Testing for Dark Matter in the Outskirts of Globular Clusters
Bibliographic record
Abstract
Abstract The proper motions of stars in the outskirts of globular clusters are used to estimate cluster velocity dispersion profiles as far as possible within their tidal radii. We use individual color–magnitude diagrams to select high-probability cluster stars for 25 metal-poor globular clusters within 20 kpc of the Sun, 19 of which have substantial numbers of stars at large radii. Of the 19, 11 clusters have a falling velocity dispersion in the 3–6 half-mass radii range, 6 are flat, and 2 plausibly have a rising velocity dispersion. The profiles are all in the range expected from simulated clusters that started at high redshift in a zoom-in cosmological simulation. The 11 clusters with falling velocity dispersion profiles are consistent with no dark matter above the Galactic background. The six clusters with approximately flat velocity dispersion profiles could have local dark matter, but are ambiguous. The two clusters with rising velocity dispersion profiles are consistent with a remnant local dark matter halo, but need membership confirmation and detailed orbital modeling to further test these preliminary results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".