Evolution of Star Cluster Within Galaxy using Self-consistent Hybrid Hydro/N-body Simulation

TL;DR

This paper presents Enzo-N, a GPU-accelerated hybrid simulation code that models the co-evolution of star clusters and galaxies, capturing complex phenomena like core collapse and tidal stripping.

Contribution

It introduces a novel hybrid hydro/N-body simulation framework combining Enzo and Nbody6++GPU, enabling efficient, self-consistent modeling of star clusters within their host galaxies.

Findings

Successfully simulates star cluster and galaxy co-evolution

Captures core collapse and tidal stripping phenomena

Demonstrates computational efficiency with GPU acceleration

Abstract

We introduce a GPU-accelerated hybrid hydro/N-body code (Enzo-N) designed to address the challenges of concurrently simulating star clusters and their parent galaxies. This task has been exceedingly challenging, primarily due to the considerable computational time required, which stems from the substantial scale difference between galaxies (~ 0.1 Mpc) and star clusters (~ pc). Yet, this significant scale separation means that particles within star clusters perceive those outside the star cluster in a semi-stationary state. By leveraging this aspect, we integrate the direct N-body code (Nbody6++GPU) into the cosmological (magneto-)hydrodynamic code (Enzo) through the utilization of the semi-stationary background acceleration approximation. We solve the dynamics of particles within star clusters using the direct N-body solver with regularization for few-body interactions, while evolving particles outside -- dark matter, gas, and stars -- using the particle-mesh gravity solver and hydrodynamic methods. We demonstrate that Enzo-N successfully simulates the co-evolution of star clusters and their parent galaxies, capturing phenomena such as core collapse of the star cluster and tidal stripping due to galactic tides. This comprehensive framework opens up new possibilities for studying the evolution of star clusters within galaxies, offering insights that were previously inaccessible.