CompactObject CEDF EOS Database GUI

A graphical user interface (GUI) application for extracting, filtering, and visualizing posterior samples from relativistic mean field (RMF) models for neutron star equations of state (EOS).

Overview

The CompactObject CEDF Posterior Tool provides researchers with easy access to approximately 170,000 samples across multiple RMF model types (NL, DDB, DDH, and GDFMX variants). These samples were derived using Bayesian inference with constraints from astrophysical observations, nuclear matter properties, and theoretical calculations within the CompactObject framework.

Features

• Interactive GUI: User-friendly interface built with CustomTkinter
• Model Selection: Access to multiple RMF model variants (NL, DDB, DDH, GDFMX)
• Flexible Sampling: Extract samples based on confidence intervals or specific criteria
• Advanced Filtering: Filter samples by nuclear matter properties and observables
• Multiple Output Formats: Export data in CSV or HDF5 format
• Comprehensive Data Access:
  • Equation of state data (pressure, density, sound speed)
  • Mass-radius relationships and tidal deformability
  • Nuclear matter properties at saturation
  • Pure neutron matter EOS

Installation

Prerequisites

• Python 3.7+
• Required packages (install via pip install -r requirements.txt):
  • customtkinter
  • numpy
  • pandas
  • matplotlib
  • h5py

Setup

1. Clone the repository:

   git clone https://github.com/tuhinbits/CompactObject-CEDF-EOS-Database-GUI.git
   cd CompactObject-CEDF-EOS-Database-GUI

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python main.py

4. Database Download: On first launch, the application will prompt you to download the CEDF EOS database (~1.71GB) from Zenodo.

Database

The application uses the CompactObject CEDF EOS Database (v1), which contains:

• 170,000+ samples from Bayesian posterior distributions
• Multiple RMF models: NL, DDB, DDH, GDFMX variants
• Comprehensive constraints: NICER observations, GW170817, nuclear experiments, χEFT, pQCD
• Rich data content: EOS tables, mass-radius curves, nuclear matter properties

Database Source: Zenodo Record 16941330

Usage

Quick Start

1. Launch the application: python main.py
2. Select a model from the dropdown menu
3. Configure sampling parameters (number of samples, confidence intervals)
4. Choose quantities to extract from the tabbed interface
5. Optionally apply filters for specific property ranges
6. Click "Run Command" to generate results
7. Access output files via "Open Results Folder"

Data Categories

• prop: General properties (weights, binding energy, etc.)
• eos: Equation of state data (pressure, energy density, sound speed)
• pnm_eos: Pure neutron matter EOS
• mr: Mass-radius relations and tidal deformability
• nmp: Nuclear matter properties at saturation

Example Use Cases

• Neutron Star Observations: Compare model predictions with NICER mass-radius measurements
• EOS Constraints: Study pressure-density relationships across different models
• Nuclear Physics: Analyze saturation properties and incompressibility
• Cooling Studies: Investigate direct Urca processes and proton fractions

Interface Components

• Sidebar: Action buttons, settings, and database management
• Main Panel: Model selection, sampling parameters, and quantity selection
• Results Panel: Command output and results management

Documentation

Detailed documentation is available in the doc/ directory:

• User guide and tutorial
• Technical details about the database structure
• Advanced usage examples

RMF Model Types

• RMF-NL: Non-linear RMF with constant couplings
• DDH/DDHy: Density-dependent model with optional modified ρ-meson coupling
• DDB/DDBy: Alternative density-dependent parameterization
• GDFMX: Extended density-dependent model

Output Formats

• CSV: Human-readable tabular format
• HDF5: Binary format for large datasets and array data
• Scalar Data: Individual sample properties
• Array Data: Density-dependent quantities (EOS, M-R curves)

Contributing

This project is currently private. For questions, issues, or collaboration requests, please contact the development team.

Citation

If you use this tool or the associated database in your research, please cite:

Cartaxo, J., Malik, T., Huang, C., et al. (2025). Constraining relativistic mean field 
models using neutron star mass-radius measurements. Zenodo. 
https://doi.org/10.5281/zenodo.16941330

License

© 2025 Center for Physics of the University of Coimbra (CFisUC). This project was funded by EuroHPC (Project No EHPC-DEV-2024D12-009).

Support

For technical support or questions about the CompactObject framework, visit the CompactObject GitHub repository.