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We propose a method to estimate the location of the Sun in the disk of theMilky Way using a method based on the Hellinger distance and constructconfidence sets on our estimate of the unknown location using a bootstrap basedmethod. Assuming the Galactic disk to be two-dimensional, the sought solarlocation then reduces to the radial distance separating the Sun from theGalactic 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 solarlocation is equivalent to the location of us earthlings who observe thevelocities of a sample of stars in the neighborhood of the Sun. This unknownlocation is estimated by undertaking pairwise comparisons of the estimateddensity of the observed set of velocities of the sampled stars, with densitiesestimated using synthetic stellar velocity data sets generated at chosenlocations in the Milky Way disk according to four base astrophysical models.The "match" between the pair of estimated densities is parameterized by theaffinity measure based on the familiar Hellinger distance. We perform a novelcross-validation procedure to establish a desirable "consistency" property ofthe proposed method.

Minimum Distance Estimation of Milky Way Model Parameters and Related Inference