Massive Interacting Binaries as an Enrichment Source for Multiple Populations in Star Clusters
Bibliographic record
Abstract
Abstract We present a suite of binary evolution models with massive primaries (10 ≤ M 1 ≤ 40 M ⊙) and periods and mass ratios chosen such that the systems undergo nonconservative mass transfer while the primaries have helium cores. We track the total mass and chemical composition of the ejecta from these systems. This material shows the abundance signatures of hot hydrogen burning that are needed to explain the abundance patterns seen in multiple populations in massive star clusters. We then calculate the total yield of a population of binary stars with masses, mass ratios, and periods consistent with their distribution in a field population. We show that the overall abundance of this material is enriched in helium, nitrogen, sodium, and aluminum, and depleted in carbon, oxygen, and magnesium, by amounts that are consistent with observations. We also show that such a population of binaries will return approximately 25% of its mass in this ejecta (compared to 4% if all the stars were single), over a characteristic timescale of about 12 Myr. We argue that massive binaries must be seriously considered as a contributor to the source of enriched material needed to explain the multiple populations in massive clusters, since essentially all massive stars are formed in binaries or higher-order multiples, massive binaries are primarily formed in clusters, and massive binaries naturally produce material of the right composition.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".