Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.
Their work uses machine learning to transform the way scientists tune particle accelerators for experiments and solve longstanding mysteries in astrophysics and cosmology.
Researchers developed a way to measure the basic properties of matter at the highest pressures thus far achieved in a controlled laboratory experiment.
In experiments at the National Ignition Facility, a SLAC-led team found new details about how supernovas boost charged particles to nearly the speed of light.
The Dark Energy Spectroscopic Instrument, which will map millions of galaxies in 3D from a mountaintop in Arizona, has reached its final milestone toward its startup.