Uncertainties in Galaxy Rotation Curves

TL;DR

This paper introduces a new tool, makemap, for robustly estimating uncertainties in galaxy rotation curves from spectral data, improving the reliability of comparisons with galaxy formation models.

Contribution

The paper presents makemap, a standalone tool that estimates velocity uncertainties from spectral data, and demonstrates its application to improve rotation curve modeling.

Findings

Estimated uncertainties vary significantly from previous estimates.

Uncertainties can be up to five times larger or smaller than prior values.

Kinematic model precision varies widely across datasets.

Abstract

Assessing the likelihood that the rotation curve of a galaxy matches predictions from galaxy formation simulations requires that the uncertainties in the circular speed as a function of radius derived from the observational data be statistically robust. Few uncertainties presented in the literature meet this requirement. In this paper we present a new standalone tool, makemap, that estimates the fitted velocity at each pixel from Gauss-Hermite fits to a 3D spectral data cube, together with its uncertainty obtained from a modified bootstrap procedure. We apply this new tool to neutral hydrogen spectra for 18 galaxies from the THINGS sample, and present new velocity maps with uncertainties. We propagate the estimated uncertainties in the velocity map into our previously-described model fitting tool DiskFit to derive new rotation curves. The uncertainties we obtain from these fits take into account not only the observational errors, but also uncertainties in the fitted systemic velocity, position of the rotation centre, inclination of the galaxy to the line of sight, and forced non-circular motion. They are therefore much better-defined than values that have previously been available. Our estimated uncertainties on the circular speeds differ from previous estimates by factors ranging up to of five, being smaller in some cases and larger in others. We conclude that kinematic models of well-resolved HI datasets vary widely in their precision and reliability, and therefore potentially in their value for comparisons with predictions from cosmological galaxy formation simulations.