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