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Published February 2022 | Accepted Version + Published
Journal Article Open

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

Abstract

We describe the public release of the Cluster Monte Carlo (CMC) code, a parallel, star-by-star N-body code for modeling dense star clusters. CMC treats collisional stellar dynamics using Hénon'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 = 10⁸) 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).

Additional Information

© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 June 4; revised 2021 October 1; accepted 2021 October 10; published 2022 January 18. We thank Kuldeep Sharma, Xiaoqi Yu, and Mike Grudić for testing this release of CMC and providing feedback, and Elena González and Miguel Martinez for useful comments and discussions. This work was supported by NSF grant AST-2009916 at Carnegie Mellon University, a New Investigator Research Grant to C.R. from the Charles E. Kaufman Foundation, and NSF grant AST-1716762 at Northwestern University. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant No. ACI-1548562. Specifically, it used the Bridges-2 system, which is supported by NSF award No. ACI-1928147, at the Pittsburgh Supercomputing Center (PSC). N.C.W. acknowledges support from the CIERA Riedel Family Graduate Fellowship. F.K. acknowledges support from the Turkish Fulbright Commission. K.K. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-2001751. P.A.-S. acknowledges support from the Ramón y Cajal Programme of the Ministry of Economy, Industry and Competitiveness of Spain, as well as the financial support of Programa Estatal de Generación de Conocimiento (ref. PGC2018-096663-B-C43) (MCIU/FEDER). N.Z.R. acknowledges support from the Dominic Orr Graduate Fellowship at Caltech. Software: The public release of CMC can be accessed, including source code and documentation, at https://clustermontecarlo.github.io/. CMC (Joshi et al. 2000, 2001; Watters et al. 2000; Fregeau et al. 2002, 2003; Chatterjee et al. 2010; Morscher et al. 2013; Pattabiraman et al. 2013; Rodriguez et al. 2021a; this work), cmctoolkit (Rui et al. 2021a, 2021b), fewbody (Fregeau & Rasio 2007; Antognini et al. 2014; Amaro-Seoane & Chen 2016), COSMIC (Breivik et al. 2020a, 2020b), matplotlib (Hunter 2007), SciPy (Virtanen et al. 2020), NumPy (Harris et al. 2020), pandas (McKinney 2010; Reback et al. 2021).

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Published - Rodriguez_2022_ApJS_258_22.pdf

Accepted Version - 2106.02643.pdf

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023