Estimating Dust Attenuation From Galactic Spectra. III. Radial variations of dust attenuation scaling relations in MaNGA galaxies
Bibliographic record
Abstract
We investigate the radial dependence of the scaling relations of dust attenuation in nearby galaxies using integral field spectroscopy (IFS) data from MaNGA. We identify ionized gas regions of kpc sizes from MaNGA galaxies, and for each region we estimate both the stellar attenuation $E(B-V)_{\rm star}$ and gas attenuation $E(B-V)_{\rm gas}$. We then quantify the correlations of 15 regional/global properties with $E(B-V)_{\rm gas}$ and $E(B-V)_{\rm star}$, 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 SDSS-based studies can be reproduced if our analysis is limited to the central region of galaxies. The scaling relations of both $E(B-V)_{\rm gas}$ and $E(B-V)_{\rm star}$ are found to strongly vary as one goes from the galactic center to outer regions, and from H$α$-bright regions to H$α$-faint regions. For $E(B-V)_{\rm gas}$, [NII]/[SII] is top ranked with a much higher correlation coefficient than any other property at $0
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".