Multiple populations in globular clusters: the possible contributions of stellar collisions
Bibliographic record
Abstract
Globular clusters were thought to be simple stellar populations, but recent photometric and spectroscopic evidence suggests that the clusters' early formation history was more complicated. In particular, clusters show star-to-star abundance variations, and multiple sequences in their colour–magnitude diagrams. These effects seem to be restricted to globular clusters, and are not found in open clusters or the field. In this paper, we combine the two competing models for these multiple populations and include a consideration of the effects of stellar collisions. Collisions are one of the few phenomena which occur solely in dense stellar environments like (proto)globular clusters. We find that runaway collisions between massive stars can produce material which has abundances comparable to the observed second generations, but that very little total mass is produced by this channel. We then add the contributions of rapidly rotating massive stars (under the assumption that massive stars are spun up by collisions and interactions), and the contribution of asymptotic giant branch stars. We find that collisions can help produce the extreme abundances which are seen in some clusters. However, the total amount of material produced in these generations is still too small (by at least a factor of 10) to match the observations. We conclude with a discussion of the additional effects which probably need to be considered to solve this particular problem.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".