Visualization of the gas in a (576 Mpc/h)^3 volume CRK-HACC simulation. Shown are the density (brightness) and temperature (color) integrated along a slab spanning the full volume, including a zoom-in. Image: Michael Buehlmann and the HACC Team, Argonne National Laboratory
This project will usher in a new era of cosmological simulations by fully exploiting the power of DOE’s exascale systems, providing scientific results that will be a critical input for ongoing and upcoming cosmological surveys.
The Universe is dominated by its ‘dark’ sector, with two mysterious components accounting for 95% of the cosmic matter-energy content. The dark energy component is the cause of a yet to be understood late-time cosmic acceleration. The dark matter component dominates over normal matter, and while its gravitational presence is confirmed via multiple observations, it eludes direct detection because it does not appear to emit or absorb light. Major sky surveys are ongoing and being constructed by a large international community to unravel the physics of the dark Universe and to investigate other fundamental cosmological questions. Large-scale simulations that connect the physics of the dark sector to observations in the visible domain are essential to this global endeavor. Such simulations are the basis of synthetic sky catalogs that are crucial to developing and testing analysis capabilities for the surveys, delivering predictions for the nonlinear regime of structure formation inaccessible by any other means, and helping determine the systematics error budget, a critical task to ensure that the anticipated level of cosmological constraints can be attained.
This INCITE project will usher in a new era of cosmological simulations by fully exploiting the power of DOE’s Frontier and Aurora supercomputers. The team will employ two exascale- ready cosmology codes: the Hardware/Hybrid Accelerated Cosmology Code (HACC) and Nyx. The team’s simulations will be transformed into synthetic sky catalogs across wavebands and provide predictions for a range of cosmological observables using both direct analysis performed on simulations as well as transfer learning combining the strengths of all the simulations. The scientific results will be an important input for all major ongoing and upcoming U.S.-led cosmological surveys, including DOE’s Dark Energy Spectroscopic Instrument (DESI); NSF and DOE’s Rubin’s Legacy Survey of Space and Time (LSST), South Pole Telescope (SPT), and CMB-S4; and NASA’s Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) and the Nancy Grace Roman Space Telescope
