PHANGS Hubble Space Telescope Treasury Survey: Globular Cluster Systems in 17 Nearby Spiral Galaxies
Bibliographic record
Abstract
Abstract We present new catalogs of likely globular clusters (GCs) in 17 nearby spiral galaxies studied as part of the PHANGS Hubble Space Telescope (HST) Treasury Survey. The galaxies were imaged in five broadband filters from the near-ultraviolet through the I band. PHANGS-HST has produced catalogs of stellar clusters of all ages by selecting extended sources (from multiple concentration index measurements) followed by morphological classification (centrally concentrated and symmetric or asymmetric, multiple peaks, and contaminants) by visually examining the V-band images and separately by a machine-learning algorithm which classified larger samples to reach fainter limits. From both cluster catalogs, we select an initial list of candidate GCs which have B − V ≥ 0.5 and V − I ≥ 0.73 mag, then remove likely contaminants (including reddened young clusters, background galaxies misclassified by the neural network, and chance superpositions/blends of stars) after a careful visual inspection. We find that ≈86% of the color-selected candidates classified as spherically symmetric and 68% of those classified as centrally concentrated but asymmetric are likely to be GCs. The luminosity functions of the GC candidates in two of our 17 galaxies, NGC 628 and NGC 3627, are atypical, and continue to rise at least 1 mag fainter than the expected turnover near M V ∼ −7.4. These faint candidate GCs have more extended spatial distributions than their bright counterparts, and may reside in the disk rather than the bulge/halo, similar to faint GCs previously discovered in M101. These faint clusters may be somewhat younger since the age–metallicity degeneracy makes it difficult to determine precise cluster ages from integrated colors once they reach ≈1 Gyr.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".