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

## TL;DR

This paper introduces CosmoGAN, a generative adversarial network that produces high-fidelity weak lensing convergence maps, serving as a fast and accurate alternative to computationally expensive simulations in cosmology.

## Contribution

The work demonstrates that GANs can generate weak lensing maps with statistical properties matching full simulations, enabling efficient cosmological data analysis.

## Key findings

- GAN-generated maps match the statistical summaries of real simulations
- CosmoGAN reduces computational costs significantly
- High-fidelity maps are produced with high statistical confidence

## 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.