Multiple stellar populations and their influence on blue stragglers
Bibliographic record
Abstract
It has become clear in recent years that globular clusters are not simple stellar populations, but may host chemically distinct subpopulations, typically with an enhanced helium abundance. These helium-rich populations can make up a substantial fraction of all cluster stars. One of the proposed formation channels for blue straggler stars is the physical collision and merger of two stars. In the context of multiple populations, collisions between stars with different helium abundances should occur and contribute to the observed blue straggler population. This will affect the predicted blue straggler colour and luminosity function. We quantify this effect by calculating models of mergers resulting from collisions between stars with different helium abundances and using these models to model a merger population. We then compare these results to four observed clusters, NGC 1851, 2808, 5634 and 6093. As in previous studies our models deviate from the observations, particularly in the colour distributions. However, our results are consistent with observations of multiple populations in these clusters. In NGC 2808, our best-fitting models include normal and helium-enhanced populations, in agreement with helium enhancement inferred in this cluster. The other three clusters show better agreement with models that do not include helium enhancement. We discuss future prospects to improve the modelling of blue straggler populations and the role that the models we present here can play in such a study.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".