Accelerator engineering

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.

Presented by Diana Gamzina. In particle accelerators, electrons are pushed to extreme energies by electromagnetic fields that oscillate inside evacuated metal cavities. Those cavities are usually made of copper.