Presenter: Dr. Mahdi Qezlou, HETDEX-Cosmology Postdoctoral Fellow, University of Texas at Austin
Venue: POB 4.304
Listen to the seminar here

Abstract:
Continuing the recent theme at the CosmicAI talks, I discuss “surrogate modeling” for inferring cosmological and astrophysical parameters with the help of machine learning. The available and upcoming observations require accurate simulations of the complex physical processes, including the non-linear gravity and gas dynamics. Using these detailed simulations at each Monte Carlo Markov Chain (MCMC) step in an inferential framework is computationally prohibitive. To overcome this, machine learning (surrogate) models can be trained to learn the map from the parameters of interest to the observables. 

In particular, I will highlight a multi-fidelity Gaussian Process framework designed to maximize the constraining power on a high-dimensional input parameter space while minimizing the computational cost. I will also demonstrate how these emulators enable unprecedented accuracy in parameter inference, paving the way for robust scientific discoveries with current and future cosmological surveys.