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According to Sciencedaily, an international team of researchers at GSI/FAIR has successfully created a machine learning model that streamlines the study of heavy element formation. The tool addresses one of the most significant hurdles in nuclear astrophysics: the immense computing power required to simulate the rapid neutron capture, or r-process, which occurs during supernova explosions and neutron star mergers.
The mechanics of the r-process
During extreme cosmic events, atomic nuclei rapidly absorb free neutrons. As these neutrons transform into protons, the nuclei grow larger, eventually forming many of the heavy elements found in nature today. Because modeling every parameter of these reactions is computationally taxing, scientists have historically had to rely on simplified models that may sacrifice accuracy for speed.
The new system, known as RHINE (r-process heating implementation in hydrodynamic simulations with neural networks), utilizes deep learning to estimate energy release during these reactions. This "heating" effect is critical because it determines how matter is expelled from stellar explosions and influences the light produced by kilonovae.
Deep learning optimizes nuclear calculations
The RHINE model functions by first training a neural network on an extensive library of reference calculations that include complete nuclear reaction networks. Once trained, the AI can approximate heating rates during hydrodynamical simulations with only a fraction of the usual computational effort.
- Reduces the need for simplified models in nuclear astrophysics.
- Provides accurate estimates of energy release during r-process events.
- Enables faster modeling of matter expulsion speeds and kilonova light production.
- Validates high degrees of agreement against existing reference data.
"Our new model RHINE, which uses artificial intelligence, offers an efficient alternative," said Dr. Oliver Just, first author of the study at GSI/FAIR. The researchers have demonstrated that this approach can save a tremendous amount of computing time while maintaining high fidelity to known data.
"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. This advancement allows for more nuanced modeling of r-process heating, which is now recognized as an essential factor in understanding the evolution of matter in the universe.