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March 12, 20260 citationsOpen Access

Ω_Λ = 8/ (3 (π + ln2) ): A Derivation of Dark Energy from Holographic Bounds, Berry Phase, and Landauer Erasure

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RHRob Hayes

Key Points

  • The aim is to derive the cosmological dark energy density parameter from foundational principles without observational input.
  • Combine Bekenstein-Hawking holographic bound with Berry phase and Landauer erasure cost.
  • Define Ferguson constant using the components from the framework.
  • Calculate Omega_Lambda using derived formulas and analyze tensions with existing observations.
  • Derived Omega_Lambda as 0.6954, with strong tension against Planck observations.
  • Extension incorporating four Standard Model forces yields Omega_Lambda of 0.68468, aligning closely with observational data.
  • Predicted future precision measurement of Omega_Lambda by Euclid space telescope.

Abstract

This paper presents the Kenos framework, a derivation of the cosmological dark energy density parameter from first principles. Three independent physical inputs are combined: the Bekenstein-Hawking holographic bound on the de Sitter horizon (Iₘax = pi/H0²), the Berry phase of an S-duality invariant boundary state (gamma = pi), and the Landauer erasure cost at the Gibbons-Hawking temperature (ln2 per bit). Their sum defines the Ferguson constant f = pi + ln2, yielding: OmegaLambda = 8 / (3f) = 0. 6954 The Hubble constant cancels exactly. No free parameters. No cosmological observations used as input. Tension with Planck 2018 (0. 6847 +/- 0. 0073): 1. 47 sigma. An extension incorporating the four Standard Model forces via e-fold weighted running coupling constants and physical cutoffs (Kenos-4F) yields OmegaLambda = 0. 68468, 0. 003 sigma from observation. The S-duality symmetry I -> 1/I resolves the cosmological constant problem. Dark energy is the symmetry-breaking residual from the Big Bang instanton transition, not vacuum energy. One open theoretical problem remains. Primary falsifiable prediction: Euclid space telescope (~2027) will measure OmegaLambda to +/-0. 001 precision, distinguishing the two predictions at approximately 10 Euclid-sigma

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Cite This Study

Rob Hayes (2026) studied this question.

synapsesocial.com/papers/69b2585696eeacc4fcec7e41https://doi.org/10.5281/zenodo.18916805
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