Opacities of Singly and Doubly Ionised Neodymium and Uranium for Kilonova Emission Modeling

arxiv(2023)

引用 1|浏览36
暂无评分
摘要
In 2017, the electromagnetic counterpart AT2017gfo to the binary neutron star merger GW170817 was observed by all major telescopes on Earth. While it was immediately clear that the transient following the merger event, is powered by the radioactive decay of r-process nuclei, only a few tentative identifications of light r-process elements have been made so far. One of the major limitations for the identification of heavy nuclei based on light curves or spectral features is incomplete or missing atomic data. While some progress has been made on lanthanide atomic data over the last few years, for actinides very little atomic data is available. We perform atomic structure calculations of neodymium ($Z = 60$) as well as the corresponding actinide uranium ($Z = 92$). Using two different codes (FAC and HFR) for the calculation of the atomic data, we investigate the accuracy of the calculated data (energy levels and electric dipole transitions) and their effect on kilonova opacities. For the FAC calculations, we optimise the local central potential and the number of included configurations and use a dedicated calibration technique to improve the agreement between theoretical and available experimental atomic energy levels (AELs). For ions with vast amounts of experimental data available, the presented opacities agree quite well with previous estimations. On the other hand, the optimisation and calibration method cannot be used for ions with only a few available AELs. For these cases, where no experimental nor benchmarked calculations are available, a large spread in the opacities estimated from the atomic data obtained with the various atomic structure codes is observed, most likely due to the uncertainty in the density of low-lying levels predicted by theory. We find that the opacity of uranium is almost double the neodymium opacity.
更多
查看译文
关键词
kilonova emission modeling,ionised neodymium,uranium
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要