Sunday 09 March 2025
Physicists have made a significant breakthrough in their quest to better understand the behavior of subatomic particles, specifically the interactions between protons and neutrons within atomic nuclei.
The research team used artificial intelligence (AI) to develop a new approach for predicting the properties of these interactions. By training neural networks on vast amounts of data from previous experiments, they were able to create a powerful tool that can accurately calculate the energies and radii of atomic nuclei.
This breakthrough has far-reaching implications for our understanding of the fundamental forces of nature. The AI-powered model can be used to study the behavior of particles at high energies, which is crucial for advancing our knowledge of the universe and developing new technologies.
One of the key challenges facing physicists is the complexity of nuclear interactions. These interactions involve the exchange of particles called quarks and gluons between protons and neutrons, which are themselves made up of quarks and antiquarks. This complexity makes it difficult to accurately predict the properties of atomic nuclei.
The AI-powered model developed by the research team uses a technique called neural networks to learn from vast amounts of data on nuclear interactions. Neural networks are designed to mimic the way that our brains process information, with layers of interconnected nodes that can recognize patterns in large datasets.
By training these neural networks on data from previous experiments, the researchers were able to create a model that can accurately predict the energies and radii of atomic nuclei. This model is much faster and more accurate than traditional methods, which rely on complex calculations involving large numbers of particles and interactions.
The implications of this breakthrough are significant. The AI-powered model can be used to study the behavior of particles at high energies, which is crucial for advancing our knowledge of the universe and developing new technologies.
For example, the model could be used to better understand the properties of dark matter, a mysterious substance that makes up about 27% of the universe’s mass-energy budget. Dark matter is thought to be composed of weakly interacting massive particles (WIMPs), which interact with normal matter only through the weak nuclear force and gravity.
The AI-powered model could also be used to study the behavior of quarks and gluons at high energies, which is crucial for developing new particle accelerators that can explore the properties of these particles in greater detail.
Cite this article: “Physicists Harness AI to Unlock Secrets of Atomic Nuclei”, The Science Archive, 2025.
Physics, Artificial Intelligence, Atomic Nuclei, Quarks, Gluons, Dark Matter, Particle Accelerators, Weak Nuclear Force, Neural Networks, Subatomic Particles
