Speaker: Jack Collins
Title: Representation Learning for Collider Events
Host: Da Liu
Zoom: https://zoom.us/j/186024391
Abstract:
Collider events, when imbued with a metric which characterizes the 'distance' between two events, can be thought of as populating a data manifold in a metric space. The geometric properties of this manifold reflect the physics encoded in the distance metric. I will show how the geometry of collider events can be probed using a class of machine learning architectures called Variational Autoencoders.
