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ABSTRACT We present a detailed chemical-abundance analysis of a highly r-process-enhanced (RPE) star, 2MASS J00512646-1053170, using high-resolution spectroscopic observations with Hubble Space Telescope/STIS in the UV and Magellan/MIKE in the optical. We determined abundances for 41 elements in total, including 23 r-process elements and rarely probed species such as Al ii, Ge i, Mo ii, Cd i, Os ii, Pt i, and Au i. We find that [Ge/Fe] = +0.10, which is an unusually high Ge enhancement for such a metal-poor star and indicates contribution from a production mechanism decoupled from that of Fe. We also find that this star has the highest Cd abundance observed for a metal-poor star to date. We find that the dispersion in the Cd abundances of metal-poor stars can be explained by the correlation of Cd i abundances with the stellar parameters of the stars, indicating the presence of NLTE effects. We also report that this star is now only the sixth star with Au abundance determined. This result, along with abundances of Pt and Os, uphold the case for the extension of the universal r-process pattern to the third r-process peak and to Au. This study adds to the sparse but growing number of RPE stars with extensive chemical-abundance inventories and highlights the need for not only more abundance determinations of these rarely probed species, but also advances in theoretical NLTE and astrophysical studies to reliably understand the origin of r-process elements.
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This content will become publicly available on March 25, 2026
Graph-based Recursive Relations for Computing and Analyzing r -process Abundances
Abstract We develop recursive relations among abundances in anr-process network evolving neutron captures, photodisintegrations and beta decays through the use of the matrix-tree and matrix-forest theorems. Since these theorems are based on results from graph theory, we term the relations the GrRproc (GraphicalR-process) relations. We validate the relations by using them to computer-process abundances in network calculations in different astrophysical environments. We also illustrate how they can be used to follow complex reaction flows quantitatively in an evolvingr-process network through the concept of contribution paths. Such contribution paths show how particular reactions govern the evolution of abundance features during the nucleosynthesis and, consequently, can clarify the role of key nuclear data and astrophysical environments in that evolution. The Python open-source package that implements the tool is freely available.
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- Award ID(s):
- 2020275
- PAR ID:
- 10598051
- Publisher / Repository:
- AAS
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 982
- Issue:
- 2
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 139
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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