- Dealing with Multiple models (such as RMF, polytrope etc) to generate Equation of State (EOS) of neutron star. (EOSgenerators Package)
- Solves the Tolman-Oppenheimer-Volkoff equation for a spherically symmetric compact object out of given equation of state of neutron star. (TOVsolver Package)
- 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
[2]. Huang, C., Tolos, L., Providência, C., and Watts, A., “Constraining a relativistic mean field model using neutron star mass-radius measurements II: Hyperonic models”, arXiv:2410.14572, 2024.
[3]. Huang, C. and Zheng, X.-P., “Bayesian Insights into post-Glitch Dynamics: Model comparison and parameter constraint from decades long observation data of the Crab pulsar”, arXiv:2409.18432, 2024
"The inference conducted here relies on the framework in \textit{CompactObject} \cite{CompactObject} package\footnote{https://chunhuangphy.github.io/CompactOject/}. This is an open source full-scope package designed to implement Bayesian constraints on the neutron star EOS. The other work based on this package is ...."
1.https://chunhuangphy.github.io/CompactOject/CompactObject-TOV package website
- Routine to check a valid equation of state input
- Return the mass, radius, and tidal deformability, and compute the corresponding speed of sound.
- 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.)
- Test cases and documentation
- 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.
- 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.
Below are commands to install and update the package as well as a link to pypi.
- Install package
pip install CompactObject-TOV
- Update package
pip install CompactObject-TOV --upgrade
When you call the package, if you need to do EoS computation just
import EOSgeneratorsif you need TOV solver, just
import TOVsolverif you need to do Bayesian inference, just
import InferenceWorkflow- 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 - Out put M in Mass of sun, radius in km, unit-less for spped of sound and tidal deformability.
Distributed under the MIT License. See LICENSE.txt for more information.
- Chun Huang - chun.h@wustl.edu
- Nicole Osborn - n.osborn@wustl.edu
- Nathan Whitsett - whitsett.n@wustl.edu
Project Link: [https://github.com/ChunHuangPhy/EoS_inference]
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.