Greatly Enhanced Merger Rates of Compact-object Binaries in Non-spherical Nuclear Star Clusters
Bibliographic record
Abstract
Abstract The Milky Way and a significant fraction of galaxies are observed to host a central massive black hole (MBH) embedded in a non-spherical nuclear star cluster. We study the secular orbital evolution of compact-object binaries in these environments and characterize the excitation of extremely large eccentricities that can lead to mergers by gravitational radiation. We find that the eccentricity excitation occurs most efficiently when the nodal precession timescale of the binary’s orbit around the MBH due to the non-spherical cluster becomes comparable (within a factor of ∼10) to the timescale on which the binary is torqued by the MBH due to the Lidov–Kozai (LK) mechanism. We show that in this regime the perturbations due to the cluster increase the fraction of systems that reach extreme eccentricities ( ) by a factor of ∼10–100 compared to the idealized case of a spherical cluster, increasing the merger rates of compact objects by a similar factor. We identify two main channels that lead to this extreme eccentricity excitation: (i) chaotic diffusion of the eccentricities due to resonance overlap; (ii) cluster-driven variations of the mutual inclinations between the binary orbit and its center-of-mass orbit around the MBH, which can intensify the LK oscillations. We estimate that our mechanism can produce BH–BH and BH–neutron star binary merger rates of up to and , respectively. Thus, we propose the cluster-enhanced LK mechanism as a new channel for the merger of compact-object binaries, competing with scenarios that invoke isolated binary evolution or dynamical formation in globular clusters.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".