Graph-based Recursive Relations for Computing and Analyzing <i>r</i>-process Abundances

Abstract We develop recursive relations among abundances in an r -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 (Graphical R -process) relations. We validate the relations by using them to compute r -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 evolving r -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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Publication Details

Journal
The Astrophysical Journal
Published
2025-03-25
DOI
https://doi.org/10.3847/1538-4357/adb625
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
0.00

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article

Graph-based Recursive Relations for Computing and Analyzing r-process Abundances

B. S. Meyer, Mengke Li
The Astrophysical Journal
Bayesian Modeling and Causal Inference
article

Graph-based Recursive Relations for Computing and Analyzing r-process Abundances

B. S. Meyer, Mengke Li
article en

Abstract

Abstract We develop recursive relations among abundances in an r -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 (Graphical R -process) relations. We validate the relations by using them to compute r -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 evolving r -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.

The Astrophysical JournalVol. 982(2)
National Aeronautics and Space Administration
Openalex Percentile: Top 98%
Bayesian Modeling and Causal Inference
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