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تسجيل مستخدم جديدPropagation of Hauser-Feshbach uncertainty estimates to r-process nucleosynthesis: Benchmark of statistical property models for neutron rich nuclei far from stability
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Multimessenger observations of the neutron star merger event GW170817 have re-energized the debate over the astrophysical origins of the most massive elements via the r-process nucleosynthesis. A key aspect of such studies is comparing astronomical observations to theoretical nucleosynthesis yields in a meaningful way. To perform realistic nucleosynthesis calculations, understanding the uncertainty in microphysics details such as nuclear reaction rates is as essential as understanding uncertainties in modeling the astrophysical environment. We present an investigation of neutron capture rate calculations uncertainty away from stability using the Hauser-Feshbach model. We provide a quantitative measure of the calculations dependability when we extrapolate models of statistical properties to nuclei in an r-process network. We select several level density and gamma-ray strength models appropriate for neutron-capture and use them to calculate the reaction rate for each nucleus in the network. We observe how statistical properties affect the theoretical reaction rates. The rates are then sampled with the Monte Carlo technique and used in network calculations to map the range of possible r-process abundances. The results show that neutron capture rates can vary by a couple of orders of magnitude between calculations. Phenomenological models provide smoother results than semi-microscopic. They cannot, however, reproduce nuclear structure changes such as shell closures. While semi-microscopic models predict nuclear structure effects away from stability, it is not clear that these results are quantitatively accurate. The effect of the uncertainty on r-process yields is large enough to impede comparisons between observation and calculations. Progress in developing better microscopic models of gamma strengths and level densities is urgently needed to improve the fidelity of r-process models.
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