It can help operators optimize the performance of X-ray lasers, electron microscopes, medical accelerators and other devices that depend on high-quality beams.
FACET-II will pave the way for a future generation of particle colliders and powerful light sources, opening avenues in high-energy physics, medicine, and materials, biological and energy science.
Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.
Their work uses machine learning to transform the way scientists tune particle accelerators for experiments and solve longstanding mysteries in astrophysics and cosmology.
Siegfried Glenzer's team and collaborators from Tel Aviv University are working on a method that could make proton accelerators 100 times smaller without giving up any of their power.