Fast and accurate simulations of the non-linear evolution of the cosmic density field are a major component of many cosmological analyses, but the computational time and storage required to run them can be exceedingly large. For this reason, we use generative adversarial networks (GANs) to learn a compressed representation of the 3D matter density field that is fast and easy to sample, and for the first time show that GANs are capable of generating samples at the level of accuracy of other conventional methods. Using sub-volumes from a suite of GADGET-2 N-body simulations, we demonstrate that a deep-convolutional GAN can generate samples that capture both large- and small-scale features of the matter density field, as validated through a variety of $n$-point statistics. The use of a data scaling that preserves high-density features and a heavy-tailed latent space prior allow us to obtain state of the art results for fast 3D cosmic web generation. In particular, the mean power spectra from generated samples agree to within $5\%$ up to $k=3$ and within $10\%$ for $k\le 5$ when compared with N-body simulations, and similar accuracy is obtained for a variety of bispectra. By modeling the latent space with a heavy-tailed prior rather than a standard Gaussian, we better capture sample variance in the high-density voxel PDF and reduce errors in power spectrum and bispectrum covariance on all scales. Furthermore, we show that a conditional GAN can smoothly interpolate between samples conditioned on redshift. Deep generative models, such as the ones described in this work, provide great promise as fast, low-memory, high-fidelity forward models of large-scale structure.

中文翻译：

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带有重尾生成对抗网络的非线性3D宇宙网络仿真

快速而准确的宇宙密度场非线性演化模拟是许多宇宙学分析的主要组成部分，但运行它们所需的计算时间和存储空间可能非常大。因此，我们使用生成对抗网络（GAN）来学习快速且易于采样的3D物质密度场的压缩表示，并且这首次证明GAN能够以其他常规方法。通过使用GADGET-2 N体模拟套件中的子体积，我们证明了深度卷积GAN可以生成捕获物质密度场的大尺度和小尺度特征的样本，这已通过多种方法得到了验证。$ñ$点统计。保留高密度特征和重型尾部潜在空间的数据缩放功能的使用使我们能够获得用于3D宇宙网快速生成的最新技术成果。特别是，生成的样本的平均功率谱在$5％$ 取决于 $ķ=3$ 并在 $10％$ 对于 $ķ\le 5$当与N体模拟进行比较时，对于各种双谱都可以获得相似的精度。通过使用重尾先验模型而不是标准高斯模型对潜在空间建模，我们可以更好地捕获高密度体素PDF中的样本方差，并在所有尺度上减少功率谱和双谱协方差的误差。此外，我们证明了条件GAN可以平滑地插值以红移为条件的样本之间。深度生成模型（如本工作中描述的模型）为大规模结构的快速，低内存，高保真正向模型提供了广阔的前景。