Effects of Close Binary Evolution on the Main-sequence Morphology of Young Star Clusters
Bibliographic record
Abstract
Abstract Star clusters are the building blocks of galaxies. They are composed of stars of nearly equal age and chemical composition, allowing us to use them as chronometers and as testbeds for gauging stellar evolution. It has become clear recently that massive stars are formed preferentially in close binaries, in which mass transfer will drastically change the evolution of the stars. This is expected to leave a significant imprint in the distribution of cluster stars in the Hertzsprung–Russell diagram. Our results, based on a dense model grid of more than 50,000 detailed binary-evolution calculations, show several distinct, coeval main-sequence (MS) components, most notably an extended MS turnoff region, and a group of near-critical rotating stars that is spread over a large luminosity range on the red side of the classical MS. We comprehensively demonstrate the time evolution of the features in an animation, and we derive analytic expressions to describe these features. We find quantitative agreement with results based on recent photometric and spectroscopic observations. We conclude that while other factors may also be at play, binary evolution has a major impact on the MS morphology of young star clusters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".