The Total Merger Rate of Compact Object Binaries in the Local Universe
Bibliographic record
Abstract
Using a population synthesis approach, we compute the total merger rate in the local universe for double neutron stars, double black holes, and black hole-neutron star binaries. These compact binaries are the prime source candidates for gravitational wave detection by LIGO and VIRGO. We account for mergers originating both from field populations and from dense stellar clusters, where dynamical interactions can significantly enhance the production of double compact objects. For both populations we use the same treatment of stellar evolution. Our results indicate that the merger rates of double neutron stars and black hole-neutron star binaries are strongly dominated by field populations, while merging black hole binaries are formed much more effectively in dense stellar clusters. The overall merger rate of double compact objects depends sensitively on the (largely unknown) initial mass fraction contained in dense clusters ( f cl ). For f cl ≲ 0.0001, the Advanced LIGO detection rate will be dominated by field populations of double neutron star mergers, with a small but significant number of detections, ~20 yr −1 . However, for a higher mass fraction in clusters, f cl ≳ 0.001, the detection rate will be dominated by numerous mergers of double black holes originating from dense clusters, and it will be considerably higher, ~25-300 yr −1 . In addition, we show that, once mergers of double black holes are detected, it is easy to differentiate between systems formed in the field and in dense clusters, since the chirp mass distributions are strikingly different. If significant field populations of double black hole mergers are detected, this will also place very strong constraints on common-envelope evolution in massive binaries. Finally, we point out that there may exist a population of merging black hole binaries in intergalactic space.
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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 | 0.003 |
| 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 | 0.001 |
| 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".