2024MNRAS.532L..48O

In the conventional approach to decomposing a rotation curve into a set of contributions from mass model components, the measurements of the rotation curve at different radii are taken to be independent. It is clear, however, that radial correlations are present in such data, for instance (but not only) because the orbital speed depends on the mass distribution at all (or, minimally, inner) radii. We adopt a very simple parametric form for a covariance matrix and constrain its parameters using Gaussian process regression. Applied to the rotation curve of the Milky Way, this suggests the presence of correlations between neighbouring rotation curve points with amplitudes of $\lt 10\, \mathrm{km}\, \mathrm{s}^{-1}$ over length scales of 1.5-$2.5\, \mathrm{kpc}$ regardless of the assumed dark halo component. We show that accounting for such covariance can result in a ∼50 per cent lower total mass estimate for the Milky Way than when it is neglected, and that the uncertainty in model parameters increases such that it seems more representative of the uncertainty in the rotation curve measurement. The statistical uncertainty associated with the covariance is comparable to or exceeds the total systematic uncertainty budget. Our findings motivate including more detailed treatment of rotation curve covariance in future analyses.