Scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory are on a quest to understand the most elusive components of the universe. Their work has the potential to answer enduring questions about cosmic evolution and challenge long-held scientific paradigms.
"The nature of dark matter and dark energy is not understood," said Salman Habib, an Argonne Distinguished Fellow and director of the lab's Computational Science division. "We know the two exist, but we don't understand what they are, nor the fundamental principles governing their existence."
To address these mysteries, researchers at Argonne are developing intricate sky maps that integrate real cosmic observations with extensive computer simulations of the universe. This initiative, known as Dark Sky Mining, is part of the Aurora Early Science Program supported by the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility.
Aurora, an exascale supercomputer capable of performing over a quintillion calculations per second, is pivotal in rapidly simulating and refining numerous physics-driven cosmic scenarios. By integrating advanced artificial intelligence (AI) and statistical methods, researchers aim to achieve a deeper understanding of dark matter and dark energy, along with their connections to galaxy distribution and behavior in the universe.
Visible matter constitutes only 5% of the universe. Dark matter accounts for 27%, while dark energy comprises the remaining 68%. The concept of dark matter gained prominence in the 1930s when Swiss astronomer Fritz Zwicky observed that galaxy clusters, like the Coma cluster, could not stay bound together based on gravitational principles alone. Zwicky hypothesized an unseen "dark matter" to explain the phenomenon.
Subsequent studies in the 1960s and 1970s, along with modern techniques such as gravitational lensing, have further validated the existence of dark matter. Meanwhile, observations of supernovae revealed the accelerating expansion of the universe, pointing to the influence of dark energy.
"Remarkably, dark energy was just what was needed to make the large-scale clustering of galaxies come out right and, somehow, keep the cosmological model intact," explained Habib.
While dark matter's gravitational effects make it detectable, dark energy presents a distinct challenge by altering the universe's expansion rate. Habib noted, "Dark energy is a very different proposition because it makes clear a fundamental problem in describing how gravity works. And that's what causes a lot of heartburn."
"If you give me a model for how dark matter interacts with itself, I can put that in my simulation code and predict how the mass distribution will change," said Habib. Researchers can then adjust models to align with observations, uncovering new insights.
Aurora's advanced AI techniques also revolutionize problem-solving. Emulation, a machine learning-based approach, reduces the need for thousands of simulations by identifying parameter sets that best match observations. "We can make the process way more efficient," added Habib.
For Habib, this endeavor is more than a scientific pursuit. "What we're doing right now is simply an extension of humanity's long history of connection to the stars," he reflected. "The ability to look deep into the universe is pretty astounding because it also tells us a lot about our own place in the big scheme of things."
Related Links
Argonne National Laboratory
Space Technology News - Applications and Research
| Subscribe Free To Our Daily Newsletters |
| Subscribe Free To Our Daily Newsletters |
