The Redshift Evolution of the Binary Black Hole Mass Distribution from Dense Star Clusters
Bibliographic record
Abstract
Abstract Gravitational-wave detectors are unveiling a population of binary black hole (BBH) mergers out to redshifts z ≈ 1, and are starting to constrain how the BBH population evolves with redshift. We present predictions for the redshift evolution of the BBH mass and spin distributions for systems originating from dense star clusters. Utilizing a grid of 144 state-of-the-art dynamical models for globular clusters, we demonstrate that BBH merger rates peak at higher redshifts for larger black hole primary masses M 1 . Specifically, for M 1 ≳ 40 M ⊙ , the BBH merger rate reaches its peak at redshift z ≈ 2.1, while for M 1 ≲ 20 M ⊙ , the peak occurs at z ≈ 1.1, assuming that the cluster formation rate peaks at z = 2.2. The average BBH primary mass also increases from ∼10 M ⊙ at z = 0 to ∼30 M ⊙ at z = 10. We show that ∼20% BBHs contain massive remnants from next-generation mergers, with this fraction increasing (decreasing) for larger (smaller) primary masses. This difference is not large enough to significantly alter the effective spins of the BBH population originating from globular clusters, and we find that their effective spin distribution does not evolve across cosmic time. These findings can be used to distinguish BBHs from dense star clusters by future gravitational-wave observations.
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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 | 0.001 |
| 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".