In fundamental physics our aim is to understand the building blocks of the nature and their interactions, and we study these phenomena with some of the biggest and most complex scientific experiments on earth. The rapid progress of artificial intelligence and machine learning is having a profound effect on fundamental physics, from the ways data are generated and analyzed to the ways experiments are operated and designed, ultimately both changing the ways we purse science and expanding the potential for new discoveries. In this talk I will discuss how AI is massively improving the way we analyze data in fundamental physics, both at large collider experiments like ATLAS at the Large Hadron Collider at CERN and at the upcoming MAGIS-100 experiment, a large baseline atom interferometer that will search for gravitational waves and dark matter.
Michael Kagan, Staff Scientist, SLAC National Accelerator Laboratory
Host: Timothy Kovachy