The ACS Fornax Cluster Survey. III. Globular Cluster Specific Frequencies of Early-type Galaxies
Bibliographic record
Abstract
Abstract The globular cluster (GC) specific frequency (S N ), defined as the number of GCs per unit galactic luminosity, represents the efficiency of GC formation (and survival) compared to field stars. Despite the naive expectation that star cluster formation should scale directly with star formation, this efficiency varies widely across galaxies. To explore this variation, we measure the z-band GC specific frequency ( ) for 43 early-type galaxies (ETGs) from the Hubble Space Telescope (HST)/Advanced Camera for Surveys (ACS) Fornax Cluster Survey. Combined with the homogeneous measurements of in 100 ETGs from the HST/ACS Virgo Cluster Survey from Peng et al., we investigate the dependence of on mass and environment over a range of galaxy properties. We find that behaves similarly in the two galaxy clusters, despite the clusters’ order-of-magnitude difference in mass density. The is low in intermediate-mass ETGs (−20 < M z < −23) and increases with galaxy luminosity. It is elevated at low masses, on average, but with a large scatter driven by galaxies in dense environments. The densest environments with the strongest tidal forces appear to strip the GC systems of low-mass galaxies. However, in low-mass galaxies that are not in strong tidal fields, denser environments correlate with enhanced GC formation efficiencies. Normalizing by inferred halo masses, the GC mass fraction, η = (3.36 ± 0.2) × 10−5, is constant for ETGs with stellar masses , in agreement with previous studies. The lack of correlation between the fraction of GCs and the nuclear light implies only a weak link between the infall of GCs and the formation of nuclei.
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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 | 0.001 |
| 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) | 0.003 | 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".