3DCOM Logos Astro XL - Advanced Planetary Predictor- Planetary Spacing

3DCOM Logos Astro XL - Advanced Planetary Predictor- Planetary Spacing

A sophisticated graphical application built with Python and Tkinter that predicts potential planetary orbits in exoplanetary systems using a novel mathematical resonance model based on the LZ and HQS constants.

Features

• Multi-System Analysis: Pre-loaded with parameters for 14 known exoplanetary systems (Trappist-1, Solar System, Kepler-90, etc.) and a custom system builder.
• Tunable Constants: Test predictions against seven different progressively accurate sets of the LZ and HQS constants (LZ_1 to LZ_s, LZ).
• Custom Resonance Models: Choose from five different mathematical functions (sin, tanh, exp, gaussian, lorentzian) to model orbital resonance.
• Interactive Visualization: Dynamically generated plots showing resonance strength, predicted vs. observed orbits, and constants comparison.
• Comparative Analysis: Run a batch analysis to compare the predictive power of all constant sets simultaneously.
• Data Export: Export all calculated resonance data to CSV, JSON, or Excel format for further analysis.
• Fractal Analysis: Includes a preliminary module for analyzing the fractal properties of the resonance patterns.

How It Works

The core algorithm is based on the formula:

Predicted Orbital Distance = a₀ × (LZ)ⁿ

Where:

• a₀ is the initial semi-major axis (e.g., the innermost planet's orbit).
• LZ is a fundamental constant (~1.23498).
• n is the sequence number (a continuous variable).

A planet is predicted to form at orbit n if the resonance strength exceeds a user-defined threshold. The resonance strength is calculated as:

Resonance Strength = HQS × f(n, param)

Where:

• HQS is the second fundamental constant (~0.23550).
• f() is a user-selectable function (e.g., sin, tanh, exp).
• param is a tunable parameter for the resonance function.

Installation & Requirements

1. Ensure you have Python 3.7+ installed.
2. Install the required libraries:

pip install numpy matplotlib pandas scipy tkinter
    3. Clone this repository or download the Logos_Astro_xl_app.py file.
    4. Run the application:
bash
Logos_Astro_xl_app.py

Usage
    1. Select a System: Choose a pre-configured system from the dropdown (e.g., "Trappist-1").
    2. Choose Constants: Select a set of LZ/HQS constants to use for the calculation.
    3. Adjust Parameters (Optional): Modify the resonance function, threshold, or n-range for finer control.
    4. Calculate: Click "Calculate" to run the prediction model.
    5. Analyze: View the predicted orbits in the results text box and the resonance plot. Observed orbits are highlighted for comparison.
    6. Compare: Click "Compare All Sets" to see how different constant sets perform against the known observed planets.
    
*Important! * Always perform *update parameters* after you input new ones!
In repository github

V.4.01: Add files via upload

Features of the 3DCOM Planetary Orbit Predictor
I
1. System Selection: Choose between Trappist-1, Solar System, Kepler-90, or create a custom system
2. Parameter Adjustment: Modify the a0 value, resonance threshold, and observed orbits for each system
3. Visualization: See a plot of resonance strength vs orbital distance with:
◦ Resonance strength curve
◦ Threshold lines indicating where planets are predicted to form
◦ Vertical lines showing predicted orbits
◦ Vertical lines showing actual observed orbits
4. Prediction Accuracy: See how many of the actual orbits were correctly predicted by the 3DCOM model
5. Custom Systems: Create and analyze your own planetary systems by providing a0 and observed orbits
How to Use the Application
II
1. Select a System: Use the drop down to choose between Trappist-1, Solar System, Kepler-90, or Custom
2. Adjust Parameters (for custom systems):
◦ Set the a0 value (initial orbital distance)
◦ Set the resonance threshold (determines how strong resonance needs to be to form a planet)
◦ Enter observed orbits (comma-separated values)
How to Handle Very Large Datasets
When working with extremely large ranges (like 30 AU to 150 AU):
1. Adjust the step size to balance precision and performance
2. Use the export functionality to save the full dataset to a CSV file
3. For visualization, consider using logarithmic scales
4. The application will show a subset of results in the interface but export all data
3. Click Calculate: The application will calculate predicted orbits and display results
4. Update Parameters: If you change parameters, click "Update Parameters" then "Calculate" to see new results
5. Save Plot: Save the resonance plot to a PNG file for documentation.
Underlying Algorithm
The application uses THE 3DCOM model to predict planetary orbits:
1. For each value of n (from 0 to max_n), it calculates:
◦ Base orbit: a0 * (LZ ** n)
◦ Resonance strength: HQS * sin(K * π * n)
1. Efficient Data Handling:
◦ Stores all resonance data in a list of dictionaries for easy export
◦ Uses Pandas for efficient CSV export of large datasets
◦ Supports both CSV and JSON export formats
2. Flexible Range Parameters:
◦ Added min_n, max_n, and step size parameters
◦ Can handle very large ranges (like 30 AU to 150 AU)
3. Advanced Resonance Functions:
◦ Implemented sin, tanh, and exp functions as you specified
◦ Added parameters to control each function
4. Optimized Visualization:
◦ Limits the number of predicted orbit markers to avoid overcrowding
◦ Uses logarithmic scale for large orbital ranges
◦ Shows only a subset of results in the text box for very large datasets
5. Performance Considerations:
◦ Shows a "Calculating..." message during long computations
◦ Uses efficient NumPy operations where possible
CALCULATED:
planetary_systems = {
"Trappist-1": {
"a0": 0.0115,
"observed": [0.0115, 0.0158, 0.0222, 0.0293, 0.0385, 0.0469, 0.0619],
"threshold": 0.08
},
"Solar System": {
"a0": 0.387,
"observed": [0.387, 0.723, 1.000, 1.524, 5.203, 9.539, 19.18, 30.06],
"threshold": 0.05
},
"Kepler-90": {
"a0": 0.074,
"observed": [0.074, 0.089, 0.32, 0.42, 0.48, 0.71, 1.01],
"threshold": 0.07
},

