The Unexpected Kinematics of Multiple Populations in NGC 6362: Do Binaries Play a Role?*
Bibliographic record
Abstract
Abstract We present a detailed analysis of the kinematic properties of the multiple populations (MPs) in the low-mass Galactic globular cluster (GC) NGC 6362 based on a sample of about 500 member stars for which radial velocities (RVs), and Fe and Na abundances have been homogeneously derived. At distances from the cluster center larger than about 0.5r h , we find that first-generation (FG–Na-poor) and second-generation (SG–Na-rich) stars show hints of different line-of-sight velocity dispersion profiles, with FG stars being dynamically hotter. This is the first time that differences in the velocity dispersion of MPs are detected using only RVs. While kinematic differences between MPs in GCs are usually described in terms of anisotropy differences driven by the different radial distributions, this explanation hardly seems viable for NGC 6362, where SG and FG stars are spatially mixed. We demonstrate that the observed difference in the velocity dispersion profiles can be accounted for by the effect of binary stars. In fact, thanks to our multi-epoch RV measurements, we find that the binary fraction is significantly larger in the FG sample (f ∼ 14%) than in the SG population (f < 1%), and we show that such a difference can inflate the velocity dispersion of FG with respect to SG by the observed amount in the relevant radial range. Our results nicely match the predictions of state-of-the art N-body simulations of the co-evolution of MPs in GCs that include the effects of binaries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".