Influence of Spontaneous Fission Rates on the r-process Nucleosynthesis

Abstract

The effects of spontaneous fission on r-process nucleosynthesis are investigated in the hot wind r-process scenario. We perform network calculations using three sets of spontaneous fission rates to study how the abundance pattern is shaped when different sets of fissioning nuclei are encountered by the r-process nuclear flow. The relative contributions from spontaneous fission, neutron-induced fission, and β-delayed fission to the nucleosynthesis process are studied by calculating the corresponding fission flow. We show that the relative contributions of various fission channels in r-process nucleosynthesis depend on the astrophysical conditions and fission models used. By using the spontaneous fission rates from a modified Swiatecki’s formula with isospin and blocking effects, the spontaneous fission and neutron-induced fission play an equally important role in r-process nucleosynthesis under an extreme neutron-rich astrophysical scenario with Y e = 0.1. The fissioning nuclei are located in different regions of the nuclear chart when different spontaneous fission models are used. The fission fragment distributions of fissioning nuclei in different regions have apparent diversity, which affects the mass regions where fission products are deposited, leading to the difference of the final abundance around the second r-process peak and rare-earth subpeak.

Impact of Nuclear β-decay Half-life Uncertainties on the r-process Simulations

“…Nuclear fission has an important effect on r-process simulations (Goriely et al 2013;Shibagaki et al 2016;Hao et al 2022), which is highly dependent on the production of transuranium elements (Z > 92). Figure 3 shows the influence of nuclear β-decay half-life predictions on the abundance evolution of transuranium elements.…”

“…From Figure 3, we know that the abundances of transuranium elements start to decrease after about 2 s when the fission plays important roles in the r-process simulations. The transuranium nuclei generally produce two to three fission fragments, which distribute to the abundances around A = 100-180 (Lemaître et al 2021;Hao et al 2022). These fission products further undergo a multitude of reactions or decays after being produced by fission, which finally fill in the troughs before the second and rare-earth peaks.…”

“…However, the r-process site has not been unambiguously identified so far. In this work, a hot wind trajectory parameterized as in Meyer (2002), Hao et al (2022) is used to perform r-process calculations. Two initial electron fractions Y e = 0.1 and 0.3 are used to study the influence of different astrophysical conditions instead of using the environments from specific r-process sites.…”

“…where τ 0 and τ 1 are the expansion timescales, T 9,0 and ρ 0 + ρ 1 are the initial temperature and initial density. As in Hao et al (2022), the parameters in Equations (1) and (2) are set to be T 9,0 = 10, τ 0 = 0.035 s, τ 1 = 1.0 s, ρ 0 = 1.4985 × 10 6 g cm −3 , and ρ 1 = 1.5 × 10 3 g cm −3 , respectively. The reaction network in our NucNet calculations includes the nuclei with Z 102.…”

“…The reaction network in our NucNet calculations includes the nuclei with Z 102. For their properties and the related reaction data, we have taken them from the JINA REACLIB database (Cyburt et al 2010), while the α-decay half-lives, βdecay half-lives, fission rates, and fission fragment yields are updated as following: the α-decay half-lives are updated by the experimental values from the National Nuclear Data Center (NNDC); 4 the fission rates and fission fragment yields are updated by the predictions from Hao et al (2022), which are calculated with a phenomenological formula in Bao et al (2015) and the general fission model (Schmidt et al 2016), respectively; the β-decay half-life predictions are replaced by the predictions with seven β-decay models, spanning from the phenomenological formula Zhou (Shi et al 2021), the gross theory RHB+GT (Fang et al 2022), the microscopic nuclear models FRDM+QRPA (Möller et al 2018), RHB+QRPA (Marketin et al 2016), SHFB+QRPA (Minato et al 2022), and SHFB+FAM (Ney et al 2020), to the machine-learning method WS4+NN (Li et al 2022), in order to investigate the influence of β-decay half-lives on the r-process simulations. It should be mentioned that the machine-learning model WS4 +NN has smaller errors in known regions, but the errors would become larger and larger when going away from the known regions, so one should take caution when using the extrapolated results of machine learning.…”