CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

IF 16.281
Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou, Jan M. Kratochvil
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引用次数: 104

Abstract

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.

Abstract Image

CosmoGAN:使用生成对抗网络创建高保真弱透镜收敛映射
从实验数据推断模型参数在包括宇宙学在内的许多科学中都是一个巨大的挑战。这通常严重依赖于高保真度的数值模拟,这在计算上是非常昂贵的。深度学习技术在生成建模中的应用重新引起了人们对使用高维密度估计器作为完全成熟的仿真计算廉价模拟器的兴趣。这些生成模型有可能在科学模拟领域产生巨大的转变,但为了实现这种转变,我们需要在科学应用所需的精确制度下研究这些生成器的性能。为此,在这项工作中,我们将生成对抗网络应用于生成弱透镜收敛映射的问题。我们表明,我们的生成器网络生成的地图具有高统计置信度,与完全模拟的地图具有相同的汇总统计。
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期刊介绍: Computational Astrophysics and Cosmology (CompAC) is now closed and no longer accepting submissions. However, we would like to assure you that Springer will maintain an archive of all articles published in CompAC, ensuring their accessibility through SpringerLink's comprehensive search functionality.
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