Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emulators of fully-fledged simulations. These generative models have the potential to make a dramatic shift in the field of scientific simulations, but for that shift to happen we need to study the performance of such generators in the precision regime needed for science applications. To this end, in this work we apply Generative Adversarial Networks to the problem of generating weak lensing convergence maps. We show that our generator network produces maps that are described by, with high statistical confidence, the same summary statistics as the fully simulated maps.

中文翻译：

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CosmoGAN：使用生成对抗网络创建高保真弱镜头会聚图

从实验数据推断模型参数是包括宇宙学在内的许多科学的巨大挑战。这通常严重地依赖于高保真度的数值模拟，这在计算上过于昂贵。深度学习技术在生成模型中的应用引起了人们对于使用高维密度估计器作为成熟模拟的廉价计算仿真器的兴趣。这些生成模型有可能在科学模拟领域发生巨大变化，但是要使这种变化发生，我们需要在科学应用所需的精度范围内研究此类发生器的性能。为此，在这项工作中，我们将生成对抗网络应用于生成弱镜头收敛图的问题。