Binary Black Hole Formation with Detailed Modeling: Stable Mass Transfer Leads to Lower Merger Rates
Bibliographic record
Abstract
Abstract Rapid binary population synthesis codes are often used to investigate the evolution of compact-object binaries. They typically rely on analytical fits of single-star evolutionary tracks and parameterized models for interactive phases of evolution (e.g., mass transfer on a thermal timescale, determination of dynamical instability, and common envelope) that are crucial to predict the fate of binaries. These processes can be more carefully implemented in stellar structure and evolution codes such as MESA. To assess the impact of such improvements, we compare binary black hole mergers as predicted in models with the rapid binary population synthesis code COSMIC to models ran with MESA simulations through mass transfer and common-envelope treatment. We find that results significantly differ in terms of formation paths, the orbital periods and mass ratios of merging binary black holes, and consequently merger rates. While common-envelope evolution is the dominant formation channel in COSMIC, stable mass transfer dominates in our MESA models. Depending upon the black hole donor mass, and mass-transfer and common-envelope physics, at subsolar metallicity, COSMIC overproduces the number of binary black hole mergers by factors of 2–35 with a significant fraction of them having merger times orders of magnitude shorter than the binary black holes formed when using detailed MESA models. Therefore we find that some binary black hole merger rate predictions from rapid population syntheses of isolated binaries may be overestimated by factors of ∼ 5–500. We conclude that the interpretation of gravitational-wave observations requires the use of detailed treatment of these interactive binary phases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".