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.
翻译:从实验数据中推断模型参数是包括宇宙学在内的许多科学领域面临的一个重大挑战,这往往严重依赖高忠诚数字模拟,这种模拟在计算上代价极高。在基因模型中应用深深学习技术再次引起人们的兴趣,即使用高维密度估计器作为计算成本低廉的全面模拟模拟器。这些基因模型有可能在科学模拟领域带来巨大的转变,但为了实现这一转变,我们需要在科学应用所需的精密系统中研究这类生成器的性能。为此,我们运用Generalization Adversarial网络来研究生成微弱透镜聚合图的问题。我们显示,我们的发电机网络制作的地图在统计上具有高度信心,与完全模拟的地图所描述的汇总统计数据相同。