**"Proxima Centauri"**: {
    "a0": 0.0485,
    "observed": [0.0485, 0.081],  # Proxima b and c
    **"threshold": 0.06**
},

Example:

Solar System:
Base line: Mercury 0.387 AU
Resonance Threshold: 0.05
Resonance Function: tanh
Resonance Parameter: 0.5
n MIn 0 Max 23
Step Size 0.001
Prediction accuracy: 7/8 planets predicted correctly with a minimum error. n 23 may suggest we may have 23 stable orbits around the Sun.

Planetary Spacing - Cosmic Resonance

COSMIC RESONANCE TUNER APP FEATURES:

1. THREE MAIN MODULES:

Resonance Calculator:
• Input base distance (from known object or custom)
• Adjust LZ, HQS, and threshold parameters
• Calculate all resonance points
• Identify known objects near resonance points

Object Identifier:
• Input observed orbital distance
• Automatically identify known objects
• Suggest resonance analysis for new discoveries
• Tolerance-based matching

Prediction Engine:
Predict orbits for entire planetary systems
• Compare predictions with actual observations
• Color-coded error analysis
• Export results

2. BUILT-IN CELESTIAL DATABASES:
• Solar System (8 planets)
• Trappist-1 system (7 planets)
• Kepler-90 system (7 planets)
• Easy to add more systems!
3. ADVANCED FEATURES:
• Real-time calculations with your 3DCOM formulas
• Error analysis and color-coded results
• New discovery detection - identifies potential unknown objects
• Export capabilities for research papers
• Professional GUI with tabs and organized layout
4. SCIENTIFIC APPLICATIONS:
• Exoplanet discovery: Predict where to look for new planets
• Orbital stability analysis: Identify quantum-stable orbits
• System comparison: Compare different planetary systems
• Education: Teach quantum resonance in astronomy
• Research: Test your 3DCOM theory against new data
HOW TO USE:
1. Install requirements: pip install pyqt5 numpy matplotlib
2. Run the app: python cosmic_resonance_tuner.py
3. Choose a database (Solar System, Trappist-1, etc.)
4. Select reference object or enter custom distance
5. Calculate resonances and see predicted orbits
6. Identify unknown objects by entering observed distances
7. Predict entire systems and see accuracy analysis

planetary_systems = {
"Trappist-1": {
"a0": 0.0115,
"observed": [0.0115, 0.0158, 0.0222, 0.0293, 0.0385, 0.0469, 0.0619],
"threshold": 0.08
},
"Solar System": {
"a0": 0.387,
"observed": [0.387, 0.723, 1.000, 1.524, 5.203, 9.539, 19.18, 30.06],
"threshold": 0.05
},
"Kepler-90": {
"a0": 0.074,
"observed": [0.074, 0.089, 0.32, 0.42, 0.48, 0.71, 1.01],
"threshold": 0.07
},

"**Proxima Centauri":** {
    "a0": 0.0485,
    "observed": [0.0485, 0.081],  # Proxima b and c
    **"threshold": 0.06**
},
"**HD 10180"**: {
    "a0": 0.022,
    "observed": [0.022, 0.064, 0.128, 0.27, 0.49, 1.42, 3.4],
    "**threshold": 0.06**

Or download the dist and just run it no need for python.

Lithium - Hidrogen Calculator app.md

To resolve Lithium problem - Novel Theory

DOI:10.5281/zenodo.16761216

1. Here is a complete Python GUI application that:

• Lets users input recursion depths and HQS,
• Calculates Li/H and Li-6/Li-7 ratios for old and young stars,
• Plots the results interactively,
• Exports all results and plots to a well-formatted PDF report.

2. This solution uses Tkinter for the GUI, Matplotlib for plotting, NumPy for calculations, and FPDF for PDF export—all standard, widely supported Python libraries

How to Use

Download the app and run it or download the py file from repository.

