The Luminous Convolution Model

TL;DR

The paper introduces the Luminous Convolution Model (LCM), a heuristic approach that predicts galaxy rotation curves by transforming photon frequency shifts due to spacetime curvature, offering an alternative to dark matter and modified gravity models.

Contribution

The LCM provides a novel method for modeling galaxy rotation curves using Lorentz-type transformations of photon frequencies based on spacetime curvature, tested on multiple galaxy data sets.

Findings

LCM successfully predicts rotation curves across diverse spiral galaxies.

LCM's accuracy depends on the luminous matter mass-to-light ratio.

Compared to NFW and MOND models, LCM shows competitive performance.

Abstract

We present a heuristic model for predicting the rotation curves of spiral galaxies. The Luminous Convolution Model (LCM) utilizes Lorentz-type transformations of very small changes in photon frequencies from curved space-times to construct a model predictive of galaxy rotation profile observations. These frequency changes are derived from the Schwarzschild red-shift result or the analogous result from a Kerr wave equation. The LCM maps the small curvatures of the emitter galactic frame onto those of the receiver galactic frame, and then returns the map to the associated flat frames where measurements are made. This treatment rests upon estimates of the luminous matter in both the emitter and receiver galaxies to determine these small curvatures. The LCM is tested on a sample of 23 galaxies, represented in 35 different data sets. LCM fits are compared to those of the Navarro, Frenk and White (NFW) Dark Matter Model, and/or to the Modified Newtonian Dynamics (MOND) model when possible. The high degree of sensitivity of the LCM to the initial assumption of a luminous mass-to-light ratio ($M_L/L$) is shown. We demonstrate that the LCM is successful across a wide range of spiral galaxies for predicting the observed rotation curves.