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The s-process, a production mechanism based on slow-neutron capture during stellar evolution, is the origin of about half the elements heavier than iron. Abundance predictions for s-process nucleosynthesis depend strongly on the relevant neutron-capture and $beta$-decay rates, as well as on the details of the stellar model being considered. Here, we have used a Monte-Carlo approach to evaluate the nuclear uncertainty in s-process nucleosynthesis. We considered the helium burning of massive stars for the weak s-process and low-mass asymptotic-giant-branch stars for the main s-process. Our calculations include a realistic and general prescription for the temperature dependent uncertainty for the reaction cross sections. We find that the adopted uncertainty for (${rm n},gamma$) rates, tens of per cent on average, effects the production of s-process nuclei along the line of $beta$-stability, and that the uncertainties in $beta$-decay from excited state contributions, has the strongest impact on branching points.
We investigated the impact of uncertainties in neutron-capture and weak reactions (on heavy elements) on the s-process nucleosynthesis in low-mass stars using a Monte-Carlo based approach. We performed extensive nuclear reaction network calculations
The main s-process taking place in low mass stars produces about half of the elements heavier than iron. It is therefore very important to determine the importance and impact of nuclear physics uncertainties on this process. We have performed extensi
Thermonuclear supernovae originating from the explosion of a white dwarf accreting mass from a companion star have been suggested as a site for the production of $p$ nuclides. Such nuclei are produced during the explosion, in layers enriched with see
The propagation of uncertainties in reaction cross sections and rates of neutron-, proton-, and $alpha$-induced reactions into the final isotopic abundances obtained in nucleosynthesis models is an important issue in studies of nucleosynthesis and Ga
Several thousand tracers from a 2D model of a thermonuclear supernova were used in a Monte Carlo post-processing approach to determine p-nuclide abundance uncertainties originating from nuclear physics uncertainties in the reaction rates.