Estimating Dust Attenuation from Galactic Spectra. III. Radial Variations of Dust Attenuation Scaling Relations in MaNGA Galaxies
Bibliographic record
Abstract
Abstract We investigate the radial dependence of the scaling relations of dust attenuation in nearby galaxies using integral field spectroscopy data from MaNGA. We identify ionized gas regions of kiloparsec size from MaNGA galaxies, and for each region we estimate both the stellar attenuation E ( B − V ) star and gas attenuation E ( B − V ) gas . We then quantify the correlations of 15 regional/global properties with E ( B − V ) gas and E ( B − V ) star , using both the feature importance obtained with the Random Forest regression technique and the Spearman correlation coefficients. The importance of stellar mass, metallicity, and nebular velocity dispersion found previously from studies based on the Sloan Digital Sky Survey can be reproduced if our analysis is limited to the central region of galaxies. The scaling relations of both E ( B − V ) gas and E ( B − V ) star are found to vary strongly as one goes from the galactic center to outer regions, and from H α -bright regions to H α -faint regions. For E ( B − V ) gas , [N ii ]/[S ii ] is top-ranked with a much higher correlation coefficient than any other property at 0 < R ≲ R e , while [O iii ]/[O ii ] outperforms [N ii ]/[S ii ] as the leading property in the outermost region. For E ( B − V ) star , stellar age shows the strongest correlation with no/weak dependence on radial distance, although Σ H α and specific star formation rate present similarly strong correlations with E ( B − V ) star in the galactic center. We find H α -bright regions to generally show stronger correlations with E ( B − V ) gas , while H α -faint regions are more strongly correlated with E ( B − V ) star , although this depends on individual properties and radial distance. The implications of our results for studies of high- z galaxies are discussed.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".