Minimum Distance Estimation of Milky Way Model Parameters and Related
  Inference
  We propose a method to estimate the location of the Sun in the disk of the
Milky Way using a method based on the Hellinger distance and construct
confidence sets on our estimate of the unknown location using a bootstrap based
method. Assuming the Galactic disk to be two-dimensional, the sought solar
location then reduces to the radial distance separating the Sun from the
Galactic center and the angular separation of the Galactic center to Sun line,
from a pre-fixed line on the disk. On astronomical scales, the unknown solar
location is equivalent to the location of us earthlings who observe the
velocities of a sample of stars in the neighborhood of the Sun. This unknown
location is estimated by undertaking pairwise comparisons of the estimated
density of the observed set of velocities of the sampled stars, with densities
estimated using synthetic stellar velocity data sets generated at chosen
locations in the Milky Way disk according to four base astrophysical models.
The "match" between the pair of estimated densities is parameterized by the
affinity measure based on the familiar Hellinger distance. We perform a novel
cross-validation procedure to establish a desirable "consistency" property of
the proposed method.
