift-dependent differences between half-light and half-mass radii that result from spatially varying dust columns within especially massive star-forming galaxies.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".