基于潜在重叠扩散的大型、快速且准确的HI强度图
Large, fast and accurate HI intensity maps with latent overlap diffusion
AI总结：
本文提出了一种新的机器学习管道，利用隐式重叠扩散模型在已训练的流体动力学模拟基础上，生成暗物质晕质量密度图和三维21厘米亮度温度信号，从而高效预测21厘米功率谱。
摘要：
中性氢21厘米发射的分布是强有力的宇宙学和天体物理学探针，因为它追踪了宇宙各时期下的暗物质和冷气体分布。然而，预测可观测信号受到所需流体动力学模拟计算成本的限制。我们介绍了一种新的机器学习管道，一旦在流体动力学模拟上训练，就能从仅暗物质的模拟开始生成暗物质晕质量密度图和三维21厘米亮度温度信号。我们使用基于注意机制的ResUNet（HALO）来预测暗物质晕图，然后通过训练的条件变分扩散模型（LODI）处理以生成21厘米亮度温度图。LODI在较小的子体积上训练，然后通过一种称为“潜在重叠”的新方法无缝组合到512倍大的体积中。我们证明，一旦在25^3 (Mpc/h)^3体积模拟上训练，我们可以在未见过的暗物质图（相同宇宙学）上预测21厘米功率谱，对于k <= 10 h Mpc^-1的波数，在非线性区域深处，误差在10%以内，计算成本约为两分钟。尽管在特定体积上展示，我们的方法设计用于可扩展到任意大的模拟。
英文摘要：
The distribution of 21 cm emission from neutral hydrogen is a powerful cosmological and astrophysical probe, as it traces the underlying dark matter and cold gas distributions throughout cosmic times. However, the prediction of observable signals is hindered by the large computational costs of the required hydrodynamic simulations. We introduce a novel machine learning pipeline that, once trained on a hydrodynamical simulation, is able to generate both halo mass density maps and the three-dimensional 21 cm brightness temperature signal, starting from a dark matter-only simulation. We use an attention-based ResUNet (HALO) to predict dark matter halo maps, which are then processed through a trained conditional variational diffusion model (LODI) to produce 21 cm brightness temperature maps. LODI is trained on smaller sub-volumes that are then seamlessly combined in 512-times larger volume using a new method, called `latent overlap'. We demonstrate that, once trained on 25^3 (Mpc/h)^3 volume simulations, we are able to predict the 21 cm power spectrum on an unseen dark matter map (with the same cosmology) to within 10% for wavenumbers k <= 10 h Mpc^-1, deep inside the non-linear regime, with a computational effort of the order of two minutes. While demonstrated on this specific volume, our approach is designed to be scalable to arbitrarily large simulations.
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