Fewer companions in the crowd: the low close binary fraction in globular clusters from <i>Gaia</i> RVS
Bibliographic record
Abstract
ABSTRACT In dense environments like globular clusters (GCs), dynamical interactions can disrupt or harden close binaries, none the less, detailed comparisons with field binary fractions remain limited. Here, we present an analysis of the close binary fraction in a carefully selected sample of field stars and 10 GCs using Gaia Radial Velocity Spectrometer data, which is among the largest samples of GCs analysed using multi-epoch spectroscopy to date. By assessing the peak-to-peak variations of the sources’ radial velocity (RV), we estimate the close binary fractions through a method that fits the distribution as the product of two Gaussian distributions. By applying the same RV-variability method to both cluster members and field stars, we ensure a homogeneous and inclusive comparison between the two environments. Despite matching stellar parameters between the field and GC samples, our findings confirm that GCs possess a significantly lower close binary fraction than field stars. Interestingly, we do not detect any clear trend of binary fraction with cluster metallicity; metal-rich and metal-poor GCs are uniformly binary-poor (within uncertainties). We discuss possible interpretations, including dynamical hardening in dense environments and the effects of common envelope evolution, which may lead to companion accretion or merger events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".