QMRG: Python Framework for Modified Gravity Analysis

Project Description

Quantum-Modified Rotating Gravity (QMRG) is a two-scale modified gravity framework that successfully explains galaxy rotation curves without requiring dark matter. This project aims to enhance the existing Python analysis framework, improve computational efficiency, and expand validation against larger galaxy databases.

Deliverables

1. Enhanced Solver Engine
   • Optimize Gauss-Newton fitting algorithm for better convergence
   • Implement MCMC sampling for parameter uncertainty estimation
   • Add support for additional galaxy databases (THINGS, Ursa Major)
2. Interactive Visualization Dashboard
   • Create web-based interface for exploring QMRG fits
   • Real-time parameter adjustment and fit visualization
   • Export capabilities for publication-quality figures
3. Comparative Analysis Tools
   • Direct comparison with MOND predictions
   • Statistical analysis of fit quality across galaxy types
   • Automated reporting and validation pipelines
4. Documentation and Testing
   • Comprehensive unit tests for all components
   • User-friendly documentation and tutorials
   • Docker container for reproducible research

Required Skills

• Python (advanced: NumPy, SciPy, Pandas, Matplotlib)
• Basic understanding of astrophysics/gravity theories
• Experience with numerical optimization and statistical analysis
• Web development (optional, for dashboard component)

Mentor Availability

The primary mentor has extensive experience with the QMRG framework and is available throughout the GSoC period for weekly meetings and code reviews. Contact: seflflieswithsanta725@gmail.com. Additional domain experts will be available for consultation on modified gravity theory and astrophysical applications.

Project Timeline

• Weeks 1-2: Familiarization with existing codebase and setup
• Weeks 3-6: Enhanced solver development and optimization
• Weeks 7-10: Interactive dashboard implementation
• Weeks 11-14: Comparative analysis and validation
• Weeks 15-18: Documentation, testing, and final integration

Evaluation Criteria

• Code quality and test coverage
• Performance improvements in fitting algorithms
• User experience of visualization tools
• Scientific rigor of comparative analysis
• Documentation completeness

Resources

• Existing QMRG codebase and SPARC database
• Access to computational resources for large-scale analysis
• Scientific literature and domain expert consultation
• Open-source Python ecosystem and visualization libraries