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.

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

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