Effective Dark Matter Phenomena from Unitary Retarded Probability Dynamics

Read full text
Authors: AI Trinity Labo
Cited by: 0

Abstract

This work proposes an effective gravitational framework in which phenomena commonly attributed to dark matter arise from a non-instantaneous temporal response of gravity rather than from additional unseen matter components. The central idea is to treat gravity as a retarded, difference-type response to the scalar source, motivated by Unitary Retarded Probability Dynamics (URPD). Instead of modifying the field equations of general relativity or introducing new particles, the model alters the temporal structure of solutions by incorporating a causal memory kernel. This construction ensures that static, high-frequency, and short-wavelength limits automatically recover standard gravitational behavior, preserving consistency with solar-system tests, gravitational waves, and local dynamics. The framework is formulated using standard linear-response tools, allowing the gravitational correction to be expressed through a frequency-dependent response function. This representation makes explicit which temporal and spatial scales contribute to the effective enhancement observed at galactic scales, while remaining suppressed elsewhere. For exponential memory kernels, the nonlocal formulation is shown to be equivalent to a local relaxation-type equation, providing a tractable dynamical interpretation. The resulting model reproduces key qualitative features of galactic dynamics typically attributed to dark matter, without introducing additional degrees of freedom. This work is intended as an effective, phenomenological bridge between galactic-scale observations and deeper non-Markovian dynamics, leaving open both empirical tests and future connections to the full URPD framework.

Fields of Study & Subjects

Cosmology and Gravitation Theories Dark Matter and Cosmic Phenomena Relativity and Gravitational Theory

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
2 February 2026
Document type
Preprint