Prospects for understanding the physics of the Universe

The remarkable progress in cosmology over the last decades has been driven by the close interplay between theory and observations. Observational discoveries have led to a standard model of cosmology with ingredients that are not present in the standard model of particle physics – dark matter, dark energy, and a primordial origin for cosmic structure. Their physical nature remains a mystery, motivating a new generation of ambitious sky surveys. However, it has become clear that formidable modelling and analysis challenges stand in the way of establishing how these ingredients fit into fundamental physics. I will discuss progress in harnessing advanced machine-learning techniques to address these challenges, giving some illustrative examples.

MS Teams link:

https://teams.microsoft.com/l/meetup-join/19:1a195312c8ce4d54b44a3d7629749659@thread.tacv2/1606316242215?context=%7B%22Tid%22:%22fc6602ef-8e88-4f1d-a206-e14a3bc19af2%22,%22Oid%22:%22ead6ea6e-5b43-439c-a2ff-7ee7bd91ab2a%22%7D