This repository now contains a comprehensive theoretical physics testing framework that extends beyond simple signal-vs-noise analysis to include:

1. Semi-classical testing (Post-Newtonian corrections)
2. Strong-curvature regime models (Planck-scale physics)
3. Unified analysis pipeline with consistent metadata format

warp-sensitivity-analysis/
├── analyze_sensitivity.py          # Original signal vs noise pipeline
├── generate_sensitivity_curve.py   # Noise curve generator
├── mock_data.*                     # Test data files
├── sensitivity_*.*                 # Analysis outputs
├── 
├── semi_classical/                 # Post-Newtonian analysis
│   ├── compute_pn_corrections.py  # PN expansion generator
│   ├── analyze_pn_tests.py        # Experimental comparison
│   ├── theory_params.am           # Theory configuration
│   ├── pn_config.am               # PN expansion settings
│   ├── pn_waveforms.ndjson        # Generated PN corrections
│   ├── pn_summary.am              # PN metadata
│   └── pn_data/                   # Experimental datasets
│       ├── ligo_data.csv          # LIGO sensitivity curves
│       ├── ligo_data.am           # LIGO metadata
│       └── atomic_interf.am       # Atomic interferometry data
│
└── strong_curvature/              # Planck-scale models
    ├── generate_2d_blackhole.py   # 2D black hole toy models
    ├── minisuperspace_cosmo.py    # FRW minisuperspace cosmology
    ├── compare_strong_models.py   # Model unification
    ├── blackhole_config.am        # Black hole parameters
    ├── cosmo_config.am            # Cosmology parameters
    ├── blackhole_data.ndjson      # Generated black hole data
    ├── blackhole_summary.am       # Black hole metadata
    └── unified_summary.am         # Combined analysis metadata

Input: Theory parameters → PN expansion → Experimental comparison

# Generate Post-Newtonian corrections
cd semi_classical/
python compute_pn_corrections.py \
    --theory theory_params.am \
    --pn-config pn_config.am \
    --out pn_waveforms.ndjson \
    --oam pn_summary.am

# Analyze against experimental data
python analyze_pn_tests.py \
    --pn-data pn_waveforms.ndjson \
    --pn-meta pn_summary.am \
    --exp-data pn_data/ligo_data.csv \
    --exp-meta pn_data/ligo_data.am \
    --out pn_analysis.ndjson \
    --oam pn_analysis.am

Output:

• PN corrections up to specified order
• Observational signatures and scaling laws
• Goodness-of-fit vs experimental constraints
• Parameter ranges surviving precision tests

Input: Model configurations → Toy model generation → Regime classification

# Generate 2D black hole models
cd strong_curvature/
python generate_2d_blackhole.py \
    --model-config blackhole_config.am \
    --out blackhole_data.ndjson \
    --oam blackhole_summary.am

# Generate minisuperspace cosmology
python minisuperspace_cosmo.py \
    --cosmo-config cosmo_config.am \
    --out cosmo_data.ndjson \
    --oam cosmo_summary.am

# Unify and compare models
python compare_strong_models.py \
    --models blackhole_data.ndjson cosmo_data.ndjson \
    --meta blackhole_summary.am cosmo_summary.am \
    --out unified_strong_models.ndjson \
    --oam unified_summary.am

Output:

• Curvature invariants (Ricci, Kretschmann scalars)
• Quantum gravity parameter (curvature/Planck scale)
• Regime classification (classical/transition/quantum)
• Parameter ranges requiring full quantum gravity

All stages use consistent AsciiMath metadata files (.am) containing:

[ key1 = value1, key2 = "string_value", key3 = 1.23e-4, ... ]

This enables automated pipeline chaining and parameter tracking across analysis stages.

• Symbolic PN expansion using SymPy for warp drive metrics
• Observational signatures in gravitational wave detectors
• Parameter constraints from precision tests (LIGO, atomic interferometry)
• Order-by-order comparison of theoretical predictions vs data

• 2D black hole toy models with exact curvature calculations
• FRW minisuperspace cosmology for early universe scenarios
• Quantum gravity indicators based on Planck-scale ratios
• Regime boundaries between classical and quantum gravity

• Consistent data format (NDJSON + AsciiMath metadata)
• Automated pipeline with configurable parameters
• Cross-regime comparison of theoretical predictions
• Scalable framework for additional model types

✅ Completed:

• Original sensitivity analysis pipeline
• Semi-classical PN correction generator
• Strong-curvature toy model framework
• Unified metadata and analysis structure

🔄 Working:

• PN corrections successfully generated
• 2D black hole models computed
• Model comparison and regime classification

🔧 Minor Issues:

• JSON serialization errors in some analysis scripts (fixable)
• Frequency range parsing in PN config files (fixed)

1. Fix remaining JSON serialization in analysis scripts
2. Add more experimental datasets (atomic interferometry, pulsar timing)
3. Extend toy models (higher-dimensional black holes, cosmological perturbations)
4. Implement parameter space exploration with automated constraint mapping
5. Add visualization tools for regime boundaries and observational prospects

This framework now provides a complete theoretical physics testing pipeline that can explore warp drive signatures across energy scales from current detector sensitivity up to Planck-scale quantum gravity.