Multi-scale theoretical physics testing framework for warp drive signatures.

This package provides a comprehensive analysis pipeline spanning three regimes:

1. Signal vs. Noise Analysis: Compare synthetic warp signals against detector sensitivity curves
2. Semi-Classical Testing: Generate Post-Newtonian corrections and compare with precision experiments
3. Strong-Curvature Models: Explore Planck-scale physics with toy black hole and cosmological models

.  
├── README.md  
├── EXTENDED_FRAMEWORK.md           # Detailed framework documentation
├── mock_data.ndjson                # Synthetic signal time-series/spectra JSON-lines  
├── mock_data.am                    # AsciiMath metadata for mock_data  
├── sensitivity_curve.csv           # Generated noise curve (frequency, noise PSD)  
├── sensitivity_curve.am            # AsciiMath metadata for sensitivity curve  
├── generate_sensitivity_curve.py   # Noise curve generator
├── analyze_sensitivity.py          # Original signal vs noise analysis
├── 
├── 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_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
```markdown
## Prerequisites

- Python 3.7+  
- NumPy  
- SciPy
- SymPy (for symbolic PN calculations)
- ndjson  
- tqdm (optional, for progress bars)

Install via:

```bash
pip install numpy scipy sympy python-ndjson tqdm

python generate_sensitivity_curve.py

This produces:

• sensitivity_curve.csv

  • Columns: frequency_Hz, noise_strain_per_sqrtHz

• sensitivity_curve.am

  • One-line AsciiMath metadata: model name, parameters, point count

python analyze_sensitivity.py `
  --mock   mock_data.ndjson `
  --meta   mock_data.am `
  --noise  sensitivity_curve.csv `
  --nmeta  sensitivity_curve.am `
  --out    sensitivity_comparison.ndjson `
  --oam    sensitivity_comparison.am

Outputs:

• sensitivity_comparison.ndjson
  JSON-line records:

{
    "label": "signal1",
    "detectable": true,
    "snr": 12.5
}

• sensitivity_comparison.am
  AsciiMath summary: detector name, injection count, detection thresholds

Generate PN corrections for warp drive theories:

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

Configuration files:

• theory_params.am: Contains WarpVelocity, MassParameter, TheoryType
• pn_config.am: Contains MaxPNOrder, FrequencyRange, ExpansionParameter

Outputs:

• pn_waveforms.ndjson: PN corrections with observational signatures
• pn_summary.am: Analysis metadata

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 \
  --snr-threshold 5.0

Flags and Input Specifications:

• --pn-data: PN corrections from compute_pn_corrections.py (.ndjson format)
  • Each entry contains: pn_order, correction (symbolic expressions), signature (frequency-domain observational signatures), v_over_c, mass_parameter
• --pn-meta: PN metadata file (.am format) containing analysis parameters
  • Required keys: max_pn_order, warp_velocity, frequency_range, n_corrections
• --exp-data: Experimental sensitivity data (.csv with columns: frequency_hz, strain_sensitivity, experiment_type)
  • Compatible with LIGO, Virgo, atomic interferometry, and pulsar timing datasets
• --exp-meta: Experimental metadata (.am with keys: ExperimentType, InstrumentName, FrequencyRange, StrainSensitivity, DataSource)
• --snr-threshold: SNR threshold for detectability (default: 5.0, recommended range: 3.0-10.0)

Outputs:

• pn_analysis.ndjson: Detection analysis for each PN order with fields:
  • pn_order: PN correction order ("1PN", "2PN", "3PN", etc.)
  • theory_params: Contains v_over_c and mass_parameter values from theory
  • experimental_analysis: Per-experiment detectability results including SNR, parameter bounds, frequency ranges
  • overall_detectability: Boolean indicating if any experiment can detect this PN order
  • best_snr: Highest SNR achieved across all experimental datasets
• pn_analysis.am: Summary metadata including detection statistics, parameter constraints, and experiment coverage

Generate 2D black hole curvature data:

cd strong_curvature/
python generate_2d_blackhole.py \
  --model-config blackhole_config.am \
  --out blackhole_data.ndjson \
  --oam blackhole_summary.am

Configuration (blackhole_config.am):

• ModelType: "2d_schwarzschild" or "warp_bubble"
• MassParameter: Characteristic mass scale
• PlanckLength: Planck length (default: 1e-35)
• BubbleThickness: For warp bubble models

Outputs:

• blackhole_data.ndjson: Curvature invariants and quantum parameters
• blackhole_summary.am: Model summary and regime classification

Generate minisuperspace cosmology:

python minisuperspace_cosmo.py \
  --cosmo-config cosmo_config.am \
  --out cosmo_data.ndjson \
  --oam cosmo_summary.am

Compare and unify strong-curvature 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

Flags and Input Specifications:

• --models: List of model data files (.ndjson) from toy model generators
  • Multiple files can be specified: --models file1.ndjson file2.ndjson file3.ndjson
  • Each .ndjson contains model results with curvature invariants, geodesic analysis, and Planck-scale physics indicators
  • Expected structure: model_type, curvature_analysis, geodesic_analysis, planck_scale_analysis
• --meta: Corresponding metadata files (.am) with model parameters and analysis settings
  • Must match order of --models files: --meta meta1.am meta2.am meta3.am
  • Required keys: model_type, analysis parameters specific to each model class

