Bayesian Inference for Radio Observations - Going beyond deconvolution
  Radio interferometers suffer from the problem of missing information in their
data, due to the gaps between the antennas. This results in artifacts, such as
bright rings around sources, in the images obtained. Multiple deconvolution
algorithms have been proposed to solve this problem and produce cleaner radio
images. However, these algorithms are unable to correctly estimate
uncertainties in derived scientific parameters or to always include the effects
of instrumental errors. We propose an alternative technique called Bayesian
Inference for Radio Observations (BIRO) which uses a Bayesian statistical
framework to determine the scientific parameters and instrumental errors
simultaneously directly from the raw data, without making an image. We use a
simple simulation of Westerbork Synthesis Radio Telescope data including
pointing errors and beam parameters as instrumental effects, to demonstrate the
use of BIRO.
