SLAC cosmologists are using multiple images of the same quasars, produced by massive galaxies’ gravitational pull, to calibrate cosmic distances. Their work may help resolve long-standing debates about how quickly the universe is expanding.

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.