用图神经网络生成宇宙大尺度结构，60万星系点云可还原引力演化规律
Score Matching on Large Geometric Graphs for Cosmology Generation
- 基于等变图网络与拓扑感知噪声调度，实现对宇宙大尺度结构的生成
- 成功生成60万暗晕点云，比现有扩散模型更准确捕捉聚类统计特征
- 适合天体物理模拟、宇宙学研究者快速构建高保真仿真数据
生成模型在宇宙学模拟中具有潜力，但面临可扩展性、物理一致性及领域对称性保持等挑战，限制其作为N-body模拟替代方案的应用。本文提出一种基于得分的生成模型，结合等变图神经网络，从先验信息出发模拟跨宇宙学参数的星系引力聚集过程，尊重周期边界条件，并可扩展至完整模拟中的全部星系数量。引入一种新型拓扑感知噪声调度，对大规模几何图至关重要。所提等变得分模型成功生成了高达60万暗晕的全尺度宇宙学点云，满足周期性与均匀先验要求，在捕捉聚类统计特性方面优于现有扩散模型，且具备显著计算优势。该工作推动了宇宙学研究，构建出更贴近真实引力聚集过程的生成模型，为宇宙大尺度结构演化提供高效、物理一致的仿真工具。
原文摘要 · Abstract (English)
Generative models are a promising tool to produce cosmological simulations but face significant challenges in scalability, physical consistency, and adherence to domain symmetries, limiting their utility as alternatives to $N$-body simulations. To address these limitations, we introduce a score-based generative model with an equivariant graph neural network that simulates gravitational clustering of galaxies across cosmologies starting from an informed prior, respects periodic boundaries, and scales to full galaxy counts in simulations. A novel topology-aware noise schedule, crucial for large geometric graphs, is introduced. The proposed equivariant score-based model successfully generates full-scale cosmological point clouds of up to 600,000 halos, respects periodicity and a uniform prior, and outperforms existing diffusion models in capturing clustering statistics while offering significant computational advantages. This work advances cosmology by introducing a generative model designed to closely resemble the underlying gravitational clustering of structure formation, moving closer to physically realistic and efficient simulators for the evolution of large-scale structures in the universe.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。