Master Colloquium
Sviatoslav Khukhlaev	SCHEDULED

MPIfR

MOJAVE (Monitoring Of Jets in Active galactic nuclei with VLBA Experiments) was organized as a long-term monitoring program to repeatedly image a large sample of bright, radio-loud AGN at milliarcsecond resolution, and these observations continue today. Until now, all images have been reconstructed with the help of the traditional imaging algorithm CLEAN, which defines the final image resolution by the restoring beam and does not provide any uncertainty estimations. In turn, modern Bayesian algorithms, for example RESOLVE, are capable of retrieving more continuous and spatially ’thin’ structural patterns, especially in the brighter areas of the source. They also naturally provide uncertainties of the reconstructions, which are invaluable for any statistical analysis. In this thesis, I use RESOLVE in an automatic regime on all 12000 MOJAVE VLBI observations and compare the performance with CLEAN. I show that RESOLVE is generally able to reconstruct images with high accuracy and uncover small-scale details. I perform core fits in the image domain and highlight the consistency between the estimated brightness temperatures and those from the analogous visibility-domain approach. These findings indicate the potential of using modern methods alongside CLEAN to improve the image-domain analysis.