The First Star-by-star $N$-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model

TL;DR

This paper presents the first star-by-star simulation of the Milky Way galaxy using a novel N-body/hydrodynamics method combined with machine learning, enabling unprecedented resolution and scalability in galaxy modeling.

Contribution

The authors developed a new integration scheme that couples N-body/hydrodynamics with a surrogate model, overcoming short-timescale limitations and achieving billion-particle galaxy simulations.

Findings

Simulated the Milky Way with 300 billion particles.

Achieved scalability over 10,000 CPU cores.

Broken the billion-particle barrier in galaxy simulations.

Abstract

A major goal of computational astrophysics is to simulate the Milky Way Galaxy with sufficient resolution down to individual stars. However, the scaling fails due to some small-scale, short-timescale phenomena, such as supernova explosions. We have developed a novel integration scheme of $N$-body/hydrodynamics simulations working with machine learning. This approach bypasses the short timesteps caused by supernova explosions using a surrogate model, thereby improving scalability. With this method, we reached 300 billion particles using 148,900 nodes, equivalent to 7,147,200 CPU cores, breaking through the billion-particle barrier currently faced by state-of-the-art simulations. This resolution allows us to perform the first star-by-star galaxy simulation, which resolves individual stars in the Milky Way Galaxy. The performance scales over $10^4$ CPU cores, an upper limit in the current state-of-the-art simulations using both A64FX and X86-64 processors and NVIDIA CUDA GPUs.