Inferring Galactic magnetic field model parameters using IMAGINE - An Interstellar MAGnetic field INference Engine

TL;DR

This paper introduces IMAGINE, an open-source Bayesian inference engine designed to systematically estimate and compare Galactic magnetic field models using diverse observational data, improving transparency and reproducibility in the field.

Contribution

The paper presents IMAGINE, a modular framework that standardizes GMF parameter inference, enabling statistical model comparison and proper treatment of stochastic components using advanced Bayesian methods.

Findings

Combining synchrotron emission and Faraday depth improves model fitting.

IMAGINE facilitates robust parameter inference and model comparison.

Open-source tool enhances transparency in GMF research.

Abstract

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