Global dust attenuation in disc galaxies: strong variation with specific star formation and stellar mass, and the importance of sample selection
Bibliographic record
Abstract
We study the relative dust attenuation–inclination relation in 78 721 nearby galaxies using the axis ratio dependence of optical–near-IR colour, as measured by the Sloan Digital Sky Survey, the Two Micron All Sky Survey, and the Wide-field Infrared Survey Explorer. In order to avoid to the greatest extent possible attenuation-driven biases, we carefully select galaxies using dust attenuation-independent near- and mid-IR luminosities and colours. Relative u-band attenuation between face-on and edge-on disc galaxies along the star-forming main sequence varies from ∼0.55 mag up to ∼1.55 mag. The strength of the relative attenuation varies strongly with both specific star formation rate and galaxy luminosity (or stellar mass). The dependence of relative attenuation on luminosity is not monotonic, but rather peaks at M3.4 μm ≈ −21.5, corresponding to M* ≈ 3 × 1010 M⊙. This behaviour stands seemingly in contrast to some older studies; we show that older works failed to reliably probe to higher luminosities, and were insensitive to the decrease in attenuation with increasing luminosity for the brightest star-forming discs. Back-of-the-envelope scaling relations predict the strong variation of dust optical depth with specific star formation rate and stellar mass. More in-depth comparisons using the scaling relations to model the relative attenuation require the inclusion of star–dust geometry to reproduce the details of these variations (especially at high luminosities), highlighting the importance of these geometrical effects.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".