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Binary Black Hole Mergers from Globular Clusters: Implications for Advanced LIGO
Open accessAbstract
The predicted rate of binary black hole mergers from galactic fields can vary over several orders of magnitude and is extremely sensitive to the assumptions of stellar evolution. But in dense stellar environments such as globular clusters, binary black holes form by well-understood gravitational interactions. In this Letter, we study the formation of black hole binaries in an extensive collection of realistic globular cluster models. By comparing these models to observed Milky Way and extragalactic globular clusters, we find that the mergers of dynamically formed binaries could be detected at a rate of ∼100 per year, potentially dominating the binary black hole merger rate. We also find that a majority of cluster-formed binaries are more massive than their field-formed counterparts, suggesting that Advanced LIGO could identify certain binaries as originating from dense stellar environments.
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Cited by
Abbott, B. P., Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., et al. (2019) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ab3800 · Cited by 895
Self-citationGeorge, D., & Huerta, E. A. (2018) · Physics Letters B · doi:10.1016/j.physletb.2017.12.053 · Cited by 373
Metadata
- Authors
- Rodriguez, C. L., Morscher, M., Pattabiraman, B., Chatterjee, S., Haster, C.-J., & Rasio, F. A.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2015-07-30
- Volume
- 115
- Issue
- 5
- Pages
- 051101-051101
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2097082581
- Cited by
- 411
- References
- 0
- FWCI
- 22.96
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
