Accelerators

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

By tinkering and troubleshooting, Aalayah Spencer helps turn researchers’ ideas into state-of-the-art science experiments.

Experiments visualize how 2D perovskite structures change when excited.

Two GEM Fellows reflect on their summer internships at SLAC and share their thoughts on representation and mentorship.

They saw how the material finds a path to contorting and flexing to avoid being irreversibly crushed.

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

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.