Dust Attenuation Curves at z ~ 0.8 from LEGA-C : Precise Constraints on the Slope and 2175Å Bump Strength
Bibliographic record
Abstract
We present a novel approach to measure the attenuation curves of 485 individual star-forming galaxies with M* > 1010 M⊙ based on deep optical spectra from the VLT/VIMOS LEGA-C survey and multi-band photometry in the COSMOS field. Most importantly, we find that the attenuation curves in the rest-frame 3000-4500 Å range are typically almost twice as steep as the Milky Way, LMC, SMC, and Calzetti attenuation curves, which is in agreement with recent studies of the integrated light of present-day galaxies. The attenuation at 4500 Å and the slope strongly correlate with the galaxy inclination: face-on galaxies show less attenuation and steeper curves compared to edge-on galaxies, suggesting that geometric effects dominate observed variations in attenuation. Our new method produces 2175 Å UV bump detections for 260 individual galaxies. Even though obvious correlations between UV bump strength and global galaxy properties are absent, strong UV bumps are most often seen in face-on, lower-mass galaxies (10 < log10(M*/M⊙) < 10.5) with low overall attenuation. Finally, we produce a typical attenuation curve for star-forming galaxies at z ~ 0.8; this prescription represents the effect of dust on the integrated spectral energy distributions of high-redshift galaxies more accurately than commonly used attenuation laws.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
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".