Compact-object Mergers in the Galactic Center: Evolution in Triaxial Clusters
Bibliographic record
Abstract
Abstract There is significant observational evidence that a large fraction of galactic centers, including those in the Milky Way and M31, host a supermassive black hole (SMBH) embedded in a triaxial nuclear star cluster. In this work, we study the secular orbital evolution of binaries in these environments and characterize the regions and morphological properties of nuclear star clusters that lead to gravitational wave mergers and/or tidal captures. We show that even a modest level of triaxiality in the density distribution of a cluster (an ellipsoid with axis ratios of 0.7 and 0.95) dramatically enhances the merger rates in the central parsecs of the Galaxy by a factor of up to ∼10–30 relative to a spherical density distribution. Moreover, we show that the merger fraction of binaries with semimajor axes in the range 10–100 au remains above 10% for the entire central parsec of the cluster, reaching values close to unity at a distance of ∼0.2–0.4 pc from the SMBH. We understand this large merger efficiency in terms of two distinct mechanisms: (i) eccentricity oscillations driven by the dominant axisymmetric part of the cluster potential that are enhanced by the slow modulation of a binary’s angular momentum from the triaxial contribution, similar to the well-known octupole-level dynamics in three-body systems; and (ii) chaotic diffusion of eccentricities arising when the nodal precession timescale of a binary’s orbit about the SMBH becomes comparable to its characteristic secular timescale. Overall, our results indicate that galactic centers are significantly more collisional than previously thought, with mergers taking place up to the effective radii of their nuclear star clusters.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".