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Abstract Heavy elements are synthesized by ther-process in neutron star mergers and potentially in rare supernovae linked to strong magnetic fields. Expensive hydrodynamic simulations of these extreme environments are usually postprocessed to calculate the nucleosynthesis. In contrast, here we follow a site-independent approach based on three key parameters: electron fraction, entropy, and expansion timescale. Our model reproduces the results based on hydrodynamic simulations. Moreover, the 120,000 astrophysical conditions analyzed allow us to systematically and generally explore the astrophysical conditions of ther-process, also beyond those found in current simulations. Our results show that a wide range of conditions produce very similar abundance patterns explaining the observed robustness of ther-process between the second and third peak. Furthermore, we cannot find a single condition that produces the full solarr-process pattern from first to third peak. Instead, a superposition of at least two or three conditions or components is required to reproduce the typicalr-process pattern as observed in the solar system and very old stars. The different final abundances are grouped into eight nucleosynthesis clusters, which can be used to select representative conditions for comparisons to observations and investigations of the nuclear physics input.
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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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