The Red Supergiant Binary Fraction of the Large Magellanic Cloud
Bibliographic record
Abstract
Abstract The binary fraction of unevolved massive stars is thought to be 70%–100% but there are few observational constraints on the binary fraction of the evolved version of a subset of these stars, the red supergiants (RSGs). Here we identify a complete sample of RSGs in the Large Magellanic Cloud (LMC) using new spectroscopic observations and archival UV, IR, and broadband optical photometry. We find 4090 RSGs with , with 1820 of them having , which we believe is our completeness limit. We additionally spectroscopically confirmed 38 new RSG + B-star binaries in the LMC, bringing the total known up to 55. We then estimated the binary fraction using a k-nearest neighbors algorithm that classifies stars as single or binary based on photometry with a spectroscopic sample as a training set. We take into account observational biases such as line-of-sight stars and binaries in eclipse while also calculating model-dependent corrections for RSGs with companions that our observations were not designed to detect. Based on our data, we find an initial result of for RSGs with O- or B-type companions. Using the Binary Population and Spectral Synthesis models to correct for unobserved systems, this corresponds to a total RSG binary fraction of . This number is in broad agreement with what we would expect given an initial OB binary distribution of 70%, a predicted merger fraction of 20%–30%, and a binary interaction fraction of 40%–50%.
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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 | 0.000 |
| 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".