Bright, relatively isolated star clusters in PHANGS–<i>HST</i> galaxies: Aperture corrections, quantitative morphologies, and comparison with synthetic stellar population models
Bibliographic record
Abstract
ABSTRACT Using PHANGS–HST NUV-U-B-V-I imaging of 17 nearby spiral galaxies, we study samples of star clusters and stellar associations, visually selected to be bright and relatively isolated, for three purposes: to compute aperture corrections for star cluster photometry, to explore the utility of quantitative morphologies in the analysis of clusters and associations, and to compare to synthetic stellar population models. We provide a technical summary of our procedures to determine aperture corrections, a standard step in the production of star cluster candidate catalogues, and compare to prior work. We also use this specialized sample to launch an analysis into the measurement of star cluster light profiles. We focus on one measure, M20 (normalized second-order moment of the brightest 20 per cent of pixels), applied previously to study the morphologies of galaxies. We find that M20 in combination with UB-VI colours, yields a parameter space where distinct loci are formed by single-peaked symmetric clusters, single-peaked asymmetric clusters, and multipeaked associations. We discuss the potential applications for using M20 to gain insight into the formation and evolution of clusters and associations. Finally, we compare the colour distributions of this sample with various synthetic stellar population models. One finding is that the standard procedure of using a single-metallicity SSP track to fit the entire population of clusters in a given galaxy should be revisited, as the oldest globular clusters will be more metal-poor compared to clusters formed recently.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".