# IMAGINE: modeling the Galactic magnetic field

## TL;DR

IMAGINE introduces a Bayesian inference framework with an open-source pipeline to model the Milky Way's magnetic field, integrating diverse observational data and priors for improved accuracy.

## Contribution

It presents a novel modular software pipeline that enables Bayesian parameter estimation for Galactic magnetic field models using multiple observational datasets.

## Key findings

- Open-source software pipeline developed
- Bayesian inference applied to Galactic magnetic field modeling
- Integration of diverse observational data sets

## Abstract

The IMAGINE Consortium aims to bring modeling of the magnetic field of the Milky Way to a next level, by using Bayesian inference. IMAGINE includes an open-source modular software pipeline that optimizes parameters in a user-defined Galactic magnetic field model against various selected observational datasets. Bayesian priors can be added as external probabilistic constraints of the model parameters. These conference proceedings describe the science goals of the IMAGINE Consortium, the software pipeline and its inputs, viz observational data sets, Galactic magnetic field models, and Bayesian priors.