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.

SLAC works with two small businesses to make its ACE3P software easier to use in supercomputer simulations for optimizing the shapes of accelerator structures.

An extension of the Stanford Research Computing Facility will host several data centers to handle the unprecedented data streams that will be produced by a new generation of scientific projects.

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.