Dynamical Blueprints for Galaxies
Lawrence M. Widrow, Brent Pym, and John Dubinski

TL;DR
This paper develops an axisymmetric equilibrium model for late-type galaxies, including the Milky Way, using Bayesian methods to fit observational data and explore the galaxy's structural and dynamical properties.
Contribution
It introduces a novel phase space distribution function-based model for galaxies and applies Bayesian inference to constrain parameters from observations.
Findings
Models fit observational data but vary in structural parameters.
Most models develop bars with pattern speeds of 40-50 km/s/kpc.
The Milky Way's bar likely formed recently.
Abstract
We present an axisymmetric, equilibrium model for late-type galaxies which consists of an exponential disk, a Sersic bulge, and a cuspy dark halo. The model is specified by a phase space distribution function which, in turn, depends on the integrals of motion. Bayesian statistics and the Markov Chain Monte Carlo method are used to tailor the model to satisfy observational data and theoretical constraints. By way of example, we construct a chain of 10^5 models for the Milky Way designed to fit a wide range of photometric and kinematic observations. From this chain, we calculate the probability distribution function of important Galactic parameters such as the Sersic index of the bulge, the disk scale length, and the disk, bulge, and halo masses. We also calculate the probability distribution function of the local dark matter velocity dispersion and density, two quantities of paramount…
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