Researchers Use Machine Learning To Map The Evolution Of The Universe

Researchers Use Machine Learning To Map The Evolution Of The Universe

By KAVLI INSTITUTE

Researchers seek to understand our Universe by making model predictions to match observations. Historically, they have been able to model simple or highly simplified physical systems, jokingly dubbed the “spherical cows,” with pencils and paper. Later, the arrival of computers enabled them to model complex phenomena with numerical simulations. For example, researchers have programmed supercomputers to simulate the motion of billions of particles through billions of years of cosmic time, a procedure known as the N-body simulations, in order to study how the Universe evolved to what we observe today.

“Now with machine learning, we have developed the first neural network model of the Universe, and demonstrated there’s a third route to making predictions, one that combines the merits of both analytic calculation and numerical simulation,” said Yin Li, a Postdoctoral Researcher at the Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo, and jointly the University of California, Berkeley.

At the beginning of our Universe, things were extremely uniform. As time went by, the denser parts grew denser and sparser parts became sparser due to gravity, eventually forming a foam-like structure known as the “cosmic web.” To study this structure formation process, researchers have tried many methods, including analytic calculations and numerical simulations. Analytic methods are fast, but fail to produce accurate results for large density fluctuations. On the other hand, numerical (N-body) methods simulate structure formation accurately, but tracking gazillions of particles is costly, even on supercomputers. Thus, to model the Universe, scientists often face the accuracy versus efficiency trade-off.

Li says the power of AI emulation will scale up in the future. N-body simulations are already heavily optimized, and as a first attempt, his team’s AI model still has large room for improvement. Also, more complicated phenomena incur a larger cost on simulation, but not likely so on emulation. Li and his colleagues expect a bigger performance gain from their AI emulator when they move on to including other effects, such as hydrodynamics, into the simulations.

“It won’t be long before we can uncover the initial conditions of and the physics encoded in our Universe along this path,” he said.

This study was published in the Proceedings of the National Academy of Sciences of the United States of America on June 24, 2019. DOI: 10.1073/pnas.1821458116

Image Credit: S. He et al./PNAS 2019