CHARM：使用自回归多阶段网络创建暗物质晕
CHARM: Creating Halos with Auto-Regressive Multi-stage networks
AI总结：
本文提出CHARM，利用多阶段神经样条流从低分辨率粒子网格模拟直接生成暗物质晕和星系目录，并实现无偏宇宙学推断。
摘要：
为了最大化从宇宙学数据集中提取的信息量，需要能够准确表征这些观测结果的模拟。然而，传统模拟通过估计粒子间相互作用来在引力作用下演化粒子（即N体模拟），其计算成本高昂，难以扩展到即将到来的数据集所需的大体积和高分辨率。此外，对星系分布进行建模通常需要识别已维里化的暗物质晕，而对于大型N体模拟而言，这同样是耗时且耗内存的过程，进一步加剧了计算开销。在本研究中，我们提出CHARM，这是一种通过直接根据暗物质密度场的大尺度分布匹配暗物质晕的空间、质量和速度统计特性来创建模拟暗物质晕目录的新方法。我们开发了基于多阶段神经样条流的网络，在红移z=0.5处直接学习这一映射，使用计算成本更低的低分辨率粒子网格模拟，而不依赖高分辨率N体模拟。我们表明，模拟暗物质晕目录和绘制的星系目录在实空间和红移空间中均具有与N体模拟所得相同的统计性质。最后，我们利用基于模拟的推断，将这些模拟目录用于基于红移空间星系功率谱、双谱和小波统计量的宇宙学推断，首次使用加速的正向模型模拟开展推断，并获得了具有良好校准后验的无偏宇宙学约束。该代码是Simons Collaboration on Learning the Universe的一部分，已公开发布于https://github.com/shivampcosmo/CHARM。
英文摘要：
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle interactions (N-body simulations) are computationally expensive and prohibitive to scale to the large volumes and resolutions necessary for the upcoming datasets. Moreover, modeling the distribution of galaxies typically involves identifying virialized dark matter halos, which is also a time- and memory-consuming process for large N-body simulations, further exacerbating the computational cost. In this study, we introduce CHARM, a novel method for creating mock halo catalogs by matching the spatial, mass, and velocity statistics of halos directly from the large-scale distribution of the dark matter density field. We develop multi-stage neural spline flow-based networks to learn this mapping at redshift z=0.5 directly with computationally cheaper low-resolution particle mesh simulations instead of relying on the high-resolution N-body simulations. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space. Finally, we use these mock catalogs for cosmological inference using redshift-space galaxy power spectrum, bispectrum, and wavelet-based statistics using simulation-based inference, performing the first inference with accelerated forward model simulations and finding unbiased cosmological constraints with well-calibrated posteriors. The code was developed as part of the Simons Collaboration on Learning the Universe and is publicly available at \url{https://github.com/shivampcosmo/CHARM}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
