New Machine-Learning Tool Diagnoses Electron Beams in an Efficient, Noninvasive Way

Researchers at the Department of Energy’s Stanford Linear Accelerator Center National Accelerator Laboratory have been working on a virtual diagnostics that will use machine learning to obtain critical information about beam quality in an efficient and noninvasive manner. According to this article, this method would be used to diagnose virtually any machine that uses electron beams, whether it’s an electron microscope for imaging of ultrasmall objects or a medical accelerator used in cancer therapy.