Binary star population of the Sculptor dwarf galaxy
Bibliographic record
Abstract
Aims. We aim to compute the binary fraction of “classical” dwarf spheroidal galaxies (dSphs) that are satellites of the Milky Way (MW). This value can offer insights into the binary fraction in environments that are less dense and more metal-poor than our own galaxy. Additionally, knowledge of the binary fraction in dwarf galaxies is important with respect to avoiding overestimations of their dark matter content, inferred from stellar kinematics. Methods. We refined an existing method from the literature, placing an emphasis on providing robust uncertainties on the value of the binary fraction. We applied this modified method to a VLT/FLAMES dataset for Sculptor, specifically acquired for the purpose of velocity monitoring of individual stars, as well as to literature datasets for other six MW “classical” dSphs. In all cases, the targeted stars were mainly red giant branch stars, with expected masses of around 0.8 M ⊙ . The VLT/FLAMES dataset offers the most precise binary fractions compared to literature datasets, due to its time baseline of 12 yr, along with at least nine repeated observations for each star. Results. We found that the binary fraction of Sculptor is 0.55 −0.19 +0.17 . We find that it is important to take into account the Roche lobe overflow for constraining the period distribution of binary stars. In contrast to what has recently been proposed in the literature, our analysis indicates that there is no evidence to support varying the properties of the binary stellar population or their deviations from those established for the solar neighborhood, based on the sample of MW dSphs analyzed here.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".