THE ACS FORNAX CLUSTER SURVEY. XII. DIFFUSE STAR CLUSTERS IN EARLY-TYPE GALAXIES
Bibliographic record
Abstract
ABSTRACT Diffuse star clusters (DSCs) are old and dynamically hot stellar systems that have lower surface brightness and more extended morphology than globular clusters (GCs). Using the images from Hubble Space Telescope (HST)/ACS Fornax Cluster Survey, we find that 12 out of 43 early-type galaxies (ETGs) in the Fornax Cluster host significant numbers of DSCs. Together with literature data from the HST/ACS Virgo Cluster Survey, where 18 out of 100 ETGs were found to host DSCs, we systematically study the relationship of DSCs with GCs and their host galaxy environment. Two DSC hosts are post-merger galaxies, with most of the other hosts either having low mass or showing clear disk components. We find that while the number ratio of DSCs to GCs is nearly constant in massive galaxies, the DSC-to-GC ratio becomes systematically higher in lower-mass hosts. This suggests that DSCs may be more efficient at forming (or surviving) in low-density environments. DSC hosts are not special either in their position in the cluster or in the galactic color–magnitude diagram. Why some disk and low-mass galaxies host DSCs while others do not is still a puzzle, however. The mean ages of DSC hosts and nonhosts are similar at similar masses, implying that formation efficiency rather than survival is the reason behind different DSC number fractions in ETGs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".