New machine learning tool diagnoses electron beams in an efficient, non-invasive way

For the past few years, researchers at the Department of Energy’s SLAC National Accelerator Laboratory have been developing “virtual diagnostics” that use machine learning to obtain crucial information about electron beam quality in an efficient, non-invasive way. Now, a new virtual diagnostic approach incorporates additional information about the beam that allows the method to work in situations where conventional diagnostics have failed.