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

• URL: http://arxiv.org/abs/2510.23330v1
• Date: Mon, 27 Oct 2025 13:45:55 GMT
• Title: The First Star-by-star $N$-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model
• Authors: Keiya Hirashima, Michiko S. Fujii, Takayuki R. Saitoh, Naoto Harada, Kentaro Nomura, Kohji Yoshikawa, Yutaka Hirai, Tetsuro Asano, Kana Moriwaki, Masaki Iwasawa, Takashi Okamoto, Junichiro Makino,
• Abstract summary: A major goal of computational astrophysics is to simulate the Milky Way Galaxy with sufficient resolution down to individual stars.<n>We have developed a novel integration scheme of $N$-body/hydrodynamics simulations working with machine learning.<n>With this method, we reached 300 billion particles using 148,900 nodes, equivalent to 7,147,200 CPU cores.<n>This resolution allows us to perform the first star-by-star galaxy simulation, which resolves individual stars in the Milky Way Galaxy.
• Score: 0.11371613777160501
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics [70.27068115318681]
  We introduce a neural-based ''subgrid black hole'' that learns the small-scale local dynamics and embeds it within direct simulations.<n>Thanks to the great speedup in fine-scale evolution, our approach captures intrinsic variability in accretion-driven feedback, allowing dynamic coupling between the central black hole and galaxy-scale gas.
  arXiv  Detail & Related papers  (2025-12-01T11:47:49Z)
• GaussGym: An open-source real-to-sim framework for learning locomotion from pixels [78.05453137978132]
  We present a novel approach for photorealistic robot simulation that integrates 3D Gaussian Splatting as a drop-in within vectorized physics simulators.<n>This enables unprecedented speed -- exceeding 100,000 steps per second on consumer GPU.<n>We additionally demonstrate its applicability in a sim-to-real robotics setting.
  arXiv  Detail & Related papers  (2025-10-17T06:34:52Z)
• ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback [0.7324709841516586]
  We introduce new high-resolution galaxy simulations accelerated by a surrogate model that reduces the computation cost by approximately 75 percent.<n>Massive stars with a Zero Age Main Sequence mass of more than about 10 $mathrmM_odot$ explode as core-collapse supernovae (CCSNe)<n>Our new approach achieves high-resolution fidelity while reducing computational costs, effectively bridging the physical scale gap and enabling multi-scale simulations.
  arXiv  Detail & Related papers  (2024-10-30T18:00:02Z)
• Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations [0.7927502566022343]
  We develop a method combining machine learning and Gibbs sampling to predict how a supernova affects the surrounding gas. Our method can replace the SN sub-grid models and help properly simulate un-resolved SN feedback in galaxy formation simulations.
  arXiv  Detail & Related papers  (2023-11-14T19:00:03Z)
• Quantum algorithm for collisionless Boltzmann simulation of self-gravitating systems [0.0]
  We propose an efficient quantum algorithm to solve the collisionless Boltzmann equation (CBE)<n>We extend the algorithm to perform quantum simulations of self-gravitating systems, incorporating the method to calculate gravity.<n>It will allow us to perform large-scale CBE simulations on future quantum computers.
  arXiv  Detail & Related papers  (2023-03-29T06:59:00Z)
• Robust Field-level Likelihood-free Inference with Galaxies [0.0]
  We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. We find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder.
  arXiv  Detail & Related papers  (2023-02-27T19:26:13Z)
• Learning cosmology and clustering with cosmic graphs [0.0]
  We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project. We first show that GNNs can learn to compute the power spectrum of galaxy catalogues with a few percent accuracy. We then train GNNs to perform likelihood-free inference at the galaxy-field level.
  arXiv  Detail & Related papers  (2022-04-28T18:00:02Z)
• Satellite galaxy abundance dependency on cosmology in Magneticum simulations [101.18253437732933]
  We build an emulator of satellite abundance based on cosmological parameters. We find that $A$ and $beta$ depend on cosmological parameters, even if weakly. We also show that satellite abundance cosmology dependency differs between full-physics (FP) simulations, dark-matter only (DMO) and non-radiative simulations.
  arXiv  Detail & Related papers  (2021-10-11T18:00:02Z)
• Megaverse: Simulating Embodied Agents at One Million Experiences per Second [75.1191260838366]
  We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research. Megaverse is up to 70x faster than DeepMind Lab in fully-shaded 3D scenes with interactive objects. We use Megaverse to build a new benchmark that consists of several single-agent and multi-agent tasks.
  arXiv  Detail & Related papers  (2021-07-17T03:16:25Z)
• DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning [70.80563014913676]
  We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images. We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
  arXiv  Detail & Related papers  (2020-11-24T22:51:08Z)
• Predicting Localized Primordial Star Formation with Deep Convolutional Neural Networks [0.0]
  We investigate applying 3D deep convolutional neural networks as fast surrogate models of the formation and feedback effects of primordial stars. We present the surrogate model to predict localized primordial star formation; the feedback model will be presented in a subsequent paper. To our knowledge, this is the first model that can predict primordial star forming regions that match highly-resolved cosmological simulations.
  arXiv  Detail & Related papers  (2020-11-02T22:32:27Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.