Fulfilling a prediction of Einstein’s theory of General Relativity, researchers report the first-ever recordings of X-ray emissions from the far side of a black hole.

The research could lead to a better understanding of extreme astrophysical environments and the development of compact high-energy radiation sources for science.

She toured the lab’s powerful X-ray laser, looked at the construction of the world’s largest digital camera, and discussed climate research, industries of the future, and diversity, equity and inclusion in the sciences.

From the invisible world of elementary particles to the mysteries of the cosmos, recipients of this prestigious award for early career scientists explore nature at every level.

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.

Rebecca Leane and colleagues showed dark matter could heat planets in our galaxy to incredible temperatures. Here, she explains how that works and how it could pave the way for sensitive new searches for the mysterious substance.

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.