Public lecture presented by Arianna Gleason

Edward Hohenstein, Emma McBride and Caterina Vernieri study what happens to molecules hit by light, recreate extreme states of matter like those inside stars and planets, and search for new physics phenomena at the most fundamental level.

An analysis of the first three years of Dark Energy Survey data is consistent with predictions from the current best model of the universe. Nevertheless, hints remain from DES and other experiments that matter in the current universe is a...

An international collaboration aims to create a 3D map of the universe, unraveling the mysterious nature of dark energy.

For the first time, DES scientists can combine measurements of the distribution of matter, galaxies, and galaxy clusters to advance our understanding of dark energy.

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.

Rubin Observatory will bring new capabilities to the studies of dark matter and dark energy.

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.

Q-NEXT will tackle next-generation quantum science challenges through a public-private partnership, ensuring U.S. leadership in an economically crucial arena.

Their work uses machine learning to transform the way scientists tune particle accelerators for experiments and solve longstanding mysteries in astrophysics and cosmology.