Washington DC [US], July 8 (ANI): Researchers have developed an artificial intelligence-based simulation that significantly speeds up the modelling of how neutron star mergers produce some of the universe’s heaviest elements. The new tool could improve predictions of these powerful cosmic explosions while helping scientists better connect astronomical observations with experiments on Earth.
Created by an international research team at GSI/FAIR, the machine learning model allows scientists to simulate complex nuclear reactions occurring during neutron star mergers and other extreme stellar events far more efficiently than previous methods. The findings were published in the journal Physical Review D.
Many chemical elements found throughout the universe are created during extreme cosmic events, including supernova explosions and neutron star mergers. These events generate the energy required 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 these neutrons then transform into protons, allowing the nuclei to grow larger and eventually form many of the heavy elements found in nature.
Simulating these reactions remains one of the biggest challenges in nuclear astrophysics because the calculations require enormous computing power.
“Researchers around the world strive to make these complex reactions understandable through theoretical simulations. However, modelling 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 in the Nuclear Astrophysics & Structure department 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), uses machine learning, specifically a deep learning neural network, to estimate the amount of energy released during nuclear reactions in the r-process while hydrodynamic simulations are running.
This energy release, known as heating, plays an important role in determining how matter is expelled during stellar explosions. It influences both the speed of the ejected material and the light produced afterward. In neutron star mergers, this bright emission is observed as a kilonova.
Instead of performing every nuclear calculation during each simulation, the AI model is first trained using an extensive database of reference calculations that include complete nuclear reaction networks. Once trained, it can accurately estimate heating rates using only a fraction of the computational resources required previously.
“First, the ML models are trained using a large number of reference calculations produced with a full set of nuclear reactions. Subsequently, the models are adopted in running hydrodynamical simulations to approximate the heating rates during the r-process with minimal effort,” explained Dr. Zewei Xiong, a scientist in GSI/FAIR’s Nuclear Astrophysics & Structure department and a key developer of the machine learning models.
“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. We also deduced from the results that r-process heating is an important effect that should be better accounted for in future modelling,” Xiong added.
The researchers said RHINE could enable more detailed simulations in the future while significantly reducing the computing resources needed. Improved models may eventually help bridge experiments at the upcoming FAIR research facility with astronomical observations of stellar explosions and neutron star mergers. (ANI)
About The Author
