The impact of H II regions on giant molecular cloud properties in nearby galaxies sampled by PHANGS ALMA and MUSE
Bibliographic record
Abstract
Context. The final stages of molecular cloud evolution involve cloud disruption due to feedback by massive stars, with recent literature suggesting the importance of early (i.e., pre-supernova) feedback mechanisms. Aims. We aim to determine whether feedback from massive stars in H II regions has a measurable impact on the physical properties of molecular clouds at a characteristic scale of ~ 100 pc, and whether the imprint of feedback on the molecular gas depends on the local galactic environment. Methods. We identified giant molecular clouds (GMCs) associated with H II regions for a sample of 19 nearby galaxies from catalogs of GMCs and H II regions released by the PHANGS-ALMA and PHANGS-MUSE surveys, using the overlap of the CO and Hα emission as the key criterion for physical association. We compared the distributions of GMC and H II region properties for paired and non-paired objects. We investigated correlations between GMC and H II region properties among galaxies and across different galactic environments to determine whether GMCs that are associated with H II regions have significantly distinct physical properties compared to the parent GMC population. Results. We identify trends between the Hα luminosity of an H II region and the CO peak brightness and molecular mass of GMCs that we tentatively attribute to a direct physical connection between the matched objects, and which arise independently of the underlying environmental variations of GMC and H II region properties within galaxies. The study of the full sample nevertheless hides a large galaxy-to-galaxy variability. Conclusions. At the ~100 pc scales accessed by the PHANGS-ALMA and PHANGS-MUSE data, pre-supernova feedback mechanisms in H II regions have a subtle but measurable impact on the properties of the surrounding molecular gas, as inferred from CO observations.
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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) | 0.001 | 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".