Smear fitting: a new image-deconvolution method for interferometric data

A new technique is presented for producing images from interferometric data. The method, 'smear fitting', makes the constraints necessary for interferometric imaging double as a model, with uncertainties, of the sky brightness distribution. It does this by modelling the sky with a set of functions and then convolving each component with its own elliptical Gaussian to account for the uncertainty in its shape and location that arises from noise. This yields much sharper resolution than clean for significantly detected features, without sacrificing any sensitivity. Using appropriate functional forms for the components provides both a scientifically interesting model and imaging constraints that tend to be better than those used by traditional deconvolution methods. This allows it to avoid the most serious problems that limit the imaging quality of those methods. Comparisons of smear fitting to clean and maximum entropy are given, using both real and simulated observations. It is also shown that the famous Rayleigh criterion (resolution = wavelength/baseline) is inappropriate for interferometers as it does not consider the reliability of the measurements.