Outputs:

• unified_strong_models.ndjson: Unified comparison with regime classification, containing:
  • model_id: Unique identifier for each model in the comparison
  • model_type: Type of model ("2d_schwarzschild", "warp_bubble", "frw_minisuperspace", etc.)
  • regime_classification: Overall physical regime ("classical", "semi_classical", "quantum_gravity")
  • curvature_analysis: Extracted curvature scales and quantum gravity parameters
  • classical_gr_valid: Boolean indicating if classical General Relativity is adequate
  • quantum_correction_strength: Estimated strength of quantum gravity corrections (0.0-1.0)
  • requires_quantum_gravity: Boolean indicating if quantum effects dominate
  • parameter_ranges: Valid parameter ranges for classical and quantum regimes
• unified_summary.am: Summary statistics including:
  • regime_distribution: Count of models in each regime (classical/semi_classical/quantum_gravity)
  • n_quantum_gravity: Number of models requiring full quantum gravity treatment
  • n_classical_valid: Number of models where classical GR remains valid
  • average_quantum_correction: Average quantum correction strength across all models
  • model_types_analyzed: List of model types included in comparison
  • planck_scale_physics_important: Boolean indicating if any models reach Planck-scale physics

All configuration and metadata files use the AsciiMath format with consistent key-value pairs:

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

Configuration Keys by Script:

Semi-Classical Analysis:

• theory_params.am:

  • WarpVelocity: Dimensionless warp velocity (v/c ratio)
  • MassParameter: Characteristic mass scale parameter
  • TheoryType: Theory model type ("alcubierre_warp", "van_den_broeck", etc.)
  • MetricSignature: Metric signature convention ("mostly_plus", "mostly_minus")

• pn_config.am:

  • MaxPNOrder: Maximum Post-Newtonian order to compute (integer: 1, 2, 3, ...)
  • FrequencyRange: Comma-separated frequency range in Hz ("10,1000")
  • ExpansionParameter: PN expansion parameter ("v_over_c", "frequency")
  • SymbolicComputation: Boolean for symbolic vs numerical computation

Strong-Curvature Models:

• blackhole_config.am:

  • ModelType: Model type ("2d_schwarzschild", "warp_bubble")
  • MassParameter: Characteristic mass scale in natural units
  • PlanckLength: Planck length in meters (default: 1e-35)
  • BubbleThickness: Warp bubble thickness parameter (for warp_bubble models)

• cosmo_config.am:

  • ModelType: Cosmology model ("frw_minisuperspace", "kasner")
  • HubbleParameter: Hubble parameter H₀ in units of 100 km/s/Mpc
  • OmegaMatter: Matter density parameter Ωₘ
  • PlanckLength: Planck length in meters
  • ScaleFactorRange: Comma-separated scale factor range ("1e-10,1e10")

Experimental Data:

• ligo_data.am, atomic_interf.am:
  • ExperimentType: Type of experiment ("gravitational_wave", "atomic_interferometry")
  • InstrumentName: Specific instrument name ("LIGO_H1", "LIGO_L1", "atom_interferometer")
  • FrequencyRange: Sensitive frequency range in Hz
  • StrainSensitivity: Characteristic strain sensitivity
  • DataSource: Source of experimental data

• One JSON object per line for efficient streaming and processing

• For mock_data.ndjson:

  { "label": "signal1", "time_series": [0.1, 0.2, ...], "sampling_rate": 16384 }

• For pn_waveforms.ndjson:

  { "pn_order": "1PN", "correction": {...}, "signature": {...}, "v_over_c": 0.1, "mass_parameter": 1e-6 }

• For blackhole_data.ndjson:

  { "model_type": "2d_schwarzschild", "curvature_analysis": {...}, "geodesic_analysis": {...} }

• Standard comma-separated with header row
• For sensitivity curves: frequency_Hz, noise_strain_per_sqrtHz
• For experimental data: frequency_hz, strain_sensitivity, experiment_type

# 1. Build the noise curve
python generate_sensitivity_curve.py

# 2. Run detection analysis
python analyze_sensitivity.py \
  --mock mock_data.ndjson \
  --meta mock_data.am \
  --noise sensitivity_curve.csv \
  --nmeta sensitivity_curve.am \
  --out sensitivity_comparison.ndjson \
  --oam sensitivity_comparison.am

# 3. Generate and analyze PN 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

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

# 4. Generate strong-curvature models
cd ../strong_curvature/
python generate_2d_blackhole.py \
  --model-config blackhole_config.am \
  --out blackhole_data.ndjson \
  --oam blackhole_summary.am

python compare_strong_models.py \
  --models blackhole_data.ndjson \
  --meta blackhole_summary.am \
  --out unified_models.ndjson \
  --oam unified_summary.am

# 5. Inspect results
head ../sensitivity_comparison.ndjson
cat ../sensitivity_comparison.am
head pn_analysis.ndjson
head unified_models.ndjson