Multi-resolution kinematic modelling of nearby galaxies: a demonstration using MHONGOOSE observations

TL;DR

This paper introduces a new multi-resolution kinematic modeling method for nearby galaxies, combining data across various spatial resolutions to improve accuracy in rotation curves and velocity profiles, demonstrated with MHONGOOSE observations.

Contribution

The paper presents a novel self-consistent approach to combine multi-resolution kinematic models, reducing systematic errors in galaxy rotation curves compared to single-resolution methods.

Findings

Combined rotation curves have smaller systematic errors.

Method suppresses unphysical radial fluctuations.

Approach enhances the use of multi-resolution data.

Abstract

We present a novel method of combining kinematic models obtained at multiple spatial resolution levels in a self-consistent manner. The MHONGOOSE survey has mapped atomic hydrogen emission in $30$ nearby dwarf and spiral galaxies. Each galaxy is imaged at multiple resolution levels with unprecedented dynamic range in spatial resolution (from $\sim 10''$ to $ 90''$) and HI sensitivity, with the latter varying by almost a factor of $30$ across all resolution scales. We use radial weighting functions to combine kinematic models from all resolution levels. The weights are derived from the residuals of model fits to a set of observations of synthetic model galaxies with known rotation curves and geometries. We obtain combined (weighted and smoothed) inclination and position angle profiles for each galaxy. These suppress the sharp, often unphysical radial fluctuations arising in single-resolution profiles. We then fit the rotation speed and velocity dispersion profiles at each resolution level with the geometric profiles fixed to the combined profiles, finally combining these using the same weighting and smoothing approach. The combined rotation curves utilise all of the available information and have smaller typical systematic errors compared to those obtained using a single resolution level, particularly near the centres and outer edges of models. This initial demonstration is promising; there is scope to further refine the process to use such information-rich observations to their full potential.