Physics and Astronomy Colloquium
Collision Course: Particle Physics meets Machine Learning
Presented by Jesse Thaler. Associate Professor of PhysicsDirector, NSF AI Institute for Artificial Intelligence and Fundamental Interactions
Modern machine learning has had an outsized impact on many scientific fields, and particle physics is no exception. What is special about particle physics, though, is the vast amount of theoretical and experimental knowledge that we already have about many problems in the field. In this colloquium, I present two cases studies involving quantum chromodynamics (QCD) at the Large Hadron Collider (LHC), highlighting the fascinating interplay between theoretical principles and machine learning strategies. First, by cataloging the space of all possible QCD measurements, we (re)discovered technology relevant for self-driving cars. Second, by quantifying the similarity between two LHC collisions, we unlocked a class of non-parametric machine learning techniques based on optimal transport. In addition to providing new quantitative insights into QCD, these techniques enable new ways to visualize data from the LHC.
3:30 pm, Friday, April 30, 2021
Via Zoom. Contact the department for password
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A schedule of talks within the Department of Physics and Astronomy is available on the P&A web site at http://physics.unm.edu/pandaweb/events/index.php