Introduction

RNS constructs models of rapidly rotating relativistic stars in equilibrium. The ACME RNS training material is authored by Nikolaos Stergioulas and is available in the ACME-RNS GitLab repository.

The tutorial notebook drives the RNS C executable from Python through rns_helpers.py, parses the output, and explores single models, stellar sequences, equilibrium surfaces in mass, angular momentum, and central energy density, and selected astrophysical applications.

Installation

Create the environment from the tutorial repository:

conda env create -f environment.yml
conda activate acme-rns

Build the RNS executables:

cd rns_build
make

This produces the standard and higher-resolution executables rns, rns_high, rns_vh, and rns_xh, plus hng for EOS tables. The Python wrapper expects these binaries and the bundled eos/ directory under rns_build/.

The detailed RNS manual is included as rns_build/manual.pdf.

Test Example

The source repository includes several command-line examples in rns_build/examples.test. A simple single-model run is:

cd rns_build
./rns -f eos/eosC -t model -e 2e15 -r 0.59 -d 0

The notebook in this section runs the same executable through Python and organizes the parsed output as dictionaries and data frames.

Citation

For scientific use of RNS, cite Stergioulas & Friedman 1995, ApJ 444, 306, and the RNS documentation bundled with this tutorial.