Binary Yellow Supergiants in the Magellanic Clouds. I. Photometric Candidate Identification
Bibliographic record
Abstract
Abstract Recent works have constrained the binary fraction of evolved populations of massive stars in local galaxies such as red supergiants and Wolf–Rayet stars, but the binary fraction of yellow supergiants (YSGs) in the Hertzsprung gap remains unconstrained. Binary evolution theory predicts that the Hertzsprung gap is home to multiple populations of binary systems with varied evolutionary histories. In this paper, we develop a method to distinguish single YSGs from YSG plus O- or B-type main-sequence binaries using optical and ultraviolet photometry, and then apply this method to identify candidate YSG binaries in the Magellanic Clouds. After constructing a set of combined stellar atmosphere models, we find that optical photometry is, given typical measurement and reddening uncertainties, sufficient to discern single YSGs from YSG+OB binaries if the OB-star is at least ∼5M ⊙ for T eff,YSG ∼ 4000 K, but requires a ∼20M ⊙ OB star for YSGs up to T eff,YSG ∼ 9000 K. For these hotter YSG temperatures, ultraviolet photometry allows binaries with OB companions as small as ∼7M ⊙ to be identified. We use color–color spaces developed from these models to search for evidence of excess blue or ultraviolet light in a set of ∼1000 YSG candidates in the Magellanic Clouds. We identify hundreds of candidate YSG binary systems and report a preliminary fraction of YSGs that show a blue/UV color excess of 20%–60%. Spectroscopic follow-up is now required to confirm the true nature of this population.
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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 | 0.001 | 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 | 0.001 |
| 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".