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.

Core Functionality

CompactObject offers a range of functionalities essential for constraining the EOS of neutron stars:

1. Equation of State (EOS) Generation
    - Utilizes multiple physics/meta models, including Relativistic Mean Field (RMF), strange star, quark star, polytrope, and speed of sound models, to generate neutron star EOS.
    - [EOSgenerators](https://github.com/ChunHuangPhy/EoS_inference/blob/main/EOSgenerators) Package

2. Tolman-Oppenheimer-Volkoff (TOV) Solver
    - Solves the TOV equations for a spherically symmetric compact object based on a given neutron star EOS.
    - [TOVsolver](https://github.com/ChunHuangPhy/EoS_inference/blob/main/TOVsolver) Package

3. Bayesian Inference Workflow
    - Implements neutron star EOS inference using Nested Sampling.
    - Provide options for single machine users using MCMC sampling by emcee.
    - Integrates constraints from nuclear experiments, neutron star mass and/or radius observations (from X-ray timing and/or radio timing), and tidal measurements from gravitational wave detections.
    - [InferenceWorkflow](https://github.com/ChunHuangPhy/EoS_inference/blob/main/InferenceWorkflow) Package

CompactObject offers a range of functionalities essential for constraining the EOS of neutron stars:

1. Equation of State (EOS) Generation
    - Utilizes multiple physics/meta models, including Relativistic Mean Field (RMF), strange star, quark star, polytrope, and speed of sound models, to generate neutron star EOS.
    - [EOSgenerators](https://github.com/ChunHuangPhy/EoS_inference/blob/main/EOSgenerators) Package

2. Tolman-Oppenheimer-Volkoff (TOV) Solver
    - Solves the TOV equations for a spherically symmetric compact object based on a given neutron star EOS.
    - [TOVsolver](https://github.com/ChunHuangPhy/EoS_inference/blob/main/TOVsolver) Package

3. Bayesian Inference Workflow
    - Implements neutron star EOS inference using Nested Sampling.
    - Provide options for single machine users using MCMC sampling by emcee.
    - Integrates constraints from nuclear experiments, neutron star mass and/or radius observations (from X-ray timing and/or radio timing), and tidal measurements from gravitational wave detections.
    - [InferenceWorkflow](https://github.com/ChunHuangPhy/EoS_inference/blob/main/InferenceWorkflow) Package

Includes

CompactObject includes the following components to facilitate neutron star EOS inference analysis:

1.  EOS Output Validation Routine
    - Compute various type of EOS by different models
    - Checks the validity of EOS inputs.

2. Mass, Radius, and Tidal Deformability Calculator
    - Returns mass, radius, tidal deformability, by solve TOV equation, and computes the corresponding speed of sound.

3. Sample TOV Solver Notebook
    - [Test_TOVsolver.ipynb](https://github.com/ChunHuangPhy/EoS_inference/blob/main/Test_Case/test_TOVsolver.ipynb)
    - Demonstrates how to solve the TOV equation with a given EOS.

4. Sample EOS Generators Notebook
    - [test_EOSgenerators.ipynb](https://github.com/ChunHuangPhy/EoS_inference/blob/main/Test_Case/test_EOSgenerators.ipynb)
    - Showcases all integrated EOS computations, including:
        - Polytrope
        - Speed of Sound Model
        - RMF Model
        - Strange Star Model
        - Quark Star Model

5.  Sample Analysis and Tutorial Notebook
    - [test_Inference.ipynb](https://github.com/ChunHuangPhy/EoS_inference/blob/main/Test_Case/test_Inference.ipynb)
    - Demonstrates the entire pipeline of Bayesian inference using supported EOS models, constructing priors and likelihoods, and the types of likelihoods supported in this project. Also provide a MCMC based emcee example for people don't have access to High Performance Computer. This is specifically focus on the RMF EOS,
    However, please check this notebook before all other notebook,
    since here we showcase all the likelihood
    - Other Inference pipline that using different EOS are
        - [MIT bag inference](https://github.com/ChunHuangPhy/CompactObject/blob/main/Test_Case/test_Bayesian_inference_MITbag_EOS.ipynb)
        - [Strangeon Star inference](https://github.com/ChunHuangPhy/CompactObject/blob/main/Test_Case/test_Bayesian_inference_Strangeon_EOS.ipynb)
        - [Polytrope inference](https://github.com/ChunHuangPhy/CompactObject/blob/main/Test_Case/test_Inference_polytrope.ipynb) 

> Note: Please review these notebooks before starting your own project to familiarize yourself with the coding routines.