Dark Flow Reinterpreted as a Super-Horizon Gravitational Gradient

This paper reinterprets Dark Flow as a structural horizon‑gradient phenomenon rather than a bulk motion of matter. Building on the Creation, Structural Memory, and Dark Matter (Structural Binding) papers, it shows that large‑scale anisotropies arise naturally from inherited curvature gradients that extend beyond the observable universe. These Super‑Horizon Gradient Geometries (SHGGs) create directional biases in cosmic structure formation without requiring super‑luminal motion, exotic forces, or violations of isotropy.The result is a coherent structural explanation of Dark Flow: a persistent, large‑scale curvature gradient preserved by structural memory and expressed through the binding behavior of dark‑matter structures. This framework resolves long‑standing tensions between observation and ΛCDM predictions by reframing Dark Flow as a geometric inheritance rather than a dynamical anomaly.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-17
DOI
https://doi.org/10.5281/zenodo.18272326
Primary Topic
Cosmology and Gravitation Theories
Type
article
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article

Dark Flow Reinterpreted as a Super-Horizon Gravitational Gradient

Brian Rieckmann
Zenodo (CERN European Organization for Nuclear Research)
Cosmology and Gravitation Theories
article

Dark Flow Reinterpreted as a Super-Horizon Gravitational Gradient

Brian Rieckmann
article en

Abstract

This paper reinterprets Dark Flow as a structural horizon‑gradient phenomenon rather than a bulk motion of matter. Building on the Creation, Structural Memory, and Dark Matter (Structural Binding) papers, it shows that large‑scale anisotropies arise naturally from inherited curvature gradients that extend beyond the observable universe. These Super‑Horizon Gradient Geometries (SHGGs) create directional biases in cosmic structure formation without requiring super‑luminal motion, exotic forces, or violations of isotropy.The result is a coherent structural explanation of Dark Flow: a persistent, large‑scale curvature gradient preserved by structural memory and expressed through the binding behavior of dark‑matter structures. This framework resolves long‑standing tensions between observation and ΛCDM predictions by reframing Dark Flow as a geometric inheritance rather than a dynamical anomaly.

Zenodo (CERN European Organization for Nuclear Research)
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