Galactic Dark Matter Halos and Globular Cluster Populations. III. Extension to Extreme Environments
Bibliographic record
Abstract
Abstract The total mass in the globular cluster (GC) system of a galaxy is empirically a near-constant fraction of the total mass of the galaxy across a range of 10 5 in galaxy mass. This trend is radically unlike the strongly nonlinear behavior of total stellar mass M ⋆ versus M h . We discuss extensions of this trend to two more extreme situations: (a) entire clusters of galaxies and (b) the ultra-diffuse galaxies (UDGs) recently discovered in Coma and elsewhere. Our calibration of the ratio from normal galaxies, accounting for new revisions in the adopted mass-to-light ratio for GCs, now gives as the mean absolute mass fraction. We find that the same ratio appears valid for galaxy clusters and UDGs. Estimates of in the four clusters we examine tend to be slightly higher than for individual galaxies, but more data and better constraints on the mean GC mass in such systems are needed to determine if this difference is significant. We use the constancy of to estimate total masses for several individual cases; for example, the total mass of the Milky Way is calculated to be . Physical explanations for the uniformity of are still descriptive, but point to a picture in which massive dense star clusters in their formation stages were relatively immune to the feedback that more strongly influenced lower-density regions where most stars form.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".