QUANTIFYING MASS SEGREGATION AND NEW CORE RADII FOR 54 MILKY WAY GLOBULAR CLUSTERS
Bibliographic record
Abstract
We present core radii for 54 Milky Way globular clusters determined by fitting King–Michie models to cumulative projected star count distributions. We find that fitting star counts rather than surface brightness profiles produces results that differ significantly due to the presence of mass segregation. The sample in each cluster is further broken down into various mass groups, each of which is fit independently, allowing us to determine how the concentration of each cluster varies with mass. The majority of the clusters in our sample show general agreement with the standard picture that more massive stars will be more centrally concentrated. We find that core radius versus stellar mass can be fit with a two-parameter power law. The slope of this power law is a value that describes the amount of mass segregation present in the cluster, and is measured independently of our distance from the cluster. This value correlates strongly with the core relaxation time and physical size of each cluster. Supplementary figures are also included showing the best fits and likelihood contours of fit parameters for all 54 clusters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".