Cosmology and Gravitation

Geometric Machine Learning for cosmological galaxy modelsConfirmed

by Christian Kragh Jespersen (Princeton University)

America/Toronto
PI/4-405 - Bob Room (Perimeter Institute for Theoretical Physics)

PI/4-405 - Bob Room

Perimeter Institute for Theoretical Physics

60
Description

Galaxies are the medium through which we study the structure of the universe. However, widely applied statistical models of galaxies are generally over-simplified: even recently proposed models cannot capture the dependencies on environment or formation history. To solve this problem, I will introduce Graph Neural Networks (GNNs), a general and ideal tool for physical modelling. Geometrically constrained GNNs vastly improve our models, and allow us to ask detailed questions about the importance of formation history and environment for cosmological galaxy modeling. I will also prove a surprising equivalence between these two aspects of galaxy formation.

Organised by

Niayesh Afshordi, Selim Hotinli