MIND Dragging: A Dynamic Reference-Frame Framework for Galaxy Rotation Coherence Without Dark Matter

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Authors: Alvaro Fabian BRICIO ARZUBIDE
Cited by: 0

Abstract

This work introduces MIND Dragging (Momentum-Induced Dynamic Dragging), a physically motivated framework that explains the coherence of galactic rotation curves through the emergence of a dynamic reference frame generated by collective angular momentum, without invoking dark matter. The central result demonstrates that the observed flat rotation velocity of a galaxy can be predicted independently from the total angular momentum of its stellar disk using the relation vflat=LtotalMtotal Reffv_{\mathrm{flat}} = \frac{L_{\mathrm{total}}}{M_{\mathrm{total}}\,R_{\mathrm{eff}}}vflat=MtotalReffLtotal where all quantities are directly measurable. We present a series of increasingly realistic tests, including: Idealized synthetic stellar populations, Hybrid datasets incorporating realistic velocity dispersions, Independent recovery of the Milky Way’s observed rotation velocity with ≤1.1–1.6% relative error,without fitting the rotation curve itself. Crucially, angular momentum is computed from individual stellar phase-space data, avoiding circularity and enabling a falsifiable prediction. The results indicate that MIND Dragging constitutes an observable manifestation of a dynamic spacetime frame induced by distributed angular momentum. The framework remains fully compatible with General Relativity and does not require modification of gravity or exotic matter components. A dedicated Theoretical Outlook section outlines clear routes toward a covariant formulation within GR and Einstein–Cartan theory, as well as a first-principles description of the Keplerian-to-MIND transition.

Fields of Study & Subjects

Galaxies: Formation Evolution Phenomena Cosmology and Gravitation Theories Stellar planetary and galactic studies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
1 January 2026
Language
EN
Document type
Preprint