A Nuclear Equation of State Inferred from Stellar r-Process Abundances: Data

<p>This repository contains the raw MCMC posteriors as H5 files for the three cases presented in &quot;A Nuclear Equation of State Inferred from Stellar <em>r</em>-Process Abundances&quot; (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p> <p>Also included are Python scripts with a variety of functions to read the H5 files and interpret the data with <a href="https://git.ligo.org/lscsoft/lalsuite">LALSuite</a>. These include:</p> <ul> <li>reading the data contained in the H5 file (likelihood, acceptance, and values for each MCMC step)</li> <li>generating a corner plot of the data</li> <li>finding the maximum likelihood in the posterior distribution</li> <li>calculating a neutron star mass-radius curve for a posterior EOS</li> <li>calculating pressure and density for a posterior EOS</li> <li>calculating observables (M_TOV, R_1.4, and L) associated with an EOS</li> </ul> <p>The scripts are written for Python3 compatibility and depend on:</p> <ul> <li><a href="https://pypi.org/project/lalsuite/">lalsuite</a></li> <li><a href="https://docs.h5py.org/en/stable/">h5py</a></li> <li><a href="https://numpy.org/">numpy</a></li> <li><a href="https://scipy.org/">scipy</a></li> <li><a href="https://matplotlib.org/">matplotlib</a></li> <li><a href="https://corner.readthedocs.io/en/latest/">corner</a></li> </ul> <p>For more information and how to use these scripts, see the comments in `example.py` or contact Erika Holmbeck.</p> <p>If any of our posterior samples are used in your work, we ask that you appropriately cite this repository and the original paper (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p>