Inferring Galactic magnetic field model parameters using IMAGINE - An
  Interstellar MAGnetic field INference Engine
  Context. The Galactic magnetic field (GMF) has a huge impact on the evolution
of the Milky Way. Yet currently there exists no standard model for it, as its
structure is not fully understood. In the past many parametric GMF models of
varying complexity have been developed that all have been fitted to an
individual set of observational data complicating comparability. Aims. Our goal
is to systematize parameter inference of GMF models. We want to enable a
statistical comparison of different models in the future, allow for simple
refitting with respect to newly available data sets and thereby increase the
research area's transparency. We aim to make state-of-the-art Bayesian methods
easily available and in particular to treat the statistics related to the
random components of the GMF correctly. Methods. To achieve our goals, we built
IMAGINE, the Interstellar Magnetic Field Inference Engine. It is a modular open
source framework for doing inference on generic parametric models of the
Galaxy. We combine highly optimized tools and technology such as the MultiNest
sampler and the information field theory framework NIFTy in order to leverage
existing expertise. Results. We demonstrate the steps needed for robust
parameter inference and model comparison. Our results show how important the
combination of complementary observables like synchrotron emission and Faraday
depth is while building a model and fitting its parameters to data. IMAGINE is
open-source software available under the GNU General Public License v3 (GPL-3)
at: https://gitlab.mpcdf.mpg.de/ift/IMAGINE
