The Information-Geometric Theory of Dimensional Flow: Explaining Quantum Phenomena, Mass, Dark Energy and Gravity Without Spacetime

Mikhail Liashkov

No waf5m_v1, OSF Preprints from Center for Open Science

Abstract: This paper presents a novel theoretical framework based on information geometry and scale-dependent dimensionality that offers unified explanations for phenomena across all physical scales. The proposed dimensional flow theory demonstrates how effective dimensionality varies with scale, creating a natural hierarchy that explains quantum behaviors as projections from lower-dimensional spaces to higher-dimensional observation space. This approach resolves quantum paradoxes while preserving determinism and locality at the fundamental level. The framework successfully derives the mass spectrum of elementary particles and coupling constants from dimensional parameters, establishing a geometric foundation for the Standard Model without fine-tuning. At galactic scales, the theory provides excellent agreement with SPARC database observations of rotation curves without invoking dark matter. Cosmologically, it reinterprets redshift observations as manifestations of a static universe with a dimensional gradient, rather than an expanding universe. This eliminates the need for inflation, dark energy, and a beginning of time, while maintaining consistency with observational constraints. Gravitational phenomena emerge from dimensional gradients rather than spacetime curvature, and cosmic microwave background features appear as dimensional tomography rather than echoes of a primordial state. The framework's remarkable predictive power across diverse phenomena, coupled with its significant reduction in free parameters compared to current models, suggests that physical reality may be fundamentally based on information-geometric principles and scale-dependent dimensionality rather than an evolving spacetime.

Date: 2025-04-11
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/67fa745b44142cb1a4cc4acb/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:waf5m_v1

DOI: 10.31219/osf.io/waf5m_v1

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().