/EoS_inference

An open-source package for neutron star whole workflow Bayesian nference constraining Neutron star EOS package

Primary LanguageJupyter NotebookMIT LicenseMIT

About The Project

  1. Dealing with complex Relativistic Mean field (RMF) theory to generate Equation of State (EOS) of neutron star. (EOSgenerators Package)
  2. Solves the Tolman-Oppenheimer-Volkoff equation for a spherically symmetric compact object out of given equation of state of neutron star. (TOVsolver Package)
  3. Implementing Neutron state EOS inference by Nested Sampling, draw constraints from Nuclear experiments, Neutron star mass (and/or) radius observations (from X-ray timing and/or radio timing) (and/or) Tidal measurement from Gravitational wave detection. That all workflow is inside this folder. (InferenceWorkflow Package)

Project papers list based these package: (Please consider cite them, if you are using this package)

[1]. [Huang, C., Raaijmakers, G., Watts, A. L., Tolos, L., and Providência, C., “Constraining fundamental nuclear physics parameters using neutron star mass-radius measurements I: Nucleonic models”,Monthly Notices of the Royal Astronomical Society,2024, 10.1093/mnras/stae844,529, https://academic.oup.com/mnras/article/529/4/4650/7634362

- check this project website: https://chunhuangphy.github.io/EoS_inference/

CompactObject-TOV package website

Inlcudes

  1. Routine to check a valid equation of state input
  2. Return the mass, radius, and tidal deformability, and compute the corresponding speed of sound.
  3. Sample TOV solver Notebook, Sample RMF Equation of state solver Notebook and Sample Analysis Notebook on Equation of state Inference and tutorial on the github to show off what we can do currently and how to use our code. (please read them before you start to work on your own project, to familiar with the coding routine.)
  4. Test cases and documentation

v.1.3 new features:

  1. Added computation function of generating Relativistic mean field theory(RMF) model EOS functionality. Defined two files fastRMF_EOS and RMF_EOS, which the fastRMF_EOS is speed up by numba, which need gcc compiler, could be hard to implement in windows, so we leave the options for users.

v.1.5 new features:

  1. Added Whole workflow of Bayesian inference of neutron star equation of state. Include defining prior by InferenceWorkflow.prior, which included two types: flat distribution and gaussian type. Include defining liklihood generated from nuclear and astrophysical constraint.

Installation

Below are commands to install and update the package as well as a link to pypi.

PyPi
  1. Install package 
    pip install CompactObject-TOV
  2. Update package 
    pip install CompactObject-TOV --upgrade

When you call the package, if you need to do EoS computation just

import EOSgenerators

if you need TOV solver, just

import TOVsolver

if you need to do Bayesian inference, just

import InferenceWorkflow

Physics notations

  1. CGS units is using here, for input quantity (equation of state): Pressure (P) and Energy density (rho). P is in $MeV/fm^{-3}$, same for rho. However, to omit a lot of the repeat of c,G. We set P as rescaled: (value in $MeV/fm^{-3}$)*G/c^4, for rho we have (value in $MeV/fm^{-3}$)*G/c^2
  2. Out put M in Mass of sun, radius in km, unit-less for spped of sound and tidal deformability.

License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Project Link: [https://github.com/ChunHuangPhy/EoS_inference]

DOI

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Acknowledgments

Use this space to list resources you find helpful and would like to give credit to. Here included a few of my favorites to kick things off! We would like to acknowledge the support of Code/Astro workshop to make this project happen, we all learned a lot from that 5 day intensive workshop.

Chun want to thank Professor Anna Watts, Dr. Geert Raaijmakers and Jeannie Kuijper for asistance on coding and help on providing me basic strategy of how to solve this problem.

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