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

@article{Mustafa2019CosmoGANCH,
  title={CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks},
  author={Mustafa Mustafa and Deborah Bard and Wahid Bhimji and Zarija Lukic and Rami Al-Rfou and Jan Michael Kratochvil},
  journal={Computational Astrophysics and Cosmology},
  year={2019},
  volume={6},
  pages={1-13}
}
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… Expand
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