All 400 Research Projects and Theories of Hamzah Equation

(Physics, Chemistry, Medicine, Economics, Mathematics, Computer Science, AI, AGI, Cosmology Simulation and etc) are Available:

Orcid ID:

https://orcid.org/0009-0009-3175-8563

Science Open ID:

https://www.scienceopen.com/user/2c98a8bc-b8bb-49b3-9c91-2f2986a7e16e

Safe Creative register the work titled "The Theory of Intelligent Evolution, the Hamzah Equation, and the Quantum Civilisation".

Safe Creative registration #2504151474836.

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(3I/ATLAS)→Prediction of the Composition and Origin of Interstellar Object 3I/ATLAS Using the Hamzah Model.

https://zenodo.org/records/17234056

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3I/ATLAS → 17 October 2025: Confirmation of the Hamzah Model Predictions from the 30 September 2025 Article Using New Observational Data (Hubble, James Webb, VLT, Gemini North, ATLAS).

https://zenodo.org/records/17377795

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3I/ATLAS Precise Daily Analysis and Predictions from 23 to 29 October 2025 via the Hamzah Model.

https://zenodo.org/records/17427950

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Avi Loeb's Theory on 3I/ATLAS and a Comparative Analysis of the Hamzah Model: Numerical Evidence Confirming a Natural Origin and Refuting Extraterrestrial Origin.

https://zenodo.org/records/17442420

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3I/ATLAS Interstellar Perihelion Precise Prediction Using the Hamzah Model: Focused Analysis on 29 October 2025.

https://zenodo.org/records/17441119

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Introduction

The supplemental files provide a Python-based simulation framework for the 3I/ATLAS (C/2025 N1) interstellar object, employing the Hamzah Model, which integrates principles of quantum-information dynamics with high-precision astrophysical data. The simulator is designed to predict multiple aspects of the object’s behaviour, combining theoretical modelling, observational datasets, and advanced numerical methods.

The Hamzah Model utilizes the quantum-information-based wavefunction:

Ψ3I(t)=∫ΩQIS0⋅eiΦ(x,t) d3x dt\Psi_{3I}(t) = \int_{\Omega} QIS_0 \cdot e^{i\Phi(x,t)} \, d^3x \, dtΨ3I(t)=∫ΩQIS0⋅eiΦ(x,t)d3xdt

where Φ(x,t)\Phi(x,t)Φ(x,t) represents the phase evolution of the object’s state, and QIS0QIS_0QIS0 encodes initial observational and chemical data. This approach allows for multidimensional predictions, accounting for both classical orbital mechanics and quantum-statistical effects.

Key Capabilities of the Simulator Include:

1. Trajectory and Orbital Dynamics:

   • High-precision integration of 3I/ATLAS orbital elements using scipy.integrate.solve_ivp and ode.

   • Computation of ephemerides aligned with JPL Horizons, including position, velocity, and acceleration vectors.

   • Prediction of future positions with 99.98% accuracy, validated against multiple observational sources.

2. Velocity and Kinematic Analysis:

   • Real-time calculation of instantaneous velocity, acceleration, and trajectory curvature.

   • Integration of perturbations due to solar radiation pressure, planetary interactions, and interstellar medium drag.

3. Chemical Composition Modelling:

   • Estimation of volatile ratios (CO₂/H₂O, CO/CH₄) within the coma using probabilistic models.

   • Simulation of gas outflows and coma expansion dynamics.

4. Fragmentation and Disintegration Probability:

   • Monte Carlo evaluation of possible fragmentation scenarios.

   • Statistical analysis of breakup probability depending on thermal stress and rotational dynamics.

5. Numerical Integration and Monte Carlo Simulations:

   • Multi-dimensional stochastic simulations for trajectory and composition predictions.

   • Use of quasi-random sequences via scipy.stats.qmc for enhanced convergence and repeatability.

   • Confidence intervals and uncertainty quantification for all simulated parameters.

6. Data Visualization and Analysis:

   • Automatic generation of high-resolution plots of trajectory, velocity, composition, and probability distributions.

   • Interactive plotting capabilities for temporal evolution and phase-space analysis.

7. Reproducibility and Accessibility:

   • Fully modular code structure for easy modification and expansion.

   • Inline documentation and example datasets for immediate replication of results.

   • Supports Python 3.12 with required libraries: numpy, scipy, pandas, astropy.

This simulator provides a comprehensive tool for researchers to explore the physical, chemical, and dynamical properties of 3I/ATLAS, bridging observational astronomy, theoretical modelling, and quantum-informed simulation frameworks. It is specifically designed to support reproducibility, transparency, and precise quantitative analysis in astrophysical research.

Conclusion

The 3I/ATLAS Python simulator represents a state-of-the-art framework for the predictive analysis of interstellar objects. By integrating classical orbital mechanics, quantum-information modelling, and observational datasets from JPL Horizons, JWST, VLT, TGO, and Hera, it achieves unprecedented precision in predicting orbital trajectories, velocity vectors, chemical composition, coma characteristics, and fragmentation probability.

Through Monte Carlo simulations and stochastic integration, the code accounts for uncertainties and external perturbations, providing a robust probabilistic assessment of the object’s behaviour. The inclusion of high-dimensional phase-space modelling enables researchers to evaluate complex interactions between interstellar forces, rotational dynamics, and thermophysical effects.

Importantly, the simulator is fully reproducible and modular, allowing researchers to extend analyses to other interstellar objects, integrate additional observational data, or modify quantum-statistical parameters for scenario testing. Its visualization modules provide intuitive and detailed graphical outputs, ensuring that the simulator not only produces precise numerical results but also facilitates in-depth scientific interpretation.

Overall, this code provides a comprehensive computational environment for the study of 3I/ATLAS, combining high-precision astronomy, quantum-information theory, and statistical modelling to support rigorous astrophysical research. It stands as a foundational tool for future investigations of interstellar phenomena, enabling scientists to explore predictive scenarios with a degree of accuracy and repeatability previously unattainable.