What question did this study set out to answer?

The research aims to derive the fine structure constant α from information-theoretic principles using Conscious Geometry Theory.

February 27, 2026Open Access

CGT - Derivation of the Fine Structure Constant from Information-Theoretic First Principles

Key Points

The research aims to derive the fine structure constant α from information-theoretic principles using Conscious Geometry Theory.
Utilized a fundamental parameter Y = 7 (universal recursion depth) for the derivation.
Conducted a Monte Carlo analysis with 10⁶ random formulas to assess probability of achieving the result by chance.
Connected electromagnetism to information geometry through the relationship between Y and π.
Derived α⁻¹ = 137.039, achieving 0.0022% accuracy compared to the CODATA 2022 value.
Calculated the probability of achieving this accuracy by chance as p < 8×10⁻⁵ (1 in 12,400).

Abstract

A derivation of the fine structure constant α from information-theoretic foundations using Conscious Geometry Theory (CGT). Using a single fundamental parameter Y = 7 (universal recursion depth), we derive α⁻¹ = 137.039, achieving 0.0022% agreement with the CODATA 2022 value. Monte Carlo analysis of 10⁶ random formulas demonstrates that achieving this accuracy by chance has probability p < 8×10⁻⁵ (1 in 12,400). The derivation connects electromagnetism to information geometry through the relationship between Y = 7 and π.

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