Knowing a magnet’s past will allow scientists to customize particle beams more precisely in the future. As accelerators stretch for higher levels of performance, understanding subtle effects, such as those introduced by magnetic history, is becoming more critical.

Edelen draws on machine learning to fine tune particle accelerators, while Kurinsky develops dark matter detectors informed by quantum information science.

The facility, LCLS-II, will soon sharpen our view of how nature works on ultrasmall, ultrafast scales, impacting everything from quantum devices to clean energy.

SLAC’s Matt Garrett and Susan Simpkins talk about tech transfer that brings innovations from the national lab to the people, including advances for medical devices and self-driving vehicles.

Over the past few years, Kathleen Ratcliffe and Tien Fak Tan have worked together to help build the superconducting accelerator that will drive new scientific discoveries at SLAC’s X-ray laser.

Less than a millionth of a billionth of a second long, attosecond X-ray pulses allow researchers to peer deep inside molecules and follow electrons as they zip around and ultimately initiate chemical reactions.

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.