Bayesian Imaging of Interferometric Data from Polarized Electromagnetic Signals

We present an imaging algorithm for polarimetric interferometric data from radio telescopes. It is based on Bayesian statistics and thereby able to provide uncertainties and to incorporate prior information such as positivity of the total emission (Stokes I) or consistency constraints (polarized fraction can only be between 0% and 100%). By comparing our results to the output of the de-facto standard algorithm called CLEAN, we show that these constraints paired with a consistent treatment of measurement uncertainties throughout the algorithm significantly improve image quality. In particular, our method reveals that depolarization canals in CLEAN images do not necessarily indicate a true absence of polarized emission, e.g., after frequency averaging, but can also stem from uncertainty in the polarization direction. This demonstrates that our Bayesian approach can distinguish between true depolarization and mere uncertainty, providing a more informative representation of polarization structures.