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Modeling Dense Star Clusters in the Milky Way and Beyond with the Cluster Monte Carlo Code

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arxiv 2106.02643 v3 pith:6T2AUZFQ submitted 2021-06-04 astro-ph.GA astro-ph.IM

Modeling Dense Star Clusters in the Milky Way and Beyond with the Cluster Monte Carlo Code

classification astro-ph.GA astro-ph.IM
keywords clustercodestarclustersevolutionmodelingstar-by-starstellar
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We describe the public release of the Cluster Monte Carlo Code (CMC) a parallel, star-by-star $N$-body code for modeling dense star clusters. CMC treats collisional stellar dynamics using H\'enon's method, where the cumulative effect of many two-body encounters is statistically reproduced as a single effective encounter between nearest-neighbor particles on a relaxation timescale. The star-by-star approach allows for the inclusion of additional physics, including strong gravitational three- and four-body encounters, two-body tidal and gravitational-wave captures, mass loss in arbitrary galactic tidal fields, and stellar evolution for both single and binary stars. The public release of CMC is pinned directly to the COSMIC population synthesis code, allowing dynamical star cluster simulations and population synthesis studies to be performed using identical assumptions about the stellar physics and initial conditions. As a demonstration, we present two examples of star cluster modeling: first, we perform the largest ($N = 10^8$) star-by-star $N$-body simulation of a Plummer sphere evolving to core collapse, reproducing the expected self-similar density profile over more than 15 orders of magnitude; second, we generate realistic models for typical globular clusters, and we show that their dynamical evolution can produce significant numbers of black hole mergers with masses greater than those produced from isolated binary evolution (such as GW190521, a recently reported merger with component masses in the pulsational pair-instability mass gap).

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Predicting intermediate-mass black hole formation in star clusters with machine learning

    astro-ph.GA 2026-05 unverdicted novelty 7.0

    Machine learning regressors trained on Rapster simulations forecast that globular clusters rarely host black holes above 100 solar masses while a few nuclear star clusters may exceed this threshold.

  2. Second-Generation Mass Peak in the Gravitational-Wave Population as a Probe of Globular Clusters

    astro-ph.HE 2026-04 unverdicted novelty 6.0

    Dynamical formation in globular clusters produces a robust second black-hole mass peak at ~70 solar masses from second-generation mergers when the first-generation spectrum is truncated by pair-instability supernovae.

  3. Signatures of a subpopulation of hierarchical mergers in the GWTC-4 gravitational-wave dataset

    gr-qc 2026-01 unverdicted novelty 6.0

    GWTC-4 data show a transition to nearly all hierarchical mergers above 46 solar masses, with the hierarchical rate peaking at 15.7 solar masses, indicating mass-dependent substructure in black hole spins.