PHANGS-HST Catalogs for ∼100,000 Star Clusters and Compact Associations in 38 Galaxies. II. Physical Properties from Decision-tree-based Spectral Energy Distribution Fitting of NUV-<i>U</i>-<i>B</i>-<i>V</i> <i>-</i> <i>I</i> Photometry with Categorical Priors Set by H<i>α</i> Emission, Cluster Morphology, and Other Auxiliary Information
Bibliographic record
Abstract
Abstract This paper is the second in a series presenting the catalogs and properties of the largest sample to date of ∼100,000 star clusters and compact associations, in 38 spiral galaxies observed by the PHANGS-HST Treasury survey. Here, we present spectral energy distribution (SED) fitting techniques used to compute the age, mass, and reddening for each object. Our decision-tree-based strategy incorporates categorical priors on model age, reddening, and metallicity determined from additional observed parameters: localized H α emission, source morphology, and demographic-specific locations in the UBVI color–color diagram. This approach is implemented to mitigate model degeneracies, particularly between young dusty clusters and old clusters with minimal dust, which can have identical optical colors. Results based on H α narrowband imaging from the ground and from Hubble Space Telescope are intercompared, and contrasted with previous SED-fitting efforts. The fraction of the population that is subject to such priors is ∼14%, and of this subset, ∼63% of old globular clusters (GCs) have ages that change by a factor of 10 or more relative to unconstrained fits with single metallicity ( Z ⊙ ) simple stellar population models. The demographics of the population are examined through age–mass and age–reddening diagrams (for individual galaxies as well as aggregated over the sample), and the GC mass function. We demonstrate relationships between cluster age–mass diagrams and properties of parent galaxies (galaxy morphology and location relative to the galaxy main sequence). We outline continuing efforts to improve the inference of physical properties, including the incorporation of JWST infrared photometry and updated synthesis models.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".