A Parallax-based Distance Estimator for Spiral Arm Sources

TL;DR

This paper introduces a Bayesian-based computer program that improves distance estimates to spiral arm sources in the Milky Way by leveraging recent parallax measurements and spiral structure models, enhancing our understanding of galactic structure.

Contribution

The paper presents a novel Bayesian method that combines parallax data, kinematic information, and spiral arm signatures to accurately estimate distances to sources in the Milky Way.

Findings

Improved accuracy in distance estimation to spiral arm sources.

Realistic visualization of the Milky Way's spiral structure.

Enhanced understanding of galactic distribution of star-forming regions.

Abstract

The spiral arms of the Milky Way are being accurately located for the first time via trigonometric parallaxes of massive star forming regions with the BeSSeL Survey, using the Very Long Baseline Array and the European VLBI Network, and with the Japanese VERA project. Here we describe a computer program that leverages these results to significantly improve the accuracy and reliability of distance estimates to other sources that are known to follow spiral structure. Using a Bayesian approach, sources are assigned to arms based on their (l,b,v) coordinates with respect to arm signatures seen in CO and HI surveys. A source's kinematic distance, displacement from the plane, and proximity to individual parallax sources are also considered in generating a full distance probability density function. Using this program to estimate distances to large numbers of star forming regions, we generate a realistic visualization of the Milky Way's spiral structure as seen from the northern hemisphere.