The Role of Natal Kicks in Forming Asymmetric Compact Binary Mergers
Bibliographic record
Abstract
Abstract In their most recent observing run, the LIGO-Virgo-KAGRA Collaboration observed gravitational waves from compact binary mergers with highly asymmetric mass ratios, including both binary black holes (BBHs) and neutron star-black holes (NSBHs). It appears that NSBHs with mass ratios q ≃ 0.2 are more common than equally asymmetric BBHs, but the reason for this remains unclear. We use the binary population synthesis code cosmic to investigate the evolutionary pathways leading to the formation and merger of asymmetric compact binaries. We find that within the context of isolated binary stellar evolution, most asymmetric mergers start off as asymmetric stellar binaries. Because of the initial asymmetry, these systems tend to first undergo a dynamically unstable mass transfer phase. However, after the first star collapses into a compact object, the mass ratio is close to unity and the second phase of mass transfer is usually stable. According to our simulations, this stable mass transfer fails to shrink the orbit enough on its own for the system to merge. Instead, the natal kick received by the second-born compact object during its collapse is key in determining how many of these systems can merge. For the most asymmetric systems with mass ratios of q ≤ 0.1, the merging systems in our models receive an average kick magnitude of 255 km s−1 during the second collapse, while the average kick for non-merging systems is 59 km s−1. Because lower mass compact objects, like neutron stars, are expected to receive larger natal kicks than higher mass BHs, this may explain why asymmetric NSBH systems merge more frequently than asymmetric BBH systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".