论文标题
与生成对抗网络的模拟宇宙多场
Emulating cosmological multifields with generative adversarial networks
论文作者
论文摘要
我们探索使用深度学习从骆驼项目的最先进的流体动力模拟产生多场图像的可能性。我们使用一个生成的对抗网络来生成三个代表气体密度（MGA），中性氢密度（HI）和磁场幅度（B）的图像。该模型生成的每个示例中的每个地图的质量看起来都非常有前途。在这项研究中考虑的GAN能够生成地图，其图像的平均值和标准偏差与训练数据的概率密度分布的平均值和标准偏差是一致的。每个磁场生成图的自动功率光谱的平均值和标准偏差与从Illustristng的地图计算出的那些图非常吻合。此外，在模拟器产生的所有情况下，字段之间的互相关与数据集的互相关均符合。这意味着发电机每个输出中的所有三个地图都编码相同的基础宇宙学和天体物理学。
We explore the possibility of using deep learning to generate multifield images from state-of-the-art hydrodynamic simulations of the CAMELS project. We use a generative adversarial network to generate images with three different channels that represent gas density (Mgas), neutral hydrogen density (HI), and magnetic field amplitudes (B). The quality of each map in each example generated by the model looks very promising. The GAN considered in this study is able to generate maps whose mean and standard deviation of the probability density distribution of the pixels are consistent with those of the maps from the training data. The mean and standard deviation of the auto power spectra of the generated maps of each field agree well with those computed from the maps of IllustrisTNG. Moreover, the cross-correlations between fields in all instances produced by the emulator are in good agreement with those of the dataset. This implies that all three maps in each output of the generator encode the same underlying cosmology and astrophysics.
