图神经网络上的Galerkin：用深度生成模型实现强大的合成星系目录
Galaxies on graph neural networks: towards robust synthetic galaxy catalogs with deep generative models
摘要：
未来的天文成像巡天将提供宇宙学参数的精确限制，如暗能量。然而，为这些调查制作合成数据，以测试和验证分析方法，受到非常高的计算成本的影响。特别是，生成足够大体积和高分辨率的模拟星系目录很快就会在计算上变得无法实现。在本文中，我们解决了这个问题的深层生成模型创建强大的模拟星系目录，可用于测试和开发未来的弱透镜调查的分析管道。我们在一个定制的图卷积网络上构建我们的模型，将每个星系放在一个图节点上，然后连接每个引力约束系统内的图。我们训练我们的模型与现实的星系种群的宇宙学模拟捕捉星系的二维和三维方向。来自模型的样本表现出与模拟中的样本相当的统计特性。据我们所知，这是在天体物理学/宇宙学背景下图形生成模型的第一个实例。
英文摘要：
The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, production of synthetic data for these surveys, to test and validate analysis methods, suffers from a very high computational cost. In particular, generating mock galaxy catalogs at sufficiently large volume and high resolution will soon become computationally unreachable. In this paper, we address this problem with a Deep Generative Model to create robust mock galaxy catalogs that may be used to test and develop the analysis pipelines of future weak lensing surveys. We build our model on a custom built Graph Convolutional Networks, by placing each galaxy on a graph node and then connecting the graphs within each gravitationally bound system. We train our model on a cosmological simulation with realistic galaxy populations to capture the 2D and 3D orientations of galaxies. The samples from the model exhibit comparable statistical properties to those in the simulations. To the best of our knowledge, this is the first instance of a generative model on graphs in an astrophysical/cosmological context.
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