Hierarchical triples versus globular clusters: binary black hole merger eccentricity distributions compete and evolve with redshift
Bibliographic record
Abstract
ABSTRACT The formation mechanisms of merging binary black holes (BBHs) observed by the LIGO–Virgo–KAGRA collaboration remain uncertain. Detectable eccentricity provides a powerful diagnostic for distinguishing between different formation channels, but resolving their eccentricity distributions requires the detection of a large number of eccentric mergers. Future gravitational wave detectors such as the Einstein Telescope and Cosmic Explorer will detect tens of thousands of BBH mergers out to redshifts $z \ge 10$, making it critical to understand the redshift-dependent evolution of eccentricity distributions. We simulate this evolution for two key channels: dynamical assembly in globular clusters (GCs), which leads to rapid, eccentric mergers, and hierarchical triples in the field, where three-body dynamics can induce eccentricity in the inner binary. When considering all BBH mergers, the GC channel dominates overall, consistent with previous studies. However, when focusing on mergers with detectable eccentricity in next-generation detectors, we find that hierarchical triples dominate the eccentric merger rate at $0\le z \le 4$, with GC mergers becoming competitive at higher redshifts. Across all model variations, eccentric mergers in the local Universe ($z\lesssim 1$) have significant contributions from field triples, challenging the common view that such systems primarily form in dense environments. We show that, regardless of cluster and stellar evolution uncertainties, hierarchical triples contribute at least 30 per cent of eccentric mergers across a large range of redshifts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".