Wide binaries from the H3 survey: the thick disc and halo have similar wide binary fractions
Bibliographic record
Abstract
ABSTRACT Due to the different environments in the Milky Way’s disc and halo, comparing wide binaries in the disc and halo is key to understanding wide binary formation and evolution. By using Gaia Early Data Release 3, we search for resolved wide binary companions in the H3 survey, a spectroscopic survey that has compiled ∼150 000 spectra for thick-disc and halo stars to date. We identify 800 high-confidence (a contamination rate of 4 per cent) wide binaries and two resolved triples, with binary separations mostly between 103 and 105 au and a lowest [Fe/H] of −2.7. Based on their Galactic kinematics, 33 of them are halo wide binaries, and most of those are associated with the accreted Gaia-Sausage-Enceladus galaxy. The wide binary fraction in the thick disc decreases toward the low metallicity end, consistent with the previous findings for the thin disc. Our key finding is that the halo wide binary fraction is consistent with the thick-disc stars at a fixed [Fe/H]. There is no significant dependence of the wide binary fraction on the α-captured abundance. Therefore, the wide binary fraction is mainly determined by the iron abundance, not their disc or halo origin nor the α-captured abundance. Our results suggest that the formation environments play a major role for the wide binary fraction, instead of other processes like radial migration that only apply to disc stars.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".