Sizing Up the Milky Way: A Bayesian Mixture Model Meta-Analysis of Photometric Scale Length Measurements
Timothy C. Licquia, Jeffrey A. Newman

TL;DR
This paper uses Bayesian meta-analysis to refine the Milky Way's disk scale length by combining multiple measurements, accounting for errors, and updating Galactic models with improved parameters.
Contribution
It introduces a Bayesian mixture-model approach for meta-analyzing photometric measurements, providing a more reliable estimate of the Milky Way's disk scale length.
Findings
Estimated scale length of 2.64±0.13 kpc from combined data.
Separate estimates: 2.71+0.22−0.20 kpc (visible) and 2.51+0.15−0.13 kpc (infrared).
Updated Galactic stellar mass to approximately 4.8×10^{10} M_⊙.
Abstract
The exponential scale length () of the Milky Way's (MW's) disk is a critical parameter for describing the global physical size of our Galaxy, important both for interpreting other Galactic measurements and helping us to understand how our Galaxy fits into extragalactic contexts. Unfortunately, current estimates span a wide range of values and often are statistically incompatible with one another. Here, we perform a Bayesian meta-analysis to determine an improved, aggregate estimate for , utilizing a mixture-model approach to account for the possibility that any one measurement has not properly accounted for all statistical or systematic errors. Within this machinery we explore a variety of ways of modeling the nature of problematic measurements, and then employ a Bayesian model averaging technique to derive net posterior distributions that incorporate any model-selection…
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