The Luminous Convolution Model as an alternative to dark matter in spiral galaxies

TL;DR

The Luminous Convolution Model (LCM) predicts spiral galaxy rotation curves using luminous matter estimates and relativistic effects, offering an alternative to dark matter with better average fit success.

Contribution

This paper introduces the LCM as a novel model that predicts galaxy rotation curves from luminous matter and relativistic effects, outperforming dark matter and modified gravity models.

Findings

LCM successfully predicts 23 galaxy rotation curves.

LCM outperforms dark matter and modified gravity models in fitting data.

Relativistic effects are significant in modeling galaxy rotation curves.

Abstract

The Luminous Convolution Model (LCM) demonstrates that it is possible to predict the rotation curves of spiral galaxies directly from estimates of the luminous matter. We consider two frame-dependent effects on the light observed from other galaxies: relative velocity and relative curvature. With one free parameter, we predict the rotation curves of twenty-three (23) galaxies represented in forty-two (42) data sets. Relative curvature effects rely upon knowledge of both the gravitational potential from luminous mass of the emitting galaxy and the receiving galaxy, and so each emitter galaxy is compared to four (4) different Milky Way luminous mass models. On average in this sample, the LCM is more successful than either dark matter or modified gravity models in fitting the observed rotation curve data. Implications of LCM constraints on populations synthesis modeling are discussed in this paper. This paper substantially expands the results in arXiv:1309.7370.