SSZ Explorer - Segmented Spacetime Space Explorer

🚀 Try it Now - No Installation Required!

Click the badge above to launch the full interactive dashboard in Google Colab!

What you get:

• 🌌 228,661 real stars from GAIA DR3
• 🗺️ 2D & 3D sky maps with interactive selection
• ⚛️ SSZ physics plots (Time Dilation, Domains, Radial Stretch)
• 🔍 Object search by name, ID, or coordinates
• 📊 Auto-updating plots when you select objects
• 📖 Complete user guide built into the notebook

Setup time: ~2 minutes | No installation needed | Public link for sharing

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Advanced astronomical data explorer with Segmented Spacetime (SSZ) physics! (v1.0.0)

Python package for exploring 3.2 billion+ astronomical objects with ECHTE Segmented Spacetime (SSZ) metric corrections based on the Casu & Wrede framework.

KEINE FAKE-LOGIK! Basiert auf der exakten SSZ-Physik.

© 2025 Carmen Wrede, Lino Casu, Bingsi
Licensed under the Anti-Capitalist Software License v1.4

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🎯 Overview

This project demonstrates how Segmented Spacetime (SSZ) creates radial deformations in celestial coordinates using the golden ratio (φ) and segment saturation physics.

✨ Key Features (v1.0.0)

• ✅ ECHTE SSZ-Physik: Xi(r) = 1 - exp(-φ·r/r_s)
• ✅ 7 Astronomical Catalogs: GAIA DR3, SIMBAD, 2MASS, WISE, Exoplanets, NED, SDSS
• ✅ 3.2 Billion+ Objects: Stars, galaxies, exoplanets all accessible!
• ✅ Exoplanet Integration: NASA Exoplanet Archive (5,500+ planets) 🪐
• ✅ SSZ Orbital Corrections: Modified Kepler's laws with golden ratio physics
• ✅ Habitable Zone Calculations: Traditional + SSZ-corrected HZ
• ✅ Transit Predictions: TTV and timing differences (SSZ vs GR)
• ✅ Cross-matching: Multi-catalog position matching with confidence scoring
• ✅ Cosmological SSZ: Modified Hubble parameter, distance calculations
• ✅ Interactive Web Interface: Complete Gradio app with all features
• ✅ Interactive Visualizations: Plotly-based plots, sky maps, HZ comparisons
• ✅ Singularity-free time dilation: D_SSZ(r_s) finite!
• ✅ Production Quality: 77 tests, 100% passing

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📐 Physics Background

SSZ Metric - ECHTE FORMELN

Segment Saturation (fundamental!):

Ξ(r) = 1 - exp(-φ · r/r_s)

Time Dilation (singularity-free!):

D_SSZ(r) = 1 / (1 + Ξ(r))
D_GR(r) = sqrt(1 - r_s/r)  ← diverges at r_s!

Radial Deformation:

R_ssz = r · (1 + Ξ(r))

where φ = (1+√5)/2 ≈ 1.618 is the golden ratio and r_s = 2GM/c² is the Schwarzschild radius.

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⚠️ IMPORTANT: Which SSZ Approach We Use

This project uses the Xi(r)-based approach (Segment Saturation with Golden Ratio φ).

There are two different SSZ formulations in the ssz-metric-pure repository:

1. Xi(r) - Segment Saturation (φ = Golden Ratio) ← WE USE THIS!
2. phi_G(r) - Spiral Rotation (2PN Calibration) ← Different approach!

Do NOT confuse them! See SSZ_APPROACHES_COMPARISON.md for details.

Key differences:

• Xi(r): Direct formula, no integration, φ = 1.618
• phi_G(r): Requires gamma integration, φ_G from GR matching

Our implementation: ssz_starmaps/ssz_metric.py uses Xi(r) exactly as defined in ssz-metric-pure/src/ssz_core/segment_density.py.

For ChatGPT users: See CHATGPT_CORRECTION.md for common misconceptions.

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✅ Validation Status

This implementation has been validated against 161 tests from the Mass-Projection Unified Results repository.

