Tracing the Dynamical History of the Globular Cluster 47 Tucanae
Bibliographic record
Abstract
We use two stellar populations in the globular cluster 47 Tucanae to trace its dynamical history: blue stragglers and low mass main sequence stars. We assumed that the blue stragglers were formed through stellar collisions in all regions of the cluster. We find that in the core of the cluster, models of collisional blue stragglers agree well with the observations as long as blue stragglers are still continuing to form and the mass function in the cluster is extremely biased towards massive stars (x=-8 where a Salpeter mass function has x=+1.35). We show that such an extreme mass function is supported by direct measurements of the luminosity function of main sequence stars in the centre of the cluster. In the middle region of our dataset (25'' to 130'' from the cluster centre), blue straggler formation seems to have stopped about half a Gyr ago. In the outskirts of the cluster, our models are least successful at reproducing the blue straggler data. Taken at face value, they indicate that blue straggler formation has been insignificant over the past billion years, and that a Salpeter mass function applies. However, it is more likely that the dominant formation mechanism in this part of the cluster is not the collisional one, and that our models are not appropriate for this region of the cluster. We conclude that blue stragglers can be used as tracers of dynamics in globular clusters, despite our incomplete understanding of how and where they were formed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".