Computer simulations yield a much more accurate picture of these states of matter.

At SLAC’s FACET facility, researchers have produced an intense electron beam by 'sneaking’ electrons into plasma, demonstrating a method that could be used in future compact discovery machines that explore the subatomic world.

SLAC researchers say their new method could make it easier to study interactions of ultrabright X-rays with matter

Combination of research methods reveals causes of capacity fading, giving scientists better insight to design advanced batteries for electric vehicles

The Large Synoptic Survey Telescope will track billions of objects for 10 years, creating unprecedented opportunities for studies of cosmic mysteries.

SLAC scientists find a new way to explain how a black hole’s plasma jets boost particles to the highest energies observed in the universe. The results could also prove useful for fusion and accelerator research on Earth.

SLAC receives three awards for the development of quantum technology for dark matter searches and quantum computing

Researchers from SLAC and around the world increasingly use machine learning to handle Big Data produced in modern experiments and to study some of the most fundamental properties of the universe.

Their work will deepen our understanding of matter in extreme conditions and fundamental particle physics.

As members of the lab’s Computer Science Division, they develop the tools needed to handle ginormous data volumes produced by the next generation of scientific discovery machines.

These stripes of electron spin and charge are exciting because of their possible link to a phenomenon that could transform society by making electrical transmission nearly 100 percent efficient.

SLAC and Stanford researchers demonstrate that brain-mimicking ‘neural networks’ can revolutionize the way astrophysicists analyze their most complex data, including extreme distortions in spacetime that are crucial for our understanding of the universe.