Accelerators

Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.

Institute of Electrical and Electronics Engineers recognizes his contributions to developing electron beams that power unique ‘electron cameras’ and could advance X-ray lasers.

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

Their work uses machine learning to transform the way scientists tune particle accelerators for experiments and solve longstanding mysteries in astrophysics and cosmology.

Marking the beginning of the LCLS-II era, the first phase of the major upgrade comes online.

It combines human knowledge and expertise with the speed and efficiency of “smart” computer algorithms.

Researchers have squeezed a high-energy electron beam into tight bundles using terahertz radiation, a promising advance in watching the ultrafast world of atoms unfold.

A cheap technique could detect neutrinos in polar ice, eventually allowing researchers to expand the energy reach of IceCube without breaking the bank.

Siqi Li develops connections with people and concepts while working on new technologies for accelerators.

Just as engineers once compressed some of the power of room-sized mainframes into desktop PCs, so too have the researchers shown how to pack some of the punch delivered by today’s ginormous particle accelerators onto a tiny silicon chip.