AI 中文总结
研究提出基于扩散的生成模型，以暗物质密度场为输入生成含位置和物理性质的星系点云。在IllustrisTNG模拟目录上训练，能再现星系与暗物质场关系及多种分布，还可绕过晕发现，为相关实验提供实用引擎及新推断途径。
AI 中文摘要
我们提出了一种基于扩散的生成模型，用于从暗物质密度场构建逼真的星系目录。该模型以三维暗物质密度场为输入，直接生成具有位置和物理性质（包括恒星形成率（SFR））的星系点云，而无需识别暗物质晕或次晕。我们在IllustrisTNG流体动力学模拟的星系目录上训练该模型。生成的目录再现了星系与潜在暗物质场之间的空间对应关系，优先填充密集区域和丝状结构。它们还准确再现了SFR和恒星质量的一维和二维分布、星系自功率谱以及星系 - 暗物质交叉功率谱。该模型通过对未解析的小尺度结构进行边缘化来生成与输入密度场标称分辨率以下的结构相关的星系，而不是依赖于解析的晕目录。通过优化的扩散采样计划，它在单个GPU上大约10秒内就能在(151.3 Mpc)^3的体积上生成SFR>1 M⊙/yr的目录。因此，我们的模型为即将到来的星系红移调查和线强度映射实验提供了一个实用的引擎，为基于模拟的推断提供了一条绕过晕发现并直接将场级暗物质统计与可观测星系群体联系起来的途径。
英文摘要
We present a diffusion-based generative model for constructing realistic galaxy catalogues from dark matter density fields. The model takes a three-dimensional dark matter density field as input and generates galaxies directly as a point cloud with positions and physical properties, including star formation rate (SFR), without identifying dark matter haloes or subhaloes. We train the model on galaxy catalogues from the IllustrisTNG hydrodynamical simulation. The generated catalogues reproduce the spatial correspondence between galaxies and the underlying dark matter field, preferentially populating dense regions and filamentary structures. They also accurately reproduce the one- and two-dimensional distributions of SFR and stellar mass, the galaxy auto-power spectrum, and the galaxy--dark matter cross-power spectrum. The model generates galaxies associated with structures below the nominal resolution of the input density field by marginalising over unresolved small-scale structure rather than relying on a resolved halo catalogue. With an optimised diffusion sampling schedule, it generates a catalogue with SFR $> 1 ~\rm M_\odot/yr$ over a $(151.3 ~\rm Mpc)^3$ volume in approximately 10 seconds on a single GPU. Our model therefore provides a practical engine for producing large mock ensembles for upcoming galaxy redshift surveys and line-intensity mapping experiments, and offers a path toward simulation-based inference that bypasses halo finding and directly connects field-level dark matter statistics to observable galaxy populations.
Comments9 pages, 9 figures, submitted to MNRAS
