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

The camera will explore cosmic mysteries as part of the Rubin Observatory’s Legacy Survey of Space and Time.

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.

Observations of dwarf galaxies around the Milky Way have yielded simultaneous constraints on three popular theories of dark matter.

New research could offer insights into the formation of planets like Earth and inform the design of more resilient materials.

It could offer insights into the evolution of planetary systems and guide scientists hoping to harness nuclear fusion as a new source of energy.

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.

Blandford’s major contributions range from energetic jets ripping forth from colossal black holes to cosmic “magnifying” glasses to gravitational waves.

Physicists at SLAC and Stanford propose that the influence of cosmic rays on early life may explain nature’s preference for a uniform “handedness” among biology’s critical molecules.

Learning how liquid silicates behave at these extreme temperatures and pressures has been a longstanding challenge in the geosciences.