A Photometric Survey of Globular Cluster Systems in Brightest Cluster Galaxies
Bibliographic record
Abstract
Abstract Hubble Space Telescope imaging for 26 giant early-type galaxies, all drawn from the MAST archive, is used to carry out photometry of their surrounding globular cluster (GC) systems. Most of these targets are Brightest Cluster Galaxies and their distances range from 24–210 Mpc. The catalogs of photometry, completed with DOLPHOT, are publicly available. The GC color indices are converted to [Fe/H] through a combination of 12 Gyr single stellar population models and direct spectroscopic calibration of the fiducial color index (F475W–F850LP). All of the resulting metallicity distribution functions (MDFs) can be accurately matched by bimodal-Gaussian functions. The GC systems in all of the galaxies also exhibit shallow metallicity gradients with projected galactocentric distance that average Z ∼ R gc − 0.3 . Several parameters of the MDFs including the means, dispersions, and blue/red fractions are summarized. Perhaps the most interesting new result is the trend of blue/red GC fraction with galaxy mass, which connects with predictions from recent simulations of GC formation within hierarchical assembly of large galaxies. The observed trend reveals two major transition stages: for low-mass galaxies, the metal-rich (red) GC fraction f(red) increases steadily with galaxy mass, until halo mass M h ≃ 3 × 1012 M ⊙. Above this point, more than half the metal-poor (blue) GCs come from accreted satellites and f(red) starts declining. But above a still higher transition point near M h ≃ 1014 M ⊙, the data hint that f(red) may start to increase again because the metal-rich GCs also become dominated by accreted systems.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".