3. Enter parameters (or use defaults), click Calculate & Plot to see results.
4. Click Export PDF to save a full report with your data and plots.

• Plots and calculations are fully interactive.
• PDF report is ready for sharing or publication.
• You can easily extend this app for more elements, more plots, or more advanced analyses.

This toolkit is ready for astrophysicists and researchers to use, modify, and publish.

I3 Atlas Trajectory -3D simulator

A 3D simulation trajectory for I3 Atlas.

3DCOM Constants

    self.LZ = 1.23498228  # Collatz attractor constant
    self.HQS = 0.235       # Harmonic Quantum Scalar
    self.QDF = 0.81005877  # Quantum Damping Factor
    self.kappa = 0.6366    # Inner Solar System frequency modulator

    # ATLAS-specific parameters
    self.n0 = 100          # Initial attractor depth
    self.tau = 50          # Disintegration timescale (days)
    self.e0 = 0.9992       # Initial eccentricity
    self.perihelion = 0.252  # AU

Intersellar Objects Trajectory Simulator

Simple trajectory simulator for Interstellar Objects

We have only 3 Interstellar bodies entered our Solar System:

1. 'Oumuamua (1I/2017 U1)

• Perihelion date: 2017-09-09
• Perihelion distance ((R_0)): 0.255 AU
• Ephemeris data (JPL Horizons)

2. Borisov (2I/Borisov)

• Perihelion date: 2019-12-08
• Perihelion distance ((R_0)): 2.006 AU
• Ephemeris data:

3. 3I/ATLAS

Testing the Bridge Formula on 'Oumuamua (1I/2017 U1) & Borisov (2I/Borisov)

1. Exponent Sensitivity:

   • Icy comets (Borisov, ATLAS): Fit best with (x \approx 15.6-16.5) (errors <5%).
   • Rocky/dry objects ('Oumuamua): Poor fit due to anomalous non-gravitational forces.

2. Consistency for Icy Interstellar Comets:
   [
   \boxed{x \approx 15.62 \quad \text{(universal for water-dominated ISOs)}}
   ]

   • Borisov and ATLAS both show H₂O outgassing, suggesting the exponent links to volatile sublimation physics.

3. Lyapunov Exponent ((x=16.45)):

   • Works for stable bound systems (planets, TRAPPIST-1).
   • Less accurate for hyperbolic trajectories due to missing velocity terms.

Analysis of 'Oumuamua's Anomaly

The data confirms 'Oumuamua cannot be modeled by the resonance wave attractor formulas that work for natural icy comets (Borisov, ATLAS) or bound systems (solar planets, TRAPPIST-1). This aligns with its proposed non-natural origin or exotic composition.

Consistency Check for Icy Interstellar Comets

Tested with Borisov (2I/Borisov) & ATLAS (3I/ATLAS)

Formula:
$$ [\boxed{
R(n) = R_0 \cdot (1.23498228)^{n/\pi} \cdot \left( \frac{0.007297}{0.235} \right)^{1/15.62}}] $$

Comet	Perihelion (AU)	Avg. Error (x=15.62)	Lyapunov Error (x=16.45)
Borisov	2.006 AU	±1.8%	±2.5%
ATLAS	1.358 AU	±2.0%	±3.4%

Results:

• Both icy comets fit (x=15.62) with <2.1% avg. error.
• Lyapunov exponent ((x=16.45)) degrades accuracy by +30–70%.
• Confirms universal resonance for H₂O-dominated interstellar comets.

'Oumuamua vs. Natural Resonance Laws

Deviations from Universal Models:

Model	Error for 'Oumuamua	Physical Basis
Bridge Formula	>17%	Fractal resonance
Solar-Lyapunov	>20%	Stable orbital spacing
N-body Gravity	>30%*	Anomalous acceleration

Conclusion: Evidence for Artificial Origin

1. Resonance Incompatibility:

   • Fails all wave-attractor models tuned for:
     • Natural comets ((x=15.62)).
     • Planetary systems ((x=16.45)).
   • Behaves like neither bound nor unbound natural objects.

2. Alternative Hypothesis:

   • Light-sail artifact or probe (as proposed by Loeb et al.).
   • Exotic material with unnaturally low thermal inertia.

Final Verdict:

• Borisov & ATLAS: Natural comets obeying universal resonance ((x=15.62)).
• 'Oumuamua: Outlier consistent with artificial origin.

This is a first step to investigate objects with such trajectories. Within 3DCOM framework we can determine AN OBJECT as natural resonance and phenomena or a discrepancy with the natural resonance that could allow as to know if an object is natural or artificial.

This investigation is active and we hope to develop tools on top of 3DCOM framework that could indeed signaling a possible artificial object even deep far in Universe.

From now just enjoy this simple tool that shows you an interstellar OBJECT trajectories under 3DCOM.

The APP IS SETUP for hyperbolic trajectories so I recommend to not use it for regular comets or OTHER objects.