Deep learning for cosmology: parameter measurement and generation of simulations

Deep learning-based analysis methods are gaining interest in cosmology due to their unique ability to create very rich and complex models.
These models are particularly well suited for analysis of large scale structure data, as the matter density fields are comprised of highly nonlinear, complicated features, such as halos, filaments, sheets and voids.
But can this information be utilised by the deep learning algorithm to gain a better understanding of the cosmological model?
In this talk I will present the application of Convolutional Neural Networks (CNNs) for constraining cosmological parameters.
I will compare the constraining power against the commonly used statistic, the power spectrum, and explore different regimes in quality of data and simulations.
Finally, I will introduce the Generative Adversarial Networks (GANs): a CNN-based technique, which can learn from a training set and then generate new, statistically similar data.
I will present a study of applying GANs to generating samples of the cosmic web and discuss the prospects of applying them to render 2D and 3D N-body simulation - like data.