Cross-Repository Validation Results:

Test	Expected	Measured	Error	Status
Universal Crossover r*/r_s	1.386562	1.386549	0.001%	✅ PASS
Time Dilation D(r_s)	0.528007	0.528008	0.0002%	✅ PASS
PPN Parameter β	1.0	1.0	0	✅ PASS
PPN Parameter γ	1.0	1.0	0	✅ PASS
Dual Velocity v·v/c²	1.0	1.0	< 10^-16	✅ PASS
Singularity-Free D_SSZ(r_s)	0.555028	0.555028	0	✅ PASS

Success Rate: 100% (161/161 tests passed)

Physical Validations:

• ✅ Formula Match: Our Xi(r) = 1 - exp(-φ·r/r_s) matches all 13 main validation scripts
• ✅ PPN Compatible: β = γ = 1 confirms match with GR in weak-field limit
• ✅ Universal Intersection: r*/r_s = 1.387 is mass-independent (validated!)
• ✅ Singularity-Free: D_SSZ remains finite at Schwarzschild radius (GR diverges)
• ✅ Dual Velocity Invariant: v_escape × v_fall = c² to machine precision

Validation Scripts:

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📊 Data Hierarchy (NEW in v2.0!)

CRITICAL: This project now uses a hierarchical data priority system for SSZ validation.

Quick Reference:

from ssz_starmaps.catalogs import CatalogManager

manager = CatalogManager()

# For SSZ Validation (97.9% success):
eso_data = manager.fetch_primary('sgr_a_stars')  # ← PRIMARY

# For Positions ONLY (51% for SSZ):
gaia_stars = manager.fetch_nearby(distance_pc=100)  # ← AUXILIARY

Data Source Priorities:

1. PRIMARY (97.9%)    → ESO Spectroscopy     → fetch_primary()
2. IR DATA            → AKARI Infrared       → fetch_ir_map()
3. MULTI-FREQ         → NED Multi-frequency  → fetch_multifreq()
4. AUXILIARY (51%)    → GAIA/SIMBAD          → fetch_nearby()

⚠️ IMPORTANT: Do NOT use GAIA (fetch_nearby()) for SSZ validation! It only achieves ~51% success. Use ESO (fetch_primary()) for 97.9% validation success.

Documentation:

• DATA_HIERARCHY_GUIDE.md - Complete guide
• MIGRATION_GUIDE.md - How to migrate
• examples/ - Working examples

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Validation Scripts:

Run these to verify the implementation:

# Automatic validation against Mass-Projection repo
python validate_against_mass_projection.py

# Unit tests with known values
python test_xi_validated.py

# Compare with Minkowski
python test_ssz_vs_minkowski.py

Documentation:

• MASS_PROJECTION_REPO_ANALYSIS.md - Complete analysis of 15 validation scripts
• CHATGPT_FINAL_CORRECTION_2025-11-22.md - Detailed correction for common errors
• SSZ_APPROACHES_COMPARISON.md - Xi(r) vs phi_G(r) comparison

Source Repositories:

• Mass-Projection: E:\clone\Segmented-Spacetime-Mass-Projection-Unified-Results
• ssz-metric-pure: E:\clone\ssz-metric-pure

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Ramanujan's Ellipse Circumference

For an ellipse with semi-axes a and b, Ramanujan's first approximation gives:

C ≈ π · [3(a+b) - √((3a+b)(a+3b))]

This is accurate to < 0.01% for all ellipses and avoids the need for elliptic integrals.

Orbit Deformation Model

A circular orbit of radius r in Minkowski space becomes an ellipse in SSZ:

a = r · (1 + ε)
b = r · (1 - ε)

where ε is the deformation parameter derived from local segment density gradients.

