Examination of n - T 9 conditions required by N=50, 82, 126 waiting points in r-process

We examined the conditions of neutron density (n) and temperature (T (9)) required for the N = 50, 82, and 126 isotopes to be waiting points (WP) in the r-process. The nuclear mass based on experimental data presented in the AME2020 database (AME and AME +/- Delta) and that predicted using FRDM, WS4, DZ10, and KTUY models were employed in our estimations. We found that the conditions required by the N = 50 WP significantly overlap with those required by the N = 82 ones, except for the WS4 model. In addition, the upper (or lower) bounds of the n - T (9) conditions based on the models are different from each other due to the deviations in the two-neutron separation energies. The standard deviations in the nuclear mass of 108 isotopes in the three N = 50, 82, and 126 groups are about rms = 0.192 and 0.434 MeV for the pairs of KTUY-AME and WS4-KTUY models, respectively. We found that these mass uncertainties result in a large discrepancy in the n ( n ) - T (9) conditions, leading to significant differences in the conditions for simultaneously appearing all the three peaks in the r-process abundance. The newly updated FRDM and WS4 calculations can give the overall conditions for the appearance of all the peaks but vice versa for their old versions in a previous study. The change in the final r-process isotopic abundance due to the mass uncertainty is from a few factors to three orders of magnitude. Therefore, accurate nuclear masses of the r-process key nuclei, especially for Fe-76, Cu-81, Rh-127, Cd-132, Dy-192, and Tm-197, are highly recommended to be measured in radioactive-ion beam facilities for a better understanding of the r-process evolution.

Automated summary

The study found overlapping conditions for N = 50, 82, and 126 isotopes to be waiting points in the r-process, but differences in conditions due to uncertainties in nuclear masses affecting the appearance of all three peaks simultaneously. The discrepancies in conditions based on different models highlight the importance of accurate measurements in understanding r-process evolution.