AI for astrophysics: Algorithms help chart the origins of heavy elements | Science & Technology

The origin of heavy elements in our universe is theorized to be the result of neutron star collisions, which produce conditions hot and dense enough for free neutrons to merge with atomic nuclei and form new elements in a split-second window of time. Testing this theory and answering other astrophysical questions requires predictions for a vast range of masses of atomic nuclei. Los Alamos National Laboratory scientists are front and center in using machine learning algorithms (an application of artificial intelligence) to successfully model the atomic masses of the entire nuclide chart — the combination of all possible protons and neutrons that defines elements and their isotopes.

“Many thousands of atomic nuclei that have yet to be measured may exist in nature,” said Matthew Mumpower, a theoretical physicist and co-author on several recent papers detailing atomic masses research. “Machine learning algorithms are very powerful, as they can find complex correlations in data, a result that theoretical nuclear physics models struggle to efficiently produce. These correlations can provide information to scientists about ‘missing physics’ and can in turn be used to strengthen modern nuclear models of atomic masses.”

Simulating the rapid neutron-capture process

Most recently, Mumpower and his colleagues, including former Los Alamos summer student Mengke Li and postdoc Trevor Sprouse, authored a paper in Physics Letters B that described simulating an important astrophysical process with a physics-based machine learning mass model. The r process, or rapid neutron-capture process, is the astrophysical process that occurs in extreme environments, like those produced by neutron star collisions. Heavy elements may result from this “nucleosynthesis”; in fact, half of the heavy isotopes up to bismuth and all of thorium and uranium in the universe may have been created by the r process.

But modeling the r process requires theoretical predictions of atomic masses currently…

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