Accelerators

Three physicists talk about how they got started, their work at SLAC and what they would say to others considering a career in STEM.

Teaching machine learning the basics of accelerator physics is particularly useful in situations where actual data don’t exist.

From the invisible world of elementary particles to the mysteries of the cosmos, recipients of this prestigious award for early career scientists explore nature at every level.

It can help operators optimize the performance of X-ray lasers, electron microscopes, medical accelerators and other devices that depend on high-quality beams.

At the Machine Shop, Pete Franco crafts beautiful, intricate and precise parts for the lab’s latest scientific tools.

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.

Institute of Electrical and Electronics Engineers recognizes his contributions to developing electron beams that power unique ‘electron cameras’ and could advance X-ray lasers.

This leap in capability will allow scientists to investigate quantum and chemical systems more directly than ever before.

Their work uses machine learning to transform the way scientists tune particle accelerators for experiments and solve longstanding mysteries in astrophysics and cosmology.

Marking the beginning of the LCLS-II era, the first phase of the major upgrade comes online.

It combines human knowledge and expertise with the speed and efficiency of “smart” computer algorithms.