Learning Particle Physics from Machines
Daniel Whiteson (University of California, Irvine)
Abstract: Recent advances in artificial intelligence offer opportunities to disrupt the traditional strategies for discovery of new particles in high energy collisions. I will describe the new machine-learning techniques, explain why they are particularly well suited for particle physics, present selected results that demonstrate their new capabilities, and present a strategy for translating their learned strategies into human understanding.
Bio: Daniel Whiteson is a professor of experimental particle physics at the University of California, Irvine, and a fellow of the American Physical Society. He conducts research using the Large Hadron Collider at CERN. He received his PhD at Berkeley.
HEP - phenomenologyHEP - theorymathematical physics
Audience: researchers in the topic
Series comments: Description: Weekly research seminar of the NHETC at Rutgers University
Livestream link is available on the webpage.
Organizers: | Christina Pettola*, Sung Hak Lim, Vivek Saxena*, Erica DiPaola* |
*contact for this listing |