This session focuses on the latest breakthroughs and ideas from artificial intelligence which are transforming the field of particle physics, providing attendees the audience with both theoretical and experimental perspectives on the ongoing transformation. Topics to be discussed include the influence of artificial intelligence on event and particle reconstruction in particle detectors; theoretical model building and optimization, large scale simulations and theoretical predictions, physics-inspired machine learning algorithms and realtime AI for detection of exotic physics signals.
The session consists of prerecorded videos by speakers and respondents on specific topics followed by a live moderated panel discussion. Session participants include: Prof. Sergei Gleyzer (Alabama), Prof. Meenakshi Narain (Brown), Prof. Jesse Thaler (MIT), Prof. Daniel Whiteson (UCI), Prof. Harrison Prosper (FSU), Prof. Risi Kondor (UChicago/Flatiron), Prof. Rose Yu (UCSD), Prof. Taritree Wongjirad(Tufts) and Dr. Savannah Thais (Princeton).
Panelists will describe the state of these important areas as well as the potential for future AI-driven development and discovery potential in new experiments such as the High-Luminosity Large Hadron Collider (HL-LHC) and Deep Underground Neutrino Experiment (DUNE), expected to come online in the later parts of this decade. Panelists will also offer insights that fundamental physics may offer to the field of machine learning to underpin the information theory of deep learning in order to lead to more powerful and explainable AI algorithms of the future.