CosmoGAN: Using Neural Nets to Study Dark Matter
“We were looking for two things: to be accurate and to be fast,” said co-author Zarija Lukic, a research scientist in the Computational Cosmology Center at Berkeley Lab. “GANs offer hope of being near…
Self-Supervised Learning for Sky Surveys
Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. Our researchers show that, without the need fo…
Modeling 3D Map of Adolescent Universe
Using extremely faint light from galaxies 10.8 billion light years away, scientists including researchers from CRD's Computational Cosmology Center have created one of the most complete, three-dimensi…
Understanding Dark Energy
Scientists believe that dark energy—the mysterious force that is accelerating cosmic expansion—makes up about 70 percent of the mass and energy of the universe. But because they don’t know what it is,…
New York Times coverage of the 2nd Planck data release
The conference to announce the second release of data from the Planck satellite mission, as covered by the New York Times. C3 scientists lead the use of NERSC supercomputers for Planck data analysis.
In recent years astrophysics has undergone a renaissance, transforming from a data-starved to data-driven science. A new generation of experiments are gathering data sets so massive that their analysis will require the use of leading-edge, high-performance computing resources. Furthermore, interpreting this data requires powerful modeling using numerical simulations and state-of-the-art statistical and machine learning methods. Continuing decades-long collaboration in this field, the Computational Research and Physics Divisions at LBNL have formed the Computational Cosmology Center (C3).
C3 is bringing together astrophysicists and computational scientists whose goals are to develop the tools, techniques, and technologies to meet the scientific challenges of modern cosmology. Our activities include:
- Developing the tools needed to manage the data from the ongoing and future Cosmic Microwave Background missions, like the CMB-S4 and Simons Observatory.
- Building astrophysical transients discovery pipelines, used for finding Supernovae type Ia and other transients.
- Simulating the large-scale structure of the universe using Nyx code, focusing on the intergalactic medium and Lyman alpha forest.
- Developing machine learning surrogate models to augment expensive numerical simulations, as well as to improve the process of scientific inference.
- Computing support for large sky surveys, like DESI and the Dark Energy Science Collaboration of the Vera Rubin Observatory.
Group leader: Zarija Lukić