Description
Chair: Kyunghyun Cho
The main goal of cosmology, is to perform parameter inference and model selection, from astronomical observations. But, uniquely, it is a field that has to do this limited to a single experiment, the Universe that we live in. With very powerful existing and upcoming cosmological surveys, we need to leverage state-of-the-art inference techniques to extract as much information as possible from our data. In this talk, I will begin present Machine Learning based methods to perform inference in cosmology, such as simulation-based inference, and stochastic control sampling approaches. I will finish by showing how these methods are being used to improve our knowledge of the Universe.
The timetable has not been filled yet.
