Submitted:
13 August 2025
Posted:
13 August 2025
Read the latest preprint version here
Abstract
Keywords:
MSC: 2020: Primary: 20N05, 51H10, 57R18; Secondary: 37C55, 37N20, 53Z05
1. Introduction
2. Theoretical Framework
3. Matrix Development and Properties
3.1. Foundations
3.2. Topological and Projective Definition of the Imaginary Number
3.3. Verification of Quaternion Structure
3.3.1. Multiplication Rules
3.4. Motivation for the Operator and Its Algebraic Rules
3.5. Comparative Analysis: The Alpha Group and Known Algebraic Structures
3.5.1. Algebra Generated by
- ,
- (idempotent operator),
- i and non-commutative: .
3.5.2. Matrix Representation
3.5.3. Verification Example
3.5.4. Norm and Multiplicativity
3.6. Alpha Group Matrix Construction
3.7. Symmetric and Antisymmetric Decomposition
4. Rotations in Different Dimensions
4.1. 2D Rotations
4.2. 3D Rotations
4.3. 4D Rotations in Alpha Group
4.4. Dynamical Behavior of the Alpha Group Matrix
4.5. Relation of the parameter to known mathematical structures
4.6. Generalization of Classical Rotations
5. Methodology
5.0.1. Symmetric and Antisymmetric Decomposition
5.0.2. Monte Carlo Simulation
- numpy as np: Used for numerical operations, such as creating matrices, handling complex numbers, and manipulating vectors. NumPy is essential for defining and decomposing the matrix , as well as for Monte Carlo simulations.
- matplotlib.pyplot as plt: Used to create trajectory plots, including three-dimensional visualizations.
- mpl_toolkits.mplot3d.Axes3D: Provides the necessary tools for plotting graphs in three dimensions.
- matplotlib.cm: Allows the use of colormaps, such as plasma, to color trajectories based on time evolution.
- def monte_carlo_simulation(theta, steps=100000): defines a function that performs a Monte Carlo simulation based on a given parameter . The optional argument steps sets the number of iterations (default: 100,000). This function typically generates random trajectories or samples according to a stochastic process, allowing the study of dynamic behavior or statistical properties of a system as a function of .
5.0.3. 3D Projection and Visualization
5.0.4. Parameter Sweep and Boundary Regimes
- : approximates a locally Euclidean structure,
- : intermediate topology with rotational coupling,
- : represents the compactified boundary, associated with hyperbolic topology and attractor-like behavior.
6. Results
6.1. Singularity at and the Topology Transformation
- For : the system exhibits a locally Euclidean structure, modeled as a bundle over the 3-sphere , corresponding to the classical quaternionic topology.
- At : a compactified topology emerges, modeled by a bundle over the 4-sphere , where spatial infinity is topologically mapped to a single point, similar to the Alexandroff compactification,29].
6.2. The Role of the Vector as an Infinity Operator
Topological Bundles
- Bundle over (local): For small , the geometry reflects Euclidean behavior and quaternionic symmetry, consistent with -based fiber bundles.
- Bundle over (global): At , the vector induces a compactification along a radial dimension, effectively transforming the ambient space from into the 4-sphere by the inclusion of a single point at infinity.
6.3. Regime Change Analysis in
6.4. Numerical Validation and Error Analysis
7. Conclusion
8. Final Considerations
Code Availability
References
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| Group | Units | Key Relations | Topology/Geometry |
|---|---|---|---|
| Alpha group | Nontrivial projective structure, compactification to | ||
| Quaternion | topology, 4D algebra | ||
| Complex | i | topology, 2D field | |
| SU(2) | Double cover of SO(3) |
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