Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields. We leverage diffusion generative models to address two tasks of importance to cosmology -- as an emulator for cold dark matter density fields conditional on input cosmological parameters $\Omega_m$ and $\sigma_8$, and as a parameter inference model that can return constraints on the cosmological parameters of an input field. We show that the model is able to generate fields with power spectra that are consistent with those of the simulated target distribution, and capture the subtle effect of each parameter on modulations in the power spectrum. We additionally explore their utility as parameter inference models and find that we can obtain tight constraints on cosmological parameters.

翻译：宇宙学模拟在揭示物理参数对场统计特性的影响以及基于密度场信息约束参数方面发挥着关键作用。我们利用扩散生成模型处理宇宙学中的两项重要任务——作为条件于输入宇宙学参数$\Omega_m$和$\sigma_8$的冷暗物质密度场仿真器，以及作为能够对输入场返回宇宙学参数约束的参数推断模型。研究表明，该模型能够生成功率谱与模拟目标分布一致的场，并捕捉各参数对功率谱调制的细微影响。此外，我们探索了其作为参数推断模型的效用，发现可获得对宇宙学参数的严格约束。