Researchers across the lab are developing AI tools to harness data and particle beams in real time and make molecular movies, speeding up the discovery process in the era of big data.

David Cesar, Julia Gonski and W.L. Kimmy Wu will each receive $2.75 million issued over five years for their research in X-ray and ultrafast science, new physics and primordial gravitational waves.

The method could lead to the development of new materials with tailored properties, with potential applications in fields such as climate change, quantum computing and drug design.

SLAC experimentalists and theorists collaborate to develop critical detector components, data analysis tools, and theoretical models for the HL-LHC upgrade, which will investigate the Higgs boson and pursue physics beyond the Standard Model.

Wan-Lin Hu’s job is to improve the way people and artificial intelligence collaborate to run SLAC’s complex machines.

What could smaller particle accelerators look like in the future? SLAC scientists are working on innovations that could give more researchers access to accelerator science.

Teams at SLAC installed new experimental hutches with cutting-edge instruments that will harness the upgraded facility’s new capabilities and expand the breadth of research done at the facility.

The future of experimental particle physics is exciting –  and energy intensive. SLAC physicists are thinking about how to make one proposal, the Cool Copper Collider, more sustainable.

With up to a million X-ray flashes per second, 8,000 times more than its predecessor, it transforms the ability of scientists to explore atomic-scale, ultrafast phenomena that are key to a broad range of applications, from quantum materials to clean...

Leora Dresselhaus-Marais, Claudio Emma,  Bernhard Mistlberger and Johanna Nelson Weker will pursue cutting-edge research into decarbonizing steel production, theoretical physics, generating more intense particle beams, and improving X-ray microscopes.

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.