Bayesian polarization calibration and imaging in very long baseline interferometry

Context . Extracting polarimetric information from very long baseline interferometry (VLBI) data is demanding but vital to understanding the synchrotron radiation process and the magnetic fields of celestial objects, such as active galactic nuclei (AGNs). However, conventional CLEAN -based calibration and imaging methods provide suboptimal resolution without uncertainty estimation of calibration solutions, while requiring manual steering from an experienced user. Aims . We present a Bayesian polarization calibration and imaging method for millimeter and centimeter VLBI datasets, which explores the posterior distribution of antenna-based gains, polarization leakages, and polarimetric images jointly from precalibrated data. To validate our method, we compare our results with the CLEAN and regularized maximum likelihood (RML)-based software ehtim . Methods . We reconstructed the posterior distribution of Stokes images, gains, and leakages from real and synthetic datasets in the framework of Bayesian imaging software resolve using variational inference methods. Polarization constraints are enforced in the model. Furthermore, polarization calibration with several sources and multiple intermediate frequencies (IFs) is supported in order to maximize the parallactic angle coverage and identify instrumental corruptions per IF. Results . We demonstrate our calibration and imaging method with observations of the quasar 3C273 with the Very Long Baseline Array (VLBA) at 15 GHz and the blazar OJ287 with the Global Millimeter VLBI Array (GMVA) and the Atacama Large Millimeter/submillimeter Array (ALMA) at 86 GHz. Compared to the CLEAN method, our approach provides physically realistic images that satisfy the positivity of the total intensity and polarization constraints and can reconstruct complex source structures composed of various spatial scales. In contrast to conventional imaging and calibration methods, our method systematically accounts for calibration uncertainties in the final images and provides uncertainties of Stokes images and calibration solutions. Conclusions . Our Bayesian polarization calibration and imaging method explores the posterior distribution of calibration solutions and reconstructs physically plausible high-resolution images from VLBI data. The automated Bayesian approach for calibration and imaging will be able to obtain high-fidelity polarimetric images using high-quality data from next-generation radio arrays. The pipeline developed for this work is publicly available.