The Bolocam Galactic Plane Survey. VIII. A Mid-Infrared Kinematic Distance Discrimination Method
Timothy P. Ellsworth-Bowers, Jason Glenn, Erik Rosolowsky, Steven, Mairs, Neal J. Evans II, Cara Battersby, Adam Ginsburg, Yancy L. Shirley,, John Bally

TL;DR
This paper introduces a Bayesian method combining millimeter and mid-infrared data to accurately determine distances to molecular cloud clumps, resolving the kinematic distance ambiguity for large Galactic surveys.
Contribution
It presents a novel distance discrimination technique using mid-infrared absorption and Galactic models, improving distance estimates for thousands of molecular cloud objects.
Findings
Resolved kinematic distance ambiguity for 618 objects
Achieved 92% agreement with existing KDA resolutions
Placed some objects at or beyond the tangent distance
Abstract
We present a new distance estimation method for dust-continuum-identified molecular cloud clumps. Recent (sub-)millimeter Galactic plane surveys have cataloged tens of thousands of these objects, but detailed study of their physical properties requires robust distance determinations. We derive Bayesian distance probability density functions (DPDFs) for 770 objects from the Bolocam Galactic Plane Survey in the longitude range 7.5 < l < 65. The DPDF formalism is based on kinematic distances, and uses external data sets to place prior distance probabilities to resolve the kinematic distance ambiguity (KDA) for objects in the inner Galaxy. We present priors related to the mid-infrared absorption of dust in dense molecular regions and the Galactic distribution of molecular gas. By assuming a numerical model of Galactic mid-infrared emission and simple radiative transfer, we match the…
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