Bibliographic record
Abstract
Star clusters in six nearby spiral galaxies are examined using archive images from the Wide Field Planetary Camera 2 (WFPC2) on board the Hubble Space Telescope ( HST ). The galaxies have previously been studied from the ground and some of them are known to possess rich populations of "young massive clusters." Comparison with the HST images indicates a success rate of ∼75% for the ground-based cluster detections, with typical contaminants being blends or loose groupings of several stars in crowded regions. The luminosity functions (LFs) of cluster candidates identified on the HST images are analyzed and compared with existing data for the Milky Way and the LMC. The LFs are well approximated by power laws of the form dN ( L )/ dL ∝ L α , with slopes in the range -2.4 ≲ α≲-2.0. The steeper slopes tend to be found among fits covering brighter magnitude intervals, although direct hints of a variation in the LF slope with magnitude are seen only at low significance in two galaxies. The surface density of star clusters at a reference magnitude of M V = -8, Σ , scales with the mean star formation rate (SFR) per unit area, Σ SFR . Assuming that the LF can be generally expressed as dN ( L )/ dL = cA Σ L α , where A is the galaxy area, γ ∼ 1.0–1.4, α = -2.4, and the normalization constant c is determined from the WFPC2 data analyzed here, the maximum cluster luminosity expected in a galaxy from random sampling of the LF is estimated as a function of Σ SFR and A . The predictions agree well with existing observations of galaxies spanning a wide range of Σ SFR values, suggesting that sampling statistics play an important role in determining the maximum observed luminosities of star clusters in galaxies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".