Globular cluster formation histories, masses, and radii inferred from gravitational waves
Bibliographic record
Abstract
ABSTRACT Globular clusters (GCs) are found in all types of galaxies and harbour some of the most extreme stellar systems, including black holes that may dynamically assemble into merging binary black holes (BBHs). Uncertain GC properties, including when they formed, their initial masses and sizes, affect their production rate of BBH mergers. Using the gravitational-wave transient catalogue (GWTC-3), we measure that dynamically assembled BBHs – those that are consistent with isotropic spin directions – make up ${61^{+29}_{-44}\%}$ of the total merger rate, with a local merger rate of ${10.9^{+16.8}_{-9.3}}$ Gpc−3 yr−1 rising to ${58.9^{+149.4}_{-46.0}}$ Gpc−3 yr−1 at z = 1. We assume that this inferred rate describes the contribution from GCs and compare it against the Cluster Monte Carlo (cmc) simulation catalogue to directly fit for the GC initial mass function, virial radius distribution, and formation history. We find that GC initial masses are consistent with a Schechter function with slope ${\beta _m = -1.9^{+0.8}_{-0.8}}$ . Assuming a mass function slope of βm = −2 and a mass range between 104–$10^8\, \mathrm{ M}_\odot$ , we infer a GC formation rate at z = 2 of ${5.0^{+9.4}_{-4.0}}$ Gpc−3 yr−1, or ${2.1^{+3.9}_{-1.7}}\times 10^6\, \mathrm{ M}_\odot$ Gpc−3 yr−1 in terms of mass density. We find that the GC formation rate probably rises more steeply than the global star formation rate between z = 0 and z = 3 (82 per cent credibility) and implies a local number density that is ${f_\mathrm{ev} = 22.6^{+29.9}_{-16.2}}$ times higher than the observed density of survived GCs. This is consistent with expectations for cluster evaporation, but may suggest that other environments contribute to the rate of BBH mergers with significantly tilted spins.
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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 | 0.000 | 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".