CompactObject v2.0.0

Summary

The CompactObject package is an open-source software framework developed to constrain the neutron star equation of state (EOS) through Bayesian statistical inference. It integrates astrophysical observational constraints from X-ray timing, gravitational wave events, and radio measurements, as well as nuclear experimental constraints derived from perturbative Quantum Chromodynamics (pQCD) and Chiral Effective Field Theory ($\chi$EFT). The package supports a diverse range of EOS models, including meta-model like and several physics-motivated EOS models. It comprises three independent components: an EOS generator module that currently provided seven EOS choices, a Tolman–Oppenheimer–Volkoff (TOV) equation solver, enabling solve Mass Radius and Tidal deformability as observables, and a comprehensive Bayesian inference workflow module, including a whole pipeline of implementing EOS Bayesian inference. Each component can be independently utilized in various scientific research contexts, like nuclear physics and astrophysics. Additionally, CompactObject is designed to synergize with existing software such as CompOSE https://compose.obspm.fr, enabling the use of the CompOSE EOS database to expand the available EOS options.

What we can do now

• modify a better documentation: https://chunhuangphy.github.io/CompactObject/
• add more available equation of state:
  1. Polytrope
  2. Speed of Sound Model
  3. RMF Model
  4. Density dependent RMF
  5. Strange Star Model
  6. MIT bag Quark Star Model
• add whole workflow inference pipeline for each of the available equation of state.
  1. RMF model inference pipeline: https://chunhuangphy.github.io/CompactObject/test_Inference.html
  2. Density dependent RMF model inference pipeline: https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_DDH.html
  3. Strange Star model inference pipeline: https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_Strangeon_EOS.html
  4. MIT bag model inference pipeline: https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_MITbag_EOS.html
  5. Polytrope inference pipeline: https://chunhuangphy.github.io/CompactObject/test_Inference_polytrope.html
  6. Speed of sound model inference pipeline: https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_SpeedOfSound_EOS.html
• add more available likelihood like $\chi$EFT, pQCD from nuclear physics.
• add documentation to showcase how to use these equation of state in https://chunhuangphy.github.io/CompactObject/test_EOSgenerators.html
• Summarize and submit a JOSS paper
• add functionality to accommodate the CompOSE database to enable more equation of state.
• unified the units system in the package to make it more friendly.

Compact-Object EOS inference package

release new version, the published version in https://academic.oup.com/mnras/article/529/4/4650/7634362.

• renewed the paper link
• debug the Jupyter notebook illustration

CompactObject-TOV v.1.8.1

The final version of full space Neutron star equation of state bayesian inference package.

update on

• Add the whole bayesian workflow package to define bayesian inference
• Add whole detailed documentation about how to do it and basical physics
• Add three well-documented instruction and websites.

CompactObject TOV solver v1.3.1

This release we fixed the related bugs

• can correctly generate mass, radius, and tidal property of neutron star, and speed of sound.
• add outputMR function to TOV solver, give the users an option to only use TOV solver to solve mass radius, save running time.
• modified illustration jupyter notebook, showing off what we can do.
• add 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.

CompactObject TOV solver v1.1.0

A user friendly TOV solver, can produce all the parameters you need to do next step Bayesian inference, or just to understand neutron structure