TDE 11.0 Vacuum Tension, Derived Shadow-Field Parameters, and a Variable-Speed-of-Light Cosmology (Research Proposal)

The Two-Energy Theory (TDE) project began in April 2025 as an intuitive, optimistic thought experiment motivated by a simple premise: mass may not be merely a “given” parameter, but could instead be an emergent manifestation of an underlying process—specifically, resonance and the geometry of stable patterns within the vacuum. From this starting point, TDE developed a conceptual framework built around two fields (the Shadow Field and the Light Field) and a resonance mechanism interpreted as a principle selecting stable states. Across subsequent iterations, as definitions were refined and a working formalism was developed, the project was confronted with numerical data. In particular, the spectral versions (TDE 10. 4/10. 5) achieve baryon-mass fits at the few-MeV level under a frozen core of parameters. This is not presented as confirmation of the theory, but as an empirical anchor that motivates further testing and external constraints. This proposal (TDE 11. 0) extends the interpretation of the vacuum parameter Mbare ≈ 450 MeV as a measure of Shadow-Field “stiffness” (vacuum tension), treating the vacuum as an effective elastic medium. This leads to derived medium parameters—most notably the vacuum cell length ℓ₀ = ℏc / Mbare and the tension scale τ₀ = Mbare² / (ℏc) —and to a cautious hypothesis that the observed speed of light may be an emergent propagation speed that depends on the vacuum state. The hypothesis is explicitly constrained by hard consistency gates: Solar-System PPN tests (Cassini), Type Ia supernova time dilation, GNSS/GPS metrology requirements, and early-Universe consistency. TDE is not a confirmed theory. It is a private research program and a hypothesis-driven thought experiment pursued under the principles of falsifiability and consistency with established physics. Calculations and formalization are assisted by AI tools, with a transparent division of roles: the concept and assumptions are authored by the proposer, while AI is used to support mathematical formulation, numerical cross-checking, and argument structure. Publishing successive versions serves as documentation of the process—with the intent that, even if the hypothesis ultimately fails, the record may retain value as a case study in constructing and confronting a physical proposal with data.