论文标题
AI辅助超分辨率宇宙学模拟
AI-assisted super-resolution cosmological simulations
论文作者
论文摘要
银河形成的宇宙学模拟受到有限的计算资源的限制。我们从人工智能（特别是深度学习）的持续快速进步中得出来解决这个问题。已经开发出神经网络从高分辨率（HR）图像数据中学习，然后制作出不同低分辨率（LR）图像的准确超分辨率（SR）版本。我们将这种技术应用于LR宇宙学N体模拟，生成SR版本。具体而言，我们能够通过产生更多的颗粒并从初始位置预测其位移来增强模拟分辨率。因此，我们的结果可以看作是新的仿真实现本身，而不是预测，例如其密度领域。此外，生成过程是随机的，使我们能够在大规模环境下采样小规模模式。 Our model learns from only 16 pairs of small-volume LR-HR simulations, and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to $16\,h^{-1}\mathrm{Mpc}$, and the HR halo mass function to within $10 \%$ down to $10^{11} \, M_\odot$.我们成功地将模型部署在比训练模拟框大1000倍的框中，这表明可以快速生成高分辨率模拟调查。我们得出的结论是，AI援助有可能在大型宇宙学中彻底改变小型星系物理学的建模。
Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data, and then make accurate super-resolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore our results can be viewed as new simulation realizations themselves rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations, and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to $16\,h^{-1}\mathrm{Mpc}$, and the HR halo mass function to within $10 \%$ down to $10^{11} \, M_\odot$. We successfully deploy the model in a box 1000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy formation physics in large cosmological volumes.
