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Atomic Masses with Machine Learning for the Astrophysical R Process by Mengke Li; Trevor M. Sprouse; Bradley S. Meyer; Matthew R. Mumpower is a Physics and Astronomy article available to read on EtoBox.

What is Atomic Masses with Machine Learning for the Astrophysical R Process about?

The astrophysical r process plays a vital role in the production of heavy elements. Modeling of the r process is sensitive to masses and further requires knowledge of masses beyond current experimental reach. Therefore, simulations of the r process offer a unique test bed for predicting mass extrapolations. We take a Machine-Learning (ML) approach to model the masses across the entire chart of nuclides. For the first time, we simulate r-process nucleosynthesis with a physics-based ML mass model. We compare simulated abundances to solar data in order to evaluate the model's performance far from stability. The resulting r-process abundances up to thorium and uranium qualitatively match those of the observed solar system abundance pattern, with the characteristic peaks well positioned. We propagate the mass uncertainties obtained from the ML model to r-process abundance yields to estimate an uncertainty band associated with our approach. The size of the uncertainty band is approximately one order of magnitude which aligns with the uncertainty reported using alternative techniques.

Who reads Atomic Masses with Machine Learning for the Astrophysical R Process?

Author

Mengke Li; Trevor M. Sprouse; Bradley S. Meyer; Matthew R. Mumpower

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Physics and Astronomy (Physical Sciences)