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Speaker: Dr. Daniel Ratner, Stanford Linear Accelerator Center and Jefferson Lab
Abstract:
Across the DOE, the wealth of data, robust automation, and stringent requirements for control, simulation, and data acquisition, make “Big Science” experiments — particle accelerators, photon sources, telescopes, etc. — ideal targets for AI/ML. At SLAC, the Machine Learning Initiative was created to address these challenges throughout the lab’s science mission. In this talk, I will present several AI applications from the accelerator domain, including autonomous optimization and anomaly detection. I will also talk about extensions to other large-scale experiments, principally observation of gravitational waves.