Atomic masses with machine learning for the astrophysical r process

Journal Article · 06 December 2023 · Physics Letters. B

DOI: https://doi.org/10.1016/j.physletb.2023.138385 · OSTI ID:2229702

Li, Mengke  ^[1]; Sprouse, Trevor M.  ^[2]; Meyer, Bradley S. ^[1]; Mumpower, Matthew R.  ^[2]

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1. Clemson Univ., SC (United States)
2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); National Aeronautics and Space Administration (NASA)
• Grant/Contract Number: 89233218CNA000001; 80NSSC20K0338
• OSTI ID: 2229702
• Report Number(s): LA-UR-23-28350; TRN: US2407497
• Journal Information: Physics Letters. B, Vol. 848; ISSN 0370-2693
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English