STELLAR ENCOUNTER RATE IN GALACTIC GLOBULAR CLUSTERS
Bibliographic record
Abstract
The high stellar densities in the cores of globular clusters cause significant stellar interactions. These stellar interactions can produce close binary mass-transferring systems involving compact objects and their progeny, such as X-ray binaries and radio millisecond pulsars. Comparing the numbers of these systems and interaction rates in different clusters drives our understanding of how cluster parameters affect the production of close binaries. In this paper we estimate stellar encounter rates (Gamma) for 124 Galactic globular clusters based on observational data as opposed to the methods previously employed, which assumed "King-model" profiles for all clusters. By deprojecting cluster surface brightness profiles to estimate luminosity density profiles, we treat "King-model" and "core-collapsed" clusters in the same way. In addition, we use Monte-Carlo simulations to investigate the effects of uncertainties in various observational parameters (distance, reddening, surface brightness) on Gamma, producing the first catalog of GC stellar encounter rates with estimated errors. Comparing our results with published observations of likely products of stellar interactions (numbers of X-ray binaries, numbers of radio millisecond pulsars, and gamma-ray luminosity) we find both clear correlations and some differences with published results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".