Sebek’s extraordinary career at SSRL includes helping build the facility’s original electron injector back in the 1980s and working on almost all of its electrical systems since.

The long – but not too long – cavity would ping-pong X-ray pulses inside of a particle accelerator facility to help capture nature’s fastest movements.

The algorithm pairs machine-learning techniques with classical beam physics equations to avoid massive data crunching.

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.

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.

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.

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.

It uses terahertz radiation to power a miniscule copper accelerator structure.

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