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The Fine Structure of the Arithmetic Vacuum

Read in Spanish License: MIT Python 3.9+ Precision ORCID X CODATA 2022 DOI PapersOpen in Colab

Exact derivation of the Fine-Structure Constant ($\alpha^{-1}$) via Modular Information Thermodynamics.

This repository contains the source code, validation scripts, and manuscript for the paper "The Fine Structure of the Arithmetic Vacuum". We present a closed-form solution for $\alpha^{-1}$ based on the thermodynamic impedance of a $\mathbb{Z}/6\mathbb{Z}$ modular substrate, matching experimental data with absolute precision.


📄 Abstract

The fine-structure constant, $\alpha$, has long been considered an arbitrary free parameter in the Standard Model. In this work, we propose that $\alpha$ emerges from the interaction between an ideal geometric topology and the informational impedance of a discrete modular substrate ($\mathbb{Z}/6\mathbb{Z}$).

The Master Equation

$$ \Large \alpha^{-1} = (4\pi^3 + \pi^2 + \pi) - \frac{R_{fund}^3}{4} - \left(1 + \frac{1}{4\pi}\right)R_{fund}^5 $$

Where $R_{fund} = (6 \log_2 3)^{-1}$ represents the informational impedance of the vacuum.

Theoretical Breakdown

The formula is structured as a perturbative expansion where each term corresponds to a specific physical layer of the vacuum:

  • $\mathbf{4\pi^3 + \pi^2 + \pi}$ (Geometric Order 0): Represents the "bare" topology of a 3+1 dimensional space-time. It sums the phase-space volumes of the bulk (3D), the horizon surface (2D), and the $U(1)$ fiber (1D).
  • $-\frac{1}{4} R_{fund}^3$ (Thermal Correction): A first-order correction account for the entropic cost of information processing. The $1/4$ factor is consistent with the Bekenstein-Hawking area-entropy law ($S = A/4$).
  • $-(1 + \frac{1}{4\pi})R_{fund}^5$ (Screening Order): Represents vacuum polarization and charge screening. It combines a scalar field interaction with a 3D Gauss-law spherical scattering term.

This formulation reproduces the CODATA 2022 recommended value with an absolute precision of $1.5 \times 10^{-14}$, effectively making the theoretical prediction indistinguishable from current experimental uncertainty.

🏆 Key Results

Our theoretical derivation is compared directly against the latest metrological standards.

Component Physical Meaning Numerical Value
Order 0 Geometric Topology ($4\pi^3 + \dots$) 137.036303775...
Order 1 Thermal Fluctuation ($-R^3/4$) -0.000290689...
Order 2 Charge Screening $-R^5(1+1/4\pi)$ -0.000013880...
Total Theoretical Prediction 137.035999206...
Reference CODATA 2022 (Experiment) 137.035999206...

Absolute Discrepancy: ~ $1.5 \times 10^{-14}$ (0.0000 ppb)
Statistical Significance: $P < 10^{-10}$

🌌 The Broader Framework: Modular Substrate Theory

The derivation of $\alpha^{-1}$ presented here is not an isolated numerical coincidence. It is a specific application of the Modular Substrate Theory (MST), a comprehensive framework that utilizes the same $\mathbb{Z}/6\mathbb{Z}$ geometry to unify:

  • Cosmology: Resolving the Hubble ($H_0$) and $S_8$ tensions.
  • Particle Physics: Classifying exotic hadrons ($d^*, T_{cc}^+$) via geometric compression.
  • Mathematics: Linking Quantum Thermodynamics to the Riemann Hypothesis.

To explore the full theoretical foundation and other derivations:

Repo MST

Discover how the same impedance $R_{\text{fund}}$ governs phenomena across 60 orders of magnitude.

🛠️ Scientific Reproducibility

To ensure transparency and facilitate immediate verification by the scientific community, all computational analysis is provided via cloud-hosted environments. These notebooks are pre-configured with the necessary arbitrary-precision libraries (mpmath).

Research Domain Interactive Notebook Key Validations & Theoretical Outputs
⚛️ Quantum Electrodynamics Open in Colab • Exact derivation of $\alpha^{-1}$ ($10^{-14}$ precision)
$R_{\text{fund}}$ entropic cost calculation
• Perturbative convergence analysis

Verification Steps

  1. Click the "Open in Colab" badge above for the corresponding domain.
  2. Execute: Go to Runtime > Run all (or press Ctrl + F9).
  3. Audit: The script will automatically install dependencies and perform the 100-digit precision audit.
  4. Compare: Evaluate the Theoretical Final Value against the CODATA 2022 reference provided in the output.

Note: A minimum of 100 decimal places (mp.dps = 100) is used to ensure that the $10^{-14}$ precision is not affected by standard floating-point rounding errors.

📂 Repository Structure

├── README.md                          # Project overview
├── COPYRIGHT.md                       
├── LICENSE                            
├── Notebooks/
│   └──  Validation_Alpha.ipynb        # Interactive Colab/Jupyter Notebook
└── paper/
    ├── Arithmetic-Vacuum-Alpha.pdf    # Full paper (Preprint)
    └── Arithmetic-Vacuum-Alpha.tex    # LaTeX source code

📚 Citation

If you use this work or code in your research, please cite the following:

@article{peinador2026fine,
  title={The Fine Structure of the Arithmetic Vacuum: Exact Derivation of $\alpha^{-1}$ via Modular Renormalization},
  author={Peinador Sala, José Ignacio},
  journal={Zenodo},
  year={2026},
  url={[https://github.com/NachoPeinador/Arithmetic-Vacuum-Alpha](https://github.com/NachoPeinador/Arithmetic-Vacuum-Alpha)}
  doi = {10.5281/zenodo.18611630},
  note = {Version 1.0.0}
}

🛡️ License

This project is licensed under the MIT License - see the LICENSE file for details.

The scientific manuscript is available under CC BY 4.0.

✉️ Contact

José Ignacio Peinador Sala Independent Researcher, Institute of Modular Algebraic Structures Valladolid, Spain

📧 joseignacio.peinador@gmail.com


Dedicated to the open science community and the pursuit of fundamental understanding outside traditional academic boundaries.

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First-principles derivation of the fine-structure constant (α⁻¹) via Modular Information Thermodynamics (ℤ/6ℤ). Includes Python validation code demonstrating 10⁻¹⁴ precision against CODATA 2022, effectively eliminating alpha as a free parameter.

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