In recent years, deep learning models have been successfully employed for augmenting low-resolution cosmological simulations with small-scale information, a task known as "super-resolution". So far, these cosmological super-resolution models have relied on generative adversarial networks (GANs), which can achieve highly realistic results, but suffer from various shortcomings (e.g. low sample diversity). We introduce denoising diffusion models as a powerful generative model for super-resolving cosmic large-scale structure predictions (as a first proof-of-concept in two dimensions). To obtain accurate results down to small scales, we develop a new "filter-boosted" training approach that redistributes the importance of different scales in the pixel-wise training objective. We demonstrate that our model not only produces convincing super-resolution images and power spectra consistent at the percent level, but is also able to reproduce the diversity of small-scale features consistent with a given low-resolution simulation. This enables uncertainty quantification for the generated small-scale features, which is critical for the usefulness of such super-resolution models as a viable surrogate model for cosmic structure formation.

The current accelerated expansion of the universe is driven by mysterious dark energy. Upcoming astronomical imaging surveys, such as LSST at Rubin Observatory, are set to provide unprecedented precise measurements of cosmological parameters, including this dark energy, using measurements such as weak gravitational lensing. Weak lensing is measured by looking for coherent patterns in galaxy shapes, which can be caused by the fact that the cosmic matter distribution coherently distorts spacetime, affecting the appearances of nearby galaxy images in similar ways. However, there are still challenges facing cosmologists on their path from data to science. One of these challenges is the effect of intrinsic alignments – where galaxies are not oriented randomly in the sky, but rather tend to point towards other galaxies.

The current accelerated expansion of the Universe is driven by mysterious dark energy. Upcoming astronomical imaging surveys, such as LSST at Rubin Observatory, are set to provide unprecedented precise measurements of cosmological parameters, including this dark energy, using measurements such as weak gravitational lensing. Weak lensing is measured by looking for coherent patterns in galaxy shapes, which can be caused by the fact that the cosmic matter distribution coherently distorts spacetime, affecting the appearances of nearby galaxy images in similar ways. However, there are still challenges facing cosmologists on their path from data to science. One of these challenges is the effect of intrinsic alignments – where galaxies are not oriented randomly in the sky, but rather tend to point towards other galaxies.

A universe evolves over billions upon billions of years, but researchers have developed a way to create a complex simulated universe in less than a day. The technique, published in this week's Proceedings of the National Academy of Sciences, brings together machine learning, high-performance computing and astrophysics and will help to usher in a new era of high-resolution cosmology simulations. Cosmological simulations are an essential part of teasing out the many mysteries of the universe, including those of dark matter and dark energy. But until now, researchers faced the common conundrum of not being able to have it all -- simulations could focus on a small area at high resolution, or they could encompass a large volume of the universe at low resolution. Carnegie Mellon University Physics Professors Tiziana Di Matteo and Rupert Croft, Flatiron Institute Research Fellow Yin Li, Carnegie Mellon Ph.D. candidate Yueying Ni, University of California Riverside Professor of Physics and Astronomy Simeon Bird and University of California Berkeley's Yu Feng surmounted this problem by teaching a machine learning algorithm based on neural networks to upgrade a simulation from low resolution to super resolution.

Cosmological simulations are indispensable for understanding our Universe, from the creation of the cosmic web to the formation of galaxies and their central black holes.

Using a bit of machine learning magic, astrophysicists can now simulate vast, complex universes in a thousandth of the time it takes with conventional methods. The new approach will help usher in a new era in high-resolution cosmological simulations, its creators report in a study published online on May 4, 2021, in Proceedings of the National Academy of Sciences. "At the moment, constraints on computation time usually mean we cannot simulate the universe at both high resolution and large volume," says study lead author Yin Li, an astrophysicist at the Flatiron Institute in New York City. "With our new technique, it's possible to have both efficiently. In the future, these AI-based methods will become the norm for certain applications."

IMAGE: The leftmost simulation ran at low resolution. Using machine learning, researchers upscaled the low-res model to create a high-resolution simulation (right). A universe evolves over billions upon billions of years, but researchers have developed a way to create a complex simulated universe in less than a day. The technique, published in this week's Proceedings of the National Academy of Sciences, brings together machine learning, high-performance computing and astrophysics and will help to usher in a new era of high-resolution cosmology simulations. Cosmological simulations are an essential part of teasing out the many mysteries of the universe, including those of dark matter and dark energy.