Toward a Zero-Parameter Model for Galaxy Rotation Curve Data

TL;DR

This paper introduces the Luminous Convolution Model (LCM), a new approach that explains galaxy rotation curves without dark matter by interpreting spectral data through galaxy curvature, and demonstrates its potential as a zero-parameter predictive model.

Contribution

The paper proposes the LCM as a novel interpretation of rotation curve data that reduces free parameters, aiming to predict luminous mass solely from observational data.

Findings

LCM explains rotation curves across 25 galaxies without dark matter.

The free parameter relates to galaxy curvature and varies with luminous mass models.

Predictions for the Milky Way's inner mass profile are provided.

Abstract

Modeling the luminous mass components of spiral galaxies in standard gravity poses a challenge due to the missing mass problem. However, with the addition of cold dark matter, the missing mass problem can be circumvented at the cost of additional free parameters to the theory. The Luminous Convolution Model (LCM) reconsiders how we interpret rotation curve data, such that Doppler-shifted spectra measurements can constrain luminous mass discovery. For a sample of 25 galaxies of varying morphologies and sizes, we demonstrate an ansatz for relative galaxy curvatures that can explain the missing mass. We solve for the LCM free parameter, which we report as a ratio of radial densities of the emitter, to receiver galaxy baryonic mass, to an exponent of $1.63$. Here, we show that this exponent is sensitive to which Milky Way luminous mass model one chooses. We then make a first prediction regarding the Milky Way mass profile in the inner one kpc. Thus, with a bound on the LCM free parameter, we pave the way for future work, where the LCM will tested as a zero-parameter model to predict luminous mass from rotation curve data.