CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
This addresses the problem of costly simulations for cosmological inference, offering a computationally inexpensive emulator, though it appears incremental as it applies existing GAN methods to a specific domain.
The paper tackles the computational expense of high-fidelity numerical simulations for cosmology by using Generative Adversarial Networks (CosmoGAN) to generate weak lensing convergence maps, showing that the generated maps match the summary statistics of fully simulated maps with high statistical confidence.
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.