用生成模型模拟宇宙暗物质分布，加速引力理论检验
Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies
- 用扩散与流模型生成三维暗物质密度场，条件可控
- 扩散模型准确复现功率谱和三阶关联函数，泛化能力强
- 新多输出结构提升参数表征，适合高精度宇宙学分析
下一代星系巡天有望在宇宙尺度上实现高精度引力检验。但要发挥这一潜力，需精确建模非线性宇宙网结构。本文探索条件生成模型，通过基于分数的（扩散）和基于流的方法生成三维暗物质密度场。结果表明，扩散模型能准确再现物质功率谱和三阶相关函数，即使面对未见过的配置也表现良好；而基于流的方法虽速度显著提升、精度略有下降，但在高阶统计量上表现不佳。为改进条件生成，我们提出一种新型多输出模型，以更好地提取宇宙学参数特征表示。研究结果为偏离标准引力的探测提供了高精度且计算成本更低的强大工具，推动更全面高效的宇宙学分析。
原文摘要 · Abstract (English)
Next-generation galaxy surveys promise unprecedented precision in testing gravity at cosmological scales. However, realising this potential requires accurately modelling the non-linear cosmic web. We address this challenge by exploring conditional generative modelling to create 3D dark matter density fields via score-based (diffusion) and flow-based methods. Our results demonstrate the power of diffusion models to accurately reproduce the matter power spectra and bispectra, even for unseen configurations. They also offer a significant speed-up with slightly reduced accuracy, when flow-based reconstructing the probability distribution function, but they struggle with higher-order statistics. To improve conditional generation, we introduce a novel multi-output model to develop feature representations of the cosmological parameters. Our findings offer a powerful tool for exploring deviations from standard gravity, combining high precision with reduced computational cost, thus paving the way for more comprehensive and efficient cosmological analyses
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。