Researchers have developed a new artificial intelligence-powered simulation that could significantly improve our understanding of how the universe creates many of its heaviest elements.
Created by an international team at GSI Helmholtzzentrum für Schwerionenforschung GmbH and the FAIR facility, the machine learning model allows scientists to simulate the complex nuclear reactions that occur during neutron star mergers far more efficiently than before. Their findings were published in the journal Physical Review D.
Many of the chemical elements found throughout the universe — including gold, platinum, and uranium — are forged during extreme cosmic events, including supernova explosions and neutron star mergers. These enormous explosions generate the energy needed to produce heavy atomic nuclei through a process known as rapid neutron capture, or the r-process.
During the r-process, atomic nuclei rapidly absorb free neutrons. Some of those neutrons then transform into protons, allowing the nuclei to grow larger and eventually form heavy elements.
Simulating these reactions has been one of the biggest challenges in nuclear astrophysics because the calculations require tremendous computing power.
"Researchers around the world strive to make these complex reactions understandable through theoretical simulations. However, modeling all parameters requires incredible computing power, which is why the models often have to be simplified," said Dr. Oliver Just, first author of the study and a researcher at GSI/FAIR. "Our new model RHINE, which uses artificial intelligence, offers an efficient alternative."
The new system, called RHINE (r-process heating implementation in hydrodynamic simulations with neural networks), relies on a deep learning neural network to estimate how much energy is released during nuclear reactions in the r-process while hydrodynamic simulations are running.
This energy release, called heating, plays an important role in determining how matter is expelled during stellar explosions. It can influence both the speed of the ejected material and the light produced afterward. In neutron star mergers, that brilliant glow is observed as a kilonova.
Instead of performing every nuclear calculation during each simulation, the AI is first trained using an extensive library of reference calculations that include complete nuclear reaction networks. Once trained, it can accurately estimate the heating rates with only a fraction of the computational effort.
"With detailed comparisons, we validated our ML scheme against reference data. The high degree of agreement suggests that the use of ML models can save a tremendous amount of computing time," explained Dr. Zewei Xiong, a key developer of the machine learning models.
The researchers say RHINE could enable much more detailed simulations in the future while dramatically reducing the computing resources required. Those improved models may eventually help connect experiments at the upcoming FAIR research facility with observations of stellar explosions made by astronomers.
The RHINE source code has been made publicly available so other researchers can build on the work. The project was co-funded by the European Research Council.
