Multiscale stellar associations across the star formation hierarchy in PHANGS–HST nearby galaxies: methodology and properties
Bibliographic record
Abstract
ABSTRACT We develop a method to identify and determine the physical properties of stellar associations using Hubble Space Telescope (HST) NUV−U−B−V−I imaging of nearby galaxies from the Physics at High Angular Resolution in Nearby GalaxieS with the Hubble Space Telescope (PHANGS–HST) survey. We apply a watershed algorithm to density maps constructed from point source catalogues Gaussian smoothed to multiple physical scales from 8 to 64 pc. We develop our method on two galaxies that span the distance range in the PHANGS–HST sample: NGC 3351 (10 Mpc) and NGC 1566 (18 Mpc). We test our algorithm with different parameters such as the choice of detection band for the point source catalogue (NUV or V), source density image filtering methods, and absolute magnitude limits. We characterize the properties of the resulting multiscale associations, including sizes, number of tracer stars, number of associations, and photometry, as well as ages, masses, and reddening from spectral energy distribution fitting. Our method successfully identifies structures that occupy loci in the UBVI colour–colour diagram consistent with previously published catalogues of clusters and associations. The median ages of the associations increase from log(age/yr) = 6.6 to log(age/yr) = 6.9 as the spatial scale increases from 8 to 64 pc for both galaxies. We find that the youngest stellar associations, with ages <3 Myr, indeed closely trace H ii regions in H α imaging, and that older associations are increasingly anticorrelated with the H α emission. Owing to our new method, the PHANGS–HST multiscale associations provide a far more complete census of recent star formation activity than found with previous cluster and compact association catalogues.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".