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 (fcl). For fcl � 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, fcl � 0.001, the detection rate will be dominated by numerous mergers of double black holes
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".