The MUSE Atlas of Disks (MAD): resolving star formation rates and gas metallicities on <100 pc scales†
Bibliographic record
Abstract
We study the physical properties of the ionized gas in local discs using the sample of 38 nearby ∼10 8.5-11.2M Star-Forming Main-Sequence (SFMS) galaxies observed so far as part of the MUSE Atlas of Disks (MAD).Specifically, we use all strong emission lines in the MUSE wavelength range 4650-9300 Å to investigate the resolved ionized gas properties on ∼100 pc scales.This spatial resolution enables us to disentangle H II regions from the diffuse ionized gas (DIG) in the computation of gas metallicities and star formation rates (SFRs) of star-forming regions.The gas metallicities generally decrease with radius.The metallicity of the H II regions is on average ∼0.1 dex higher than that of the DIG, but the metallicity radial gradient in both components is similar.The mean metallicities within the inner galaxy cores correlate with the total stellar mass of the galaxies.On our < 100 pc scales, we find two correlations previously reported at kpc scales: a spatially resolved mass-metallicity relation (RMZR) and a spatially resolved SFMS (RSFMS).We find no secondary dependence of the RMZR with the SFR density.We find that both resolved relations have a local origin, as they do not depend on the total stellar mass.The observational results of this paper are consistent with the inside-out scenario for the growth of galactic disks.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".