Date: **June 20th 2019, 11am**

Speaker:** Doogesh Kodi Ramanah (IAP)
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Title: **Fast complex dynamics emulators for cosmological inference**

Room: **Cassini**

**Abstract**

I will present an overview of our recent work in developing various aspects of Bayesian forward modelling machinery for an optimal exploitation of state-of-the-art galaxy redshift surveys. I will focus on the development of a generative model for mapping dark matter simulations to 3D halo fields using physically motivated neural networks. We employ the Wasserstein distance as a metric to train our halo painting emulator and demonstrate its efficacy in predicting 3D halo distributions using summary statistics such as the power spectrum and bispectrum. I will subsequently briefly review our novel cosmological parameter inference framework that extracts several orders of magnitude more information from the cosmic expansion relative to standard approaches, and a sophisticated likelihood that is robust to unknown foreground contaminations.