Date: June 20th 2019, 11am
Speaker: Doogesh Kodi Ramanah (IAP)
Title: Fast complex dynamics emulators for cosmological inference
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.