Stellar mass segregation as separating classifier between globular clusters and ultrafaint dwarf galaxies
Bibliographic record
Abstract
ABSTRACT We have determined the amount of stellar mass segregation in over 50 globular clusters and ultrafaint dwarf galaxy candidates based on deep HST- and ground-based photometry. We find that the amount of mass segregation in globular clusters is strongly correlated with their relaxation time and that all clusters with relaxation times of the order of their ages or longer have little to no mass segregation. For each cluster, the amount of mass segregation seen is fully compatible with the amount expected by dynamical evolution from initially unsegregated clusters, showing that globular clusters formed without primordial mass segregation among their low-mass stars. Ultrafaint dwarf galaxy candidates split into two groups, star clusters which follow the same trend between relaxation time and amount of mass segregation as globular clusters and dark-matter dominated dwarf galaxies that are unsegregated despite having relaxation times smaller than a Hubble time. Stellar abundance and velocity dispersion data, where available, confirm our classification. After classification of the ultrafaint dwarf galaxy candidates, we find that outer halo star clusters have average densities inside their half-light radii of 0.03 ≲ ρh ≲ 1 M⊙ pc–3, while dwarf galaxies have stellar densities of 0.001 ≲ ρh ≲ 0.03 M⊙ pc–3. The reason for this separation in density is most likely a combination of the initial conditions by which the systems formed and the requirement to withstand external tidal forces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".