Stellar associations powering H <scp>ii</scp> regions – I. Defining an evolutionary sequence
Bibliographic record
Abstract
ABSTRACT Connecting the gas in H ii regions to the underlying source of the ionizing radiation can help us constrain the physical processes of stellar feedback and how H ii regions evolve over time. With PHANGS–MUSE, we detect nearly 24 000 H ii regions across 19 galaxies and measure the physical properties of the ionized gas (e.g. metallicity, ionization parameter, and density). We use catalogues of multiscale stellar associations from PHANGS–HST to obtain constraints on the age of the ionizing sources. We construct a matched catalogue of 4177 H ii regions that are clearly linked to a single ionizing association. A weak anticorrelation is observed between the association ages and the $\mathrm{H}\, \alpha$ equivalent width $\mathrm{EW}(\mathrm{H}\, \alpha)$, the $\mathrm{H}\, \alpha/\mathrm{FUV}$ flux ratio, and the ionization parameter, log q. As all three are expected to decrease as the stellar population ages, this could indicate that we observe an evolutionary sequence. This interpretation is further supported by correlations between all three properties. Interpreting these as evolutionary tracers, we find younger nebulae to be more attenuated by dust and closer to giant molecular clouds, in line with recent models of feedback-regulated star formation. We also observe strong correlations with the local metallicity variations and all three proposed age tracers, suggestive of star formation preferentially occurring in locations of locally enhanced metallicity. Overall, $\mathrm{EW}(\mathrm{H}\, \alpha)$ and log q show the most consistent trends and appear to be most reliable tracers for the age of an H ii region.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".