Globular clusters and supermassive black holes in galaxies: further analysis and a larger sample
Bibliographic record
Abstract
We explore several correlations between various large-scale galaxy properties, particularly total globular cluster population (NGC), the central black hole mass (M•), velocity dispersion (nominally σe) and bulge mass (Mdyn). Our data sample of 49 galaxies, for which both NGC and M• are known, is larger than used in previous discussions of these two parameters and we employ the same sample to explore all pairs of correlations. Further, within this galaxy sample, we investigate the scatter in each quantity, with emphasis on the range of published values for σe and effective radius (Re) for any one galaxy. We find that these two quantities in particular are difficult to measure consistently and caution that precise intercomparison of galaxy properties involving Re and σe is particularly difficult. Using both conventional χ2-minimization and Monte Carlo Markov Chain fitting techniques, we show that quoted observational uncertainties for all parameters are too small to represent the true scatter in the data. We find that the correlation between Mdyn and NGC is stronger than either the M•–σe or the M•–NGC relations. We suggest that this is because both the galaxy bulge population and NGC were fundamentally established at an early epoch during the same series of star-forming events. By contrast, although the seed for M• was likely formed at a similar epoch, its growth over time is less similar from galaxy to galaxy and thus less predictable.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".