Speaker
Description
Jefferson Laboratory is a single-purpose, DOE national lab which serves the nuclear physics community through the Continuous Electron Beam Accelerator Facility (CEBAF). This presentation will give a broad view of the ways machine learning (ML) is being used at the lab, with a bias toward applications in accelerator physics. An ML-based system to classify accelerating cavity trips is nearing deployment and some features of the implementation will be highlighted. A few challenges preventing the emergence of a "killer app" will be raised with the aim of promoting discussion during the workshop. Finally, we describe steps being taken at the lab to equip scientists with the skills to leverage machine learning in their areas of expertise and how possible collaboration between laboratories could further enhance proficiency in this field.
