Where the Blue Stragglers Roam: Searching for a Link between Formation and Environment
Bibliographic record
Abstract
The formation of blue stragglers is still not completely understood, particularly the relationship between formation environment and mechanism. We use a large, homogeneous sample of blue stragglers in the cores of 57 globular clusters to investigate the relationships between blue straggler populations and their environments. We use a consistent definition of "blue straggler" based on position in the color-magnitude diagram and normalize the population relative to the number of red giant branch stars in the core. We find that the previously determined anticorrelation between blue straggler frequency and total cluster mass is present in the purely core population. We find some weak anticorrelations with central velocity dispersion and with half-mass relaxation time. The blue straggler frequency does not show any trend with any other cluster parameter. Even though collisions may be expected to be a dominant blue straggler formation process in globular cluster cores, we find no correlation between the frequency of blue stragglers and the collision rate in the core. We also investigated the blue straggler luminosity function shape and found no relationship between any cluster parameter and the distribution of blue stragglers in the color-magnitude diagram. Our results are inconsistent with some recent models of blue straggler formation that include collisional formation mechanisms and may suggest that almost all observed blue stragglers are formed in binary systems.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".