用Transformer模型将星系画进暗物质模拟，快速生成真实星系分布。
Galactification: painting galaxies onto dark matter only simulations using a transformer-based model
- 基于Transformer，输入暗物质密度和速度场，输出星系点云及物理属性。
- 生成的星系统计量与真实模拟一致，且对参数变化响应正确。
- 适合需要快速生成星系数据的宇宙学研究者使用。
将星系形成与大尺度结构联系起来对于解释宇宙学观测至关重要。虽然流体动力学模拟能准确刻画星系的关联特性，但其计算成本高，难以在匹配现代巡天体积的范围内运行。为此，我们开发了一种框架，可基于低成本的纯暗物质模拟快速生成条件化的星系模拟数据。提出一种多模态的Transformer模型，输入三维暗物质密度场和速度场，输出对应星系点云及其物理属性。结果表明，训练后的模型能忠实再现多种星系统计量，并正确捕捉其随宇宙学和天体物理参数变化的趋势，是首个能够同时还原所有相关星系属性、完整空间分布及其条件依赖关系的加速正向模型。
原文摘要 · Abstract (English)
Connecting the formation and evolution of galaxies to the large-scale structure is crucial for interpreting cosmological observations. While hydrodynamical simulations accurately model the correlated properties of galaxies, they are computationally prohibitive to run over volumes that match modern surveys. We address this by developing a framework to rapidly generate mock galaxy catalogs conditioned on inexpensive dark-matter-only simulations. We present a multi-modal, transformer-based model that takes 3D dark matter density and velocity fields as input, and outputs a corresponding point cloud of galaxies with their physical properties. We demonstrate that our trained model faithfully reproduces a variety of galaxy summary statistics and correctly captures their variation with changes in the underlying cosmological and astrophysical parameters, making it the first accelerated forward model to capture all the relevant galaxy properties, their full spatial distribution, and their conditional dependencies in hydrosimulations.
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