SLAC’s astrophysicists and cosmologists pursue top-priority research on topics including dark matter and dark energy, the formation of galaxies and cosmic evolution.
Dwarf Galaxy 3.
(Visualization by Ralf Kaehler and Tom Abel. Simulation by John Wise, Tom Abel/The Kavli Foundation/SLAC National Accelerator Laboratory/Stanford University)
Rebecca Leane and colleagues showed dark matter could heat planets in our galaxy to incredible temperatures. Here, she explains how that works and how...
For the first time, DES scientists can combine measurements of the distribution of matter, galaxies, and galaxy clusters to advance our understanding of dark...
SLAC cosmologists are using multiple images of the same quasars, produced by massive galaxies’ gravitational pull, to calibrate cosmic distances. Their work may help...
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.
Researchers developed a way to measure the basic properties of matter at the highest pressures thus far achieved in a controlled laboratory experiment.
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.
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.
Researchers developed a way to measure the basic properties of matter at the highest pressures thus far achieved in a controlled laboratory experiment.