1. Significantly improved estimates of neutron capture cross sections relevant to the r process
- Author
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R. B. Cakirli, Aaron Couture, and R. F. Casten
- Subjects
Physics ,Nuclear physics ,Neutron capture ,Nuclear Theory ,Neutron cross section ,Order (ring theory) ,r-process ,Production (computer science) ,Approx ,Nuclear Experiment ,Nucleon ,Energy (signal processing) - Abstract
Background: The $r$ process is thought to be a dominant mechanism for the production of medium mass and heavy nuclei. Despite extensive study, neither the process nor its site, and, consequently, the origin of these elements, is well understood. One of the principal reasons is the lack of adequate knowledge of neutron capture cross sections in neutron-rich nuclei, especially up to a few hundred keV. Existing statistical model calculations differ widely and are unreliable more than a few nucleons beyond stability.Purpose: To provide a new, more reliable empirical method to estimate neutron capture cross sections.Method: To use an entirely empirical approach by exploiting a newly discovered correlation between two-neutron separation energies and neutron capture cross sections.Results: It is shown that there is a compact correlation between neutron capture cross sections and ${S}_{2n}$ for ${S}_{2n}$ values from 10 to 16 MeV, encompassing nuclei in five mass regions comprising seven classes of nuclei from $A\ensuremath{\approx}110$ through the actinides over Maxwellian energy distributions from $kT=5$ to 100 keV. As a consequence, many unknown cross sections can be predicted by interpolation, with accuracies on the order of $\ifmmode\pm\else\textpm\fi{}25%$. In other cases, the cross sections can still be predicted with greatly improved accuracy compared to current models. Finally, it is shown that, in very neutron-rich nuclei, measurements of ${S}_{2n}$ can replace more difficult or impossible neutron capture measurements to estimate $r$-process abundances.Conclusions: A new approach to estimate neutron capture cross section allows much improved predictions of these key ingredients into $r$-process calculations, perhaps providing an enhanced ability to model this process and to better define its site(s).
- Published
- 2021