  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 $<10\,\mathrm{km}\,\mathrm{s}^{-1}$ 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 $\sim 50$ per cent
lower total mass estimate for the Milky Way than when it is neglected, and that
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.
