The Zipped Universe: A Unified Elastic Framework for Galactic Dynamics and Cosmological Expansion

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Authors: Michel Burgardus Albertus Zwanenburg
Cited by: 0

Abstract

This dataset contains the full manuscript of the Zipped Framework (ZIPT), a theoretical model that unifies galactic dynamics and late-time cosmological expansion through a single, scale-invariant elastic property of spacetime. ZIPT replaces the need for non-baryonic dark matter and dark energy with a fundamental elastic response that activates below a critical acceleration threshold (the Delta Offset, $\Delta \approx 1.2 \times 10^{-10},\mathrm{m/s^2}$). The framework is governed by a single geometric scaling parameter, the Zwanenburg-degree ($\gamma = 1.585$), which simultaneously reproduces flat rotation curves in spiral galaxies, rising curves in low-surface-brightness systems, and enhanced velocity dispersions in pressure-supported elliptical galaxies. Cosmological expansion is interpreted as a macroscopic stress-relaxation process of spacetime, providing a natural explanation for the observed Hubble tension without invoking dark energy. The manuscript includes: Mathematical derivation of the ZIPT acceleration and potential Numerical validation of galactic rotation and virial equilibrium Cosmological implications and stress-relaxation modeling Predictions and falsifiability criteria for future observational tests Formal intellectual property declaration asserting authorship and rights This work provides a parsimonious, falsifiable, and numerically consistent alternative to standard dark-sector models, linking galactic and cosmological phenomena through a single elastic principle. Zenodo Hashtags #ZippedFramework #ZIPT #TheoreticalPhysics #Cosmology #GalacticDynamics #ElasticSpacetime #DarkMatterAlternative #HubbleTension #GalaxyRotationCurves #EllipticalGalaxies #Astrophysics #FalsifiablePhysics #MichelZwanenburg

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
5 January 2026
Document type
Dataset