Machine Learning and Science Forum: Representation Learning for Particle Collider Events

Forum

May 4, 2020
1:30pm to 2:30pm
Virtual Participation

Participate remotely using this Zoom link.

Abstract: 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.

Full details about this meeting will be posted here: https://bids.github.io/MLStatsForum/.

The Machine Learning and Science Forum (formerly the Berkeley Statistics and Machine Learning Forum) meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. To receive email notifications about upcoming meetings, or to request more information, please contact berkeleymlforum@gmail.comAll interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend.

Speaker(s)

Jack Collins

Physics, SLAC

Jack Collins is a postdoctoral researcher in theoretical particle physics at SLAC National Accelerator Laboratory. He received his PhD in Theoretical Particle Physics from Cornell University.