The Luminous Convolution Model
  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.
