Robust radio interferometric calibration

Existing calibration algorithms in radio interferometry implicitly assume the noise to be Gaussian. However, outliers in the data due to interference or due to errors in the sky model would have adverse effects on processing based on a Gaussian noise model. Most of the shortcomings of calibration such as the loss in flux or coherence, and the appearance of spurious sources could be attributed to the deviations of the underlying noise model. In this paper, we demonstrate our previous proposal to improve the robustness of calibration by using a noise model based on the Student´s t distribution. Unlike Gaussian noise model based calibration, traditional nonlinear least squares minimization would not directly extend to a case when we have a Student´s t noise model. Therefore, we use the Expectation-Conditional Maximization Either (ECME) algorithm for calibration. We give simulation results to show the robustness of the proposed calibration method as opposed to traditional Gaussian noise model based calibration, especially in preserving the flux of weaker sources that are not included in the calibration model.