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Representation Learning for Particle Collider Events - Livestream

Jack Collins

Collider events at the Large Hadron Collider, 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 Variational Autoencoders.

Speaker: Jack Collins, SLAC

Monday, 05/04/20


Website: Click to Visit



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Berkeley Institute for Data Science

, CA