Minimum Distance Estimation of Milky Way Model Parameters and Related Inference

TL;DR

This paper introduces a novel statistical method using Hellinger distance and bootstrap techniques to estimate the Sun's location in the Milky Way by comparing stellar velocity densities across different models.

Contribution

It develops a new approach for Milky Way parameter estimation based on density comparisons and bootstrap confidence sets, advancing astrophysical inference methods.

Findings

Successfully estimates the Sun's galactic position.

Provides confidence sets for the estimated location.

Demonstrates consistency of the proposed method.

Abstract

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.