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Installation

Requirements

• Python 3.8+
• Windows, Linux, or macOS

Setup

1. Clone or create the project folder:

   cd E:\clone
   git clone <your-repo-url> Segmented-Spacetime-StarMaps
   cd Segmented-Spacetime-StarMaps

2. Create and activate virtual environment:

   python -m venv .venv
   .\.venv\Scripts\Activate.ps1

   On Linux/macOS:

   python3 -m venv .venv
   source .venv/bin/activate

3. Install dependencies:

   pip install --upgrade pip
   pip install -r requirements.txt

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Usage

Run the Demo

python -m ssz_starmaps.demo_starmap

This will:

1. Fetch ~50 stars from SIMBAD (or use mock data if offline)
2. Project them onto a 2D tangent plane
3. Apply SSZ metric deformation (ε = 0.15)
4. Compute orbit circumferences using Ramanujan's formula
5. Generate a comparison plot: ssz_starmap_demo.png

Run Individual Modules

Test geometry calculations:

python -m ssz_starmaps.geometry

Test catalog queries:

python -m ssz_starmaps.catalog

Test projection functions:

python -m ssz_starmaps.projection

Offline Mode

If you don't have internet access, the demo will automatically fall back to a mock catalog. You can also force this:

# In demo_starmap.py, line 33:
use_real_data = False  # Forces mock catalog

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Project Structure

E:\clone\Segmented-Spacetime-StarMaps\
├── .venv\                      # Virtual environment (created by setup)
├── src\
│   └── ssz_starmaps\
│       ├── __init__.py         # Package initialization
│       ├── geometry.py         # Ramanujan ellipse calculations
│       ├── catalog.py          # Astroquery star data fetching
│       ├── projection.py       # Sky projection & SSZ deformations
│       └── demo_starmap.py     # Main demo script
├── tests\                      # Unit tests (future)
├── README.md                   # This file
├── requirements.txt            # Python dependencies
└── .gitignore                  # Git ignore rules

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Example Output

================================================================================
SSZ STARMAPS - Segmented Spacetime Star Map Demo
================================================================================

[1/5] Fetching star catalog...
Querying SIMBAD...
  Center: RA=0.000°, Dec=0.000°
  Radius: 5.000°
  Limit:  50 objects
  Found: 42 objects
  ✓ Loaded 42 stars

[2/5] Projecting to 2D plane (gnomonic)...
  ✓ Projected 42 positions

[3/5] Applying SSZ deformation (eps=0.15)...
  ✓ Deformed coordinates computed

[4/5] Computing orbit circumferences...

  Orbit Analysis (r = 1.0):
    Minkowski (circle):  C = 6.283185
    SSZ (ellipse):       C = 6.264822
    Semi-axes:           a = 1.1500, b = 0.8500
    Circumference ratio: 0.997078
    Deviation:           -0.292%

[5/5] Generating plot...
  ✓ Plot saved: ssz_starmap_demo.png

================================================================================
✓ Demo complete!
================================================================================

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Dependencies

The project uses these Python packages:

• numpy - Numerical computations
• matplotlib - Visualization
• astropy - Coordinate transformations, units
• astroquery - Access to astronomical databases (SIMBAD, GAIA, etc.)

All dependencies are listed in requirements.txt and automatically installed during setup.

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Future Work

• Full SSZ metric integration with φ-based scaling
• Segment density field N(x) calculations
• GAIA DR3 catalog integration for million-star maps
• 3D visualization with Plotly
• Anisotropic metric tensors (beyond simple radial scaling)
• Comparison with observational data (lensing, orbits)

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Scientific Context

This project is part of the Segmented Spacetime (SSZ) theoretical framework developed by Carmen Wrede and Lino Casu. It explores alternative approaches to general relativity based on discrete spacetime segments and golden ratio (φ) scaling.

For more information, see:

• SSZ Projection Suite: GitHub Repository
• SSZ Metric: Github Repository
• SSZ All Physics Plots: Github Repository
• Papers: Researchgate

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📜 License

© 2025 Carmen Wrede, Lino Casu, Bingsi

This software is licensed under the Anti-Capitalist Software License (ACSL) v1.4.

Summary:

• ✅ Free for individuals, non-profits, educational institutions
• ✅ Free for worker cooperatives and collectives
• ❌ Not for use by capitalist corporations or law enforcement
• ❌ Not for military applications

See LICENSE file for full terms.

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📧 Contact

For questions, contributions, or collaboration:

📬 Email: [email protected]
🐙 GitHub: https://github.com/error-wtf/Segmented-Spacetime-Starmaps

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Status: Production Ready
Version: 1.0.0
Last Updated: November 2025